Health econ stuff

In a post I published a few weeks ago I mentioned that I had decided against including some comments and observations I had written about health economics in that post because the post was growing unwieldy, but that I might post that stuff later on in a separate post. This post will include those observations, as well as some additional details I added to the post later. This sort of post is the sort of post that usually does not get past the ‘draft’ stage (in wordpress you can save posts you intend to publish later on as drafts), and as is usually the case for posts like these I already regret having written it, for multiple reasons. I should warn you from the start that this post is very long and will probably take you some time to read.

Anyway, the starting point for this post was some comments related to health insurance and health economics which I left on SSC in the past. A lot more people read those comments on SSC than will read this post so the motivation for posting it here was not to ‘increase awareness’ of the ideas and observations included in some kind of general sense; my primary motivation for adding this stuff here is rather that it’s a lot easier for me personally to find stuff I’ve written when it’s located here on this blog rather than elsewhere on the internet, and I figure that some of the things I wrote back then are topics which might well come up again later, and it would be convenient for me in that case to have a link at hand. Relatedly I have added many additional comments and observations in this post not included in the primary exchange, which it is no longer possible for me to do on SSC as my comments are no longer editable on that site.

Although the starting point for the post was as mentioned a comment exchange, I decided early on against just ‘quoting myself’ in this post, and I have thus made some changes in wording and structure in order to increase the precision of the statements included and in order to add a bit of context making the observations below easier to read and understand (and harder to misread). Major topics to which the observations included in this post relate are preventable diseases, the level of complexity that is present in the health care sector, and various topics which relate to health care cost growth. Included in the post are some perhaps not sufficiently well known complications which may arise in the context of the discussion of how different financing schemes may relate to various outcomes, and to cost growth. Much of the stuff included will probably be review to people who’ve read my previous posts on health economics, but that’s to be expected considering the nature of this post.

Although ‘normative stuff’ is not what interests me most – I generally tend to prefer discussions where the aim is to identify what happens if you do X, and I’ll often be happy to leave the discussion of whether outcome X or Y is ‘best’ to others – I do want to start out with stating a policy preference, as this preference was the starting point for the aforementioned debate that lead to the origination of this post. At the outset I should thus make clear that I would in general be in favour of changes to the financial structure of health care systems where people who take avoidable risks which systematically and demonstrably increase their expected health care expenditures at the population level pay a larger proportion of the cost than do people who did not take such avoidable risks.

Most developed societies have health care systems which are designed in a way that implicitly to some extent subsidize unhealthy behaviours. An important note in this context is incidentally that one way of looking at these things is that if you are not explicitly demanding people who behave in risky ways which tend to increase their expected costs to pay more for their health care (/insurance), then you are in fact by virtue of not doing this implicitly subsidizing those unhealthy individuals/behaviours. I mention this because some people might not like the idea of ‘subsidizing healthy behaviours’ (‘health fascism’) – which from a certain point of view is what you do if you charge people who behave in unhealthy ways more. Maybe some people would take issue with words like ‘subsidy’ and ‘implicit’, but regardless of what you call these things the major point that is important to have in mind here is that if one group of people (e.g. ‘unhealthy people’) cost more to treat (/are ill more often, get illnesses related to their behaviours, etc., etc.) than another group of people (‘healthy people’), then if you need to finance this shortfall – which you do, as you face a budget constraint – there are only two basic ways to do this; you can either charge the high-cost group (‘unhealthy people’) more, or you can require the other group (‘healthy people’) to make up the difference. Any scheme which deals with such a case of unequal net contribution rates are equivalent either to one of those schemes or a mix of the two, regardless of what you call things and how it’s done, and regardless of which groups we are talking about (old people also have higher health care expenditures than do young people, and most health care systems implicitly redistribute income from the young to the old). If you’re worried about ‘health fascism’ and the implications of subsidizing healthy behaviours (/’punishing’ unhealthy behaviours) you should at least keep in mind that if the health care costs of people who live healthy lives and people who do not are dissimilar then any system that deals with this issue – which all systems must – can either choose to ‘subsidize’ healthy behaviours or unhealthy behaviours; there’s no feasible way to design a ‘neutral system’ if the costs of the groups are dissimilar.

Having said all this, the very important next point is then that it is much more difficult to make simple schemes that would accomplish an outcome in which people who engage in unhealthy behaviours are required to pay more without at the same time introducing a significant number of new problems than people who are not familiar with this field would probably think it is. And it’s almost certainly much harder to evaluate if the proposed change actually accomplished what you wanted to accomplish than you think it is. Even if we are clear about what we want to accomplish and can all agree that that is what we are aiming for – i.e. we are disregarding the political preferences of large groups of voters and whether the setup in question is at all feasible to accomplish – this stuff is really much harder than it looks, for many reasons.

Let’s start out by assuming that smoking increases the risk of disease X by 50%. Say you can’t say which of the cases of X are caused by smoking, all you know is that smoking increases the risk at the population level. Say you don’t cover disease X at all if someone smokes, that is, smokers are required to pay the full treatment cost out of pocket if they contract disease X. It’s probably not too controversial to state that this approach might by some people be perceived of as not completely ‘fair’ to the many smokers who would have got disease X even if they had not smoked (a majority in this particular case, though of course the proportion will vary with the conditions and the risk factors in question). Now, a lot of the excess health care costs related to smoking are of this kind, and it is actually a pretty standard pattern in general with risk factors – smoking, alcohol, physical inactivity, etc. You know that these behaviours increase risk, but you usually can’t say for certain which of the specific cases you observe in clinical practice are actually (‘perfectly’/’completely’/’partially’?) attributable to the behaviour. And quite often the risk increase associated with a specific behaviour is actually really somewhat modest, compared to the relevant base rates, meaning that many of the people who engage in behaviours which increase risk and who get sick might well have got sick even if they hadn’t engaged in those risky behaviours.

On top of this problem usually it’s also the case that risk factors interact with each other. Smoking increases the risk of cancer of the esophagus, but so does alcohol and obesity, and if a person both smokes and drinks the potential interaction effect may not be linear – so you most likely often can’t just identify individual risk factors in specific studies and then pool them later and add them all together to get a proper risk assessment. A further complication is that behaviours may both increase as well as decrease risk – to stick with the example, diets high in fruits and vegetables both lower the risk of cancer of the esophagus. Exercise probably does as well – we know that exercise has important and highly complex effects on immune system function (see e.g. this post). Usually a large number of potential risk factors is at play at the same time, there may be multiple variables which lower risk and are also important to include if you want a proper risk assessment, and even if you knew in theory which interaction terms were likely to be relevant, you might even so find yourself in a situation unable to estimate the interaction terms of interest – this might take high-powered studies with large numbers of patients, which may not be available or the results of such high-powered studies may not apply to your specific subgroup of patients. Cost-effectiveness is also an issue – it’s expensive to assess risk properly. One take-away is that you’ll still have a lot of unfairness in a modified contribution rate model, and even evaluating fairness aspects of the change may be difficult to impossible because to some extent this question is unknowable. You might find yourself in a situation where you charge the obese guy more because obesity means he’s high risk, but in reality he is actually lower risk than is the non-fat guy who is charged a lower rate, because he also exercises and eats a lot of fruits and vegetables, which the other guy doesn’t.

Of course the above paragraph took it for granted that it was even possible to quantify the excess costs attributable to a specific condition. That may not be easy at all to do, and there may be large uncertainties involved. The estimated excess cost will depend upon a variety of factors which may or may not be of interest to the party performing the analysis, for example it may be very important which time frame you’re looking at and which discounting methodology is applied (see e.g. the last paragraph in this post). The usual average vs marginal cost problem (see the third-last paragraph in the post to which I link in the previous sentence – this post also has more on this topic) also applies here and is related to ‘the fat guy who exercises and is low-risk’-problem; ideally you’d want to charge people with higher health care utilization levels more (again, in a setting where we assume the excess cost is associated with life-style variables which are modifiable – this was our starting point), but if there’s a large amount of variation in costs across individuals in the specific subgroups of interest and you only have access to average costs rather than individual-level costs, then a scheme only taking into account the differences in the averages may be very sub-optimal when you look at it from the viewpoint of the individual. Care needs to be taken to avoid problems like e.g. Simpson’s paradox.

Risk factors are not the only things that cluster; so do diseases. An example:

“An analysis of the Robert Koch-Institute (RKI) from 2012 shows that more than 50 % of German people over 65 years suffer from at least one chronic disease, approximately 50 % suffer from two to four chronic diseases, and over a quarter suffer from five or more diseases [3].” (link)

78.3 % of the type 2 diabetics also suffered from hypertension in that study. Does this fact make it easier or harder to figure out what is ‘the true cost contribution’ of ‘type 2 diabetes’ and ‘hypertension’ (and, what we’re ultimately interested in in this setting – the ‘true cost contribution’ of the unhealthy behaviours which lead some individuals to develop type 2 diabetics and hypertension who would not otherwise have developed diabetes and/or hypertension (…/as early as they did)? It should be noted that diabetes was estimated to account for 11 % of total global healthcare expenditure on adults in 2013 (link). That already large proportion is expected to rise substantially in the decades to come – if you’re interested in cost growth trajectories, this is a major variable to account for. Attributability is really tricky here, and perhaps even more tricky in the case of hypertension – but for what it’s worth, according to a CDC estimate hypertension cost the US $46 billion per year, or ~$150/per person per year.

