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Background Subjective life expectancy is considered relevant in predicting mortality and future demand for health services as well as for explaining peoples' decisions in several life domains, such as the perceived impact of health behaviour changes on future health outcomes. Such expectations and in particular subjective expectations regarding future health-related quality of life remain understudied. The purpose of this study was to investigate individuals' subjective quality adjusted life years (QALYs) expectation from age 65 onwards in a representative sample of the Dutch generic public. Methods A web-based questionnaire was administered to a sample of the adult population from the Netherlands. Information on subjective expectations regarding length and future health-related quality of life were combined into one single measure of subjective expected QALYs from age 65 onwards. This subjective QALY expectation was related to background, health and lifestyle variables. The implications of using different methods to construct our main outcome measure were addressed. Results Mean subjective expected QALYs from age 65 onwards was 11 QALYs (range —9 to 40 QALYs). Individuals with unhealthier lifestyles, chronic diseases, severe disorders or lower age of death of next of kin reported lower QALY expectations. Indicators were varyingly associated with either subjective life expectancy or future health-related quality of life, or both. Conclusion Extending the concept of subjective life expectancy by correcting for expected quality of life appears to generate important additional information contributing to our understanding of people's perceptions regarding ageing and lifestyle choices.

Background Subjective survival probabilities (SSPs) are considered relevant in relation to lifestyle as lifestyle improvements may improve health and lower mortality risk. Objective To study individuals' SSP in a population of elderly (i.e. 60 years and older) from 15 European countries. Methods Data from the second wave of the Survey of Health, Ageing and Retirement in Europe (SHARE) were used. Individuals were asked about their chances to live up to age [T] or more. These SSPs were related to general characteristics, health and lifestyle. In addition, cross‐country comparisons were made. The validity of the probabilistic elicitation format used for collecting SSPs was also addressed. Results The average subjective probability of surviving the next 9–15 years was around 57%. Mean SSPs varied significantly across age, with lower means at higher ages. Cross‐country comparisons showed lowest mean in the Czech Republic (42%) and the highest in Denmark (64%). SSPs correlated with socio‐demographic, socio‐economic and also strongly with (objective) health characteristics except for obesity. Smokers reported significantly lower SSPs compared to non‐smokers, but no difference was found between non‐smokers and quitters. Excessive alcohol consumers reported significantly higher SSPs than moderate consumers and abstainers, but this only held for female excessive drinkers. Physical inactivity was negatively associated with SSPs, but this relation was attenuated at higher ages. In this context, important cross‐country differences were found. Conclusions Subjective survival probabilities are informative and relevant in relation to lifestyle decisions and can be validly obtained in elder people. The results from this study provide interesting implications for health policy, health communication strategies and future research.

Understanding subjective longevity expectations is important, but measurement is not straightforward. Two common elicitation formats are the direct measurement of a subjective point estimate of life expectancy and the assessment of survival probabilities to a range of target ages. This study presents one of the few direct comparisons of these two methods. Results from a representative sample of the Dutch population indicate that respondents on average gave higher estimates of longevity using survival probabilities (83.6 years) compared to point estimates (80.2 years). Individual differences between elicitation methods were smaller for younger respondents and for respondents with a higher socioeconomic status. The correlation between the subjective longevity estimations was moderate, but their associations with respondents' characteristics were similar. Our results are in line with existing literature and suggest that findings from both elicitation methods may not be directly comparable, especially in certain subgroups of the population. Implications of inconsistent and focal point answers, rounding and anchoring require further attention. More research on the measurement of subjective expectations is required.

The Consideration of Future Consequences (CFC) Scale is a measure of the extent to which individuals consider and are influenced by the distant outcomes of current behavior. In this study, the authors conducted factor analysis to investigate the factor structure of the 12-item CFC Scale. The authors found evidence for a multiple factor solution including one completely present-oriented factor consisting of all 7 present-oriented items, and one or two future-oriented factors consisting of the remaining future-oriented items. Further evidence indicated that the present-oriented factor and the 12-item CFC Scale perform similarly in terms of internal consistency and convergent validity. The structure and content of the future-oriented factor(s) is unclear. From the findings, the authors raise questions regarding the construct validity of the CFC Scale, the interpretation of its results, and the usefulness of the CFC scale in its current form in applied research.

Which costs and benefits to consider in economic evaluations of healthcare interventions remains an area of much controversy. Unrelated medical costs in life-years gained is an important cost category that is normally ignored in economic evaluations, irrespective of the perspective chosen for the analysis. National guidelines for pharmacoeconomic research largely endorse this practice, either by explicitly requiring researchers to exclude these costs from the analysis or by leaving inclusion or exclusion up to the discretion of the analyst. However, the inclusion of unrelated medical costs in life-years gained appears to be gaining support in the literature. This article provides an overview of the discussions to date. The inclusion of unrelated medical costs in life-years gained seems warranted, in terms of both optimality and internal and external consistency. We use an example of a smoking-cessation intervention to highlight the consequences of different practices of accounting for costs and effects in economic evaluations. Only inclusion of all costs and effects of unrelated medical care in life-years gained can be considered both internally and externally consistent. Including or excluding unrelated future medical costs may have important distributional consequences, especially for interventions that substantially increase length of life. Regarding practical objections against inclusion of future costs, it is important to note that it is becoming increasingly possible to accurately estimate unrelated medical costs in life-years gained. We therefore conclude that the inclusion of unrelated medical costs should become the new standard.

