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Research: Can Money Buy Happiness? | Stanford Graduate School of Business
The basis for these reference values is unclear but one argument is that it should be based on the health opportunity costs of funding decisions. Empirical estimates of the marginal cost per unit of health produced by a healthcare system have been proposed to capture the health opportunity costs of new funding decisions.
Based on a systematic search, we identified eight studies that have sought to estimate a reference value through empirical estimation of the marginal cost per unit of health produced by a healthcare system for England, Spain, Australia, The Netherlands, Sweden, South Africa and China. We review these eight studies to provide an overview of the key methodological approaches taken to estimate the marginal cost per unit of health produced by the healthcare system with the aim to help inform future estimates for additional countries.
The lead author for each of these papers was invited to contribute to the current paper to ensure all the key methodological issues encountered were appropriately captured. These included consideration of the key variables required and their measurement, accounting for endogeneity of spending to health outcomes, the inclusion of lagged spending, discounting and future costs, the use of analytical weights, level of disease aggregation, expected duration of health gains, and modelling approaches to estimating mortality and morbidity effects of health spending, research paper on money spending.
Subsequent research estimates for additional countries should 1 carefully consider the specific context and data available, 2 clearly and transparently report the assumptions made and include stakeholder perspectives on their appropriateness and acceptability, and 3 assess the sensitivity of the preferred central estimate to these assumptions. The estimated costs and effects of investments in healthcare are used to guide funding decisions, but this approach is limited if the health opportunity cost of an investment is unknown.
Under a constrained budget, the health opportunity cost of a new investment is the health lost elsewhere from reducing funding to an existing service. An estimate of health opportunity cost can therefore allow decision makers to invest in new health technologies or interventions that are expected to generate net health gains, allowing for the expected health gains forgone elsewhere in the healthcare system, thus ensuring efficient reimbursement decisions when the goal is to improve population health [ 2 ].
Precisely which healthcare intervention s are forgone when a new intervention is funded is rarely known. Empirically estimating the marginal cost per unit of health produced by the healthcare system offers a practical alternative to determine an expectation on health opportunity costs.
Seminal work from Claxton et al. This has been followed by estimates in Spain [ 4 ], Australia [ 5 ], The Netherlands [ 67 ], Sweden [ 8 ], South Africa [ 9 ] and China [ 10 ], which all employ different methodological approaches based on available data.
While such estimates may be constrained by uncertainty in the data and the methodological approaches taken, they can be explicit about their uncertainty, the assumptions made and the directional impact these may have on the estimated marginal cost per health unit. This paper provides an overview of previously published methods used to estimate the marginal cost per unit of health produced by the healthcare system. We include in our discussion the eight research paper on money spending published to date that we have identified that have estimated the marginal cost per unit of health produced by a healthcare system for seven different countries.
These papers were identified through a systematic review of the literature see Electronic Supplementary Material [ESM] 1. Studies were included that assessed the impact of healthcare spending on health outcomes within a country and translated the results into a cost per quality-adjusted life-year QALY or disability-adjusted life-year DALY estimate, research paper on money spending.
Studies that sought to estimate the relationship based on cross-country data, sought to estimate the cost per QALY or DALY based on the estimated relationship between spending and outcomes estimated for another country, and those that were not peer reviewed, published in English, or published prior to were excluded.
This led to the final eight studies included in the review. The lead author for each of these papers was invited to contribute to the current paper to ensure all the key methodological issues encountered could be appropriately captured. Across all eight studies, approaches to estimating the cost per unit of health can be split into two parts: modelling population-level health outcomes against health spending and other control variables to estimate the health spending elasticity; and modelling to extrapolate the estimated effect to impact on a lifetime generic measure of health.
We compare and contrast the approaches taken to address key methodological issues critical to both parts. Key issues are those that were identified by the study authors and therefore are also likely to be relevant to researchers wishing to undertake similar research. Thresholds used to inform funding decisions and to draw recommendations in the published literature may reflect a range of considerations other than opportunity costs [ 11 ].
Some authors have recently emphasised that decision rules are context-dependent and differ by the perspective taken by decisions makers and by the budget constraints, whether fixed or variable, faced by them [ 1213 ]. According to the two-perspective approach framework presented by Brouwer et al. However, in the most commonly operating context where fixed budgets are allocated to healthcare and coverage decisions are taken from a healthcare system perspective, information on the health opportunity cost of healthcare funding decisions becomes the relevant information to inform cost-effectiveness thresholds [ 13 ].
Estimating the relationship between healthcare spending and health outcomes presents several challenges. The key component to addressing this research question is to identify variation in health spending that is unrelated to variation in health status, and to then estimate the health effects of such exogenous variation. In an ideal world, researchers would link the exogenous variations in healthcare spending to all the affected individuals and calculate the resulting changes in health over their lifetimes.
Information on individual-level health spending across all areas of healthcare would be ideal. If the aim is to assess the impact of public spending on health, as would be the case for health opportunity cost estimates attempting to guide public reimbursement decisions, then additional health spending information such as private insurance and patient out-of-pocket spending are relevant as model covariates.