Anyway, you look at the data and you make guesses, but the point is that doctor Smith won’t know for certain if Mr. Hanson would have had a stroke even if he hadn’t smoked or not. A proposal of not providing payment for a health care service or medical product in the case of an ‘obviously risky-behaviour-related-health-condition’ may sometimes appear to be an appealing proposition and you sometimes see people make this sort of proposal in discussions of this nature, but it tends to be very difficult when you look at the details to figure out just what those ‘obviously risky-behaviour-related-health-conditions’ are, and even harder to make even remotely actuarially fair adjustments to the premiums and coverage patterns to reflect the risk. Smoking and lung cancer is a common example of a relatively ‘clean’ case, but most cases are ‘less clean’ and even here there are complications; a substantial proportion of lung cancer cases are not caused by tobacco – occupational exposures also cause a substantial proportion of cases, and: “If considered in its own disease category […] lung cancer in never smokers would represent the seventh leading cause of cancer mortality globally, surpassing cancers of the cervix, pancreas, and prostate,5 and among the top 10 causes of death in the United States.” (link) Occupational exposures (e.g. asbestos) are not likely to fully account for all cases, and for example it has also been found that other variables, including previous pneumonia infections and tuberculosis, affect risk (here are a couple of relevant links to some previous coverage I wrote on these topics).

I think many people who have preferences of this nature (‘if it’s their own fault they’re sick, they should pay for it themselves’) underestimate how difficult it may be to make changes which could be known with a reasonable level of certainty to actually have the intended consequences, even assuming everybody agreed on the goal to be achieved. This is in part because there are many other aspects and complications which need to be addressed as well. Withholding payment in the case of costly preventative illness may for example in some contexts increase cost, rather than decrease them. The risk of complications of some diseases – an important cost driver in the context of diabetes – tends to be dependent on post-diagnosis behavioural patterns. The risk of developing diabetes complications will depend upon the level of glycemic control. If you say you won’t cover complications at all in the case of ‘self-inflicted disease X’, then you also to some extent tend to remove the option of designing insurance schemes which might lower cost and complication rates post-diagnosis by rewarding ‘good’ (risk-minimizing) behaviours post-diagnosis and punishing ‘bad’ (risk-increasing) behaviours. This is not desirable in the context of diseases where post-diagnosis behaviour is an important component of the cost function, as it certainly is in the diabetes context. There are multiple potential mechanisms here, some of which are disease specific (e.g. suboptimal diet in a diagnosed type 2 diabetic) and some of which may not be (a more general mechanism could e.g. be lowered compliance/adherence to treatment in the uncovered populations because they can’t afford the drugs which are required to treat their illness; though the cost-compliance link is admittedly not completely clear in the general case, there are certainly multiple diseases where lowered compliance to treatment would be expected to increase cost long-term).

And again, also in the context of complications fairness issues are not as simple to evaluate as people might like them to be; some people may have a much harder time controlling their disease than others, or they may be more susceptible to complications given the same behaviour. Some may already have developed complications by the time of diagnosis. Such issues make it difficult to design simple rules which would achieve what you want them to achieve without having unfortunate side-effects; for example a rule that a microvascular diabetes-related complication is automatically ‘your own fault’ (so we won’t pay for it), which might be motivated by the substantial amount of research linking glycemic control with complication risk, would punish some diabetics who have had the disease for a longer amount of time (many complications are not only strongly linked to Hba1c but also display a substantial degree of duration-dependence; for example in type 1 diabetics one study found that diabetic retinopathy was present in 13% of patients with a duration of disease less than 5 years, whereas the corresponding figure was 90% for individuals with a disease duration of 10–15 years (Sperling et al., p. 393). I also recall reading a study finding that Hba1c itself is increasing with diabetes duration, which may be partly accounted for by the higher risk of hypoglycemia related to hypoglycemia-unawareness-syndromes in individuals with long-standing disease), individuals with diseases which are relatively hard to control (perhaps due to genetics, or maybe again due to the fact that they have had the disease for a longer amount of time; the presence of hypoglycemia unawareness is as alluded to above to a substantial degree duration-dependent, and this problem increases the risk of hospitalizations, which are expensive), diabetics who developed complications before they knew they were sick (a substantial proportion of type 2 diabetics develop some degree of microvascular damage pre-diagnosis), and diabetics with genetic variants which confer an elevated risk of complications (“observations suggest that involvement of genetic factors is increasing the risk of complications” (Sperling et al., p. 226), and for example in the DCCT trial familial clustering of both neuropathy and retinopathy was found; clustering which persisted after controlling for Hba1c – for more on these topics, see e.g. Sperling et al.’s chapter 11).

Other decision rules would similarly lead to potentially problematic incentives and fairness issues; for example requiring individuals to meet a specific Hba1c goal might be more desirable than to just not cover complications, but that one also leads to potential problems; ideally such an Hba1c goal should be individualized, because of the above-mentioned complexities and others I have not mentioned here; to require a newly-diagnosed individual to meet the same goals as someone who has had diabetes for decades does not make sense, and neither does it make sense to require these two groups to meet exactly the same Hba1c goal as the middle-aged female diabetic who desires to become pregnant (diabetes greatly increases the risk of pregnancy complications, and strict glycemic control is extremely important in this patient group). It’s important to note that these issues don’t just relate to whether or not the setup is perceived of as fair, but it also relates to whether or not you would expect the intended goals to actually be met or not when you implement the rule. If you were to require that a long-standing diabetic with severe hypoglycemia unawareness had to meet the same Hba1c goal as the newly diagnosed individual, this might well lead to higher overall cost, because said individual might suffer a large number of hypoglycemia-related hospitalizations which would have been avoidable if a more lax requirement was imposed; when you decrease Hba1c you decrease the risk of long-term complications, but you increase the risk of hypoglycemia. A few numbers might make it easier to make sense of how expensive hospitalizations really are, and why I emphasize them here. In this diabetes-care publication they assign a cost for an inpatient day for a diabetes-related hospitalization at $2,359 and an emergency visit at ~$800. The same publication estimates the total average annual excess expenditures of diabetics below the age of 45 at $4,394. Going to the hospital is really expensive (43% of the total medical costs of diabetes are accounted for by hospital inpatient care in that publication).

A topic which was brought up in the SSC discussion was the question of the extent to which private providers have a greater incentive to ‘get things right’ in terms of assessing risk. I don’t take issue with this notion in general, but there are a lot of complicating factors in the health care context. One factor of interest is that it is costly to get things right. If you’re looking at this from an insurance perspective, larger insurance providers may be better at getting things right because they can afford to hire specialists who provide good cost estimates – getting good cost estimates is really hard, as I’ve noted above. Larger providers translate into fewer firms, which increases firm concentration and may thus increase collusion risk, which may again increase the prices of health care services. Interestingly if your aim is to minimize health care cost growth increased market power of private firms may actually be a desirable state of affairs/goal, because cost growth is a function of both unit prices and utilization levels, and higher premiums are likely to translate into lower utilization rates, which may lower overall costs and -cost growth. I decided to include this observation here also in order to illustrate that what is an optimal outcome depends on what your goal is, and in the setting of the health care sector you sometimes need to be very careful about thinking about what your actual goal is, and which other goals might be relevant.

When private insurance providers become active in a market that also includes a government entity providing a level of guaranteed coverage, total medical outlays may easily increase rather than decrease. The firms may meed an unmet need, but some of that unmet need may be induced demand (here’s a related link). Additionally, the bargaining power of various groups of medical personnel may change in such a setting, leading to changes in compensation schedules which may not be considered desirable/fair. An increase in total outlays may or may not be considered a desirable outcome, but this does illustrate once again the point that you need to be careful about what you are trying to achieve.

There’s a significant literature on how the level of health care integration, both at the vertical and horizontal level, both in terms of financial structure and e.g. in terms of service provision structure, may impact health care costs, and this is an active area of research where we in some contexts do not yet know the answers.

Even when cost minimization mechanisms are employed in the context of private firms and the firm in question is efficient, the firm may not internalize all relevant costs. This may paradoxically lead to higher overall cost, due to coverage decisions taken ‘upstream’ influencing costs ‘downstream’ in an adverse manner; I have talked about this topic on this blog before. A diabetic might be denied coverage of glucose testing materials by his private insurer, and that might mean that the diabetic instead gets hospitalized for a foreseeable and avoidable complication (hypoglycemic coma due to misdosing), but because it might not be the same people paying for the testing material and the subsequent hospitalization it might not matter to the people denying coverage of the testing materials, and/so they won’t take it into account when they’re making their coverage decisions. That sort of thing is quite common in the health care sector – different entities pay for and receive payments for different things, and this is once again a problem to keep in mind if you’re interested in health care evaluation; interventions which seem to lower cost may not do so in reality, because the intervention lead to higher health care utilization elsewhere in the system. If incentives are not well-aligned things may go badly wrong, and they are often not well-aligned in the health care sector. When both the private and public sectors are involved in either the financial arrangements and/or actual health service provision – which is the default health care system setup for developed societies – this usually leads to highly complex systems, where the scope for such problems to appear seems magnified, rather than the opposite. I would assume that in many cases it matters a lot more that incentives are well-aligned than which specific entity is providing insurance or health care in the specific context, in part a conclusion drawn from the coverage included in Simmons, Wenzel & Zgibor‘s book.

