Econstudentlog

Infectious Disease Surveillance (II)

Some more observation from the book below.

“There are three types of influenza viruses — A, B, and C — of which only types A and B cause widespread outbreaks in humans. Influenza A viruses are classified into subtypes based on antigenic differences between their two surface glycoproteins, hemagglutinin and neuraminidase. Seventeen hemagglutinin subtypes (H1–H17) and nine neuraminidase subtypes (N1–N9) have been identifed. […] The internationally accepted naming convention for influenza viruses contains the following elements: the type (e.g., A, B, C), geographical origin (e.g., Perth, Victoria), strain number (e.g., 361), year of isolation (e.g., 2011), for influenza A the hemagglutinin and neuraminidase antigen description (e.g., H1N1), and for nonhuman origin viruses the host of origin (e.g., swine) [4].”

“Only two antiviral drug classes are licensed for chemoprophylaxis and treatment of influenza—the adamantanes (amantadine and rimantadine) and the neuraminidase inhibitors (oseltamivir and zanamivir). […] Antiviral resistant strains arise through selection pressure in individual patients during treatment [which can lead to treatment failure]. […] they usually do not transmit further (because of impaired virus fitness) and have limited public health implications. On the other hand, primarily resistant viruses have emerged in the past decade and in some cases have completely replaced the susceptible strains. […] Surveillance of severe influenza illness is challenging because most cases remain undiagnosed. […] In addition, most of the influenza burden on the healthcare system is because of complications such as secondary bacterial infections and exacerbations of pre-existing chronic diseases, and often influenza is not suspected as an underlying cause. Even if suspected, the virus could have been already cleared from the respiratory secretions when the testing is performed, making diagnostic confirmation impossible. […] Only a small proportion of all deaths caused by influenza are classified as influenza-related on death certificates. […] mortality surveillance based only on death certificates is not useful for the rapid assessment of an influenza epidemic or pandemic severity. Detection of excess mortality in real time can be done by establishing specific monitoring systems that overcome these delays [such as sentinel surveillance systems, US].”

“Influenza vaccination programs are extremely complex and costly. More than half a billion doses of influenza vaccines are produced annually in two separate vaccine production cycles, one for the Northern Hemisphere and one for the Southern Hemisphere [54]. Because the influenza virus evolves constantly and vaccines are reformulated yearly, both vaccine effectiveness and safety need to be monitored routinely. Vaccination campaigns are also organized annually and require continuous public health efforts to maintain an acceptable level of vaccination coverage in the targeted population. […] huge efforts are made and resources spent to produce and distribute influenza vaccines annually. Despite these efforts, vaccination coverage among those at risk in many parts of the world remains low.”

“The Active Bacterial Core surveillance (ABCs) network and its predecessor have been examples of using surveillance as information for action for over 20 years. ABCs has been used to measure disease burden, to provide data for vaccine composition and recommended-use policies, and to monitor the impact of interventions. […] sites represent wide geographic diversity and approximately reflect the race and urban-to-rural mix of the U.S. population [37]. Currently, the population under surveillance is 19–42 million and varies by pathogen and project. […] ABCs has continuously evolved to address challenging questions posed by the six pathogens (H. influenzae; GAS [Group A Streptococcus], GBS [Group B Streptococcus], S.  pneumoniae, N. meningitidis, and MRSA) and other emerging infections. […] For the six core pathogens, the objectives are (1) to determine the incidence and epidemiologic characteristics of invasive disease in geographically diverse populations in the United States through active, laboratory, and population-based surveillance; (2) to determine molecular epidemiologic patterns and microbiologic characteristics of isolates collected as part of routine surveillance in order to track antimicrobial resistance; (3) to detect the emergence of new strains with new resistance patterns and/or virulence and contribute to development and evaluation of new vaccines; and (4) to provide an infrastructure for surveillance of other emerging pathogens and for conducting studies aimed at identifying risk factors for disease and evaluating prevention policies.”

“Food may become contaminated by over 250 bacterial, viral, and parasitic pathogens. Many of these agents cause diarrhea and vomiting, but there is no single clinical syndrome common to all foodborne diseases. Most of these agents can also be transmitted by nonfoodborne routes, including contact with animals or contaminated water. Therefore, for a given illness, it is often unclear whether the source of infection is foodborne or not. […] Surveillance systems for foodborne diseases provide extremely important information for prevention and control.”

