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Impact of Socioeconomic Differences on Distributional Cost-effectiveness Analysis.

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.

SUBMITTER: Yang F

PROVIDER: S-EPMC7488816 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Impact of Socioeconomic Differences on Distributional Cost-effectiveness Analysis.

Yang Fan F Angus Colin C Duarte Ana A Gillespie Duncan D Walker Simon S Griffin Susan S

Medical decision making : an international journal of the Society for Medical Decision Making 20200701 5

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 ...[more]

PMID: 32608317

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Project description:BackgroundPhysical inactivity is the fourth highest cause of death globally and is a major contributor to increases in healthcare expenditure. Improving public open spaces such as parks in areas of low socio-economic position (SEP) may increase recreational physical activity in disadvantaged populations. We assessed the cost-effectiveness of the installation of a play-space in a large metropolitan park in a low socioeconomic area based on changes in physical activity.MethodsObservational data of visitor counts and activities undertaken in the park before the installation of the new play-scape (T1), at two months (T2) and 14 months post-installation (T3) were obtained for the intervention and a control park (with no refurbishment) located in a high SEP metropolitan area. Observed sitting, standing, and moderate and vigorous-intensity physical activity were converted to yearly MET-h according to age. Costs of the play-scape and ongoing maintenance were obtained from the organisation managing the refurbishment. The incremental cost-effectiveness ratio (ICER) (ratio of incremental cost to incremental effect) was calculated based on the incremental increase in MET-h from T1 to T3 assuming a 20-year lifetime of the play-scape. Observation counts combining moderate and vigorous activity were used in the sensitivity analysis.ResultsWhen compared with T1, at T3 the new play-scape resulted in an overall incremental net gain of 114,114 MET-h (95% UI: 80,476 - 146,096) compared with the control park and an incremental cost effectiveness ratio (or cost per MET-h gained per park visitor) of AUD $0.58 (95% UI: $0.44-$0.80). The sensitivity analysis combining moderate and vigorous activity into one category showed an increase in estimated incremental MET-h of 118,190 (95% CI: 83,528 - 149,583) and a lower incremental cost per MET-h gained of AUD $0.56 (95% UI: $0.43-$0.77).ConclusionsUsing a benchmark of cost-effectiveness for physical activity interventions of AUD $0.60-$1.30, this study suggests that the installation of a play-scape located in a low SEP area is cost-effective based on its potential to facilitate increases in MET-h. It provides much needed preliminary evidence and requires replication elsewhere.

Project description:BackgroundWe aimed to evaluate the cost-effectiveness of an artificial intelligence-(AI) based diabetic retinopathy (DR) screening system in the primary care setting for both non-Indigenous and Indigenous people living with diabetes in Australia.MethodsWe performed a cost-effectiveness analysis between January 01, 2022 and August 01, 2023. A decision-analytic Markov model was constructed to simulate DR progression in a population of 1,197,818 non-Indigenous and 65,160 Indigenous Australians living with diabetes aged ≥20 years over 40 years. From a healthcare provider's perspective, we compared current practice to three primary care AI-based screening scenarios-(A) substitution of current manual grading, (B) scaling up to patient acceptance level, and (C) achieving universal screening. Study results were presented as incremental cost-effectiveness ratio (ICER), benefit-cost ratio (BCR), and net monetary benefits (NMB). A Willingness-to-pay (WTP) threshold of AU$50,000 per quality-adjusted life year (QALY) and a discount rate of 3.5% were adopted in this study.FindingsWith the status quo, the non-Indigenous diabetic population was projected to develop 96,269 blindness cases, resulting in AU$13,039.6 m spending on DR screening and treatment during 2020-2060. In comparison, all three intervention scenarios were effective and cost-saving. In particular, if a universal screening program was to be implemented (Scenario C), it would prevent 38,347 blindness cases, gain 172,090 QALYs and save AU$595.8 m, leading to a BCR of 3.96 and NMB of AU$9,200 m. Similar findings were also reported in the Indigenous population. With the status quo, 3,396 Indigenous individuals would develop blindness, which would cost the health system AU$796.0 m during 2020-2060. All three intervention scenarios were cost-saving for the Indigenous population. Notably, universal AI-based DR screening (Scenario C) would prevent 1,211 blindness cases and gain 9,800 QALYs in the Indigenous population, leading to a saving of AU$19.2 m with a BCR of 1.62 and NMB of AU$509 m.InterpretationOur findings suggest that implementing AI-based DR screening in primary care is highly effective and cost-saving in both Indigenous and non-Indigenous populations.FundingThis project received grant funding from the Australian Government: the National Critical Research Infrastructure Initiative, Medical Research Future Fund (MRFAI00035) and the NHMRC Investigator Grant (APP1175405). The contents of the published material are solely the responsibility of the Administering Institution, a participating institution or individual authors and do not reflect the views of the NHMRC. This work was supported by the Global STEM Professorship Scheme (P0046113), the Fundamental Research Funds of the State Key Laboratory of Ophthalmology, Project of Investigation on Health Status of Employees in Financial Industry in Guangzhou, China (Z012014075). The Centre for Eye Research Australia receives Operational Infrastructure Support from the Victorian State Government. W.H. is supported by the Melbourne Research Scholarship established by the University of Melbourne. The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Dataset Information

Impact of Socioeconomic Differences on Distributional Cost-effectiveness Analysis.

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Impact of Socioeconomic Differences on Distributional Cost-effectiveness Analysis.

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