1. Cognitive Dysfunction in Older Adults With Diabetes: What a Clinician Needs to Know. I’ve talked about these topics before here on the blog (see e.g. these posts on related topics), but this is a good summary article. I have added some observations from the paper below:
“Although cognitive dysfunction is associated with both type 1 and type 2 diabetes, there are several distinct differences observed in the domains of cognition affected in patients with these two types. Patients with type 1 diabetes are more likely to have diminished mental flexibility and slowing of mental speed, whereas learning and memory are largely not affected (8). Patients with type 2 diabetes show decline in executive function, memory, learning, attention, and psychomotor efficiency (9,10).”
“So far, it seems that the risk of cognitive dysfunction in type 2 diabetes may be influenced by glycemic control, hypoglycemia, inflammation, depression, and macro- and microvascular pathology (14). The cumulative impact of these conditions on the vascular etiology may further decrease the threshold at which cognition is affected by other neurological conditions in the aging brain. In patients with type 1 diabetes, it seems as though diabetes has a lesser impact on cognitive dysfunction than those patients with type 2 diabetes. […] Thus, the cognitive decline in patients with type 1 diabetes may be mild and may not interfere with their functionality until later years, when other aging-related factors become important. […] However, recent studies have shown a higher prevalence of cognitive dysfunction in older patients (>60 years of age) with type 1 diabetes (5).”
“Unlike other chronic diseases, diabetes self-care involves many behaviors that require various degrees of cognitive pliability and insight to perform proper self-care coordination and planning. Glucose monitoring, medications and/or insulin injections, pattern management, and diet and exercise timing require participation from different domains of cognitive function. In addition, the recognition, treatment, and prevention of hypoglycemia, which are critical for the older population, also depend in large part on having intact cognition.
The reason a clinician needs to recognize different domains of cognition affected in patients with diabetes is to understand which self-care behavior will be affected in that individual. […] For example, a patient with memory problems may forget to take insulin doses, forget to take medications/insulin on time, or forget to eat on time. […] Cognitively impaired patients using insulin are more likely to not know what to do in the event of low blood glucose or how to manage medication on sick days (34). Patients with diminished mental flexibility and processing speed may do well with a simple regimen but may fail if the regimen is too complex. In general, older patients with diabetes with cognitive dysfunction are less likely to be involved in diabetes self-care and glucose monitoring compared with age-matched control subjects (35). […] Other comorbidities associated with aging and diabetes also add to the burden of cognitive impairment and its impact on self-care abilities. For example, depression is associated with a greater decline in cognitive function in patients with type 2 diabetes (36). Depression also can independently negatively impact the motivation to practice self-care.”
“Recently, there is an increasing discomfort with the use of A1C as a sole parameter to define glycemic goals in the older population. Studies have shown that A1C values in the older population may not reflect the same estimated mean glucose as in the younger population. Possible reasons for this discrepancy are the commonly present comorbidities that impact red cell life span (e.g., anemia, uremia, renal dysfunction, blood transfusion, erythropoietin therapy) (45,46). In addition, A1C level does not reflect glucose excursions and variability. […] Thus, it is prudent to avoid A1C as the sole measure of glycemic goal in this population. […] In patients who need insulin therapy, simplification, also known as de-intensification of the regimen, is generally recommended in all frail patients, especially if they have cognitive dysfunction (37,49). However, the practice has not caught up with the recommendations as shown by large observational studies showing unnecessary intensive control in patients with diabetes and dementia (50–52).”
“With advances in the past few decades, we now see a larger number of patients with type 1 diabetes who are aging successfully and facing the new challenges that aging brings. […] Patients with type 1 diabetes are typically proactive in their disease management and highly disciplined. Cognitive dysfunction in these patients creates significant distress for the first time in their lives; they suddenly feel a “lack of control” over the disease they have managed for many decades. The addition of autonomic dysfunction, gastropathy, or neuropathy may result in wider glucose excursions. These patients are usually more afraid of hyperglycemia than hypoglycemia — both of which they have managed for many years. However, cognitive dysfunction in older adults with type 1 diabetes has been found to be associated with hypoglycemic unawareness and glucose variability (5), which in turn increases the risk of severe hypoglycemia (54). The need for goal changes to avoid hypoglycemia and accept some hyperglycemia can be very difficult for many of these patients.”
“From 2006 to 2013, use increased for metformin (from 47.6 to 53.5%), dipeptidyl peptidase 4 inhibitors (0.5 to 14.9%), and insulin (17.1 to 23.0%) but declined for sulfonylureas (38.8 to 30.8%) and thiazolidinediones (28.5 to 5.6%; all P < 0.001). […] The overall rate of severe hypoglycemia remained the same (1.3 per 100 person-years; P = 0.72), declined modestly among the oldest patients (from 2.9 to 2.3; P < 0.001), and remained high among those with two or more comorbidities (3.2 to 3.5; P = 0.36). […] During the recent 8-year period, the use of glucose-lowering drugs has changed dramatically among patients with T2DM. […] The use of older classes of medications, such as sulfonylureas and thiazolidinediones, declined. During this time, glycemic control of T2DM did not improve in the overall population and remained poor among nearly a quarter of the youngest patients. Rates of severe hypoglycemia remained largely unchanged, with the oldest patients and those with multiple comorbidities at highest risk. These findings raise questions about the value of the observed shifts in drug utilization toward newer and costlier medications.”
“Our findings are consistent with a prior study of drug prescribing in U.S. ambulatory practice conducted from 1997 to 2012 (2). In that study, similar increases in DPP-4 inhibitor and insulin analog prescribing were observed; these changes were accompanied by a 61% increase in drug expenditures (2). Our study extends these findings to drug utilization and demonstrates that these increases occurred in all age and comorbidity subgroups. […] In contrast, metformin use increased only modestly between 2006 and 2013 and remained relatively low among older patients and those with two or more comorbidities. Although metformin is recommended as first-line therapy (26), it may be underutilized as the initial agent for the treatment of T2DM (27). Its use may be additionally limited by coexisting contraindications, such as chronic kidney disease (28).”
“The proportion of patients with a diagnosis of diabetes who did not fill any glucose-lowering medications declined slightly (25.7 to 24.1%; P < 0.001).”
That is, one in four people who had a diagnosis of type 2 diabetes were not taking any prescription drugs for their health condition. I wonder how many of those people have read wikipedia articles like this one…
“When considering treatment complexity, the use of oral monotherapy increased slightly (from 24.3 to 26.4%) and the use of multiple (two or more) oral agents declined (from 33.0 to 26.5%), whereas the use of insulin alone and in combination with oral agents increased (from 6.0 to 8.5% and from 11.1 to 14.6%, respectively; all P values <0.001).”
“Between 1987 and 2011, per person medical spending attributable to diabetes doubled (4). More than half of the increase was due to prescription drug spending (4). Despite these spending increases and greater utilization of newly developed medications, we showed no concurrent improvements in overall glycemic control or the rates of severe hypoglycemia in our study. Although the use of newer and more expensive agents may have other important benefits (44), further studies are needed to define the value and cost-effectiveness of current treatment options.”
“Using the 2011–2013 Medical Expenditure Panel Survey, bivariate and regression analyses were conducted to compare demographic characteristics, medical service use, diabetes care, and health status among privately insured adult respondents with diabetes, aged 18–64 years (N = 1,461) by lower (<200% of the federal poverty level) and higher (≥200% of the federal poverty level) income and deductible vs. no deductible (ND), low deductible ($1,000/$2,400) (LD), and high deductible (>$1,000/$2,400) (HD). The National Health Interview Survey 2012–2014 was used to analyze differences in medical debt and delayed/avoided needed care among adult respondents with diabetes (n = 4,058) by income. […] Compared with privately insured respondents with diabetes with ND, privately insured lower-income respondents with diabetes with an LD report significant decreases in service use for primary care, checkups, and specialty visits (27%, 39%, and 77% lower, respectively), and respondents with an HD decrease use by 42%, 65%, and 86%, respectively. Higher-income respondents with an LD report significant decreases in specialty (28%) and emergency department (37%) visits.”
“The reduction in ambulatory visits made by lower-income respondents with ND compared with lower-income respondents with an LD or HD is far greater than for higher-income patients. […] The substantial reduction in checkup (preventive) and specialty visits by those with a lower income who have an HDHP [high-deductible health plan, US] implies a very different pattern of service use compared with lower-income respondents who have ND and with higher-income respondents. Though preventive visits require no out-of-pocket costs, reduced preventive service use with HDHPs is well established and might be the result of patients being unaware of this benefit or their concern about findings that could lead to additional expenses (31). Such sharply reduced service use by low-income respondents with diabetes may not be desirable. Patients with diabetes benefit from assessment of diabetes control, encouragement and reinforcement of behavior change and medication use, and early detection and treatment of diabetes complications or concomitant disease.”
“OBJECTIVE To study long-term mortality, causes of death, and end-stage renal disease (ESRD) in people diagnosed with type 1 diabetes at age 15–29 years.
RESEARCH DESIGN AND METHODS This nationwide, population-based cohort with type 1 diabetes diagnosed during 1978–1982 (n = 719) was followed from diagnosis until death, emigration, or September 2013. Linkages to the Norwegian Cause of Death Registry and the Norwegian Renal Registry provided information on causes of death and whether ESRD was present.
RESULTS During 30 years’ follow-up, 4.6% of participants developed ESRD and 20.6% (n = 148; 106 men and 42 women) died. Cumulative mortality by years since diagnosis was 6.0% (95% CI 4.5–8.0) at 10 years, 12.2% (10.0–14.8) at 20 years, and 18.4% (15.8–21.5) at 30 years. The SMR [standardized mortality ratio] was 4.4 (95% CI 3.7–5.1). Mean time from diagnosis of diabetes to ESRD was 23.6 years (range 14.2–33.5). Death was caused by chronic complications (32.2%), acute complications (20.5%), violent death (19.9%), or any other cause (27.4%). Death was related to alcohol in 15% of cases. SMR for alcohol-related death was 6.8 (95% CI 4.5–10.3), for cardiovascular death was 7.3 (5.4–10.0), and for violent death was 3.6 (2.3–5.3).
CONCLUSIONS The cumulative incidence of ESRD was low in this cohort with type 1 diabetes followed for 30 years. Mortality was 4.4 times that of the general population, and more than 50% of all deaths were caused by acute or chronic complications. A relatively high proportion of deaths were related to alcohol.”
Some additional observations from the paper:
“Studies assessing causes of death in type 1 diabetes are most frequently conducted in individuals diagnosed during childhood (1–7) or without evaluating the effect of age at diagnosis (8,9). Reports on causes of death in cohorts of patients diagnosed during late adolescence or young adulthood, with long-term follow-up, are less frequent (10). […] Adherence to treatment during this age is poor and the risk of acute diabetic complications is high (13–16). Mortality may differ between those with diabetes diagnosed during this period of life and those diagnosed during childhood.”
“Mortality was between four and five times higher than in the general population […]. The excess mortality was similar for men […] and women […]. SMR was higher in the lower age bands — 6.7 (95% CI 3.9–11.5) at 15–24 years and 7.3 (95% CI 5.2–10.1) at 25–34 years — compared with the higher age bands: 3.7 (95% CI 2.7–4.9) at 45–54 years and 3.9 (95% CI 2.6–5.8) at 55–65 years […]. The Cox regression model showed that the risk of death increased significantly by age at diagnosis (HR 1.1; 95% CI 1.1–1.2; P < 0.001) and was eight to nine times higher if ESRD was present (HR 8.7; 95% CI 4.8–15.5; P < 0.0001). […] the underlying cause of death was diabetes in 57 individuals (39.0%), circulatory in 22 (15.1%), cancer in 18 (12.3%), accidents or intoxications in 20 (13.7%), suicide in 8 (5.5%), and any other cause in 21 (14.4%) […] In addition, diabetes contributed to death in 29.5% (n = 43) and CVD contributed to death in 10.9% (n = 29) of the 146 cases. Diabetes was mentioned on the death certificate for 68.2% of the cohort but for only 30.0% of the violent deaths. […] In 60% (88/146) of the cases the review committee considered death to be related to diabetes, whereas in 40% (58/146) the cause was unrelated to diabetes or had an unknown relation to diabetes. According to the clinical committee, acute complications caused death in 20.5% (30/146) of the cases; 20 individuals died as a result of DKA and 10 from hypoglycemia. […] Chronic complications caused the largest proportion of deaths (47/146; 32.2%) and increased with increasing duration of diabetes […]. Among individuals dying as a result of chronic complications (n = 47), CVD caused death in 94% (n = 44) and renal failure in 6% (n = 3). ESRD contributed to death in 22.7% (10/44) of those dying from CVD. Cardiovascular death occurred at mortality rates seven times higher than those in the general population […]. ESRD caused or contributed to death in 13 of 14 cases, when present.”
“Violence (intoxications, accidents, and suicides) was the leading cause of death before 10 years’ duration of diabetes; thereafter it was only a minor cause […] Insulin was used in two of seven suicides. […] According to the available medical records and autopsy reports, about 20% (29/146) of the deceased misused alcohol. In 15% (22/146) alcohol-related ICD-10 codes were listed on the death certificate (18% [19/106] of men and 8% [3/40] of women). In 10 cases the cause of death was uncertain but considered to be related to alcohol or diabetes […] The SMR for alcohol-related death was high when considering the underlying cause of death (5.0; 95% CI 2.5–10.0), and even higher when considering all alcohol-related ICD-10 codes listed on the death certificate (6.8; 95% CI 4.5–10.3). The cause of death was associated with alcohol in 21.8% (19/87) of those who died with less than 20 years’ diabetes duration. Drug abuse was noted on the death certificate in only two cases.”
“During follow-up, 33 individuals (4.6%; 22 men and 11 women) developed ESRD as a result of diabetic nephropathy. Mean time from diagnosis of diabetes to ESRD was 23.6 years (range 14.2–33.5 years). Cumulative incidence of ESRD by years since diagnosis of diabetes was 1.4% (95% CI 0.7–2.7) at 20 years and 4.8% (95% CI 3.4–6.9) at 30 years.”
“This study highlights three important findings. First, among individuals who were diagnosed with type 1 diabetes in late adolescence and early adulthood and had good access to health care, and who were followed for 30 years, mortality was four to five times that of the general population. Second, 15% of all deaths were associated with alcohol, and the SMR for alcohol-related deaths was 6.8. Third, there was a relatively low cumulative incidence of ESRD (4.8%) 30 years after the diagnosis of diabetes.
We report mortality higher than those from a large, population-based study from Finland that found cumulative mortality around 6% at 20 years’ and 15% at 30 years’ duration of diabetes among a population with age at onset and year of diagnosis similar to those in our cohort (10). The corresponding numbers in our cohort were 12% and 18%, respectively; the discrepancy was particularly high at 20 years. The SMR in the Finnish cohort was lower than that in our cohort (2.6–3.0 vs. 3.7–5.1), and those authors reported the SMR to be lower in late-onset diabetes (at age 15–29 years) compared with early-onset diabetes (at age 23). The differences between the Norwegian and Finnish data are difficult to explain since both reports are from countries with good access to health care and a high incidence of type 1 diabetes.”
However the reason for the somewhat different SMRs in these two reasonably similar countries may actually be quite simple – the important variable may be alcohol:
“Finland and Norway are appropriate to compare because they share important population and welfare characteristics. There are, however, significant differences in drinking levels and alcohol-related mortality: the Finnish population consumes more alcohol and the Norwegian population consumes less. The mortality rates for deaths related to alcohol are about three to four times higher in Finland than in Norway (30). […] The markedly higher SMR in our cohort can probably be explained by the lower mortality rates for alcohol-related mortality in the general population. […] In conclusion, the high mortality reported in this cohort with an onset of diabetes in late adolescence and young adulthood draws attention to people diagnosed during a vulnerable period of life. Both acute and chronic complications cause substantial premature mortality […] Our study suggests that increased awareness of alcohol-related death should be encouraged in clinics providing health care to this group of patients.”
The links above are links to topics I looked up while reading the second half of the book. The first link is quite relevant to the book’s coverage as a comprehensive longitudinal Grade of Membership (-GoM) model is covered in chapter 17. Relatedly, chapter 18 covers linear latent structure (-LLS) models, and as observed in the book LLS is a generalization of GoM. As should be obvious from the nature of the links some of the stuff included in the second half of the text is highly technical, and I’ll readily admit I was not fully able to understand all the details included in the coverage of chapters 17 and 18 in particular. On account of the technical nature of the coverage in Part 2 I’m not sure I’ll cover the second half of the book in much detail, though I probably shall devote at least one more post to some of those topics, as they were quite interesting even if some of the details were difficult to follow.
I have almost finished the book at this point, and I have already decided to both give the book five stars and include it on my list of favorite books on goodreads; it’s really well written, and it provides consistently highly detailed coverage of very high quality. As I also noted in the first post about the book the authors have given readability aspects some thought, and I am sure most readers would learn quite a bit from this text even if they were to skip some of the more technical chapters. The main body of Part 2 of the book, the subtitle of which is ‘Statistical Modeling of Aging, Health, and Longevity’, is however probably in general not worth the effort of reading unless you have a solid background in statistics.
This post includes some observations and quotes from the last chapters of the book’s Part 1.
“The proportion of older adults in the U.S. population is growing. This raises important questions about the increasing prevalence of aging-related diseases, multimorbidity issues, and disability among the elderly population. […] In 2009, 46.3 million people were covered by Medicare: 38.7 million of them were aged 65 years and older, and 7.6 million were disabled […]. By 2031, when the baby-boomer generation will be completely enrolled, Medicare is expected to reach 77 million individuals […]. Because the Medicare program covers 95 % of the nation’s aged population […], the prediction of future Medicare costs based on these data can be an important source of health care planning.”
“Three essential components (which could be also referred as sub-models) need to be developed to construct a modern model of forecasting of population health and associated medical costs: (i) a model of medical cost projections conditional on each health state in the model, (ii) health state projections, and (iii) a description of the distribution of initial health states of a cohort to be projected […] In making medical cost projections, two major effects should be taken into account: the dynamics of the medical costs during the time periods comprising the date of onset of chronic diseases and the increase of medical costs during the last years of life. In this chapter, we investigate and model the first of these two effects. […] the approach developed in this chapter generalizes the approach known as “life tables with covariates” […], resulting in a new family of forecasting models with covariates such as comorbidity indexes or medical costs. In sum, this chapter develops a model of the relationships between individual cost trajectories following the onset of aging-related chronic diseases. […] The underlying methodological idea is to aggregate the health state information into a single (or several) covariate(s) that can be determinative in predicting the risk of a health event (e.g., disease incidence) and whose dynamics could be represented by the model assumptions. An advantage of such an approach is its substantial reduction of the degrees of freedom compared with existing forecasting models (e.g., the FEM model, Goldman and RAND Corporation 2004). […] We found that the time patterns of medical cost trajectories were similar for all diseases considered and can be described in terms of four components having the meanings of (i) the pre-diagnosis cost associated with initial comorbidity represented by medical expenditures, (ii) the cost peak associated with the onset of each disease, (iii) the decline/reduction in medical expenditures after the disease onset, and (iv) the difference between post- and pre-diagnosis cost levels associated with an acquired comorbidity. The description of the trajectories was formalized by a model which explicitly involves four parameters reflecting these four components.”
As I noted earlier in my coverage of the book, I don’t think the model above fully captures all relevant cost contributions of the diseases included, as the follow-up period was too short to capture all relevant costs to be included in the part iv model component. This is definitely a problem in the context of diabetes. But then again nothing in theory stops people from combining the model above with other models which are better at dealing with the excess costs associated with long-term complications of chronic diseases, and the model results were intriguing even if the model likely underperforms in a few specific disease contexts.
“Models of medical cost projections usually are based on regression models estimated with the majority of independent predictors describing demographic status of the individual, patient’s health state, and level of functional limitations, as well as their interactions […]. If the health states needs to be described by a number of simultaneously manifested diseases, then detailed stratification over the categorized variables or use of multivariate regression models allows for a better description of the health states. However, it can result in an abundance of model parameters to be estimated. One way to overcome these difficulties is to use an approach in which the model components are demographically-based aggregated characteristics that mimic the effects of specific states. The model developed in this chapter is an example of such an approach: the use of a comorbidity index rather than of a set of correlated categorical regressor variables to represent the health state allows for an essential reduction in the degrees of freedom of the problem.”
“Unlike mortality, the onset time of chronic disease is difficult to define with high precision due to the large variety of disease-specific criteria for onset/incident case identification […] there is always some arbitrariness in defining the date of chronic disease onset, and a unified definition of date of onset is necessary for population studies with a long-term follow-up.”
“Individual age trajectories of physiological indices are the product of a complicated interplay among genetic and non-genetic (environmental, behavioral, stochastic) factors that influence the human body during the course of aging. Accordingly, they may differ substantially among individuals in a cohort. Despite this fact, the average age trajectories for the same index follow remarkable regularities. […] some indices tend to change monotonically with age: the level of blood glucose (BG) increases almost monotonically; pulse pressure (PP) increases from age 40 until age 85, then levels off and shows a tendency to decline only at later ages. The age trajectories of other indices are non-monotonic: they tend to increase first and then decline. Body mass index (BMI) increases up to about age 70 and then declines, diastolic blood pressure (DBP) increases until age 55–60 and then declines, systolic blood pressure (SBP) increases until age 75 and then declines, serum cholesterol (SCH) increases until age 50 in males and age 70 in females and then declines, ventricular rate (VR) increases until age 55 in males and age 45 in females and then declines. With small variations, these general patterns are similar in males and females. The shapes of the age-trajectories of the physiological variables also appear to be similar for different genotypes. […] The effects of these physiological indices on mortality risk were studied in Yashin et al. (2006), who found that the effects are gender and age specific. They also found that the dynamic properties of the individual age trajectories of physiological indices may differ dramatically from one individual to the next.”
“An increase in the mortality rate with age is traditionally associated with the process of aging. This influence is mediated by aging-associated changes in thousands of biological and physiological variables, some of which have been measured in aging studies. The fact that the age trajectories of some of these variables differ among individuals with short and long life spans and healthy life spans indicates that dynamic properties of the indices affect life history traits. Our analyses of the FHS data clearly demonstrate that the values of physiological indices at age 40 are significant contributors both to life span and healthy life span […] suggesting that normalizing these variables around age 40 is important for preventing age-associated morbidity and mortality later in life. […] results [also] suggest that keeping physiological indices stable over the years of life could be as important as their normalizing around age 40.”
“The results […] indicate that, in the quest of identifying longevity genes, it may be important to look for candidate genes with pleiotropic effects on more than one dynamic characteristic of the age-trajectory of a physiological variable, such as genes that may influence both the initial value of a trait (intercept) and the rates of its changes over age (slopes). […] Our results indicate that the dynamic characteristics of age-related changes in physiological variables are important predictors of morbidity and mortality risks in aging individuals. […] We showed that the initial value (intercept), the rate of changes (slope), and the variability of a physiological index, in the age interval 40–60 years, significantly influenced both mortality risk and onset of unhealthy life at ages 60+ in our analyses of the Framingham Heart Study data. That is, these dynamic characteristics may serve as good predictors of late life morbidity and mortality risks. The results also suggest that physiological changes taking place in the organism in middle life may affect longevity through promoting or preventing diseases of old age. For non-monotonically changing indices, we found that having a later age at the peak value of the index […], a lower peak value […], a slower rate of decline in the index at older ages […], and less variability in the index over time, can be beneficial for longevity. Also, the dynamic characteristics of the physiological indices were, overall, associated with mortality risk more significantly than with onset of unhealthy life.”
“Decades of studies of candidate genes show that they are not linked to aging-related traits in a straightforward manner […]. Recent genome-wide association studies (GWAS) have reached fundamentally the same conclusion by showing that the traits in late life likely are controlled by a relatively large number of common genetic variants […]. Further, GWAS often show that the detected associations are of tiny effect […] the weak effect of genes on traits in late life can be not only because they confer small risks having small penetrance but because they confer large risks but in a complex fashion […] In this chapter, we consider several examples of complex modes of gene actions, including genetic tradeoffs, antagonistic genetic effects on the same traits at different ages, and variable genetic effects on lifespan. The analyses focus on the APOE common polymorphism. […] The analyses reported in this chapter suggest that the e4 allele can be protective against cancer with a more pronounced role in men. This protective effect is more characteristic of cancers at older ages and it holds in both the parental and offspring generations of the FHS participants. Unlike cancer, the effect of the e4 allele on risks of CVD is more pronounced in women. […] [The] results […] explicitly show that the same allele can change its role on risks of CVD in an antagonistic fashion from detrimental in women with onsets at younger ages to protective in women with onsets at older ages. […] e4 allele carriers have worse survival compared to non-e4 carriers in each cohort. […] Sex stratification shows sexual dimorphism in the effect of the e4 allele on survival […] with the e4 female carriers, particularly, being more exposed to worse survival. […] The results of these analyses provide two important insights into the role of genes in lifespan. First, they provide evidence on the key role of aging-related processes in genetic susceptibility to lifespan. For example, taking into account the specifics of aging-related processes gains 18 % in estimates of the RRs and five orders of magnitude in significance in the same sample of women […] without additional investments in increasing sample sizes and new genotyping. The second is that a detailed study of the role of aging-related processes in estimates of the effects of genes on lifespan (and healthspan) helps in detecting more homogeneous [high risk] sub-samples”.
“The aging of populations in developed countries requires effective strategies to extend healthspan. A promising solution could be to yield insights into the genetic predispositions for endophenotypes, diseases, well-being, and survival. It was thought that genome-wide association studies (GWAS) would be a major breakthrough in this endeavor. Various genetic association studies including GWAS assume that there should be a deterministic (unconditional) genetic component in such complex phenotypes. However, the idea of unconditional contributions of genes to these phenotypes faces serious difficulties which stem from the lack of direct evolutionary selection against or in favor of such phenotypes. In fact, evolutionary constraints imply that genes should be linked to age-related phenotypes in a complex manner through different mechanisms specific for given periods of life. Accordingly, the linkage between genes and these traits should be strongly modulated by age-related processes in a changing environment, i.e., by the individuals’ life course. The inherent sensitivity of genetic mechanisms of complex health traits to the life course will be a key concern as long as genetic discoveries continue to be aimed at improving human health.”
“Despite the common understanding that age is a risk factor of not just one but a large portion of human diseases in late life, each specific disease is typically considered as a stand-alone trait. Independence of diseases was a plausible hypothesis in the era of infectious diseases caused by different strains of microbes. Unlike those diseases, the exact etiology and precursors of diseases in late life are still elusive. It is clear, however, that the origin of these diseases differs from that of infectious diseases and that age-related diseases reflect a complicated interplay among ontogenetic changes, senescence processes, and damages from exposures to environmental hazards. Studies of the determinants of diseases in late life provide insights into a number of risk factors, apart from age, that are common for the development of many health pathologies. The presence of such common risk factors makes chronic diseases and hence risks of their occurrence interdependent. This means that the results of many calculations using the assumption of disease independence should be used with care. Chapter 4 argued that disregarding potential dependence among diseases may seriously bias estimates of potential gains in life expectancy attributable to the control or elimination of a specific disease and that the results of the process of coping with a specific disease will depend on the disease elimination strategy, which may affect mortality risks from other diseases.”
In a post I published a few weeks ago I mentioned that I had decided against including some comments and observations I had written about health economics in that post because the post was growing unwieldy, but that I might post that stuff later on in a separate post. This post will include those observations, as well as some additional details I added to the post later. This sort of post is the sort of post that usually does not get past the ‘draft’ stage (in wordpress you can save posts you intend to publish later on as drafts), and as is usually the case for posts like these I already regret having written it, for multiple reasons. I should warn you from the start that this post is very long and will probably take you some time to read.
Anyway, the starting point for this post was some comments related to health insurance and health economics which I left on SSC in the past. A lot more people read those comments on SSC than will read this post so the motivation for posting it here was not to ‘increase awareness’ of the ideas and observations included in some kind of general sense; my primary motivation for adding this stuff here is rather that it’s a lot easier for me personally to find stuff I’ve written when it’s located here on this blog rather than elsewhere on the internet, and I figure that some of the things I wrote back then are topics which might well come up again later, and it would be convenient for me in that case to have a link at hand. Relatedly I have added many additional comments and observations in this post not included in the primary exchange, which it is no longer possible for me to do on SSC as my comments are no longer editable on that site.
Although the starting point for the post was as mentioned a comment exchange, I decided early on against just ‘quoting myself’ in this post, and I have thus made some changes in wording and structure in order to increase the precision of the statements included and in order to add a bit of context making the observations below easier to read and understand (and harder to misread). Major topics to which the observations included in this post relate are preventable diseases, the level of complexity that is present in the health care sector, and various topics which relate to health care cost growth. Included in the post are some perhaps not sufficiently well known complications which may arise in the context of the discussion of how different financing schemes may relate to various outcomes, and to cost growth. Much of the stuff included will probably be review to people who’ve read my previous posts on health economics, but that’s to be expected considering the nature of this post.
