“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.”
Here’s my first post about the book. I was disappointed by some of the chapters in the second half of the book and I think a few of them were quite poor. I have been wondering what to cover from the second half, in part because some of the authors seem to proceed as if e.g. the work of these authors does not exist (key quote: Our findings do not support continued widespread efforts to boost self-esteem in the hope that it will by itself foster improved outcomes) – I was thinking this about the authors of the last chapter, on ‘Changing self-esteem through competence and worthiness training’, in particular; their basic argument seems to be that since CWT (Competence and Worthiness Training) has been shown to improve self-esteem, ‘good things will follow’ people who make use of such programs. Never mind the fact that causal pathways between self-esteem and life outcomes are incredibly unclear, never mind that self-esteem is not the relevant outcome measure (and studies with good outcome measures do not exist), and never mind that effect persistence over time is unknown, to take but three of many problems with the research. They argue/conclude in the chapter that CWT is ’empirically validated’, an observation which almost made me laugh. I’m in a way slightly puzzled that whereas doctors contributing to Springer publications and similar are always supposed to disclose conflicts of interest in the publications, no similar demands are made in the context of the psychological literature; these people obviously make money off of these things, and yet they’re the ones evaluating the few poor studies that have been done, often by themselves, while pretending to be unbiased observers with no financial interests in whether the methods are ‘validated’ or not. Oh well.
Although some chapters are poor (‘data-poor and theory rich’, might not be a bad way to describe them – note that the ‘data poor’ part relates both to low amounts of data and the use of data of questionable quality; I’m thinking specifically about the use of measures of ‘implicit self-esteem’ in chapter 6 – the authors seem confused about the pattern of results and seem to have a hard time making sense of them (they seem to keep having to make up new ad-hoc explanations for why ‘this makes sense in context’), but I don’t think the results are necessarily that confusing; the variables probably aren’t measuring what they think they’re measuring, not even close, and the two different types of measures probably aren’t remotely measuring anything similar (I have a really hard time figuring out why anyone would ever think that they do), so it makes good sense that findings are all over the place..), chapter 8, on ‘Self-esteem as an interpersonal signal, was however really great and I thought I should share some observations from that chapter here – I have done this below. Interestingly, people who read the first post about the book would in light of the stuff included in that chapter do well to forget my personal comments in the first post about me having low self-esteem; interpersonal outcomes seem to be likely to be better if you think the people with whom you interact have high self-esteem (there are exceptions, but none of them seem relevant in this context), whether or not that’s actually true. Of course the level of ‘interaction’ going on here on the blog is very low, but even so… (I may be making a similar type of mistake the authors make in the last chapter here, by making unwarranted assumptions, but anyway…).
Before moving on, I should perhaps point out that I just finished the short Springer publication Appointment Planning in Outpatient Clinics and Diagnostic Facilities. I’m not going to blog this book separately as there frankly isn’t enough stuff in there for it to make sense to devote an entire blog post to it, but I thought I might as well add a few remarks here before moving on. The book contains a good introduction to some basic queueing theory, and quite a few important concepts are covered which people working with those kinds of things ought to know about (also, if you’ve ever had discussions about waiting lists and how ‘it’s terrible that people have to wait so long’ and ‘something has to be done‘, the discussion would have had a higher quality if you’d read this book first). Some chapters of the book are quite technical – here are a few illustrative/relevant links dealing with stuff covered in the book: Pollaczek–Khinchine formula, Little’s Law, the Erlang C formula, the Erlang B formula, Laplace–Stieltjes transform. The main thing I took away from this book was that this stuff is a lot more complicated that I’d thought. I’m not sure how much the average nurse would get out of this book, but I’m also not sure how much influence the average nurse has on planning decisions such as those described in this book – little, I hope. Sometimes a book contains a few really important observations and you sort of want to recommend the book based simply on these observations, because a lot of people would benefit from knowing exactly those things; this book is like that, as planners on many different decision-making levels would benefit from knowing the ‘golden rules’ included in section 7.1. When things go wrong due to mismanagement and very long waiting lists develop, it’s obvious that however you look at it, if people had paid more attention to those aspects, this would probably not have happened. An observation which is critical to include in the coverage of a book like this is that it may be quite difficult for an outside observer (e.g. a person visiting a health clinic) to evaluate the optimality of scheduling procedures except in very obvious cases of inefficiently long queues. Especially in the case of excess capacity most outsiders do not know enough to evaluate these systems fairly; what may look like excess capacity to the outsider may well be a necessary buffer included in the planning schedule to keep waiting times from exploding at other points in time, and it’s really hard to tell those apart if you don’t have access to relevant data. Even if you do, things can be, complicated (see the links above).
Okay, back to the self-esteem text – some observations from the second half of the book below…
“low self-esteem is listed as either a diagnostic criterion or associated feature of at least 24 mental disorders in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV- TR). Low self-esteem and an insufficient ability to experience self-relevant positive emotions such as pride is particularly strongly linked to depression, to such a degree that some even suggest conceptualizing self-esteem and depression as opposing end points of a bipolar continuum […] The phenomenology of low self-esteem – feeling incompetent and unworthy, unfit for life – inevitably translates into experiencing existence as frightening and futile. This turns life for the person lacking in self-esteem into a chronic emergency: that person is psychologically in a constant state of danger, surrounded by a feeling of impending disaster and a sense of helplessness. Suffering from low self-esteem thus involves having one’s consciousness ruled by fear, which sabotages clarity and efficiency (Branden, 1985). The main goal for such a person is to keep the anxieties, insecurities, and self-doubts at bay, at whatever cost that may come. On the other hand, a person with a satisfying degree of self-respect, whose central motivation is not fear, can afford to rejoice in being alive, and view existence as a more exciting than threatening affair.” [from chapter 7, on ‘Existential perspective on self-esteem’ – I didn’t particularly like that chapter and I’m not sure to which extent I agree with the observations included, but I thought I should add the above to illustrate which kind of stuff is also included in the book.]
“Although past research has emphasized how social environments are internalized to shape self-views, researchers are increasingly interested in how self-views are externalized to shape one’s social environment. From the externalized perspective, people will use information about another’s self-esteem as a gauge of that person’s worth […] self-esteem serves a “status-signaling” function that complements the status-tracking function […] From this perspective, self-esteem influences one’s self-presentational behavior, which in turn influences how others view the self. This status-signaling system in humans should work much like the status-signaling models developed in non-human animals [Aureli et al. and Kappeler et al. are examples of places to go if you’re interested in knowing more about this stuff] […] Ultimately, these status signals have important evolutionary outcomes, such as access to mates and consequent reproductive success. In essence, self-esteem signals important status-related information to others in one’s social world. […] the basic notion here is that conveying high (or low) self-esteem provides social information to others.”
“In an effort to understand their social world, people form lay theories about the world around them. These lay theories consist of information about how characteristics covary within individuals […] Research on the status-signaling function of self-esteem […] and on self-esteem stereotypes […] report a consistent positive bias in the impressions formed about high self-esteem individuals and a consistent negative bias about those with low self-esteem. In several studies conducted by Cameron and her colleagues […], when Canadian and American participants were asked to rate how the average person would describe a high self-esteem individual, they universally reported that higher self-esteem people were attractive, intelligent, warm, competent, emotionally stable, extraverted, open to experience, conscientious, and agreeable. Basically, on all characteristics in the rating list, high self-esteem people were described as superior. […] Whereas people sing the praises of high self-esteem, low self-esteem is viewed as a “fatal flaw.” In the same set of studies, Cameron and her colleagues […] found that participants attributed negative characteristics to low self-esteem individuals. Across all of the characteristics assessed, low self-esteem people were seen as inferior. They were described as less attractive, less intelligent, less warm, less competent, less sociable, and so forth. The only time that the stereotypes of low self-esteem individuals were rated as “more” than the group of high self-esteem individuals was on negative characteristics, such as experiencing more negative moods and possessing more interpersonally disadvantageous characteristics (e.g., jealousy). […] low self-esteem individuals were seen just as negatively as welfare recipients and mentally ill people on most characteristics […] All cultures do not view self-esteem in the same way. […] There is some evidence to suggest that East Asian cultures link high self-esteem with more negative qualities”
“Zeigler-Hill and his colleagues […] presented participants with a single target, identified as low self-esteem or high self-esteem, and asked for their evaluations of the target. Whether the target was identified as low self-esteem by an explicit label (Study 3), a self-deprecating slogan on a T-shirt (Study 4), or their email address (Study 5, e.g., sadeyes@), participants rated an opposite-sex low self-esteem target as less romantically desirable than a high self-esteem target […]. However, ascribing negative characteristics to low self-esteem individuals is not just limited to decisions about an opposite-sex target. Zeigler-Hill and colleagues demonstrated that, regardless of match or mismatch of perceiver-target gender, when people thought a target had lower self-esteem they were more likely to ascribe negative traits to him or her, such as being lower in conscientiousness […] Overall, people are apt to assume that people with low self-esteem possess negative characteristics, whereas those with high self-esteem possess positive characteristics. Such assumptions are made at the group level […] and at the individual level […] According to Cameron and colleagues […], fewer than 1% of the sample ascribed any positive characteristics to people with low self-esteem when asked to give open-ended descriptions. Furthermore, on the overwhelming majority of characteristics assessed, low self-esteem individuals were rated more negatively than high self-esteem individuals”
“Although for the most part it is low self-esteem that people associate with negative qualities, there is a dark side to being labeled as having high self-esteem. People who are believed to have high self-esteem are seen as more narcissistic […], self-absorbed, and egotistical […] than those believed to possess low self-esteem. Moreover, the benefits of being seen as high self-esteem may be moderated by gender. When rating an opposite-sex target, men were often more positive toward female targets with moderate self-esteem than those with high self-esteem”
“Not only might perceptions of others’ self-esteem influence interactions among relative strangers, but they may also be particularly important in close relationships. Ample evidence demonstrates that a friend or partner’s self-esteem can have actual relational consequences […]. Relationships involving low self-esteem people tend to be less satisfying and less committed […], due at least in part to low self-esteem people’s tendency to engage in defensive, self-protective behavior and their enhanced expectations of rejection […]. Mounting evidence suggests that people can intuit these disadvantages, and thus use self-esteem as an interpersonal signal. […] Research by MacGregor and Holmes (2007) suggests that people expect to be less satisfied in a romantic relationship with a low self-esteem partner than a high self-esteem partner, directly blaming low self-esteem individuals for relationship mishaps […] it appears that people use self-esteem as a signal to indicate desirability as a mate: People report themselves as less likely to date or have sex with those explicitly labeled as having “low self-esteem” compared to those labeled as having “high self-esteem” […] Even when considering friendships, low self-esteem individuals are rated less socially appealing […] In general, it appears that low self-esteem individuals are viewed as less-than-ideal relationship partners.”
“Despite people’s explicit aversion to forming social bonds with low self-esteem individuals, those with low self-esteem do form close relationships. Nevertheless, even these established relationships may suffer when one person detects another’s low self-esteem. For example, people believe that interactions with low self-esteem friends or family members are more exhausting and require more work than interactions with high self-esteem friends and family […]. In the context of romantic relationships, Lemay and Dudley’s (2011) findings confirm the notion that relationships with low self-esteem individuals require extra relationship maintenance (or “work”) as people attempt to “regulate” their romantic partner’s insecurities. Specifically, participants who detected their partner’s low self-esteem tended to exaggerate affection for their partner and conceal negative sentiments, likely in an effort to maintain harmony in their relationship. Unfortunately, this inauthenticity was actually associated with decreased relationship satisfaction for the regulating partner over time. […] MacGregor and colleagues […] have explored a different type of communication in close relationships. Their focus was on capitalization, which is the disclosure of positive personal experiences to others […]. In two experiments […], participants who were led to believe that their close other had low self-esteem capitalized less positively (i.e., enthusiastically) compared to control participants. […] Moreover, in a study involving friend dyads, participants reported capitalizing less frequently with their friend to the extent they perceived him or her as having low self-esteem […] low self-esteem individuals are actually no less responsive to others’ capitalization attempts than are high self-esteem partners. Despite this fact, MacGregor and Holmes (2011) found that people are reluctant to capitalize with low self-esteem individuals precisely because they expect them to be less responsive than high self-esteem partners. Thus people appear to be holding back from low self-esteem individuals unnecessarily. Nevertheless, the consequences may be very real given that capitalization is a process associated with personal and interpersonal benefits”
“Cameron (2010) asked participants to indicate how much they tried to conceal or reveal their self-feelings and insecurities with significant others (best friends, romantic partners, and parents). Those with lower self-esteem reported attempting to conceal their insecurities and self-doubts to a greater degree than those with higher self-esteem. Thus, even in close relationships, low self-esteem individuals appear to see the benefit of hiding their self-esteem. Cameron, Hole, and Cornelius (2012) further investigated whether concealing self-esteem was linked with relational benefits for those with low self-esteem. In several studies, participants were asked to report their own self-esteem and then to provide their “self-esteem image”, or what level of self-esteem they thought they had conveyed to their significant others. Participants then indicated their relationship quality (e.g., satisfaction, commitment, trust). Across all studies and across all relationship types studied (friends, romantic partners, and parents), people reporting a higher self-esteem image, regardless of their own self-esteem level, reported greater relationship quality. […] both low and high self-esteem individuals benefit from believing that a high self-esteem image has been conveyed, though this experience may feel “inauthentic” for low self-esteem people. […] both low and high self-esteem individuals may hope to been seen as they truly are by their close others. […] In a recent meta-analysis, Kwang and Swann (2010) proposed that individuals desire verification unless there is a high risk for rejection. Thus, those with negative self-views may desire to be viewed positively, but only if being seen negatively jeopardizes their relationship. From this perspective, romantic partners should signal high self-esteem during courtship, job applicants should signal high self-esteem to potential bosses, and politicians should signal high self-esteem to their voters. Once the relationship has been cemented (and the potential for rejection has been reduced), however, people should desire to be seen as they are. Importantly, the results of the meta-analysis supported this proposal. While this boundary condition has shed some light on this debate, more research is needed to understand fully under what contexts people are motivated to communicate either positive or negative self-views.”
“it appears that people’s judgments of others’ self-esteem are partly well informed, yet also based on inaccurate stereotypes about characteristics not actually linked to self-esteem. […] Traits that do not readily manifest in behavior, or are low in observability, should be more difficult to detect accurately (see Funder & Dobroth, 1987). Self-esteem is one of these “low-observability” traits […] Although the operationalization of accuracy is tricky […], it does appear that people are somewhat accurate in their impressions of self-esteem […] research from various laboratories indicates that both friends […] and romantic partners […] are fairly accurate in judging each other’s self-esteem. […] However, people may also use information that has nothing to do with the appearances or behaviors of target. Instead, people may make judgements about another’s personality traits based on how they perceive their own traits […] people tend to project their own characteristics onto others […] People’s ratings of others’ self-esteem tend to be correlated with their own, be it for friends or romantic partners”
I read this book for different reasons than the ones that usually apply when I find myself reading medical textbooks; I’d wish those reasons did not exist.
The book is nice; there’s a lot of information in there despite the low page count, and it’s slightly less technical than are some related books (e.g. this one, which I’ve also briefly had a look at). The book has a lot of data, but as a result of the nature of the publication a lot of information related to this data is missing from the coverage; for example we’re told that “[o]ral ulcers are present in 20% of patients”, but we’re not told how many patients this estimate is based upon, and/or how confident we are that that number is correct. You have to take some stuff on faith and I’ve read the book assuming that given the background of the author, his reported estimates are at least in the right ballpark. The author might argue that providing such additional ‘meta-data’ as well to the readers could have easily doubled the page-count without adding much information which is relevant to the people reading the publication, and if he did do that I’m not actually sure I’d necessarily disagree with him; it’s a good book in terms of what it sets out to do, and I can’t really blame the author for not writing a different book. Part of why I chose to give the book a high rating is also that I liked it much better than some other superficially similar short books I’ve read, e.g. this one.
I’ve added some observations from the book below and added some hopefully helpful links which may make the post a bit easier to read.
“Based on the definitions of lupus given in Chapter 2, 50% of all cases of lupus are systemic lupus erythematosus, evenly divided into organ-threatening (25%) and non-organ-threatening (25%) categories. The remainder of cases are cutaneous lupus (40%), mixed connective tissue disease and/or overlap syndromes (10%), and drug-induced lupus (<1%). There are seven undifferentiated connective tissue disease patients for every lupus patient [a different way to say this: “For every patient with SLE [Systemic Lupus Erythematosus], there are six or seven who display lupus-like symptoms without meeting SLE criteria”]. The true incidence and prevalence of systemic lupus in the United States are difficult to ascertain. Surveys have shown that only one in three patients told by a physician that he or she has SLE meets the American College of Rheumatology criteria. […] Once thought to be a benign process, MCTD [mixed connective tissue disease] has a 20-year mortality rate of over 50%. […] Approximately one white male in 10,000, one white female in 1,000, and one African American female in 250 in the United States have SLE. People of color are diagnosed with lupus more frequently than are Caucasians, but this statistic can get complicated. For example, Filipinos and Chinese are diagnosed with lupus much more frequently than are Japanese or Malays. […] Nearly 90% of persons with SLE are female, as opposed to 80% with chronic cutaneous lupus and 50% with drug-induced lupus. Most develop the disorder during their reproductive years. The female-to-male ratio is 2:1 before puberty, as high as 8:1 during years of active menstruation, and 2.3:1 for patients over the age of 60″
“Pathogenesis undergoes an often-gradual process consisting of several phases: predisposition, benign autoimmunity, prodrome, and clinical systemic lupus erythematosus. Only one person in 10 who possess lupus susceptibility genes ever develops full-blown lupus. Many individuals have “subclinical autoimmunity,” or undifferentiated connective tissue disorders where the process is attenuated. […] At least 30 susceptibility genes for SLE have been identified, and their presence varies widely depending on race, ethnicity, and geography. […] Most of the lupus-associated genes have odds ratios (relative risks) of less than 2.5 (1 would indicate no predisposition), and they are only of minimal clinical value […] Lupus patients are normally fertile. However, only 67% of pregnancies in SLE patients are successful, compared to 85% in the general population. […] A woman with lupus has a 2% risk of her son and a 10% risk of her daughter having lupus […]
As only 28% of monozygotic twins both have SLE, environmental factors clearly play a role. […] The development of autoantibodies precedes the first symptoms of SLE by two to nine years.”
