Investigating public values in health care priority - Chileans´ preference for national health care

BACKGROUND

This study aims to assess preferences and values for priority setting in healthcare in Chile through an original and innovative survey method. Based on the answers from a previous survey that look into the barriers the Chilean population face, this study considers the preferences of the communities overcoming those barriers. As a result six programs were identified: (1) new infrastructure, (2) better healthcare coverage, (3) increasing physicians/specialists, (4) new informatics systems, (5) new awareness healthcare programs, and (6) improving availability of drugs.

METHODS

We applied an innovative survey method developed for this study to sample subjects to prioritize these programs by their opinion and by allocating resources. The survey also asked people's preferences for a distributive justice principle for healthcare to guide priority setting of services in Chile. The survey was conducted with a sample of 1142 individuals.

RESULTS

More than half of the interviewees (56.4%) indicated a single program as their first priority, while 20.1% selected two of them as their first priority. To increase the number of doctors/specialists and improve patient-doctor communication was the program that obtained the highest priority. The second and third priorities correspond to improving and investing in infrastructure and expanding the coverage of healthcare insurances. Additionally, the results showed that equal access for equal healthcare is the principle selected by the majority to guide distributive justice for the Chilean health system.

CONCLUSIONS

This study shows how a large population sample can participate in major decision making of national health policies, including making a choice of a distributive justice principle. Despite the complexity of the questions asked, this study demonstrated that with an innovative method and adequate guidance, average population is capable of engaging in expressing their preferences and values. Results of this study provide policy-makers useful community generated information for prioritizing policies to improve healthcare access.

Economic evaluation of meningococcal vaccines: considerations for the future

In 2018, a panel of health economics and meningococcal disease experts convened to review methodologies, frameworks, and decision-making processes for economic evaluations of vaccines, with a focus on evaluation of vaccines targeting invasive meningococcal disease (IMD). The panel discussed vaccine evaluation methods across countries; IMD prevention benefits that are well quantified using current methods, not well quantified, or missing in current cost-effectiveness methodologies; and development of recommendations for future evaluation methods. Consensus was reached on a number of points and further consideration was deemed necessary for some topics. Experts agreed that the unpredictability of IMD complicates an accurate evaluation of meningococcal vaccine benefits and that vaccine cost-effectiveness evaluations should encompass indirect benefits, both for meningococcal vaccines and vaccines in general. In addition, the panel agreed that transparency in the vaccine decision-making process is beneficial and should be implemented when possible. Further discussion is required to ascertain: how enhancing consistency of frameworks for evaluating outcomes of vaccine introduction can be improved; reviews of existing tools used to capture quality of life; how indirect costs are considered within models; and whether and how the weighting of quality-adjusted life-years (QALY), application of QALY adjustment factors, or use of altered cost-effectiveness thresholds should be used in the economic evaluation of vaccines.

Rare disease prevention and treatment: the need for a level playing field

Pharmacogenetic tests are being used increasingly to prevent rare and potentially life-threatening adverse drug reactions. For many tests, however, cost-effectiveness is hard to demonstrate, and with the exception of a few cases, widespread implementation remains a distant prospect. Many orphan drugs for rare diseases are also not cost effective but are nonetheless normally reimbursed. In this article, we argue that the health technology assessment of pharmacogenetic tests aimed to prevent rare but severe adverse drug reactions should be on a level playing field with orphan drugs. This is supported by a number of arguments, concerning the severity, rarity and iatrogenic nature of adverse drug reactions, the distribution of benefits and costs and societal preference towards prevention over treatment.