In terms of the incentive structures of the people involved in the health care sector, this stuff is of course also adding another layer of complexity. In all sectors of the economy you have people with different interests who interact with each other, and when incentives change outcomes change. Outcomes may be car batteries, or baseball bats, or lectures. Evaluating outcomes is easier in some settings than in others, and I have already touched upon some of the problems that might be present when you’re trying to evaluate outcomes in the health care context. How easy it is to evaluate outcomes will naturally vary across sub-sectors of the health care sector but a general problem which tends to surface here is the existence of various forms of asymmetrical information. There are multiple layers, but a few examples are worth mentioning. To put it bluntly, the patient tends to know his symptoms and behavioural patterns – which may be disease-relevant, and this aspect is certainly important to include when discussing preventative illnesses caused at least in part by behaviours which increase the risk of said illnesses – better than his doctor, and the doctor will in general tend to know much more about the health condition and potential treatment options than will the patient. The patient wants to get better, but he also wants to look good in the eyes of the doctor, which means he might not be completely truthful when interacting with the doctor; he might downplay how much alcohol he drinks, misrepresent how often he exercises, or he may lie about smoking habits or about how much he weighs. These things make risk-assessments more difficult than they otherwise might have been. As for the GPs, usually we here have some level of regulation which restricts their behaviour to some extent, and part of the motivation for such regulation is to reduce the level of induced demand which might otherwise be the result of information asymmetry in the context of stuff like relevant treatment effects. If a patient is not sufficiently competent to evaluate the treatments he receives (‘did the drug the doctor ordered really work, or would I have gotten better without it?’), there’s a risk he might be talked into undergoing needless procedures or take medications for which he has no need, especially if the doctor who advises him has a financial interest in the treatment modality on offer.

General physicians have different incentives from nurses and specialists working in hospitals, and all of these groups may experience conflicts of interests when they’re dealing with insurance providers and with each other. Patients as mentioned have their own set of incentives, which may not align perfectly with those of the health care providers. Different approaches to how to deal with such problems lead to different organizational setups, all of which influence both the quantity and quality of care, subject to various constraints. It’s an active area of research whether decreasing competition between stakeholders/service providers may decrease costs; one thing that is relatively clear from diabetes research with which I have familiarized myself is that when different types of care providers coordinate activities, this tends to lead to better outcomes (and sometimes, but not always, lower costs), because some of the externalized costs become internalized by virtue of the coordination. It seems very likely to me that conclusions to such questions will be different for different subsectors of the health care sector. A general point might be that more complex diseases should be expected to be more likely to generate cost savings from increased coordination than should relatively simple diseases (if you’re fuzzy about what the concept of disease complexity refers to, this post includes some relevant observations). This may be important, because complex diseases also should probably tend to be more expensive to treat in general, because the level of need in patients is higher.

It’s perhaps hardly surprising, considering the problems I’ve already discussed related to how difficult it may be to properly assess costs, that there’s a big discussion to be had about how to even estimate costs (and benefits) in specific contexts, and that people write books about these kinds of things. A lot of things have already been said on this topic and a lot more could be said, but one general point perhaps worth repeating is that it may in the health care sector be very difficult to figure out what things (‘truly’) cost (/’is worth’). If you only have a public sector entity dealing with a specific health problem and patients are not charged for receiving treatment, it may be very difficult to figure out what things ‘should’ cost because relevant prices are simply missing from the picture. You know what the government entity paid the doctors in wages and what it paid for the drugs, but the link between payment and value is sometimes a bit iffy here. There are ways to at least try to address some of these issues, but as already noted people write books about these kinds of things so I’m not going to provide all the highlights here – I refer to the previous posts I’ve written on these topics instead.

Another important related point is that medical expenditures and medical costs are not synonyms. There are many costs associated with illness which are not directly related to e.g. a payment to a doctor. People who are ill may be less productive while they are at work, they may have more sick-days, they may retire earlier, their spouse may cut down on work hours to take care of them instead of going to work, a family caretaker may become ill as a result of the demands imposed by the caretaker role (for example Alzheimer’s disease significantly increases the risk of depression in the spouse). Those costs are relevant, there are literatures on these things, and in some contexts such ‘indirect costs’ (e.g. lower productivity at work and early retirement) may make up a very substantial proportion of the total costs of a health condition. I have seen diabetes cost estimates which indicated that the indirect costs may account for as much as 50 % of the total costs.

If there’s a significant disconnect between total costs and medical expenditures then minimizing expenditures may not be desirable from an economic viewpoint. A reasonable assessment model will/should in the context of models of outlays include both a monetary cost parameter and a quality/quantity (ideally both) parameter; if you neglect to take account of the latter, in some sense you’re only dealing with what you pay out, not what you get for that payment (which is relevant). If you don’t take into account indirect costs you implicitly allow cost switching practices to potentially muddle the picture and make assessments more difficult; for example if you provide fewer long-term care facilities then the number of people involved in ‘informal care’ (e.g. family members having to take care of granny) will go up, and that is going to have secondary effects downstream which should also be assessed (you improve the budget in the context of the long-term care facilities, but you may at the same time increase demands on e.g. psychiatric institutions and marginally lower especially the female labour market participation rate. The net effect may still be positive, but the point is that an evaluation will/should include costs like these in the analysis, at least if you want anything remotely close to the full picture).

Let’s return to those smokers we talked about earlier. A general point not mentioned yet is that if you don’t cover smokers in the public sector because of cost considerations, many of them may also not be covered by private insurance either. This is because a group of individuals that is high risk and expensive to treat will be demanded high premiums (or the insurance providers would go out of business), and for the sake of this discussion we’re now assuming smokers are expensive. If that is so, many of them probably would not be able to afford the premiums demanded. Now, one of the health problems which are very common in smokers is chronic obstructive pulmonary disease (COPD). Admission rates for COPD patients differ as much as 10-fold between European countries, and one of the most important parameters regarding pharmacoeconomics is the hospitalization rate (both observations are from this text). What does this mean? It means that we know that admission rate from COPD is highly responsive to the treatment regime; populations well-treated have much fewer hospitalizations. 4% of all Polish hospitalizations are due to COPD. If you remove the public sector subsidies, the most likely scenario you get seems to me to be a poor-outcomes scenario with lots of hospitalizations. Paying for those is likely to be a lot more expensive than it is to treat the COPD pharmacologically in the community. And if smokers aren’t going to be paying for it, someone else will have to do that. If you both deny them health insurance and refuse them treatment if they cannot pay for it they may just die of course, but in most cost-assessment models that’s a high-cost outcome, not a low-cost outcome (e.g. due to lost work-life productivity etc. Half of people with COPD are of working age, see the text referred to above.). This is one example where the ‘more fair’ option might lead to higher costs, rather than lower costs. Some people might still consider such an outcome desirable, it depends on the maximand of interest, but such outcomes are worth considering when assessing the desirability of different systems.

A broadly similar dynamic, in the context of post-diagnosis behaviour and links to complications and costs, may be present in the context of type 2 diabetes. I know much more about diabetes than I do about respirology, but certainly in the case of diabetes this is a potentially really big problem. Diabetics who are poorly regulated tend to die a lot sooner than other people, they develop horrible complications, they stop being able to work, etc. etc. Some of those costs you can ignore if you’re willing to ‘let them die in the streets’ (as the expression goes), but a lot of those costs are indirect costs due to lower productivity, and those costs aren’t going anywhere, regardless of who may or may not be paying the medical bills of these people. Even if they have become sick due to a high-risk behaviour of their own choosing, their health care costs post-diagnosis will still be highly dependent upon their future medical care and future health insurance coverage. Denying them coverage for all diabetes-related costs post-diagnosis may, paradoxical though it may seem to some, not be the cost-minimizing option.

I already talked about information asymmetries. Another problematic aspect linked to information management also presents itself here in a model of this nature (‘deny all diabetes-related coverage to known diabetics’); people who suspect they might be having type 2 diabetes may choose not to disclose this fact to a health care provider because of the insurance aspect (denial of coverage problems). Insurance providers can of course (and will try to) counter this by things like mandatory screening protocols, but this is expensive, and even assuming they are successful you again not only potentially neglect to try to minimize the costs of the high-cost individuals in the population (the known diabetics, who might be cheaper long-term if they had some coverage), you also price a lot of non-diabetics out of the market (because premiums went up to pay for the screening). And some of those non-diabetics are diabetics to-be, who may get a delayed diagnosis as a result, with an associated higher risk of (expensive) complications. Again, as in the smoking context if the private insurer does not cover the high-cost outcomes someone else will have to do that, and the blind diabetic in a wheel-chair is not likely to be able to pay for his dialysis himself.

More information may in some situations lead to a breakdown in insurance markets. This is particularly relevant in the context of genetics and genetic tests. If you have full information, or close to it, the problem you have to some extent stops being an insurance problem and instead becomes a problem of whether or not to, and to which extent you want to-, explicitly compensate people for having been dealt a bad hand by nature. To put it in very general terms, insurance is a better framework for diseases which can in principle be cured than it is for chronic conditions where future outlays are known with a great level of certainty; the latter type of disease tends to be difficult to handle in an insurance context.