“Immune complexes and apoptotic cells circulate in the bloodstream and need to be disposed of so they do not settle in tissues (which causes inflammation) or release chemicals (e.g., cytokines, chemokines) which also promote inflammation. In SLE, this clearance fails due to a variety of mechanisms: defective phagocytosis, altered transport by complement receptors, defective regulation of T helper cells by regulatory T cells, inadequate production or function of regulatory cells that kill or suppress autoreactive B cells, low production of interleukin 2 by T cells, and defects in apoptosis that permit the survival of effector T and autoreactive B cells […] Tissue damage is produced by the deposition of circulating immune complexes into tissue, which in turn activates endothelial cells, cytokines, and chemokines. In the kidneys, this produces inflammation, followed by proliferation and ultimately fibrosis (scarring). Complement activation, overloading of the Complement Receptor 1 (CR1) transport system, antibodies to complement components […], and congenital or acquired deficiency in complement components also lead to tissue inflammation and damage. Lupus is also characterized by accelerated atherosclerosis.”
“Half of persons with systemic lupus erythematosus present with organ-threatening disease. The remaining individuals do not present with cardiopulmonary, hepatic, or renal symptoms; central nervous system (CNS) vasculitis; hemolytic anemia; or thrombocytopenia on initial evaluation. Organ involvement is relatively easy to diagnose […] On the other hand, it can often take one to two years and several physician consultations before the presence of organ-sparing lupus is ascertained. Rashes can shorten the length to time of diagnosis, but young, healthy-appearing women with non-specific symptoms of fatigue and aching are often thought to have other processes or are given psychosocial explanations. […] Lupus patients complain of a sense of malaise and fatigue. Over 90% with the disease report this to their physician, and its lack of specificity can be problematic. […] Arthralgias are present in at least 80% of SLE patients, whereas observable inflammatory arthritis involving two or more joints is found in 50% at some point in the course of the disease. […] Two-thirds of lupus patients self-report sensitivity to sunlight. […] Present in a little more than one-third of lupus patients, the butterfly rash is one of the disease’s most recognizable features.”
“Oral ulcers are present in 20% of patients with SLE […] Patients with lupus may complain of pain on taking a deep breath, shortness of breath, windedness, wheezing, or chest pains. The most common problem relates to pleurisy. At autopsy, most lupus patients show evidence of pleural scarring or prior inflammation. Manifested by pain or a catching sensation on taking a deep breath, pleural symptoms are present in 60% of lupus patients, and frank effusions noted in 25%, during a lifetime. […] Seen in 1% to 9% of persons with SLE, patients with acute lupus pneumonitis (ALP) present acutely with shortness of breath and fever. Often treated telephonically as a pulmonary infection with antibiotics, the process progresses rapidly if high doses of corticosteroids are not prescribed […] ALP has an 80% mortality rate if not diagnosed within two weeks of onset; recovery is usually rapid with appropriate treatment. […] Pericardial involvement is found in 60% of lupus patients at autopsy and incidentally, and asymptomatically in 25% on 2-D echocardiogram. Frank effusions are seen in 25% during the course of disease but in fewer than 5% of patients at any given point in time […] Myocardial dysfunction (often from subclinical inflammation) is found on stress echocardiography in 40% with SLE, but only 5% to 10% ever experience frank myocarditis. […] Coronary artery disease, hypertension, insulin resistance, metabolic syndrome, hyperhomocystinemia, and hyperlipidemia are more common in lupus patients […] Most lupus patients complain of at least some intermittent cognitive impairment […] Autoimmune thyroiditis, type 1 diabetes, autoimmune adrenalitis, premenstrual disease flares, and elevated prolactin levels have increased prevalence in SLE. […] Thirty percent of individuals with SLE have some form of renal involvement; in half of these cases, it mandates organ-specific therapy […] Lupus nephritis is associated with high morbidity and mortality.”
“Lupus costs the American public approximately $20 billion a year in lost wages, disability, hospitalizations, medical visits, and medication […] Direct costs account for one-third, and indirect costs two-thirds, of this amount.[1,3] The overwhelming majority of lupus patients with non-organ-threatening disease are employed full time, while 50% with organ involvement are disabled […] Part-time employment is possible for many lupus patients. Total permanent disability is not to be taken lightly. Disabled patients tend to be less independent, less socially interactive, and more depressed, and to have less self-esteem. […] Currently, most patients with systemic lupus erythematosus survive at least 20 years, although their quality of life is not always optimal. Historically, 40% of deaths in lupus patients with serious disease were from inflammation and occurred within two years of diagnosis. Approximately 10% of deaths took place over the following 10 years. The remaining 40% of lupus patients died 12 to 25 years after diagnosis, mostly from infections and complications of chronic steroid therapy and immunosuppression. This “bimodal” curve has been altered in the past 10 years. Death due to lupus during the first two years is becoming less common; individuals with serious SLE still have a 10- to 30-year shortened life expectancy due to complications of therapy. […] Patients with drug-induced lupus, chronic cutaneous lupus, and non-organ-threatening SLE without antiphospholipid antibodies have a normal survival rate. […] Approximately 5% of persons with SLE experience spontaneous remission without treatment.”
This book actually probably didn’t really merit two posts, but given that I wrote a part one earlier on I felt I had to write a part two as well now that I’ve finished it. I’ve also recently read Josephine Tey’s The Daughter of Time and Agatha Christie’s Cards on the Table, but as mentioned earlier I’ve been thinking about getting rid of the fiction coverage here and so I won’t cover those here in any detail – all I’ll say is that Cards on the Table was awesome (5 stars on goodreads), and Tey was an enjoyable read (4 stars … do recall if you read it that it’s a work of fiction).
Goerling’s book is a neat little book – I liked it. It’s not super comprehensive, but it’s the kind of book that can be read without problems by both patients and their caregivers as well as doctors and other health care professionals. Many people will/would probably benefit from reading this book. Occasional talk about stuff like ‘Mindfulness-Based Stress Reduction’ and similar stuff subtracted a star or two along the way, but most of the stuff is actually quite interesting. I’ve added a few observations from the second half of the book below:
“With the favorable trend regarding survival of cancer in the Western world, there is an increasing focus among patients, clinicians, researchers, and politicians regarding cancer survivors’ health and well-being. Their number is rapidly growing and more than 3 % of the adult populations in Western countries have survived cancer for 5 years or more. Cancer survivors are at increased risk for a variety of late effects after treatment, some life-threatening such as secondary cancer and cardiac diseases, others might negatively impact on their daily functioning and quality of life. The latter might include fatigue, anxiety disorders and difficulties returning to work while depression does not seem to be more common among survivors than in the general population. […] Today, the relative 5-year survival is 60–65 % for patients diagnosed with cancer (American Cancer Society 2012, Verdecchia et al. 2007). In Norway, cancer survivors alive ≥5 years from diagnosis represent 3.3 % of the total population (The Cancer Registry of Norway 2010). For some cancer types such as testicular cancer, breast cancer, and Hodgkin’s lymphoma, the 5-year relative survival exceeds 90 %. According to cancer types the most common survivor groups are survivors of female breast, prostate, colorectal, and gynecologic cancer (American Cancer Society 2012).”
“Treatment-related solid second cancers are usually diagnosed at a latency of 10–30 years after radiotherapy, and their development is related to the radiation dose within the target field, but also to scattered irradiation beyond the field borders. […] During the last two decades increasing documentation has emerged that cytotoxic drugs in a dose-dependent manner are carcinogenic leading to an increased risk of leukemia […], but also of solid tumors […] Dependent of their previous treatment long-term cancer survivors may develop asymptomatic or symptomatic left ventricle dysfunction, heart failure, premature coronary atherosclerosis, arrhythmia, or sudden cardiac death, most often due to myocardial infarction (Lenihan et al. 2013). Mediastinal radiotherapy and treatment with certain cytotoxic drugs (antracyclines, trastuzumab) represent well-known cardiotoxic risk factors, with clear dose–effect associations to cardiac dysfunction.” [treatment for cancer can be really bad for you, but often the alternative isn’t great either…]
“For the cancer survivor to be able to make the optimal decisions regarding own present and future health, they need information regarding the long-term health risks they face and how to best handle them. The literature indicates that today’s cancer survivors are not aware of their risks for later adverse health events […] These findings might not only relate to lacking information per se. We must also assume that the survivors have an ambivalent wish for information about future health risks.”
“CBT strives to be evidence based and much effort has been put in scientific research, including large randomized controlled studies. In patients suffering from cancer, CBT has been demonstrated to improve anxiety and depressive symptoms, self-esteem, immune functions, quality of life, optimism, self-efficacy, compliance, coping effectiveness and satisfaction, and to decrease cancer-related fatigue, cortisol levels, pain, and distress (Andersen et al. 2007; Daniels and Kissane 2008; Greer et al. 1992; Hopko et al. 2005; Lee et al. 2006; Manne et al. 2007; Mefford et al. 2007; Moorey et al. 1998; Osborn et al. 2006; Penedo et al. 2007; Tatrow 2006; Witek-Janusek et al. 2008; Wojtyna et al. 2007).”
“psycho-oncological interventions seem to influence treatment adherence, but its relevance for survival is controversial (Chow et al. 2004; Smedslund and Ringdal 2004; Spiegel et al. 1989). A systematic Cochrane review examining the effectiveness of psychosocial interventions in breast cancer patients on survival outcome showed insufficient evidence for such an effect (Edwards et al. 2008).”
“In a very impressive paper Laurie Lyckholm (2001) reports on handling stress, burnout, and grief in the practice of oncology. Causes of stress are seen in insufficient personal or vacation time, a sense of failure, unrealistic expectations, anger, frustration, as well as feelings of inadequacy or self preservations, reimbursement, and other issues related to managed care and third party payers, and last but not least grieving. Burnout can manifest itself in substance abuse, marital conflict, overeating and substantial weight gain, higher frequency of mistakes in clinical care, inappropriate emotional outbursts, interaction problems, depression and anxiety disorders, and even suicide. Lack of or inadequate training of communication and management skills are also considered causes of burnout (Ramirez et al. 1996). In a survey of 7,288 physicians in the United States, 45.8 % reported at least one of the following symptoms of burnout: loss of enthusiasm for work, feelings of cynicism (depersonalisation), and low sense of personal accomplishment (Shanafelt et al. 2012).”
I finished Lloyd et. al, but I’ll cover that one later – I’m a bit behind on the book blogging as there are a few books I haven’t covered, but I don’t really give a crap about that right now. I might get to those books or I might not. On a related note I’ve been thinking about dropping the fiction book blogging altogether and just limit coverage of those books to whatever I can be arsed to write on goodreads.
I usually don’t find it hard to justify spending time reading a specific book – I don’t have many non-inferior ways to spend my time – but in the grand scheme of things this one was/is particularly easy to justify reading. I consider it not particularly likely that I’ll get cancer (other causes of death are statistically much more likely, and many of them can be expected to kill me before, say, those prostate basal cells start acting up enough for me to get a cancer diagnosis), but assuming I’m still alive in a decade or so there’s a high likelihood someone in my family and/or social circle will have gotten cancer in the meantime. When that happens, it’s probably a good idea to have read a book like this. At least it can’t hurt. I should note that although I did not know this when I started out, some of the observations in the book are quite relevant to areas outside the cancer setting. For instance I behaved like a jerk towards a good friend last week, and I’d have at least to some extent decreased the likelihood of behaving in such a manner if I’d read and taken to heart the remarks on how to optimize communication strategies included in chapter 3 of this book before that specific social exchange took place.
On a related note, “Those who suffer from depression tend to withdraw from friendships and relationships, causing loneliness and isolation. Maintaining networks of family and friends may prevent this from happening.” This quote is actually from Lloyd et al. but I figured I should include it here; as that book also makes clear, the mental health profile of people with chronic diseases like DM is somewhat complicated and I’m not sure how to categorize my current state of mind, but there are some depressive thoughts there and I’m really trying to remind myself of stuff like this these days. Yesterday I went to the chess club despite having absolutely no desire to go at all, and today I went to a Mensa meeting for the first time in a few months – not because I wanted to, but because my social interaction patterns over the last few weeks in particular have been deeply problematic (i.e., I have had pretty much no social interaction).
Anyway, enough blather – below some observations from the first half of the book:
“Several meta-analyses and large multi-centre studies have shown that, during the time of cancer diagnosis, about 30 % of the patients suffer from a mental health condition (Mitchell et al. 2011; Singer et al. 2010, 2013a; Vehling et al. 2012). Less is known however about the course of those conditions during the cancer trajectory. Available evidence suggests that their frequency does not decrease considerably over time (Bringmann et al. 2008).
Known risk factors for mental disorders in cancer patients are pain, high symptom burden, fatigue, mental health problems in the past and disability […] There are no consistent correlates of depression in cancer patients (Mitchell et al. 2011).”
“Vocational rehabilitation of cancer patients differs remarkably between countries. For example, while in Scandinavia about 63 % of all patients returned to work after a total laryngectomy (Natvig 1983) and 50 % did so in France (Schraub et al. 1995) only 11 % could return in Spain (Herranz et al. 1999). Predictors of successful return to work are flexible working arrangements, counseling, training and rehabilitation services, younger age, educational attainment, male gender, less physical symptoms and continuity of care (Mehnert 2011).”
“Although the data reviewing sex and/or gender as a primary variable in cancer is quite limited there is a body of literature that is highly informative and is worth a brief review. As it relates to psychological distress, women report more psychological distress overall than do men. This information has been confirmed by many international studies using a wide variety of screening instruments and in diverse cancer populations […] In terms of willingness to report vulnerabilities based on gender, women do report more requests for help (Merckaert et al. 2010) and accept more help (Curry et al. 2002)”
“Pistrang and Barker found that male partner support (high empathy and low withdrawal) plays a pivotal role in the woman’s adaptation and psychological well-being (Pistrang and Barker 1995). […] in a large study of caregivers, Kim et al. (2006) reported that female cancer patients felt that their male partners were very supportive when it came to practical tasks but that they did not provide the emotional support that was so important to them. In essence, men were much more comfortable with demanding and ongoing practical and physical tasks than with the emotional components of the experience. This misalignment has significant implications not only for couples but whenever men and women try to support and connect with each other during times of stress or crisis. […]
“One of the well-documented gender differences found in the literature is the stress response. When under stress, women have been shown to reach out to others and to ‘‘tend and befriend,’’ (Taylor et al. 2000) as an initial response to control their sense of danger and fear. Women feel secure in reaching out to others when trying to manage the stress associated to their vulnerability and do not experience any diminution of self-esteem by asking for help. […] Unlike women, men may experience a sense of diminished self-esteem by sharing their vulnerabilities with others. Although women are adept at prospectively sharing their emotional concerns to reduce their immediate sense of threat, it is only in retrospect that men are generally comfortable sharing their fears and concerns with others, once the sense of threat is reduced to manageable levels. The ways in which many women and men manage their vulnerabilities (women seeking emotional connection and men seeking space and time to think) have significant implications within the context of caregiving. […] when people are under stress they are more likely to revert into their habitual behavioral patterns. In essence, they become more like caricatures of themselves. There are some common behaviors that men and women produce in different frequencies that are generalizations (to be at least considered but never assumed) in the clinical setting.”
“although women are still the primary caregivers for seriously ill family members, men are increasingly taking on the role as primary caregiving role from 25 % in 1987 to 39 % in 2004, (Kim et al. 2006, 2007).”
“There is growing evidence that early integration of palliative care—several months prior to death—not only reduces distress and improves quality of life, but also decreases health care utilization and lastly costs (Temel et al. 2010, 2011; Zhang et al. 2009). Evidence seems to be sufficient for the American Society for Clinical Oncology (ASCO) to recommend early palliative care as best practice in some cancer diagnoses (Smith et al. 2012).”
“Anxiety […] plays an important if not dominant role in symptom perception and expression especially in pain. It is well known from multiple studies in neuropsychology and -physiology that uncertainty and pain are directly linked (Brown et al. 2008; Yoshida et al. 2013).”
“The lack of a common metric makes it difficult to precisely assess the extent of psychological impairment among cancer family caregivers, and the subgroup of caregivers who are at greatest risk; however, it is noteworthy that, across almost all metrics, caregivers consistently have anxiety, depression, and psychological distress rates two or more times that of the general population (Kurtz et al. 2004; Grov et al. 2005; Grunfeld et al. 2004; Northouse et al. 2001; Williams et al. 2013). The lack of precision in the research literature around caregiver psychological impairment in no way obscures what is undoubtedly a major burden for cancer family caregivers. Several studies which concurrently measured psychological impairment in patients and family caregivers, found the family caregivers had higher rates of impairment than the patients with cancer (Braun et al. 2007; Kim et al. 2005; Matthews 2003; Mellon et al. 2006).”
“Out-of-pocket payments play a dominant role in LMICs [low- and middle-income countries] where they cover about 50 percent of health care expenditures. [They] are less important in the high-income countries [but] there seems to be a tendency toward an increase of patient cost-sharing in countries where it traditionally has played a minor role […] This is not only explained by a concern to fight moral hazard and overconsumption, but it also reflects the increasing pressure on the public financing part of the system.” [In ‘low-income-countries’ out-of-pocket expenditures in 2008 made up on average 67.4 % of total expenditures in health, whereas the corresponding numbers for ‘lower middle income countries’, ‘upper middle income countries’ and ‘high income countries’ were 46.8%, 30.2% and 14.4% respectively. The global average was 22.5%. Note that total out-of-pocket expenditures incurred in high-income countries (in e.g. dollars) may make up a much larger share of the total global out-of-pocket expenditures than you might believe from those numbers alone – recall that high income countries spend approximately 100 times as much money on health per person than do low-income countries (low income countries spent on average $23 on health per capita in 2008, whereas high income countries spent $2414 per capita).]
