Why do we need fair tests of treatments?And you'll find a review by Xavier Bonfill at Annals
What are fair tests of treatments?
What can be done to improve tests of treatments?
How can YOU help to improve tests of treatments?
The turn to HRQL arose from the recognition that observable characteristics of the body could not provide an adequate account of the overall burden of chronic disease on individual patients or on the population as a whole. But the HRQL concept merely tried to add the experience of the patient to a notion of health defined by observable damage to the body or objective disease. It is not adequate to talk about the subjective experience of objectively defined health states. Health is not an objective state because neither death nor disease are always bad for patients. Most patients now choose to limit the medical care they receive at the end of life because they do not see death as the worst possible outcome. Similarly, it is possible for older adults to live well despite multiple chronic diseases. HRQL has not provided a fundamentally new metric for health and has not succeeded in redirecting clinical trials or activity to a more patient centered set of goals.
Any adequate, comprehensive, and valid approach to health must focus on personal agency, or the capacity of the person to achieve his or her goals. This is the only way we will understand and achieve individual well- being. This personal agency is both a means to well- being and an intrinsically valuable component of it. We conceptualize health and well- being as capabilities rather than as objective or subjective states: “our capability to lead the kind of lives we have reason to value.” To explore and define a new sense of health as agency, we will need to understand the various ways in which health and action are related.A deep view of bioethics and the concept of health, all together. Good read for this summer.
The value of a diagnostic arises not because of its direct effect on a patient’s health but because of the information it provides on a patient’s likely response to a particular therapy. Personalized diagnostic testing reduces – though does not eliminate – the trial-and-error associated with empirical medicine, where physicians and their patients try an initial set of therapies and decide to continue or discontinue them on the basis of realized efficacy and side effects. In this manner, personalized diagnostic tests transform medical care from what economists call “experience goods,” whose quality can only be determined through consumption, to “search goods,” whose quality can be learned before consumption
Personalized diagnostic testing offers several advantages over an empirical, trial-and-error approach to medicine. These benefits include the avoidance of side effects, potentially reduced financial costs of therapy (e.g., if a patient is identified as a likely nonresponder to an expensive therapy and the alternative is cheaper), potentially reduced opportunity costs of time – not just in terms of physician visits but also time lost on an ineffective or even harmful treatment, and improved or earlier access to effective care. Not only do patients receive value from personalized testing and treatment, but providers and health care systems benefit by avoiding ineffective, or wasteful, health care that accompanies less targeted, traditional treatment approaches. Specifically, a diagnostic test will be most valuable when the therapy being evaluated is expensive relative to alternatives, when side effects are frequent and severe (thereby making the empirical approach relatively less safe), and when delay from an alternate therapy can severely harm an individual’s health (e.g., metastatic cancer)The concept is clear, its measurement is still uncertain.
Argument 1: the whole health maximisation assumption underlying the approach is misconceived. health care is about more than just promoting health. Other objectives commonly include financial protection (e.g. from the out-of-pocket expenseof costly interventions), innovation, and all those listed earlier
Argument 2: innovation is stifled by the strict application acost-effectiveness threshold that is too low
Argument 3: the use of standard outcome measures, like theEQ-5D QALY or averted DALYs, underestimates the health benefits of cancer treatments
Argument 4: the assessment of benefit excludes the beneficial effects that treatment and its consequences have on those who care for cancer patients
Argument 5: the opportunity cost argument is weak. There are always efficiency savings that can be found in any systemwhich mean that the alleged sacrifice of health represented by the threshold is spurious. the actual sacrifice is much smaller
Argument 6: cancer is a scary disease and people who suffer from it deserve to have access to treatments that would fail aconventional cost-effectiveness test
Argument 7: for some cancer patients a costly and not very effective treatment may offer a “last chance” to someone in despair. such a situation might exist if no intervention of any kind existed for these patients or if the patient suffered from a rare form of cancer
Argument 8: cancer is a “severe” disease and should accordingly be given a higher priority than less severe diseases
Argument 9: many cancer patients have a short life expectancy even with treatment. a quasi-utilitarian argument might cite the law of diminishing marginal value: even small gains for such people are to be valued more highly than the same gains of equivalent quality of life for people with an already long expectation of life. alternatively, there is the more direct emotional appeal “Our moral response to the imminence of death demands that we rescue the doomed proof"
These arguments fall into two broad groups. Some are questionsof social value: how should we value health gains of particular kindsand should we value them differently according as they accrueto different people? Others are questions of fact: would informa-tion about the quantitative size of the effects in question lead us to conclude that cancer is indeed a special case? The burden of proof in both cases lies with those making the assertion that cancer is, indeed, special. That burden of proof is not impossible to bear.Is cancer a special case? The question may apply to many diseases and will provide more difficulties than answers. In the end any analysis relies on distributive justice principles and according to different views you'l apply different prioritisation criteria.