Although ‘normative stuff’ is not what interests me most – I generally tend to prefer discussions where the aim is to identify what happens if you do X, and I’ll often be happy to leave the discussion of whether outcome X or Y is ‘best’ to others – I do want to start out with stating a policy preference, as this preference was the starting point for the aforementioned debate that lead to the origination of this post. At the outset I should thus make clear that I would in general be in favour of changes to the financial structure of health care systems where people who take avoidable risks which systematically and demonstrably increase their expected health care expenditures at the population level pay a larger proportion of the cost than do people who did not take such avoidable risks.
Most developed societies have health care systems which are designed in a way that implicitly to some extent subsidize unhealthy behaviours. An important note in this context is incidentally that one way of looking at these things is that if you are not explicitly demanding people who behave in risky ways which tend to increase their expected costs to pay more for their health care (/insurance), then you are in fact by virtue of not doing this implicitly subsidizing those unhealthy individuals/behaviours. I mention this because some people might not like the idea of ‘subsidizing healthy behaviours’ (‘health fascism’) – which from a certain point of view is what you do if you charge people who behave in unhealthy ways more. Maybe some people would take issue with words like ‘subsidy’ and ‘implicit’, but regardless of what you call these things the major point that is important to have in mind here is that if one group of people (e.g. ‘unhealthy people’) cost more to treat (/are ill more often, get illnesses related to their behaviours, etc., etc.) than another group of people (‘healthy people’), then if you need to finance this shortfall – which you do, as you face a budget constraint – there are only two basic ways to do this; you can either charge the high-cost group (‘unhealthy people’) more, or you can require the other group (‘healthy people’) to make up the difference. Any scheme which deals with such a case of unequal net contribution rates are equivalent either to one of those schemes or a mix of the two, regardless of what you call things and how it’s done, and regardless of which groups we are talking about (old people also have higher health care expenditures than do young people, and most health care systems implicitly redistribute income from the young to the old). If you’re worried about ‘health fascism’ and the implications of subsidizing healthy behaviours (/’punishing’ unhealthy behaviours) you should at least keep in mind that if the health care costs of people who live healthy lives and people who do not are dissimilar then any system that deals with this issue – which all systems must – can either choose to ‘subsidize’ healthy behaviours or unhealthy behaviours; there’s no feasible way to design a ‘neutral system’ if the costs of the groups are dissimilar.
Having said all this, the very important next point is then that it is much more difficult to make simple schemes that would accomplish an outcome in which people who engage in unhealthy behaviours are required to pay more without at the same time introducing a significant number of new problems than people who are not familiar with this field would probably think it is. And it’s almost certainly much harder to evaluate if the proposed change actually accomplished what you wanted to accomplish than you think it is. Even if we are clear about what we want to accomplish and can all agree that that is what we are aiming for – i.e. we are disregarding the political preferences of large groups of voters and whether the setup in question is at all feasible to accomplish – this stuff is really much harder than it looks, for many reasons.
Let’s start out by assuming that smoking increases the risk of disease X by 50%. Say you can’t say which of the cases of X are caused by smoking, all you know is that smoking increases the risk at the population level. Say you don’t cover disease X at all if someone smokes, that is, smokers are required to pay the full treatment cost out of pocket if they contract disease X. It’s probably not too controversial to state that this approach might by some people be perceived of as not completely ‘fair’ to the many smokers who would have got disease X even if they had not smoked (a majority in this particular case, though of course the proportion will vary with the conditions and the risk factors in question). Now, a lot of the excess health care costs related to smoking are of this kind, and it is actually a pretty standard pattern in general with risk factors – smoking, alcohol, physical inactivity, etc. You know that these behaviours increase risk, but you usually can’t say for certain which of the specific cases you observe in clinical practice are actually (‘perfectly’/’completely’/’partially’?) attributable to the behaviour. And quite often the risk increase associated with a specific behaviour is actually really somewhat modest, compared to the relevant base rates, meaning that many of the people who engage in behaviours which increase risk and who get sick might well have got sick even if they hadn’t engaged in those risky behaviours.
On top of this problem usually it’s also the case that risk factors interact with each other. Smoking increases the risk of cancer of the esophagus, but so does alcohol and obesity, and if a person both smokes and drinks the potential interaction effect may not be linear – so you most likely often can’t just identify individual risk factors in specific studies and then pool them later and add them all together to get a proper risk assessment. A further complication is that behaviours may both increase as well as decrease risk – to stick with the example, diets high in fruits and vegetables both lower the risk of cancer of the esophagus. Exercise probably does as well – we know that exercise has important and highly complex effects on immune system function (see e.g. this post). Usually a large number of potential risk factors is at play at the same time, there may be multiple variables which lower risk and are also important to include if you want a proper risk assessment, and even if you knew in theory which interaction terms were likely to be relevant, you might even so find yourself in a situation unable to estimate the interaction terms of interest – this might take high-powered studies with large numbers of patients, which may not be available or the results of such high-powered studies may not apply to your specific subgroup of patients. Cost-effectiveness is also an issue – it’s expensive to assess risk properly. One take-away is that you’ll still have a lot of unfairness in a modified contribution rate model, and even evaluating fairness aspects of the change may be difficult to impossible because to some extent this question is unknowable. You might find yourself in a situation where you charge the obese guy more because obesity means he’s high risk, but in reality he is actually lower risk than is the non-fat guy who is charged a lower rate, because he also exercises and eats a lot of fruits and vegetables, which the other guy doesn’t.
Of course the above paragraph took it for granted that it was even possible to quantify the excess costs attributable to a specific condition. That may not be easy at all to do, and there may be large uncertainties involved. The estimated excess cost will depend upon a variety of factors which may or may not be of interest to the party performing the analysis, for example it may be very important which time frame you’re looking at and which discounting methodology is applied (see e.g. the last paragraph in this post). The usual average vs marginal cost problem (see the third-last paragraph in the post to which I link in the previous sentence – this post also has more on this topic) also applies here and is related to ‘the fat guy who exercises and is low-risk’-problem; ideally you’d want to charge people with higher health care utilization levels more (again, in a setting where we assume the excess cost is associated with life-style variables which are modifiable – this was our starting point), but if there’s a large amount of variation in costs across individuals in the specific subgroups of interest and you only have access to average costs rather than individual-level costs, then a scheme only taking into account the differences in the averages may be very sub-optimal when you look at it from the viewpoint of the individual. Care needs to be taken to avoid problems like e.g. Simpson’s paradox.
Risk factors are not the only things that cluster; so do diseases. An example:
“An analysis of the Robert Koch-Institute (RKI) from 2012 shows that more than 50 % of German people over 65 years suffer from at least one chronic disease, approximately 50 % suffer from two to four chronic diseases, and over a quarter suffer from five or more diseases .” (link)
78.3 % of the type 2 diabetics also suffered from hypertension in that study. Does this fact make it easier or harder to figure out what is ‘the true cost contribution’ of ‘type 2 diabetes’ and ‘hypertension’ (and, what we’re ultimately interested in in this setting – the ‘true cost contribution’ of the unhealthy behaviours which lead some individuals to develop type 2 diabetics and hypertension who would not otherwise have developed diabetes and/or hypertension (…/as early as they did)? It should be noted that diabetes was estimated to account for 11 % of total global healthcare expenditure on adults in 2013 (link). That already large proportion is expected to rise substantially in the decades to come – if you’re interested in cost growth trajectories, this is a major variable to account for. Attributability is really tricky here, and perhaps even more tricky in the case of hypertension – but for what it’s worth, according to a CDC estimate hypertension cost the US $46 billion per year, or ~$150/per person per year.
Anyway, you look at the data and you make guesses, but the point is that doctor Smith won’t know for certain if Mr. Hanson would have had a stroke even if he hadn’t smoked or not. A proposal of not providing payment for a health care service or medical product in the case of an ‘obviously risky-behaviour-related-health-condition’ may sometimes appear to be an appealing proposition and you sometimes see people make this sort of proposal in discussions of this nature, but it tends to be very difficult when you look at the details to figure out just what those ‘obviously risky-behaviour-related-health-conditions’ are, and even harder to make even remotely actuarially fair adjustments to the premiums and coverage patterns to reflect the risk. Smoking and lung cancer is a common example of a relatively ‘clean’ case, but most cases are ‘less clean’ and even here there are complications; a substantial proportion of lung cancer cases are not caused by tobacco – occupational exposures also cause a substantial proportion of cases, and: “If considered in its own disease category […] lung cancer in never smokers would represent the seventh leading cause of cancer mortality globally, surpassing cancers of the cervix, pancreas, and prostate,5 and among the top 10 causes of death in the United States.” (link) Occupational exposures (e.g. asbestos) are not likely to fully account for all cases, and for example it has also been found that other variables, including previous pneumonia infections and tuberculosis, affect risk (here are a couple of relevant links to some previous coverage I wrote on these topics).
I think many people who have preferences of this nature (‘if it’s their own fault they’re sick, they should pay for it themselves’) underestimate how difficult it may be to make changes which could be known with a reasonable level of certainty to actually have the intended consequences, even assuming everybody agreed on the goal to be achieved. This is in part because there are many other aspects and complications which need to be addressed as well. Withholding payment in the case of costly preventative illness may for example in some contexts increase cost, rather than decrease them. The risk of complications of some diseases – an important cost driver in the context of diabetes – tends to be dependent on post-diagnosis behavioural patterns. The risk of developing diabetes complications will depend upon the level of glycemic control. If you say you won’t cover complications at all in the case of ‘self-inflicted disease X’, then you also to some extent tend to remove the option of designing insurance schemes which might lower cost and complication rates post-diagnosis by rewarding ‘good’ (risk-minimizing) behaviours post-diagnosis and punishing ‘bad’ (risk-increasing) behaviours. This is not desirable in the context of diseases where post-diagnosis behaviour is an important component of the cost function, as it certainly is in the diabetes context. There are multiple potential mechanisms here, some of which are disease specific (e.g. suboptimal diet in a diagnosed type 2 diabetic) and some of which may not be (a more general mechanism could e.g. be lowered compliance/adherence to treatment in the uncovered populations because they can’t afford the drugs which are required to treat their illness; though the cost-compliance link is admittedly not completely clear in the general case, there are certainly multiple diseases where lowered compliance to treatment would be expected to increase cost long-term).
And again, also in the context of complications fairness issues are not as simple to evaluate as people might like them to be; some people may have a much harder time controlling their disease than others, or they may be more susceptible to complications given the same behaviour. Some may already have developed complications by the time of diagnosis. Such issues make it difficult to design simple rules which would achieve what you want them to achieve without having unfortunate side-effects; for example a rule that a microvascular diabetes-related complication is automatically ‘your own fault’ (so we won’t pay for it), which might be motivated by the substantial amount of research linking glycemic control with complication risk, would punish some diabetics who have had the disease for a longer amount of time (many complications are not only strongly linked to Hba1c but also display a substantial degree of duration-dependence; for example in type 1 diabetics one study found that diabetic retinopathy was present in 13% of patients with a duration of disease less than 5 years, whereas the corresponding figure was 90% for individuals with a disease duration of 10–15 years (Sperling et al., p. 393). I also recall reading a study finding that Hba1c itself is increasing with diabetes duration, which may be partly accounted for by the higher risk of hypoglycemia related to hypoglycemia-unawareness-syndromes in individuals with long-standing disease), individuals with diseases which are relatively hard to control (perhaps due to genetics, or maybe again due to the fact that they have had the disease for a longer amount of time; the presence of hypoglycemia unawareness is as alluded to above to a substantial degree duration-dependent, and this problem increases the risk of hospitalizations, which are expensive), diabetics who developed complications before they knew they were sick (a substantial proportion of type 2 diabetics develop some degree of microvascular damage pre-diagnosis), and diabetics with genetic variants which confer an elevated risk of complications (“observations suggest that involvement of genetic factors is increasing the risk of complications” (Sperling et al., p. 226), and for example in the DCCT trial familial clustering of both neuropathy and retinopathy was found; clustering which persisted after controlling for Hba1c – for more on these topics, see e.g. Sperling et al.’s chapter 11).
Other decision rules would similarly lead to potentially problematic incentives and fairness issues; for example requiring individuals to meet a specific Hba1c goal might be more desirable than to just not cover complications, but that one also leads to potential problems; ideally such an Hba1c goal should be individualized, because of the above-mentioned complexities and others I have not mentioned here; to require a newly-diagnosed individual to meet the same goals as someone who has had diabetes for decades does not make sense, and neither does it make sense to require these two groups to meet exactly the same Hba1c goal as the middle-aged female diabetic who desires to become pregnant (diabetes greatly increases the risk of pregnancy complications, and strict glycemic control is extremely important in this patient group). It’s important to note that these issues don’t just relate to whether or not the setup is perceived of as fair, but it also relates to whether or not you would expect the intended goals to actually be met or not when you implement the rule. If you were to require that a long-standing diabetic with severe hypoglycemia unawareness had to meet the same Hba1c goal as the newly diagnosed individual, this might well lead to higher overall cost, because said individual might suffer a large number of hypoglycemia-related hospitalizations which would have been avoidable if a more lax requirement was imposed; when you decrease Hba1c you decrease the risk of long-term complications, but you increase the risk of hypoglycemia. A few numbers might make it easier to make sense of how expensive hospitalizations really are, and why I emphasize them here. In this diabetes-care publication they assign a cost for an inpatient day for a diabetes-related hospitalization at $2,359 and an emergency visit at ~$800. The same publication estimates the total average annual excess expenditures of diabetics below the age of 45 at $4,394. Going to the hospital is really expensive (43% of the total medical costs of diabetes are accounted for by hospital inpatient care in that publication).
A topic which was brought up in the SSC discussion was the question of the extent to which private providers have a greater incentive to ‘get things right’ in terms of assessing risk. I don’t take issue with this notion in general, but there are a lot of complicating factors in the health care context. One factor of interest is that it is costly to get things right. If you’re looking at this from an insurance perspective, larger insurance providers may be better at getting things right because they can afford to hire specialists who provide good cost estimates – getting good cost estimates is really hard, as I’ve noted above. Larger providers translate into fewer firms, which increases firm concentration and may thus increase collusion risk, which may again increase the prices of health care services. Interestingly if your aim is to minimize health care cost growth increased market power of private firms may actually be a desirable state of affairs/goal, because cost growth is a function of both unit prices and utilization levels, and higher premiums are likely to translate into lower utilization rates, which may lower overall costs and -cost growth. I decided to include this observation here also in order to illustrate that what is an optimal outcome depends on what your goal is, and in the setting of the health care sector you sometimes need to be very careful about thinking about what your actual goal is, and which other goals might be relevant.
When private insurance providers become active in a market that also includes a government entity providing a level of guaranteed coverage, total medical outlays may easily increase rather than decrease. The firms may meed an unmet need, but some of that unmet need may be induced demand (here’s a related link). Additionally, the bargaining power of various groups of medical personnel may change in such a setting, leading to changes in compensation schedules which may not be considered desirable/fair. An increase in total outlays may or may not be considered a desirable outcome, but this does illustrate once again the point that you need to be careful about what you are trying to achieve.
There’s a significant literature on how the level of health care integration, both at the vertical and horizontal level, both in terms of financial structure and e.g. in terms of service provision structure, may impact health care costs, and this is an active area of research where we in some contexts do not yet know the answers.
Even when cost minimization mechanisms are employed in the context of private firms and the firm in question is efficient, the firm may not internalize all relevant costs. This may paradoxically lead to higher overall cost, due to coverage decisions taken ‘upstream’ influencing costs ‘downstream’ in an adverse manner; I have talked about this topic on this blog before. A diabetic might be denied coverage of glucose testing materials by his private insurer, and that might mean that the diabetic instead gets hospitalized for a foreseeable and avoidable complication (hypoglycemic coma due to misdosing), but because it might not be the same people paying for the testing material and the subsequent hospitalization it might not matter to the people denying coverage of the testing materials, and/so they won’t take it into account when they’re making their coverage decisions. That sort of thing is quite common in the health care sector – different entities pay for and receive payments for different things, and this is once again a problem to keep in mind if you’re interested in health care evaluation; interventions which seem to lower cost may not do so in reality, because the intervention lead to higher health care utilization elsewhere in the system. If incentives are not well-aligned things may go badly wrong, and they are often not well-aligned in the health care sector. When both the private and public sectors are involved in either the financial arrangements and/or actual health service provision – which is the default health care system setup for developed societies – this usually leads to highly complex systems, where the scope for such problems to appear seems magnified, rather than the opposite. I would assume that in many cases it matters a lot more that incentives are well-aligned than which specific entity is providing insurance or health care in the specific context, in part a conclusion drawn from the coverage included in Simmons, Wenzel & Zgibor‘s book.
In terms of the incentive structures of the people involved in the health care sector, this stuff is of course also adding another layer of complexity. In all sectors of the economy you have people with different interests who interact with each other, and when incentives change outcomes change. Outcomes may be car batteries, or baseball bats, or lectures. Evaluating outcomes is easier in some settings than in others, and I have already touched upon some of the problems that might be present when you’re trying to evaluate outcomes in the health care context. How easy it is to evaluate outcomes will naturally vary across sub-sectors of the health care sector but a general problem which tends to surface here is the existence of various forms of asymmetrical information. There are multiple layers, but a few examples are worth mentioning. To put it bluntly, the patient tends to know his symptoms and behavioural patterns – which may be disease-relevant, and this aspect is certainly important to include when discussing preventative illnesses caused at least in part by behaviours which increase the risk of said illnesses – better than his doctor, and the doctor will in general tend to know much more about the health condition and potential treatment options than will the patient. The patient wants to get better, but he also wants to look good in the eyes of the doctor, which means he might not be completely truthful when interacting with the doctor; he might downplay how much alcohol he drinks, misrepresent how often he exercises, or he may lie about smoking habits or about how much he weighs. These things make risk-assessments more difficult than they otherwise might have been. As for the GPs, usually we here have some level of regulation which restricts their behaviour to some extent, and part of the motivation for such regulation is to reduce the level of induced demand which might otherwise be the result of information asymmetry in the context of stuff like relevant treatment effects. If a patient is not sufficiently competent to evaluate the treatments he receives (‘did the drug the doctor ordered really work, or would I have gotten better without it?’), there’s a risk he might be talked into undergoing needless procedures or take medications for which he has no need, especially if the doctor who advises him has a financial interest in the treatment modality on offer.
General physicians have different incentives from nurses and specialists working in hospitals, and all of these groups may experience conflicts of interests when they’re dealing with insurance providers and with each other. Patients as mentioned have their own set of incentives, which may not align perfectly with those of the health care providers. Different approaches to how to deal with such problems lead to different organizational setups, all of which influence both the quantity and quality of care, subject to various constraints. It’s an active area of research whether decreasing competition between stakeholders/service providers may decrease costs; one thing that is relatively clear from diabetes research with which I have familiarized myself is that when different types of care providers coordinate activities, this tends to lead to better outcomes (and sometimes, but not always, lower costs), because some of the externalized costs become internalized by virtue of the coordination. It seems very likely to me that conclusions to such questions will be different for different subsectors of the health care sector. A general point might be that more complex diseases should be expected to be more likely to generate cost savings from increased coordination than should relatively simple diseases (if you’re fuzzy about what the concept of disease complexity refers to, this post includes some relevant observations). This may be important, because complex diseases also should probably tend to be more expensive to treat in general, because the level of need in patients is higher.
It’s perhaps hardly surprising, considering the problems I’ve already discussed related to how difficult it may be to properly assess costs, that there’s a big discussion to be had about how to even estimate costs (and benefits) in specific contexts, and that people write books about these kinds of things. A lot of things have already been said on this topic and a lot more could be said, but one general point perhaps worth repeating is that it may in the health care sector be very difficult to figure out what things (‘truly’) cost (/’is worth’). If you only have a public sector entity dealing with a specific health problem and patients are not charged for receiving treatment, it may be very difficult to figure out what things ‘should’ cost because relevant prices are simply missing from the picture. You know what the government entity paid the doctors in wages and what it paid for the drugs, but the link between payment and value is sometimes a bit iffy here. There are ways to at least try to address some of these issues, but as already noted people write books about these kinds of things so I’m not going to provide all the highlights here – I refer to the previous posts I’ve written on these topics instead.
Another important related point is that medical expenditures and medical costs are not synonyms. There are many costs associated with illness which are not directly related to e.g. a payment to a doctor. People who are ill may be less productive while they are at work, they may have more sick-days, they may retire earlier, their spouse may cut down on work hours to take care of them instead of going to work, a family caretaker may become ill as a result of the demands imposed by the caretaker role (for example Alzheimer’s disease significantly increases the risk of depression in the spouse). Those costs are relevant, there are literatures on these things, and in some contexts such ‘indirect costs’ (e.g. lower productivity at work and early retirement) may make up a very substantial proportion of the total costs of a health condition. I have seen diabetes cost estimates which indicated that the indirect costs may account for as much as 50 % of the total costs.
If there’s a significant disconnect between total costs and medical expenditures then minimizing expenditures may not be desirable from an economic viewpoint. A reasonable assessment model will/should in the context of models of outlays include both a monetary cost parameter and a quality/quantity (ideally both) parameter; if you neglect to take account of the latter, in some sense you’re only dealing with what you pay out, not what you get for that payment (which is relevant). If you don’t take into account indirect costs you implicitly allow cost switching practices to potentially muddle the picture and make assessments more difficult; for example if you provide fewer long-term care facilities then the number of people involved in ‘informal care’ (e.g. family members having to take care of granny) will go up, and that is going to have secondary effects downstream which should also be assessed (you improve the budget in the context of the long-term care facilities, but you may at the same time increase demands on e.g. psychiatric institutions and marginally lower especially the female labour market participation rate. The net effect may still be positive, but the point is that an evaluation will/should include costs like these in the analysis, at least if you want anything remotely close to the full picture).
Let’s return to those smokers we talked about earlier. A general point not mentioned yet is that if you don’t cover smokers in the public sector because of cost considerations, many of them may also not be covered by private insurance either. This is because a group of individuals that is high risk and expensive to treat will be demanded high premiums (or the insurance providers would go out of business), and for the sake of this discussion we’re now assuming smokers are expensive. If that is so, many of them probably would not be able to afford the premiums demanded. Now, one of the health problems which are very common in smokers is chronic obstructive pulmonary disease (COPD). Admission rates for COPD patients differ as much as 10-fold between European countries, and one of the most important parameters regarding pharmacoeconomics is the hospitalization rate (both observations are from this text). What does this mean? It means that we know that admission rate from COPD is highly responsive to the treatment regime; populations well-treated have much fewer hospitalizations. 4% of all Polish hospitalizations are due to COPD. If you remove the public sector subsidies, the most likely scenario you get seems to me to be a poor-outcomes scenario with lots of hospitalizations. Paying for those is likely to be a lot more expensive than it is to treat the COPD pharmacologically in the community. And if smokers aren’t going to be paying for it, someone else will have to do that. If you both deny them health insurance and refuse them treatment if they cannot pay for it they may just die of course, but in most cost-assessment models that’s a high-cost outcome, not a low-cost outcome (e.g. due to lost work-life productivity etc. Half of people with COPD are of working age, see the text referred to above.). This is one example where the ‘more fair’ option might lead to higher costs, rather than lower costs. Some people might still consider such an outcome desirable, it depends on the maximand of interest, but such outcomes are worth considering when assessing the desirability of different systems.
A broadly similar dynamic, in the context of post-diagnosis behaviour and links to complications and costs, may be present in the context of type 2 diabetes. I know much more about diabetes than I do about respirology, but certainly in the case of diabetes this is a potentially really big problem. Diabetics who are poorly regulated tend to die a lot sooner than other people, they develop horrible complications, they stop being able to work, etc. etc. Some of those costs you can ignore if you’re willing to ‘let them die in the streets’ (as the expression goes), but a lot of those costs are indirect costs due to lower productivity, and those costs aren’t going anywhere, regardless of who may or may not be paying the medical bills of these people. Even if they have become sick due to a high-risk behaviour of their own choosing, their health care costs post-diagnosis will still be highly dependent upon their future medical care and future health insurance coverage. Denying them coverage for all diabetes-related costs post-diagnosis may, paradoxical though it may seem to some, not be the cost-minimizing option.
I already talked about information asymmetries. Another problematic aspect linked to information management also presents itself here in a model of this nature (‘deny all diabetes-related coverage to known diabetics’); people who suspect they might be having type 2 diabetes may choose not to disclose this fact to a health care provider because of the insurance aspect (denial of coverage problems). Insurance providers can of course (and will try to) counter this by things like mandatory screening protocols, but this is expensive, and even assuming they are successful you again not only potentially neglect to try to minimize the costs of the high-cost individuals in the population (the known diabetics, who might be cheaper long-term if they had some coverage), you also price a lot of non-diabetics out of the market (because premiums went up to pay for the screening). And some of those non-diabetics are diabetics to-be, who may get a delayed diagnosis as a result, with an associated higher risk of (expensive) complications. Again, as in the smoking context if the private insurer does not cover the high-cost outcomes someone else will have to do that, and the blind diabetic in a wheel-chair is not likely to be able to pay for his dialysis himself.
More information may in some situations lead to a breakdown in insurance markets. This is particularly relevant in the context of genetics and genetic tests. If you have full information, or close to it, the problem you have to some extent stops being an insurance problem and instead becomes a problem of whether or not to, and to which extent you want to-, explicitly compensate people for having been dealt a bad hand by nature. To put it in very general terms, insurance is a better framework for diseases which can in principle be cured than it is for chronic conditions where future outlays are known with a great level of certainty; the latter type of disease tends to be difficult to handle in an insurance context.
People who have one disease may develop other diseases as time progresses, and having disease X may increase or decrease the risk of disease Y. People study such disease variability patterns, and have done so for years, but there’s still a lot of stuff we don’t know – here’s a recent post on these topics. Such patterns are interesting for multiple reasons. One major motivation for studying these things is that ‘different’ diseases may have common mechanisms, and the identification of these mechanisms may lead to new treatment options. A completely different motivation for studying these things relate rather to the kind of stuff covered in this post, where you instead wonder about economic aspects; for example, if the smoker stops smoking he may gain weight and eventually develop type 2 diabetes instead of developing some smoking-related condition. Is this outcome better or worse than the other? It’s important to keep in mind when evaluating changes in compensation schedules/insurance structures that diseases are not independent, and this is a problem regardless of whether you’re interested in total costs or ‘just’ direct outlays. Say you’re ‘only’ worried about outlays and you are trying to figure out if it is a good idea to deny coverage to smokers, and you know that ex-smokers are likely to gain weight and have an increased risk of type 2 diabetes. Then the relevant change in cost is not the money you save on smoking-related illness, it’s the cost change you arrive at when after you account for those savings also account for the increased cost of treating type 2 diabetes. Disease interdependencies are probably as complex as risk factor interdependencies – the two phenomena are to some extent representing the same basic phenomenon – so this makes true cost evaluation even harder than it already was. Not all relevant costs at the societal level are of course medical costs; if people live longer, and they rely partly on a pension scheme to which they are no longer contributing, that cost is also relevant.
If a group of people who live longer cost more than a group of people who do not live as long, and you need to cover the associated shortfall, then – as we concluded in the beginning – there are really only two ways to handle this: Make them pay more than the people who do not live as long, or make the people who do not live as long pay more to cover the shortfall. Another way to look at this is that in this situation you can either tax people ‘for not living long enough’, or you can tax people for ‘not dying at the appropriate time’. On the other hand (?), if a group of people who die early turns out to be the higher-cost group in the relevant comparison (perhaps because they have shorter working lives and so pay into the system for a shorter amount of time), then you can deal with this problem by… either taxing them for ‘not living long enough’ or by punishing the people who live long lives for ‘not dying at the appropriate time’. No, of course it doesn’t matter which group is high cost, the solution mechanism is the same in both cases – make one of the groups pay more. And every time you tweak things you change the incentives of various people, and implicit effects like these hide somewhere in the background.
It’s been a long time since I last posted one of these posts, so a great number of links of interest has accumulated in my bookmarks. I intended to include a large number of these in this post and this of course means that I surely won’t cover each specific link included in this post in anywhere near the amount of detail it deserves, but that can’t be helped.
“For those diagnosed with ASD in childhood, most will become adults with a significant degree of disability […] Seltzer et al […] concluded that, despite considerable heterogeneity in social outcomes, “few adults with autism live independently, marry, go to college, work in competitive jobs or develop a large network of friends”. However, the trend within individuals is for some functional improvement over time, as well as a decrease in autistic symptoms […]. Some authors suggest that a sub-group of 15–30% of adults with autism will show more positive outcomes […]. Howlin et al. (2004), and Cederlund et al. (2008) assigned global ratings of social functioning based on achieving independence, friendships/a steady relationship, and education and/or a job. These two papers described respectively 22% and 27% of groups of higher functioning (IQ above 70) ASD adults as attaining “Very Good” or “Good” outcomes.”