“User charges do have a negative effect on health care consumption. The evidence is overwhelming for co-payments in the developed insurance systems. […] The evidence is almost equally strong for the effects of user charges in LMICs. Introducing or increasing user fees has almost always and everywhere led to a decrease of utilization […] both in developed health insurance systems and in LMICs, the evidence suggests that the decrease in utilization may have negative effects on the quality of care […] Most studies find that cost sharing leads to a decrease in the utilization of essential medication, defined as medication that is necessary to maintain or improve health. Often adherence to a regimen of maintenance medication goes down with patients skipping doses or stretching out refills. With a few exceptions […], higher cost-sharing for, and therefore lower utilization of, prescription drugs, has led to greater use of inpatient and emergency medical services by chronically ill patients [effects like these, I should point out, may well make cost-sharing a less than ideal cost-saving mechanism; emergency services are incredibly expensive compared to ‘routine management’] […] cross-price effects are also significant. Again, the evidence for the developed countries and the LMICs goes in the same direction. Two- or three-tier plans for prescription drugs in the US, introducing differentiated cost sharing for different categories of drugs, have clear effects on the pattern of drug use. […] user charges are a strongly regressive component in the health care financing structure of developed countries […] A large majority of studies suggest that user charges lead to a stronger reduction in utilization among the poor than among the rich (James et al. 2006).”
Insurance and the demand for medical care:
“two main empirical findings from research to date are these: (1) the aggregate or average consumer demand curve, whether Marshallian (uncompensated) or Hicksian (compensated), slopes downward and to the right. (2) Demand curves are significantly price responsive at all consumer income levels. These conclusions are at variance with common perceptions of medical care demand by non-economists, who traditionally have asserted that non-poor consumers only use medical care when they have to do so because they are sick or are ordered to do so by their physician, and that only lower income households would restrain their demand for needed care because of cost sharing.”
“When the consumer has price-sensitive demand for care, the influence of deductibles on spending is complex because a deductible in effect faces the consumer with a two part block tariff: full price up to a certain level of spending, and then low or zero marginal price. Since the marginal price is different depending on whether the deductible is covered or not, the consumer has to consider the distribution of expected expenses […] While the actual analytics of demand responsiveness are complicated by a deductible […] the main intuitive finding is obvious: the lower the deductible the higher the demand for care, other things equal.”
“A traditional discussion about choosing the “right” (desired) hospital output is associated with the role of quality and the trade-off between lower costs and higher quality. This trade-off is based on the assumption that higher quality implies more costs. This is likely to be so in efficient hospitals. However, inefficient hospitals may have room to improve simultaneously in both dimensions.”
“Whenever hospitals are funded by case payments they prefer to receive more patients for treatment while hospitals funded by capitation (to treat people in a defined catchment area) will invest more in keeping patients treated at primary care level when clinically feasible. […] several health systems make referral by a GP a necessary condition to visit a specialist. […] Gatekeeping determines to a considerable extent the demand faced by the hospital. Moreover, referrals to the hospital depend on both the incentives faced by GPs and on the formal relationship between primary care and hospitals.”
“One of the main issues in measuring economies of scale (productivity, in general) in hospitals is the role of quality. More efficient hospitals are more likely to have a lower marginal cost of providing quality, and accordingly they may supply a higher quality level in equilibrium (which is likely to raise costs and mask their efficiency advantage). […] Under regulated prices, quality is the main “competitive tool” of hospitals and it is used intensively. Whenever both price and quality are available instruments to the hospital, the effort to attract patients is spread over both of them. […] response to an unexpected demand surge for hospital services is more likely to be met by early discharges to free up capacity than rationing admissions.”
The economics of the biopharmaceutical industry:
“The US research-based biopharmaceutical industry invests 15-17 percent of sales in R&D, and the R&D cost of bringing a new compound to market is estimated at over $1bn. […] The cost of developing an approved new medical entity (NME), measured as a discounted present value at launch, [was] $138 million in the 1970s […] the global nature of pharmaceutical R&D raises issues of appropriate cross-national price differentials and cost sharing. National regulators have incentives to free-ride, driving domestic prices to country-specific marginal cost, leaving others to pay for the joint costs of R&D. The long R&D lead times – on average roughly twelve years from drug discovery to product approval – make the incentives for short run free-riding by individual countries particularly acute because negative effects will be delayed for years and hard to attribute. […] In practice, the ability of pharmaceutical firms to price-discriminate is undermined by government policies […] the design of each country’s price regulatory system affects not only its prices and availability of drugs but also availability in other countries through price spillovers in the short run, and through R&D incentives in the long run. […] North America accounted for 45.9 percent of global pharmaceutical sales in 2007, compared to 31.1 percent for Europe”
“The theoretically optimal insurance/reimbursement contract for drugs must deter both insurance-induced over-use by patients and excessive prices by manufacturers, while paying prices sufficiently to reward appropriate R&D, taking into account the global scope of pharmaceutical sales. […] An important conclusion is that patient cost sharing alone cannot simultaneously provide optimal incentives for efficient use of drugs, control of patient moral hazard and optimal provider incentives for R&D. In addition, given the global nature of pharmaceutical utilization, creating optimal R&D incentives require appropriate price differentials across countries […] generally, regulatory systems that induce price convergence across countries are likely to reduce social welfare. […] Overall, countries that use direct price controls do not consistently have lower prices than countries that use other indirect means to constrain prices”
“In the US, generics now account for almost seventy percent of all prescriptions but only about 16 percent of sales, due to their low prices. Although US prices for on-patent drugs are on average 20-40 percent higher in the US than in other industrialized countries, US generic prices are lower […] many middle and low income countries have relatively high generic prices […] and uncertain generic quality. […] Empirical studies of generic entry has shown, not surprisingly, that generic prices are inversely related to number of generic competitors […]; generic entry is more likely for compounds with large markets […], [and in] chronic disease markets”
“the FDA is required by statute to consider risks and benefits to patients. Costs […] is beyond the FDA’s purview. […] Currently the US lags other countries in the use of comparative and/or cost-effectiveness as an input to reimbursement decisions.”
Here’s the first post about the book. If you want to know what I’ve been doing over the last few days, look at the red thingy. I’ve read roughly 700 pages so far. Book-blogging takes time, so I’ve been emphasizing reading over blogging.
This book covers a lot of stuff. There’s a lot more in there than I can justify covering here. That said, in a way I also feel that it’s necessary to note how little stuff the book actually covers: In more than a few chapters I’ve added remarks such as, ‘this topic is covered in much more detail in Juth and Munthe‘, or ‘for a much more comprehensive review, see Goldstein’s book‘. The book also covers stuff covered in greater detail here, here and here; as mentioned before I know a bit about these things already, though I haven’t felt like I’ve really had a great overview of the material. Having read books like this one, this one or perhaps this one may help understand some issues presented in specific chapters better, but you don’t really need to have done that – in most cases the chapters can stand on their own. I should mention that in one specific chapter (about addiction) I basically wrote in the margin at one point that the authors didn’t seem to know what they were talking about here, and that they should familiarize themselves with the medical- and neuroscientific research on the stuff they’d written about there (addiction) before writing any more stuff on that (specific) subtopic. It wasn’t a big part of the chapter though, and that has only happened once; most chapters are great, and none are what I’d really term ‘weak’ – I’m currently at either four or five stars on goodreads, probably a little bit closer to five than four. I should note that I have had similar ‘these guys don’t seem to know a lot about what the non-economists have found out about this stuff’- experiences as I had when reading the addiction chapter previously a few times when covering labour economics topics during my coursework; sometimes it seems to me that economists who’re very fond of their models (and the models of their antecessors) don’t really have a clue what’s really going on because they refuse to learn what people in other fields have already found out (perhaps because they assume that related work will not help them in their model-building efforts? (…if so, I think they’re wrong)) – it always bothers me.
Anyway, some observations from the book below:
“From an economist’s perspective, infectious diseases are distinguished from many other health issues by the central role played by externalities.1 Control of infectious diseases yields both positive externalities (prevention and treatment can delay or reduce spread of infection to uninfected individuals) and negative externalities (overuse of treatment can lead to drug resistance, which has global consequences for treatment effectiveness). […] vaccination, an important tool in the prevention of infectious diseases, presents a classic public goods problem. Society gains from individual vaccination because of herd immunity, but this value is not recognized by individuals, who have an incentive to free-ride on vaccination by other individuals. […] disease reporting and eradication efforts are also public goods. […] a country’s incentives to control a freely moving disease like malaria are determined as much by its ability to stop the inflow of infected individuals as by the ability to control the disease within its own borders. Reducing malaria in a country could have transboundary benefits by incentivizing infection control in its neighboring countries as well. This principle also applies more generally to the challenge of global disease eradication. […] Eradication is a binary public good: the maximum benefits are achieved when the disease is completely gone.”
“Together, all infectious diseases account for more than 25 percent of premature death globally.”
“In sum, obtaining accurate information about potential epidemics is as much about reporting incentives as it is about detection technology.”
“from an economic perspective, disease burden may be a poor criterion to use for allocating treatment resources.”
“(OECD) nations commonly spend between 5 percent and 14 percent of their health dollar on mental health care […] this implies that OECD countries devote between 0.3 percent and 1.1 percent of their national incomes to treatment of mental disorders.2 […] It is important to note that the patterns of spending on mental health care are different from those observed in international comparisons of health care spending. […] there is more variation in mental health spending levels across nations than there is for health care. […] The commitment by OECD countries to promote community-based treatment and inclusion of people with mental disorders into the mainstream of society while also accepting the responsibility for public protection creates a policy tension that […] shapes public mental health spending. […] there have been notable reductions in the inpatient psychiatric capacity in virtually all OECD countries [since the 1960s]. […] [There is] growing variation in how each society sees the function of the psychiatric hospital. […] in France and the United States, two countries that spend similar shares of GDP on mental health care, France allocates roughly 80 percent of mental health spending on inpatient care (Verdoux 2007) and the United States about 36 percent (Mark et al. 2007). […] Mental health spending in the US as a share of total health spending has declined from nearly 11 percent in the 1970s to 6.2 percent in 2003”
“Cost-effectiveness evaluations of evidence-based treatments for depression suggest that they produce gains in Quality Adjusted Life Years (QALYs) at levels comparable to other medical treatments […] rates of treatment for the mental disorders, with some of the strongest effectiveness of care evidence, such as depression and anxiety disorders, are quite low […] mental health services are frequently funded and/or supplied by several bureaucratic departments all operating under fixed budgets. […] There may therefore exist opportunities for cost shifting. That is, strict rationing of mental health services may be seen as an opportunity to expand monies available for general medical care while allowing people with mental disorders to obtain care from the social care sector. […] recently the creation of combined trusts (mental health and social care) has tried to use organizational design to blunt incentives to cost shift created by fragmentation in financing.”
Public sector health care financing:
“In general, it can be shown to be efficient for the consumer’s cost-share to be lower when he or she incurs large health care costs, but higher with relatively low costs. This can be accomplished via a plan with an initial deductible (under which consumers are responsible for 100 percent of their health care costs in a given period of time, up to the limit of the deductible), followed by one or more intervals of partial cost sharing, perhaps up to some maximum (a “stop-loss” provision) beyond which the plan pays 100 percent of any additional costs. […a related observation from another chapter: “In the pure theory of insurance, Arrow (1963) showed that, with proportional administrative loading, optimal coverage is full coverage above a deductible” – this result is called ‘Arrow’s theorem of the deductible’ and lots of people have written stuff about that one…] […] The theoretical analysis of the efficient degree of consumer cost-sharing has focused on the trade-off between the gain from more complete insurance against the associated inefficiency of over-utilization, but in practice, the appropriate degree of cost-sharing should also depend on certain other factors, in particular, on the relative costs of administering plans with different degrees of cost-sharing. […] Patient cost-sharing as a means of controlling health services utilization and aggregate health care costs is an example of what in the health economics literature is called “demand-side incentives” (that is, incentives that affect the patients who use health services). A prominent theme in the health economics literature in recent years has been that services utilization and total health care spending in a given population also depend strongly on the incentives of the providers of health services who treat the patients and advise them on what services they should utilize (“supply-side incentives”). If utilization can be effectively controlled through supply-side incentives, the case for high user fees is less strong”
“In comparing the equity and efficiency properties of the social insurance model of funding health care with the general-revenue financing model, the first point that should be made is that, for those populations for which membership in the public plan is compulsory (which may be the entire population), the contributions that the insured are required to pay toward funding the plan […] are equivalent to a tax. […] This equivalence has two important consequences. First, it means that the equity and efficiency properties of the social insurance system can only meaningfully be analyzed as part of the overall system of raising government revenue for all purposes: As previously argued, it is not meaningful to separately analyze the equity and efficiency properties of the revenue raised for some particular purpose. In this sense, therefore, social insurance funding of health care involves the same issues as those arising when funding is from general revenue. Second, once it is recognized that the contributions paid into the social insurance system is only one of many sources of government revenue, it becomes clear that it is not in general efficient to match the revenues raised from this source with a particular kind of spending (health care). If one wants to explain why many countries still try, at least to some extent, to match health care expenditures under their public plans to specific types of revenue (such as social insurance contributions), one must appeal to other factors […], not economic efficiency or equity.”
Health care cost growth:
“The dominant factor contributing to rising spending is the development and diffusion of new medical technology […] The conclusion that technology is a primary driver of cost growth is based on a wide body of literature […] Alarm over health care cost growth is typically centered on the rise in health care expenditures at the population level. Expenditures reflect both unit costs (prices) and utilization patterns (quantities). Some interventions may reduce unit prices, but, because of the utilization response, may not reduce expenditures. […] This helps explain why innovative technology often raises expenditures in the health care sector, even though it is perceived to lower cost in other industries. For example, as technology reduced unit cost in the information technology sector, spending growth in the overall sector increased 26 percent annually from 1982 to 1996 (Haimowitz 1997). Expenditures are also not limited to any particular disease. Individuals cured of one disease inevitably get another. It is possible that reductions in expenditures on one disease may increase overall spending if competing conditions are more expensive. Finally, cost growth at the population level may not reflect trends in cost growth for particular services. Efforts to constrain spending in one area may simply generate greater spending in other areas. For example, in the United States, as inpatient spending growth slowed following implementation of prospective payment systems (PPS), outpatient spending soared (Miller and Sulvetta 1992).”
“In assessing cost containment strategies it is crucial to distinguish between those interventions that affect the trajectory of cost growth versus those that affect the level. […] This distinction is important in assessing the ability of systems which are more conservative in their adoption of new technology to control cost growth. A system that adopts new technology more slowly than another system may have the same rate of cost growth if the baseline level of costs is lower. For example, if a given country has a base spending rate that is 20 percent below that of another country, it will experience the same cost growth if it utilizes a new technology 20 percent less frequently.”
“decreased utilization associated with cost sharing does not disproportionately impact necessary care, as proponents of cost sharing would hope and standard economic theory would predict. Patients apparently reduce use of appropriate and inappropriate care in similar proportions […] Consistent with this view, many recent studies suggest patients reduce use of prescription drugs when faced with modestly higher copayments […] cost sharing has been demonstrated to have disproportionately negative effects on the quality and delivery of health care among low-income populations […] Adverse events, lower adherence, and decreased management of illness are associated with increased patient cost sharing […] the longer term consequences on health associated with lower utilization of high value services have yet to be fully evaluated. […] Because cost sharing is associated with lower costs, many health care payers view cost sharing as a means to reduce growth in health care (Chernew 2004). Yet there is virtually no evidence examining the impact of cost sharing on cost growth. It is possible higher cost sharing lowers spending, but does not alter the trajectory of spending growth. […] Although the debate about the relationship between physician and hospital supply and spending and costs will continue, it is important to note that much of this literature is related to the level of costs, not the trajectory. The limited evidence on cost growth suggests that even in the most successful settings […] the share of GDP devoted to health care still rises, albeit at a somewhat slower rate than in other markets.”
“Many observers have noted that the health care expenditures of individuals with chronic disease are much greater than expenditures of individuals without such disease […] The share of obese Medicare beneficiaries increased from 9.4 percent in 1987 to 22.5 percent in 2002 […] For this reason, some believe that initiatives aimed at improving health will save money. […] [However] most preventive services are not cost saving from a societal perspective. […] In general […] evidence of […] savings associated with disease management and pay for performance is weak. […] it is likely too optimistic to assume that better health will substantially lower the trajectory of health care spending. Health care costs were growing rapidly well before the epidemic of obesity and health care cost growth among the healthy persists. […] Because healthier beneficiaries live longer, and may demand a range of quality of life improving services, it would not be prudent to assume that better health, as desirable as it is, will substantially slow cost growth.”
I’ve read about and blogged this topic before, but this is the first academic text on the topic I’ve read. I liked the book and gave it four stars on goodreads. It’s a typical Springer publication, i.e. it’s a collection of relevant studies/papers published on the topic; there are fourteen papers/chapters included in the book. Given the nature of the book there’s some overlap across chapters, but that’s to be expected and it doesn’t really matter much. The book was published in 2011 so it’s reasonably up to date even though things are happening fast in this area.
Some of the authors of the studies included in the book assume that the reader possesses a level of knowledge about microbiology which goes way beyond what you’d get from reading an intro text like Hardy, and although I’ve also previously browsed one of the books you’d actually need to have read in order to understand the details (Brooks, Butel & Morse), I’ve of course long ago forgotten much of that stuff and so occasionally felt a bit lost while reading the book. There’s some good stuff in there though, and many of the chapters I did not find that hard to read although some details eluded me. It’s my impression that you probably will not get much out of the book if you’ve never read a microbiology text before (I actually feel a bit sad having to write that as the topics covered are very important in terms of future public health, and so in a way I’d really wish as many people as possible actually read this book, or at the very least familiarized themselves in some other way with the problems covered in the book).
“This book serves a twin purpose in helping to construct a more informed evidence base for coherent policy making while, at the same time, providing practical advice for health professionals in the prevention and control of HAIs.”
