The impact of healthcare spending on health outcomes: A meta-regression analysis

Gallet, Craig A.; Doucouliagos, Hristos

doi:10.1016/j.socscimed.2017.02.024

Cited by 122 publications

(87 citation statements)

References 31 publications

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“…Figure displays posterior distributions of α , β , and the sum of α and β for various prior distributions of α (the base case model and Scenarios 1 and 2). It can be observed that the instantaneous impact of spending on mortality ( α ) in our study is much smaller than the estimates of Claxton, Martin, et al () and Gallet and Doucouliagos (). Furthermore, the lagged impact ( β ) of spending on mortality is bigger than the instantaneous impact.…”

Section: Resultscontrasting

confidence: 76%

“…Furthermore, the lagged impact ( β ) of spending on mortality is bigger than the instantaneous impact. Taken together ( α + β ), the impact of spending on mortality is bigger than estimated by Gallet and Doucouliagos in their meta‐analysis but still much smaller than the impact of spending on mortality estimated by Claxton, Martin, et al (Gallet & Doucouliagos, ). Furthermore, Figure suggests that a more informative prior, that is, one based on previous studies, shifts the posterior of α to the left.…”

Section: Resultsmentioning

confidence: 63%

“…In two alternative scenarios, we investigated the impact of more informative priors for α based on previous research and varied the number of years included in the sample. In Scenario 1, we used the estimates of a recent meta-analysis of studies investigating the impact of health spending (total health spending, not CVD specific) on mortality, which estimated an elasticity of −0.13 (−0.20/−0.06) (Gallet & Doucouliagos, 2017). In Scenario 2, we used the outcome elasticity with respect to circulatory problems as estimated by Claxton, Martin, et al (2015) using 2006/2007expenditure and 2006/2007 mortality data, which estimated an elasticity of −1.43 (−1.86/−1.00).…”

Section: Regression Model Specificationmentioning

confidence: 99%

“…Woods, Revill, Sculpher, and Claxton (2016) reported similar estimates (€21,000-28,000) per QALY by extrapolating estimates of the English k-threshold using income elasticity of demand for health, and gross domestic product. To estimate the k-threshold, we will use a Bayesian regression modelling framework, which is in line with the cost-effectiveness decision framework and allows to incorporate results from previous studies on the impact of health spending on mortality (Claxton, Martin, et al, 2015;Gallet & Doucouliagos, 2017).…”

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confidence: 99%

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A cost‐effectiveness threshold based on the marginal returns of cardiovascular hospital spending

et al. 2018

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Traditionally, threshold levels of cost‐effectiveness have been derived from willingness‐to‐pay studies, indicating the consumption value of health ( v‐ thresholds). However, it has been argued that v‐ thresholds need to be supplemented by so‐called k‐ thresholds, which are based on the marginal returns to health care. The objective of this research is to estimate a k‐ threshold based on the marginal returns to cardiovascular disease (CVD) hospital care in the Netherlands. To estimate a k ‐threshold for hospital care on CVD, we proceed in two steps: First, we estimate the impact of hospital spending on mortality using a Bayesian regression modelling framework, using data on CVD mortality and CVD hospital spending by age and gender for the period 1994–2010. Second, we use life tables in combination with quality of life data to convert these estimates into a k ‐threshold expressed in euros per quality‐adjusted life year gained. Our base case estimate resulted in an estimate of 41,000 per quality‐adjusted life year gained. In our sensitivity analyses, we illustrated how the incorporation of prior evidence into the estimation pushes estimates downwards. We conclude that our base case estimate of the k ‐threshold may serve as a benchmark value for decision making in the Netherlands as well as for future research regarding k‐ thresholds.

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Section: Resultscontrasting

confidence: 76%

Section: Resultsmentioning

confidence: 63%

Section: Regression Model Specificationmentioning

confidence: 99%

mentioning

confidence: 99%

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A cost‐effectiveness threshold based on the marginal returns of cardiovascular hospital spending

et al. 2018

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“…Campanella et al (2015) applied meta-analysis to assess the impact of electronic health record (EHR) on healthcare quality and the impact of Public Reporting on clinical outcomes (Campanella et al 2016). Gallet and Doucouliagos (2017) applied meta-regression analysis (MRA), and examine the healthcare spending elasticity for the mortality rate and the spending elasticity for life expectancy. Shor et al (2017)conducted meta-analyses and meta-regressions to examine the relationship between immigration and mortality from Latin American countries to OECD countries, and the overall results suggested no immigrant mortality advantage, and the relative risk of mortality largely depends on life course stages.…”

Section: Why Meta-analysis Is Used To Re-examine the Impact Of Healthmentioning

confidence: 99%

Impact of healthcare insurance on medical expense in China: new evidence from meta-analysis

et al. 2017

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This paper is aimed to make sense of the real effect of implement of social healthcare insurance on one's medical expense in China. Due to previous studies drew various and inconsistent conclusions on this issue, this works intend to apply meta-analysis to the problem. For 31 related studies, we first implement an advanced conditional Dirichlet-based Bayesian semi-parametric model specific to meta-analysis, and come to a primary conclusion that healthcare possesses little probability reducing one's medical expense in China. Further, the authors conduct random effects meta-regression and find that heterogeneity exists among the observed effect sizes. Mixed effects model shows that the age variation may is actually the heterogeneity source. The coefficients for Nonold and Old are respectively 0.29 and 0.54, implying that when researching on the medical expense for the elderly, it is more likely to conclude the medical insurance could increase medical spending. The coefficients for IV and OLS are both National Center for Mathematics and Interdisciplinary Sciences, CAS, Beijing, China remarkably negative at 90% confidence level. This suggests when directly using Instrument Variable (IV) approach and OLS method to assess the implementation effect for the healthcare insurance, it is inclined to result in the reduced impact on medical expense. We deduce this is because this two methods can't solve the sample-selection bias when compared with the Two-part model and difference-in-difference (DID) model. Based on the results and discussion, we finally propose suggests for the government.

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Community and Health

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2019

Handbook of Health Social Work

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The impact of healthcare spending on health outcomes: A meta-regression analysis

Cited by 122 publications

References 31 publications

A cost‐effectiveness threshold based on the marginal returns of cardiovascular hospital spending

A cost‐effectiveness threshold based on the marginal returns of cardiovascular hospital spending

Impact of healthcare insurance on medical expense in China: new evidence from meta-analysis

Community and Health

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