Prevention of treatable infectious diseases: A game-theoretic approach

We model outcomes of voluntary prevention using an imperfect vaccine, which confers protection only to a fraction of vaccinees for a limited duration. Our mathematical model combines a single-player game for the individual-level decision to get vaccinated, and a compartmental model for the epidemic dynamics. Mathematical analysis yields a characterization for the effective vaccination coverage, as a function of the relative cost of prevention versus treatment; note that cost may involve monetary as well as non-monetary aspects. Three behaviors are possible. First, the relative cost may be too high, so individuals do not get vaccinated. Second, the relative cost may be moderate, such that some individuals get vaccinated and voluntary vaccination alleviates the epidemic. In this case, the vaccination coverage grows steadily with decreasing relative cost of vaccination versus treatment. Unlike previous studies, we find a third case where relative cost is sufficiently low so epidemics may be averted through the use of prevention, even for an imperfect vaccine. However, we also found that disease elimination is only temporary-as no equilibrium exists for the individual strategy in this third case-and, with increasing perceived cost of vaccination versus treatment, the situation may be reversed toward the epidemic edge, where the effective reproductive number is 1. Thus, maintaining relative cost sufficiently low will be the main challenge to maintain disease elimination. Furthermore, our model offers insight on vaccine parameters, which are otherwise difficult to estimate. We apply our findings to the epidemiology of measles.

The value of disease prevention vs treatment

We present an integrated valuation model for diseases that are life-threatening. The model extends the standard one-period value-per-statistical-life model to three health prospects: healthy, ill, and dead. We derive willingness-to-pay values for prevention efforts that reduce a disease's incidence rate as well as for treatments that lower the corresponding health deterioration and mortality rates. We find that the demand value of prevention always exceeds that of treatment. People often overweight small risks and underweight large ones. We use the rank dependent utility framework to explore how the demand for prevention and treatment alters when people evaluate probabilities in a non-linear manner. For incidence and mortality rates associated with common types of cancers, the inverse-S shaped probability weighting found in experimental studies leads to a significant increase in the demand values of both treatment and prevention.

Public preferences for prioritizing preventive and curative health care interventions: a discrete choice experiment

BACKGROUND

Setting fair health care priorities counts among the most difficult ethical challenges our societies are facing.

OBJECTIVE

To elicit through a discrete choice experiment the Belgian adult population's (18-75 years; N = 750) preferences for prioritizing health care and investigate whether these preferences are different for prevention versus cure.

METHODS

We used a Bayesian D-efficient design with partial profiles, which enables considering a large number of attributes and interaction effects. We included the following attributes: 1) type of intervention (cure vs. prevention), 2) effectiveness, 3) risk of adverse effects, 4) severity of illness, 5) link between the illness and patient's health-related lifestyle, 6) time span between intervention and effect, and 7) patient's age group.

RESULTS

All attributes were statistically significant contributors to the social value of a health care program, with patient's lifestyle and age being the most influential ones. Interaction effects were found, showing that prevention was preferred to cure for disease in young adults, as well as for severe and lethal disease in people of any age. However, substantial differences were found in the preferences of respondents from different age groups, with different lifestyles and different health states.

CONCLUSIONS

Our study suggests that according to the Belgian public, contextual factors of health gains such as patient's age and health-related lifestyle should be considered in priority setting decisions. The studies, however, revealed substantial disagreement in opinion between different population subgroups.

A systematic review of stated preference studies reporting public preferences for healthcare priority setting

BACKGROUND

There is current interest in incorporating weights based on public preferences for health and healthcare into priority-setting decisions.

OBJECTIVE

The aim of this systematic review was to explore the extent to which public preferences and trade-offs for priority-setting criteria have been quantified, and to describe the study contexts and preference elicitation methods employed.

METHODS

A systematic review was performed in April 2013 to identify empirical studies eliciting the stated preferences of the public for the provision of healthcare in a priority-setting context. Studies are described in terms of (i) the stated preference approaches used, (ii) the priority-setting levels and contexts, and (iii) the criteria identified as important and their relative importance.