The quote above is from the preface of the book. The papers included in the book cover a wide variety of topics; one chapter deals with the ‘total scale’ of the problem of healthcare associated infections (HAIs), another chapter deals with (among other things) how antibiotic treatment regimes and the development of resistant strains in the community and/or health care institutions are associated, one chapter deals with the epidemiology of drug resistant strains of bacteria and how to properly categorize drug resistance (which can take on many forms), and quite a few chapters focus on specific HAIs (C. difficile, MRSA, VRE, ESBL-producing bacteria, CRE, Acinetobacter baumannii, and MDR (multi-drug resistant) Pseudomonas all get a chapter each). Many intervention studies are covered and the focus is not just on identifying the extent of the problem but also on finding ways to counter the problems; one chapter deals specifically with antibiotic stewardship, which is one of the main ways to try to stop the spread of antibiotic resistance, but many other chapters cover that topic as well in the specific setting. Another key strategic element in any intervention strategy, infection control measures (hand hygiene, patient isolation, etc.), is likewise covered in many of the chapters, and as the studies included have a very ‘evidence-based medicine approach’ to these matters important but potentially embarrasing problems like compliance problems on the part of health care providers [it’s harder to convince doctors to wash their hands than it is to convince nurses..] are not overlooked. The book is not US-centric; countless international studies are included, and a specific chapter is reserved to dealing with MDR infections in low-resource health care settings. The institutional setting is important and is covered in a few chapters, and included in that discussion are observations related to how things like reimbursement methodology may impact health care provider behaviours and how faulty incentive structures on the institutional level may aggravate the problems with resistance development e.g. by failing to address collective action problems in this area.
As might be inferred from the comments above, there’s way too much stuff in there for it to make sense for me to cover it all here. However I have added some observations from the book below, emphasizing some important points and observations along the way and adding a few comments here and there.
“What is required is tackling of the problem at its root cause, namely the gross over use of antibiotics.” […let’s just start out with that one, so that people will not falsely assume that this aspect is not covered in the book.]
“In broad terms, there are two means by which patients can develop multi-resistant infections—they can either develop their own resistant pathogen, or they can acquire someone else’s strain.
Emergence of new resistant pathogens is directly related to antimicrobial selection pressure either via the mutation of new resistance genes or the alteration of bacterial ecology (e.g. in the gut) that facilitates the transfer of naturally occurring or emergent resistance genes from one bacterial class to another […] antibiotic use in food production can have the same effect as direct human antibiotic misuse, since it can select for both resistant pathogens (e.g. fluoroquinolone-resistant Campylobacter in chicken meat) or resistance genes such that food consumption results in either direct fecal colonisation or acquisition of resistance genes by routine gut flora [3,4]. Antibiotic stewardship is therefore not simply a hospital issue.” […]
“The global burden of healthcare associated infections (HAI) is currently unknown, despite international efforts to fill this gap in our knowledge. Where the size of the burden of HAI has been quantified, the greatest impact is in those countries with least resources to measure and manage them. […] 3.5–10.5% of hospitalised patients in industrialised countries may experience HAI (E.C.D.C. 2008), while greater than 25% of hospitalised patients in developing world nations may be affected (W.H.O. 2005). […] While in 2000, 70 countries did not screen donated blood for HIV, hepatitis B or hepatitis C, currently the risk of bacterial infection from transfusion is greater than the risk of acquiring these viruses. Reuse of contaminated needles or syringes during injections in limited resource settings poses a major threat for transmission of infection, accounting for an estimated 21 million hepatitis B infections, 2 million hepatitis C infections and over 95,000 HIV infections. […] Of the 8.8 million deaths in children under the age of 5 years, infectious diseases account for 5.5 million (63%) […] clinicians in developing countries tend to diagnose and prescribe medication empirically. People with undetected resistance then receive antibiotics to which their isolate is not susceptible. For example, one study in western Kenya found that more than half of the patients treated empirically for bacterial diarrhea were given ineffective antibiotics. Among patients with shigella, this number exceeded 80% (Shapiro et al. 2001). […] In developing countries, antibiotics are a scarce resource, and most clinics and hospitals can barely afford common first-line agents, much less second and third-line alternatives […] variation in prices of antibiotics is considerable. The wholesale price differential between amoxicillin and co-amoxiclav, for example, is on the order of a factor of 20 (Forster 2010). This means that where resistant bacteria necessitate the use of co-amoxiclav, only 5% of the patients can be treated for the same budget as with amoxicillin. […] In coastal Kenya, resistance to chloramphenicol, amoxicillin, cotrimoxazole, and gentamicin in Gram-negative sepsis is common, and susceptibility remains only to two rarely used drugs, ciprofloxacin and cefotaxime. The cost of treating a 15 kg child with sepsis would be $0.38–2.30 for gentamicin and chloramphenicol versus $73–108 for the effective drugs […] In Thailand, only 9% of antibiotics administered in a teaching hospital were appropriate to the patient’s condition, and 36% of patients were given antibiotics without evidence of an infection […]
“The underused vaccines that could have the biggest effect on antibiotic use in hospitals are against Streptococcus pneumoniae and Haemophilus influenzae type b. To these should be added one of the new vaccines against Rotavirus, the main cause of dehydrating diarrhea, which kills 400,000–500,000 infants and children in developing countries annually. Even though Rotavirus is, in fact, a virus, reducing its incidence will reduce antibiotic use. The most appropriate treatment for rotavirus and other causes of watery diarrhea is oral rehydration therapy, but since antibiotics are used inappropriately in many cases, reducing the number of cases will reduce antibiotic use.” [..vaccines against viruses may help decrease the number of bacteria resistant to antibiotics – yep, this stuff is complicated..]
“HAI are recognised as among the most common adverse outcomes from hospitalisation in the US; approximately 1.7 million HAI are reported across the US each year, which are associated with around 99,000 deaths per year. Around a third of HAI are urinary tract infections, one fifth are surgical site infections, 15% are pneumonia and 14% are bloodstream infections (C.D.C. 2010).” […] Estimates in Europe are that approximately 4.1 million patients per year experience HAI, and that attributable deaths are of the order of 37,000 per year (E.C.D.C. 2005–2010).” [These estimates are somewhat uncertain and I’m not sure how much you should read into the fact that they differ in the way that they do, with fewer but more lethal HAIs in the US. Before you read a lot into it, you should certainly note that there is huge regional variation in the data here.] […]
“Surgical prophylaxis is a common area of overuse as shown in many publications. Measured by total DDDs [defined daily doses], it can amount to around one third of a hospital’s total antibiotic use. This illustrates the potential for ecological damage although surgeons often ask whether 24 h or even single dose prophylaxis can really select for resistance. The simple answer is yes, but of course much of the problem is extension of prophylaxis beyond the perioperative period, often for several days in critical patients, perhaps until all lines and drains are removed. There is no evidence base in favour of such practices.” […]
“Since 2002, increasing rates of CDI [Clostridium difficile Infection] with a more severe course, higher mortality (from 4.7 to 13.8%) and more complications (from 7.1 to 18.2%) have been reported in Canada […] Of all patients who develop CDI in the hospital setting, approximately 80–90% have used antibiotics in the previous 3 months. […] MRSA can survive for months in hospital environment […] and it can be isolated on clinical equipment, as well as on general surfaces especially close to patient’s area, such as curtains, beds, lockers and over-bed tables […] Before contact precautions are implemented, MRSA carriers may have already contaminated their environment with MRSA. […] Cross-transmission between patients may occur via HCWs [health care workers’] hands after touching contaminated environmental surfaces […] One study showed that 10% of HCWs fingertips were contaminated with MRSA after contact with MRSA positive patient’s environment […] There is now reasonable evidence that rates of MRSA, C.difficile, VRE and multi resistant Gram-negatives can be reversed by modulating use of key agents such as cephalosporins and quinolones […] The real problem for the future, of course, is how to do this without “squeezing the balloon”, transferring the resistance selection pressure to other classes of agents. This highlights another paradox, that of current antibiotic policies which tend to lead to a lack of diversity of use of different classes of antibiotics. Diversity of use is probably one of the best strategies to delay emergence of resistance, although a lack of choice of truly different drug classes makes its implementation problematic. Moreover, the holy grail, and the most difficult thing is to achieve total reduction in prescribing while not compromising patient outcomes. Again, this isn’t something current strategies are good at achieving.” […]
“ESBL-producing bacteria are not only present in hospitals from endemic nosocomial sources but are introduced into the hospital from other health care facilities (particularly high rates occur in care of the elderly homes […] but also from individuals coming
from the community (Ben-Ami et al. 2006). […] This community carriage is an important facet of ESBL control [Again, what happens outside the hospitals matter a great deal…] […]
“Carbapenems have the broadest antimicrobial spectrum of any beta-lactam antibiotic and are frequently used as first-line agents for the treatment of severe infections caused by multiresistant Gram-negative bacteria […] The emergence and spread of carbapenem-resistant Enterobacteriaceae (CRE) are therefore a major concern for patient safety and public health. Infections due to CRE may lead to increased likelihood of treatment failure and growing reliance on third-line agents and combination therapy, with doubtful therapeutic efficacy and increased potential for toxic side-effects […] It also increases the cost of treatment […] CRE differ from most other multidrug-resistant bacterial pathogens in that there is no reliable treatment available (Schwaber and Carmeli 2008). […] two cases of panresistant CRE were recently reported from a hospital in New York […panresistant strains are basically untreatable, US.] […] Patients with CRE infection are at high risk of treatment failure and adverse outcomes, including increased mortality and morbidity, longer length of hospital stay, and higher treatment costs when compared to infections caused by susceptible strains. Several studies have reported high percentages of crude in-hospital mortality— some over 50%—among patients infected with CRE […] the magnitude of the excess mortality directly attributable to CRE is difficult to quantify […] Overall, uncertainties persist in individual patient-level analyses regarding which prior antibiotic exposures are most important as risk factors for acquisition of, transmission of and infection with CRE. Similarly, ecologic studies using aggregate datalevel analyses do not show a clear-cut picture.” […]
“Antibiotic policies are crucial but they cannot be effective without active infection control program[s]. A hospital with a strong infection control program without an antibiotic stewardship component would tackle transmission of multi-resistant organisms such as VRE but would not prevent individual patients from getting colonised or infected with resistant microbes. On the other hand, strong antibiotic stewardship would be expected to control the menace of multi-resistant organism but in absence of an infection control program, transmission of organisms (even if not multiply resistant) would be easy and would adversely affect patient care.” […]
“A retrospective, risk-adjusted, cohort study of 80 patients with Acinetobacter bacteraemia conducted in Korea demonstrated that those infected with imipenemresistant strains had a significantly higher 30-day cumulative mortality rate than those infected with imipenem-susceptible strains (57.5% versus 27.5%) […] This was mainly due to a higher rate of inappropriate antimicrobial therapy. […] Carbapenems are the mainstay of treatment for severe infections. However, carbapenem-resistant A. baumannii strains have emerged worldwide. […] A considerable proportion of multi-drug resistant A. baumannii strains are susceptible only to polymyxins, which prompted the use of an old antibiotic in recent years. […] Polymyxins are polypeptide antibiotics that act as detergents on the bacterial cell wall. They were introduced in 1940 but they were abandoned in the 1980s due to the occurrence of nephrotoxicity and neurotoxicity. […] Reported nephrotoxicity ranges between 8 and 36%. […] Reported neurotoxicity ranges between 7 and 29%, with oral and perioral paresthesias, visual disturbances and polyneuropathy […] [So basically what has happened is that doctors have been forced to restart using drugs they threw away 30 years ago because those drugs caused kidney failure and severe nerve damage. These old drugs are currently the only drugs that work against some MDR infections, and no new drugs are even close to being developed at this point]. […]
P. aeruginosa is the second most common cause of health-care associated pneumonia, of hospital-acquired pneumonia and of ventilator-associated pneumonia (VAP). It is also reported as the cause of 9% of hospital-acquired urinary tract infections (UTIs). […] It is estimated that the rate of colonization and/or infection by MDR P. aeruginosa is 0.5 episodes/1,000 patient-days in the general ward and 29.9 to 36.7/100 patients in the ICU (Agodi et al. 2007; Peňa et al. 2009). […] infections by MDR P. aeruginosa have a significant impact on mortality. A retrospective study of our group in non-neutropenic hosts in the general ward disclosed 22.2% mortality of infections by MDR P. aeruginosa compared to 0% of infections by susceptible isolates […]. For ICU infections caused by MDR P. aeruginosa mortality ranges between 22% and 77%; this ranges between 12% and 23% when ICU infections are caused by susceptible isolates (Shorr 2009).” […]
“Antibiotic effectiveness can be viewed as a shared resource in which current use depletes future value and imposes costs on society in the form of longer hospitalization, higher mortality rates, and the diversion of resources into the provision of newer and more expensive drugs. In making treatment decisions, prescribers should weigh the favorable effects of applying antibiotics to improve a patient’s health against the negative consequences for the public and future drug effectiveness (Laxminarayan 2003b). However, clinicians usually ignore the future therapeutic risks to society associated with antibiotic use and instead focus on the direct benefits of antibiotic treatment to their patients. […] In the absence of a good pipeline of new drugs, it is the balance between the individual patient and society as a whole, otherwise known as the ecological perspective, that has to be clearly established and debated. We need to get clever, quickly. […] In the long term, new antibiotics are needed […] However, as a gap of 10–15 years has been identified (European Centre for Disease Prevention and Control and European Medicines Agency 2009), immediate action is needed to conserve the power of the available arsenal.”
My brief goodreads review of the book, which I read yesterday (I gave it two stars):
“Closer to one star than three. Multiple spelling errors, no inline citations, questionable coverage of the literature, the authors frequently repeat themselves. Not recommended.”
This is a poor book, and I was close to giving it one star. I was considering modifying the review above today, as I got started blogging the book; when I wrote the review I’d assumed they’d just put the sources in the back of the book because the idea never crossed my mind that somone might decide to write a book like this without providing any sources (…I mean, that would be insane – and they’re psychiatry professors..). See more details about this aspect below. Normally spelling errors don’t bother me that much, but I spotted four of them in the first 50 pages alone (plus two words that were not separated by a space) and that’s just completely unacceptable; if you make that many mistakes which make it into the final publication then you don’t care enough about your book. I have had good experiences with the Wiley-Blackwell publications I’ve read, but the fact that they allowed this book through is a strong point against them.
The book is not heavy on data, but they do talk quite a bit about various findings from the literature. The problem is that you have to take everything they talk about on faith, and I’m just not that kind of person. No specific studies are mentioned (sentences like, ‘we base this on X&Y’s publication (20XX)’ are completely absent), the number of studies used to establish the conclusions are unknown, effect sizes are rarely talked about except in a very general sense (and you’ve no idea where the numbers are coming from so often such estimates are not actually helpful), limitations of the studies on which the conclusions are based are not covered in any amount of detail. I have the distinct impression that the authors are really bad at math, due to coverage of some specific conditions and the reported risks associated with them, but I won’t go into that in detail here as I consider those specific findings uninteresting (and if they knew basic probability theory, so presumably would the authors). Some of the findings they talk about I’m sure are correct as they are well known and -established in the literature, but there are other claims in the book which contradict what has been found in studies I’ve read in the past on the topic, so I certainly see no great need to just take it for granted that the authors are right. The fact that they won’t go into the details of which studies they are basing their conclusions on and so on frankly just make them look bad, especially considering the overall data quality one has to work with in this area and the limitations imposed by this data quality problem – basically what they’ve ended up with is a book filled with postulates. They don’t even include a literature overview in the final pages of the book, like say in an appendix – the book has an index, but no literature list, so basically they just decided to publish a book in which they don’t even tell you which sources they’re basing their conclusions on. From the point of view of someone who’d only consider evidence which you can test/verify to be valid, this book would be completely worthless as they may just have made it all up. I’m sure they haven’t, but this approach really stinks. One might argue that given that the book’s main focus is on the clinical perspective (how to deal with patients), not epidemiology, lack of sourcing may not be that big of a deal – one major problem here, however, is that writing textbooks this way certainly isn’t the way to promote evidence-based approaches in the future. Given the problematic history of mental health care, this is sure as hell not an area where you get points in my book for not dealing with the data in some detail and dealing with questions related e.g. to how we know what we know, and how and why those conclusions we’ve drawn may be wrong.
As usual when reading this kind of material, I was mildly annoyed by the fact that the authors take for granted the view that all suicides should be prevented. There’s such a thing as a good suicide. I’m not exactly surprised that this notion is absent from the book, but I am still a bit annoyed.