People who have one disease may develop other diseases as time progresses, and having disease X may increase or decrease the risk of disease Y. People study such disease variability patterns, and have done so for years, but there’s still a lot of stuff we don’t know – here’s a recent post on these topics. Such patterns are interesting for multiple reasons. One major motivation for studying these things is that ‘different’ diseases may have common mechanisms, and the identification of these mechanisms may lead to new treatment options. A completely different motivation for studying these things relate rather to the kind of stuff covered in this post, where you instead wonder about economic aspects; for example, if the smoker stops smoking he may gain weight and eventually develop type 2 diabetes instead of developing some smoking-related condition. Is this outcome better or worse than the other? It’s important to keep in mind when evaluating changes in compensation schedules/insurance structures that diseases are not independent, and this is a problem regardless of whether you’re interested in total costs or ‘just’ direct outlays. Say you’re ‘only’ worried about outlays and you are trying to figure out if it is a good idea to deny coverage to smokers, and you know that ex-smokers are likely to gain weight and have an increased risk of type 2 diabetes. Then the relevant change in cost is not the money you save on smoking-related illness, it’s the cost change you arrive at when after you account for those savings also account for the increased cost of treating type 2 diabetes. Disease interdependencies are probably as complex as risk factor interdependencies – the two phenomena are to some extent representing the same basic phenomenon – so this makes true cost evaluation even harder than it already was. Not all relevant costs at the societal level are of course medical costs; if people live longer, and they rely partly on a pension scheme to which they are no longer contributing, that cost is also relevant.

If a group of people who live longer cost more than a group of people who do not live as long, and you need to cover the associated shortfall, then – as we concluded in the beginning – there are really only two ways to handle this: Make them pay more than the people who do not live as long, or make the people who do not live as long pay more to cover the shortfall. Another way to look at this is that in this situation you can either tax people ‘for not living long enough’, or you can tax people for ‘not dying at the appropriate time’. On the other hand (?), if a group of people who die early turns out to be the higher-cost group in the relevant comparison (perhaps because they have shorter working lives and so pay into the system for a shorter amount of time), then you can deal with this problem by… either taxing them for ‘not living long enough’ or by punishing the people who live long lives for ‘not dying at the appropriate time’. No, of course it doesn’t matter which group is high cost, the solution mechanism is the same in both cases – make one of the groups pay more. And every time you tweak things you change the incentives of various people, and implicit effects like these hide somewhere in the background.

March 31, 2017 Posted by | Cancer/oncology, Diabetes, Economics, health care, rambling nonsense | Leave a comment

Economic Analysis in Healthcare (II)

This is my second and last post about the book, which will include some quotes from the second half of the book, as well as some comments.

“Different countries have adopted very different health care financing systems. In fact, it is arguable that the arrangements for financing of health care are more variable between different countries than the financing of any other good or service. […] The mechanisms adopted to deal with moral hazard are similar in all systems, whilst the mechanisms adopted to deal with adverse selection and incomplete coverage are very different. Compulsory insurance is used by social insurance and taxation [schemes] to combat adverse selection and incomplete coverage. Private insurance relies instead on experience rating to address adverse selection and a mix of retrospective reimbursement and selective contracting and vertical integration to deal with incomplete coverage.”

I have mentioned this before here on the blog (and elsewhere), but it is worth reiterating because you seem to sometimes encounter people who do not know this; there are some problems you’ll have to face when you’re dealing with insurance markets which will be there regardless of which entity is in charge of the insurance scheme. It doesn’t matter if your insurance system is government based or if the government is not involved in the insurance scheme at all, moral hazard will be there either way as a potential problem and you’re going to have to deal with that somehow. In econ 101 you tend to learn that ‘markets are great’, but this is one of those problems which are not going to go away by privatization.

On top of common problems faced by all insurers/insurance systems, different types of -systems will also tend to face a different mix of potential problems, some of which are likely to merit special attention in the specific setting in question. Some problems tend to be much more common in some specific settings than they are in others, which means that to some extent when you’re deciding on what might be ‘the ‘best’ institutional setup’, part of what you’re deciding on is which problem you are most concerned about addressing. In an evaluation context it should be pointed out in that context that the fact that most systems are mixes of different systems rather than ‘pure systems’, which they are, means that evaluation problems tend to be harder than they might otherwise have been. To add to this complexity as noted above the ways insurers deal with the same problem may not necessarily be the same in different institutional setups, which is worth having in mind when performance is evaluated (i.e., the fact that country A has included in the insurance system a feature X intending to address problem Q does not mean that country B, which has not included X in the system, does not attempt to address problem Q; B may just be using feature Y instead of feature X to do so).

Chapter 7 of the book deals with Equity in health care, and although I don’t want to cover that chapter in any detail a few observations from the text I did find worth including in this post:

“In the 1930s, only 43% of the [UK] population were covered by the national insurance scheme, mainly men in manual and low-paid occupations, and covered only for GP services. Around 21 million people were not covered by any health insurance, and faced potentially catastrophic expenditure should they become ill.”

“The literature on equity in the finance of health care has focused largely on the extent to which health care is financed according to ability to pay, and in particular on whether people with different levels of income make […] different payments, which is a vertical equity concern. Much less attention has been paid to horizontal equity, which considers the extent to which people with the same income make the same payments. […] There is horizontal inequity if people with the same ability to pay for health care, for example the same income, pay different amounts for it. […] tax-based payments and social health insurance payments tend to have less horizontal inequity than private health insurance payments and direct out-of-pocket payments. […] there are many concepts of equity that could be pursued; these are limited only by our capacity to think about the different ways in which resources could be allocated. It is unsurprising therefore that so many concepts of equity are discussed in the literature.”

Chapter 8 is about ‘Health care labour markets’. Again I won’t cover the chapter in much detail – people interested in such topics might like to have a look at this paper, which I concluded from a brief skim looks like it covers a few of the topics also discussed in the chapter – but I did want to include a few data:

“[S]alaries and wages paid to health care workers account for a substantial component of total health expenditure: the average country devotes over 40% of its government-funded health expenditure to paying its health workforce […], though there are regional variations [from ~30% in Africa to ~50% in the US and the Middle East – the data source is WHO, and the numbers are from 2006]. […] The WHO estimates there are around 59 million paid health workers worldwide […], around nine workers for every 1 000 population, with around two-thirds of the total providing health care and one third working in a non-clinical capacity.”

The last few chapters of the book cover mostly topics I have dealt with before, in more detail – for example are most topics covered here which are also covered in Gray et al. covered in much more detail in the latter book, which is natural as this text is mostly an introductory undergraduate text whereas the Gray et al. text is not (the latter book was based on material taught in a course called ‘Advanced Methods of Cost-Effectiveness Analysis’) – or topics in which I’m not actually all that interested (e.g. things like ‘extra-welfarism‘). Below I have added some quotes from the remaining chapters. I apologize in advance for repeating myself, given the fact that I probably covered a lot of this stuff back when I covered Gray et al., but on the other hand I read that book a while ago anyway:

“Simply providing information on costs and benefits is in itself not evaluative. Rather, in economic evaluation this information is structured in such a way as to enable alternative uses of resources to be judged. There are many criteria that might be used for such judgements. […] The criteria that are the focus of economic analysis are efficiency and equity […] in practice efficiency is dealt with far more often and with greater attention to precise numerical estimates. […] In publicly provided health programmes, market forces might be weak or there might be none at all. Economic evaluation is largely concerned with measuring efficiency in areas where there is public involvement and there are no markets to generate the kind of information – for example, prices and profits – that enable us to judge this. […] The question of how costs and benefits are to be measured and weighed against each other is obviously a fundamental issue, and indeed forms the main body of work on the topic. The answers to this question are often pragmatic, but they also have very strong guides from theory.”

“[M]any support economic evaluation as a useful technique even where it falls short of being a full cost–benefit analysis [‘CBA’ – US], as it provides at least some useful information. A partial cost–benefit analysis usually means that some aspects of cost or benefit have been identified but not valued, and the usefulness of the information depends on whether we believe that if the missing elements were to be valued they would alter the balance of costs and benefits. […] A special case of a partial economic evaluation is where costs are valued but benefits are not. […] This kind of partial efficiency is dealt with by a different type of economic evaluation known as cost-effectiveness analysis (CEA). […] One rationale for CEA is that whilst costs are usually measured in terms of money, it may be much more difficult to measure benefits that way. […] Cost-effectiveness analysis tries to identify where more benefit can be produced at the same cost or a lower cost can be achieved for the same benefit. […] there are many cases where we may wish to compare alternatives in which neither benefits nor costs are held constant. In this case, a cost-effectiveness ratio (CER) – the cost per unit of output or effect – is calculated to compare the alternatives, with the implication that the lower the CER the better. […] CBA seeks to answer whether or not a particular output is worth the cost. CEA seeks to answer the question of which among two or more alternatives provides the most output for a given cost, or the lowest cost for a given output. CBA therefore asks whether or not we should do things, while CEA asks what is the best way to do things that are worth doing.”

“The major preoccupation of economic evaluation in health care has been measurement of costs and benefits – what should be measured and how it should be measured – rather than the aims of the analysis. […] techniques such as CBA and CEA are […] defined by measurement rather than economic theory. […] much of the economic evaluation literature gives the label cost-minimisation analysis to what was traditionally called CEA, and specifically restricts the term CEA to choices between alternatives that have similar types of effects but differing levels of effect and costs. […] It can be difficult to specify what the appropriate measure of effect is in CEA. […] care is […] required to ensure that whichever measure of effect is chosen does not mislead or bias the analysis – for example, if one intervention is better at preventing non-fatal heart attacks but is worse at preventing fatal attacks, the choice of effect measure will be crucial.”