“[W]e evaluated the adult outcomes for 45 individuals diagnosed with ASD prior to age 18, and compared this with the functioning of 35 patients whose ASD was identified after 18 years. Concurrent mental illnesses were noted for both groups. […] Comparison of adult outcome within the group of subjects diagnosed with ASD prior to 18 years of age showed significantly poorer functioning for those with co-morbid Intellectual Disability, except in the domain of establishing intimate relationships [my emphasis. To make this point completely clear, one way to look at these results is that apparently in the domain of partner-search autistics diagnosed during childhood are doing so badly in general that being intellectually disabled on top of being autistic is apparently conferring no additional disadvantage]. Even in the normal IQ group, the mean total score, i.e. the sum of the 5 domains, was relatively low at 12.1 out of a possible 25. […] Those diagnosed as adults had achieved significantly more in the domains of education and independence […] Some authors have described a subgroup of 15–27% of adult ASD patients who attained more positive outcomes […]. Defining an arbitrary adaptive score of 20/25 as “Good” for our normal IQ patients, 8 of thirty four (25%) of those diagnosed as adults achieved this level. Only 5 of the thirty three (15%) diagnosed in childhood made the cutoff. (The cut off was consistent with a well, but not superlatively, functioning member of society […]). None of the Intellectually Disabled ASD subjects scored above 10. […] All three groups had a high rate of co-morbid psychiatric illnesses. Depression was particularly frequent in those diagnosed as adults, consistent with other reports […]. Anxiety disorders were also prevalent in the higher functioning participants, 25–27%. […] Most of the higher functioning ASD individuals, whether diagnosed before or after 18 years of age, were functioning well below the potential implied by their normal range intellect.”
ii. Premature mortality in autism spectrum disorder. This is a Swedish matched case cohort study. Some observations from the paper:
“The aim of the current study was to analyse all-cause and cause-specific mortality in ASD using nationwide Swedish population-based registers. A further aim was to address the role of intellectual disability and gender as possible moderators of mortality and causes of death in ASD. […] Odds ratios (ORs) were calculated for a population-based cohort of ASD probands (n = 27 122, diagnosed between 1987 and 2009) compared with gender-, age- and county of residence-matched controls (n = 2 672 185). […] During the observed period, 24 358 (0.91%) individuals in the general population died, whereas the corresponding figure for individuals with ASD was 706 (2.60%; OR = 2.56; 95% CI 2.38–2.76). Cause-specific analyses showed elevated mortality in ASD for almost all analysed diagnostic categories. Mortality and patterns for cause-specific mortality were partly moderated by gender and general intellectual ability. […] Premature mortality was markedly increased in ASD owing to a multitude of medical conditions. […] Mortality was significantly elevated in both genders relative to the general population (males: OR = 2.87; females OR = 2.24)”.
“Individuals in the control group died at a mean age of 70.20 years (s.d. = 24.16, median = 80), whereas the corresponding figure for the entire ASD group was 53.87 years (s.d. = 24.78, median = 55), for low-functioning ASD 39.50 years (s.d. = 21.55, median = 40) and high-functioning ASD 58.39 years (s.d. = 24.01, median = 63) respectively. […] Significantly elevated mortality was noted among individuals with ASD in all analysed categories of specific causes of death except for infections […] ORs were highest in cases of mortality because of diseases of the nervous system (OR = 7.49) and because of suicide (OR = 7.55), in comparison with matched general population controls.”
iii. Adhesive capsulitis of shoulder. This one is related to a health scare I had a few months ago. A few quotes:
“Adhesive capsulitis (also known as frozen shoulder) is a painful and disabling disorder of unclear cause in which the shoulder capsule, the connective tissue surrounding the glenohumeral joint of the shoulder, becomes inflamed and stiff, greatly restricting motion and causing chronic pain. Pain is usually constant, worse at night, and with cold weather. Certain movements or bumps can provoke episodes of tremendous pain and cramping. […] People who suffer from adhesive capsulitis usually experience severe pain and sleep deprivation for prolonged periods due to pain that gets worse when lying still and restricted movement/positions. The condition can lead to depression, problems in the neck and back, and severe weight loss due to long-term lack of deep sleep. People who suffer from adhesive capsulitis may have extreme difficulty concentrating, working, or performing daily life activities for extended periods of time.”
Some other related links below:
The prevalence of a diabetic condition and adhesive capsulitis of the shoulder.
“Adhesive capsulitis is characterized by a progressive and painful loss of shoulder motion of unknown etiology. Previous studies have found the prevalence of adhesive capsulitis to be slightly greater than 2% in the general population. However, the relationship between adhesive capsulitis and diabetes mellitus (DM) is well documented, with the incidence of adhesive capsulitis being two to four times higher in diabetics than in the general population. It affects about 20% of people with diabetes and has been described as the most disabling of the common musculoskeletal manifestations of diabetes.”
Adhesive Capsulitis (review article).
“Patients with type I diabetes have a 40% chance of developing a frozen shoulder in their lifetimes […] Dominant arm involvement has been shown to have a good prognosis; associated intrinsic pathology or insulin-dependent diabetes of more than 10 years are poor prognostic indicators.15 Three stages of adhesive capsulitis have been described, with each phase lasting for about 6 months. The first stage is the freezing stage in which there is an insidious onset of pain. At the end of this period, shoulder ROM [range of motion] becomes limited. The second stage is the frozen stage, in which there might be a reduction in pain; however, there is still restricted ROM. The third stage is the thawing stage, in which ROM improves, but can take between 12 and 42 months to do so. Most patients regain a full ROM; however, 10% to 15% of patients suffer from continued pain and limited ROM.”
Musculoskeletal Complications in Type 1 Diabetes.
“The development of periarticular thickening of skin on the hands and limited joint mobility (cheiroarthropathy) is associated with diabetes and can lead to significant disability. The objective of this study was to describe the prevalence of cheiroarthropathy in the well-characterized Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) cohort and examine associated risk factors […] This cross-sectional analysis was performed in 1,217 participants (95% of the active cohort) in EDIC years 18/19 after an average of 24 years of follow-up. Cheiroarthropathy — defined as the presence of any one of the following: adhesive capsulitis, carpal tunnel syndrome, flexor tenosynovitis, Dupuytren’s contracture, or a positive prayer sign [related link] — was assessed using a targeted medical history and standardized physical examination. […] Cheiroarthropathy was present in 66% of subjects […] Cheiroarthropathy is common in people with type 1 diabetes of long duration (∼30 years) and is related to longer duration and higher levels of glycemia. Clinicians should include cheiroarthropathy in their routine history and physical examination of patients with type 1 diabetes because it causes clinically significant functional disability.”
Musculoskeletal disorders in diabetes mellitus: an update.
“Diabetes mellitus (DM) is associated with several musculoskeletal disorders. […] The exact pathophysiology of most of these musculoskeletal disorders remains obscure. Connective tissue disorders, neuropathy, vasculopathy or combinations of these problems, may underlie the increased incidence of musculoskeletal disorders in DM. The development of musculoskeletal disorders is dependent on age and on the duration of DM; however, it has been difficult to show a direct correlation with the metabolic control of DM.”
Musculoskeletal Disorders of the Hand and Shoulder in Patients with Diabetes.
“In addition to micro- and macroangiopathic complications, diabetes mellitus is also associated with several musculoskeletal disorders of the hand and shoulder that can be debilitating (1,2). Limited joint mobility, also termed diabetic hand syndrome or cheiropathy (3), is characterized by skin thickening over the dorsum of the hands and restricted mobility of multiple joints. While this syndrome is painless and usually not disabling (2,4), other musculoskeletal problems occur with increased frequency in diabetic patients, including Dupuytren’s disease [“Dupuytren’s disease […] may be observed in up to 42% of adults with diabetes mellitus, typically in patients with long-standing T1D” – link], carpal tunnel syndrome [“The prevalence of [carpal tunnel syndrome, CTS] in patients with diabetes has been estimated at 11–30 % […], and is dependent on the duration of diabetes. […] Type I DM patients have a high prevalence of CTS with increasing duration of disease, up to 85 % after 54 years of DM” – link], palmar flexor tenosynovitis or trigger finger [“The incidence of trigger finger [/stenosing tenosynovitis] is 7–20 % of patients with diabetes comparing to only about 1–2 % in nondiabetic patients” – link], and adhesive capsulitis of the shoulder (5–10). The association of adhesive capsulitis with pain, swelling, dystrophic skin, and vasomotor instability of the hand constitutes the “shoulder-hand syndrome,” a rare but potentially disabling manifestation of diabetes (1,2).”
“The prevalence of musculoskeletal disorders was greater in diabetic patients than in control patients (36% vs. 9%, P < 0.01). Adhesive capsulitis was present in 12% of the diabetic patients and none of the control patients (P < 0.01), Dupuytren’s disease in 16% of diabetic and 3% of control patients (P < 0.01), and flexor tenosynovitis in 12% of diabetic and 2% of control patients (P < 0.04), while carpal tunnel syndrome occurred in 12% of diabetic patients and 8% of control patients (P = 0.29). Musculoskeletal disorders were more common in patients with type 1 diabetes than in those with type 2 diabetes […]. Forty-three patients [out of 100] with type 1 diabetes had either hand or shoulder disorders (37 with hand disorders, 6 with adhesive capsulitis of the shoulder, and 10 with both syndromes), compared with 28 patients [again out of 100] with type 2 diabetes (24 with hand disorders, 4 with adhesive capsulitis of the shoulder, and 3 with both syndromes, P = 0.03).”
Association of Diabetes Mellitus With the Risk of Developing Adhesive Capsulitis of the Shoulder: A Longitudinal Population-Based Followup Study.
“A total of 78,827 subjects with at least 2 ambulatory care visits with a principal diagnosis of DM in 2001 were recruited for the DM group. The non-DM group comprised 236,481 age- and sex-matched randomly sampled subjects without DM. […] During a 3-year followup period, 946 subjects (1.20%) in the DM group and 2,254 subjects (0.95%) in the non-DM group developed ACS. The crude HR of developing ACS for the DM group compared to the non-DM group was 1.333 […] the association between DM and ACS may be explained at least in part by a DM-related chronic inflammatory process with increased growth factor expression, which in turn leads to joint synovitis and subsequent capsular fibrosis.”
It is important to note when interpreting the results of the above paper that these results are based on Taiwanese population-level data, and type 1 diabetes – which is obviously the high-risk diabetes subgroup in this particular context – is rare in East Asian populations (as observed in Sperling et al., “A child in Helsinki, Finland is almost 400 times more likely to develop diabetes than a child in Sichuan, China”. Taiwanese incidence of type 1 DM in children is estimated at ~5 in 100.000).
iv. Parents who let diabetic son starve to death found guilty of first-degree murder. It’s been a while since I last saw one of these ‘boost-your-faith-in-humanity’-cases, but they in my impression do pop up every now and then. I should probably keep at hand one of these articles in case my parents ever express worry to me that they weren’t good parents; they could have done a lot worse…
v. Freedom of medicine. One quote from the conclusion of Cochran’s post:
“[I]t is surely possible to materially improve the efficacy of drug development, of medical research as a whole. We’re doing better than we did 500 years ago – although probably worse than we did 50 years ago. But I would approach it by learning as much as possible about medical history, demographics, epidemiology, evolutionary medicine, theory of senescence, genetics, etc. Read Koch, not Hayek. There is no royal road to medical progress.”
I agree, and I was considering including some related comments and observations about health economics in this post – however I ultimately decided against doing that in part because the post was growing unwieldy; I might include those observations in another post later on. Here’s another somewhat older Westhunt post I at some point decided to bookmark – I in particular like the following neat quote from the comments, which expresses a view I have of course expressed myself in the past here on this blog:
“When you think about it, falsehoods, stupid crap, make the best group identifiers, because anyone might agree with you when you’re obviously right. Signing up to clear nonsense is a better test of group loyalty. A true friend is with you when you’re wrong. Ideally, not just wrong, but barking mad, rolling around in your own vomit wrong.”
“Approximately 59% of all health care expenditures attributed to diabetes are for health resources used by the population aged 65 years and older, much of which is borne by the Medicare program […]. The population 45–64 years of age incurs 33% of diabetes-attributed costs, with the remaining 8% incurred by the population under 45 years of age. The annual attributed health care cost per person with diabetes […] increases with age, primarily as a result of increased use of hospital inpatient and nursing facility resources, physician office visits, and prescription medications. Dividing the total attributed health care expenditures by the number of people with diabetes, we estimate the average annual excess expenditures for the population aged under 45 years, 45–64 years, and 65 years and above, respectively, at $4,394, $5,611, and $11,825.”
“Our logistic regression analysis with NHIS data suggests that diabetes is associated with a 2.4 percentage point increase in the likelihood of leaving the workforce for disability. This equates to approximately 541,000 working-age adults leaving the workforce prematurely and 130 million lost workdays in 2012. For the population that leaves the workforce early because of diabetes-associated disability, we estimate that their average daily earnings would have been $166 per person (with the amount varying by demographic). Presenteeism accounted for 30% of the indirect cost of diabetes. The estimate of a 6.6% annual decline in productivity attributed to diabetes (in excess of the estimated decline in the absence of diabetes) equates to 113 million lost workdays per year.”
viii. Effect of longer term modest salt reduction on blood pressure: Cochrane systematic review and meta-analysis of randomised trials. Did I blog this paper at some point in the past? I could not find any coverage of it on the blog when I searched for it so I decided to include it here, even if I have a nagging suspicion I may have talked about these findings before. What did they find? The short version is this:
“A modest reduction in salt intake for four or more weeks causes significant and, from a population viewpoint, important falls in blood pressure in both hypertensive and normotensive individuals, irrespective of sex and ethnic group. Salt reduction is associated with a small physiological increase in plasma renin activity, aldosterone, and noradrenaline and no significant change in lipid concentrations. These results support a reduction in population salt intake, which will lower population blood pressure and thereby reduce cardiovascular disease.”
ix. Some wikipedia links:
Heroic Age of Antarctic Exploration (featured).
Kuiper belt (featured).
Treason (one quote worth including here: “Currently, the consensus among major Islamic schools is that apostasy (leaving Islam) is considered treason and that the penalty is death; this is supported not in the Quran but in the Hadith.“).
Savant syndrome (“It is estimated that 10% of those with autism have some form of savant abilities”). A small sidenote of interest to Danish readers: The Danish Broadcasting Corporation recently featured a series about autistics with ‘special abilities’ – the show was called ‘The hidden talents’ (De skjulte talenter), and after multiple people had nagged me to watch it I ended up deciding to do so. Most of the people in that show presumably had some degree of ‘savantism’ combined with autism at the milder end of the spectrum, i.e. Asperger’s. I was somewhat conflicted about what to think about the show and did consider blogging it in detail (in Danish?), but I decided against it. However I do want to add here to Danish readers reading along who’ve seen the show that they would do well to repeatedly keep in mind that a) the great majority of autistics do not have abilities like these, b) many autistics with abilities like these presumably do quite poorly, and c) that many autistics have even greater social impairments than do people like e.g. (the very likeable, I have to add…) Louise Wille from the show).
Black Death (“Over 60% of Norway’s population died in 1348–1350”).
Renault FT (“among the most revolutionary and influential tank designs in history”).
Weierstrass function (“an example of a pathological real-valued function on the real line. The function has the property of being continuous everywhere but differentiable nowhere”).
Void coefficient. (“a number that can be used to estimate how much the reactivity of a nuclear reactor changes as voids (typically steam bubbles) form in the reactor moderator or coolant. […] Reactivity is directly related to the tendency of the reactor core to change power level: if reactivity is positive, the core power tends to increase; if it is negative, the core power tends to decrease; if it is zero, the core power tends to remain stable. […] A positive void coefficient means that the reactivity increases as the void content inside the reactor increases due to increased boiling or loss of coolant; for example, if the coolant acts as a neutron absorber. If the void coefficient is large enough and control systems do not respond quickly enough, this can form a positive feedback loop which can quickly boil all the coolant in the reactor. This happened in the RBMK reactor that was destroyed in the Chernobyl disaster.”).
Gregor MacGregor (featured) (“a Scottish soldier, adventurer, and confidence trickster […] MacGregor’s Poyais scheme has been called one of the most brazen confidence tricks in history.”).
This is my second and last post about the book, which will include some quotes from the second half of the book, as well as some comments.
“Different countries have adopted very different health care financing systems. In fact, it is arguable that the arrangements for financing of health care are more variable between different countries than the financing of any other good or service. […] The mechanisms adopted to deal with moral hazard are similar in all systems, whilst the mechanisms adopted to deal with adverse selection and incomplete coverage are very different. Compulsory insurance is used by social insurance and taxation [schemes] to combat adverse selection and incomplete coverage. Private insurance relies instead on experience rating to address adverse selection and a mix of retrospective reimbursement and selective contracting and vertical integration to deal with incomplete coverage.”
I have mentioned this before here on the blog (and elsewhere), but it is worth reiterating because you seem to sometimes encounter people who do not know this; there are some problems you’ll have to face when you’re dealing with insurance markets which will be there regardless of which entity is in charge of the insurance scheme. It doesn’t matter if your insurance system is government based or if the government is not involved in the insurance scheme at all, moral hazard will be there either way as a potential problem and you’re going to have to deal with that somehow. In econ 101 you tend to learn that ‘markets are great’, but this is one of those problems which are not going to go away by privatization.
On top of common problems faced by all insurers/insurance systems, different types of -systems will also tend to face a different mix of potential problems, some of which are likely to merit special attention in the specific setting in question. Some problems tend to be much more common in some specific settings than they are in others, which means that to some extent when you’re deciding on what might be ‘the ‘best’ institutional setup’, part of what you’re deciding on is which problem you are most concerned about addressing. In an evaluation context it should be pointed out in that context that the fact that most systems are mixes of different systems rather than ‘pure systems’, which they are, means that evaluation problems tend to be harder than they might otherwise have been. To add to this complexity as noted above the ways insurers deal with the same problem may not necessarily be the same in different institutional setups, which is worth having in mind when performance is evaluated (i.e., the fact that country A has included in the insurance system a feature X intending to address problem Q does not mean that country B, which has not included X in the system, does not attempt to address problem Q; B may just be using feature Y instead of feature X to do so).
Chapter 7 of the book deals with Equity in health care, and although I don’t want to cover that chapter in any detail a few observations from the text I did find worth including in this post:
“In the 1930s, only 43% of the [UK] population were covered by the national insurance scheme, mainly men in manual and low-paid occupations, and covered only for GP services. Around 21 million people were not covered by any health insurance, and faced potentially catastrophic expenditure should they become ill.”
“The literature on equity in the finance of health care has focused largely on the extent to which health care is financed according to ability to pay, and in particular on whether people with different levels of income make […] different payments, which is a vertical equity concern. Much less attention has been paid to horizontal equity, which considers the extent to which people with the same income make the same payments. […] There is horizontal inequity if people with the same ability to pay for health care, for example the same income, pay different amounts for it. […] tax-based payments and social health insurance payments tend to have less horizontal inequity than private health insurance payments and direct out-of-pocket payments. […] there are many concepts of equity that could be pursued; these are limited only by our capacity to think about the different ways in which resources could be allocated. It is unsurprising therefore that so many concepts of equity are discussed in the literature.”
Chapter 8 is about ‘Health care labour markets’. Again I won’t cover the chapter in much detail – people interested in such topics might like to have a look at this paper, which I concluded from a brief skim looks like it covers a few of the topics also discussed in the chapter – but I did want to include a few data:
“[S]alaries and wages paid to health care workers account for a substantial component of total health expenditure: the average country devotes over 40% of its government-funded health expenditure to paying its health workforce […], though there are regional variations [from ~30% in Africa to ~50% in the US and the Middle East – the data source is WHO, and the numbers are from 2006]. […] The WHO estimates there are around 59 million paid health workers worldwide […], around nine workers for every 1 000 population, with around two-thirds of the total providing health care and one third working in a non-clinical capacity.”
The last few chapters of the book cover mostly topics I have dealt with before, in more detail – for example are most topics covered here which are also covered in Gray et al. covered in much more detail in the latter book, which is natural as this text is mostly an introductory undergraduate text whereas the Gray et al. text is not (the latter book was based on material taught in a course called ‘Advanced Methods of Cost-Effectiveness Analysis’) – or topics in which I’m not actually all that interested (e.g. things like ‘extra-welfarism‘). Below I have added some quotes from the remaining chapters. I apologize in advance for repeating myself, given the fact that I probably covered a lot of this stuff back when I covered Gray et al., but on the other hand I read that book a while ago anyway:
“Simply providing information on costs and benefits is in itself not evaluative. Rather, in economic evaluation this information is structured in such a way as to enable alternative uses of resources to be judged. There are many criteria that might be used for such judgements. […] The criteria that are the focus of economic analysis are efficiency and equity […] in practice efficiency is dealt with far more often and with greater attention to precise numerical estimates. […] In publicly provided health programmes, market forces might be weak or there might be none at all. Economic evaluation is largely concerned with measuring efficiency in areas where there is public involvement and there are no markets to generate the kind of information – for example, prices and profits – that enable us to judge this. […] The question of how costs and benefits are to be measured and weighed against each other is obviously a fundamental issue, and indeed forms the main body of work on the topic. The answers to this question are often pragmatic, but they also have very strong guides from theory.”
“[M]any support economic evaluation as a useful technique even where it falls short of being a full cost–benefit analysis [‘CBA’ – US], as it provides at least some useful information. A partial cost–benefit analysis usually means that some aspects of cost or benefit have been identified but not valued, and the usefulness of the information depends on whether we believe that if the missing elements were to be valued they would alter the balance of costs and benefits. […] A special case of a partial economic evaluation is where costs are valued but benefits are not. […] This kind of partial efficiency is dealt with by a different type of economic evaluation known as cost-effectiveness analysis (CEA). […] One rationale for CEA is that whilst costs are usually measured in terms of money, it may be much more difficult to measure benefits that way. […] Cost-effectiveness analysis tries to identify where more benefit can be produced at the same cost or a lower cost can be achieved for the same benefit. […] there are many cases where we may wish to compare alternatives in which neither benefits nor costs are held constant. In this case, a cost-effectiveness ratio (CER) – the cost per unit of output or effect – is calculated to compare the alternatives, with the implication that the lower the CER the better. […] CBA seeks to answer whether or not a particular output is worth the cost. CEA seeks to answer the question of which among two or more alternatives provides the most output for a given cost, or the lowest cost for a given output. CBA therefore asks whether or not we should do things, while CEA asks what is the best way to do things that are worth doing.”
“The major preoccupation of economic evaluation in health care has been measurement of costs and benefits – what should be measured and how it should be measured – rather than the aims of the analysis. […] techniques such as CBA and CEA are […] defined by measurement rather than economic theory. […] much of the economic evaluation literature gives the label cost-minimisation analysis to what was traditionally called CEA, and specifically restricts the term CEA to choices between alternatives that have similar types of effects but differing levels of effect and costs. […] It can be difficult to specify what the appropriate measure of effect is in CEA. […] care is […] required to ensure that whichever measure of effect is chosen does not mislead or bias the analysis – for example, if one intervention is better at preventing non-fatal heart attacks but is worse at preventing fatal attacks, the choice of effect measure will be crucial.”
“[Health] indicators are usually measures of the value of health, although not usually expressed in money terms. As a result, a third important type of economic evaluation has arisen, called cost–utility analysis (CUA). […] the health measure usually used in CUA is gains in quality-adjusted life years […] it is essentially a composite measure of gains in life expectancy and health-related quality of life. […] the most commonly used practice in CUA is to use the QALY and moreover to assume that each QALY is worth the same irrespective of who gains it and by what route. […] Similarly, CBA in practice focuses on sums of benefits compared to sums of costs, not on the distribution of these between people with different characteristics. It also does not usually take account of whether society places different weights on benefits experienced by different people; for example, there is evidence that many people would prefer health services to put a higher priority on improving the health of younger rather than older people (Tsuchiya et al., 2003).”
“Because CEA does not give a direct comparison between the value of effects and costs, decision rules are far more complex than for CBA and are bounded by restrictions on their applicability. The problem arises when the alternatives being appraised do not have equal costs or benefits, but instead there is a trade-off: the greater benefit that one of the alternatives has is achieved at a higher cost [this is not a rare occurrence, to put it mildly…]. The key problem is how that trade-off is to be represented, and how it can then be interpreted; essentially, encapsulating cost-effectiveness in a single index that can unambiguously be interpreted for decision-making purposes.”
“Although cost-effectiveness analysis can be very useful, its essential inability to help in the kind of choices that cost–benefit analysis allows – an absolute recommendation for a particular activity rather than one contingent on a comparison with alternatives – has proved such a strong limitation that means have been sought to overcome it. The key to this has been the cost-effectiveness threshold or ceiling ratio, which is essentially a level of the CER that any intervention must meet if it is to be regarded as cost-effective. It can also be interpreted as the decision maker’s willingness to pay for a unit of effectiveness. […] One of the problems with this kind of approach is that it is no longer consistent with the conventional aim of CEA. Except under special conditions, it is not consistent with output maximisation constrained by a budget. […] It is useful to distinguish between a comparator that is essentially ‘do nothing about the problem […]’ and one that is ‘another way of doing something about that problem’. The CER that arises from the second of these is […] an incremental cost-effectiveness ratio (ICER) […] in most cases the ICER is the correct measure to use. […] A problem [with using ICERs] is that if only the ICER is evaluated, it must be assumed that the alternative used in the comparator is itself cost-effective; if it is not, the ICER may mislead.”
“The basis of economic costing is […] quite distinct from accounting or financial cost approaches. The process of costing involves three steps: (1) identify and describe the changes in resource use, both increases and decreases, that are associated with the options to be evaluated; (2) quantify those changes in resource use in physical units; and (3) value those resources. […] many markets are not fully competitive. For example, the wages paid to doctors may be a reflection of the lobbying power of medical associations or restrictions to licensing, rather than the value of their skills […] The prices of drugs may reflect the effect of government regulations on licensing, pricing and intellectual property. Deviations of price from opportunity cost may arise from factors such as imperfect competition […] or from distortions to markets created by government interventions. Where these are known, prices should be adjusted […] In practice, such adjustments are difficult to make and would rely on good information on the underlying costs of production, which is often not available. Further, where the perspective is that of the health service, there is an argument for not adjusting prices, on the grounds that the prevailing prices, even if inefficient, are those they must pay and are relevant to their budget. […] Where prices are used, it is important to consider whether the option being evaluated will, if implemented, result in price changes. […] Valuing resource use becomes still more difficult in cases where there are no markets. This includes the value of patients’ time in seeking and receiving care or of caregivers’ time in providing informal supportive care. The latter can be an important element of costs and […] may be particularly important in the evaluation of health care options that rely on such inputs.”
“[A]lthough the emphasis in economic evaluation is on marginal changes in costs and benefits, the available data frequently relate to average costs […] There are two issues with using average cost data. First, the addition to or reduction in costs from increased or decreased resource use may be higher, lower or the same as the average cost. Unfortunately, knowing what the relationship is between average and marginal cost requires information on the latter – the absence of which is the reason average costs are used! Secondly, average cost data obscure potentially important issues with respect to the technical efficiency of providers. If average costs are derived in one setting, for example a hospital, this assumes that the hospital is using the optimal combination of inputs. If average costs are derived from multiple settings, they will include a variety of underlying production technologies and a variety of underlying levels of production efficiency. Average costs are therefore less than ideal, because they comprise a ‘black box’ of underlying cost and production decisions. […] Approaches to costing fall into two broad types: macro- or ‘top-down’ costing, and micro- or ‘bottom-up’ costing […] distinguished largely on the basis of the level of disaggregation […] A top-down approach may involve using pre-existing data on total or average costs and apportioning these in some way to the options being evaluated. […] In contrast, a bottom-up approach identifies, quantifies and values resources in a disaggregated way, so that each element of costs is estimated individually and they are summed up at the end. […] The separation of top-down and bottom-up costing approaches is not always clear. For example, often top-down studies are used to calculate unit costs, which are then combined with resource use data in bottom-up studies.”
“Health care programmes can affect both length and quality of life; these in turn interact with both current and future health care use, relating both to the condition of interest and to other conditions. Weinstein and Stason (1977) argue that the cost of ‘saving’ life in one way should include the future costs to the health service of death from other causes. […] In practice, different analysts respond to this issue in different ways: examples may be found of economic evaluations of mammography screening that do […] and do not […] incorporate future health care costs. Methodological differences of this sort reduce the ability to make valid comparisons between results. In practical terms, this issue is a matter of researcher discretion”.