RESULTS

Thirty-nine studies applying 40 elicitation methods reported in 41 papers met the inclusion criteria. The discrete choice experiment method was most commonly applied (n = 18, 45.0 %), but other approaches, including contingent valuation and the person trade-off, were also used. Studies prioritised health systems (n = 4, 10.2 %), policies/programmes/services/interventions (n = 16, 41.0 %), or patient groups (n = 19, 48.7 %). Studies generally confirmed the importance of a wide range of process, non-health and patient-related characteristics in priority setting in selected contexts, alongside health outcomes. However, inconsistencies were observed for the relative importance of some prioritisation criteria, suggesting context and/or elicitation approach matter.

CONCLUSIONS

Overall, findings suggest caution in directly incorporating public preferences as weights for priority setting unless the methods used to elicit the weights can be shown to be appropriate and robust in the priority-setting context.

Australian Public Preferences for the Funding of New Health Technologies

Background. Ethical, economic, political, and legitimacy arguments support the consideration of public preferences in health technology decision making. The objective was to assess public preferences for funding new health technologies and to compare a profile case best-worst scaling (BWS) and traditional discrete choice experiment (DCE) method. Methods. An online survey consisting of a DCE and BWS task was completed by 930 adults recruited via an Internet panel. Respondents traded between 7 technology attributes. Participation quotas broadly reflected the population of Queensland, Australia, by gender and age. Choice data were analyzed using a generalized multinomial logit model. Results. The findings from both the BWS and DCE were generally consistent in that respondents exhibited stronger preferences for technologies offering prevention or early diagnosis over other benefit types. Respondents also prioritized technologies that benefit younger people, larger numbers of people, those in rural areas, or indigenous Australians; that provide value for money; that have no available alternative; or that upgrade an existing technology. However, the relative preference weights and consequent preference orderings differed between the DCE and BWS models. Further, poor correlation between the DCE and BWS weights was observed. While only a minority of respondents reported difficulty completing either task (22.2% DCE, 31.9% BWS), the majority (72.6%) preferred the DCE over BWS task. Conclusions. This study provides reassurance that many criteria routinely used for technology decision making are considered to be relevant by the public. The findings clearly indicate the perceived importance of prevention and early diagnosis. The dissimilarity observed between DCE and profile case BWS weights is contrary to the findings of previous comparisons and raises uncertainty regarding the comparative merits of these stated preference methods in a priority-setting context.

Prevention praised, cure preferred: results of between-subjects experimental studies comparing (monetary) appreciation for preventive and curative interventions

Background

'An ounce of prevention is worth a pound of cure’ is a common saying, and indeed, most health economic studies conclude that people are more willing to pay for preventive measures than for treatment activities. This may be because most health economic studies ask respondents to compare preventive measures with treatment, and thus prompt respondents to consider other uses of resources. However, psychological theorizing suggests that, when methods do not challenge subjects to consider other uses of resources, curative treatment is favored over prevention. Could it be that while prevention is praised, cure is preferred?

Methods

In two experimental studies, we investigated, from a psychological perspective and using a between-subjects design, whether prevention or treatment is preferred and why. In both studies, participants first read a lung cancer prevention or treatment intervention scenario that varied on the prevention-treatment dimension, but that were the same on factors like 'costs per saved life’ and kind of disease. Then participants completed a survey measuring appreciation (general and monetary) as well as a number of potential mediating variables.

Results

Both studies clearly demonstrated that, when the design was between-subjects, participants had greater (general and monetary) appreciation for treatment interventions than for preventive interventions with perceived urgency of the intervention quite consistently mediating this effect. Differences in appreciation of treatment over preventive treatment were shown to be .59 (Study 1) and .45 (Study 2) on a 5-point scale. Furthermore, participants thought that health insurance should compensate more for the treatment than for preventive measures, differences of 16% (Study 1), and 22% (Study 2). When participants were asked to directly compare both interventions on the basis of a short description, they preferred the preventive intervention.

Conclusion

It appears that people claim to prefer prevention when they are asked to consider other use of resources, but otherwise they prefer treatment. This preference is related to perceived urgency. The preference for treatment may be related to the prevention-treatment dimension itself, but also to variations on other dimensions that are inherently linked to prevention and treatment (like different efficacy rates and costs per treatment).