“[Health] indicators are usually measures of the value of health, although not usually expressed in money terms. As a result, a third important type of economic evaluation has arisen, called cost–utility analysis (CUA). […] the health measure usually used in CUA is gains in quality-adjusted life years […] it is essentially a composite measure of gains in life expectancy and health-related quality of life. […] the most commonly used practice in CUA is to use the QALY and moreover to assume that each QALY is worth the same irrespective of who gains it and by what route. […] Similarly, CBA in practice focuses on sums of benefits compared to sums of costs, not on the distribution of these between people with different characteristics. It also does not usually take account of whether society places different weights on benefits experienced by different people; for example, there is evidence that many people would prefer health services to put a higher priority on improving the health of younger rather than older people (Tsuchiya et al., 2003).”

“Because CEA does not give a direct comparison between the value of effects and costs, decision rules are far more complex than for CBA and are bounded by restrictions on their applicability. The problem arises when the alternatives being appraised do not have equal costs or benefits, but instead there is a trade-off: the greater benefit that one of the alternatives has is achieved at a higher cost [this is not a rare occurrence, to put it mildly…]. The key problem is how that trade-off is to be represented, and how it can then be interpreted; essentially, encapsulating cost-effectiveness in a single index that can unambiguously be interpreted for decision-making purposes.”

“Although cost-effectiveness analysis can be very useful, its essential inability to help in the kind of choices that cost–benefit analysis allows – an absolute recommendation for a particular activity rather than one contingent on a comparison with alternatives – has proved such a strong limitation that means have been sought to overcome it. The key to this has been the cost-effectiveness threshold or ceiling ratio, which is essentially a level of the CER that any intervention must meet if it is to be regarded as cost-effective. It can also be interpreted as the decision maker’s willingness to pay for a unit of effectiveness. […] One of the problems with this kind of approach is that it is no longer consistent with the conventional aim of CEA. Except under special conditions, it is not consistent with output maximisation constrained by a budget. […] It is useful to distinguish between a comparator that is essentially ‘do nothing about the problem […]’ and one that is ‘another way of doing something about that problem’. The CER that arises from the second of these is […] an incremental cost-effectiveness ratio (ICER) […] in most cases the ICER is the correct measure to use. […] A problem [with using ICERs] is that if only the ICER is evaluated, it must be assumed that the alternative used in the comparator is itself cost-effective; if it is not, the ICER may mislead.”

“The basis of economic costing is […] quite distinct from accounting or financial cost approaches. The process of costing involves three steps: (1) identify and describe the changes in resource use, both increases and decreases, that are associated with the options to be evaluated; (2) quantify those changes in resource use in physical units; and (3) value those resources. […] many markets are not fully competitive. For example, the wages paid to doctors may be a reflection of the lobbying power of medical associations or restrictions to licensing, rather than the value of their skills […] The prices of drugs may reflect the effect of government regulations on licensing, pricing and intellectual property. Deviations of price from opportunity cost may arise from factors such as imperfect competition […] or from distortions to markets created by government interventions. Where these are known, prices should be adjusted […] In practice, such adjustments are difficult to make and would rely on good information on the underlying costs of production, which is often not available. Further, where the perspective is that of the health service, there is an argument for not adjusting prices, on the grounds that the prevailing prices, even if inefficient, are those they must pay and are relevant to their budget. […] Where prices are used, it is important to consider whether the option being evaluated will, if implemented, result in price changes. […] Valuing resource use becomes still more difficult in cases where there are no markets. This includes the value of patients’ time in seeking and receiving care or of caregivers’ time in providing informal supportive care. The latter can be an important element of costs and […] may be particularly important in the evaluation of health care options that rely on such inputs.”

“[A]lthough the emphasis in economic evaluation is on marginal changes in costs and benefits, the available data frequently relate to average costs […] There are two issues with using average cost data. First, the addition to or reduction in costs from increased or decreased resource use may be higher, lower or the same as the average cost. Unfortunately, knowing what the relationship is between average and marginal cost requires information on the latter – the absence of which is the reason average costs are used! Secondly, average cost data obscure potentially important issues with respect to the technical efficiency of providers. If average costs are derived in one setting, for example a hospital, this assumes that the hospital is using the optimal combination of inputs. If average costs are derived from multiple settings, they will include a variety of underlying production technologies and a variety of underlying levels of production efficiency. Average costs are therefore less than ideal, because they comprise a ‘black box’ of underlying cost and production decisions. […] Approaches to costing fall into two broad types: macro- or ‘top-down’ costing, and micro- or ‘bottom-up’ costing […] distinguished largely on the basis of the level of disaggregation […] A top-down approach may involve using pre-existing data on total or average costs and apportioning these in some way to the options being evaluated. […] In contrast, a bottom-up approach identifies, quantifies and values resources in a disaggregated way, so that each element of costs is estimated individually and they are summed up at the end. […] The separation of top-down and bottom-up costing approaches is not always clear. For example, often top-down studies are used to calculate unit costs, which are then combined with resource use data in bottom-up studies.”

“Health care programmes can affect both length and quality of life; these in turn interact with both current and future health care use, relating both to the condition of interest and to other conditions. Weinstein and Stason (1977) argue that the cost of ‘saving’ life in one way should include the future costs to the health service of death from other causes. […] In practice, different analysts respond to this issue in different ways: examples may be found of economic evaluations of mammography screening that do […] and do not […] incorporate future health care costs. Methodological differences of this sort reduce the ability to make valid comparisons between results. In practical terms, this issue is a matter of researcher discretion”.

The stuff included in the last paragraph above is closely linked to stuff covered in the biodemography text I’m currently reading, and I expect to cover related topics in some detail in the future here on the blog. Below a few final observations from the book about discounting:

“It is generally accepted that future costs should be discounted in an economic evaluation and, in CBA, it is also relatively non-controversial that benefits, in monetary terms, should also be discounted. In contrast, there is considerable debate surrounding the issue of whether to discount health outcomes such as QALYs, and what the appropriate discount rate is. […] The debate […] concentrates on the issue of whether people have a time preference for receiving health benefits now rather than in the future in the same way that they might have a time preference for gaining monetary benefits now rather than later in life. Arguments both for and against this view are plausible, and the issue is currently unresolved. […] The effect of not discounting health benefits is to improve the cost-effectiveness of all health care programmes that have benefits beyond the current time period, because not discounting increases the magnitude of the health benefits. But as well as affecting the apparent cost-effectiveness of programmes relative to some benchmark or threshold, the choice of whether to discount will also affect the cost-effectiveness of different health care programmes relative to each other […] Discounting health benefits tends to make those health care programmes with benefits realised mostly in the future, such as prevention, less cost-effective relative to those with benefits realised mostly in the present, such as cure.”

March 5, 2017 Posted by | Books, Economics, health care | Leave a comment

Economic Analysis in Healthcare (I)

“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.”

February 20, 2017 Posted by | Books, Economics, health care | Leave a comment

Self-Esteem (II)

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”

November 12, 2014 Posted by | Books, Economics, health care, Psychology | Leave a comment

The Oxford Handbook of Health Economics

I don’t know in how much detail I’ll cover this book in the week to come but there’s some interesting stuff in there and I figure I might as well start ‘work-blogging’ a bit again. It is my goal to read at least 100 pages/day in this book over the next week’s time, meaning I should be finished at the end of next week (the book has 937 pages, excluding the index at the end – I’ve read 165 pages so far). 100 pages per day isn’t actually all that much and I may decide to work more than that, but the book is occasionally a little technical and I set that goal also because I knew it would be achievable (plus it’s not the only work I’ll be doing). I’ll probably cover plenty of stuff here which will end up not being directly relevant to my work, but I don’t think any of you will object strongly to this – almost none of the stuff I cover here on the blog has anything to do with my university activities anyway. I’m in the very rare situation that stuff which I might have read anyway because I find it interesting actually turns out to be relevant to my work/studies.

I don’t know how difficult the material covered in this book will be to understand for people without a background in economics; it’s much easier to make correct assumptions about such things if you’re covering stuff which is technically not within ‘your field’ either (e.g. psychology textbooks). Fortunately unlike Mas-Colell this is not a (not very well…) disguised math textbook, so I think it actually makes sense to cover some of the stuff from the book here on the blog – it made pretty much no sense to cover the stuff in Mas-Colell which was a big part of why I didn’t do it. I don’t know to which extent it’ll be necessary to add links and comments etc. along the way to aid understanding and I will probably underestimate the need – if you don’t get what they’re talking about even though I seem to be assuming that you ought to get it, just ask questions in the comments and I’ll try to clarify. I have done work on some of the topics covered in the book before, so it’s not like I don’t know anything about these things. Only one chapter so far has been what I’d term ‘model-heavy’, and although I probably won’t talk much about that one there should still be plenty of stuff in the book which it makes sense to cover here. I should note that I’m very aware of the fact that time spent covering the book is time not spent reading, and although I may derive a small benefit from covering the material here I won’t let blogging interfere with the reading goal. So I don’t know how much I’ll blog in the week to come – we’ll see.