The stuff included in the last paragraph above is closely linked to stuff covered in the biodemography text I’m currently reading, and I expect to cover related topics in some detail in the future here on the blog. Below a few final observations from the book about discounting:
“It is generally accepted that future costs should be discounted in an economic evaluation and, in CBA, it is also relatively non-controversial that benefits, in monetary terms, should also be discounted. In contrast, there is considerable debate surrounding the issue of whether to discount health outcomes such as QALYs, and what the appropriate discount rate is. […] The debate […] concentrates on the issue of whether people have a time preference for receiving health benefits now rather than in the future in the same way that they might have a time preference for gaining monetary benefits now rather than later in life. Arguments both for and against this view are plausible, and the issue is currently unresolved. […] The effect of not discounting health benefits is to improve the cost-effectiveness of all health care programmes that have benefits beyond the current time period, because not discounting increases the magnitude of the health benefits. But as well as affecting the apparent cost-effectiveness of programmes relative to some benchmark or threshold, the choice of whether to discount will also affect the cost-effectiveness of different health care programmes relative to each other […] Discounting health benefits tends to make those health care programmes with benefits realised mostly in the future, such as prevention, less cost-effective relative to those with benefits realised mostly in the present, such as cure.”
“This book is written to provide […] a useful balance of theoretical treatment, description of empirical analyses and breadth of content for use in undergraduate modules in health economics for economics students, and for students taking a health economics module as part of their postgraduate training. Although we are writing from a UK perspective, we have attempted to make the book as relevant internationally as possible by drawing on examples, case studies and boxed highlights, not just from the UK, but from a wide range of countries”
I’m currently reading this book. The coverage has been somewhat disappointing because it’s mostly an undergraduate text which has so far mainly been covering concepts and ideas I’m already familiar with, but it’s not terrible – just okay-ish. I have added some observations from the first half of the book below.
“Health economics is the application of economic theory, models and empirical techniques to the analysis of decision making by people, health care providers and governments with respect to health and health care. […] Health economics has evolved into a highly specialised field, drawing on related disciplines including epidemiology, statistics, psychology, sociology, operations research and mathematics […] health economics is not shorthand for health care economics. […] Health economics studies not only the provision of health care, but also how this impacts on patients’ health. Other means by which health can be improved are also of interest, as are the determinants of ill-health. Health economics studies not only how health care affects population health, but also the effects of education, housing, unemployment and lifestyles.”
“Economic analyses have been used to explain the rise in obesity. […] The studies show that reasons for the rise in obesity include: *Technological innovation in food production and transportation that has reduced the cost of food preparation […] *Agricultural innovation and falling food prices that has led to an expansion in food supply […] *A decline in physical activity, both at home and at work […] *An increase in the number of fast-food outlets, resulting in changes to the relative prices of meals […]. *A reduction in the prevalence of smoking, which leads to increases in weight (Chou et al., 2004).”
“[T]he evidence is that ageing is in reality a relatively small factor in rising health care costs. The popular view is known as the ‘expansion of morbidity’ hypothesis. Gruenberg (1977) suggested that the decline in mortality that has led to an increase in the number of older people is because fewer people die from illnesses that they have, rather than because disease incidence and prevalence are lower. Lower mortality is therefore accompanied by greater morbidity and disability. However, Fries (1980) suggested an alternative hypothesis, ‘compression of morbidity’. Lower mortality rates are due to better health amongst the population, so people not only live longer, they are in better health when old. […] Zweifel et al. (1999) examined the hypothesis that the main determinant of high health care costs amongst older people is not the time since they were born, but the time until they die. Their results, confirmed by many subsequent studies, is that proximity to death does indeed explain higher health care costs better than age per se. Seshamani and Gray (2004) estimated that in the UK this is a factor up to 15 years before death, and annual costs increase tenfold during the last 5 years of life. The consensus is that ageing per se contributes little to the continuing rise in health expenditures that all countries face. Much more important drivers are improved quality of care, access to care, and more expensive new technology.”
“The difference between AC [average cost] and MC [marginal cost] is very important in applied health economics. Very often data are available on the average cost of health care services but not on their marginal cost. However, using average costs as if they were marginal costs may mislead. For example, hospital costs will be reduced by schemes that allow some patients to be treated in the community rather than being admitted. Given data on total costs of inpatient stays, it is possible to calculate an average cost per patient. It is tempting to conclude that avoiding an admission will reduce costs by that amount. However, the average includes patients with different levels of illness severity, and the more severe the illness the more costly they will be to treat. Less severely ill patients are most likely to be suitable for treatment in the community, so MC will be lower than AC. Such schemes will therefore produce a lower cost reduction than the estimate of AC suggests.
A problem with multi-product cost functions is that it is not possible to define meaningfully what the AC of a particular product is. If different products share some inputs, the costs of those inputs cannot be solely attributed to any one of them. […] In practice, when multi-product organisations such as hospitals calculate costs for particular products, they use accounting rules to share out the costs of all inputs and calculate average not marginal costs.”
“Studies of economies of scale in the health sector do not give a consistent and generalisable picture. […] studies of scope economies [also] do not show any consistent and generalisable picture. […] The impact of hospital ownership type on a range of key outcomes is generally ambiguous, with different studies yielding conflicting results. […] The association between hospital ownership and patient outcomes is unclear. The evidence is mixed and inconclusive regarding the impact of hospital ownership on access to care, morbidity, mortality, and adverse events.“
“Public goods are goods that are consumed jointly by all consumers. The strict economics definition of a public good is that they have two characteristics. The first is non-rivalry. This means that the consumption of a good or service by one person does not prevent anyone else from consuming it. Non-rival goods therefore have large marginal external benefits, which make them socially very desirable but privately unprofitable to provide. Examples of nonrival goods are street lighting and pavements. The second is non-excludability. This means that it is not possible to provide a good or service to one person without letting others also consume it. […] This may lead to a free-rider problem, in which people are unwilling to pay for goods and services that are of value to them. […] Note the distinction between public goods, which are goods and services that are non-rival and non-excludable, and publicly provided goods, which are goods or services that are provided by the government for any reason. […] Most health care products and services are not public goods because they are both rival and excludable. […] However, some health care, particularly public health programmes, does have public good properties.”
“[H]ealth care is typically consumed under conditions of uncertainty with respect to the timing of health care expenditure […] and the amount of expenditure on health care that is required […] The usual solution to such problems is insurance. […] Adverse selection exists when exactly the wrong people, from the point of view of the insurance provider, choose to buy insurance: those with high risks. […] Those who are most likely to buy health insurance are those who have a relatively high probability of becoming ill and maybe also incur greater costs than the average when they are ill. […] Adverse selection arises because of the asymmetry of information between insured and insurer. […] Two approaches are adopted to prevent adverse selection. The first is experience rating, where the insurance provider sets a different insurance premium for different risk groups. Those who apply for health insurance might be asked to undergo a medical examination and
to disclose any relevant facts concerning their risk status. […] There are two problems with this approach. First, the cost of acquiring the appropriate information may be high. […] Secondly, it might encourage insurance providers to ‘cherry pick’ people, only choosing to provide insurance to the low risk. This may mean that high-risk people are unable to obtain health insurance at all. […] The second approach is to make health insurance compulsory. […] The problem with this is that low-risk people effectively subsidise the health insurance payments of those with higher risks, which may be regarded […] as inequitable.”
“Health insurance changes the economic incentives facing both the consumers and the providers of health care. One manifestation of these changes is the existence of moral hazard. This is a phenomenon common to all forms of insurance. The suggestion is that when people are insured against risks and their consequences, they are less careful about minimising them. […] Moral hazard arises when it is possible to alter the probability of the insured event, […] or the size of the insured loss […] The extent of the problem depends on the price elasticity of demand […] Three main mechanisms can be used to reduce moral hazard. The first is co-insurance. Many insurance policies require that when an event occurs the insured shares the insured loss […] with the insurer. The co-insurance rate is the percentage of the insured loss that is paid by the insured. The co-payment is the amount that they pay. […] The second is deductibles. A deductible is an amount of money the insured pays when a claim is made irrespective of co-insurance. The insurer will not pay the insured loss unless the deductible is paid by the insured. […] The third is no-claims bonuses. These are payments made by insurers to discourage claims. They usually take the form of reduced insurance premiums in the next period. […] No-claims bonuses typically discourage insurance claims where the payout by the insurer is small. “
“The method of reimbursement relates to the way in which health care providers are paid for the services they provide. It is useful to distinguish between reimbursement methods, because they can affect the quantity and quality of health care. […] Retrospective reimbursement at full cost means that hospitals receive payment in full for all health care expenditures incurred in some pre-specified period of time. Reimbursement is retrospective in the sense that not only are hospitals paid after they have provided treatment, but also in that the size of the payment is determined after treatment is provided. […] Which model is used depends on whether hospitals are reimbursed for actual costs incurred, or on a fee-for-service (FFS) basis. […] Since hospital income [in these models] depends on the actual costs incurred (actual costs model) or on the volume of services provided (FFS model) there are few incentives to minimise costs. […] Prospective reimbursement implies that payments are agreed in advance and are not directly related to the actual costs incurred. […] incentives to reduce costs are greater, but payers may need to monitor the quality of care provided and access to services. If the hospital receives the same income regardless of quality, there is a financial incentive to provide low-quality care […] The problem from the point of view of the third-party payer is how best to monitor the activities of health care providers, and how to encourage them to act in a mutually beneficial way. This problem might be reduced if health care providers and third-party payers are linked in some way so that they share common goals. […] Integration between third-party payers and health care providers is a key feature of managed care.“
One of the prospective imbursement models applied today may be of particular interest to Danes, as the DRG system is a big part of the financial model of the Danish health care system – so I’ve added a few details about this type of system below:
“An example of prospectively set costs per case is the diagnostic-related groups (DRG) pricing scheme introduced into the Medicare system in the USA in 1984, and subsequently used in a number of other countries […] Under this scheme, DRG payments are based on average costs per case in each diagnostic group derived from a sample of hospitals. […] Predicted effects of the DRG pricing scheme are cost shifting, patient shifting and DRG creep. Cost shifting and patient shifting are ways of circumventing the cost-minimising effects of DRG pricing by shifting patients or some of the services provided to patients out of the DRG pricing scheme and into other parts of the system not covered by DRG pricing. For example, instead of being provided on an inpatient basis, treatment might be provided on an outpatient basis where it is reimbursed retrospectively. DRG creep arises when hospitals classify cases into DRGs that carry a higher payment, indicating that they are more complicated than they really are. This might arise, for instance, when cases have multiple diagnoses.”
Some stuff from the chapters dealing with the UK:
“we now know that reducing the HbA1c too far and fast in some patients can be harmful . This is a particularly important issue, where primary care is paid through the Quality Outcomes Framework (QoF), a general practice “pay for performance” programme . A major item within QoF, is the proportion of patients below HbA1c criteria: such reporting is not linked to rates of hypoglycaemia, ambulance call outs or hospitalisation, i.e., a practice could receive a high payment through achieving the QoF target, but with a high hospitalisation/ambulance callout rate.”
“nationwide audit data for England 2009–2010 showed that […] targets for HbA1c (≤7.5%/58.5 mmol/mol), blood pressure (BP) (<140/80 mmHg) and total cholesterol (
One thing that is perhaps worth noting here before moving any further is that the fact that you have actual data on this stuff is in itself indicative of an at least reasonable standard of care, compared to many places; in a lot of countries you just don’t have data on this kind of stuff, and it seems highly unlikely to me that the default assumption should be that things are going great in places where you do not have data on this kind of thing. Denmark also, incidentally, has a similar audit system, the results of which I’ve discussed in some detail before here on the blog).
“Our local audit data shows that approximately 85–90 % of patients with diabetes are managed by GPs and practice nurses in Coventry and Warwickshire. Only a small proportion of newly diagnosed patients with T2D (typically around 5–10 %) who attend the DESMOND (Diabetes Education and Self-Management for Ongoing and Newly Diagnosed) education programme come into contact with some aspect of the specialist services . […] Payment by results (PBR) has […] actively, albeit indirectly, disincentivised primary care to seek opinion from specialist services . […] Large volumes of data are collected by various services ranging between primary care, local laboratory facilities, ambulance services, hospital clinics (of varying specialties), retinal screening services and several allied healthcare professionals. However, the majority of these systems are not unified and therefore result in duplication of data collection and lack of data utilisation beyond the purpose of collection. This can result in missed opportunities, delayed communication, inability to use electronic solutions (prompts, alerts, algorithms etc.), inefficient use of resources and patient fatigue (repeated testing but no apparent benefit). Thus, in the majority of the regions in England, the delivery of diabetes care is disjointed and lacks integration. Each service collects and utilises data for their own “narrow” purpose, which could be used in a holistic way […] Potential consequences of the introduction of multiple service providers are fragmentation of care, reductions in continuity of care and propagation of a reluctance to refer on to a more specialist service . […] There are calls for more integration and less fragmentation in health-care , yet so far, the major integration projects in England have revealed negligible, if any, benefits [25, 32]. […] to provide high quality care and reduce the cost burden of diabetes, any integrated diabetes care models must prioritise prevention and early aggressive intervention over downstream interventions (secondary and tertiary prevention).”
“It is estimated that 99 % of diabetes care is self-management […] people with diabetes spend approximately only 3 h a year with healthcare professionals (versus 8757 h of self-management)” [this is a funny way of looking at things, which I’d never really considered before.]
“In a traditional model of diabetes care the rigid divide between primary and specialist care is exacerbated by the provision of funding. For example the tariff system used in England, to pay for activity in specialist care, can create incentives for one part of the system to “hold on” to patients who might be better treated elsewhere. This system was originally introduced to incentivise providers to increase elective activity and reduce waiting times. Whilst it has been effective for improving access to planned care, it is not so well suited to achieving the continuity of care needed to facilitate integrated care .”
“Currently in the UK there is a miss-match between what the healthcare policies require and what the workforce is actually being trained for. […] For true integrated care in diabetes and the other long term condition specialties to work, the education and training needs for both general practitioners and hospital specialists need to be more closely aligned.”
The chapter on Germany (Baden-Württemberg):
“An analysis of the Robert Koch-Institute (RKI) from 2012 shows that more than 50 % of German people over 65 years suffer from at least one chronic disease, approximately 50 % suffer from two to four chronic diseases, and over a quarter suffer from five or more diseases . […] Currently the public sector covers the majority (77 %) of health expenditures in Germany […] An estimated number of 56.3 million people are living with diabetes in Europe . […] The mean age of the T2DM-cohort [from Kinzigtal, Germany] in 2013 was 71.2 years and 53.5 % were women. In 2013 the top 5 co-morbidities of patients with T2DM were essential hypertension (78.3 %), dyslipidaemia (50.5 %), disorders of refraction and accommodation (38.2 %), back pain (33.8 %) and obesity (33.3 %). […] T2DM in Kinzigtal was associated with mean expenditure of 5,935.70 € per person in 2013 (not necessarily only for diabetes care ) including 40 % from inpatient stays, 24 % from drug prescriptions, 19 % from physician remuneration in ambulatory care and the rest from remedies and adjuvants (e.g., insulin pen systems, wheelchairs, physiotherapy, etc.), work incapacity or rehabilitation.”
“Zhang et al.  […] reported that globally, 12 % of health expenditures […] per person were spent on diabetes in 2010. The expenditure varies by region, age group, gender, and country’s income level.”
“Over the years many approaches [have been] introduced to improve the quality and continuity of care for chronic diseases. […] the Dutch minister of health approved, in 2007, the introduction of bundled-care (known is the Netherlands as a ‘chain-of-care’) approach for integrated chronic care, with special attention to diabetes. […] With a bundled payment approach – or episode-based payment – multiple providers are reimbursed a single sum of money for all services related to an episode of care (e.g., hospitalisation, including a period of post-acute care). This is in contrast to a reimbursement for each individual service (fee-for-service), and it is expected that this will reduce the volume of services provided and consequently lead to a reduction in spending. Since in a fee-for-service system the reimbursement is directly related to the volume of services provided, there is little incentive to reduce unnecessary care. The bundled payment approach promotes [in theory… – US] a more efficient use of services  […] As far as efficiency […] is concerned, after 3 years of evaluation, several changes in care processes have been observed, including task substitution from GPs to practice nurses and increased coordination of care [31, 36], thus improving process costs. However, Elissen et al.  concluded that the evidence relating to changes in process and outcome indicators, remains open to doubt, and only modest improvements were shown in most indicators. […] Overall, while the Dutch approach to integrated care, using a bundled payment system with a mixed payer approach, has created a limited improvement in integration, there is no evidence that the approach has reduced morbidity and premature mortality: and it has come at an increased cost.”
“In 2013 Sweden spent the equivalent of 4,904 USD per capita on health [OECD average: 3,453 USD], with 84 % of the expenditure coming from public sources [OECD average: 73 %]. […] Similarly high proportions [of public spending] can be found in the Netherlands (88 %), Norway (85 %) and Denmark (84 %) . […] Sweden’s quality registers, for tracking the quality of care that patients receive and the corresponding outcomes for several conditions, are among the most developed across the OECD . Yet, the coordination of care for patients with complex needs is less good. Only one in six patients had contact with a physician or specialist nurse after discharge from hospital for stroke, again with substantial variation across counties. Fewer than half of patients with type 1 diabetes […] have their blood pressure adequately controlled, with a considerable variation (from 26 % to 68 %) across counties . […] at 260 admissions per 100,000 people aged over 80, avoidable hospital admissions for uncontrolled diabetes in Sweden’s elderly population are the sixth highest in the OECD, and about 1.5 times higher than in Denmark.”
“Waiting times [in Sweden] have long been a cause of dissatisfaction . In an OECD ranking of 2011, Sweden was rated second worst . […] Sweden introduced a health-care guarantee in 2005 [guaranteeing fast access in some specific contexts]. […] Most patients who appeal under the health-care guarantee and [are] prioritised in the “queue” ha[ve] acute conditions rather than medical problems as a consequence of an underlying chronic disease. Patients waiting for a hip replacement or a cataract surgery are cured after surgery and no life-long follow-up is needed. When such patients are prioritised, the long-term care for patients with chronic diseases is “crowded out,” lowering their priority and risking worse outcomes. The health-care guarantee can therefore lead to longer intervals between checkups, with difficulties in accessing health care if their pre-existing condition has deteriorated.”
“Within each region / county council the care of patients with diabetes is divided. Patients with type 1 diabetes get their care at specialist clinics in hospitals and the majority of patients with type 2 diabetes in primary care . Patients with type 2 diabetes who have severe complications are referred to the Diabetes Clinics at the hospital. Approximately 10 % of all patients with type 2 continue their care at the hospital clinics. They are almost always on insulin in high doses often in combination with oral agents but despite massive medication many of these patients have difficulties to achieve metabolic balance. Patients with advanced complications such as foot ulcers, macroangiopathic manifestations and treatment with dialysis are also treated at the hospitals.”
Do keep in mind here that even if only 10% of type 2 patients are treated in a hospital setting, type 2 patients may still make up perhaps half or more of the diabetes patients treated in a hospital setting; type 2 prevalence is much, much higher than type 1 prevalence. Also, in view of such treatment- and referral patterns the default assumption when doing comparative subgroup analyses should always be that the outcomes of type 2 patients treated in a hospital setting should be expected to be much worse than the outcomes of type 2 patients treated in general practice; they’re in much poorer health than the diabetics treated in general practice, or they wouldn’t be treated in a hospital setting in the first place. A related point is that regardless of how great the hospitals are at treating the type 2 patients (maybe in some contexts there isn’t actually much of a difference in outcomes between these patients and type 2 patients treated in general practice, even though you’d expect there to be one?), that option will usually not be scalable. Also, it’s to be expected that these patients are more expensive than the default type 2 patient treated by his GP [and they definitely are: “Only if severe complications arise [in the context of a type 2 patient] is the care shifted to specialised clinics in hospitals. […] these patients have the most expensive care due to costly treatment of for example foot ulcers and renal insufficiency”]; again, they’re sicker and need more comprehensive care. They would need it even if they did not get it in a hospital setting, and there are costs associated with under-treatment as well.
“About 90 % of the children [with diabetes in Sweden] are classified as having Type 1 diabetes based on positive autoantibodies and a few percent receive a diagnosis of “Maturity Onset Diabetes of the Young” (MODY) . Type 2 diabetes among children is very rare in Sweden.”
Lastly, some observations from the final chapter:
“The paradox that we are dealing with is that in spite of health professionals wanting the best for their patients on a patient by patient basis, the way that individuals and institutions are organised and paid, directly influences the clinical decisions that are made. […] Naturally, optimising personal care and the provider/purchaser-commissioner budget may be aligned, but this is where diabetes poses substantial problems from a health system point of view: The majority of adverse diabetes outcomes […] are many years in the future, so a system based on this year’s budget will often not prioritise the future […] Even for these adverse “diabetes” outcomes, other clinical factors contribute to the end result. […] attribution to diabetes may not be so obvious to those seeking ways to minimise expenditure.”
[I incidentally tried to get this point across in a recent discussion on SSC, but I’m not actually sure the point was understood, presumably because I did not explain it sufficiently clearly or go into enough detail. It is my general impression, on a related note, that many people who would like to cut down on the sort of implicit public subsidization of unhealthy behaviours that most developed economies to some extent engage in these days do not understand well enough the sort of problems that e.g. the various attribution problems and how to optimize ‘post-diagnosis care’ (even if what you want to optimize is the cost minimization function…) cause in specific contexts. As I hope my comments indicate in that thread, I don’t think these sorts of issues can be ignored or dealt with in some very simple manner – and I’m tempted to say that if you think they can, you don’t know enough about these topics. I say that as one of those people who would like people who engage in risky behaviours to pay a larger (health) risk premium than they currently do].
[Continued from above, …problems from a health system point of view:]
“Payment for ambulatory diabetes care , which is essentially the preventative part of diabetes care, usually sits in a different budget to the inpatient budget where the big expenses are. […] good evidence for reducing hospitalisation through diabetes integrated care is limited […] There is ample evidence [11, 12] where clinicians own, and profit from, other services (e.g., laboratory, radiology), that referral rates are increased, often inappropriately […] Under the English NHS, the converse exists, where GPs, either holding health budgets, or receiving payments for maintaining health budgets , reduce their referrals to more specialist care. While this may be appropriate in many cases, it may result in delays and avoidance of referrals, even when specialist care is likely to be of benefit. [this would be the under-treatment I was talking about above…] […] There is a mantra that fragmentation of care and reductions in continuity of care are likely to harm the quality of care , but hard evidence is difficult to obtain.”
“The problems outlined above, suggest that any health system that fails to take account of the need to integrate the payment system from both an immediate and long term perspective, must be at greater risk of their diabetes integration attempts failing and/or being unsustainable. […] There are clearly a number of common factors and several that differ between successful and less successful models. […] Success in these models is usually described in terms of hospitalisation (including, e.g., DKA, amputation, cardiovascular disease events, hypoglycaemia, eye disease, renal disease, all cause), metabolic outcomes (e.g., HbA1c ), health costs and access to complex care. Some have described patient related outcomes, quality of life and other staff satisfaction, but the methodology and biases have often not been open to scrutiny. There are some methodological issues that suggest that many of those with positive results may be illusory and reflect the pre-existing landscape and/or wider changes, particular to that locality. […] The reported “success” of intermediate diabetes clinics run by English General Practitioners with a Special Interest led to extension of the model to other areas. This was finally tested in a randomised controlled trial […] and shown to be a more costly model with no real benefit for patients or the system. Similarly in East Cambs and Fenland, the 1 year results suggested major reductions in hospitalisation and costs in practices participating fully in the integrated care initiative, compared with those who “engaged” later . However, once the trends in neighbouring areas and among those without diabetes were accounted for, it became clear that the benefits originally reported were actually due to wider hospitalisation reductions, not just in those with diabetes. Studies of hospitalisation /hospital costs that do not compare with rates in the non-diabetic population need to be interpreted with caution.”
“Kaiser Permanente is often described as a great diabetes success story in the USA due to its higher than peer levels of, e.g., HbA1c testing . However, in the 2015 HEDIS data, levels of testing, metabolic control achieved and complication rates show quality metrics lower than the English NHS, in spite of the problems with the latter . Furthermore, HbA1c rates above 9 % remain at approximately 20 %, in Southern California  or 19 % in Northern California , a level much higher than that in the UK […] Similarly, the Super Six model […] has been lauded as a success, as a result of reductions in patients with, e.g., amputations. However, these complications were in the bottom quartile of performance for these outcomes in England  and hence improvement would be expected with the additional diabetes resources invested into the area. Amputation rates remain higher than the national average […] Studies showing improvement from a low baseline do not necessarily provide a best practice model, but perhaps a change from a system that required improvement. […] Several projects report improvements in HbA1c […] improvements in HbA1c, without reports of hypoglycaemia rates and weight gain, may be associated with worse outcomes as suggested from the ACCORD trial .”
The book provides a good overview of studies and clinical trials which have attempted to improve the coordination of diabetes treatment in specific areas. The book covers research from all over the world – the UK, the US, Hong Kong, South Africa, Germany, Netherlands, Sweden, Australia. The language of the publication is quite good, considering the number of non-native English speaking contributors. An at least basic understanding of medical statistics is probably required for one to properly read and understand this book in full.
The book is quite good if you want to understand how people have tried to improve (mainly type 2) diabetes treatment ‘from an organizational point of view’ (the main focus here is not on new treatment options, but on how to optimize care delivery and make the various care providers involved work better together, in a way that improves outcomes for patients (at an acceptable cost?), which is to a large extent an organizational problem), but it’s actually also probably quite a nice book if you simply want to know more about how diabetes treatment systems differ across countries; the contributors don’t assume that the readers know how e.g. the Swedish approach to diabetes care differs from that of e.g. Pennsylvania, so many chapters contain interesting details on how specific countries/health care providers handle specific aspects of e.g. care delivery or finance.
What people mean by ‘integrated care’ varies a bit depending on whom you ask (patients and service providers may emphasize different dimensions when thinking about these topics), as should also be clear from the quotes below; however I assumed it might be a good idea to start out the post with the quote above, so that people who might have no idea what ‘integrated diabetes care’ is did not start out reading the post completely in the dark. In short, a big problem in health service delivery contexts is that care provision is often fragmented and uncoordinated, for many reasons. Ideally you might like doctors working in general practice to collaborate smoothly and efficiently with hospital staff and various other specialists involved in diabetes care (…and perhaps also with social services and mental health care providers…), but that kind of coordination often doesn’t happen, leading to what may well be sub-optimal care provision. Collaboration and a ‘desirable’ (whatever that might mean) level of coordination between service providers doesn’t happen automatically; it takes money, effort and a lot of other things (that the book covers in some detail…) to make it happen – and so often it doesn’t happen, at least there’s a lot of room for improvement even in places where things work comparatively well. Some quotes from the book on these topics:
“it is clear that in general, wherever you are in the world, service delivery is now fragmented . Such fragmentation is a manifestation of organisational and financial barriers, which divide providers at the boundaries of primary and secondary care, physical and mental health care, and between health and social care. Diverse specific organisational and professional cultures, and differences in terms of governance and accountability also contribute to this fragmentation . […] Many of these deficiencies are caused by organisational problems (barriers, silo thinking, accountability for budgets) and are often to the detriment of all of those involved: patients, providers and funders – in extreme cases – leading to lose-lose-lose-situations […] There is some evidence that integrated care does improve the quality of patient care and leads to improved health or patient satisfaction [10, 11], but evidence of economic benefits remain an issue for further research . Failure to improve integration and coordination of services along a “care continuum” can result in suboptimal outcomes (health and cost), such as potentially preventable hospitalisation, avoidable death, medication errors and adverse drug events [3, 12, 13].”
“Integrated care is often described as a continuum [10, 24], actually depicting the degree of integration. This degree can range from linkage, to coordination and integration , or segregation (absence of any cooperation) to full integration , in which the integrated organisation is responsible for the full continuum of care responsible for the full continuum of care […] this classification of integration degree can be expanded by introducing a second dimension, i.e., the user needs. User need should be defined by criteria, like stability and severity of condition, duration of illness (chronic condition), service needed and capacity for self-direction (autonomy). Accordingly, a low level of need will not require a fully integrated system, then [10, 24] […] Kaiser Permanente is a good example of what has been described as a “fully integrated system. […] A key element of Kaiser Permanente’s approach to chronic care is the categorisation of their chronically ill patients into three groups based on their degree of need“.