“Since 1995, the Centers for Disease Control and Prevention (CDC) has routinely used an automated statistical outbreak detection algorithm that compares current reports of each Salmonella serotype with the preceding 5-year mean number of cases for the same geographic area and week of the year to look for unusual clusters of infection [5]. The sensitivity of Salmonella serotyping to detect outbreaks is greatest for rare serotypes, because a small increase is more noticeable against a rare background. The utility of serotyping has led to its widespread adoption in surveillance for food pathogens in many countries around the world [6]. […] Today, a new generation of subtyping methods […] is increasing the specificity of laboratory-based surveillance and its power to detect outbreaks […] Molecular subtyping allows comparison of the molecular “fingerprint” of bacterial strains. In the United States, the CDC coordinates a network called PulseNet that captures data from standardized molecular subtyping by PFGE [pulsed field gel electrophoresis]. By comparing new submissions and past data, public health officials can rapidly identify geographically dispersed clusters of disease that would otherwise not be apparent and evaluate them as possible foodborne-disease outbreaks [8]. The ability to identify geographically dispersed outbreaks has become increasingly important as more foods are mass-produced and widely distributed. […] Similar networks have been developed in Canada, Europe, the Asia Pacifc region, Latin America and the Caribbean region, the Middle Eastern region and, most recently, the African region”.

“Food consumption and practices have changed during the past 20 years in the United States, resulting in a shift from readily detectable, point-source outbreaks (e.g., attendance at a wedding dinner), to widespread outbreaks that occur over many communities with only a few illnesses in each community. One of the changes has been establishment of large food-producing facilities that disseminate products throughout the country. If a food product is contaminated with a low level of pathogen, contaminated food products are distributed across many states; and only a few illnesses may occur in each community. This type of outbreak is often difficult to detect. PulseNet has been critical for the detection of widely dispersed outbreaks in the United States [17]. […] The growth of the PulseNet database […] and the use of increasingly sophisticated epidemiological approaches have led to a dramatic increase in the number of multistate outbreaks detected and investigated.”

“Each year, approximately 35 million people are hospitalized in the United States, accounting for 170 million inpatient days [1,2]. There are no recent estimates of the numbers of healthcare-associated infections (HAI). However, two decades ago, HAI were estimated to affect more than 2 million hospital patients annually […] The mortality attributed to these HAI was estimated at about 100,000 deaths annually. […] Almost 85% of HAI in the United States are associated with bacterial pathogens, and 33% are thought to be preventable [4]. […] The primary purpose of surveillance [in the context of HAI] is to alert clinicians, epidemiologists, and laboratories of the need for targeted prevention activities required to reduce HAI rates. HAI surveillance data help to establish baseline rates that may be used to determine the potential need to change public health policy, to act and intervene in clinical settings, and to assess the effectiveness of microbiology methods, appropriateness of tests, and allocation of resources. […] As less than 10% of HAI in the United States occur as recognized epidemics [18], HAI surveillance should not be embarked on merely for the detection of outbreaks.”

“There are two types of rate comparisons — intrahospital and interhospital. The primary goals of intrahospital comparison are to identify areas within the hospital where HAI are more likely to occur and to measure the efficacy of interventional efforts. […] Without external comparisons, hospital infection control departments may [however] not know if the endemic rates in their respective facilities are relatively high or where to focus the limited fnancial and human resources of the infection control program. […] The CDC has been the central aggregating institution for active HAI surveillance in the United States since the 1960s.”

“Low sensitivity (i.e., missed infections) in a surveillance system is usually more common than low specificity (i.e., patients reported to have infections who did not actually have infections).”