Below some observations from the first 7 chapters/160 pages:

“Health care spending has been capturing a growing share of the GDP in all OECD countries between 1970 and 2008. In the median OECD country, health care spending was 5.1 percent of GDP in 1970 and increased to 9.1 percent by 2008 […] A persistent outlier in health care spending has been the US, where health care spending increased from 7.0 percent of the GDP in 1970 to 16.0 percent in 2008. […] Denmark was also an early outlier, actually spending a greater percentage of its GDP on health than the US (7.9%) in 1970. By 2008, Denmark had similar health care spending levels as the median OECD country, a result of a very slow rate of growth in health care spending between 1970 and 2008.” [Actually one might argue that this last part shouldn’t be news to ‘regular readers’ as I’ve covered the Danish numbers before here on the blog in a post in Danish. Note that the data in that post seem to indicate that there may have been a structural shift around the year 2000 or so – the cost share has risen relatively fast since that time, compared to earlier. Denmark is above 11% of GDP now.]

“Growth rates in health care spending are […] compared using the average annual growth rates of health care spending adjusted for inflation and population growth. Using this measure, the average annual rate of health care spending growth was 3.9 percent in the median OECD country from 1970 to 2008 […] The rate of health care spending growth was higher than inflation in every OECD country during the overall time period. […] In 2008, approximately three-quarters of the health spending was from public funds in the median OECD country, while the remaining quarter was from private funds. […] Of the private health care dollars in the median OECD country, 72 percent were out-of-pocket expenses in 2008. Private health insurance is responsible for a small proportion of health spending in most OECD countries. […] Across the OECD, about 15 percent of all tax revenue is devoted to health care –  a proportion that is steadily increasing.”

“Three sectors of health care represents over half of the total health care spending in most OECD countries: inpatient hospital care, outpatient medical services, and pharmaceuticals. […] Inpatient hospital spending declined rapidly during the period from a median of 48.5 percent in 1970 to 32.3 percent in 2008. […] During the same time period, the length of stay for inpatient care has fallen by approximately 58 percent. […] The median OECD country has seen a slight increase in the share of pharmaceutical expenditures from 17.5 percent […] to 13.8 percent [sic; I’m sure those numbers were mixed up during editing.]  […] Real GDP per capita increased by 120 percent from 1970 to 2008 in the median country of the OECD. […] health spending per capita increased by 314 percent […] after controlling for inflation and population growth […] Health spending grew an average of 1.8 percent per year faster than GDP in the median OECD country […] The percent of the population over the age 65 [increased] 40 percent in the median OECD country. […] Life expectancy increased by 8.8 years […] while fertility rates declined by 31 percent. […] Every OECD country had an increase in the number of physicians per 1000 capita between 1970 and 2008 […] The median OECD country had a 223 percent increase.” […but note also that: “on the whole, healthcare wages does not drive health spending growth”]

“Chronic diseases is creating a growing burden on health care spending. […] within the US, 85 percent of health spending was attributed to people with chronic diseases in 2006 (Anderson 2007).”

“Low and middle-income countries (LMICs) […] account for 84 percent of the world’s population, 90 percent of the world’s disease burden […], 24 percent of the world’s GDP, and only 13 percent of global health expenditure. […] The lower the country income level, the higher tends to be the share of out-of-pocket payments […] and the lower the share of revenues (e.g. tax, insurance premiums) which flows through financing agents. […] Even in a country like India, 83 percent of total expenditures on health is from private sources, and of this 94 percent is from out-of-pocket payments. […] [there is] only 0.3 physicians and one nurse per 1000 people in low-income countries. SSA has the lowest density of physicians (one doctor for every 5.000 people), and South Asia of nurses (one nurse for every 1430 people).” [I was very surprised when looking at the data in this chapter; everybody knows that South-Saharan Africa sucks, but South Asia do almost as badly on a lot of health metrics – and on some of them they do even worse than SSA.] […] “Within overall low coverage levels, there are considerable within-country inequalities by socioeconomic group […] In low-income countries, children from the highest wealth quintile have double the measles immunization coverage of the lowest wealth quintile, and there is a seven-fold difference between highest and lowest wealth quintiles in presence of a skilled birth attendant at birth” [note that this latter difference likely translates into a lot of dead babies; infant mortality rates are high these places.]

“There is some evidence from developing countries to suggest that while the public share of revenue may increase as countries grow richer, the public share of [health care] provision shrinks.”

“In low-income countries […] resource limitations make it difficult to provide universally even a limited package of high priority interventions […] The commonly recommended solution is to target resources to the poorest, but there is little evidence so far that such targeting can be done effectively, or that it is cost-effective relative to broader approaches”

“In sum, education is strongly related to health, with both reserve causality and direct effects. However, the extent to which the correlation between education and health reflects direct causality, reverse causality, or omitted factors is not known. Although mechanisms by which health affect educational attainment are well-understood, how education affects health is not. […] it seems unlikely that any one mechanism alone can explain the effect of education on health.” [I wrote a review paper on this topic a while back, coming to some roughly similar conclusions. I won’t cover this stuff in detail here because it’d just be review, but if you want to know more you’re welcome to ask. I incidentally think I may have written about this topic on the blog previously, but I’m not certain how detailed my coverage was.]

“Both income and wealth have strong independent correlations with health, net of education and other measures of SES. Assessing causality is difficult, however. Income and wealth improve access to health inputs (such as medical care and food), but health improves one’s ability to participate in the labour market and earn a decent wage. Illness also raises health care spending, thus reducing wealth […do note here that: “onset of a new illness reduces household wealth by far more than the household’s out-of-pocket health expenditures […] A large share of this reduction in wealth is attributable to a decline in labor earnings.]. Additionally, “third factors” – such as education – may determine both financial ressources and health status. Despite these caveats, many public health researchers have attributed the health-income gradient to a causal effect running from income to health. Some have even gone as far as labeling income “one of the most profound influences on mortality” (Wilkinson 1990: 412). Initial research seemed to support this view – in one such study, McDonough et al. (1997) estimated that a move from a household income of $20.000-$30.000 to a household income greater than $70.000 (in 1993 dollars) was associated with a halving of the odds of adult mortality. It was difficult to fathom that an association so large could be entirely due to omitted variables or reverse causality. However, more recent studies suggest that the direction of causality is far from clear and, furthermore, that it varies considerably by age. Among adults, the negative impact of poor health on income and wealth appears to account for a sizeable part of the correlation between financial ressources and health. […] Careful studies that look for the effect of income on health find little evidence to support this causal link in samples of older individuals in developed countries. […] a preponderance of evidence suggests that in developed countries today, income does not have a large causal effect on adult health, whereas adult health has a large effect on adult income. […] In the last two decades, economists’ most substantial contributions to this literature have involved untangling causal mechanisms.” [This is not news to me as I’ve done some work on this subject during my studies. But it seems like this often surprises people who don’t know what (at least some) economists/econometricians do.]

February 2, 2014 Posted by | Books, Economics, health care | Leave a comment

Suicide risk management: A manual for health professionals

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.”

November 24, 2013 Posted by | Books, health care, Psychology | Leave a comment

Handbook of critical care (II)

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.”

August 26, 2013 Posted by | Books, health care, Infectious disease, Medicine, Microbiology, Nephrology, Neurology, Pharmacology | Leave a comment

The Knowledgeable Patient: Communication and Participation in Health (A Cochrane Handbook)

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’ [19]). 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 [20]. […]

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 [28]. […]

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 [17]. […]

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 [8]. 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 [10] and application to an individual patient, complex statistical concepts have to be communicated in ways that are easy to understand and motivating too [11]. Consumers’ existing understandings of risk information, their individual personal life circumstances [12] 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 [19]. […] 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 [25]. 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 [1]. […] 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 [6]. […] 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 [20]. […]

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 [1]. […] 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 [15]. […]

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 [33]. 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 [38]. […]

At its simplest, health literacy is the ability to seek, find, understand and use health information [3]. […] 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 [31].”

August 11, 2013 Posted by | Books, health care, Medicine | Leave a comment

Making Choices in Health: WHO Guide to Cost-Effectiveness Analysis

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):

Table 3
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.

January 30, 2013 Posted by | Books, Economics, health care | Leave a comment

Change in the prevalence of obesity and use of health care in Denmark: an observational study

Wildenschild, Kjøller, Sabroe, Erlandsen and Heitmann has published a new study on this. Some stuff from the paper:

“In recent years, health care utilization has increased steadily. Data from Statistics Denmark show that the average number of consultations with a general practitioner increased from 7.2/year in 1999 to 8.0/year in 2005 for women and from 4.5/year to 5.3/year for men during the same period. […] Concurrently, with the increased utilization of health care, the prevalence of obesity among those aged 16–99 years increased from 5.5% in 1987 to 11.4% in 2005 according to the DHIS.[3] This rise in prevalence of obesity is in accordance with findings from other Danish studies [5,6] and with the development seen in other industrialized countries.[7,8]
Considering the higher incidence of somatic and psychological illness among obese people, it is conceivable that some of the increase in utilization of health care might be attributed to the increase in the prevalence of obesity. Studies examining the impact of the rising prevalence of obesity on the development of health care utilization are generally absent in previous literature, but several studies have shown an association between obesity per se and utilization of various types of health care.[9–22] […]
The purpose of this study was therefore to examine the impact of the rising prevalence of obesity on utilization of health care in Denmark in 1987–2005. The hypothesis was that the prevalence of obesity would be associated with utilization of health care and thus that the rise in utilization could be partly attributed to the rise in the prevalence of obesity. Another purpose was to examine whether the utilization of health care of obese people has changed during the period.”