Some observations from the book below:
“based on available data, globally suicide is believed to account for an average of 10–15 deaths for every 100 000 persons each year, and for each completed suicide there are believed to be up to 20 failed suicide attempts.” [believed by whom, you ask? I have no idea] […] “in the USA and many other countries (particularly in wealthy or developed states), suicide continues to be one of the three leading causes of death in young people between the ages of 15 and 24.” […]
“The majority of studies on risk factors for suicide have been conducted in developed countries using the psychological autopsy methodology. Psychological autopsy studies in the West have consistently demonstrated strong associations between suicide and mental disorder, reporting that 90% of people who die by suicide have one or more diagnosable mental illness […] Using the same type of psychological autopsy methodology, studies conducted in developing countries have not demonstrated as robust an association between suicide and mental disorder as purported in the West.” […] Other identified significant risk factors include current or past suicide behaviour, availability of and access to lethal means, exposure to trauma or abuse, severe psychosocial stressors, interpersonal loss, family history of suicide and mental disorder, alcohol and drug misuse, lack of significant relationships and social isolation, chronic physical illness, disabling pain, lack of internal coping abilities, and lack of access to health and social services and supports.” […]
“In North America, studies indicate that the majority (up to two-thirds) of those who die by suicide have had contact with a health care professional for various physical and emotional complaints in the month before their death. […] individuals at risk are often never identified” […]
“In many parts of the world mental illness fails to be recognized as a legitimate health disorder and people with mental illness continue to be misunderstood as weak, lazy, attention seeking, crazy or stupid. Fear of being thought of or being labelled as mentally ill and fear of the ridicule, discrimination, social exclusion, loss of friends, loss of employment or loss of opportunity that may result likely contributes to the secrecy and silence that keeps people from reaching out and receiving help. […] Regardless of the reasons, many of those who die by suicide do not seek help and do not inform others of their plans. Moreover, some who are contemplating suicide or who are committed to completing suicide may not reveal their thoughts or plans even when directly asked.” […]
“Protective factors are those factors and experiences that are believed to reduce the risk for suicide and suicide behaviours and increase a person’s ability to cope with and manage stress and face life’s challenges. […] Protective factors are less well established than are risk factors and the scientific data to support their notation is generally not very strong. […] In the opinion of the authors of this manual, these factors have not been adequately demonstrated to prevent suicide. Many of them are simply negative restatements of known risk factors” […]
“A number of risk factors have been strongly linked to both suicide and suicide behaviours. Distal risk factors can be understood as predisposing factors that may increase a person’s vulnerability to suicide. […] Proximal risk factors include factors which augment current vulnerability for suicide as well as factors which may precipitate or trigger suicide or suicide behaviours.” […]
“In North America, Western Europe (including the UK) and most other countries for which data are available, suicide rates generally increase with increasing age. Projected on top of this trend are three peaks representing periods of increased risk: adolescence/young adulthood, middle age and old age. In general, suicide rates rise sharply in late adolescence and early adulthood, before leveling off through early midlife, then rising again in middle age and then again after age 70. In developed countries the highest suicide rates are found in the elderly. […] In general, suicide behaviours in the elderly are more likely to be lethal as compared to younger age groups. […] In most countries, suicide deaths occur more frequently in men than in women. In the United States, suicide rates are four times higher in men. […] In many Asian countries, including India for example, the rates of suicide death, particularly in rural areas, are almost equal for men and women. In China, female suicide rates are 25% higher than male suicide rates.” [Considering the kind of data likely to be available, I don’t trust this finding very much but I thought the observation was still interesting. Cultural factors causing differences in reported rates is one of many potential drivers here which should at least be considered a potentially contributing factor (e.g. greater shame associated with the suicide of a son (/an heir) than a daughter).] […]
“Suicidal ideation refers to thoughts, fantasies, ruminations and preoccupations about death, self-harm and self-inflicted death. Suicidal ideation can be both ‘passive’ and ‘active’. A person who is actively thinking about killing themselves and is having thoughts of initiating a suicide process that will lead to their death is experiencing active suicide ideation. A person who has thoughts about wanting to ‘disappear’, wishing they could just go to sleep and never wake up, or thoughts that they would rather not be alive, but who does not have thoughts of actively initiating a suicide process that would lead to their death, is experiencing passive suicide ideation. Active suicide ideation confers greater risk than passive suicide ideation and the greater the magnitude and persistence of the suicidal thoughts, the higher the risk for eventual suicide. […] Suicide ideation occurs along a continuum of frequency (fleeting to persistent), intensity (manageable to intolerable or uncontrollable), duration (chronic to acute) and persistence (intermittent to persistent), and can be associated with different levels of intent (no wish or desire to die to strong desire to die) as well as motivation.” […]
“In general, men tend to choose more violent means and women less violent means. Globally, hanging, firearms and poisoning are the most common lethal means for suicide – hanging being the most common in both genders. […] In developing countries, particularly in agricultural areas, ingestion of pesticides is the most common method of suicide. […] an estimated 30% of suicide deaths globally are attributable to the ingestion of pesticide.” […]
“Suicide attempts are 10–20 times more prevalent than completed suicides and up to 50% of those who die by suicide have made at least one previous attempt. These figures are likely underestimates of the true prevalence of suicide attempts as many attempts likely go undetected […] past suicide attempts are a major risk factor for suicide death. Up to one-fifth of people who attempt suicide will reattempt (most within a year) and reattempts are often associated with more lethal means, lower chance of rescue and survival, and higher likelihood of serious medical consequences.” […]
“the suicide rate among single adults is twice that of married adults, and rates among those who are divorced, separated or widowed are four to five times higher than those for married individuals.” […]
“Identification of ‘suicide risk factors’ does not allow a completely accurate prediction of when or if a specific individual will in fact die by suicide. Thus, suicide assessment scales that rely on the cataloguing of patient risk factors, although a useful clinical aid in the assessment of suicide risk, cannot by themselves be used successfully to predict who will commit suicide. […] It is the weighting and confluence of specific suicide risk factors rather than the number of risk factors present that must be considered in determining risk” […]
“Suicidal thoughts are relatively common amongst adolescents. […] Suicidal ideation in and of itself does not indicate psychopathology or need for intervention in teenagers. In children, however, expression of suicidal ideation warrants serious attention. Young children may not appreciate the ‘finality’ of death and therefore may unwittingly commit suicide, not realizing that they will not come back. […] Many […] warning signs are nonspecific and ambiguous, and taken separately may be just a normal part of growing up. On the other hand, if these warning signs represent a clear change in a young person’s personality, behaviour or functioning they may be signals of a serious underlying problem.” […]
“Although many universal and targeted interventions for suicide prevention have been implemented in countries and communities around the world, few have been empirically studied and evaluated in either developing or developed countries. Of those that have been evaluated, few have been shown to impact suicide rates. […] A number of interventions popularly considered to be very effective in reducing suicide rates, including suicide telephone hotlines and school-based suicide-education programmes, have shown little or no substantial positive effect on decreasing suicide rates.” […]
“suicide does not occur in a vacuum. Once the individual ends his or her life, there are clinicians, family members, friends and communities that may require support. […] Experience of shock and disbelief is normal in the first few hours or days following the loss of a loved one. Once the initial shock of the loss has dissipated, most people slowly begin the process of recognizing and accepting the loss. Feelings of intense sadness, anger, hopelessness, helplessness and guilt often wax and wane throughout the day, with periods of extreme intensity becoming less overwhelming and less persistent over time. Thoughts about not wanting to be alive anymore, that life is not worth living, and of wanting to reunite with the deceased are not uncommon […] After six months to one year, the pain associated with the grief generally becomes less intrusive, less intense and less persistent. Although there may be reexperiencing of intense grief when confronted with reminders of the loss, and periods of feeling sad, angry and empty, these grief experiences no longer prevent the person for moving on with their life and doing what they need to do, such as returning to work, returning to school, reconnecting in their personal relationships, participating in social and recreational activities, and caring for their families and children.” […]
“Often the most meaningful way to help someone who has experienced loss is to simply listen to them. […] Acknowledge and validate their feelings. […] Do not tell them not to cry or get angry. […] Do not tell them how you think they should feel. […] Give them space and time to talk about their loss. […] Assist problem-solving around practical issues and concerns.”
i. Here are my thoughts on the upcoming Danish election:
(And here are my thoughts on voting in general.)
“Office in the morning. This morning my dining-room was finished with green serge hanging and gilt leather, which is very handsome.
This night I sat up late to make up my accounts ready against to-morrow for my Lord. I found him to be above 80l. in my debt, which is a good sight, and I bless God for it.”
Here’s a background article. I learned about the existence of this diary through Bryson. I should point out that navigation is easier at the site where the diary is located; you can get brief explanations of key terms simply by hovering over the linked words, and so you often don’t really need to click any links.
I don’t actually think the lecture is all that great, but I watched it anyway and I figured I might as well blog it.
iv. I got a draw against a ~2050 Elo opponent last Monday – you can watch the game here, I was white. This was actually I game I was reasonably satisfied with – my opponent was the one who offered the draw, which was in itself a small victory (I graciously accepted). The draw was not a result of a blunder in a time scramble or something like that; I played semi-accurate moves and so did my opponent, and so we ended up in a dead drawn position. I didn’t exactly play ambitiously in this game but with opponents like this most people will probably consider a draw a satisfactory outcome (my opponent was in the top 25 in the last Danish Championship), and keeping it simple seemed the best strategy, especially as I got completely crushed in the first game I played against him. Today I won a bullet game against a much stronger opponent, but I often do that and those games don’t really count nearly as much as these; games like this one are serious games, and in these kinds of time controls it seems I can still play along with some of the best players in the club. This is nice. After 8 games of the tournament my performance rating is now slightly above 1950.
Okay – on to you guys: What have you been up to? Have you read anything during the last couple of weeks that I ought to read as well? Have you seen an amazing TV series I’ve never heard about? A good online lecture? Found an amazing website?
This is probably also a good place for a new reader to step forward and tell me a little about yourself. I like to know at least a little bit about who’s reading along here.
I’ve read roughly two-thirds of the book by now – I like it, pretty much every page contains new stuff which I didn’t know anything about and it’s quite interesting. Some more stuff from the book below, as well as some comments. As always you can click images to view them in a higher resolution.
“Obesity increases the incidence of many cancers, such as breast, prostate, and colon cancer. However, endometrial cancer is the mostly tightly linked with obesity. Estimates suggest that nearly 40 % of cases of endometrial cancer can be attributed to obesity. […] Obese women have a threefold higher risk of developing endometrial cancer than lean women . […] every increase in BMI of 5 kg/m^2 increases a woman’s risk of the developing of endometrial cancer by approximately 60 % (relative risk, 1.59; 95 % confidence interval [CI], 1.50–1.68) . Endometrial cancer in obese women is more likely to have lower risk features such as endometrioid histology and low/intermediate grade. […] An elevated waist-to-hip ratio, reflecting a preferential deposition of adipose in the abdomen, increases the risk of developing endometrial cancer by 220 % . […] Among the population as a whole, obesity increases the risk of death from endometrial cancer. In a study of 900,000 prospectively followed healthy patients, 57,145 individuals died of cancer over 16 years. The relative risk of death from endometrial cancer in this population was 6.25 for women with a BMI >40 and 2.77 with a BMI between 35 and 39 .”
“As a component of adipose tissue in obese individuals, immune cells, and specifically macrophages, secrete a variety of growth, survival, and proangiogenic factors, as well as bioactive molecules that enable tumor growth and contribute to the remodeling of the tumor microenvironment to facilitate metastases. Furthermore, reactive oxygen and nitrogen species released by activated macrophages are mutagenic and accelerate oncogenic mutations that contribute to cancer risk and progression [30,33]. So, not only does inflamed visceral adipose tissue provide an ideal milieu for the growth of metastatic endometrial cancer but proinflammatory factors also secreted by infiltrating adipose immune cells mediate systemic effects on tumor progression at distant sites, including the endometrium.”
“Taken together, current evidence suggests that through a variety of mechanisms, weight loss and physical activity reduce proproliferative signaling and counteract environmental conditions that support the initiation and progression of endometrial cancer.” […as the figure above illustrates, endometrial cancer is far from the only cancer type where behavioral factors play a large role – US.]
“Multiple epidemiologic studies demonstrate that women who use combination estrogen and progesterone oral contraceptives (OCP) decrease their risk of endometrial cancer by 50 % [78–80]. While there is no data to support a decreased efficacy in endometrial cancer protection in obese women, there are studies that suggest that obese women have a slightly decreased contraceptive efficacy compared to thin women .”
“At the cellular level, overweight and obesity are characterized by the increase in number and size of adipocytes. A lean adult has 35 million adipocytes, each containing 0.4–0.6 μg of triglycerides, whereas an extremely obese person has 125 million adipocytes, each containing 0.8–1.2 μg of triglycerides . Traditionally, adipocytes have been viewed solely as energy depots, but after the discovery of leptin in 1994 and extensive research in the field in the last decades, it has been established that the adipose tissue is an active endocrine organ. The adipocyte is a major source of secreted proteins …”
A really important point which has been repeated, explicitly or implicitly, again and again in this book, and which I thought I should emphasize here using ‘non-textbook language’, is that fat cells aren’t just inactive cells that ‘hang around’ doing nothing. They do a lot of stuff while they’re ‘hanging around’. And when you have a lot of them hanging around in the wrong places, many of the things they’re doing are really quite bad for you. As you’ve probably already inferred, the book goes into a lot more detail about mechanisms and how these things work in detail (to the extent that we even know what’s going on in the first place), but if you don’t remember much from the posts about this book this is at least, I think, one of the key points you should try to remember; adipose tissues are active tissues and they – and the secretions derived from them – play a major role in a variety of contexts, including some contexts which are highly relevant to e.g. cancer pathogenesis. There’s still a lot we don’t know because this stuff is complicated; I link to leptin above, which has been intensively studied and is also relatively intensively covered e.g. in chapter 5 of the book, and the wiki link about adipokines mentions a few others – but I should note here that there are more than 50 different types of adipokines that we know of at this point. Different types of cancer start out in different types of tissues and a diverse set of mechanisms are involved in the disease processes, and so it seems likely that different types of adipokines play different roles in different types of cancers. There are still a lot of things which are not clear, but as they put it in the conclusion of chapter 5: “There are strong epidemiological, molecular, and clinical evidences showing associations between adipokines and the incidence and clinical outcome of cancer.” It should be noted that work on this stuff is not limited to work on just ‘human data’ – lab-work using rodents, which is covered in chapter 6 of the book, has added some details and some interesting observations regarding potential mechanisms of action, and such animal models seem to support ‘a causal link’ of some sort between body weight and the development of specific types of cancers in a number of important (…to humans…) cases, including breast cancer and colon cancer. However the precise mechanisms of action are still far from clear, as they note in their conclusion in chapter 6:
“As detailed here, overweight and/or obesity is associated with an elevated risk of several cancers; however, it is clear that a common disease mechanism was not identified. Although the current literature hypothesizes at least three major components such as sex hormones, insulin-related pathologies, and adipokines, these components cannot explain every aspect of clinical features/disease courses. But as models improve both for obesity and various cancers, hopefully it will become easier to identify mechanisms of action for the relationship of body weight and cancer.”
I’ve read the first third of this book, and it’s been a quite interesting read so far. Some parts have been easier to read than others and occasionally it gets a bit technical, but overall it’s a quite readable book for someone with my background and I’m certainly learning some new stuff by reading this.
Some observations from the book:
“obesity and metabolic syndrome are linked to various chronic diseases [6,7] including cardiovascular disease, type II diabetes, and the focus of this chapter, cancer. Importantly, not all obese individuals develop the metabolic dysregulation usually associated with obesity and metabolic syndrome, and these “metabolically healthy obese” individuals do not have elevated cancer risk. An estimated 30 % of obese individuals in the USA are metabolically healthy . Conversely, some nonobese individuals can develop the metabolic perturbations usually associated with obesity, and these individuals appear to be more prone to chronic diseases including cancer . Thus, an emerging hypothesis is that the obesity-related metabolic perturbations, and not specific dietary components or increased adiposity, are at the crux of the obesity–cancer connection.” […]
“Evidence-based guidelines for cancer prevention urge maintenance of a lean phenotype . Overall, an estimated 15–20 % of all cancer deaths in the USA are attributable to overweight and obese body types . Obesity is associated with increased mortality from cancer of the prostate and stomach in men; breast (postmenopausal), endometrium, cervix, uterus, and ovaries in women; and kidney (renal cell), colon, esophagus (adenocarcinoma), pancreas, gallbladder, and liver in both genders . While the relationships between metabolic syndrome and specific cancers are less well established, first reports from the Metabolic Syndrome and Cancer Project, a European cohort study of ~580,000 adults, confirm associations between obesity (or BMI) in metabolic syndrome and risks of colorectal, thyroid, and cervical cancer .”
“During obesity, adipose tissue responds to the excess energy by increasing adipocyte size (hypertrophy) and enhancing adipocyte proliferation (hyperplasia) . Adipocyte size strongly correlates with insulin resistance and secretion of proinflammatory cytokines . Moreover, location of the adipose tissue also determines risk for metabolic diseases. […] Healthy adipose tissue must be able to rapidly respond to excess energy intake by inducing adipocyte hypertrophy and hyperplasia, remodeling of the extracellular matrix, and enhanced neovascularization to nourish the adipose tissue. In pathological states such as insulin resistance associated with obesity, rapid adipocyte hypertrophy occurs with restricted angiogenesis resulting in cellular hypoxia, and thereby resulting in local inflammation . Macrophages surrounding necrotic adipocytes phagocytize fatty acids, which are released from the adipocyte. This produces bloated, lipid overburdened macrophages, which is characteristic of chronic inflammation and often observed in obese individuals . […] inflammation is a recognized hallmark of cancer, and growing evidence continues to indicate that chronic inflammation is associated with increased cancer risk [75–77]. Several tissue-specific inflammatory lesions are established neoplastic precursors for invasive cancer, including gastritis for gastric cancer, inflammatory bowel disease for colon cancer, and pancreatitis for pancreatic cancer [78,79].”
“When lipid storage capacity in adipose tissue is exceeded, surplus lipids often accumulate within muscle, liver, and pancreatic tissue . As a consequence, hepatic and pancreatic steatosis can develop; both have been positively associated with insulin resistance and ultimately lead to impairment of lipid processing and clearance within these tissues . […] The term nonalcoholic fatty liver disease (NAFLD) refers to a disease spectrum that includes variable degrees of simple steatosis, nonalcoholic steatohepatitis (NASH), and cirrhosis [19,20]. Simple steatosis is benign, whereas NASH is defined by the presence of hepatocyte injury, inflammation, and/or fibrosis, which can lead to cirrhosis, liver failure, and hepatocellular carcinoma. […] NASH occurs in 20 % of cases of NAFLD and ~5–20 % of NASH cases progress to cirrhosis; 80 % of cryptogenic cirrhosis cases present with NASH . Of this group, ~0.5 % will eventually progress to hepatocellular carcinoma […] In Western populations, overnutrition/obesity is the most common cause of NAFLD” […] NAFLD has evolved in parallel to the obesity pandemic as the most prevalent liver disease worldwide. Whereas the fact that chronic liver inflammation as observed in nonalcoholic steatohepatitis (NASH) finally leads to the development of hepatocellular carcinoma is well accepted , its association with increased formation of adenomatous polyps and CRC has just recently been established [124,125].”
“Hyperglycemia, a hallmark of metabolic syndrome, is associated with insulin resistance, aberrant glucose metabolism, chronic inflammation, and the production of other metabolic hormones such as IGF-1, leptin, and adiponectin . […] In metabolic syndrome, the amount of bioavailable IGF-1 increases […] Elevated circulating IGF-1 is an established risk factor for many cancer types [38,39].”