It may be a useful simplification to think along the lines of: ‘Higher degree of need = a higher level of integration becomes desirable/necessary. Disease complexity is closely related to degree of need.’ Some related observations from the book:
“Diabetes is a condition in which longstanding hyperglycaemia damages arteries (causing macrovascular, e.g., ischaemic heart, peripheral and cerebrovascular disease, and microvascular disease, e.g., retinopathy, nephropathy), peripheral nerves (causing neuropathy), and other structures such as skin (causing cheiroarthropathy) and the lens (causing cataracts). Different degrees of macrovascular, neuropathic and cutaneous complications lead to the “diabetic foot.” A proportion of patients, particularly with type 2 diabetes have metabolic syndrome including central adiposity, dyslipidaemia, hypertension and non alcoholic fatty liver disease. Glucose management can have severe side effects, particularly hypoglycaemia and weight gain. Under-treatment is not only associated with long term complications but infections, vascular events and increased hospitalisation. Absence of treatment in type 1 diabetes can rapidly lead to diabetic keto-acidosis and death. Diabetes doubles the risk for depression, and on the other hand, depression may increase the risk for hyperglycaemia and finally for complications of diabetes . Essentially, diabetes affects every part of the body once complications set in, and the crux of diabetes management is to normalise (as much as possible) the blood glucose and manage any associated risk factors, thereby preventing complications and maintaining the highest quality of life. […] glucose management requires minute by minute, day by day management addressing the complexity of diabetes, including clinical and behavioural issues. While other conditions also have the patient as therapist, diabetes requires a fully empowered patient with all of the skills, knowledge and motivation every hour of the waking day. A patient that is fully engaged in self-management, and has support systems, is empowered to manage their diabetes and will likely experience better outcomes compared with those who do not have access to this support. […] in diabetes, the boundaries between primary care and secondary care are blurred. Diabetes specialist services, although secondary care, can provide primary care, and there are GPs, diabetes educators, and other ancillary providers who can provide a level of specialist care.”
In short, diabetes is a complex disease – it’s one of those diseases where a significant degree of care integration is likely to be necessary in order to achieve even close to optimal outcomes. A little more on these topics:
“The unique challenge to providers is to satisfy two specific demands in diabetes care. The first is to anticipate and recognize the onset of complications through comprehensive diabetes care, which demands meticulous attention to a large number of process-of-care measures at each visit. The second, arguably greater challenge for providers is to forestall the development of complications through effective diabetes care, which demands mastery over many different skills in a variety of distinct fields in order to achieve performance goals covering multiple facets of management. Individually and collectively, these dual challenges constitute a virtually unsustainable burden for providers. That is because (a) completing all the mandated process measures for comprehensive care requires far more time than is traditionally available in a single patient visit; and (b) most providers do not themselves possess skills in all the ancillary disciplines essential for effective care […] Diabetes presents patients with similarly unique dual challenges in mastering diabetes self-management with self-awareness, self-empowerment and self-confidence. Comprehensive Diabetes Self-Management demands the acquisition of a variety of skills in order to fulfil a multitude of tasks in many different areas of daily life. Effective Diabetes Self-Management, on the other hand, demands constant vigilance, consistent discipline and persistent attention over a lifetime, without respite, to nutritional self-discipline, monitoring blood glucose levels, and adherence to anti-diabetic medication use. Together, they constitute a burden that most patients find difficult to sustain even with expert assistance, and all-but-impossible without it.”
“Care coordination achieves critical importance for diabetes, in particular, because of the need for management at many different levels and locations. At the most basic level, the symptomatic management of acute hypo- and hyperglycaemia often devolves to the PCP [primary care provider], even when a specialist oversees more advanced strategies for glycaemic management. At another level, the wide variety of chronic complications requires input from many different specialists, whereas hospitalizations for acute emergencies often fall to hospitalists and critical care specialists. Thus, diabetes care is fraught with the potential for sometimes conflicting, even contradictory management strategies, making care coordination mandatory for success.”
“Many of the problems surrounding the provision of adequate person-centred care for those with diabetes revolve around the pressures of clinical practice and a lack of time. Good diabetes management requires attention to a number of clinical parameters
1. (Near) Normalization of blood glucose
2. Control of co-morbidities and risk factors
3. Attainment of normal growth and development
4. Prevention of Acute Complications
5. Screening for Chronic Complications
To fit all this and a holistic, patient-centred collaborative approach into a busy general practice, the servicing doctor and other team members must understand that diabetes cannot be “dealt with” coincidently during a patient consultation for an acute condition.”
“Implementation of the team model requires sharing of tasks and responsibilities that have traditionally been the purview of the physician. The term “team care” has traditionally been used to indicate a group of health-care professionals such as physicians, nurses, pharmacists, or social workers, who work together in caring for a group of patients. In a 2006 systematic review of 66 trials testing 11 strategies for improving glycaemic control for patients with diabetes, only team care and case management showed a significant impact on reducing HbA1c levels .”
Moving on, I found the chapter about Hong Kong interesting, for several reasons. The quality of Scandinavian health registries are probably widely known in the epidemiological community, but I was not aware of Hong Kong’s quality of diabetes data, and data management strategies, which seems to be high. Nor was I aware of some of the things they’ve discovered while analyzing those data. A few quotes from that part of the coverage:
“Given the volume of patients in the clinics, the team’s earliest work from the HKDR [Hong Kong Diabetes Registry, US] prioritized the development of prediction models, to allow for more efficient, data-driven risk stratification of patients. After accruing data for a decade on over 7000 patients, the team established 5-year probabilities for major diabetes-related complications as defined by the International Code for Diseases retrieved from the CMS [Clinical Management System, US]. These included end stage renal disease , stroke , coronary heart disease , heart failure , and mortality . These risk equations have a 70–90 % sensitivity and specificity of predicting outcomes based on the parameters collected in the registry.”
“The lifelong commitments to medication adherence and lifestyle modification make diabetes self-management both physically and emotionally taxing. The psychological burdens result from insulin injection, self-monitoring of blood glucose, dietary restriction, as well as fear of complications, which may significantly increase negative emotions in patients with diabetes. Depression, anxiety, and distress are prevalent mental afflictions found in patients with diabetes […] the prevalence of depression was 18.3 % in Hong Kong Chinese patients with type 2 diabetes. Furthermore, depression was associated with poor glycaemic control and self-reported hypoglycaemia, in part due to poor adherence […] a prospective study involving 7835 patients with type 2 diabetes without cardiovascular disease (CVD) at baseline […] found that [a]fter adjusting for conventional risk factors, depression was independently associated with a two to threefold increase in the risk of incident CVD .”
“Diabetes has been associated with increased cancer risk, but the underlying mechanism is poorly understood. The linkage between the longitudinal clinical data within the HKDR and the cancer outcome data in the CMS has provided important observational findings to help elucidate these connections. Detailed pharmacoepidemiological analyses revealed attenuated cancer risk in patients treated with insulin and oral anti-diabetic drugs compared with non-users of these drugs”
“Among the many challenges of patient self-management, lack of education and empowerment are the two most cited barriers . Sufficient knowledge is unquestionably important in self-care, especially in people with low health literacy and limited access to diabetes education. Several systematic reviews [have] showed that self-management education with comprehensive lifestyle interventions improved glycaemic and cardiovascular risk factor control [60–62].”
“Clinical trials are expensive because of the detail and depth of data required on each patient, which often require separate databases to be developed outside of the usual-care electronic medical records or paper-based chart systems. These databases must be built, managed, and maintained from scratch every time, often requiring double-entry of data by research staff. The JADE [Joint Asia Diabetes Evaluation] programme provides a more efficient means of collecting the key clinical variables in its comprehensive assessments, and allows researchers to add new fields as necessary for research purposes. This obviates the need for redundant entry into non-clinical systems, as the JADE programme is simultaneously a clinical care tool and prospective database. […] A large number of trials fail because of inadequate recruitment . The JADE programme has allowed for ready identification of eligible clinical trial participants because of its detailed clinical database. […] One of the greatest challenges in clinical trials is maintaining the contact between researchers and patients over many years. […] JADE facilitates long-term contact with the patient, as part of routine periodic follow-up. This also allows researchers to evaluate longer term outcomes than many previous trials, given the great expense in maintaining databases for the tracking of longitudinal outcomes.”
Lastly, some stuff on cost and related matters from the book:
“Diabetes imposes a massive economic burden on all healthcare systems, accounting for 11 % of total global healthcare expenditure on adults in 2013.”
“Often, designated service providers institute managed care programmes to standardize and control care rendered in a safe and cost-effective manner. However, many of these programmes concentrate on cost-savings rather than patient service utilization and improved clinical outcomes. [this part of the coverage is from South Africa, but these kinds of approaches are definitely not limited to SA – US] […] While these approaches may save some costs in the short-term, Managed Care Programmes which do not address patient outcomes nor reduce long term complications, ignore the fact that that the majority of the costs for treating diabetes, even in the medium term, are due to the treatment of acute and chronic complications and for inpatient hospital care . Additionally, it is well established that poor long-term clinical outcomes increase the cost burden of managing the patient with diabetes by up to 250 %. […] overall, the costs of medication, including insulin, accounts for just 7 % of all healthcare costs related to diabetes [this number varies across countries, I’ve seen estimates of 15% in the past – and as does the out-pocket share of that cost – but the costs of medications constitute a relatively small proportion of the total costs of diabetes everywhere you look, regardless of health care system and prevalence. If you include indirect costs as well, which you should, this becomes even more obvious – US]”
“[A] study of the Economic Costs of Diabetes in the U.S. in 2012  showed that for people with diabetes, hospital inpatient care accounted for 43 % of the total medical cost of diabetes.”
“There is some evidence of a positive impact of integrated care programmes on the quality of patient care [10, 34]. There is also a cautious appraisal that warns that “Even in well-performing care groups, it is likely to take years before cost savings become visible” […]. Based on a literature review from 1996 to 2004 Ouwens et al.  found out that integrated care programmes seemed to have positive effects on the quality of care. […] because of the variation in definitions of integrated care programmes and the components used cover a broad spectrum, the results should be interpreted with caution. […] In their systematic review of the effectiveness of integrated care Ouwens et al.  could report on only seven (about 54 %) reviews which had included an economic analysis. Four of them showed financial advantages. In their study Powell Davies et al.  found that less than 20 % of studies that measured economic outcomes found a significant positive result. Similarly, de Bruin et al.  evaluated the impact of disease management programmes on health-care expenditures for patients with diabetes, depression, heart failure or chronic obstructive pulmonary disease (COPD). Thirteen studies of 21 showed cost savings, but the results were not statistically significant, or not actually tested for significance. […] well-designed economic evaluation studies of integrated care approaches are needed, in particular in order to support decision-making on the long-term financing of these programmes [30, 39]. Savings from integrated care are only a “hope” as long as there is no carefully designed economic analysis with a kind of full-cost accounting.”
“The cost-effectiveness of integrated care for patients with diabetes depends on the model of integrated care used, the system in which it is used, and the time-horizon chosen . Models of cost benefit for using health coaching interventions for patients with poorly controlled diabetes have generally found a benefit in reducing HbA1c levels, but at the cost of paying for the added cost of health coaching which is not offset in the short term by savings from emergency department visits and hospitalizations […] An important question in assessing the cost of integrated care is whether it needs to be cost-saving or cost-neutral to be adopted, or is it enough to increase quality-adjusted life years (QALYs) at a “reasonable” cost (usually pegged at between $30,000 and $60,000 per QALY saved). Most integrated care programmes for patients with diabetes that have been evaluated for cost-effectiveness would meet this more liberal criterion […] In practice, integrated care programmes for patients with diabetes are often part of generalized programmes of care for patients with other chronic medical conditions, making the allocation of costs and savings with respect to integrated care for diabetes difficult to estimate. At this point, integrated care for patients with diabetes appears to be a widely accepted goal. The question becomes: which model of integrated care is most effective at reasonable cost? Answering this question depends both on what costs are included and what outcomes are measured; the answers may vary among different patient populations and different care systems.”
I have had a look at two sources, the Office of Refugee Resettlement’s annual reports to Congress for the financial years 2013 and 2014. I have posted some data from the reports below. In the cases where the page numbers are not included directly in the screen-caps, all page numbers given below are the page numbers of the pdf version of the documents.
I had some trouble with how to deal with the images included in the post; I hope it looks okay now, at least it does on my laptop – but if it doesn’t, I’m not sure I care enough to try to figure out how to resolve the problem. Anyway, to the data!
The one above is the only figure/chart from the 2014 report, but I figured it was worth including here. It’s from page 98 of the report. It’s of some note that, despite the recent drop, 42.8% of the 2014 US arrivals worked/had worked during the year they arrived; in comparison, only 494 of Sweden’s roughly 163.000 asylum seekers who arrived during the year 2015 landed a job that year (link).
All further images/charts below are from the 2013 report.
It’s noteworthy here how different the US employment gap is to e.g. the employment gap in Denmark. In Denmark the employment rate of refugees with fugitive status who have stayed in the country for 5 years is 34%, and the employment rate of refugees with fugitive status who have stayed in the country for 15 years is 37%, compared to a native employment rate of ~74% (link). But just like in Denmark, in the US it matters a great deal where the refugees are coming from:
“Since their arrival in the U.S., 59 percent of refugees in the five-year population worked at one point. This rate was highest for refugees from Latin America (85 percent) and lowest for refugees from the Middle East (48 percent), while refugees from South/Southeast Asia (61 percent) and Africa (59 percent) were positioned in between. […] The highest disparity between male and female labor force participation rates was found for respondents from the Middle East (64.1 percent for males vs. 34.5 percent for females, a gap of 30 points). A sizeable gender gap was also found among refugees from South/Southeast Asia (24 percentage points) and Africa (18 percentage points), but there was hardly any gap among Latin American refugees (3 percentage points). Among all refugee groups, 71 percent of males were working or looking for work at the time of the 2013 survey, compared with 49 percent of females.” (p.94)
Two tables (both are from page 103 of the 2013 report):
When judged by variables such as home ownership and the proportion of people who survive on public assistance, people who have stayed longer do better (Table II-16). But if you consider table II-17, a much larger proportion of the refugees surveyed in 2013 than in 2008 are partially dependent on public assistance, and it seems that a substantially smaller proportion of the refugees living in the US in the year 2013 was totally self-reliant than was the case 5 years earlier. Fortunately the 2013 report has a bit more data on this stuff (p. 107):
The table has more information on page 108, with more details about specific public assistance programs.Table II-22 includes data on how public assistance utilization has developed over time (it’s clear that utilization rates increased substantially during the half-decade observed):
Some related comments from the report:
“Use of non-cash assistance was generally higher than cash assistance. This is probably because Medicaid, the Supplemental Nutrition Assistance Program (SNAP), and housing assistance programs, though available to cash assistance households, also are available more broadly to households without children. SNAP utilization was lowest among Latin Americans (37 percent) but much higher for the other groups, reaching 89 to 91 percent among the refugees from Africa and the Middle East. […] Housing assistance varied by refugee group — as low as 4 percent for Latin American refugees and as high as 32 percent for refugees from South/Southeast Asia in the 2013 survey. In the same period, other refugee groups averaged use of housing assistance between 19 and 31 percent.” (pp. 107-108)
The report includes some specific data on Iraqi refugees – here’s one table from that section:
The employment rate of the Iraqis increased from 29.8% in the 2009 survey to 41.3% in 2013. However the US female employment rate is still actually not much different from the female employment rates you observe when you look at European data on these topics – just 29%, up from 18.8% in 2009. As a comparison, in the year 2010 the employment rate of Iraqi females living in Denmark was 28% (n=10163) (data from p.55 of the Statistics Denmark publication Indvandrere i Danmark 2011), almost exactly the same as the employment rate of female Iraqis in the US.
Of note in the context of the US data is perhaps also the fact that despite the employment rate going up for females in the time period observed, the labour market participation rate of this group actually decreased between 2009 and 2013, as it went from 42.2% to 38.1%. So more than 3 out of 5 Iraqi female refugees living in the US are outside the labour market, and almost one in four of those that are not are unemployed. A few observations from the report:
“The survey found that the overall EPR [employment rate, US] for the 2007 to 2009 Iraqi refugee group in the 2013 survey9 was 41 percent (55 percent for males and 29 percent for females), a steady increase in the overall rate from 39 percent in the 2012 survey, 36 percent in the 2011 survey, 31 percent in the 2010 survey, and 30 percent in the 2009 survey. As a point of further reference, the EPR for the general U.S. population was 58.5 percent in 2013, about 17 percentage points higher than that of the 2007 to 2009 Iraqi refugee group (41.3 percent). The U.S. male population EPR was nine percentage points higher than the rate for Iraqi males who arrived in the U.S. in 2007 to 2009 (64 percent versus 55 percent), while the rate for the Iraqi females who arrived in the U.S. in 2007 to 2009 was 24 points higher for all U.S. women (53 percent versus 29 percent). The difference between the male and female EPRs among the same group of Iraqi refugees (26 percentage points) also was much larger than the gap between male and female EPRs in the general U.S. population (11 points) […] The overall unemployment rate for the 2007 to 2009 Iraqi refugee group was 22.9 percent in the 2013 survey, about four times higher than that of the general U.S. population (6.5 percent) in 2013” (pp. 114-115).
I was debating whether to blog this book at all, as it’s neither very long nor very good, but I decided it was worth adding a few observations from the book here. You can read my goodreads review of the publication here. Whenever quotes look a bit funny in the coverage below (i.e. when you see things like words in brackets or strangely located ‘[…]’, assume that the reason for this is that I tried to improve upon the occasionally frankly horrible language of some of the contributors to the publication. If you want to know exactly what they wrote, rather than what they presumably meant to write (basic grammar errors due to the authors having trouble with the English language are everywhere in this publication, and although I did choose to do so here I do feel a bit uncomfortable quoting a publication like this one verbatim on my blog), read the book.
I went off on a tangent towards the end of the post and I ended up adding some general remarks about medical cost, insurance and various other topics. So the post may have something of interest even to people who may not be highly interested in any of the stuff covered in the book itself.
“Despite intensive recommendations, [the] influenza vaccination rate in medical staff in Poland ranges from about 20 % in physicians to 10 % in nurses. […] It has been demonstrated that vaccination of health care workers against influenza significantly decreases mortality of elderly people remaining under [long-term care]. […] Vaccinating health care workers also substantially reduces sickness absenteeism, especially in emergency units […] Concerning physicians, vaccination avoidance stemmed from the lack of knowledge of protective value of vaccine (33 %), lack of time to get vaccinated (29 %), and Laziness (24 %). In nurses, these figures amounted to 55 %, 12 %, and 5 %, respectively (Zielonka et al. 2009).”
I just loved the fact that ‘laziness’ was included here as an explanatory variable, but on the other hand the fact that one-third of doctors cited lack of knowledge about the protective value of vaccination as a reason for not getting vaccinated is … well, let’s use the word ‘interesting’. But it gets even better:
“The questions asked and opinions expressed by physicians or nurses on vaccinations showed that their knowledge in this area was far from the current evidence-based medicine recommendations. Nurses, in particular, commonly presented opinions similar to those which can be found in anti-vaccination movements and forums […] The attitude of physicians toward influenza vaccination vary greatly. In many a ward, a majority of physicians were vaccinated (70–80 %). However, in the neurology and intensive care units the proportion of vaccinated physicians amounted only to 20 %. The reason for such a small yield […] was a critical opinion about the effectiveness and safety of vaccination. Similar differences, depending on medical specialty, were observed in Germany (4–71% of vaccines) (Roggendorf et al. 2011) […] It is difficult to explain the fear of influenza vaccination among the staff of intensive care units, since these are exactly the units where many patients with most severe cases of influenza are admitted and often die (Ayscue et al. 2014). In this group of health care workers, high efficiency of influenza vaccination has been clearly demonstrated […] In the present study a strong difference between the proportion of vaccinated physicians (55 %) and nurses (21 %) was demonstrated, which is in line with some data coming from other countries. In the US, 69 % of physicians and 46 % of nurses get a vaccine shot […] and in Germany the respective percentages are 39 % and 17 % […] In China, 21 % of nurses and only 13 % of physicians are vaccinated against influenza (Seale et al. 2010a), and in [South] Korea, 91 % and 68 % respectively (Lee et al. 2008).”
“[A] survey was conducted among Polish (243) and foreign (80) medical students at the Pomeranian Medical University in Szczecin, Poland. […] The survey results reveal that about 40 % of students were regular or occasional smoker[s]. […] 60 % of students declared themselves to be non-smokers, 20 % were occasional smokers, and 20 % were regular smokers”
40 % of medical students in a rather large sample turned out to be smokers. Wow. Yeah, I hadn’t seen that one coming. I’d probably expect a few alcoholics and I would probably not have been surprised about a hypothetical higher-than-average alcohol consumption in a sample like that (they don’t talk about alcohol so I don’t have data on this, I’m just saying I wouldn’t be surprised – after all I do know that doctors are high-risk for suicide), but such a large proportion smoking? That’s unexpected. It probably shouldn’t have been, considering that this is very much in line with the coverage included in Thirlaway & Upton’s book. I include some remarks about their coverage about smoking in my third post about the book here. The important observation of note from that part of the book’s coverage is probably that most smokers want to quit and yet very few manage to actually do it. “Although the majority of smokers want to stop smoking and predict that they will have stopped in twelve months, only 2–3 per cent actually stops permanently a year (Taylor et al. 2006).” If those future Polish doctors know that smoking is bad for them, but they assume that they can just ‘stop in time’ when ‘the time’ comes – well, some of those people are probably in for a nasty surprise (and they should have studied some more, so that they’d known this?).
“A prospective study of middle-aged British men […] revealed that the self-assessment of health status was strongly associated with mortality. Men who reported poor health had an eight-fold increase in total mortality compared with those reporting excellent health. Those who assessed their health as poor were manual workers, cigarette smokers, and often heavy drinkers. Half of those with poor health suffered from chest pain on exertion and other chronic diseases. Thus, self-assessment of health status appears to be a good measure of current physical health and risk of death“.
“It is estimated that globally 3.1 million people die each year due to chronic obstructive pulmonary disease (COPD). According to the World Health Organization (WHO 2014), the disease was the third leading cause of death worldwide in 2012. [In the next chapter of the book they state that: “COPD is currently the fourth leading cause of death among adult patients globally, and it is projected that it will be the third most common cause of death by 2020.” Whether it’s the third or fourth most common cause of death, it definitely kills a lot of people…] […] Approximately 40–50 % of lifelong smokers will go on to develop COPD […] the number of patients with a primary diagnosis of COPD […] constitutes […] 1.33 % of the total population of Poland. This result is consistent with that obtained during the Polish Spirometry Day in 2011 (Dabrowiecki et al. 2013) when 1.1 % of respondents declared having had a diagnosed COPD, while pulmonary function tests showed objectively the presence of obstruction in 12.3 % of patients.”
Based on numbers like these I feel tempted to conclude that the lungs may be yet another organ in which a substantial proportion of people of advanced age experience low-level organ dysfunction arguably not severe enough to lead to medical intervention. The kidneys are similar, as I also noted when I covered Longmore et al.‘s text.
“Generally, the costs of treatment of patients with COPD are highly variable […] estimates suggest […] that the costs of treatment of moderate stages of COPD may be 3–4-fold higher in comparison with the mild form of the disease, and in the severe form they reach up to 6–10 times the basic cost […] every second person with COPD is of working age […] Admission rates for COPD patients differ as much as 10-fold between European countries (European Lung White Book 2013).”
“In the EU, the costs of respiratory diseases are estimated at 6 % of the budget allocated to health care. Of this amount, 56 % is allocated for the treatment of COPD patients. […] Studies show that one per ten Poles over 30 year of age have COPD symptoms. Each year, around 4 % of all hospitalizations are due to COPD. […] One of the most important parameters regarding pharmacoeconomics is the hospitalization rate […] a high number of hospitalizations due to COPD exacerbations in Poland dramatically increase direct medical costs.”
I bolded the quote above because I knew this but had never seen it stated quite as clearly as it’s stated here, and I may be tempted to quote that one later on. Hospitalizations are often really expensive compared to drugs people who are not hospitalized take for their various health conditions, for example you can probably buy a year’s worth of anti-diabetic drugs, or more, for the costs of just one hospital admission due to drug mis-dosing. Before you get the idea that this might have ‘obvious implications’ for how ‘one’ should structure medical insurance arrangements in terms of copay structures etc., do however keep in mind that the picture here is really confusing:
Here’s the link, with more details – the key observation is that: “There is no consistency […] in the direction of change in costs resulting from changes in compliance”. That’s not diabetes, that’s ‘stuff in general’.
It would be neat if you could e.g. tell a story about how high costs of a drug always lead to non-compliance, which lead to increased hospitalization rates, which lead to higher costs than if the drugs had been subsidized. That would be a very strong case for subsidization. Or it would be neat if you could say that it doesn’t matter whether you subsidize a drug or not, because the costs of drugs are irrelevant in terms of usage patterns – people are told to take one pill every day by their doctor, and by golly that’s what they’re doing, regardless of what those pills cost. I know someone personally who wrote a PhD thesis about a drug where that clearly wasn’t the case, and the price elasticity was supposed to be ‘theoretically low’ in that case, so that one’s obviously out ‘in general’, but the point is that people have looked at this stuff, a lot. I’m assuming you might be able to spot a dynamic like this in some situations, and different dynamics in the case of other drugs. It gets even better when you include complicating phenomena like cost-switching; perhaps the guy/organization responsible for potentially subsidizing the drug is not the same guy(/-…) as the guy who’s supposed to pay for the medical admissions (this depends on the insurance structure/setup). But that’s not always the case, and the decision as to who pays for what is not necessarily a given; it may depend e.g. on health care provider preferences, and those preferences may themselves depend upon a lot of things unrelated to patient preferences or -incentives. A big question even in the relatively simple situation where the financial structure is – for these purposes at least – simple, is also the extent to which relevant costs are even measured, and/or how they’re measured (if a guy dies due to a binding budget constraint resulting in no treatment for a health condition that would have been treatable with a drug, is that outcome supposed to be ‘very cheap’ (he didn’t pay anything for drugs, so there were no medical outlays) or very expensive (he could have worked for another two decades if he’d been treated, and those productivity losses need to be included in the calculation somehow; to focus solely on medical outlays is thus to miss the point)? An important analytical point here is that if you don’t explicitly make those deaths/productivity losses expensive, they are going to look very cheap, because the default option will always be to have them go unrecorded and untallied.
A problem not discussed in the coverage was incidentally the extent to which survey results pertaining to the cost of vaccination are worth much. You ask doctors why they didn’t get vaccinated, and they tell you it’s because it’s too expensive. Well, how many of them would you have expected to tell you they did not get vaccinated because the vaccines were too cheap? This is more about providing people with a perceived socially acceptable out than it is about finding stuff out about their actual reasons for behaving the way they do. If the price of vaccination does not vary across communities it’s difficult to estimate the price elasticity, true (if it does, you probably got an elasticity estimate right there), but using survey information to implicitly assess the extent to which the price is too high? Allow the vaccination price to vary next year/change it/etc. (or even simpler/cheaper, if those data exist; look at price variation which happened in the past and observe how the demand varied), and see if/how the doctors and nurses respond. That’s how you do this, you don’t ask people. Asking people is also actually sort of risky; I’m pretty sure a smart doctor could make an argument that if you want doctors to get vaccinated you should pay them for getting the shot – after all, getting vaccinated is unpleasant, and as mentioned there are positive externalities here in terms of improved patient outcomes, which might translate into specific patients not dying, which is probably a big deal, for those patients at least. The smart doctor wouldn’t necessarily be wrong; if the price of vaccination was ‘sufficiently low’, i.e. a ‘large’ negative number (‘if you get vaccinated, we give you $10.000’), I’m pretty sure coverage rates would go up a lot. That doesn’t make it a good idea. (Or a bad idea per se, for that matter – it depends upon the shape of the implicit social welfare function we’re playing around with. Though I must add – so that any smart doctors potentially reading along here don’t get any ideas – that a ‘large’ negative price of vaccination for health care workers is a bad idea if a cheaper option which achieves the same outcome is potentially available to the decision makers in question, which seems highly likely to me. For example vaccination rates of medical staff would also go up a lot if regular vaccinations were made an explicit condition of their employment, the refusal of which would lead to termination of their employment… There would be implicit costs of such a scheme, in terms of staff selection effects, but if you’re comparing solely those options and you’re the guy who makes the financial decisions..?)
As I stated in my goodreads review, ‘If you’re a schizophrenic and/or you have a strong interest in e.g. the metabolic effects of various anti-psychotics, the book is a must-read’. If that’s not true, it’s a different matter. One reason why I didn’t give the book a higher rating is that many of the numbers in there are quite dated, which is a bit annoying because it means you might feel somewhat uncertain about how valid the estimates included still are at this point.
As pointed out in my coverage of the human drug metabolism text there are a lot of things that can influence the way that drugs are metabolized, and this text includes some details about a specific topic which may help to illustrate what I meant by stating in that post that people ‘self-experimenting’ may be taking on risks they may not be aware of. Now, diabetics who need insulin injections are taking a drug with a narrow therapeutic index, meaning that even small deviations from the optimal dose may have serious repercussions. A lot of things influence what is actually the optimal dose in a specific setting; food (“food is like a drug to a person with diabetes”, as pointed out in Matthew Neal’s endocrinology text, which is yet another text I, alas, have yet to cover here), sleep patterns, exercise (sometimes there may be an impact even days after you’ve exercised), stress, etc. all play a role, and even well-educated diabetics may not know all the details.