I found this paper when looking for data on Danish obesity, which is not as easy to find as you’d perhaps think (for one thing, Statistics Denmark doesn’t have any data on this at all). Even though it’s not easy to find data on this, I did manage to find a 2004 study along the way which is aptly named Major increase in prevalence of overweight and obesity between 1987 and 2001 among Danish adults. The abstract:


The aim of the study was to examine the secular trends in the prevalence of obesity (BMI >or= 30.0 kg/m(2)) and overweight (25.0 <or= BMI < 30.0 kg/m(2)) in Danish adults between 1987 and 2001.


The study included self-reported weight and height of 10,094 men and 9897 women 16 to 98 years old, collected in a series of seven independent cross-sectional surveys. Prevalence and changes in prevalence of obesity and overweight stratified by sex and age groups were determined.


The prevalence of obesity more than doubled between 1987 and 2001, in men from 5.6% to 11.8% [odds ratio (OR) = 2.3, 95% confidence interval (CI) = 1.9 to 2.8, p < 0.0001] and in women from 5.4% to 12.5% (OR = 2.6, 95% CI = 2.1 to 3.2, p < 0.0001), with the largest increase among the 16- to 29-year-old subjects (men, from 0.8% to 7.5%, OR = 10.2, 95% CI = 4.1 to 25.3, p < 0.0001; women, from 1.4% to 9.0% OR = 7.0, 95% CI = 3.5 to 14.1, p < 0.0001). Between 1987 and 2001, the prevalence of overweight increased from 34% to 40% in men and from 17% to 27% in women.

The prevalence of overweight and obesity in Denmark has increased substantially between 1987 and 2001, particularly among young adults, a development that resembles that of other countries. There is clearly a need for early preventive efforts in childhood to limit the number of obesity-related complications in young adults.”

Note that this is not the study [3] mentioned in the original quote, but the numbers are nevertheless very similar; it’s quite clear that the estimated change that has taken place is within this neighbourhood – when using data like these. Note also that these results most likely underestimate the true increase over time (the type of data they have to work with is one of the main weaknesses of the original study and the authors don’t attempt to hide that); self-reported data on stuff like this are notoriously unreliable and will always cause some bias. This review article found, according to the abstract (couldn’t find a non-gated version online), that ‘The largest increase [in BMI over time] has been documented in studies based on objective data from total populations’, which is not surprising.

Back to the original study on health care utilization – what did they find?

“Principal findings
The increase in health care utilization that has occurred in recent years may in part be attributed to a rise in the prevalence of obesity. This increase is particularly seen among obese men. Health care utilization among obese women increased in 1987–2000 only and then leveled from 2000 to 2005. Including variables of obesity-related illness, such as hypertension, diabetes, and back problems, in the analyses suggested a varying significance of these conditions among the subsets of the sample but indicated that they may be at least part of the cause of the increased utilization among obese people. Among men, the association between BMI and health care utilization was dependent on age. Stratification according to age resulted in reduced statistical strength, and results were found to be significant only for those aged 45–64 years and borderline significant for those aged 25–44 and 65+ years. Among men aged 65+, the underweight had the largest probability of health care utilization, as opposed to the other age groups. This finding may be partly attributed to the presence of malignant illness in this group, indicating inverse causality”

These results make good sense to me, particularly that the strength of the association increases with age, though only up to a certain point (the weight-associated effect on -utilization is higher for middle aged than young people, and under-weight individuals is what muddles the waters when looking at the elderly segment + obesity may not be a super big issue if people actually get to reach old age in the first place); it will probably often take a few decades for obesity to cause significant health problems so it makes sense that middle aged obese people are more likely to ‘overutilize’ than people in the lower age brackets. I found this passage interesting:

“From 1987 to 2005, nonresponse to the DHIS increased from 20% in 1987 to 33% in 2005, with the largest increase in nonresponse occurring among those aged 16–24 and 25–44 years.[26] Analyses on nonresponse by BMI to the DHIS in 2005 showed that more obese than normal weight people did not participate.[26] This is in line with results from studies performed during the 1980s that indicated a greater nonresponse among obese people.[36,37] These findings imply that, over the years, nonresponse was generally larger among obese people compared with normal weight people, adding to an increasing underestimation of the prevalences of obesity in the study period. In addition, previous analyses on nonresponse in relation to health care utilization in the DHIS 2000 and 2005 have shown a positive association between nonresponse and health care utilization”

This of course leads to a conclusion which is less strong than it might have been, given better data (the lack of which is hard to blame the authors for):

“We found that the increased burden on the health care system was partly caused by obesity and a change toward an increase in health care use, particularly among obese men. It is likely that the present findings are underestimated due to a possible underestimation of weight, particularly among obese people with health problems, and potential differential selection caused by nonresponse among obese people with health problems.”

One final point, which is very important to remember when interpreting results like these, is that the increased utilization of health care ressources related to increased rates of obesity is not fully explained by (‘standard’) obesity-related illnesses:

“In the present study, associations between obesity and health care utilization were found independent of hypertension, diabetes, and back problems; thus, these illnesses did not fully mediate associations. This is in line with previous findings that suggested that associations between obesity and health care utilization can only partly be attributed to obesity-related illness such as heart disease, hypertension, high cholesterol, diabetes, and arthritis.14”

Even though you don’t get diabetes or hypertension from being fat, you’ll still have to see the doctor more often than will your friends who weigh less than you do.

March 3, 2012 Posted by | Data, Demographics, Diabetes, Economics, health care, Medicine | 2 Comments

Random stuff from the net, links, wikipedia…

1. RAND: Living Well at the End of Life (via Razib Khan). Here’s a link to one of the sources, a book which deals with some of the same questions: Approaching Death: Improving Care at the End of Life. Looks interesting, don’t have time to read it at the moment.

2. Fatal familial insomnia. “Fatal familial insomnia (FFI) is a very rare autosomal dominant inherited prion disease of the brain. It is almost always caused by a mutation to the protein PrPC, but can also develop spontaneously in patients with a non-inherited mutation variant called sporadic Fatal Insomnia (sFI). FFI is an incurable disease, involving progressively worsening insomnia, which leads to hallucinations, delirium, and confusional states like that of dementia.[1] The average survival span for patients diagnosed with FFI after the onset of symptoms is 18 months.”

Sleep’s important.

3. False consensus effect.

“In psychology, the false consensus effect is a cognitive bias whereby a person tends to overestimate how much other people agree with him or her. There is a tendency for people to assume that their own opinions, beliefs, preferences, values and habits are ‘normal’ and that others also think the same way that they do.[1] This cognitive bias tends to lead to the perception of a consensus that does not exist, a ‘false consensus’. This false consensus is significant because it increases self-esteem. The need to be “normal” and fit in with other people is underlined by a desire to conform and be liked by others in a social environment.

Within the realm of personality psychology, the false consensus effect does not have significant effects. This is because the false consensus effect relies heavily on the social environment and how a person interprets this environment. Instead of looking at situational attributions, personality psychology evaluates a person with dispositional attributions, making the false consensus effect relatively irrelevant in that domain. Therefore, a person’s personality potentially could affect the degree that the person relies on false consensus effect, but not the existence of such a trait.

The false consensus effect is not necessarily restricted to cases where people believe that their values are shared by the majority. The false consensus effect is also evidenced when people overestimate the extent of their particular belief is correlated with the belief of others. Thus, fundamentalists do not necessarily believe that the majority of people share their views, but their estimates of the number of people who share their point of view will tend to exceed the actual number.

This bias is especially prevalent in group settings where one thinks the collective opinion of their own group matches that of the larger population. Since the members of a group reach a consensus and rarely encounter those who dispute it, they tend to believe that everybody thinks the same way.

Additionally, when confronted with evidence that a consensus does not exist, people often assume that those who do not agree with them are defective in some way.[2] There is no single cause for this cognitive bias; the availability heuristic and self-serving bias have been suggested as at least partial underlying factors.

The false consensus effect can be contrasted with pluralistic ignorance, an error in which people privately disapprove but publicly support what seems to be the majority view (regarding a norm or belief), when the majority in fact shares their (private) disapproval. While the false consensus effect leads people to wrongly believe that they agree with the majority (when the majority, in fact, openly disagrees with them), the pluralistic ignorance effect leads people to wrongly believe that they disagree with the majority (when the majority, in fact, covertly agrees with them).”

4. Malthus, An Essay on the Principle of Population. Salman Khan recently made a video on the subject, here’s wikipedia.

5. Marital Rape License (warning, tvtropes link).

“Only a few decades ago, it was legal for a man to rape his wife. Sweden was the first country to explicitly criminalize it in 1965, and it has only been illegal in all fifty US states since 1993. Fifty-three countries around the world still don’t consider it a crime.

In some old patriarchal systems, a woman belonged first to her father (or closest living male relative if the father was dead) and then to her husband. Once married — and in some systems she could be married off without her consent to some old man she despised or had never met — her husband had a legal and “moral” right to her body whether she liked it or not. It gets even creepier when the bride is underage.”

We tend to take a lot of stuff for granted. Another reason why you should read Nothing To Envy.

6. Schema (psychology)

“A schema (pl. schemata or schemas), in psychology and cognitive science, describes any of several concepts including:

*An organized pattern of thought or behavior.
*A structured cluster of pre-conceived ideas.
*A mental structure that represents some aspect of the world.
*A specific knowledge structure or cognitive representation of the self.
*A mental framework centering on a specific theme, that helps us to organize social information.
*Structures that organize our knowledge and assumptions about something and are used for interpreting and processing information.