Confidential price discounts are now common among the ten health systems that participated in our study, though some had only recently begun to use these pricing arrangements on a routine basis. Several health systems had used a wide variety of discounting schemes in the past two years. The most frequent discount received by participating health systems was between 20% and 29% of official list prices; however, six participants reported their health system received one or more discount over the past two years that was valued at 60% or more of the list prices. On average, participants reported that confidential discounts were more common, complex, and significant for specialty pharmaceuticals than for primary care pharmaceuticals.If confidential discounts are huge (>60%), as they are, any cost-effectiveness analysis is adhoc and its obsolescence undermines any result. This fact is the recognition that the pricing system is not working and we are under a procurement system. As I said some months ago:The time to finish such farce has come.
The general point is that any form of choice architecture, including the use of default rules, may have little or no net effect if people are able to find otherdomains in which to counteract it. The idea of compensating behavior can be seen as a subset of the general category of strong antecedent references, but it points to a more specifi c case, in which the apparent success of the nudge is an illusion in terms of what choice architects actually care about (Hirschman,1991).Therefore with this text Sunstein is landing to the practical difficulties on nudging. Highly recommended.
What matters is welfare, not effectiveness (Sunstein,2016). A largely ineffective nudge may have positive welfare effects; an effective nudge might turn out to reduce welfare. A strong reason for nudges, as distinguished from more aggressive tools, is that they preserve freedom of choice and thus allow people to go their own way. In many contexts, that is indeed a virtue, and the ineffectiveness of nudges, for some or many, is nothing to lament. But when choosers are making clear errors, and when third-party effects are involved, the ineffectiveness of nudges provides a good reason to consider stronger measures on welfare grounds.
Debate continues to exist on whether QALYs should serve as the central means of health economics analysis. This review examines the potential shortfalls of QALYs, spanning current ethical, methodological, and contextual domains in addition to examining their suitability for regenerative medicine and future technologies. In the UK, NICE currently stipulates a threshold of £20 000 - £30 000 per QALY when evaluating new therapeutics and/or technologies for NHS adoption, and has used this tool to apply a rational and transparent process to technological adoption for over ten years. Calculating QALY or cost effectiveness thresholds is particularly complex and debate has previously been publicized on whether the value of a QALY should be dictated by first proposing the worth of a QALY and setting the healthcare budget at or below that value, or alternatively, proposing a healthcare budget and then allowing the cost of a QALY to declare itself following purchasing decisions. With the advent of cellular based therapeutics and their comparably high upfront costs, the QALY calculation methodology may need refinement to realise the financial advantages and opportunity costs such interventions may convey – particularly considering the degree of uncertainty associated with them.Meanwhile we should focus on improving comparative effectiveness of current and new technologies, specially those that are related to precision medicine.
estimate the degree of risk aversion for a sample of young healthy adults and we explore its links with a broad range of risky behaviours considered together. Second, as indicator of the overall quality of diet, we complement, for the first time, the BMI with the Healthy Eating Index (HEI), and we relate both to estimated risk preferencesSounds good, because the use of Body Mass Index is absolutely outdated and its relationship with risk aversion is crucial. This is the summary:
Our results show that risk preferences significantly differ across young adults with different, not extreme, health conditions. In particular, they reinstate the importance of conducting analyses that look separately at the two sub-samples of female and male subjects . This allows disentangling the links and interactions between preferences and key health variables such as smoking, and also to fully account for the gender-specific effects of the BMI and of alternative indicators of healthy weight.