The perfect data on health outcomes would include individual-level cause of death and health-related quality of life HRQoL data to estimate disease- research paper on money spending, age- and sex-specific trajectories in the estimation of mortality- and morbidity-related QALYs.
Obtaining exogenous variations in healthcare spending will typically rely on controlling for a large number of observed healthcare need variables and a method to control for any unobserved research paper on money spending and reverse causality.
The preferred method to control for this exogenous variation due to both unobserved covariates and reverse causality due to prior health outcomes influencing current health spending is research paper on money spending panel approach.
The advantage of this approach is that time-invariant confounding can be removed through the use of multiple cross-sections; however, this approach relies on significant data availability. In the absence of multiple cross-sections of data, instrumental variable IV estimation is an alternative approach that has been employed.
The use of IVs, discussed further in Sect. However, it is limited by the quality of the IVs and places a large burden on the researchers to present evidence for the validity of their IVs that cannot be conclusively supported with empirical evidence.
Good data are therefore required not only on healthcare spending and health outcomes but also on variables to control for healthcare need, and potentially on candidate IVs. Table 1 summarises the key methodological approaches taken, split into parts one modelling population-level research paper on money spending outcomes against health spending and other control variables to estimate the health spending elasticity and two modelling to extrapolate the estimated effect to impact on a lifetime generic measure of health.
Most studies estimate the effect of a change in health spending on mortality in part one, and then approximate and incorporate the morbidity effect of health spending to arrive at the estimated marginal cost per QALY or DALY in part two, using either area [ 1458910 ] or patient groups [ 67 ] as the unit of analysis.
Less frequently, a QALY or DALY outcome measure is used in part one. This is discussed research paper on money spending more detail in Sect. In part one, different econometric specifications are used to model the effect of a change in health spending on population-level health outcomes.
As outlined in Table 1the reviewed studies either employed panel data with unit fixed effects or cross-section data with an IV approach. Control variables used see Table 1 are similar across the studies and are used to minimise confounding due to area- or group-level determinants of health that can also influence change in healthcare spending in longitudinal approaches, or due to unit- or area-level determinants of health that can also influence change in healthcare spending in cross-sectional approaches.
Control variables must also account for potential confounding between the IVs and determinants of health in IV analyses see Sects.
In part two, some studies have assumed the effect on morbidity to be proportional to the effect on mortality, or have aimed at estimating the morbidity effects of health spending directly. Estimation of the health effects of healthcare spending requires methodological decisions to be made by researchers that may have substantial impacts on the final results.
In this section, we briefly describe the key decisions of this research paper on money spending that were made in the reviewed papers, splitting these into issues encountered when 1 modelling population-level health outcomes, normally a measure of mortality, against health spending and other control variables to estimate the health spending elasticity, and 2 approximating and incorporating the morbidity effect of health spending to arrive at the estimated marginal cost per unit of lifetime health produced.
The majority of the methodological issues identified pertain to part one, therefore this received the bulk research paper on money spending the focus; however, some of the key methodological challenges pertain to both parts.
With the exception of the health outcome measures used, these are listed under the part one challenges. Health outcome measures used is given its own section and is discussed first. Where suggestions are made, these are from the view of informing decisions about the funding of new health technologies, but it should be noted that an estimate of the effect of healthcare spending on health outcomes could also be of interest from other perspectives, research paper on money spending.
The population-level health outcomes must be relevant to the specific decision-making context. As decisions are made regarding healthcare interventions spanning a range of disease areas, a generic measure of health that accounts for changes in survival and morbidity i.
Research paper on money spending and DALYs have been shown to be largely interchangeable [ 14 ], research paper on money spending. Few countries routinely capture national HRQoL information that could be used to inform estimates of QALYs or DALYs by small geographical areas.
Therefore, due to limited data, most approaches reviewed herein have modelled the impact of health spending part one on some mortality-related measure and subsequently incorporated morbidity effects part two [ 1568 ]. Other approaches included a combined measure of mortality and morbidity as the key outcome variable in their econometric model part one through either the use of quality-adjusted life expectancy QALEcalculated as life expectancy LE adjusted by modelled EQ-5D weights [ 4 ], or through QALYs lost due to mortality corrected for disease burden and morbidity [ 7 ], potentially negating the need for some or all of part two.
A single estimate directly modelled the effect of health spending on DALYs through publicly available data on the Global Burden of Disease [ 10 ]; see ESM 3 for further information.
The most commonly estimated mortality-based health outcome measures were mortality rates [ 6910 ], years of life lost YLL [ 157 ] or LE [ 4 research paper on money spending, 8 ] see Table 1. YLL reflect survival effects and are constructed from data on mortality and conditional LE or a fixed reference age. Careful consideration should be given to how the mortality-based effects contribute to the estimation of YLL.
Where mortality rates are used as an outcome measure instead of YLL, research paper on money spending, assumptions are required to ultimately obtain survival effects. For example, both the South Research paper on money spending [ 9 ] and the Chinese mortality-based [ 10 ] cost per DALY estimates are derived from the change in mortality rates from increases in health spending using the method outlined by Ochalek et al.