A lot of drugs also affect glucose metabolism and insulin sensitivity, one of the best known drug types of this nature probably being the corticosteroids because of their widespread use in a variety of disorders, including autoimmune disorders which tend to be more common in autoimmune forms of diabetes (mainly type 1). However many other types of drugs can also influence blood glucose, and on the topic of antidepressants and antipsychotics we actually know some stuff about these things and about how various medications influence glucose levels; it’s not a big coincidence that people have looked at this, they’ve done that because it has become clear that “[m]any medications, in particular psychotropics, including antidepressants, antipsychotics, and mood stabilizers, are associated with elevations in blood pressure, weight gain, dyslipidemias, and/or impaired glucose homeostasis.” (p. 49). Which may translate into an increased risk of type 2 diabetes, and impaired glucose control in diabetics. Incidentally the authors of this text observes in the text that: “Our research group was among the first in the field to identify a possible link between the development of obesity, diabetes, and other metabolic derangements (e.g., lipid abnormalities) and the use of newer, second-generation antipsychotic medications.” Did the people who took these drugs before this research was done/completed know that their medications might increase their risk of developing diabetes? No, because the people prescribing it didn’t know, nor did the people who developed the drugs. Some probably still don’t know, including some of the medical people prescribing these medications. But the knowledge is out there now, and the effect size is in the case of some drugs argued to be large enough to be clinically relevant. In the context of a ‘self-experimentation’-angle the example is also interesting because the negative effect in question here is significantly delayed; type 2 diabetes takes time to develop, and this is an undesirable outcome which you’re not going to spot the way you might link a headache the next day to a specific drug you just started out with (another example of a delayed adverse event is incidentally cancer). You’re not going to spot dyslipidemia unless you keep track of your lipid levels on your own or e.g. develop xanthomas as a consequence of it, leading you to consult a physician. It helps a lot if you have proper research protocols and large n studies with sufficient power when you want to discover things like this, and when you want to determine whether an association like this is ‘just an association’ or if the link is actually causal (and then clarifying what we actually mean by that, and whether the causal link is also clinically relevant and/or for whom it might be clinically relevant). Presumably many people taking all kinds of medical drugs these days are taking on risks which might in a similar manner be ‘hidden from view’ as was the risk of diabetes in people taking second-generation antipsychotics in the near-past; over time epidemiological studies may pick up on some of these risks, but many will probably remain hidden from view on account of the amount of complexity involved. Even if a drug ‘works’ as intended in the context of the target variable in question, you can get into a lot of trouble if you only focus on the target variable (“if a drug has no side effects, then it is unlikely to work“). People working in drug development know this.
The book has a lot of blog-worthy stuff so I decided to include some quotes in the coverage below. The quotes are from the first half of the book, and this part of the coverage actually doesn’t talk much about the effects of drugs; it mainly deals with epidemiology and cost estimates. I thus decided to save the ‘drug coverage’ to a later post. It should perhaps be noted that some of the things I’d hoped to learn from Ru-Band Lu et al.’s book (blog coverage here) was actually included in this one, which was nice.
“Those with mental illness are at higher risk and are more likely to suffer the severe consequences of comorbid medical illness. Adherence to treatment is often more difficult, and other factors such as psychoneuroendocrine interactions may complicate already problematic treatments. Additionally, psychiatric medications themselves often have severe side effects and can interact with other medications, rendering treatment of the mental illness more complicated. Diabetes is one example of a comorbid medical illness that is seen at a higher rate in people with mental illness.”
“Depression rates have been studied and are increased in type 1 and type 2 diabetes. In a meta-analysis, Barnard et al. reviewed 14 trials in which patients with type 1 diabetes were surveyed for rates of depression.16 […] subjects with type 1 diabetes had a 12.0% rate of depression compared with a rate of 3.4% in those without diabetes. In noncontrolled trials, they found an even higher rate of depression in patients with type 1 diabetes (13.4%). However, despite these overall findings, in trials that were considered of an adequate design, and with a substantially rigorous depression screening method (i.e., use of structured clinical interview rather than patient reported surveys), the rates were not statistically significantly increased (odds ratio [OR] 2.36, 95% confidence interval [CI] 0.69–5.4) but had such substantial variation that it was not sufficient to draw a conclusion regarding type 1 diabetes. […] When it comes to rates of depression, type 2 diabetes has been studied more extensively than type 1 diabetes. Anderson et al. compiled a large metaanalysis, looking at 42 studies involving more than 21,000 subjects to assess rates of depression among patients with type 1 versus type 2 diabetes mellitus.18 Regardless of how depression was measured, type 1 diabetes was associated with lower rates of depression than type 2 diabetes. […] Depression was significantly increased in both type 1 and type 2 diabetes, with increased ORs for subjects with type 1 (OR = 2.9, 95% CI 1.6 –5.5, […] p=0.0003) and type 2 disease (OR = 2.9, 95% CI 2.3–3.7, […] p = 0.0001) compared with controls. Overall, with multiple factors controlled for, the risk of depression in people with diabetes was approximately twofold. In another large meta-analysis, Ali et al. looked at more than 51,000 subjects in ten different studies to assess rates of depression in type 2 diabetes mellitus. […] the OR for comorbid depression among the diabetic patients studied was higher for men than for women, indicating that although women with diabetes have an overall increased prevalence of depression (23.8 vs. 12.8%, p = 0.0001), men with diabetes have an increased risk of developing depression (men: OR = 1.9, 95% CI = 1.7–2.1 vs. women: OR = 1.3, 95% CI = 1.2–1.4). […] Research has shown that youths 12–17 years of age with type 1 diabetes had double the risk of depression compared with a teenage population without diabetes.21 This amounted to nearly 15% of children meeting the criteria for depression.”
“As many as two-thirds of patients with diabetes and major depression have been ill with depression for more than 2 years.44 […] Depression has been linked to decreased adherence to self-care regimens (exercise, diet, and cessation of smoking) in patients with diabetes, as well as to the use of diabetes control medications […] Patients with diabetes and depression are twice as likely to have three or more cardiac risk factors such as smoking, obesity, sedentary lifestyle, or A1c > 8.0% compared with patients with diabetes alone.47 […] The costs for individuals with both major depression and diabetes are 4.5 times greater than for those with diabetes alone.53”
“A 2004 cross-sectional and longitudinal study of data from the Health and Retirement Study demonstrated that the cumulative risk of incident disability over an 8-year period was 21.3% for individuals with diabetes versus 9.3% for those without diabetes. This study examined a cohort of adults ranging in age from 51 to 61 years from 1992 through 2000.”
“Although people with diabetes comprise just slightly more than 4% of the U.S. population,3 19% of every dollar spent on health care (including hospitalizations, outpatient and physician visits, ambulance services, nursing home care, home health care, hospice, and medication/glucose control agents) is incurred by individuals with diabetes” (As I noted in the margin, these are old numbers, and prevalence in particular is definitely higher today than it was when that chapter was written, so diabetics’ proportion of the total cost is likely even higher today than it was when that chapter was written. As observed multiple times previously on this blog, most of these costs are unrelated to the costs of insulin treatment and oral anti-diabetics like metformin, and indirect costs make out a quite substantial proportion of the total costs).
“In 1997, only 8% of the population with a medical claim of diabetes was treated for diabetes alone. Other conditions influenced health care spending, with 13.8% of the population with one other condition, 11.2% with two comorbidities, and 67% with three or more related conditions.6 Patients with diabetes who suffer from comorbid conditions related to diabetes have a greater impact on health services compared with those patients who do not have comorbid conditions. […] Overall, comorbid conditions and complications are responsible for 75% of total medical expenditures for diabetes.” (Again, these are old numbers)
“Heart disease and stroke are the largest contributors to mortality for individuals with diabetes; these two conditions are responsible for 65% of deaths. Death rates from heart disease in adults with diabetes are two to four times higher than in adults without diabetes. […] Adults with diabetes are more than twice as likely to have multiple diagnoses related to macrovascular disease compared to patients without diabetes […] Although the prevalence of cardiovascular disease increases with age for both diabetics and nondiabetics, adults with diabetes have a significantly higher rate of disease. […] The management of macrovascular disease, such as heart attacks and strokes, represents the largest factor driving medical service use and related costs, accounting for 52% of costs to treat diabetes over a lifetime. The average costs of treating macrovascular disease are $24,330 of a total of $47,240 per person (in year 2000 dollars) over the course of a lifetime.17 Moreover, macrovascular disease is an important determinant of cost at an earlier time than other complications, accounting for 85% of the cumulative costs during the first 5 years following diagnosis and 77% over the initial decade. [Be careful here: This is completely driven by type 2 diabetics; a 10-year old newly diagnosed type 1 diabetic does not develop heart disease in the first decade of disease – type 1s are also at high risk of cardiovascular disease, but the time profile here is completely different] […] Cardiovascular disease in the presence of diabetes affects not only cost but also the allocation of health care resources. Average annual individual costs attributed to the treatment of diabetes with cardiovascular disease were $10,172. Almost 51% of costs were for inpatient hospitalizations, 28% were for outpatient care, and 21% were for pharmaceuticals and related supplies. In comparison, the average annual costs for adults with diabetes and without cardiovascular disease were $4,402 for management and treatment of diabetes. Only 31.2% of costs were for inpatient hospitalizations, 40.3% were for outpatient care, and 28.6% were for pharmaceuticals.16“
“Of individuals with diabetes, 2% to 3% develop a foot ulcer during any given year. The lifetime incidence rate of lower extremity ulcers is 15% in the diabetic population.20 […] The rate of amputation in individuals with diabetes is ten times higher than in those without diabetes.5 Diabetic lower-extremity ulcers are responsible for 92,000 amputations each year,21 accounting for more than 60% of all nontraumatic amputations.5 The 10-year cumulative incidence of lower-extremity amputation is 7% in adults older than 30 years of age who are diagnosed with diabetes.22 […] Following amputation, the 5-year survival rate is 27%.23 […] The majority of annual costs associated with treating diabetic peripheral neuropathy are associated with treatment of ulcers […] Overall, inpatient hospitalization is a major driver of cost, accounting for 77% of expenditures associated with individual episodes of lower-extremity ulcers.24“
“By 2003, diabetes accounted for 37% of individuals being treated for renal disease in the United States. […] Diabetes is the leading cause of kidney failure, accounting for 44% of all newly diagnosed cases. […] The amount of direct medical costs for ESRD attributed to diabetes is substantial. The total adjusted costs in a 24-month period were 76% higher among ESRD patients with diabetes compared with those without diabetes. […] Nearly one half of the costs of ESRD are due to diabetes.27” [How much did these numbers change since the book was written? I’m not sure, but these estimates do provide some sort of a starting point, which is why I decided to include the numbers even though I assume some of them may have changed since the publication of the book]
“Every percentage point decrease in A1c levels reduces the risk of microvascular complications such as retinopathy, neuropathy, and nephropathy by 40%.5 However, the trend is for A1c to drift upward at an average of 0.15% per year, increasing the risk of complications and costs.17 […] A1c levels also affect the cost of specific complications associated with diabetes. Increasing levels affect overall cost and escalate more dramatically when comorbidities are present. A1c along with cardiovascular disease, hypertension, and depression are significant independent predictors of health care
costs in adults with diabetes.”
This book is not exactly the first book I’ve read on these kinds of topics (see for example my previous coverage of related topics here, here, here, here, here, and here), but the book did have some new stuff and I decided in the end that it was worth blogging, despite the fact that I did not think the book was particularly great. The book is slightly different from previous books I’ve read on related topics because normative aspects are covered in much greater detail – as they put it in the preface:
“This volume addresses normative dimensions of methodological and theoretical approaches, international experiences concerning the normative framework and the process of priority setting as well as the legal basis behind priorities. It also examines specific criteria for prioritization and discusses economic evaluation. […] Prioritization is necessary and inevitable – not only for reasons of resource scarcity, which might become worse in the next few years. But especially in view of an optimization of the supply structures, prioritization is an essential issue that will contribute to the capability and stability of healthcare systems. Therefore, our volume may give useful impulses to face challenges of appropriate prioritization.”
I’m generally not particularly interested in normative questions, preferring instead to focus on the empirical side of things, but the book did have some data as well. In the post I’ll focus on topics I found interesting, and I have made no attempt here to make the coverage representative of the sort of topics actually covered in the book; this is (as usual) a somewhat biased account of the material covered.
The book observes early and often that there’s no way around prioritization in medicine; you can’t not prioritize, because “By giving priority to one group, you ration care to the second group.” Every time you spend a dollar on cancer treatment, well, that’s a dollar you can’t spend on heart disease. So the key question in this context is how best to prioritize, rather than whether you should do it. It is noted in the text that there is a wide consensus that approaching and handling health care allocation rules explicitly is preferable to implicit rationing, a point I believe was also made in Glied and Smith. A strong argument can be made that clear and well-defined decision-rules will lead to better outcomes than implicit allocation decisions made by doctors during their day-to-day workload. The risks of leaving allocation decisions to physicians involve overtaxing medical practitioners (they are implicitly required to repeatedly take decisions which may be emotionally very taxing), problematic and unfair distribution patters of care, and there’s also a risk that such practices may erode trust between patients and physicians.
A point related to the fact that any prioritization decision made within the medical sector, regardless of whether the decision is made implicitly or explicitly, will necessarily affect all patient populations by virtue of the fact that resources used for one purpose cannot be used for another purpose, is that the health care sector is not the only sector in the economy; when you spend money on medicine that’s also money you can’t be spending on housing or education: “The competition between health-related resources and other goods is generally left to a political process. The fact that a societal budget for meeting health needs is the result of such a political process means that in all societies, some method of resolving disagreements about priorities is needed.” Different countries have different approaches to how to resolve these disagreements (and in large countries in particular, lower-level regional differences may also be important in terms of realized care provision allocation decisions), and the book covers systems applied in multiple different countries, including England, Germany, Norway, Sweden, and the US state of Oregon.
Some observations and comments:
“A well-known unfairness objection against conventional cost-effectiveness analysis is the severity of diseases objection – the objection that the approach is blind as to whether the QALYs go to severely or to slightly ill patients. Another is the objection of disability discrimination – the objection that the approach is not blind between treating a life-threatening disease when it befalls a disabled patient and treating the same disease when it befalls a non-disabled patient. An ad hoc amendment for fairness problems like these is equity weighting. Equity weights are multiplication factors that are introduced in order to make some patient group’s QALYs count more than others.”
“There were an estimated 3 million people with diabetes in England in 2009; estimates suggest that the number of people with diabetes could rise to 4.6 million by 2030. There has also been a rapid rise in gastrointestinal diseases, particularly chronic liver disease where the under-65 mortality rate has increased 5-fold since 1970. Liver disease is strongly linked to the harmful use of alcohol and rising levels of obesity. […] the poorest members of the community are at most risk of neglecting their health. This group is more likely to eat, drink and smoke to excess and fail to take sufficient exercise.22 Accordingly, life expectancy in this community is shorter and the years spent of suffering from disability are much longer. […] Generic policies are effective in the sense that aggregate levels of health status improve and overall levels of morbidity and mortality fall. However, they are ineffective in reducing health inequalities; indeed, they may make them worse. The reason is that better-off groups respond more readily to public health campaigns. […] If policy-makers [on the other hand] disinvest from the majority to narrow the inequality gap with a minority resistant to change, this could reduce aggregate levels of health status in the community as a whole. [Health behaviours also incidentally tend to be quite resistant to change in general, and we really don’t know all that much about which sort of interventions work and/or how well they work – see also Thirlaway & Upton’s coverage] […] two out of three adults [in the UK] are overweight or obese; and inequalities in health remain widespread, with people in the poorest areas living on average 7 years fewer than those in the richest areas, and spending up to 17 more years living with poor health. […] the proportion of the total health budget invested in preventive medicine and health promotion […] is small. The UK spends about 3.6 % of its entire healthcare budget on public health projects of this nature (which is more than many other EU member states).”
Let’s talk a little bit about rationing. Rationing by delay (waiting lists) is a well-known method of limiting care, but it’s far from the only way to implicitly ration care in a manner which may be hidden from view; another way to limit care provision is to ration by dilution. This may happen when patients are seen on time (do recall that waiting lists are very common in the medical sector, for very natural reasons which I’ve discussed here on the blog before), but the quality of care that is provided to patients receiving care goes down. Rationing by dilution may sometimes be a result of attempts to limit rationing by delay; if you measure hospitals on whether or not they treat people within a given amount of time, the time dimension becomes very important in the treatment context and it may thus end up dominating other decision variables which should ideally take precedence over this variable in the specific clinical context. The book mentions as an example the Bristol Eye Hospital, where it is thought that 25 patients may have lost their sights because even though they were urgent cases which should have been high priority, they were not treated in time because there was a great institutional focus on not allowing waiting times of any patients on the waiting lists to cross the allowed maximum waiting time, meaning that much less urgent cases were treated instead of the urgent cases in order to make the numbers look good. A(n excessive?) focus on waiting lists may thus limit focus on patient needs, and similar problems pop up when other goals aside from patient needs are emphasized in an institutional context; hospital reorganisations undertaken in order to improve financial efficiency may also result in lower standards of care, and in the book multiple examples of this having happened in a British context are discussed. The chapter in question does not discuss this aspect, but it seems to me likely that rationing by dilution, or at least something quite similar to this, may also happen in the context of a rapid increase in capacity as a result of an attempt to address long waiting lists; if you for example decide to temporarily take on a lot of new and inexperienced nurses to lower the waiting list, these new nurses may not provide the same level of care as do the experienced nurses already present. A similar dynamic may probably be observed in a setting where the number of nurses does not change, but each patient is allocated less time with any given nurse than was previously the case.
“Public preferences have been shown not to align with QALY maximization (or health benefit maximization) across a variety of contexts […] and considerations affecting these preferences often extend well beyond strict utilitarian concerns […] age has been shown to be among the most frequently cited variables affecting the public’s prioritization decisions […] Most people are willing to use age as a criterion at least in some circumstances and at least in some ways. This is shown by empirical studies of public views on priority setting […] most studies suggest that a majority accepts that age can have some role in priority setting. […] Oliver [(2009)] found […] a wide range of context-dependent ‘decision rules’ emerged across the decision tasks that appeared to be dependent on the scenario presented. Respondents referenced reasons including maximizing QALYs,11 maximizing life-years or post-treatment quality of life,12 providing equal access to health care, maximizing health based on perceptions of adaptation, maximizing societal productivity (including familial roles, i.e. ‘productivity ageism’), minimizing suffering, minimizing costs, and distributing available resources equitably. As an illustration of its variability, he noted that 46 of the 50 respondents were inconsistent in their reasoning across the questions. Oliver commented that underlying values influence the respondents’ decisions, but if these values are context dependent, it becomes a challenge – if not impossible – to identify a preferred, overarching rule by which to distribute resources. […] Given the empirical observations that respondents do not seem to rely upon a consistent decision rule that is independent of the prioritization context, some have suggested that deliberative judgments be used to incorporate equity considerations […]. This means that decision makers may call upon a host of different ‘rules’ to set priorities depending on the context. When the patients are of similar ages, prioritization by severity may offer a morally justifiable solution, for example. In contrast, as the age discrepancy becomes greater between the two patients, there may be a point at which ‘the priority view’ (i.e. those who in the most dire conditions take precedence) no longer holds […] There is some evidence that indicates that public preferences do not support giving priority in instances where the intervention has a poor prognosis […] If older patients have poorer health outcomes as a result of certain interventions, [this] finding might imply that in these instances, they should receive lower priority or not be eligible for certain care. […] A substantial body of evidence indicates that the utilitarian approach of QALY maximization fails to adequately capture public preferences for a greater degree of equity into health-care distribution; however, how to go about incorporating these concerns remains unresolved.”
“roughly 35 % of the […] [UK] health expenditures were spent on the 13 % of our population over the age of 65. A similar statistic holds true for the European Union as well […] the elderly, on average, have many more health needs than the non-elderly. In the United States, 23 % of the elderly have five or more chronic health problems, some life-threatening, some quality-of-life diminishing (Thorpe et al. 2010). Despite this statistic, the majority of the elderly in any given year is quite healthy and makes minimal use of the health care system. Health needs tend to be concentrated. The sickest 5 % of the Medicare population consume 39 % of total Medicare expenditures, and the sickest 10 % consume 58 % of Medicare expenditures (Schoenman 2012). […] we are […] faced with the problem of where to draw the line with regard to a very large range of health deficiencies associated with advanced age. It used to be the case in the 1970s that neither dialysis nor kidney transplantation were offered as an option to patients in end-stage kidney failure who were beyond age 65 because it was believed they were not medically suitable. That is, both procedures were judged to be too burdensome for individuals who already had diminished health status. But some centers started dialyzing older patients with good results, and consequently, the fastest growing segment of the dialysis population today (2015) is over age 75. This phenomenon has now been generalized across many areas of surgery and medicine. […] What [many new] procedures have in common is that they are very expensive: $70,000 for coronary bypass surgery (though usually much more costly due to complication rates among the hyper-elderly); $200,000 for the LVAD [Left Ventricular Assist Device]; $100,000+ per month for prolonged mechanical ventilation. […] The average older recipient of an LVAD will gain one to two extra years of life […] there are now (2015) about 5.5 million Americans in various stages of heart failure and 550,000 new cases annually. Versions of the LVAD are still being improved, but the potential is that 200,000 of these devices could be implanted annually in the United States. That would add at least $40 billion per year to the cost of the Medicare program.”
“In the USA, around 40 % of premature mortality is attributed to behavioral patterns,2 and it is estimate[d] that around $1.3 trillion annually — around a third of the total health budget — is spent on preventable diseases.3 […] among the ten leading risk factors contributing to the burden of disease in high-income countries, seven can be directly attributed to unhealthy lifestyles. […] Private health insurance takes such factors into account when calculating premiums for health insurances (Olsen 2009). In contrast, publicly funded health-care systems are mainly based on the so-called solidarity principle, which generally excludes risk-based premiums. However, in some countries, several incentive schemes such as “fat taxes” […], bonuses, or reductions of premiums […] have recently been implemented in order to incorporate aspects of personal responsibility in public health-care systems. […] [An important point in this context is that] there are fundamental questions about whether […] better health leads to lower cost. Among other things, cost reductions are highly dependent on the period of time that one considers. What services are covered by a health system, and how its financing is managed, also matters. Regarding the relative lifetime cost of smokers, obese, and healthy people (never smokers, normal body mass index [BMI]) in the Netherlands, it has been suggested that the latter, and not the former two groups, are most costly — chiefly due to longer life and higher cost of care at the end of life.44 Other research suggests that incentivizing disease management programs rather than broader prevention programs is far more effective.45 Cost savings can therefore not be taken for granted but require consideration of the condition being incentivized, the organizational specifics of the health system, and, in particular, the time horizon over which possible savings are assessed. […] Policies seeking to promote personal responsibility for health can be structured in a very wide variety of ways, with a range of different consequences. In the best case, the stars are aligned and programs empower people’s health literacy and agency, reduce overall healthcare spending, alleviate resource allocation dilemmas, and lead to healthier and more productive workforces. But the devil is often in the detail: A focus on controlling or reducing cost can also lead to an inequitable distribution of benefits from incentive programs and penalize people for health risk factors that are beyond their control.”
Below are three new lectures from the Institute of Advanced Study. As far as I’ve gathered they’re all from an IAS symposium called ‘Lens of Computation on the Sciences’ – all three lecturers are computer scientists, but you don’t have to be a computer scientist to watch these lectures.
Should computer scientists and economists band together more and try to use the insights from one field to help solve problems in the other field? Roughgarden thinks so, and provides examples of how this might be done/has been done. Applications discussed in the lecture include traffic management and auction design. I’m not sure how much of this lecture is easy to follow for people who don’t know anything about either topic (i.e., computer science and economics), but I found it not too difficult to follow – it probably helped that I’ve actually done work on a few of the things he touches upon in the lecture, such as basic auction theory, the fixed point theorems and related proofs, basic queueing theory and basic discrete maths/graph theory. Either way there are certainly much more technical lectures than this one available at the IAS channel.
I don’t have Facebook and I’m not planning on ever getting a FB account, so I’m not really sure I care about the things this guy is trying to do, but the lecturer does touch upon some interesting topics in network theory. Not a great lecture in my opinion and occasionally I think the lecturer ‘drifts’ a bit, talking without saying very much, but it’s also not a terrible lecture. A few times I was really annoyed that you can’t see where he’s pointing that damn laser pointer, but this issue should not stop you from watching the video, especially not if you have an interest in analytical aspects of how to approach and make sense of ‘Big Data’.
I’ve noticed that Scott Alexander has said some nice things about Scott Aaronson a few times, but until now I’ve never actually read any of the latter guy’s stuff or watched any lectures by him. I agree with Scott (Alexander) that Scott (Aaronson) is definitely a smart guy. This is an interesting lecture; I won’t pretend I understood all of it, but it has some thought-provoking ideas and important points in the context of quantum computing and it’s actually a quite entertaining lecture; I was close to laughing a couple of times.
“A commonplace argument in contemporary writing on trust is that we would all be better off if we were all more trusting, and therefore we should all trust more […] Current writings commonly focus on trust as somehow the relevant variable in explaining differences across cases of successful cooperation. Typically, however, the crucial variable is the trustworthiness of those who are to be trusted or relied upon. […] It is not trust per se, but trusting the right people that makes for successful relationships and happiness.”
“If we wish to understand the role of trust in society […], we must get beyond the flaccid – and often wrong – assumption that trust is simply good. This supposition must be heavily qualified, because trusting the malevolent or the radically incompetent can be foolish and often even grossly harmful. […] trust only make[s] sense in dealings with those who are or who could be induced to be trustworthy. To trust the untrustworthy can be disastrous.”
That it’s stupid to trust people who cannot be trusted should in my opinion be blatantly obvious, yet somehow to a lot of people it doesn’t seem to be at all obvious; in light of this problem (…I maintain that this is indeed a problem) the above observations are probably among the most important ones included in Hardin’s book. The book includes some strong criticism of much of the current/extant literature on trust. The two most common fields of study within this area of research are game-theoretic ‘trust games’, which according to the author are ill-named as they don’t really seem to be dealing much, if at all, with the topic of trust, and (poor) survey research which asks people questions which are hard to answer and tend to yield answers which are even harder to interpret. I have included below a few concluding remarks from the chapter on these topics:
“Both of the current empirical research programs on trust are largely misguided. The T-games [‘trust-games’], as played […] do not elicit or measure anything resembling ordinary trust relations; and their findings are basically irrelevant to the modeling and assessment of trust and trustworthiness. The only thing that relates the so-called trust game […] to trust is its name, which is wrong and misleading. Survey questions currently in wide use are radically unconstrained. They therefore force subjects to assume the relevant degrees of constraint, such as how costly the risk of failed cooperation would be. […] In sum, therefore, there is relatively little to learn about trust from these two massive research programs. Without returning their protocols to address standard conceptions of trust, they cannot contribute much to understanding trust as we generally know it, and they cannot play a very constructive role in explaining social behavior, institutions, or social and political change. These are distressing conclusions because both these enterprises have been enormous, and in many ways they have been carried out with admirable care.”
There is ‘relatively little to learn about trust from these two massive research programs’, but one to me potentially important observation, hidden away in the notes at the end of the book, is perhaps worth mentioning here: “There is a commonplace claim that trust will beget trustworthiness […] Schotter [as an aside this guy was incidentally the author of the Micro textbook we used in introductory Microeconomics] and Sopher (2006) do not find this to be true in game experiments that they run, while they do find that trustworthiness (cooperativeness in the play of games) does beget trust (or cooperation).”
There were a few parts of the coverage which confused me somewhat until it occurred to me that the author might not have read Boyd and Richerson, or other people who might have familiarized him with their line of thinking and research (once again, you should read Boyd and Richerson).
Moving on, a few remarks on social capital:
“Like other forms of capital and human capital, social capital is not completely fungible but may be specific to certain activities. A given form of social capital that is valuable in facilitating certain actions may be useless or even harmful for others. […] [A] mistake is the tendency to speak of social capital as though it were a particular kind of thing that has generalized value, as money very nearly does […] it[‘s value] must vary in the sense that what is functional in one context may not be in another.”
It is important to keep in mind that trust which leads to increased cooperation can end up leading to both good outcomes and bad:
“Widespread customs and even very local practices of personal networks can impose destructive norms on people, norms that have all of the structural qualities of interpersonal capital. […] in general, social capital has no normative valence […] It is generally about means for doing things, and the things can be hideously bad as well as good, although the literature on social capital focuses almost exclusively on the good things it can enable and it often lauds social capital as itself a wonderful thing to develop […] Community and social capital are not per se good. It is a grand normative fiction of our time to suppose that they are.”