“Among the numerous analyses of CDC hospital data carried out over the years, characteristics consistently found to be associated with higher HAI rates include affiliation with a medical school (i.e., teaching vs. nonteaching), size of the hospital and ICU categorized by the number of beds (large hospitals and larger ICUs generally had higher infection rates), type of control or ownership of the hospital (municipal, nonprofit, investor owned), and region of the country [43,44]. […] Various analyses of SENIC and NNIS/NHSN data have shown that differences in patient risk factors are largely responsible for interhospital differences in HAI rates. After controlling for patients’ risk factors, average lengths of stay, and measures of the completeness of diagnostic workups for infection (e.g., culturing rates), the differences in the average HAI rates of the various hospital groups virtually disappeared. […] For all of these reasons, an overall HAI rate, per se, gives little insight into whether the facility’s infection control efforts are effective.”

“Although a hospital’s surveillance system might aggregate accurate data and generate appropriate risk-adjusted HAI rates for both internal and external comparison, comparison may be misleading for several reasons. First, the rates may not adjust for patients’ unmeasured intrinsic risks for infection, which vary from hospital to hospital. […] Second, if surveillance techniques are not uniform among hospitals or are used inconsistently over time, variations will occur in sensitivity and specificity for HAI case finding. Third, the sample size […] must be sufficient. This issue is of concern for hospitals with fewer than 200 beds, which represent about 10% of hospital admissions in the United States. In most CDC analyses, rates from hospitals with very small denominators tend to be excluded [37,46,49]. […] Although many healthcare facilities around the country aggregate HAI surveillance data for baseline establishment and interhospital comparison, the comparison of HAI rates is complex, and the value of the aggregated data must be balanced against the burden of their collection. […] If a hospital does not devote sufficient resources to data collection, the data will be of limited value, because they will be replete with inaccuracies. No national database has successfully dealt with all the problems in collecting HAI data and each varies in its ability to address these problems. […] While comparative data can be useful as a tool for the prevention of HAI, in some instances no data might be better than bad data.”

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August 10, 2017 Posted by | Books, Data, Epidemiology, Infectious disease, Medicine, Statistics | Leave a comment

A few diabetes papers of interest

i. Long-term Glycemic Variability and Risk of Adverse Outcomes: A Systematic Review and Meta-analysis.

“This systematic review and meta-analysis evaluates the association between HbA1c variability and micro- and macrovascular complications and mortality in type 1 and type 2 diabetes. […] Seven studies evaluated HbA1c variability among patients with type 1 diabetes and showed an association of HbA1c variability with renal disease (risk ratio 1.56 [95% CI 1.08–2.25], two studies), cardiovascular events (1.98 [1.39–2.82]), and retinopathy (2.11 [1.54–2.89]). Thirteen studies evaluated HbA1c variability among patients with type 2 diabetes. Higher HbA1c variability was associated with higher risk of renal disease (1.34 [1.15–1.57], two studies), macrovascular events (1.21 [1.06–1.38]), ulceration/gangrene (1.50 [1.06–2.12]), cardiovascular disease (1.27 [1.15–1.40]), and mortality (1.34 [1.18–1.53]). Most studies were retrospective with lack of adjustment for potential confounders, and inconsistency existed in the definition of HbA1c variability.

CONCLUSIONS HbA1c variability was positively associated with micro- and macrovascular complications and mortality independently of the HbA1c level and might play a future role in clinical risk assessment.”

Two observations related to the paper: One, although only a relatively small number of studies were included in the review, the number of patients included in some of those included studies was rather large – the 7 type 1 studies thus included 44,021 participants, and the 13 type 2 studies included in total 43,620 participants. Two, it’s noteworthy that some of the associations already look at least reasonably strong, despite interest in HbA1c variability being a relatively recent phenomenon. Confounding might be an issue, but then again it almost always might be, and to give an example, out of 11 studies analyzing the association between renal disease and HbA1c variability included in the review, ten of them support a link and the only one which does not was a small study on pediatric patients which was almost certainly underpowered to investigate such a link in the first place (the base rate of renal complications is, as mentioned before here on this blog quite recently (link 3), quite low in pediatric samples).

ii. Risk of Severe Hypoglycemia in Type 1 Diabetes Over 30 Years of Follow-up in the DCCT/EDIC Study.

(I should perhaps note here that I’m already quite familiar with the context of the DCCT/EDIC study/studies, and although readers may not be, and although background details are included in the paper, I decided not to cover such details here although they would make my coverage of the paper easier to understand. I instead decided to limit my coverage of the paper to a few observations which I myself found to be of interest.)