VEGF [Vascular Endothelial Growth Factor], a heparin-binding glycoprotein produced by adipocytes and tumor cells, has angiogenic, mitogenic, and vascular permeability-enhancing activities specific for endothelial cells . Circulating levels of VEGF are increased in obese, relative to lean, humans and animals, and increased tumoral expression of VEGF is associated with poor prognosis in several obesity-related cancers . The need for nutrients and oxygen triggers tumor cells to produce VEGF, which leads to the formation of new blood vessels to nourish the rapidly growing tumor and may facilitate the metastatic spread of tumors cells .”
“Epidemiological studies indicate that obesity represents a significant risk factor for the development of various cancers such as prostate and breast cancer, leading cancers in the Western world. An impressive body of evidence, however, also indicates that the risk of colorectal adenoma, and cancer (CRC) is increased in subjects with obesity and related metabolic syndrome [2,3]. […] Colorectal cancer is the second leading cancer death in the Western world and its death rate correlates with body mass index . […] Recent CRC screening studies suggest that obesity and an increased body mass index are a significant additional risk factor for the development of colonic polyps with evidence that advanced adenomas arise in men almost a decade earlier than in women . […] menopausal status appears to modify the relationship between BMI and colon cancer with a strong association between BMI and colon cancer risk seen in premenopausal but not postmenopausal women . […] being obese prior to being diagnosed with colon cancer increases your risk of dying from the disease [29–32]. […] more and more studies are now demonstrating the location of body fat tissue is the best predictor of all-cause and colorectal cancer mortality […] colon cancer survival may be less likely for patients who are […] too thin at diagnosis .”
“In a meta-analysis of 52 studies (24 case–control and 28 cohort studies) examining the link between physical activity and colon cancer, a significant 24 % reduced risk of colon cancer in people who were most active compared with the least was found . This supports other reviews of the association between physical activity and colon cancer in the Asian and European populations [49,50]. […] Physical activity also appears to affect disease outcome and recurrence after diagnosis and treatment with the greatest effect on colon cancer incidence . […] new well-controlled clinical trials on obesity prevention and obesity treatment are necessary before therapeutic implications of WAT [White Adipose Tissue] reduction on cancer predisposition are completely understood. One of the possibly important considerations is the number of adipocytes and the accompanying stromal/vascular cells in WAT increasing in obesity and remaining increased even upon subsequent weight loss, which occurs via adipocyte size reduction. The pool of ASC [Adipose Stem Cells] is likely to remain intact and could contribute to cancer onset or progression despite calorie restriction and reduced adiposity.”
“There is general agreement that obesity is associated with an increased incidence of breast cancer in postmenopausal women (reviewed in [14–17]). […] The European Prospective Investigation into Cancer and Nutrition (EPIC) study , which had 57,923 postmenopausal participants, is of particular interest because of its large size, its prospective design, and the observations made concerning exogenous estrogens as a confounder. The results showed that a long-term weight gain was related to an increase in risk, but only in those who were not taking hormone replacement medication: compared with women with a stable body weight the relative risk for women who gained 15–20 kg was 1.5 with a confidence interval of 1.60–2.13. As reported by others, adiposity ceased to be a risk factor in current replacement therapy users, who were already at a high risk for breast cancer compared with nonusers. […] Preexisting obesity and postoperative weight gain are associated with poor prognosis in both premenopausal and postmenopausal breast cancer patients. […] A pivotal review of the literature by Chlebowski et al.  found that in 26 out of 34 studies individual studies, totaling 29,460 women, obesity was related to an increased risk of recurrence or reduced survival.”
“Daling et al.  have provided a major contribution to our understanding in the relationships between body fat mass and tumor biomarkers of progression in young breast cancer patients. In their study, not only was a combination of obesity and an absence of ER expression in premenopausal breast cancer patients aged younger than 45 years associated with an increased risk of dying from the disease, but those with BMI values in the highest quartile were more likely to have larger tumors of high histologic grade. This observation is particularly significant because it implies that large tumors in overweight/obese women grow at a faster rate than tumors of similar size from leaner women, rather than simply arising from delayed diagnosis due to palpation difficulty in obese women.”
“Wolf et al.  and Schott et al.  suggested that up to 16 % of breast cancer patients have diabetes, and that T2D may be associated with a 10–20 % excessive risk of breast cancer. […] There is ample epidemiological evidence that diabetes contributes to breast cancer risk [17,36–40]. […] Overall survival in cancer patients, with or without preexisting diabetes, has shown diabetes to be associated with an increased all-cause mortality risk. […] The Danish Breast Cancer Cooperative Group, with 18,762 newly diagnosed T2D cases, found that the recurrence with metastases was 46 % higher in obese women with a BMI of 30 kg/m^2 or greater beyond the first 5 years.”
The relationship between obesity and prostate cancer is a complicated one. […] The explanation for this confusion may rest, at least in part, in the reports that obesity as a positive risk factor for prostate cancer relates specifically with the aggressive phenotype [56–60] […] a meta-analysis by Discacciati et al.  of the results from 25 studies that examined disease stage and BMI showed not only a positive relationship between obesity and advanced prostate cancer but also a decrease in the risk for localized disease. The association between obesity and an aggressive prostate cancer phenotype is reflected in the relationship between the BMI and prostate cancer mortality rate. For example, in one large retrospective cohort study by Andersson et al.  […] there was a significantly larger prostate cancer mortality rate in the higher BMI categories”
Two studies have been reported in which meta-analysis was used to examine previously published investigations into the relationship between diabetes mellitus and prostate cancer risk [66,67]. […] [The first] meta-analysis showed that there was an inverse relationship between diabetes and prostate cancer risk, which translated to a 9 % reduction in risk. […] The overall conclusion […in the second meta-analysis] was the same: diabetic men have a significantly decreased risk of developing prostate cancer (RR = 0.84; 95% CI, 0.76–0.93). […] Gong et al.  reported a large prospective study of diabetes and prostate cancer from the USA after the two meta-analyses described above had been published that also took account of potential confounding by obesity. Men with diabetes had a 34 % lower risk of prostate cancer compared with men without diabetes that was not affected by adjustment for the BMI […] In contrast to these results, recently published studies have found that the presence of diabetes is positively associated with prostate cancers of high-grade [71–73] and late-stage tumors  ], a reversal in the observed relationship that needs to be considered in the context of the duration of the presence of T2D and the detection of prostate cancer by prostatic-specific antigen screening.”
I finished the book. It was hard to rate, in part because I as mentioned in the first post am not exactly part of the main target audience. However I think the book is reasonably well written and it’s certainly not the authors’ fault that I couldn’t always figure out exactly what was going on because I’m an ignorant fool (compared to most people who’ll read this). I ended up giving it four stars.
I covered the first chapters in my first post about the book, but I’ll not cover the rest of the book in as much detail as I did the first part. Topics covered in the remaining chapters were acute renal failure, neurological emergencies, the endocrine system, gastrointestinal disorders, infection and inflammation, hematologic emergencies, nutritional support, physical injury (including things like burns and electrical injuries, as well as near-drowning, hypothermia and heat stroke – which is incidentally quite a bit more dangerous than I’d imagined), toxicology, a chapter on scoring systems used to assess severity of illness among patients in the ICU, and lastly a brief chapter about obstetric emergencies (pre-/eclampsia and HELLP-syndrome). So a lot of ground is covered here, meaning of course also that they do not go into as much detail in many of these chapters as they did in some of the first ones from which I quoted earlier.
I think reading a book like this may cause your viewing experience associated with watching medical dramas to change at least marginally. Some stuff from the remaining part of the book, as well as some comments:
“Traumatic brain injury
Primary brain injury occurs on impact and is considered irreversible. Secondary brain injury […] results from processes initiated by primary insult that occur some time later and may be prevented or ameliorated. Management of traumatic brain injury (TBI) aims to prevent secondary brain injury.”
“Management of organ donors
Once a potential organ donor has been identified, the regional transplant coordinator should be contacted, but he or she should not be involved in the process of diagnosing brain death or obtaining consent for organ donation. In general, the following features exclude eligibility for organ donation: malignancy (except for primary cerebral, skin, or lip), HIV, hepatitis, intravenous drug abuse, active tuberculosis, and sepsis. However, the regional transplant coordinator should make the determination of eligibility. Once brain death has been declared and the family has consented to organ donation, an aggressive approach to preservation of organ function is crucial.”
“Management of hyponatremia
Correction must not exceed 20 mol/L per 48 h and generally at a rate of no more than 0.5 mmol/L per h.” I was curious to know why, so I looked it up – it turns out that really bad things can happen if adjustment is too fast – this may lead to CPM (central pontine myelinolysis). It’s a recurring theme in the book that adjustment speeds matter, and that optimal treatment does not always imply fast adjustment; to give but one other example this is also the case when it comes to treatment of DKA (“The initial aim is to inhibit ketogenesis, which is achieved with modest doses of insulin. Rapid reductions in blood glucose should be avoided”).
“Within 24 h of admission the majority of critically ill patients will develop stress-related mucosal damage. Clinically relevant bleeding causes hematemesis and/or melena; hypotension, tachycardia, or anemia occurs in 1–4% of patients. Those who develop stress-related mucosal disease, endoscopic signs of bleeding, or clinically important bleeding have a higher risk of death. […] Maintenance of an elevated intragastic pH has the potential to prevent stress-related mucosal disease. Studies have demonstrated that a pH of more than 4.0 is adequate to prevent stress ulceration. However, a pH greater than 6.0 may be necessary to maintain clotting in patients at risk from rebleeding in peptic ulcer disease. […] There are, however, concerns that the elevation in pH in patients may lead to increased episodes of pneumonia.”
“Hypergastrinemia from a gastrinoma tumor causes Zollinger–Ellison syndrome (ZES) leading to gastric acid hypersecretion. Gastrin leads to hypertrophy and hyperplasia of the parietal cells which, in turn, also results in gastric acid hypersecretion. Although a rare disease, it is life threatening. […] ZES can be cured in 30% of patients by surgical resection. More than 50% of patients with control of acid hypersecretion who are not cured will die of tumor-related causes. Surgical resection should, therefore, be pursued whenever possible.” (‘More than 50% of patients with control of acid hypersecretion who are not cured will die of tumor-related causes’ – I’m starting to like my diabetes…)
In chapter 8 it’s noted that 50% of acute liver failure cases in the UK are caused by acetaminophen overdose, and that the various forms of viral hepatitis are behind another 40% of cases.
“Severe infection is not only a common cause of admission to intensive care, but also the most common complication suffered by critically ill patients. […] Hospital-acquired pneumonia (HAP) is defined as a pneumonia diagnosed 48 h or more after admission, which was not incubating at the time of admission. In contrast to the hospital population as a whole (in whom urinary tract and wound infections are more frequent), it is the most common infection in the critically ill, and is associated with a mortality rate of up to 50%.”
No, these are not all caused by the poor hand hygiene of nurses and doctors; 10 specific risk factors are listed and it’s made clear that:
“Although community-acquired pathogens can cause HAP, there is a much higher incidence of infection caused by aerobic Gram-negative bacilli. This is possibly the result of overgrowth of the stomach with intestinal bacteria, or the direct vascular spread of organisms that have translocated across the intestinal wall into the circulation.” On a related note, “There is no clear evidence that duration of residence in itself increases the risk of [nosocomial] bacteremia.” The chapter has some great (and/but brief) descriptions of various antibiotics, antivirals and antifungal medications. Some of the descriptions make it very obvious why such drugs are not always as great as they tend to be made out to be – here’s a presumably well-known example:
“Vancomycin inhibits cell wall synthesis. It is the drug of choice in the treatment of MRSA and coagulase-negative staphylococci that are resistant to meticillin.” Sounds great. But here’s the next sentence: “However, it is nephrotoxic and ototoxic, and serum levels must be monitored carefully.” (To those who don’t speak medical textbook, vancomycin may cause kidney failure and cause you to go deaf.)
“Respiratory function is often compromised in patients with cervical cord injury […] The level of injury critically influences the effect on ventilation […] Patients with lesions above C5 (unable to move hands or arms) usually require ventilation. Patients with intact C5 innervation (can shrug shoulders and externally rotate arms) may maintain adequate respiratory function in the absence of any other pulmonary insult. Patients with lesions at C6 will usually manage without ventilatory support in the acute phase.” Spinal cord damage can cause a lot of ugly stuff to happen besides ‘just’ being unable to move limbs – there may also be systemic problems such as various gastrointestinal problems, bladder distension and urinary retention, and loss of ability to regulate normal body temperature (Poikilothermia) as well as other metabolic problems.
“Supportive care is the basis of all treatment in poisoned patients. A medical history and physical examination can help direct which toxins or poisons are involved. It is important to seek out all sources of information because obtaining a history from an attempted suicide patient may be difficult. There may be deliberate misinformation in this setting. One must always assess for coingestions, as most patients who attempt suicide will use two or more toxins. […] Specific poison assays are often unhelpful as absorption is variable and a poor guide to prognosis. […] There are a limited number of poisons that have specific antidotes […] Many antidotes are toxic in their own right and should be reserved for life-threatening poisonings.”
Here’s a link. From the description:
“Written as a teaching aid for residents and fellows in intensive care and emergency medicine, this revised edition of the Handbook of Critical Care is also a valuable reference for all medical professionals in the critical care team. The 15 chapters in this pocket-sized basic intensive care manual include eight substantial sections covering the major organ systems, as well as infection, nutrition, physical injury and toxicology, and brief chapters on scoring systems and obstetrics. The chapters feature numerous pictures, comparative tables, diagrams and lists, and provide essential information for juniors training in intensive care medicine. The definitions, etiology, clinical features and differential diagnoses are well covered, while extensive use of bullet points and numbering increases the clarity of presentation enabling readers to quickly get to the key learning points.”
I’m not a medical professional who’s part of a critical care team (surprise!) and so I don’t understand everything that’s going on in this book, but I still feel like I’m learning quite a bit. I think it’s an interesting book, but I certainly wouldn’t recommend it to someone who’s not spent more time than most obtaining an understanding of the human body, pathophysiology, some pharmacology, etc. – if you’re new to the field of medicine I think you’ll simply get blown away here and you’ll have little clue what’s going on most of the time. Khan Academy has a lot of stuff about the heart, and I’ve watched a lot of the videos dealing with this kind of stuff, though far from all of them – but a couple of textbooks plus learning all the material covered in the lectures I link to above is probably the bare minimum you need in order to understand everything that’s going on in chapter 3 alone. So, yeah…
As pointed out, even though I don’t understand everything doesn’t mean I don’t learn a lot by reading this stuff – that’s not how it works. So so far I’m enjoying this – if I could have been bothered to look up all the new terms I’ve come across here so far, I’d have learned more than I did, but that would have been too much work. I did rewatch a few Khan Academy videos to brush up on some of the concepts, but that’s pretty much all I’ve done, aside from an occasional visit to wikipedia. So I’ve read and understood some of it, and I’ve read and not fully understood other parts. That’s okay.
One thing that I’ve achieved a greater understanding of is how stuff like adverse drug reactions, nosocomial infections etc. may be much harder to avoid and deal with than I’ve tended to believe in the past. All drugs have side effects, and as a general rule I don’t think it’d be wrong to say that the more sick you are, the more extensive health interventions are required to make you better (or simply stop you from getting worse). A patient in the ICU who develops pneumonia as a (more or less direct) consequence of having been on a mechanical ventilator for an extended period of time and dies from it would most likely have died even sooner if he had not been put on a ventilator – he was so sick in the first place that he couldn’t even satisfy his own body’s oxygen demands through breathing… Many treatment options critically ill patients have to choose from (or their medical proxy has to choose from) are quite risky, but it should be kept in mind that the risks associated with not treating the conditions in question are usually much worse. Adding to all of this is the fact that quite a few diseases progress much too fast for treatment to follow diagnosis chronologically; in such cases you need to engage in treatment before you’re even certain what’s wrong. Some drugs will work one way when dosed in a specific manner, but will have more or less the opposite effect if given in a higher dose; now add interpersonal differences in drug metabolism, absorption rates, measurement problems, etc. to the mix… This is not to say that attempts to minimize errors and treatment-associated complications should not be undertaken, far from it, but I think I understand a little better at this point why these problems may sometimes be very hard to address satisfactorily, and why some of these problems are probably even best perceived of as inherent risks which simply cannot be avoided.
I decided to add a few quotes below from the first four chapters (95 pages) of the book. I’ve only added quotes from the stuff I understand. I added some wikipedia links along the way in order to make the stuff a little easier to read.
“Several types of sedative available are commonly used in the ICU […] Drug regimens should of course be matched to the particular needs of individual patients; however, generally speaking, no single drug is ideal and what follows is a summary of the advantages and disadvantages of each drug. […] All sedatives can accumulate in critically ill patients, leading to a prolonged sedative effect. This may increase the duration of mechanical ventilation, length of stay in the ICU, and length of stay in the hospital, and lead to complications such as ventilator-associated pneumonia. A strategy to reduce drug accumulation should be implemented […]
“studies that have failed to demonstrate an improved outcome in critical patients have cast doubt on the clinical value of PACs. [PAC: Pulmonary Artery Catheter, US] Nevertheless, traditional indications include the following: [Long list omitted here] […] To date, the use of PAC monitoring has not been shown to confer a clinical benefit in any of these settings.”
“A wide range of inotropes and vasopressors is available to help support CO [cardiac output] or blood pressure (BP). […] Inotropes that vasodilate and vasoconstrict are known as inodilators and inoconstrictors, respectively. Vasoconstrictors should always increase blood pressure, but may have a variable effect on CO. Many of these drugs have the potential to cause myocardial ischemia due to increases in cardiac workload, tachycarrhythmias, etc, and thus as electrocardiogram (EKG) and (preferably direct) arterial pressure monitoring are mandatory during their use. Vasoconstrictor drugs must be given into a central vein.”
“Hypertension is not uncommon in critically ill patients and may worsen myocardial ischemia and increase oxygen requirements. […] Injudicious use of hypotensive drugs reduces perfusion pressure (eg, kidney, brain, myocardium) and may lead to organ dysfunction.”
Heart failure occurs when the heart fails to maintain a CO sufficient for the metabolic needs of the body, or when it can only do so at the expense of abnormally elevated end-diastolic pressures. Heart failure is not a diagnosis as such, but a clinical syndrome; consequently the underlying disease must always be sought and treated.