The book has a chapter specifically about trust on the internet which related to the coverage included in Barak et al.‘s book, a publication which I have unfortunately neglected to blog (this book of course goes into a lot more detail). A key point in that chapter is that the internet is not really all that special in terms of these things, in the sense that to the extent that it facilitates coordination etc., it can be used to accomplish beneficial things as well as harmful things – i.e. it’s also neutrally valenced. Barak et al.‘s book has a lot more stuff about how this medium impacts communication and optimal communication strategies etc., which links in quite a bit with trust aspects, but I won’t go into this stuff here and I’m pretty sure I’ve covered related topics before here on the blog, e.g. back when I covered Hargie.
The chapter about terrorism and distrust had some interesting observations. A few quotes:
“We know from varied contexts that people can have a more positive view of individuals from a group than they have of the group.”
“Mere statistical doubt in the likely trustworthiness of the members of some identifiable group can be sufficient to induce distrust of all members of the group with whom one has no personal relationship on which to have established trust. […] This statistical doubt can trump relational considerations and can block the initial risk-taking that might allow for a test of another individual’s trustworthiness by stereotyping that individual as primarily a member of some group. If there are many people with whom one can have a particular beneficial interaction, narrowing the set by excluding certain stereotypes is efficient […] Unfortunately, however, excluding very systematically on the basis of ethnicity or race becomes pervasively destructive of community relations.”
One thing to keep in mind here is that people’s stereotypes are often quite accurate. When groups don’t trust each other it’s always a lot of fun to argue about who’s to blame for that state of affairs, but it’s important here to keep in mind that both groups will always have mental models of both the in-group and the out-group (see also the coverage below). Also it should be kept in mind that to the extent that people’s stereotypes are accurate, blaming stereotyping behaviours for the problems of the people who get stereotyped is conceptually equivalent to blaming people for discriminating against untrustworthy people by not trusting people who are not trustworthy. You always come back to the problem that what’s at the heart of the matter is never just trust, but rather trustworthiness. To the extent that the two are related, trust follows trustworthiness, not the other way around.
“There’s a fairly extensive literature on so-called generalized trust, which is trust in the anonymous or general other person, including strangers, whom we might encounter, perhaps with some restrictions on what isues would come under that trust. […] [Generalized trust] is an implausible notion. In any real-world context, I trust some more than others and I trust any given person more about some things than about others and more in some contexts than in others. […] Whereas generalized trust or group-generalized trust makes little or no sense (other than as a claim of optimism), group-generalized distrust in many contexts makes very good sense. If you were Jewish, Gypsy, or gay, you had good reason to distrust all officers of the Nazi state and probably most citizens in Nazi Germany as well. American Indians of the western plains had very good reason to distrust whites. During Milosevic’s wars and pogroms, Serbs, Croatians, and Muslims in then Yugoslavia had increasingly good reasons to distrust most members of the other groups, especially while the latter were acting as groups. […] In all of these cases, distrust is defined by the belief that members of the other groups and their representatives are hostile to one’s interests. Trust relationships between members of these various groups are the unusual cases that require explanation; the relatively group-generalized distrust is easy to understand and justify.”
“In the current circumstances of mostly Arab and Islamic terrorism against israel and the West and much of the rest of the world, it is surely a very tiny fraction of all Arabs and Islamists who are genuinely a threat, but the scale of their threat may make many Israelis and westerners wary of virtually all Arabs and Islamists […] many who are not prospects for taking terrorist action evidently sympathize with and even support these actions”
“When cooperation is organized by communal norms, it can become highly exclusionary, so that only members of the community can have cooperative relations with those in the community. In such a case, the norms of cooperativeness are norms of exclusion […] For many fundamentalist groups, continued loyalty to the group and its beliefs is secured by isolating the group and its members from many other influences so that relations within the community are governed by extensive norms of exclusion. When this happens, it is not only trust relations but also basic beliefs that are constrained. If we encounter no one with contrary beliefs our own beliefs will tend to prevail by inertia and lack of questioning and they will be reinforced by our secluded, exclusionary community. There are many strong, extreme beliefs about religious issues as well as about many other things. […] The two matters for which such staunch loyalty to unquestioned beliefs are politically most important are probably religious and nationalist commitments […] Such beliefs are often maintained by blocking our alternative views and by sanctioning those within the group who stray. […] Narrowing one’s associations to others in an isolated extremist group cripples one’s epistemology by blocking out general questioning of the group’s beliefs […] To an outsider those beliefs might be utterly crazy. Indeed, virtually all strong religious beliefs sound crazy or silly to those who do not share them. […] In some ways, the internet allows individuals and small groups to be quite isolated while nevertheless maintaining substantial contact with others of like mind. Islamic terrorists in the West can be almost completely isolated individually while maintaining nearly instant, frequent contact with other and with groups in the Middle East, Pakistan, or Afghanistan, as well as with groups of other potential terrorists in target nations.”
David Friedman recently asked a related question on SSC (he asked about why there are waiting lists for surgical procedures), and I decided that as I’d read some stuff about these topics in the past I might as well answer his question. The answer turned out to be somewhat long/detailed, and I decided I might as well post some of this stuff here as well. In a way my answer to David’s question provides belated coverage of a book I read last year, Appointment Planning in Outpatient Clinics and Diagnostic Facilities, which I have covered only in very limited detail here on the blog before (the third paragraph of this post is the only coverage of the book I’ve provided here).
Below I’ve tried to cover these topics in a manner which would make it unnecessary to also read David’s question and related comments.
The brief Springer publication Appointment Planning in Outpatient Clinics and Diagnostic Facilities has some basic stuff about operations research and queueing theory which is useful for making sense of resource allocation decisions made in the medical sector. I think this is the kind of stuff you’ll want to have a look at if you want to understand these things better.
There are many variables which are important here and which may help explain why waiting lists are common in the health care sector (it’s not just surgery). The quotes below are from the book:
“In a walk-in system, patients are seen without an appointment. […] The main advantage of walk-in systems is that access time is reduced to zero. […] A huge disadvantage of patients walking in, however, is that the usually strong fluctuating arrival stream can result in an overcrowded clinic, leading to long waiting times, high peaks in care provider’s working pressure, and patients leaving without treatment (blocking). On other moments of time the waiting room will be practically empty […] In regular appointment systems workload can be dispersed, although appointment planning is usually time consuming. A walk-in system is most suitable for clinics with short service times and multiple care providers, such as blood withdrawal facilities and pre-anesthesia check-ups for non-complex patients. If the service times are longer or the number of care providers is limited, the probability that patients experience a long waiting time becomes too high, and a regular appointment system would be justified”
“Sometimes it is impossible to provide walk-in service for all patients, for example when specific patients need to be prepared for their consultation, or if specific care providers are required, such as anesthesiologists [I noted in my reply to David that these remarks seem highly relevant for the surgery context]. Also, walk-in patients who experience a full waiting room upon arrival may choose to come back at a later point in time. To make sure that they do have access at that point, clinics usually give these patients an appointment. This combination of walk-in and appointment patients requires a specific appointment system that satisfies the following requirements:
1. The access time for appointment patients is below a certain threshold
2. The waiting time for walk-in patients is below a certain threshold
3. The number of walk-in patients who are sent away due to crowding is minimized
To satisfy these requirements, an appointment system should be developed to determine the optimal scheduling of appointments, not only on a day level but also on a week level. Developing such an appointment system is challenging from a mathematical perspective. […] Due to the high variability that is usually observed in healthcare settings, introducing stochasticity in the modeling process is very important to obtain valuable and reasonable results.”
“Most elective patients will ultimately evolve into semi-urgent or even urgent patients if treatment is extensively prolonged.” That’s ‘on the one hand’ – but of course there’s also the related ‘on the other hand’-observation that: “Quite often a long waiting list results in a decrease in demand”. Patients might get better on their own and/or decide it’s not worth the trouble to see a service provider – or they might deteriorate.
“Some planners tend to maintain separate waiting lists for each patient group. However, if capacity is shared among these groups, the waiting list should be considered as a whole as well. Allocating capacity per patient group usually results in inflexibility and poor performance”.
“mean waiting time increases with the load. When the load is low, a small increase therein has a minimal effect on the mean waiting time. However, when the load is high, a small increase has a tremendous effect on the mean waiting time. For instance, […] increasing the load from 50 to 55 % increases the waiting time by 10 %, but increasing the load from 90 to 95 % increases the waiting time by 100 % […] This explains why a minor change (for example, a small increase in the number of patients, a patient arriving in a bed or a wheelchair) can result in a major increase in waiting times as sometimes seen in outpatient clinics.”
“One of the most important goals of this chapter is to show that it is impossible to use all capacity and at the same time maintain a short, manageable waiting list. A common mistake is to reason as follows:
Suppose total capacity is 100 appointments. Unused capacity is commonly used for urgent and inpatients, that can be called in last minute. 83 % of capacity is used, so there is on average 17 % of capacity available for urgent and inpatients. The urgent/inpatient demand is on average 20 appointments per day. Since 17 appointments are on average not used for elective patients, a surplus capacity of only three appointments is required to satisfy all patient demand.
Even though this is true on average, more urgent and inpatient capacity is required. This is due to the variation in the process; on certain days 100 % of capacity is required to satisfy elective patient demand, thus leaving no room for any other patients. Furthermore, since 17 slots are dedicated to urgent and inpatients, only 83 slots are available for elective patients, which means that ρ is again equal to 1, resulting in an uncontrollable waiting list.” [ρ represents the average proportion of time which the server/service provider is occupied – a key stability requirement is that ρ is smaller than one; if it is not, the length of the queue becomes unstable/explodes. See also this related link].
“The challenge is to make a trade-off between maintaining a waiting list which is of acceptable size and the amount of unused capacity. Since the focus in many healthcare facilities is on avoiding unused capacity, waiting lists tend to grow until “something has to be done.” Then, temporarily surplus capacity is deployed, which is usually more expensive than regular capacity […]. Even though waiting lists have a buffer function (i.e., by creating a reservoir of patients that can be planned when demand is low) it is unavoidable that, even in well-organized facilities, over a longer period of time not all capacity is used.”
I think one way to think about the question of whether it makes sense to have a waiting list or whether you can ‘just use the price variable’ is that if it is possible for you as a provider to optimize over both the waiting time variable and the price variable (i.e., people demanding the service find some positive waiting time to be acceptable when it is combined with a non-zero price reduction), the result you’re going to get is always going to be at least as good as an option where you only have the option of optimizing over price – not including waiting time in the implicit pricing mechanism can be thought of as in a sense a weakly dominated strategy.
A lot of the planning stuff relates to how to handle variable demand, and input heterogeneities can be thought of as one of many parameters which may be important to take into account in the context of how best to deal with variable demand; surgeons aren’t perfect substitutes. Perhaps neither are nurses, or different hospitals (relevant if you’re higher up in the decision making hierarchy). An important aspect is the question of whether a surgeon (or a doctor, or a nurse…) might be doing other stuff instead of surgery during down-periods, and what might be the value of that other stuff s/he might be doing instead. In the surgical context, not only is demand variable over time, there are also issues such as that many different inputs need to be coordinated; you need a surgeon and a scrub nurse and an anesthesiologist. The sequential and interdependent nature of many medical procedures and inputs is likely also a factor in terms of adding complexity; whether a condition requires treatment or not, and/or which treatment may be required, may depend upon the results of a test which has to be analyzed before the treatment is started, and so you for example can’t switch the order of test and treatment, or for that matter treat patient X based on patient Y’s test results; there’s some built-in inflexibility here at the outset. This type of thing also means there are more nodes in the network, and more places where things can go wrong, resulting in longer waiting times than planned.
I think the potential gains in terms of capacity utilization, risk reduction and increased flexibility to be derived from implementing waiting schemes of some kind in the surgery context would mediate strongly against a model without waiting lists, and I think that the surgical field is far from unique in that respect in the context of medical care provision.
This will be my last post about the book. Yesterday I finished reading Darwin’s Origin of Species, which was my 100th book this year (here’s the list), but I can’t face blogging that book at the moment so coverage of that one will have to wait a bit.
In my second post about this book I had originally planned to cover chapter 7 – ‘Analysing costs’ – but as I didn’t like to spend too much time on the post I ended up cutting it short. This omission of coverage in the last post means that some themes to be discussed below are closely related to stuff covered in the second post, whereas on the other hand most of the remaining material, more specifically the material from chapters 8, 9 and 10, deal with decision analytic modelling, a quite different topic; in other words the coverage will be slightly more fragmented and less structured than I’d have liked it to be, but there’s not really much to do about that (it doesn’t help in this respect that I decided to not cover chapter 8, but doing that as well was out of the question).
I’ll start with coverage of some of the things they talk about in chapter 7, which as mentioned deals with how to analyze costs in a cost-effectiveness analysis context. They observe in the chapter that health cost data are often skewed to the right, for several reasons (costs incurred by an individual cannot be negative; for many patients the costs may be zero; some study participants may require much more care than the rest, creating a long tail). One way to address skewness is to use the median instead of the mean as the variable of interest, but a problem with this approach is that the median will not be as useful to policy-makers as will be the mean; as the mean times the population of interest will give a good estimate of the total costs of an intervention, whereas the median is not a very useful variable in the context of arriving at an estimate of the total costs. Doing data transformations and analyzing transformed data is another way to deal with skewness, but their use in cost effectiveness analysis have been questioned for a variety of reasons discussed in the chapter (to give a couple of examples, data transformation methods perform badly if inappropriate transformations are used, and many transformations cannot be used if there are data points with zero costs in the data, which is very common). Of the non-parametric methods aimed at dealing with skewness they discuss a variety of tests which are rarely used, as well as the bootstrap, the latter being one approach which has gained widespread use. They observe in the context of the bootstrap that “it has increasingly been recognized that the conditions the bootstrap requires to produce reliable parameter estimates are not fundamentally different from the conditions required by parametric methods” and note in a later chapter (chapter 11) that: “it is not clear that boostrap results in the presence of severe skewness are likely to be any more or less valid than parametric results […] bootstrap and parametric methods both rely on sufficient sample sizes and are likely to be valid or invalid in similar circumstances. Instead, interest in the bootstrap has increasingly focused on its usefulness in dealing simultaneously with issues such as censoring, missing data, multiple statistics of interest such as costs and effects, and non-normality.” Going back to the coverage in chapter 7, in the context of skewness they also briefly touch upon the potential use of a GLM framework to address this problem.
Data is often missing in cost datasets. Some parts of their coverage of these topics was to me but a review of stuff already covered in Bartholomew. Data can be missing for different reasons and through different mechanisms; one distinction is among data missing completely at random (MCAR), missing at random (MAR) (“missing data are correlated in an observable way with the mechanism that generates the cost, i.e. after adjusting the data for observable differences between complete and missing cases, the cost for those with missing data is the same, except for random variation, as for those with complete data”), and not missing at random (NMAR); the last type is also called non-ignorably missing data, and if you have that sort of data the implication is that the costs of those in the observed and unobserved groups differ in unpredictable ways, and if you ignore the process that drives these differences you’ll probably end up with a biased estimator. Another way to distinguish between different types of missing data is to look at patterns within the dataset, where you have:
“*univariate missingness – a single variable in a dataset is causing a problem through missing values, while the remaining variables contain complete information
*unit non-response – no data are recorded for any of the variables for some patients
*monotone missing – caused, for example, by drop-out in panel or longitudinal studies, resulting in variables observed up to a certain time point or wave but not beyond that
*multivariate missing – also called item non-response or general missingness, where some but not all of the variables are missing for some of the subjects.”
The authors note that the most common types of missingness in cost information analyses are the latter two. They discuss some techniques for dealing with missing data, such as complete-case analysis, available-case analysis, and imputation, but I won’t go into the details here. In the last parts of the chapter they talk a little bit about censoring, which can be viewed as a specific type of missing data, and ways to deal with it. Censoring happens when follow-up information on some subjects is not available for the full duration of interest, which may be caused e.g. by attrition (people dropping out of the trial), or insufficient follow up (the final date of follow-up might be set before all patients reach the endpoint of interest, e.g. death). The two most common methods for dealing with censored cost data are the Kaplan-Meier sample average (-KMSA) estimator and the inverse probability weighting (-IPW) estimator, both of which are non-parametric interval methods. “Comparisons of the IPW and KMSA estimators have shown that they both perform well over different levels of censoring […], and both are considered reasonable approaches for dealing with censoring.” One difference between the two is that the KMSA, unlike the IPW, is not appropriate for dealing with censoring due to attrition unless the attrition is MCAR (and it almost never is), because the KM estimator, and by extension the KMSA estimator, assumes that censoring is independent of the event of interest.
The focus in chapter 8 is on decision tree models, and I decided to skip that chapter as most of it is known stuff which I felt no need to review here (do remember that I to a large extent use this blog as an extended memory, so I’m not only(/mainly?) writing this stuff for other people..). Chapter 9 deals with Markov models, and I’ll talk a little bit about those in the following.
“Markov models analyse uncertain processes over time. They are suited to decisions where the timing of events is important and when events may happen more than once, and therefore they are appropriate where the strategies being evaluated are of a sequential or repetitive nature. Whereas decision trees model uncertain events at chance nodes, Markov models differ in modelling uncertain events as transitions between health states. In particular, Markov models are suited to modelling long-term outcomes, where costs and effects are spread over a long period of time. Therefore Markov models are particularly suited to chronic diseases or situations where events are likely to recur over time […] Over the last decade there has been an increase in the use of Markov models for conducting economic evaluations in a health-care setting […]
A Markov model comprises a finite set of health states in which an individual can be found. The states are such that in any given time interval, the individual will be in only one health state. All individuals in a particular health state have identical characteristics. The number and nature of the states are governed by the decisions problem. […] Markov models are concerned with transitions during a series of cycles consisting of short time intervals. The model is run for several cycles, and patients move between states or remain in the same state between cycles […] Movements between states are defined by transition probabilities which can be time dependent or constant over time. All individuals within a given health state are assumed to be identical, and this leads to a limitation of Markov models in that the transition probabilities only depend on the current health state and not on past health states […the process is memoryless…] – this is known as the Markovian assumption”.
The note that in order to build and analyze a Markov model, you need to do the following: *define states and allowable transitions [for example from ‘non-dead’ to ‘dead’ is okay, but going the other way is, well… For a Markov process to end, you need at least one state that cannot be left after it has been reached, and those states are termed ‘absorbing states’], *specify initial conditions in terms of starting probabilities/initial distribution of patients, *specify transition probabilities, *specify a cycle length, *set a stopping rule, *determine rewards, *implement discounting if required, *analysis and evaluation of the model, and *exploration of uncertainties. They talk about each step in more detail in the book, but I won’t go too much into this.
Markov models may be governed by transitions that are either constant over time or time-dependent. In a Markov chain transition probabilities are constant over time, whereas in a Markov process transition probabilities vary over time (/from cycle to cycle). In a simple Markov model the baseline assumption is that transitions only occur once in each cycle and usually the transition is modelled as taking place either at the beginning or the end of cycles, but in reality transitions can take place at any point in time during the cycle. One way to deal with the problem of misidentification (people assumed to be in one health state throughout the cycle even though they’ve transfered to another health state during the cycle) is to use half-cycle corrections, in which an assumption is made that on average state transitions occur halfway through the cycle, instead of at the beginning or the end of a cycle. They note that: “the important principle with the half-cycle correction is not when the transitions occur, but when state membership (i.e. the proportion of the cohort in that state) is counted. The longer the cycle length, the more important it may be to use half-cycle corrections.” When state transitions are assumed to take place may influence factors such as cost discounting (if the cycle is long, it can be important to get the state transition timing reasonably right).
When time dependency is introduced into the model, there are in general two types of time dependencies that impact on transition probabilities in the models. One is time dependency depending on the number of cycles since the start of the model (this is e.g. dealing with how transition probabilities depend on factors like age), whereas the other, which is more difficult to implement, deals with state dependence (curiously they don’t use these two words, but I’ve worked with state dependence models before in labour economics and this is what we’re dealing with here); i.e. here the transition probability will depend upon how long you’ve been in a given state.
Below I mostly discuss stuff covered in chapter 10, however I also include a few observations from the final chapter, chapter 11 (on ‘Presenting cost-effectiveness results’). Chapter 10 deals with how to represent uncertainty in decision analytic models. This is an important topic because as noted later in the book, “The primary objective of economic evaluation should not be hypothesis testing, but rather the estimation of the central parameter of interest—the incremental cost-effectiveness ratio—along with appropriate representation of the uncertainty surrounding that estimate.” In chapter 10 a distinction is made between variability, heterogeneity, and uncertainty. Variability has also been termed first-order uncertainty or stochastic uncertainty, and pertains to variation observed when recording information on resource use or outcomes within a homogenous sample of individuals. Heterogeneity relates to differences between patients which can be explained, at least in part. They distinguish between two types of uncertainty, structural uncertainty – dealing with decisions and assumptions made about the structure of the model – and parameter uncertainty, which of course relates to the precision of the parameters estimated. After briefly talking about ways to deal with these, they talk about sensitivity analysis.
“Sensitivity analysis involves varying parameter estimates across a range and seeing how this impacts on he model’s results. […] The simplest form is a one-way analysis where each parameter estimate is varied independently and singly to observe the impact on the model results. […] One-way sensitivity analysis can give some insight into the factors influencing the results, and may provide a validity check to assess what happens when particular variables take extreme values. However, it is likely to grossly underestimate overall uncertainty, and ignores correlation between parameters.”
Multi-way sensitivity analysis is a more refined approach, in which more than one parameter estimate is varied – this is sometimes termed scenario analysis. A different approach is threshold analysis, where one attempts to identify the critical value of one or more variables so that the conclusion/decision changes. All of these approaches are deterministic approaches, and they are not without problems. “They fail to take account of the joint parameter uncertainty and correlation between parameters, and rather than providing the decision-maker with a useful indication of the likelihood of a result, they simply provide a range of results associated with varying one or more input estimates.” So of course an alternative has been developed, namely probabilistic sensitivity analysis (-PSA), which already in the mid-80es started to be used in health economic decision analyses.
“PSA permits the joint uncertainty across all the parameters in the model to be addressed at the same time. It involves sampling model parameter values from distributions imposed on variables in the model. […] The types of distribution imposed are dependent on the nature of the input parameters [but] decision analytic models for the purpose of economic evaluation tend to use homogenous types of input parameters, namely costs, life-years, QALYs, probabilities, and relative treatment effects, and consequently the number of distributions that are frequently used, such as the beta, gamma, and log-normal distributions, is relatively small. […] Uncertainty is then propagated through the model by randomly selecting values from these distributions for each model parameter using Monte Carlo simulation“.
Like in the first post I cannot promise I have not already covered the topics I’m about to cover in this post before on the blog. In this post I’ll include and discuss material from two chapters of the book: the chapters on how to measure, value, and analyze health outcomes, and the chapter on how to define, measure, and value costs. In the last part of the post I’ll also talk a little bit about some research related to the coverage which I’ve recently looked at in a different context.
In terms of how to measure health outcomes the first thing to note is that there are lots and lots of different measures (‘thousands’) that are used to measure aspects of health. The symptoms causing problems for an elderly man with an enlarged prostate are not the same symptoms as the ones which are bothering a young child with asthma, and so it can be very difficult to ‘standardize’ across measures (more on this below).
A general distinction in this area is that between non-preference-based measures and preference-based measures. Many researchers working with health data are mostly interested in measuring symptoms, and metrics which do (‘only’) this would be examples of non-preference-based measures. Non-preference based measures can again be subdivided into disease- and symptom-specific measures, and non-disease-specific/generic measures; an example of the latter would be the SF-36, ‘the most widely used and best-known example of a generic or non-disease-specific measure of general health’.
Economists will often want to put a value on symptoms or quality-of-life states, and in order to do this you need to work with preference-based measures – there are a lot of limitations one confronts when dealing with non-preference-based measures. Non-preference based measures tend for example to be very different in design and purpose (because asthma is not the same thing as, say, bulimia), which means that there is often a lack of comparability across measures. It is also difficult to know how to properly trade off various dimensions included when using such metrics (for example pain relief can be the result of a drug which also increases nausea, and it’s not perfectly clear when you use such measures whether such a change is to be considered desirable or not); similar problems occur when taking the time dimension into account, where problems with aggregation over time and how to deal with this pop up. Various problems related to weighting are recurring problems; for example a question can be asked when using such measures which symptoms/dimensions included are more important? Are they all equally important? This goes for both the weighting of various different domains included in the metric, and for how to weigh individual questions within a given domain. Many non-preference-based measures contain an implicit equal-interval assumption, so that a move from (e.g.) level one to level two on the metric (e.g. from ‘no pain at all’ to ‘a little’) is considered the same as a move from (e.g.) level three to level four (e.g. ‘quite a bit’ to ‘very much’), and it’s not actually clear that the people who supply the information that goes into these metrics would consider such an approach to be a correct reflection of how they perceive these things. Conceptually related to the aggregation problem mentioned above is the problem that people may have different attitudes toward short-term and long-term health effects/outcomes, but non-preference-based measures usually give equal weight to a health state regardless of the timing of the health state. The issue of some patients dying is not addressed at all when using these measures, as they do not contain information about mortality; which may be an important variable. For all these reasons the authors argue in the text that:
“In summary, non-preference-based health status measures, whether disease specific or generic, are not suitable as outcome measures in economic evaluation. Instead, economists require a measure that combines quality and quantity of life, and that also incorporates the valuations that individuals place on particular states of health.
The outcome metric that is currently favoured as meeting these requirements and facilitating the widest possible comparison between alternative uses of health resources is the quality-adjusted life year“.
Non-preference-based tools may be useful, but you will usually need to go ‘further’ than those to be able to handle the problems economists will tend to care the most about. Some more observations from the chapter below:
“the most important challenge [when valuing health states] is to find a reliable way of quantifying the quality of life associated with any particular health state. There are two elements to this: describing the health state, which […] could be either a disease-specific description or a generic description intended to cover many different diseases, and placing a valuation on the health state. […] these weights or valuations are related to utility theory and are frequently referred to as utilities or utility values.
Obtaining utility values almost invariably involves some process by which individuals are given descriptions of a number of health states and then directly or indirectly express their preferences for these states. It is relatively simple to measure ordinal preferences by asking respondents to rank-order different health states. However, these give no information on strength of preference and a simple ranking suffers from the equal interval assumption […]; as a result they are not suitable for economic evaluation. Instead, analysts make use of cardinal preference measurement. Three main methods have been used to obtain cardinal measures of health state preferences: the rating scale, the time trade-off, and the standard gamble. […] The large differences typically observed between RS [rating scale] and TTO [time trade-off] or SG [standard gamble] valuations, and the fact that the TTO and SG methods are choice based and therefore have stronger foundations in decision theory, have led most standard texts and guidelines for technology appraisal to recommend choice-based valuation methods [The methods are briefly described here, where the ‘VAS’ corresponds to the rating scale method mentioned – the book covers the methods in much more detail, but I won’t go into those details here].”
“Controversies over health state valuation are not confined to the valuation method; there are also several strands of opinion concerning who should provide valuations. In principle, valuations could be provided by patients who have had first-hand experience of the health state in question, or by experts such as clinicians with relevant scientific or clinical expertise, or by members of the public. […] there is good evidence that the valuations made by population samples and patients frequently vary quite substantially [and] the direction of difference is not always consistent. […] current practice has moved towards the use of valuations obtained from the general public […], an approach endorsed by recent guidelines in the UK and USA explicitly recommend that population valuations are used”.
Given the very large number of studies which have been based on non-preference based instruments, it would be desirable for economists working in this field to somehow ‘translate’ the information contained in those studies so that this information can also be used for cost-effectiveness evaluations. As a result of this an increasing number of so-called ‘mapping studies’ have been conducted over the years, the desired goal of which is to translate the non-preference based measures into health state utilities, allowing outcomes and effects derived from the studies to be expressed in terms of QALYs. There’s more than one way to try to get from a non-preference based metric to a preference-based metric and the authors describe three approaches in some detail, though I’ll not discuss those approaches or details here. They make this concluding assessment of mapping studies in the text:
“Mapping studies are continuing to proliferate, and the literature on new mapping algorithms and methods, and comparisons between approaches, is expanding rapidly. In general, mapping methods seem to have reasonable ability to predict group mean utility scores and to differentiate between groups with or without known existing illness. However, they all seem to predict increasingly poorly as health states become more serious. […] all forms of mapping are ‘second best’, and the existence of a range of techniques should not be taken as an argument for relying on mapping instead of obtaining direct preference-based measurements in prospectively designed studies.”
I won’t talk too much about the chapter on how to define, measure and value costs, but I felt that a few observations from the chapter should be included in the coverage:
“When asking patients to complete resource/time questionnaires (or answer interview questions), a particularly important issue is deciding on the optimum recall period. Two types of recall error can be distinguished: simply forgetting an entire episode, or incorrectly recalling when it occurred. […] there is a trade-off between recall bias and complete sampling information. […] the longer the period of recall the greater is the likelihood of recall error, but the shorter the recall period the greater is the problem of missing information.”