Paying for prevention: challenges to health insurance coverage for biomedical HIV prevention in the United States

Reducing the incidence of HIV infection continues to be a crucial public health priority in the United States, especially among populations at elevated risk such as men who have sex with men, transgender women, people who inject drugs, and racial and ethnic minority communities. Although most HIV prevention efforts to date have focused on changing risky behaviors, the past decade yielded efficacious new biomedical technologies designed to prevent infection, such as the prophylactic use of antiretroviral drugs and the first indications of an efficacious vaccine. Access to prevention technologies will be a significant part of the next decade's response to HIV and advocates are mobilizing to achieve more widespread use of these interventions. These breakthroughs, however, arrive at a time of escalating healthcare costs; health insurance coverage therefore raises pressing new questions about priority-setting and the allocation of responsibility for public health. The goals of this Article are to identify legal challenges and potential solutions for expanding access to biomedical HIV prevention through health insurance coverage. This Article discusses the public policy implications of HIVprevention coverage decisions, assesses possible legal grounds on which insurers may initially deny coverage for these technologies, and evaluates the extent to which these denials may survive external and judicial review. Because several of these legal grounds may be persuasive, particularly denials on the basis of medical necessity, this Article also explores alternative strategies for financing biomedical HIV prevention efforts.

Behavioral economics and regulatory analysis

Behavioral economics has captured the interest of scholars and the general public by demonstrating ways in which individuals make decisions that appear irrational. While increasing attention is being focused on the implications of this research for the design of risk-reducing policies, less attention has been paid to how it affects the economic valuation of policy consequences. This article considers the latter issue, reviewing the behavioral economics literature and discussing its implications for the conduct of benefit-cost analysis, particularly in the context of environmental, health, and safety regulations. We explore three concerns: using estimates of willingness to pay or willingness to accept compensation for valuation, considering the psychological aspects of risk when valuing mortality-risk reductions, and discounting future consequences. In each case, we take the perspective that analysts should avoid making judgments about whether values are "rational" or "irrational." Instead, they should make every effort to rely on well-designed studies, using ranges, sensitivity analysis, or probabilistic modeling to reflect uncertainty. More generally, behavioral research has led some to argue for a more paternalistic approach to policy analysis. We argue instead for continued focus on describing the preferences of those affected, while working to ensure that these preferences are based on knowledge and careful reflection.

Valuing mortality risk reductions from environmental, transport, and health policies: a global meta-analysis of stated preference studies

We conduct, to our knowledge, the first global meta-analysis (MA) of stated preference (SP) surveys of mortality risk valuation. The surveys ask adults their willingness to pay (WTP) for small reductions in mortality risks, deriving estimates of the sample mean value of statistical life (VSL) for environmental, health, and transport policies. We explain the variation in VSL estimates by differences in the characteristics of the SP methodologies applied, the population affected, and the characteristics of the mortality risks valued, including the magnitude of the risk change. The mean (median) VSL in our full data set of VSL sample means was found to be around $7.4 million (2.4 million) (2005 U.S. dollars). The most important variables explaining the variation in VSL are gross domestic product (GDP) per capita and the magnitude of the risk change valued. According to theory, however, VSL should be independent of the risk change. We discuss and test a range of quality screening criteria in order to investigate the effect of limiting the MA to high-quality studies. When limiting the MA to studies that find statistically significant differences in WTP using external or internal scope tests (without requiring strict proportionality), we find that mean VSL from studies that pass both tests tend to be less sensitive to the magnitude of the risk change. Mean VSL also tends to decrease when stricter screening criteria are applied. For many of our screened models, we find a VSL income elasticity of 0.7-0.9, which is reduced to 0.3-0.4 for some subsets of the data that satisfy scope tests or use the same high-quality survey.