Prevention of unhealthy lifestyles has sometimes been promoted as simultaneously reducing costs and improving public health but this will unlikely prove to be true. Additional medical costs in life years gained due to treatment of unrelated diseases may offset possible savings in related diseases, but are often ignored both in health promotion policies and in economic evaluations of life-prolonging interventions. Many national guidelines explicitly recommend excluding these costs from economic evaluations or leave inclusion up to the discretion of the analyst. This may result in too favorable estimations of cost-effectiveness, feeding the unjustified optimism among policymakers regarding lifestyle interventions as a cost-saving option. However, prevention may still be a cost-effective way to improve public health, even when it does not result in cost savings, but this should be judged taking all future costs into account and be based on the true value for money provided by lifestyle interventions.

Background: Obesity is a major contributor to the overall burden of disease (also reducing life expectancy) and associated with high medical costs due to obesity-related diseases. However, obesity prevention, while reducing obesity-related morbidity and mortality, may not result in overall healthcare cost savings because of additional costs in life-years gained. Sector-specific financial consequences of preventing obesity are less well documented, for pharmaceutical spending as well as for other healthcare segments. Objective: To estimate the effect of obesity prevention on annual and lifetime drug spending as well as other sector-specific expenditures, i.e. the hospital segment, long-term care segment and primary healthcare. Methods: The RIVM (Dutch National Institute for Public Health and the Environment) Chronic Disease Model and Dutch cost of illness data were used to simulate, using a Markov-type model approach, the lifetime expenditures in the pharmaceutical segment and three other healthcare segments for a hypothetical cohort of obese (body mass index [BMI] ≥30 kg/m 2 ), non-smoking people with a starting age of 20 years. In order to assess the sector-specific consequences of obesity prevention, these costs were compared with the costs of two other similar cohorts, i.e. a ‘healthy-living’ cohort (non-smoking and a BMI ≥18.5 and <25 kg/m 2 ) and a smoking cohort. To assert whether preventing obesity results in cost savings in any of the segments, net present values were estimated using different discount rates. Sensitivity analyses were conducted across key input values and using a broader definition of healthcare. Results: Lifetime drug expenditures are higher for obese people than for ‘healthy-living’ people, despite shorter life expectancy for the obese. Obesity prevention results in savings on drugs for obesity-related diseases until the age of 74 years, which outweigh additional drug costs for diseases unrelated to obesity in life-years gained. Furthermore, obesity prevention will increase long-term care expenditures substantially, while savings in the other healthcare segments are small or non-existent. Discounting costs more heavily or using lower relative mortality risks for obesity would make obesity prevention a relatively more attractive strategy in terms of healthcare costs, especially for the long-term care segment. Application of a broader definition of healthcare costs has the opposite effect. Conclusions: Obesity prevention will likely result in savings in the pharmaceutical segment, but substantial additional costs for long-term care. These are important considerations for policy makers concerned with the future sustainability of the healthcare system.

Current investments in preventive lifestyle interventions are relatively low, despite the significant impact of unhealthy behaviour on population health. This raises the question of whether the criteria used in reimbursement decisions about healthcare interventions put preventive interventions at a disadvantage. In this paper, we highlight the decision-making framework used in the Netherlands to delineate the basic benefits package. Important criteria in that framework are ‘necessity’ and ‘cost-effectiveness’. Several normative choices need to be made, and these choices can have an important impact on the evaluation of lifestyle interventions, especially when making these criteria operational and quantifiable. Moreover, the implementation of the decision-making framework may prove to be difficult for lifestyle interventions. Improvements of the decision-making framework in the Netherlands are required to guarantee sound evaluations of lifestyle interventions aimed at improving health.

Current investments in preventive lifestyle interventions are relatively low, despite the significant impact of unhealthy behaviour on population health. This raises the question of whether the criteria used in reimbursement decisions about healthcare interventions put preventive interventions at a disadvantage. In this paper, we highlight the decision-making framework used in the Netherlands to delineate the basic benefits package. Important criteria in that framework are `necessity' and `cost-effectiveness'. Several normative choices need to be made, and these choices can have an important impact on the evaluation of lifestyle interventions, especially when making these criteria operational and quantifiable. Moreover, the implementation of the decision-making framework may prove to be difficult for lifestyle interventions. Improvements of the decision-making framework in the Netherlands are required to guarantee sound evaluations of lifestyle interventions aimed at improving health. Reprinted by permission of Cambridge University Press. An electronic version of this article can be accessed via the internet at http://journals.cambridge.org