“During the DCCT, the rates of SH [Severe Hypoglycemia, US], including episodes with seizure or coma, were approximately threefold greater in the intensive treatment group than in the conventional treatment group […] During EDIC, the frequency of SH increased in the former conventional group and decreased in the former intensive group so that the difference in SH event rates between the two groups was no longer significant (36.6 vs. 40.8 episodes per 100 patient-years, respectively […] By the end of DCCT, with an average of 6.5 years of follow-up, 65% of the intensive group versus 35% of the conventional group experienced at least one episode of SH. In contrast, ∼50% of participants within each group reported an episode of SH during the 20 years of EDIC.”

“Of [the] participants reporting episodes of SH, during the DCCT, 54% of the intensive group and 30% of the conventional group experienced four or more episodes, whereas in EDIC, 37% of the intensive group and 33% of the conventional group experienced four or more events […]. Moreover, a subset of participants (14% [99 of 714]) experienced nearly one-half of all SH episodes (1,765 of 3,788) in DCCT, and a subset of 7% (52 of 709) in EDIC experienced almost one-third of all SH episodes (888 of 2,813) […] Fifty-one major accidents occurred during the 6.5 years of DCCT and 143 during the 20 years of EDIC […] The most frequent type of major accident was that involving a motor vehicle […] Hypoglycemia played a role as a possible, probable, or principal cause in 18 of 28 operator-caused motor vehicle accidents (MVAs) during DCCT […] and in 23 of 54 operator-caused MVAs during EDIC”.

“The T1D Exchange Clinic Registry recently reported that 8% of 4,831 adults with T1D living in the U.S. had a seizure or coma event during the 3 months before their most recent annual visit (11). During EDIC, we observed that 27% of the cohort experienced a coma or seizure event over the 20 years of 3-month reporting intervals (∼1.4% per year), a much lower annual risk than in the T1D Exchange Clinic Registry. In part, the open enrollment of patients into the T1D Exchange may be reflected without the exclusion of participants with a history of SH as in the DCCT and other clinical trials. The current data support the clinical perception that a small subset of individuals is more susceptible to SH (7% of patients with 11 or more SH episodes during EDIC, which represents 32% of all SH episodes in EDIC) […] a history of SH during DCCT and lower current HbA1c levels were the two major factors associated with an increased risk of SH during EDIC. Safety concerns were the reason why a history of frequent SH events was an exclusion criterion for enrollment in DCCT. […] Of note, we found that participants who entered the DCCT as adolescents were more likely to experience SH during EDIC.”

“In summary, although event rates in the DCCT/EDIC cohort seem to have fallen and stabilized over time, SH remains an ever-present threat for patients with T1D who use current technology, occurring at a rate of ∼36–41 episodes per 100 patient-years, even among those with longer diabetes duration. Having experienced one or more such prior events is the strongest predictor of a future SH episode.”

I didn’t actually like that summary. If a history of severe hypoglycemia was an exclusion criterion in the DCCT trial, which it was, then the event rate you’d get from this data set is highly likely to provide a biased estimator of the true event rate, as the Exchange Clinic Registry data illustrate. The true population event rate in unselected samples is higher.

Another note which may also be important to add is that many diabetics who do not have a ‘severe event’ during a specific time period might still experience a substantial number of hypoglycemic episodes; ‘severe events’ (which require the assistance of another individual) is a somewhat blunt instrument in particular for assessing quality-of-life aspects of hypoglycemia.

iii. The Presence and Consequence of Nonalbuminuric Chronic Kidney Disease in Patients With Type 1 Diabetes.