Chronic heart failure has increased in incidence due to a decline in mortality from acute myocardial infarction, and an increase in the elderly population. It carries a significant mortality risk, with a 5-year survival rate of approximately 30%.
Acute cardiac failure is a medical emergency with a high mortality rate […] in which diagnosis of the cause and empirical treatment may have to be carried out simultaneously.”
“Pathogenesis of AMI
Rupture of an atheromatous plaque within the lumen of a coronary artery, and the subsequent formation of fresh thrombus, leads to vascular occlusion and (total) cessation of blood flow to the region of the myocardium supplied by that artery. Hypotension, hypoxemia, and local vasospasm may extend the size of the resulting infarct by compromising the blood supply of surrounding ischemic muscle. […] There is ischemic pain, typically retrosternal, spreading across the chest, and possibly radiating to the arms, throat, jaw, and back. The pain lasts for more than 20 min and may be atypical (eg, epigastric) which may confuse the diagnosis. A silent (ie, painless) infarction is more common in elderly people and those with diabetes. […] Less than 10% of patients with enzyme-proven infarcts will have two normal EKGs performed 30 min apart in the hyperacute phase. This establishes electrocardiography as an important initial investigation in the patient with a history suggestive of AMI. […] Aspirin combined with streptokinase improves the reduction in mortality rate from 25% to 42% by preventing reocclusion of thrombolyzed arteries. A dose of 150–300 mg should be given as soon as possible after the onset of symptoms […] The in-hospital mortality rate from AMI is now less than 10%, with most deaths occurring within the first few hours, often due to ventricular fibrillation (VF).”
Cardiogenic shock is a low-cardiac output state with clinical evidence of inadequate blood flow. It has been defined clinically as a syndrome characterized by hypotension (eg, systolic blood pressure <90 mmHg or 30 mmHg less than normal) or evidence of reduced tissue blood flow (eg, cold clammy skin, urine output <30 mL/h, confusion). […] Most cases of cardiogenic shock are caused by severe left ventricular failure, and will present with pulmonary edema […] The mortality rate of cardiogenic shock overall is around 80%, although this improves to approximately 50% in patients with a surgically correctable lesion. Many survivors are left with significant cardiac pathology such as angina or limiting heart failure.”
“The lungs have two major functions: to provide adequate arterial oxygenation for tissue needs and to eliminate CO2. These two functions are largely independent of each other. Respiratory failure can be classified according to the underlying pathophysiologic derangement. All types of respiratory failure may present with arterial hypoxemia and/or arterial hypercapnia.”
“In the normal resting state, 1–5% of the cardiac output is delivered to the respiratory muscles. This can increase up to tenfold in patients with shock and respiratory distress. Mechanical ventilation allows resting of the respiratory muscles.”
“Patients who have been ventilated for brief periods of time (eg, overnight ventilation following major surgery) may be liberated from mechanical ventilation rapidly […] This is in marked contrast to patients who have been critically ill for long periods of time (days), in whom the process of withdrawing ventilatory support is often protracted. Day-to-day changes in the patient’s condition during this period of respiratory convalescence often necessitate the temporary reintroduction of more substantial mechanical ventilatory support.”
“ARDS is a syndrome causing acute respiratory failure characterized by severe hypoxemia, poorly compliant (‘stiff ’) lungs, and diffuse patchy infiltration on the chest X-ray in patients in whom cardiogenic pulmonary edema has been excluded. Rather than being an isolated condition, it is recognized as the pulmonary manifestation of systemic inflammation. ARDS is now recognized as the extreme end of a spectrum of ALI [Acute Lung Injury, US – though see also this], and is defined in terms of the severity of the gas exchange defect […] The mortality rate of ARDS ranges from 30% to 40%, and has improved significantly in recent years with the advent of new ventilator strategies.”
“Pneumonia can be defined as an acute lower respiratory tract illness, which is associated with fever, symptoms and signs in the chest, and abnormalities on the chest X-ray. In patients admitted to the hospital, it carries an overall mortality of about 10%. Mortality is strongly correlated with age, chronic comorbidities, severely abnormal vital signs upon presentation, and laboratory abnormalities (eg, pH, blood urea nitrogen, Na+, glucose, hemoglobin, and PaO2).”
“[Hospital-acquired (nosocomial) pneumonia] is defined as pneumonia developing more than 2 days after admission to hospital. It is particularly common in the ICU and postoperative patients, and carries a mortality rate of up to 50%. […] Diagnosis often proves difficult, particularly in ventilated patients, because features are nonspecific and may be confused with other conditions ”
“Pulmonary embolism (PE) occurs in 15–20 patients per 1,000 of the general hospital population, of which 2–5 cases are fatal. At least 50% of patients who die from PE have had some indication of thromboembolism within the preceding 7 days. Failure to diagnose PE has adverse consequences, since 30% of patients with untreated PE die compared with 8% of treated PE. […] Pulmonary emboli usually result from the formation of asymptomatic deep vein thromboses (DVTs) in deep veins of the lower limbs, pelvis, and abdomen. Upper extremity DVTs are usually associated with indwelling catheters and may account for up to 15% of DVTs. […] Factors promoting the formation of thrombi are described by Virchow’s triad of venous stasis, abnormal vessel walls, and increased coagulability […] Thrombolytic therapy is indicated for massive PE with associated shock. Its role in massive PE, with echocardiographic evidence of right heart failure or massive PE with severe pulmonary hypertension, has demonstrated improved secondary outcomes compared with heparin, though no survival benefit has been noted. Allergic reactions and hemorrhage are the principal complications of thrombolytics, and restrict their use considerably.”
Here’s the link.
The book wasn’t particularly good (I gave it a 2 star rating on goodreads) and I didn’t spend much time on it, so I also don’t plan on spending much time on it here. There’s too much fluff and too little hard data for it to be all that interesting to me. This is not to say that it’s not a research-oriented book; it very much is, but apparently communication research at this stage is, well, yeah… There’s a lot of stuff here, but a lot of it wasn’t particularly interesting. I’d expected more from a Cochrane Handbook.
Anyway, some quotes (my bold):
“communication-related difficulties affect not only people’s feelings but also the quality, efficacy and safety of the medical and surgical treatments […] attempts to overcome the difficulties are more than just feel-good strategies. Rather, they are critical to improving people’s health and ensuring that medical mistakes are avoided. […] Communication failures can cause not only dissatisfaction but serious adverse events (an ‘injury caused by medical care’ ). In 2008–2009, the report on such events in Victorian hospitals identified that communication was a contributing factor in 20% of these events, with health information a factor in another 8% of cases . […]
A study of 1308 complaints made at a major South Australian hospital over a 30-month period found that fully 45% (n = 621) of complaints were about communication problems […] Poor communication is known to be a key contributing factor in litigation
against primary care physicians . […]
There is a long recorded history in research indicating that patients want more information than they receive. […] There is clear evidence that people want more information than they are given and that clinicians tend to overestimate the amount of information they have provided [15,16]. Roter and Makoul have noted that only 58% of people studied said their healthcare provider told them things in a way they could understand . […]
People are usually presented as the recipients of information and communication, such as advice on what to do to keep healthy, get screened, take up needed healthcare and so on. The importance of information coming from consumers or the reality of many consumers communicating with each other does not receive the same attention.
Analysis of thousands of interventions for communication and participation in fact identifies that communication and participation can be readily conceived as multidirectional. […]
People cannot change risk factors unless they know about them, want to change them, understand how to change them and receive support and assistance in that process. This makes communication – and risk communication in particular – a key component of any strategy to reduce the impact of chronic disease, particularly where there are a number of known modifiable risk factors. […] Risk communication involves informing or educating people who are exposed to a risk factor (e.g. smoking) about their risk of disease. Information on the qualitative and quantitative dimensions of the risk may be presented with a view to enabling people to make decisions about changes in lifestyle or medications to reduce that risk . A fundamental requirement for risk communication is to have concrete data from which to estimate the risk of an individual developing a disease. This is achieved by use of epidemiological research […] Risk communication is challenging for health professionals. General practitioners (GPs), and increasingly nurse practitioners, may be the first point of contact and information for many people. Apart from accurate calculation  and application to an individual patient, complex statistical concepts have to be communicated in ways that are easy to understand and motivating too . Consumers’ existing understandings of risk information, their individual personal life circumstances  and individual preferences for the formats used to communicate risk [13, 14] add another level of complexity. This chapter presents findings from qualitative research into the views of health consumers and GPs on how risk for CVD should be discussed in consultations. […]
Percentages were frequently misunderstood by consumers [/patients, US], hampering their understanding of the degree of risk. […] Clear visual representations of risk assists comprehension . […] Whilst GPs’ format preferences aligned with consumers, some felt that their patients did not respond well to numbers or charts. […] Reflecting other research findings, and highlighting their own experiences of consumers’ health literacy (or innumeracy), a number of GPs thought that the use of numbers, statistics, percentages, ratios and proportions was too difficult for some of their patients to understand or that their use might be confusing . Several GPs also commented that since they did not understand particular numbers (such as odds) themselves, they would not expect that their patients would understand such measures accurately. […]
The people who expressed high levels of satisfaction with their experiences were those who felt that their physicians treated them as equals and that their treatment decision was made with input from both the physician and the patient. Positive health outcomes, including symptom resolution and pain control, have been linked to effective communication and agreement between the physician and the patient [8,9]. […]
Adverse events are incidents in which a patient experiences unintended harm while receiving medical treatment . […] some may be due to treatment (e.g. prescribing or administering the wrong medicine or dosage), they may be known risks of a procedure (e.g. complications of a surgery or therapy) or they may be indirectly related to treatment (e.g. healthcare-acquired infection or exposure to a disease or disease risk). […] Communication following adverse events is especially sensitive and challenging. Doctors may be reluctant to apologise or inform patients that mistakes have occurred, as it may be seen as an admission of culpability and could raise liability concerns [4, 5]. Patients may suffer secondary harms, such as confusion, anxiety or distress, if poor communication strategies are used when disclosing information about adverse events, such as exposure to a disease risk . […] When an adverse event has occurred in healthcare, people generally want to be informed [4, 11]. Even with the current increasing emphasis on open disclosure, there are indications that many adverse events remain unreported, and historically, this was often the case [4, 5, 11]. Several studies of people’s preferences for disclosure indicate that communication should contain detailed information, explaining the event, how it will affect them, what steps are being taken to prevent future occurrences of the same problem and, significantly, an expression of regret. People also want access to ongoing emotional support [4, 11]. […] People at risk, in general, would prefer to be notified of their risk status even if it is distressing [6, 12–14]. […]
Information found through individual research can […] be confusing or contradictory, as indicated by Glenton’s study on the information provided by government-run online health portals. Glenton and colleagues found that health portal information is rarely supported by systematic reviews, and is frequently confusing, vague or incomplete . […]
Most of the reviews suggest that interventions to improve communication between clinicians and patients have only modest benefits on consultation processes and patient satisfaction. […]
There is no guarantee that evidence synthesised in a systematic review will lead to a clear conclusion. However, this does not make the review any less useful. As Light and Pillemer suggest, disagreements between research findings offer a valuable opportunity for the reader. Divergent outcomes may result from carrying out the same intervention in different settings, from an intervention being implemented differently or even different interventions sharing the same name. Exploring conflicting findings may teach the reader how to implement an intervention in their setting successfully in the future . […] When confronted with the huge quantity and variable quality of available research, it may be tempting to take a short cut and use one study from a reputable journal to inform a decision. […] A health professional who looks at only a few of the individual BCN trials might have difficulty assessing their quality or comparing their results, and may reach a conclusion about the effectiveness of BCNs not actually supported by all the evidence. With a systematic review, all the available evidence is brought together in one place. Though this review shows no certain outcome for the intervention, it is preferable to know this, rather than basing future decisions and programmes on limited or poorer quality evidence. […]
The pharmaceutical industry alone accounts for 25% of the United Kingdom’s business investment in R&D […]
Whilst policy and supportive initiatives can increase patient involvement in setting research agendas, the question remains: does it make a difference? Indeed, there is some concern that involving non-researchers in the process may compromise research rigour in some way . […]
At present, research evidence and the information materials derived from it for both doctors and consumers principally focus on one disease and largely ignore the interaction of diseases in patients’ lives . This means that there may be little or no information for patients with multimorbidity to support treatment, self-management or other health actions [25, 27, 36]. There may also be little information that is suitable for doctors to share with their patients when multimorbidity is present. A further problem arises because it is not possible to simply apply what is known from research and information derived from single diseases to people with multimorbidity [12, 24]. […] although many interventions exist which might theoretically be able to help improve medicines use in multimorbidity, the reality is that the research evidence that evaluates these strategies does not consider, in most cases, issues of the growing number and complexity of medicines for multimorbidity. This means we have almost no research evidence to guide practice or policy on medicines in multimorbidity even though multimorbidity is a known risk factor for medicine-related adverse events . […]
At its simplest, health literacy is the ability to seek, find, understand and use health information . […] Health literacy can be built, but the effects of poorer literacy have been the focus of much of the research so far. […] Poorer health literacy is linked to more adverse events. […] People with limited health literacy are more likely to report their health as poor. They have poorer health outcomes [7, 8]. […]
In recent years, television and the internet have become the most important resources for health information [31, 32] […in China, US], with more than half of Chinese people gaining their health knowledge from television .”
Khan Academy has, in collaboration with Stanford School of Medicine, made 15 videos about the disease (so far) with a total duration of two hours and 37 minutes – you can watch all of them here. There’s some overlap here and there, different videos covering similar stuff, and a lot of details are left out. But this is still good stuff and the videos were an enjoyable part of my day yesterday. I know I’ve covered this disease before, but given how many people have been exposed and how important it was in the past (roughly one hundred years ago one sixth of all French deaths were due to this disease), this is arguably a disease you should at least have some knowledge about. Some samples from the playlist below:
A quote from the last video above: “DOT – Directly Observed Therapy – is very important.”
There are theoretical reasons why DOT may be useful/efficient, as mentioned in the video. And I’ve seen it argued elsewhere that “treating tuberculosis with the DOTS strategy is highly cost-effective” [DOTS means “directly observed therapy, short course” – which is a specific type of DOT therapy; “a comprehensive tuberculosis management programme that focuses on low-income countries.” (see the Cochrane link for more)]. But I’m also aware that there are reasons to be skeptical as well:
The results of randomized controlled trials conducted in low-, middle-, and high-income countries provide no assurance that DOT compared with self administration of treatment has any quantitatively important effect on cure or treatment completion in people receiving treatment for tuberculosis.
PLAIN LANGUAGE SUMMARY
Directly observing people taking their tuberculosis drugs did not improve the cure rate compared with people without direct monitoring of treatment
Tuberculosis is a very serious health problem with two million people dying each year, mostly in low-income countries. Effective drugs for tuberculosis have been available since the 1940s, but the problem still abounds. People with tuberculosis need to take the drugs for at least six months, but many do not complete their course of treatment. For this reason, services for people with tuberculosis often use different approaches to encourage people to complete their course of treatment. This review found no evidence that direct observation by health workers, family members, or community members of people taking their medication showed better cure rates that [sic] people having self administered treatment. The intervention is expensive to implement, and there appears to be no sound reason to advocate its routine use until we better understand the situations in which it may be beneficial.”
“Compliance is the degree to which a patient is compliant with the instructions that are given by a healthcare professional and written on the medication label (for example, prescribed dose and time schedule).” (p.8 – I didn’t know that definition before reading the book so it made sense to me to start out with this quote, to make sure people are aware of what this book is about.)
It’s an interesting book with a lot of stuff I didn’t know and/or at the very least hadn’t thought about. A couple of the chapters were quite weak and I basically skipped most of chapter 6, which was written by a pharmaceutical marketing consultant who wrote about branding stuff which I couldn’t care less about – but most of the book was quite good. One of the chapters (chapter 8) very surprisingly included undocumented claims which were to some extent proven wrong in a previous chapter (chapter 3) – it seemed as if the authors of that chapter had not read the previous chapter in question. Here’s what they wrote at the very beginning of their chapter (chapter 8):
“Compliance is important. Better adherence to treatment regimes leads to less healthcare resource utilization overall, as fewer illness recurrence or medication errors leading to side-effects take place.” (p.109)
And here’s what Dr. Dyffrig Hughes told us in chapter 3:
From the studies evaluated, the direction and magnitude of the change in costs and consequences resulting from applying sensitivity analysis to the compliance rate was measured and taken as an indicator of the impact of non-compliance. There was consistency among studies, in that as compliance decreased (whatever the measure), the [health] benefits also decreased […] There is no consistency, however, in the direction of change in costs resulting from changes in compliance [my bold, US] […] Whilst some studies show that costs increase as compliance decreases, others showed the opposite trend. This difference did not appear to be related to the nature of the disease, the measure of non-compliance or the assumptions relating to the health benefits experienced by non-compliers.
And here’s even a figure illustrating this point:
A little more from chapter 3 on the same subject: “The economic evaluations described demonstrate that medical expenditures do not always increase because of poor compliance. However, the limitations in the methodology adopted in many of the studies would suggest that the reported changes in healthcare expenditure may not necessarily be observed in practice. It is difficult, therefore, to predict the true economic impact of non-compliance with drug therapy, particularly as evidence relating to discontinuers is often not reported. It is the case, however, that decisions on optimal treatments, based on economic criteria, are influenced by non-compliance […] Health economic evaluations often fail to include non-compliance with medications. As a significant proportion of evaluations are based on efficacy trials, attention should be given to how their findings might be generalized. In particular, as poor compliance is one of the most important elements responsible for the differences that may exist between the effectiveness and efficacy of an intervention, greater consideration should be given to compliance when generalizing from the results of a controlled clinical trial. An optimal cost-effective treatment strategy chosen on the basis of efficacy data may not be so attractive once real-world compliance figures are taken into account.”
I don’t consider this to be an unforgiveable error in a book like this with a lot of authors writing about different aspects of the problem, but it doesn’t help that the authors of chapter 8 repeat the claim that improved compliance will have cost-saving effects in their conclusion of the chapter as well, and at the very least it doesn’t make them look good to me (a more cautious and tentative approach in the introduction and the conclusion of the chapter would have suited me better). A good editor sh(/w)ould probably have caught something like this.