This approach uses YLL averted from public disease burden data in an all-cause model to estimate mortality-based survival effects, and incorporates morbidity-based effects by assuming a proportional impact on direct morbidity effects [ 910 ], using the same assumption as the English estimate [ 1 ].
Using LE, mortality rates are extrapolated to the future to predict YLL. Crucially, and regardless of the mortality measure used, the survival effects research paper on money spending be adjusted for the HRQoL in which they are expected to be lived.
Where outcomes are considered by disease area, it is important that the survival effects reflect disease-specific profiles of survival and HRQoL [ 1 ]. To account for the morbidity effects of a change in health spending, studies typically employed an assumption about the effect size relative to the change in mortality or YLL [ 1910 ]. The Australian study estimated the effect directly using available health index data, making the assumption that temporal change in HRQoL, controlling for demographic, societal and other economic variables, research paper on money spending, was due to change in health spending over the same time period [ 5 ].
The English study constructed a measure of the QALY burden of disease at the national level by making the assumption that the effect of change in spending on mortality provided a surrogate for the effect of change in spending on the QALY burden of disease estimated using variation across local health authorities see ESM 3 for further details. Other studies used a health outcome measure in their econometric models that already reflected changes in mortality and on HRQoL, and thus estimated the effect of spending on survival and morbidity simultaneously [ 47 ].
Both approaches used EQ-5D weights to estimate morbidity effects, making additional assumptions about the extent to which an annual survey sample was representative of the Dutch population [ 7 ] or modelling temporal change in HRQoL due to limited national-level HRQoL information [ 4 ].
The measurement of health spending involves the estimation of health spending within defined geographical areas, for which accompanying variables describing health needs and health outcomes are required. If the goal is to inform public reimbursement decisions through the estimation of the opportunity costs of publicly funded health spending, then the main spending independent variable should reflect public spending on health.
However, private health spending should be included as an important model covariate as its omission may result in biased estimates on health outcomes where the coefficient on health outcomes may be underestimated overestimated if the relationship between public and private spending on health is negative positive. The preferred approach is to include private health spending as a covariate in part one, research paper on money spending.
Approaches to accounting for the impact of private healthcare spending on health outcomes in the absence of private health spending data have included using socioeconomic variables as a proxy for private health insurance coverage and spending within the regression model e. Edney et al. If total public research paper on money spending on health is unavailable, then the impact of missing cost categories, either by health service or patient type, on the estimated health spending elasticity must be considered, research paper on money spending.
If the impact is deemed negligible then this should be explicitly justified [ 5 ]. Alternatively, conclusions will need to be restricted to the types of healthcare spending included; for example, by estimating the marginal cost per unit of health of hospital-based care [ 67 ].
How health spending is measured, as well as characteristics of the specific local context, will also impact on the appropriate econometric models; for example, the English analysis is the only approach to have employed separate disease-specific spending models, research paper on money spending, reflecting that expenditure data were available for each disease area, with mortality data available by cause of death research paper on money spending 1 ].
The need for healthcare, defined in relation to the research paper on money spending to benefit from healthcare, not only clearly affects health outcomes but can also predict health spending.
Variables used to represent healthcare need have included measures of health status, past healthcare use, health supply, and demographic variables such as population size and proportion of elderly people. This is not only most obvious in countries with funding mechanisms that allocate resources on the basis of needs e. Therefore, healthcare need must be included in the model to estimate the unbiased causal relationship between healthcare spending and outcomes.
While census data, as used in England and Australia, research paper on money spending, can provide population measures of need via health status or socioeconomic questions, it is typically obtained infrequently, meaning that analyses are restricted to census years [ 5 ] or outdated information is used outside of census years [ 1 ].
In the context of a model looking at variations over time [ 47916 ], longitudinal survey or administrative data have been used to account for health need with the addition of fixed effects, including time and age- and sex-specific time trends, to account for any remaining unobserved health need [ 49 ].
Regressing outcomes against spending, controlling for differences in health needs, is unlikely to produce estimates of causal effects. There are two principal reasons for this: 1 there are likely unobserved area-specific confounders, that is, omitted variables that are associated with both spending and outcomes, resulting in omitted variable bias; and 2 there may be reverse causality whereby historic health outcomes impact current health spending and current health outcomes see ESM 4 for a figure depicting the potential temporal causal relationships between health spending and health outcomes.
In the context of these factors, research paper on money spending, spending is endogenous [ 17 ] and the resultant coefficient on spending is biased. Two popular approaches to accounting for endogeneity that have been employed in the country-level estimates reviewed here include the use of panel data and IVs see Table 2, research paper on money spending.
Panel data can address endogeneity through eliminating additional unobserved confounding by controlling for time-invariant region effects and time-varying year effects and mitigating some reverse causality through inclusion of lagged spending or health outcomes in a dynamic panel data analysis [ 18 ].
HAPPINESS: Should you spend money on things or experiences? New Research!
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