“This study investigated the prevalence of nonalbuminuric chronic kidney disease in type 1 diabetes to assess whether it increases the risk of cardiovascular and renal outcomes as well as all-cause mortality. […] This was an observational follow-up of 3,809 patients with type 1 diabetes from the Finnish Diabetic Nephropathy Study. […] mean age was 37.6 ± 11.8 years and duration of diabetes 21.2 ± 12.1 years. […] During 13 years of median follow-up, 378 developed end-stage renal disease, 415 suffered an incident cardiovascular event, and 406 died. […] At baseline, 78 (2.0%) had nonalbuminuric chronic kidney disease. […] Nonalbuminuric chronic kidney disease did not increase the risk of albuminuria (hazard ratio [HR] 2.0 [95% CI 0.9–4.4]) or end-stage renal disease (HR 6.4 [0.8–53.0]) but did increase the risk of cardiovascular events (HR 2.0 [1.4–3.5]) and all-cause mortality (HR 2.4 [1.4–3.9]). […] ESRD [End-Stage Renal Disease] developed during follow-up in 0.3% of patients with nonalbuminuric non-CKD [CKD: Chronic Kidney Disease], in 1.3% of patients with nonalbuminuric CKD, in 13.9% of patients with albuminuric non-CKD, and in 63.0% of patients with albuminuric CKD (P < 0.001).”

CONCLUSIONS Nonalbuminuric chronic kidney disease is not a frequent finding in patients with type 1 diabetes, but when present, it is associated with an increased risk of cardiovascular morbidity and all-cause mortality but not with renal outcomes.”

iv. Use of an α-Glucosidase Inhibitor and the Risk of Colorectal Cancer in Patients With Diabetes: A Nationwide, Population-Based Cohort Study.

This one relates closely to stuff covered in Horowitz & Samsom’s book about Gastrointestinal Function in Diabetes Mellitus which I just finished (and which I liked very much). Here’s a relevant quote from chapter 7 of that book (which is about ‘Hepato-biliary and Pancreatic Function’):

“Several studies have provided evidence that the risk of pancreatic cancer is increased in patients with type 1 and type 2 diabetes mellitus [136,137]. In fact, diabetes has been associated with an increased risk of several cancers, including those of the pancreas, liver, endometrium and kidney [136]. The pooled relative risk of pancreatic cancer for diabetics vs. non-diabetics in a meta-analysis was 2.1 (95% confidence interval 1.6–2.8). Patients presenting with diabetes mellitus within a period of 12 months of the diagnosis of pancreatic cancer were excluded because in these cases diabetes may be an early presenting sign of pancreatic cancer rather than a risk factor [137]”.

They don’t mention colon cancer there, but it’s obvious from the research which has been done – and which is covered extensively in that book – that diabetes has the potential to cause functional changes in a large number of components of the digestive system (and I hope to cover this kind of stuff in a lot more detail later on) so the fact that some of these changes may lead to neoplastic changes should hardly be surprising. However evaluating causal pathways is more complicated here than it might have been, because e.g. pancreatic diseases may also themselves cause secondary diabetes in some patients. Liver pathologies like hepatitis B and C also display positive associations with diabetes, although again causal pathways here are not completely clear; treatments used may be a contributing factor (interferon-treatment may induce diabetes), but there are also suggestions that diabetes should be considered one of the extrahepatic manifestations of hepatitis. This stuff is complicated.

The drug mentioned in the paper, acarbose, is incidentally a drug also discussed in some detail in the book. It belongs to a group of drugs called alpha glucosidase inhibitors, and it is ‘the first antidiabetic medication designed to act through an influence on intestinal functions.’ Anyway, some quotes from the paper:

“We conducted a nationwide, population-based study using a large cohort with diabetes in the Taiwan National Health Insurance Research Database. Patients with newly diagnosed diabetes (n = 1,343,484) were enrolled between 1998 and 2010. One control subject not using acarbose was randomly selected for each subject using acarbose after matching for age, sex, diabetes onset, and comorbidities. […] There were 1,332 incident cases of colorectal cancer in the cohort with diabetes during the follow-up period of 1,487,136 person-years. The overall incidence rate was 89.6 cases per 100,000 person-years. Patients treated with acarbose had a 27% reduction in the risk of colorectal cancer compared with control subjects. The adjusted HRs were 0.73 (95% CI 0.63–0.83), 0.69 (0.59–0.82), and 0.46 (0.37–0.58) for patients using >0 to <90, 90 to 364, and ≥365 cumulative defined daily doses of acarbose, respectively, compared with subjects who did not use acarbose (P for trend < 0.001).

CONCLUSIONS Acarbose use reduced the risk of incident colorectal cancer in patients with diabetes in a dose-dependent manner.”