Second, in our sample young women do not show any significant robust associations between risk preferences and BMI. Third, for young men – but not women – the HEI index appears to be significantly and consistently associated with risk preferences: across all specifications, healthier nutritional habits, tend to be robustly associated with higher risk aversion. This, together with the lack of significance of BMI-based indexes, suggests that, for subjects with not extreme health conditions, there is a wide scope to use measures alternative (or complementary) to the BMI, as indicators of the overall quality of diet.That's it. And his final recommendation:
From a health policy perspective, our study suggests that in young adults who have not yet developed chronic or extreme health conditions, looking at a comprehensive nutritional indicator such as the HEI could provide more direct insights to the deeply rooted behavioural mechanisms that drive health behaviours than considering an indirect and increasingly questioned measure such as the BMI.Since children's obesity is one of the main challenges for health improvement, someone should take into account this message.
Our main finding from the tax simulations is that nutrient-specific taxes have much larger effects on nutrition than do product-specific taxes, without causing a larger decline in consumer utility. The intuition for this result is that nutrient-based taxes have a much broader base, so it is more difficult to substitute away from any one good in response to such taxes. For example,a 20% tax on soda decreases total purchased calories by 4.84% and decreases sugar consumption by over 10%. However, a 20% sugar tax decreases total calories by over 18% and sugar by over 16%.The larger effect of a sugar tax on nutrition comes despite the fact that it has the same effect on indirect utility as a soda tax. Dueto their negative income elasticities and the patterns of own- and cross-price elasticities we find, taxes on snacks and packaged mealshave very small effects on nutrition. Fat and salt taxes, on the other hand, have much larger effects, decreasing calories by 19% and 11%, respectively. SSB taxes, which can be thought of as a hybrid price policy that targets a set of products based on their nutritional content, also are quite effective, reducing caloric intake by over 8%. However, these taxes are less-effective and only slightly less-distortive than a broad-based sugar tax.If this is so, the next steps should be to review the initial impact and explore wether new approaches could be more succesful. Unfortunately the article doesn't explains the details of how to implement their result...
Two key steps in reducing low-value care, proposed by García-Armesto et al. (García-Armesto et al. 2013), are the following:As I said, a must read. Congratulations to the authors. Unfortunately the barrier is the price: $279. Notwithstanding that, health policy makers and managers should have it as a key reference for their decisions.
• Identifying those technologies ineffective in their usual indications or less effective than alternatives
– Dropping them from the benefits basket or making them subject to avoidable copayments
– Restricting indications to certain types of patients (choice guided by evidence of positive benefit/risk balance)
– Specifying and limiting the types of providers more suitable to offer each service (therefore substantiating indication becomes a requisite, discouraging irrelevant use)
– Capping the frequency or length of treatments
• Producing and making available guidance on a regular basis to reduce inappropriate use of procedures
– Highlighting and tackling unwarranted variations in elective surgery (naming and “shaming” to prompt query and change)
– Fostering best practices and improving coordination of care
The availability of prepaid resources for health, such as government spending, is one of many determinants of access to health care, and can lead to population health gains. Economic development is associated with an increase in spending and specifically an increase in prepaid resources. This is at the core of the pursuit for universal health coverage. This research also points to countries that deviate from the trends, spending more or less than expected, based on their level of economic development. This information is valuable to planners assessing funding gaps and financing opportunities, and can be used to provide insight into what future health financing challenges are likely. Tracking changes in health financing patterns across time and benchmarking against global trends is vital to addressing missed opportunities, ensuring access to medicines and high quality services, and the pursuit of universal health coverage.
This volume covers the main areas of quantitative methodology for the design and analysis of CER studies. The volume has four major sections—causal inference; clinical trials; research synthesis; and specialized topics. The audience includes CER methodologists, quantitative-trained researchers interested in CER, and graduate students in statistics, epidemiology, and health services and outcomes research. The book assumes a masters-level course in regression analysis and familiarity with clinical research.
