Objective: Estimates of the marginal productivity of the health sector are required for a wide range of resource allocation decisions. Founding these estimates on robust empirical analysis can inform these decisions and improve allocative efficiency. This article estimates the annual marginal productivity of the English NHS over a 10-year period (between 2003 and 2012). Methods: Data on expenditure and mortality by program budget category are used in conjunction with socioeconomic and demographic variables from the censuses for 2001 and 2011. This article applies an econometric strategy that employs an established instrumental variable approach, which is then subjected to a number of sensitivity analyses. The results of the econometric analysis, along with additional data on the burden of disease, are used to generate an estimate of the marginal productivity for each of the study years. Results: We find that an additional unit of health benefit has cost between £5000 and £15 000 per quality-adjusted life-year from 2003 to 2012. Over this period these estimates (all in current prices) have increased at a faster rate than NHS price inflation, suggesting an increase in real terms. Conclusions: These results are discussed in the context of the existing literature, and the potential policy implications for decisions about resource allocation are explored.

Considering whether or not a proposed investment (an intervention, technology, or program of care) is affordable is really asking whether the benefits it offers are greater than its opportunity cost. To say that an investment is cost-effective but not affordable must mean that the (implicit or explicit) "threshold" used to judge cost-effectiveness does not reflect the scale and value of the opportunity costs. Existing empirical estimates of health opportunity costs are based on cross-sectional variation in expenditure and mortality outcomes by program budget categories (PBCs) and do not reflect the likely effect of nonmarginal budget impacts on health opportunity costs. The UK Department of Health regularly updates the needs-based target allocation of resources to local areas of the National Health Service (NHS), creating two subgroups of local areas (those under target allocation and those over). These data provide the opportunity to explore how the effects of changes in health care expenditure differ with available resources. We use 2008-2009 data to evaluate two econometric approaches to estimation and explore a range of criteria for accepting subgroup specific effects for differences in expenditure and outcome elasticities across the 23 PBCs. Our results indicate that health opportunity costs arising from an investment imposing net increases in expenditure are underestimated unless account is taken of likely nonmarginal effects. They also indicate the benefits (reduced health opportunity costs or increased value-based price of a technology) of being able to "smooth" these nonmarginal budget impacts by health care systems borrowing against future budgets or from manufacturers offering "mortgage" type arrangements.

Background Health technology assessment has been increasingly used in China, having been legally mandated in 2019, to inform reimbursement decisions and price negotiations between the National Healthcare Security Administration and pharmaceutical companies around the price of new pharmaceuticals. The criteria currently used to judge cost-effectiveness and inform pricing negotiations, 3x GDP per capita, is based on the rule of thumb previously recommended by the World Health Organization rather than an estimate based on an empirical assessment of health opportunity costs.Objective The objective of this study was to inform a cost-effectiveness threshold for health technology assessment in China that accounts for health opportunity cost. MethodsThe elasticity of health outcomes with respect to health expenditure was estimated using variations across 30 provincial-level administrative divisions in 2017 controlling for a range of other factors and using an instrumental variable approach to account for endogeneity to assess robustness of results. The estimated elasticity was then used to calculate the cost per DALY averted by variations in Chinese health expenditure at the margin. ResultsThe range of estimates from this study, 27,923-52,247 (2017 RMB) (central estimate 37,446) per DALY averted or 47-88% of GDP per capita (central estimate 63%), shows that a cost per DALY averted cost-effectiveness threshold that reflects health opportunity costs is below 1x GDP per capita. ConclusionOur results suggest that the current cost-effectiveness threshold used in China is too high; continuing to use it risks decisions that reduce overall population health. Key points for decision makers• Health technology assessment has been increasingly used in China and the criteria currently used to judge costeffectiveness and inform pricing negotiations does not reflect an evidence-based assessment of health opportunity costs.• This article provides the first estimate of the marginal productivity of health expenditure in China which can be used to inform the health opportunity cost of funding a new technology.• Our central estimate 37,446 (2017 RMB) or 63% of GDP per capita shows that a cost per DALY averted costeffectiveness threshold that reflects health opportunity costs would be below 1x GDP per capita, suggesting that decisions made on the basis of the currently used 3x GDP per capita threshold risk resulting in net losses in overall population health.

This paper provides an educational review covering the consideration of costs for cost-effectiveness analysis (CEA), summarising relevant methods and research from the published literature. Cost data are typically generated by applying appropriate unit costs to healthcare resource-use data for patients. Trial-based evaluations and decision analytic modelling represent the two main vehicles for CEA. The costs to consider will depend on the perspective taken, with conflicting recommendations ranging from focusing solely on healthcare to the broader 'societal' perspective. Alternative sources of resource-use are available, including medical records and forms completed by researchers or patients. Different methods are available for the statistical analysis of cost data, although consideration needs to be given to the appropriate methods, given cost data are typically non-normal with a mass point at zero and a long right-hand tail. The choice of covariates for inclusion in econometric models also needs careful consideration, focusing on those that are influential and that will improve balance and precision. Where data are missing, it is important to consider the type of missingness and then apply appropriate analytical methods, such as imputation. Uncertainty around costs should also be reflected to allow for consideration on the impacts of the CEA results on decision uncertainty. Costs should be discounted to account for differential timing, and are typically inflated to a common cost year. The choice of methods and sources of information used when accounting for cost information within CEA will have an effect on the subsequent costeffectiveness results and how information is presented to decision makers. It is important that the most appropriate methods are used as overlooking the complicated nature of cost data could lead to inaccurate information being given to decision makers.Not appropriately controlling for the nature of cost data and the uncertainty around subsequent cost estimates used in cost-effectiveness analysis (CEA) could lead to inaccurate information being given to decision makers.Although checklists exist for use alongside CEA to aid transparency in the methods used, there is still poor reporting and rationalisation of statistical methods and covariate adjustments when using cost data.It is difficult to suggest a 'one size fits all' methodology when estimating and analysing cost data for CEA; therefore, it is down to the researcher to assess the nature of the cost data to determine which methods to use.

Long-term care (LTC) consists of medical and social services for individuals with chronic conditions or disability that have difficulties with their activities of daily living (e.g., Lipszyc et al., 2012; National Institute on Aging, 2017). Public LTC systems are common across countries within the Organisation for Economic Co-operation and Development (OECD) to address the failure of unregulated LTC markets (Finkelstein & McGarry, 2006;Forder et al., 1996), and public spend on LTC in most of these countries is considerable. In 2017, for example, the Netherlands, the Nordic countries and Switzerland were among those spending the most on public LTC, that is, between 2.5% and 4% of GDP. The provision of public LTC has the primary goal of improving the quality of life of the service user (Fernandez et al., 2011). In addition, LTC aims to support the health care sector in achieving a better allocation of resources by providing less costly alterna-