It’s perhaps worth mentioning that the prevalence of type 1 is relatively low in East Asian populations and that most of the patients included were type 2 (this is also clearly indicated by this observation from the paper: “The median age at the time of the initial diabetes diagnosis was 54.1 years, and the median diabetes duration was 8.9 years.”). Another thing worth mentioning is that colon cancer is a very common type of cancer, and so even moderate risk reductions here at the individual level may translate into a substantial risk reduction at the population level. A third thing, noted in Horowitz & Samsom’s coverage, is that the side effects of acarbose are quite mild, so widespread use of the drug is not out of the question, at least poor tolerance is not likely to be an obstacle; the drug may cause e.g. excessive flatulence and something like 10% of patients may have to stop treatment because of gastrointestinal side effects, but although the side effects are annoying and may be unacceptable to some patients, they are not dangerous; it’s a safe drug which can be used even in patients with renal failure (a context where some of the other oral antidiabetic treatments available are contraindicated).

v. Diabetes, Lower-Extremity Amputation, and Death.

“Worldwide, every 30 s, a limb is lost to diabetes (1,2). Nearly 2 million people living in the U.S. are living with limb loss (1). According to the World Health Organization, lower-extremity amputations (LEAs) are 10 times more common in people with diabetes than in persons who do not have diabetes. In the U.S. Medicare population, the incidence of diabetic foot ulcers is ∼6 per 100 individuals with diabetes per year and the incidence of LEA is 4 per 1,000 persons with diabetes per year (3). LEA in those with diabetes generally carries yearly costs between $30,000 and $60,000 and lifetime costs of half a million dollars (4). In 2012, it was estimated that those with diabetes and lower-extremity wounds in the U.S. Medicare program accounted for $41 billion in cost, which is ∼1.6% of all Medicare health care spending (47). In 2012, in the U.K., it was estimated that the National Health Service spent between £639 and 662 million on foot ulcers and LEA, which was approximately £1 in every £150 spent by the National Health Service (8).”

“LEA does not represent a traditional medical complication of diabetes like myocardial infarction (MI), renal failure, or retinopathy in which organ failure is directly associated with diabetes (2). An LEA occurs because of a disease complication, usually a foot ulcer that is not healing (e.g., organ failure of the skin, failure of the biomechanics of the foot as a unit, nerve sensory loss, and/or impaired arterial vascular supply), but it also occurs at least in part as a consequence of a medical plan to amputate based on a decision between health care providers and patients (9,10). […] 30-day postoperative mortality can approach 10% […]. Previous reports have estimated that the 1-year post-LEA mortality rate in people with diabetes is between 10 and 50%, and the 5-year mortality rate post-LEA is between 30 and 80% (4,1315). More specifically, in the U.S. Medicare population mortality within a year after an incident LEA was 23.1% in 2006, 21.8% in 2007, and 20.6% in 2008 (4). In the U.K., up to 80% will die within 5 years of an LEA (8). In general, those with diabetes with an LEA are two to three times more likely to die at any given time point than those with diabetes who have not had an LEA (5). For perspective, the 5-year death rate after diagnosis of malignancy in the U.S. was 32% in 2010 (16).”

“Evidence on why individuals with diabetes and an LEA die is based on a few mainly small (e.g., <300 subjects) and often single center–based (13,1720) studies or <1 year duration of evaluation (11). In these studies, death is primarily associated with a previous history of cardiovascular disease and renal insufficiency, which are also major complications of diabetes; these complications are also associated with an increased risk of LEA. The goal of our study was to determine whether complications of diabetes well-known to be associated with death in those with diabetes such as cardiovascular disease and renal failure fully explain the higher rate of death in those who have undergone an LEA.”

“This is the largest and longest evaluation of the risk of death among those with diabetes and LEA […] Between 2003 and 2012, 416,434 individuals met the entrance criteria for the study. This cohort accrued an average of 9.0 years of follow-up and a total of 3.7 million diabetes person-years of follow-up. During this period of time, 6,566 (1.6%) patients had an LEA and 77,215 patients died (18.5%). […] The percentage of individuals who died within 30 days, 1 year, and by year 5 of their initial code for an LEA was 1.0%, 9.9%, and 27.2%, respectively. For those >65 years of age, the rates were 12.2% and 31.7%, respectively. For the full cohort of those with diabetes, the rate of death was 2.0% after 1 year of follow up and 7.3% after 5 years of follow up. In general, those with an LEA were more than three times more likely to die during a year of follow-up than an individual with diabetes who had not had an LEA. […] In any given year, >5% of those with diabetes and an LEA will die.”