The efficacy/effectiveness difference he talks about relates to the fact that the results of randomized controlled trials (RCTs) could/should be considered estimates of the health effects related to something close to the ideal treatment scenario, whereas real world implementation (effectiveness) of the treatment in question will often provide patients a sometimes significantly lower health benefit in terms of average treatment effect (or similar metrics), because of differences in the composition of the two groups and the settings of the treatment protocols applied, among other things. RCTs often deliberately try to maximize compliance e.g. by excluding patients who are likely to be non-compliers, and that of course will lead to biased estimates if you apply such estimates to the total patient population. There are many variables affecting how big the potential difference between efficacy and effectiveness may be for a particular drug and they cover that stuff, as well as a lot of other stuff, in the book. Non-compliance rates are much bigger than I’d imagined, but there are a lot of reasons for this that I hadn’t considered. The fact that non-compliance is widespread can be inferred even from the definitions applied in clinical trials:
“ultimately it is the outcome that is important. This might not always require that all doses of a drug are taken. Indeed, in short-term efficacy clinical trials patients who take 80 per cent or more of their medication, based upon pill counts, are usually considered ‘compliant’.” (p.14)
You can fail to take one-fifth of the medicine and still be considered compliant. Indeed as Parkinson, Wei and McDonald put it in their chapter:
“As the reader of this chapter it might be informative to reflect on your own behaviour: can you honestly say that you have always complied fully with every tablet of every prescription and have always finished the course? A very few readers will say yes, with honesty. The reality is that nearly everyone is non-compliant; the variable is the degree of non-compliance.”
A few numbers from the book illustrating the extent of the problem:
“reports (for example, Sung et al., 1998) have suggested that only 37 percent of participants take greater than 90 per cent of all doses of statins over a two-year period. […]
[Astma:] When patients were aware of being monitored a majority (60 per cent) were fully compliant, but when unaware the majority had a compliance rate between 30 and 51 per cent (Yeung et al., 1994). […]
Significant levels of non-redemption [of prescriptions], as seen in this study, have subsequently been confirmed within the large UK general practice databases such as GPRD where there is only about 90 per cent concordance between the prescriptions issued by the GP and those recorded as being redeemed at a pharmacy by the UK Prescription Pricing Authority (Rodriguez et al., 2000). […]
Chapman et al. (2005) recently examined compliance with concomitant antihypertensive and lipid-lowering drug therapy in 8406 enrollees in a US-managed care plan […] Less than half of patients (44.7 per cent) were adherent with both therapies three months after medication initiation, a figure that decreased to 35.8 per cent at 12 months. […]
Despite international clinical guidelines recommending lipid-lowering treatment in patients with clinically evident atherosclerotic vascular disease, study after study has documented low treatment rates in this high-risk patient population, thereby creating a clinical practice and public health dilemma (Fonarow and Watson, 2003).
Only about 30 per cent of patients with established CVD and raised serum lipids, and fewer than 10 per cent of individuals eligible for primary prevention, receive lipidlowering therapy. Target total cholesterol concentrations are then achieved in fewer than 50 per cent of patients who do receive such treatment (Primatesta and Poulter, 2000).
Poor patient compliance to medication regimen is a major factor in the lack of success in treating hyperlipidaemia (Schedlbauer et al., 2004). All of the lipid-lowering drugs must be continued indefinitely; when they are stopped, plasma cholesterol concentrations generally return to pretreatment levels (Anon, 1998). […]
Up to half of the patients treated for hypertension drop out of care entirely within a year of diagnosis (ibid. [WHO, 2003b], Flack et al., 1996). […]
Non-compliance comes in many forms: depending on the disease area, as many as one in five patients fail to take the first step of collecting a prescription from the pharmacy. Many patients on short-term medications depart from recommended doses within a day or two of starting treatment. And many of those on longer-term medication may take a break from their medication or vary their dose depending on how they feel. A review of the evidence (Horne and Weinman, 1999) concluded that compliance overall is approximately 50 per cent but varies across different medication regimens, different illnesses and different treatment settings.”
A little more stuff from the book:
“Compliance depends on many factors, including the study population (better in educated compared to disadvantaged patients) type of intervention, duration of treatment, complexity of treatment, real or perceived side-effects and life circumstances (see Table 8.1). The reasons are often patient-specific, multifaceted and can change over time. Demographically, the very young, the very old, teenagers and those taking very complex treatment regimes are the least likely to comply. […]
asymptomatic and chronic diseases needing long-term treatment […] result in poorer compliance; and […] the longer the remission in chronic diseases, the lower the compliance (Blackwell, 1976). […] patient-controlled non-compliance was lower in treatment for diseases in which the relationship between non-compliance and recurrence is very clear, such as diabetes, compared to treatment for diseases in which this relationship is less clear […] Of course, cognitive deficit, helplessness, poor motivation and withdrawal all lead to forgetfulness and passive or structural noncompliance (Gitlin et al., 1989; Shaw, 1986). […] most non-compliance is intentional and results from conscious choices. […]
As a rule, patients cannot be simply classified as compliers or non-compliers. Rather, the level of compliance ranges from patients who take every prescribed dose precisely as directed to those who never do with the typical patient lying between these two extremes. The degree to which patients intend to comply with a regimen can be subdivided into patient-controlled and structural. Patient-controlled factors can be subdivided further into rational behaviour (as seen in patients with Parkinson’s disease who regulate their own dosing) and irrational behaviours (such as self-induced seizures). Structural factors are those beyond the patient’s control, such as impaired memory or difficulty accessing medication (Leppik, 1990). […]
Compliance and adherence to therapy are complex issues with no obvious ‘one size fits all’ solution available. It appears that actively involving patients in treatment decisions, empowering patients with access to medical information and providing ongoing monitoring all contribute to improved compliance and adherence rates. The challenge for health services, however, is to provide these enhanced levels of support cost-effectively.”
The book is a few years old and sometimes you can tell. I was curious along the way about how much things have changed in the meantime. I’m guessing less than would have been optimal.
I should point out lastly that I have made a goodreads profile. I haven’t added a lot of books to my profile yet, but I may decide to use that site actively in the future. At goodreads I gave the book 3 stars, corresponding to an ‘I liked it’ evalution.
I’m currently writing a topic on ‘the causal effect of education on health’, so this is a topic I’ve looked at a bit – consider this post a ‘workblog’-post, even though it’s only tangentially related to what I’m working on.
This kind of stuff – health disparities related to education and income – pops up in the public debate every now and then, see e.g. this recent article (in Danish), or this analysis by AE-rådet (also in Danish). This is ‘politics’ to some extent (see the previous post), but it’s also a question about what’s actually going on in the world, and the latter type of question is the type of question I tend to be interested in answering. I’d like to make some general points here which are sometimes overlooked:
i. People with lower education are fatter. And being fat is bad for your health.
ii. People with lower levels of education smoke more: “Well-documented declines in smoking prevalence over time have not occurred evenly throughout society (12, 13). They have been most substantial among the most educated. Thus, the least educated form increasing proportions of those who remain smokers.” Regarding alcohol the picture is more complicated (as I’ve talked about before), however it should be noted that if the variance of the quantity consumed by the highly educated is lower than for the lower educated groups, as they claim in the article I link to at the beginning of this paragraph, then it would make sense if the highly educated people who die from alcohol-related diseases die later and lose fewer years of their life to the alcohol than does the group with low education (‘the uneducated alcoholic loses 20 years, the educated alcoholic loses five…’). Either way alcohol matters much less than smoking, and the differences aren’t that big in the former case. Incidentally the causal pathways of the smoking link are still unclear: “The causal pathways between education and smoking are both complicated and contested in the literature.” (link)
iii. Lifestyle differences among different educational groups make up a big part of the difference in health outcomes: “the mediating effects of health behaviors – measured by smoking, drinking, exercising and the body mass index – account in the short run for 17% to 31% and in the long run for 23% to 45% of the entire effect of education on health, depending on gender.”
iv. An additional point related to point iii.: I haven’t looked for studies on this because it’s obvious, but the health gradient is more sensitive to stuff like income level and employment status in countries like the US than it is in Denmark. So international (non-Scandinavian?) estimates of the magnitude of educational effects and income effects on health outcomes are likely to be biased upwards, compared to what the magnitude would be in a country like Denmark where ability to pay for medical services problems are unlikely to have much influence on life expectancy at this point.
v. I’ll spell out this point even though it should be obvious by now: Many of the reasons why people with a low education on average die too soon relate to the fact that they on average make poorer choices when it comes to their health. And the stuff mentioned above is just a small part of what’s going on; you also have related stuff like information channels and compliance differences, on top of stuff like ‘likelihood of seeking proper medical attention conditional on you actually needing it, and ability to verbalize complaints so that the doctor makes the correct inferences’ (e.g. a lot of T2 diabetics don’t get diagnosed, and this lowers their life expectancy significantly).
vi. Note that whereas it’s true that some jobs are still more unhealthy than others (a traditional mechanism most people think of when they’re thinking about these things), if the connection between type of work and health risks is known people employed in such jobs would be expected to earn a risk premium – this is not super relevant when you look at education and health, but it is something to have in mind when analyzing health and income stuff.
vii. It should be noted that if you get better over time at treating people for stuff that isn’t lifestyle-related and so stop a lot of people from dying early on of other causes, then lifestyle-stuff is going to become a big driver of health disparities.
I just finished the book, which is published by Britannica Educational Publishing and edited by Kara Rogers.
It’s a little bit repetitive, but it’s really quite good. I knew a lot about the subject already, but this is my first textbook dealing specifically with this topic and there were a few places where I had ‘aha-moments’ and suddenly understood everything a lot better – I really enjoy reading books that give me such experiences.
I should point out that Khan Academy has a lot of good stuff on this subject, and the videos there go into a lot more detail than does the book – I haven’t seen all those videos, but I’ve seen enough of them to know that this is mostly good stuff. I should perhaps also point out that each link above is to a topic covered at Khan Academy, each with multiple videos of coverage. Wikipedia also has some stuff on this subject.
As an intro textbook to the subject I think the book is a decent choice, though the illustrations are somewhat lacking. All concepts are properly introduced and defined, and definitions will sometimes be repeated other places in the book (which is part of what makes it repetitive) so you don’t necessarily need to memorize everything to keep track of what’s going on. My main points of criticism would be the unnecessary amount of repetition and the fact that it doesn’t actually go into much detail. The latter point of criticism can however also be considered a plus if you don’t know very much about the subjects covered, and of course the somewhat superficial treatment of the material also means that this is by no means a hard textbook to read.
I found it hard to blog stuff from the book, because most of it is just definitions, ‘how does it all work?’, ‘what can go wrong and how does it go wrong?’, disease progression, treatment options, etc. Not a lot of numbers in there, or a lot of stuff that can easily be quoted ‘out of context’. But I figured I couldn’t blog the book without at least posting a few bits from the book, so below a few quotes (none of these are ‘old numbers’; the book was published in 2011):
“of those likely to die during the first two weeks after a major heart attack, nearly half will die within one hour of the onset of
“less than half of the persons who die from heart attacks each year in the United States survive long enough to reach the hospital.”
“While life expectancy following a heart transplant is difficult to predict, the average recipient will live 8 to 10 years.” […] The survival rate at one year is now about 84 percent and at three years about 77 percent.”
“The renal arteries deliver to the kidneys of a normal person at rest 1.2 litres (2.5 pints) of blood per minute, a volume equivalent to approximately one-quarter of the heart’s output. Thus, a volume of blood equal to all that found in the body of an adult human is processed by the kidneys once every four to five minutes.”
“In general, the rate of heartbeat varies inversely with the size of the animal. In elephants it averages 25 beats per minute, in canaries about 1,000. In humans the rate diminishes progressively from birth (when it averages 130) to adolescence but increases slightly in old age. The average adult rate is 70 beats at rest.”
A big part of the book is available at the link.
You can buy the book here, though I should note that I’m certain that free versions of the book are also available online. I started reading it yesterday and I completed it today.
The book consists of two parts: Part one deals with “Methods for Generalized Cost-Effectiveness Analysis” and part two consists of “Background Papers and Applications”. If you’re weird, like me, (or if you’re a researcher in the field…) you’ll want to read both parts. They write in the introduction that: “The main objective of this Guide is to provide policy-makers and researchers with a clear understanding of the concepts and benefits of GCEA [generalized cost-effectiveness analysis]. It provides guidance on how to undertake studies using this form of analysis and how to interpret the results.” As mentioned the book has two parts. It’s very clear that part one is written mainly for the politicians and that part two is written for the researchers – and good luck finding a politician who’ll actually read part 2 (/or part 1..?). I like to think that part one can be read and understood by most people, including certainly most readers of this blog, and I do not believe it requires a lot of knowledge about statistics or mathematics; some papers in part 2 on the other hand require math beyond the level I’ve taken for the reader to understand all the steps taken (here are a few wikipedia articles I had a look at while reading this part of the book). They repeat themselves a bit here and there, but it’s not hard to just skim passages containing stuff you’ve already dealt with elsewhere.
It should be noted that although some of it is a bit technical, there’s some good stuff in part 2 as well – for instance I really liked this table (from the fourth study in part 2, Econometric estimation of country-specific hospital costs):
Click to view full size. The obvious conclusion to draw here is that costs do not vary much across countries – no, they definitely do not… Actually I was very surprised to learn that there’s a huge amount of variation even within countries – in the same article they note that: “it must be emphasized that there is wide variation in the unit costs estimated from studies within a particular country […] These differences are sometimes of an order of magnitude, and cannot always be attributed to different methods. This implies that analysts cannot simply take the cost estimates from a single study in a country to guide their assessment of the cost-effectiveness of interventions, or the costs of scaling-up. In some cases, they could be wrong by an order of magnitude.”
In the first chapter they state that:
“It appears that the field can develop in two distinct directions, towards increasingly contextualized analyses or towards more generalized assessments. Cost-effectiveness studies and the sectoral application of CEA [cost effectiveness analyses] to a wide range of interventions can become increasingly context specific—at the individual study level by directly incorporating other social concerns such as distributional weights or a priority to treat the sick and at the sectoral level by developing complex resource allocation models that capture the full range of resource, ethical and political constraints facing decision-makers.
We fear that this direction will lead ultimately to less use of costeffectiveness information in the health policy dialogue. Highly contextualized analyses must by definition be undertaken in each context; the cost and time involved as well as the inevitable complexity of the resource allocation models will limit their practical use. The other direction for sectoral cost-effectiveness, the direction that WHO is promoting […] is to focus on the general assessment of the costs and health benefits of different interventions in the absence of various highly variable local decision constraints. A generalized league table of the cost-effectiveness of interventions for a group of populations with comparable health systems and epidemiological profiles can make the most powerful component of CEA readily available to inform health policy debates. Relative judgements on cost-effectiveness—e.g. treating tuberculosis with the DOTS strategy is highly cost-effective and providing liver transplants in cases of alcoholic cirrhosis is highly cost-ineffective—can have wide ranging influence and, as one input to an informed policy debate, can enhance allocative efficiency of many health systems.”
I’m not a health economist so I have no idea which way the field has developed since the book was written. The book isn’t exactly brand new (it’s from 2003) and so I figured one way to probe whether the recommendations have been followed in the years after the book was published was to try to figure out the extent to which one of the big ideas here, the use of Stochastic League Tables in CEAs, has been implemented. So I went to google scholar and searched for the term – and it gave me 7400+ results (and 589 since 2012). It seems to me that the use of these things at least have caught on. I incidentally have no idea to which extent researchers have now moved towards the use of GCEAs and away from the previously (?) widely used ‘incremental approach’ studies when performing these analyses. I posted the long quote above also to caution people unfamiliar with the literature against complaining about CEAs which are ‘not specific enough’ (a complaint I’ve made myself in the past…) – it may make a lot of sense to not make a CEA too specific, in order to make it more potentially useful to decisionmakers. A related point is that the idea of using CEAs in a formulaic way to decide which health interventions ‘pass the bar’ and which do not, and thus base decisions such as which health interventions should receive government support only on the outcome of CEAs, do not have much support in the field – as they put it in Murray, Lauer et al. (study 7 in the second part):
“The results of cost-effectiveness analysis should not be used in a formulaic way—starting with the intervention that has the lowest cost-effectiveness ratio, choosing the next most attractive intervention, and continuing until all resources have been used (10). There is generally too much uncertainty surrounding estimates for this approach; moreover, there are other goals of health policy in addition to improving population health. The tool is most powerful when it is used to classify interventions into broad categories such as those we used. This approach provides decision-makers with information on which interventions are low-cost ways of improving population health and which improve health at a much higher cost. This information enters the policy debate to be weighed against the effect of the interventions on other goals of health policy.”
(They also emphasize this aspect in the first part of the book). I could quote a lot of stuff from the book, but if you’re interested you’ll read it and if you’re not you’d probably not read my quotes either. If you’re interested in cost-effectiveness analyses, I think you should probably read this book – or at least the first part which is relatively easy and does not take that long to read. If you’re not interested in this stuff you should definitely stay away from it. But I think the book is a good starting point if you seek to understand some of the main concepts, issues, and tradeoffs involved when doing and interpreting CEAs.
One last thing I should note, primarily to the people who will not read the book: Many people think of the people doing stuff like cost-effectiveness analyses in this field as the bad guys. That’s because they’re the ones who keep reminding us that we can’t afford everything. When it comes to health care we don’t like to be reminded of this fact, because sometimes when it’s been decided by decisionmakers that public money should not be spent on X it means that someone will die. What I’d like to remind you of is that resource constraints don’t go away just because people prefer to ignore them; rather, when people disregard cost-effectiveness it may just mean that fewer people will be helped and more people will die than if a different course of action, perhaps the one suggested by a CEA, had been taken. CEAs may not provide the complete answer to how we should do these things and they have some limitations, but we should all keep in mind that it matters how we spend our money on this stuff, and that completely ignoring the resource constraint isn’t really a solution to the problems we face when dealing with these matters.