“The range of patient-related costs included in economic valuations can vary considerably. Some studies include only the costs incurred by patients in travelling to a hospital or clinic for treatment; others may include a wider range of costs including over-the-counter purchases of medications or equipment. However, in some studies a much broader approach is taken, in which attempts are made to capture both the costs associated with treatments and the consequences of illness in terms of absence from or cessation of work.”
An important note here which I thought I should add is that whereas many people unfamiliar with this field may translate ‘medical costs of illness’ with ‘the money that is paid to the doctor(s)’, direct medical costs will in many cases drastically underestimate the ‘true costs’ of disease. To give an example, Ferber et al. (2006) when looking at the costs of diabetes included two indirect cost components in their analysis – inability to work, and early retirement – and concluded that these two cost components made up approximately half of the total costs of diabetes. I think there are reasons to be skeptical of the specific estimate on account of the way it is made (for example if diabetics are less productive/earn less than the population in general, which seems likely if the disease is severe enough to cause many people to withdraw prematurely from the labour market, the cost estimate may be argued to be an overestimate), but on the other hand there are multiple other potentially important indirect cost components they do not include in the calculation, such as e.g. disease-related lower productivity while at work (for details on this, see e.g. this paper – that cost component may also be substantial in some contexts) and things like spousal employment spill-over effects (it is known from related research – for an example, see this PhD dissertation – that disease may impact on the retirement decisions of the spouse of the individual who is sick, not just the individual itself, but effects here are likely to be highly context-dependent and to vary across countries). Another potentially important variable in an indirect cost context is informal care provision. Here’s what they authors say about that one:
“Informal care is often provided by family members, friends, and volunteers. Devoting time and resources to collecting this information may not be worthwhile for interventions where informal care costs are likely to form a very small part of the total costs. However, in other studies non-health-service costs could represent a substantial part of the total costs. For instance, dementia is a disease where the burden of care is likely to fall upon other care agencies and family members rather than entirely on the health and social care services, in which case considering such costs would be important.
To date [however], most economic evaluations have not considered informal care costs.”
Yesterday’s SMBC was awesome, and I couldn’t help myself from including it here (click to view full size):
In a way the three words I chose to omit from the post title are rather important in order to know which kind of book this is – the full title of Gray et al.’s work is: Applied Methods of … – but as I won’t be talking much about the ‘applied’ part in my coverage here, focusing instead on broader principles etc. which will be easier for people without a background in economics to follow, I figured I might as well omit those words from the post titles. I should also admit that I personally did not spend much time on the exercises, as this did not seem necessary in view of what I was using the book for. Despite not having spent much time on the exercises myself, I incidentally did reward the authors for including occasionally quite detailed coverage of technical aspects in my rating of the book on goodreads; I feel confident from the coverage that if I need to apply some of the methods they talk about in the book later on, the book will do a good job of helping me get things right. All in all, the book’s coverage made it hard for me not to give it 5 stars – so that was what I did.
I own an actual physical copy of the book, which makes blogging it more difficult than usual; I prefer blogging e-books. The greater amount of work involved in covering physical books is also one reason why I have yet to talk about Eysenck & Keane’s Cognitive Psychology text here on the blog, despite having read more than 500 pages of that book (it’s not that the book is boring). My coverage of the contents of both this book and the Eysenck & Keane book will (assuming I ever get around to blogging the latter, that is) be less detailed than it could have been, but on the other hand it’ll likely be very focused on key points and observations from the coverage.
I have talked about cost-effectiveness before here on the blog, e.g. here, but in my coverage of the book below I have not tried to avoid making points or including observations which I’ve already made elsewhere on the blog; it’s too much work to keep track of such things. With those introductory remarks out of the way, let’s move on to some observations made in the book:
“In cost-effectiveness analysis we first calculate the costs and effects of an intervention and one or more alternatives, then calculate the differences in cost and differences in effect, and finally present these differences in the form of a ratio, i.e. the cost per unit of health outcome effect […]. Because the focus is on differences between two (or more) options or treatments, analysts typically refer to incremental costs, incremental effects, and the incremental cost-effectiveness ratio (ICER). Thus, if we have two options a and b, we calculate their respective costs and effects, then calculate the difference in costs and difference in effects, and then calculate the ICER as the difference in costs divided by the difference in effects […] cost-effectiveness analyses which measure outcomes in terms of QALYs are sometimes referred to as cost-utility studies […] but are sometimes simply considered as a subset of cost-effectiveness analysis.”
“Cost-effectiveness analysis places no monetary value on the health outcomes it is comparing. It does not measure or attempt to measure the underlying worth or value to society of gaining additional QALYs, for example, but simply indicates which options will permit more QALYs to be gained than others with the same resources, assuming that gaining QALYs is agreed to be a reasonable objective for the health care system. Therefore the cost-effectiveness approach will never provide a way of determining how much in total it is worth spending on health care and the pursuit of QALYs rather than on other social objectives such as education, defence, or private consumption. It does not permit us to say whether health care spending is too high or too low, but rather confines itself to the question of how any given level of spending can be arranged to maximize the health outcomes yielded.
In contrast, cost-benefit analysis (CBA) does attempt to place some monetary valuation on health outcomes as well as on health care resources. […] The reasons for the more widespread use of cost-effectiveness analysis compared with cost-benefit analysis in health care are discussed extensively elsewhere, […] but two main issues can be identified. Firstly, significant conceptual or practical problems have been encountered with the two principal methods of obtaining monetary valuations of life or quality of life: the human capital approach […] and the willingness to pay approach […] Second, within the health care sector there remains a widespread and intrinsic aversion to the concept of placing explicit monetary values on health or life. […] The cost-benefit approach should […], in principle, permit broad questions of allocative efficiency to be addressed. […] In contrast, cost-effectiveness analysis can address questions of productive or production efficiency, where a specified good or service is being produced at the lowest possible cost – in this context, health gain using the health care budget.”
“when working in the two-dimensional world of cost-effectiveness analysis, there are two uncertainties that will be encountered. Firstly, there will be uncertainty concerning the location of the intervention on the cost-effectiveness plane: how much more or less effective and how much more or less costly it is than current treatment. Second, there is uncertainty concerning how much the decision-maker is willing to pay for health gain […] these two uncertainties can be presented together in the form of the question ‘What is the probability that this intervention is cost-effective?’, a question which effectively divides our cost-effectiveness plane into just two policy spaces – below the maximum acceptable line, and above it”.
“Conventionally, cost-effectiveness ratios that have been calculated against a baseline or do-nothing option without reference to any alternatives are referred to as average cost-effectiveness ratios, while comparisons with the next best alternative are described as incremental cost-effectiveness ratios […] it is quite misleading to calculate average cost-effectiveness ratios, as they ignore the alternatives available.”
“A life table provides a method of summarizing the mortality experience of a group of individuals. […] There are two main types of life table. First, there is a cohort life table, which is constructed based on the mortality experience of a group of individuals […]. While this approach can be used to characterize life expectancies of insects and some animals, human longevity makes this approach difficult to apply as the observation period would have to be sufficiently long to be able to observe the death of all members of the cohort. Instead, current life tables are normally constructed using cross-sectional data of observed mortality rates at different ages at a given point in time […] Life tables can also be classified according to the intervals over which changes in mortality occur. A complete life table displays the various rates for each year of life; while an abridged life table deals with greater periods of time, for example 5 year age intervals […] A life table can be used to generate a survival curve S(x) for the population at any point in time. This represents the probability of surviving beyond a certain age x (i.e. S(x)=Pr[X>x]). […] The chance of a male living to the age of 60 years is high (around 0.9) [in the UK, presumably – US] and so the survival curve is comparatively flat up until this age. The proportion dying each year from the age of 60 years rapidly increases, so the curve has a much steeper downward slope. In the last part of the survival curve there is an inflection, indicating a slowing rate of increase in the proportion dying each year among the very old (over 90 years). […] The hazard rate is the slope of the survival curve at any point, given the instantaneous chance of an individual dying.”
“Life tables are a useful tool for estimating changes in life expectancies from interventions that reduce mortality. […] Multiple-cause life tables are a way of quantifying outcomes when there is more than one mutually exclusive cause of death. These life tables can estimate the potential gains from the elimination of a cause of death and are also useful in calculating the benefits of interventions that reduce the risk of a particular cause of death. […] One issue that arises when death is divided into multiple causes in this type of life table is competing risk. […] competing risk can arise ‘when an individual can experience more than one type of event and the occurrence of one type of event hinders the occurrence of other types of events’. Competing risks affect life tables, as those who die from a specific cause have no chance of dying from other causes during the remainder of the interval […]. In practice this will mean that as soon as one cause is eliminated the probabilities of dying of other causes increase […]. Several methods have been proposed to correct for competing risks when calculating life tables.”
“the use of published life-table methods may have limitations, especially when considering particular populations which may have very different risks from the general population. In these cases, there are a host of techniques referred to as survival analysis which enables risks to be estimated from patient-level data. […] Survival analysis typically involves observing one or more outcomes in a population of interest over a period of time. The outcome, which is often referred to as an event or endpoint could be death, a non-fatal outcome such as a major clinical event (e.g. myocardial infarction), the occurrence of an adverse event, or even the date of first non-compliance with a therapy.”
“A key feature of survival data is censoring, which occurs whenever the event of interest is not observed within the follow-up period. This does not mean that the event will not occur some time in the future, just that it has not occurred while the individual was observed. […] The most common case of censoring is referred to as right censoring. This occurs whenever the observation of interest occurs after the observation period. […] An alternative form of censoring is left censoring, which occurs when there is a period of time when the individuals are at risk prior to the observation period.
A key feature of most survival analysis methods is that they assume that the censoring process is non-informative, meaning that there is no dependence between the time to the event of interest and the process that is causing the censoring. However, if the duration of observation is related to the severity of a patient’s disease, for example if patients with more advanced illness are withdrawn early from the study, the censoring is likely to be informative and other techniques are required”.
“Differences in the composition of the intervention and control groups at the end of follow-up may have important implications for estimating outcomes, especially when we are interested in extrapolation. If we know that the intervention group is older and has a lower proportion of females, we would expect these characteristics to increase the hazard mortality in this group over their remaining lifetimes. However, if the intervention group has experienced a lower number of events, this may significantly reduce the hazard for some individuals. They may also benefit from a past treatment which continues to reduce the hazard of a primary outcome such as death. This effect […] is known as the legacy effect“.
“Changes in life expectancy are a commonly used outcome measure in economic evaluation. […] Table 4.6 shows selected examples of estimates of the gain in life expectancy for various interventions reported by Wright and Weinstein (1998) […] Gains in life expectancy from preventative interventions in populations of average risk generally ranged from a few days to slightly more than a year. […] The gains in life expectancy from preventing or treating disease in persons at elevated risk [this type of prevention is known as ‘secondary-‘ and/or ‘tertiary prevention’ (depending on the circumstances), as opposed to ‘primary prevention’ – the distinction between primary prevention and more targeted approaches is often important in public health contexts, because the level of targeting will often interact with the cost-effectiveness dimension – US] are generally greater […one reason why this does not necessarily mean that targeted approaches are always better is that search costs will often be an increasing function of the level of targeting – US]. Interventions that treat established disease vary, with gains in life-expectancy ranging from a few months […] to as long as nine years […] the point that Wright and Weinstein (1998) were making was not that absolute gains vary, but that a gain in life expectancy of a month from a preventive intervention targeted at population at average risk and a gain of a year from a preventive intervention targeted at populations at elevated risk could both be considered large. It should also be noted that interventions that produce a comparatively small gain in life expectancy when averaged across the population […] may still be very cost-effective.”
I haven’t really blogged this book in anywhere near the amount of detail it deserves even though my first post about the book actually had a few quotes illustrating how much different stuff is covered in the book.
This book is technical, and even if I’m trying to make it less technical by omitting the math in this post it may be a good idea to reread the first post about the book before reading this post to refresh your knowledge of these things.
Quotes and comments below – most of the coverage here focuses on stuff covered in chapters 3 and 4 in the book.
“Tests of null hypotheses and information-theoretic approaches should not be used together; they are very different analysis paradigms. A very common mistake seen in the applied literature is to use AIC to rank the candidate models and then “test” to see whether the best model (the alternative hypothesis) is “significantly better” than the second-best model (the null hypothesis). This procedure is flawed, and we strongly recommend against it […] the primary emphasis should be on the size of the treatment effects and their precision; too often we find a statement regarding “significance,” while the treatment and control means are not even presented. Nearly all statisticians are calling for estimates of effect size and associated precision, rather than test statistics, P-values, and “significance.” [Borenstein & Hedges certainly did as well in their book (written much later), and this was not an issue I omitted to talk about in my coverage of their book…] […] Information-theoretic criteria such as AIC, AICc, and QAICc are not a “test” in any sense, and there are no associated concepts such as test power or P-values or α-levels. Statistical hypothesis testing represents a very different, and generally inferior, paradigm for the analysis of data in complex settings. It seems best to avoid use of the word “significant” in reporting research results under an information-theoretic paradigm. […] AIC allows a ranking of models and the identification of models that are nearly equally useful versus those that are clearly poor explanations for the data at hand […]. Hypothesis testing provides no general way to rank models, even for models that are nested. […] In general, we recommend strongly against the use of null hypothesis testing in model selection.”
“The bootstrap is a type of Monte Carlo method used frequently in applied statistics. This computer-intensive approach is based on resampling of the observed data […] The fundamental idea of the model-based sampling theory approach to statistical inference is that the data arise as a sample from some conceptual probability distribution f. Uncertainties of our inferences can be measured if we can estimate f. The bootstrap method allows the computation of measures of our inference uncertainty by having a simple empirical estimate of f and sampling from this estimated distribution. In practical application, the empirical bootstrap means using some form of resampling with replacement from the actual data x to generate B (e.g., B = 1,000 or 10,000) bootstrap samples […] The set of B bootstrap samples is a proxy for a set of B independent real samples from f (in reality we have only one actual sample of data). Properties expected from replicate real samples are inferred from the bootstrap samples by analyzing each bootstrap sample exactly as we first analyzed the real data sample. From the set of results of sample size B we measure our inference uncertainties from sample to (conceptual) population […] For many applications it has been theoretically shown […] that the bootstrap can work well for large sample sizes (n), but it is not generally reliable for small n […], regardless of how many bootstrap samples B are used. […] Just as the analysis of a single data set can have many objectives, the bootstrap can be used to provide insight into a host of questions. For example, for each bootstrap sample one could compute and store the conditional variance–covariance matrix, goodness-of-fit values, the estimated variance inflation factor, the model selected, confidence interval width, and other quantities. Inference can be made concerning these quantities, based on summaries over the B bootstrap samples.”
“Information criteria attempt only to select the best model from the candidate models available; if a better model exists, but is not offered as a candidate, then the information-theoretic approach cannot be expected to identify this new model. Adjusted R2 […] are useful as a measure of the proportion of the variation “explained,” [but] are not useful in model selection […] adjusted R2 is poor in model selection; its usefulness should be restricted to description.”
“As we have struggled to understand the larger issues, it has become clear to us that inference based on only a single best model is often relatively poor for a wide variety of substantive reasons. Instead, we increasingly favor multimodel inference: procedures to allow formal statistical inference from all the models in the set. […] Such multimodel inference includes model averaging, incorporating model selection uncertainty into estimates of precision, confidence sets on models, and simple ways to assess the relative importance of variables.”
“If sample size is small, one must realize that relatively little information is probably contained in the data (unless the effect size if very substantial), and the data may provide few insights of much interest or use. Researchers routinely err by building models that are far too complex for the (often meager) data at hand. They do not realize how little structure can be reliably supported by small amounts of data that are typically “noisy.””
“Sometimes, the selected model [when applying an information criterion] contains a parameter that is constant over time, or areas, or age classes […]. This result should not imply that there is no variation in this parameter, rather that parsimony and its bias/variance tradeoff finds the actual variation in the parameter to be relatively small in relation to the information contained in the sample data. It “costs” too much in lost precision to add estimates of all of the individual θi. As the sample size increases, then at some point a model with estimates of the individual parameters would likely be favored. Just because a parsimonious model contains a parameter that is constant across strata does not mean that there is no variation in that process across the strata.”
“[In a significance testing context,] a significant test result does not relate directly to the issue of what approximating model is best to use for inference. One model selection strategy that has often been used in the past is to do likelihood ratio tests of each structural factor […] and then use a model with all the factors that were “significant” at, say, α = 0.05. However, there is no theory that would suggest that this strategy would lead to a model with good inferential properties (i.e., small bias, good precision, and achieved confidence interval coverage at the nominal level). […] The purpose of the analysis of empirical data is not to find the “true model”— not at all. Instead, we wish to find a best approximating model, based on the data, and then develop statistical inferences from this model. […] We search […] not for a “true model,” but rather for a parsimonious model giving an accurate approximation to the interpretable information in the data at hand. Data analysis involves the question, “What level of model complexity will the data support?” and both under- and overfitting are to be avoided. Larger data sets tend to support more complex models, and the selection of the size of the model represents a tradeoff between bias and variance.”
“The easy part of the information-theoretic approaches includes both the computational aspects and the clear understanding of these results […]. The hard part, and the one where training has been so poor, is the a priori thinking about the science of the matter before data analysis — even before data collection. It has been too easy to collect data on a large number of variables in the hope that a fast computer and sophisticated software will sort out the important things — the “significant” ones […]. Instead, a major effort should be mounted to understand the nature of the problem by critical examination of the literature, talking with others working on the general problem, and thinking deeply about alternative hypotheses. Rather than “test” dozens of trivial matters (is the correlation zero? is the effect of the lead treatment zero? are ravens pink?, Anderson et al. 2000), there must be a more concerted effort to provide evidence on meaningful questions that are important to a discipline. This is the critical point: the common failure to address important science questions in a fully competent fashion. […] “Let the computer find out” is a poor strategy for researchers who do not bother to think clearly about the problem of interest and its scientific setting. The sterile analysis of “just the numbers” will continue to be a poor strategy for progress in the sciences.
Researchers often resort to using a computer program that will examine all possible models and variables automatically. Here, the hope is that the computer will discover the important variables and relationships […] The primary mistake here is a common one: the failure to posit a small set of a priori models, each representing a plausible research hypothesis.”
“Model selection is most often thought of as a way to select just the best model, then inference is conditional on that model. However, information-theoretic approaches are more general than this simplistic concept of model selection. Given a set of models, specified independently of the sample data, we can make formal inferences based on the entire set of models. […] Part of multimodel inference includes ranking the fitted models from best to worst […] and then scaling to obtain the relative plausibility of each fitted model (gi) by a weight of evidence (wi) relative to the selected best model. Using the conditional sampling variance […] from each model and the Akaike weights […], unconditional inferences about precision can be made over the entire set of models. Model-averaged parameter estimates and estimates of unconditional sampling variances can be easily computed. Model selection uncertainty is a substantial subject in its own right, well beyond just the issue of determining the best model.”
“There are three general approaches to assessing model selection uncertainty: (1) theoretical studies, mostly using Monte Carlo simulation methods; (2) the bootstrap applied to a given set of data; and (3) utilizing the set of AIC differences (i.e., ∆i) and model weights wi from the set of models fit to data.”
“Statistical science should emphasize estimation of parameters and associated measures of estimator uncertainty. Given a correct model […], an MLE is reliable, and we can compute a reliable estimate of its sampling variance and a reliable confidence interval […]. If the model is selected entirely independently of the data at hand, and is a good approximating model, and if n is large, then the estimated sampling variance is essentially unbiased, and any appropriate confidence interval will essentially achieve its nominal coverage. This would be the case if we used only one model, decided on a priori, and it was a good model, g, of the data generated under truth, f. However, even when we do objective, data-based model selection (which we are advocating here), the [model] selection process is expected to introduce an added component of sampling uncertainty into any estimated parameter; hence classical theoretical sampling variances are too small: They are conditional on the model and do not reflect model selection uncertainty. One result is that conditional confidence intervals can be expected to have less than nominal coverage.”
“Data analysis is sometimes focused on the variables to include versus exclude in the selected model (e.g., important vs. unimportant). Variable selection is often the focus of model selection for linear or logistic regression models. Often, an investigator uses stepwise analysis to arrive at a final model, and from this a conclusion is drawn that the variables in this model are important, whereas the other variables are not important. While common, this is poor practice and, among other issues, fails to fully consider model selection uncertainty. […] Estimates of the relative importance of predictor variables xj can best be made by summing the Akaike weights across all the models in the set where variable j occurs. Thus, the relative importance of variable j is reflected in the sum w+ (j). The larger the w+ (j) the more important variable j is, relative to the other variables. Using the w+ (j), all the variables can be ranked in their importance. […] This idea extends to subsets of variables. For example, we can judge the importance of a pair of variables, as a pair, by the sum of the Akaike weights of all models that include the pair of variables. […] To summarize, in many contexts the AIC selected best model will include some variables and exclude others. Yet this inclusion or exclusion by itself does not distinguish differential evidence for the importance of a variable in the model. The model weights […] summed over all models that include a given variable provide a better weight of evidence for the importance of that variable in the context of the set of models considered.” [The reason why I’m not telling you how to calculate Akaike weights is that I don’t want to bother with math formulas in wordpress – but I guess all you need to know is that these are not hard to calculate. It should perhaps be added that one can also use bootstrapping methods to obtain relevant model weights to apply in a multimodel inference context.]
“If data analysis relies on model selection, then inferences should acknowledge model selection uncertainty. If the goal is to get the best estimates of a set of parameters in common to all models (this includes prediction), model averaging is recommended. If the models have definite, and differing, interpretations as regards understanding relationships among variables, and it is such understanding that is sought, then one wants to identify the best model and make inferences based on that model. […] The bootstrap provides direct, robust estimates of model selection probabilities πi , but we have no reason now to think that use of bootstrap estimates of model selection probabilities rather than use of the Akaike weights will lead to superior unconditional sampling variances or model-averaged parameter estimators. […] Be mindful of possible model redundancy. A carefully thought-out set of a priori models should eliminate model redundancy problems and is a central part of a sound strategy for obtaining reliable inferences. […] Results are sensitive to having demonstrably poor models in the set of models considered; thus it is very important to exclude models that are a priori poor. […] The importance of a small number (R) of candidate models, defined prior to detailed analysis of the data, cannot be overstated. […] One should have R much smaller than n. MMI [Multi-Model Inference] approaches become increasingly important in cases where there are many models to consider.”
“In general there is a substantial amount of model selection uncertainty in many practical problems […]. Such uncertainty about what model structure (and associated parameter values) is the K-L [Kullback–Leibler] best approximating model applies whether one uses hypothesis testing, information-theoretic criteria, dimension-consistent criteria, cross-validation, or various Bayesian methods. Often, there is a nonnegligible variance component for estimated parameters (this includes prediction) due to uncertainty about what model to use, and this component should be included in estimates of precision. […] we recommend assessing model selection uncertainty rather than ignoring the matter. […] It is […] not a sound idea to pick a single model and unquestioningly base extrapolated predictions on it when there is model uncertainty.”
“In this book we present several novel concepts in cooperative game theory, but from a computer scientist’s point of view. Especially, we will look at a type of games called non-transferable utility games. […] In this book, we extend the classic stability concept of the non-transferable utility core by proposing new belief-based stability criteria under uncertainty, and illustrate how the new concept can be used to analyse the stability of a new type of belief-based coalition formation game. Mechanisms for reaching solutions of the new stable criteria are proposed and some real life application examples are studied. […] In Chapter 1, we first provide an introduction of topics in game theory that are relevant to the concepts discussed in this book. In Chapter 2, we review some relevant works from the literature, especially in cooperative game theory and multi-agent coalition formation problems. In Chapter 3, we discuss the effect of uncertainty in the agent’s beliefs on the stability of the games. A rule-based approach is adopted and the concepts of strong core and weak core are introduced. We also discuss the effect of precision of the beliefs on the stability of the coalitions. In Chapter 4, we introduce private beliefs in non-transferable utility (NTU) games, so that the preferences of the agents are no longer common knowledge. The impact of belief accuracy on stability is also examined. In Chapter 5, we study an application of the proposed belief-based stability concept, namely the buyer coalition problem, and we see how the proposed concept can be used in the evaluation of this multi-agent coalition formation problem. In Chapter 6, we combine the works of earlier chapters and produce a complete picture of the introduced concepts: non-transferable utility games with private beliefs and uncertainty. We conclude this book in Chapter 7.”
The above quote is from the preface of the book, which I finished yesterday. It deals with some issues I was slightly annoyed about not being covered in a previous micro course; my main problem being that it seemed to me back then that the question of belief accuracy and the role of this variable was not properly addressed in the models we looked at (‘people can have mistaken beliefs, and it seems obvious that the ways in which they’re wrong can affect which solutions are eventually reached’). The book makes the point that if you look at coalition formation in a context where it is not reasonable to assume that information is shared among coalition partners (because it is in the interest of the participants to keep their information/preferences/willingness to pay private), then the beliefs of the potential coalition partners may play a major role in determining which coalitions are feasible and which are ruled out. A key point is that in the model context explored by the authors, inaccurate beliefs of agents will expand the number of potential coalitions which are available, although coalition options ruled out by accurate beliefs are less stable than ones which are not. They do not discuss the fact that this feature is unquestionably a result of implicit assumptions made along the way which may not be true, and that inaccurate beliefs may also in some contexts conceivably lead to lower solution support in general (e.g. through variables such as disagreement, or, to think more in terms of concepts specifically included in their model framework, higher general instability of solutions which can feasibly be reached, making agents less likely to explore the option of participating in coalitions in the first place due to the lower payoffs associated with the available coalitions likely to be reached – dynamics such as these are not included in the coverage). I decided early on to not blog the stuff in this book in major detail because it’s not the kind of book where this makes sense to do (in my opinion), but if you’re curious about how they proceed, they talk quite a bit about the (classical) Core and discuss why this is not an appropriate solution concept to apply in the contexts they explore, and they then proceed to come up with new and better solution criteria, developed with the aid of some new variables and definitions along the way, in order to end up with some better solution concepts, their so-called ‘belief-based cores’, which are perhaps best thought of as extensions of the classical core concept. I should perhaps point out, as this may not be completely clear, that the beliefs they talk about deal both with the ‘state of nature’ (which in part of the coverage is assumed to be basically unobservable) and the preferences of agents involved.
If you want a sort of bigger picture idea of what this book is about, I should point out that in general you have two major sub-fields of game theory, dealing with cooperative and non-cooperative games respectively. Within the sub-field of cooperative games, a distinction is made between games and settings where utilities are transferable, and games/settings where they are not. This book belongs in the latter category; it deals with cooperative games in which utilities are non-transferable. The authors in the beginning make a big deal out of the distinction between whether or not utilities are transferable, and claim that the assumption that they’re not is the more plausible one; whereas they do have a point, I however also actually think the non-transferability assumption might in some of the specific examples included in the book be a borderline questionable assumption. To give an example, the non-transferability assumption seems in one context to imply that all potential coalition partners have the same amount of bargaining power. This assumption is plausible in some contexts, but wildly implausible in others (and I’m not sure the authors would agree with me about which contexts would belong to which category).
The professor teaching the most recent course in micro I took had a background in computer science, rather than economics – he was also Asian, but this perhaps goes without saying. This book is supposedly a computer science book, and they argue in the introduction that: “instead of looking at human beings, we study the problem from an intelligent software agent’s perspective.” However I don’t think a single one of the examples included in the book would be an example you could not also have found in a classic micro text, and it’s really hard to tell in many parts of the coverage that the authors aren’t economists with a background in micro – there seems to be quite a bit of field overlap here (this field overlap incidentally extends to areas of economics besides micro, is my impression; one econometrics TA I had, teaching the programming part of the course, was also a CS major). In the book they talk a bit about coalition formation mechanisms and approaches, such as propose-and-evaluate mechanisms and auction approaches, and they also touch briefly upon stuff like mechanism design. They state in the description that: “The book is intended for graduate students, engineers, and researchers in the field of artificial intelligence and computer science.” I think it’s really weird that they don’t include (micro-)economists as well, because this stuff is obviously quite close to/potentially relevant to the kind of work some of these people are working on.
There are a lot of definitions, theorems, and proofs in this book, and as usual when doing work on game theory you need to think very carefully about the stuff they cover to be able to follow it, but I actually found it reasonably accessible – the book is not terribly difficult to read. Though I would probably advise you against reading the book if you have not at least read an intro text on game theory. Although as already mentioned the book deals with an analytical context in which utilities are non-transferable, it should be pointed out that this assumption is sort of implicit in the coverage, in the sense that the authors don’t really deal with utility functions at all; the book only deals with preference relations, not utility functions, so it probably helps to be familiar with this type of analysis (e.g. by having studied (solved some problems) dealing with the kind of stuff included in the coverage in chapter 1 of Mas-Colell).
Part of the reason why I gave the book only two stars is that the authors are Chinese and their English is terrible. Another reason is that as is usually the case in game theory, these guys spend a lot of time and effort being very careful to define their terms and make correct inferences from the assumptions they make – but they don’t really end up saying very much.