“From 2003 to 2012, the HR [hazard rate, US] for death after an LEA was 3.02 (95% CI 2.90, 3.14). […] our a priori assumption was that the HR associating LEA with death would be fully diminished (i.e., it would become 1) when adjusted for the other risk factor variables. However, the fully adjusted LEA HR was diminished only ∼22% to 2.37 (95% CI 2.27, 2.48). With the exception of age >65 years, individual risk factors, in general, had minimal effect (<10%) on the HR of the association between LEA and death […] We conducted sensitivity analyses to determine the general statistical parameters of an unmeasured risk factor that could remove the association of LEA with death. We found that even if there existed a very strong risk factor with an HR of death of three, a prevalence of 10% in the general diabetes population, and a prevalence of 60% in those who had an LEA, LEA would still be associated with a statistically significant and clinically important risk of 1.30. These findings are describing a variable that would seem to be so common and so highly associated with death that it should already be clinically apparent. […] In summary, individuals with diabetes and an LEA are more likely to die at any given point in time than those who have diabetes but no LEA. While some of this variation can be explained by other known complications of diabetes, the amount that can be explained is small. Based on the results of this study, including a sensitivity analysis, it is highly unlikely that a “new” major risk factor for death exists. […] LEA is often performed because of an end-stage disease process like chronic nonhealing foot ulcer. By the time a patient has a foot ulcer and an LEA is offered, they are likely suffering from the end-stage consequence of diabetes. […] We would […] suggest that patients who have had an LEA require […] vigilant follow-up and evaluation to assure that their medical care is optimized. It is also important that GPs communicate to their patients about the risk of death to assure that patients have proper expectations about the severity of their disease.”

vi. Trends in Health Care Expenditure in U.S. Adults With Diabetes: 2002–2011.

Before quoting from the paper, I’ll remind people reading along here that ‘total medical expenditures’ != ‘total medical costs’. Lots of relevant medical costs are not included when you focus only on direct medical expenditures (sick days, early retirement, premature mortality and productivity losses associated therewith, etc., etc.). With that out of the way…

“This study examines trends in health care expenditures by expenditure category in U.S. adults with diabetes between 2002 and 2011. […] We analyzed 10 years of data representing a weighted population of 189,013,514 U.S. adults aged ≥18 years from the Medical Expenditure Panel Survey. […] Relative to individuals without diabetes ($5,058 [95% CI 4,949–5,166]), individuals with diabetes ($12,180 [11,775–12,586]) had more than double the unadjusted mean direct expenditures over the 10-year period. After adjustment for confounders, individuals with diabetes had $2,558 (2,266–2,849) significantly higher direct incremental expenditures compared with those without diabetes. For individuals with diabetes, inpatient expenditures rose initially from $4,014 in 2002/2003 to $4,183 in 2004/2005 and then decreased continuously to $3,443 in 2010/2011, while rising steadily for individuals without diabetes. The estimated unadjusted total direct expenditures for individuals with diabetes were $218.6 billion/year and adjusted total incremental expenditures were approximately $46 billion/year. […] in the U.S., direct medical costs associated with diabetes were $176 billion in 2012 (1,3). This is almost double to eight times the direct medical cost of other chronic diseases: $32 billion for COPD in 2010 (10), $93 billion for all cancers in 2008 (11), $21 billion for heart failure in 2012 (12), and $43 billion for hypertension in 2010 (13). In the U.S., total economic cost of diabetes rose by 41% from 2007 to 2012 (2). […] Our findings show that compared with individuals without diabetes, individuals with diabetes had significantly higher health expenditures from 2002 to 2011 and the bulk of the expenditures came from hospital inpatient and prescription expenditures.”

 

August 10, 2017 Posted by | Books, Cancer/oncology, Cardiology, Diabetes, Economics, Epidemiology, Gastroenterology, Health Economics, Medicine, Nephrology, Pharmacology | Leave a comment