A schema for oneself is called a “self schema”. Schemata for other people are called “person schemata”. Schemata for roles or occupations are called “role schemata”, and schemata for events or situations are called “event schemata” (or scripts).

Schemata influence our attention, as we are more likely to notice things that fit into our schema. If something contradicts our schema, it may be encoded or interpreted as an exception or as unique. Thus, schemata are prone to distortion. They influence what we look for in a situation. They have a tendency to remain unchanged, even in the face of contradictory information. We are inclined to place people who do not fit our schema in a “special” or “different” category, rather than to consider the possibility that our schema may be faulty. As a result of schemata, we might act in such a way that actually causes our expectations to come true.”

7. Koch Snowflake Fractal (a structure with infinite perimeter but a finite area). Couldn’t remember if I’ve already blogged this at one point, but no harm done in case I have:

January 3, 2012 Posted by | Books, Genetics, health care, Khan Academy, Mathematics, Psychology, Wikipedia | Leave a comment

Terry Pratchett: Shaking Hands with Death

You should watch this:

Also, this.

November 4, 2011 Posted by | health care, Terry Pratchett | Leave a comment

Danish death panels

Mostly to the non-Danish readers. It seems there’s recently been a story about widespread use of secret DNR-codes by Danish doctors, I haven’t been able to find an article about it in English but here’s google translate. The doctors apparently systematically write in the journals of some sick people that nurses and staff should not try to save the individual in case they have a heart attack. In some cases, the code states that they shouldn’t be put in intensive care.

There’s been zero debate about this before this story broke, it was just something doctors did. A study from 2007 that apparently now has come to some journalist’s attention found that whereas almost all departments use the ‘no resuscitation in case of heart attack’ (natural enough, some people want to avoid becoming a living vegetable and people are given the choice) one third of all medical departments (n= 138) use these codes in secret, where the doctor makes the decision, often without informing the patient. 38 percent of the departments uses the codes in cases where the individual is not terminal.

Another article – which google translate translates into something that makes absolutely no sense – makes it clear that the practise is illegal, as it’s currently (on paper) illegal to decide whether a patient should be attempted resuscitated or not without informing the individual. The doctor actually can decide you should not receive treatment, but he has to inform you about the decision and your response to the decision should be put into the medical chart. I didn’t know that you could be denied resuscitation attempts but it doesn’t surprise me.

I think the health care system is one of those places where people sometimes can convince themselves that it’s better just to pretend tradeoffs don’t exist, because then they don’t have to deal with the ethical dilemmas which are all over the place. But the tradeoffs don’t go away by pretending they do, and somebody has to make some hard choices at some point. If nobody else do, the doctors have to; if everybody else just ignore the incompatibility of the current political demands (the laws) regarding medical service provision and the ressource constraints that exist in the field, well, the doctors are pretty much left with the bag.

January 17, 2011 Posted by | health care | Leave a comment

Quote of the day

There is NO single payor system that compensates physicians on health outcomes. None. There are a bunch that pay for ticking boxes (e.g. did you talk about smoking cessation, did you talk about weight loss, etc.) But not a damn one pays based on outcomes. Why you ask? Because as sure as the sun rises I and every other general surgeon would IMMEDIATELY stop operating on 1. smokers, 2. the obese, and 3 diabetics on an elective basis. They talk a big game, but every time someone points that out to the powers that be they back down. Hell, even REPORTING outcomes has caused a drop in elective CABGs in NY, a rise in emergent ones and worse outcomes across the board.

William Bromberg, in a comment here. I don’t know if that’s completely true, but I’m sure this fundamental problem is in no way limited to surgery even if that’s probably (one of?) the field(s?) where such a change in incentives structures would have the highest impact; in general, if you compensate doctors based on whether the patients get better or not, then it gets harder to get (/enough) doctors to treat the risky cases and/or the very sick.

May 18, 2010 Posted by | Cardiology, health care, Medicine | Leave a comment

Cost-effectiveness and -growth in health care

Two great illustrations:

A lot more here, via MR.

March 3, 2010 Posted by | health care | Leave a comment

Rationing: A fact of life

Limiting health care’s availability by the criterion of personal wealth rightly offends our sense of the dignity of the individual. Are the lives of the poor not of the same intrinsic value of those of the wealthy? To be fair, it is rare in the United States that poverty alone prevents the uninsured poor from receiving lifesaving intervention in a healthcare crisis. A poor man having a heart attack is not turned away from the emergency room, nor is the poor woman in labor sent away to have her baby at home. (I am not arguing that such enormities never occur, but the fact that such occurrences remain scandalous and newsworthy is a testament to their rarity.) Yet it is equally undeniable that the poor get a lesser share of the preventive care that can maintain health or of the quotidian care for the less dramatic challenges to their health.

There are two major alternatives to the allocating of health care on the basis of personal wealth. Both involve a large number of individuals agreeing (or having imposed on them) that the amount of health care they receive will not be in strict accord to how much they have paid for it. The cost will be distributed over the healthy as well as the sick, even though the benefit will inure only to those who are ill or who need health care to prevent illness. People accept the certainty of a bearable cost to avoid the risk of an unbearable one. But to the extent that these collective programs sever the connection between paying for health care and receiving it, they generate increased demand for health care. The individual feels that he has already paid for health care. When he is sick, or thinks that he is sick, he feels fully entitled to care with no consideration of cost. After all, he has already paid for it, hasn’t he? Given the limited amount of health care that may be bought with the aggregate funds of the group, this untrammeled demand for it must always result in rationing. This is true whether the collective effort is a private insurance plan or a government program. Rationing is inevitable in all collective health care financing schemes.

Rationing must occur, but it need not be admitted. Denying the truth of rationing is more common in government-run health care schemes than private ones, because the government is reluctant to have the people know this ugly fact. Government-run programs, therefore, are more likely to disguise the rationing. This plausibly deniable form of limiting health care is called implicit healthcare rationing, and it assumes many forms. Rationing by termination occurs when patients are discharged from the hospital earlier than is medically optimal. Rationing by dilution occurs when second-best rather than first-best treatment is provided. Rationing by rejection or redirection involves healthcare providers turning away patients whose care will be inadequately reimbursed. This is commonly seen now in the Medicare and Medicaid programs, because those programs reimburse providers at a rate substantially lower than private insurance plans. Perhaps more common than those forms of rationing is rationing by delay, as exemplified by the outrageous amount of time patients in Canada must wait for hip replacement surgery or colonoscopy. The unifying theme in all these forms of implicit rationing is that, without admitting it, they force some patients to forego medical care that they want and are ostensibly entitled to receive.

All modern societies ration health care. A wise society considers the options and chooses a method of doing so which best conforms to its values and capabilities. Thus we come to the terrible question we would so very much like to avoid: How shall we ration health care? How shall we explicitly ration it? So noxious a question is this, so offensive in its tacit assumptions and implications, that most politicians and wishful thinkers will deny that we need to address it at all. They will argue that the fundamental problem is one of distribution, not one of unmeetable demand. They will argue, with more enthusiasm than evidence, that an emphasis on preventive care would substantially reduce aggregate demand. Some will say we must reduce the role of government; others will argue that we should augment it. If only we will adopt their plan—they’ll say—waste, fraud, and abuse will be abolished. There will be chicken—or at least chicken soup—in every pot, and a vaccine in every arm. People love honesty, but they hate the truth. To frankly acknowledge and address the ineluctable reality of healthcare rationing is not merely to touch the proverbial third rail of American politics; it is to lie across the tracks in front of the onrushing train.

Come, let us speak of unpleasant things. How is health care to be rationed? Who gets the short end of the stick?

I’ve quoted extensively from the post, but I urge you to read it in full. The author is Eric Chevlen, an oncologist.

HT: Megan McArdle.

August 29, 2009 Posted by | Economics, health care | 2 Comments

Demand: 80.000, potential supply: 300.000.000

Yet a shortage still exists in the US – and most other places too. Yes, we are of course talking about kidneys.

I’ve said it before, I’ll say it again: The current policy kills. Read Virginia Postrell’s (very long and informative) article on the subject here.

Currently, 500-600 Danes are waiting for a kidney.

(An aside to Danish readers: Det tager kun et minut eller to at tilmelde sig donorregistret her. Uanset hvordan man har det med det nuværende system, vil denne simple handling en dag kunne redde et eller flere liv)

July 12, 2009 Posted by | health care | 2 Comments

A thought

Robin: A thought occurred to me about medical spending. There exists a classic economic model that would be consistent with medical spending being completely uncorrelated from health outcomes – that of a monopoly with the ability to implement price discrimination. (In other words, it’s exactly what you’d expect if people paid widely varying amounts of money for the same average quality of treatment.)

Doug S., in a comment here. I’d never thought of it like that, but now that the idea has been formulated, I wonder how close to the truth this is.

Robin Hanson’s idea about health care spending is, in case you didn’t know, that appr. half of all US health care spending is pure waste with no measurable effect on outcomes. This link provides a graph that illustrates the cost-effectiveness (or lack thereof) of the US health care system by plotting per-capita-spending and longevity for different countries.

October 27, 2008 Posted by | health care, USA | Leave a comment

Well, it’s a theory…

One reason we might have a “health care crisis” and rising medical costs is that we turn away almost 97% of the applicants to medical schools.

Here’s the link.

July 16, 2008 Posted by | health care, Random stuff | Leave a comment