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Impact of Socioeconomic Differences on Distributional Cost-effectiveness Analysis

Fan Yang, Colin Angus, Ana Duarte, Duncan Gillespie, Simon Walker and Susan Griffin
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Fan Yang: Centre for Health Economics, University of York, UK
Colin Angus: Sheffield Alcohol Research Group, Health Economics and Decision Science, ScHARR, University of Sheffield, UK
Ana Duarte: Centre for Health Economics, University of York, UK
Duncan Gillespie: Sheffield Alcohol Research Group, Health Economics and Decision Science, ScHARR, University of Sheffield, UK
Susan Griffin: Centre for Health Economics, University of York, UK

Medical Decision Making, 2020, vol. 40, issue 5, 606-618

Abstract: Public health decision makers value interventions for their effects on overall health and health inequality. Distributional cost-effectiveness analysis (DCEA) incorporates health inequality concerns into economic evaluation by accounting for how parameters, such as effectiveness, differ across population groups. A good understanding of how and when accounting for socioeconomic differences between groups affects the assessment of intervention impacts on overall health and health inequality could inform decision makers where DCEA would add most value. We interrogated 2 DCEA models of smoking and alcohol policies using first national level and then local authority level information on various socioeconomic differences in health and intervention use. Through a series of scenario analyses, we explored the impact of altering these differences on the DCEA results. When all available evidence on socioeconomic differences was incorporated, provision of a smoking cessation service was estimated to increase overall health and increase health inequality, while the screening and brief intervention for alcohol misuse was estimated to increase overall health and reduce inequality. Ignoring all or some socioeconomic differences resulted in minimal change to the estimated impact on overall health in both models; however, there were larger effects on the estimated impact on health inequality. Across the models, there were no clear patterns in how the extent and direction of socioeconomic differences in the inputs translated into the estimated impact on health inequality. Modifying use or coverage of either intervention so that each population group matched the highest level improved the impacts to a greater degree than modifying intervention effectiveness. When local level socioeconomic differences were considered, the magnitude of the impacts was altered; in some cases, the direction of impact on inequality was also altered.

Keywords: distributional cost-effectiveness analysis; economic evaluation; health inequality; public health (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:40:y:2020:i:5:p:606-618

DOI: 10.1177/0272989X20935883

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