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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:BackgroundIn the USA, Black and Hispanic or Latinx individuals continue to be disproportionately affected by HIV. Applying a distributional cost-effectiveness framework, we estimated the cost-effectiveness and epidemiological impact of two combination implementation approaches to identify the approach that best meets the dual objectives of improving population health and reducing racial or ethnic health disparities.MethodsWe adapted a dynamic, compartmental HIV transmission model to characterise HIV micro-epidemics in six US cities: Atlanta, Baltimore, Los Angeles, Miami, New York, and Seattle. We considered combinations of 16 evidence-based interventions to diagnose, treat, and prevent HIV transmission according to previously documented levels of scale-up. We then identified optimal combination strategies for each city, with the distribution of each intervention implemented according to existing service levels (proportional services approach) and the racial or ethnic distribution of new diagnoses (between Black, Hispanic or Latinx, and White or other ethnicity individuals; equity approach). We estimated total costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios of strategies implemented from 2020 to 2030 (health-care perspective; 20-year time horizon; 3% annual discount rate). We estimated three measures of health inequality (between-group variance, index of disparity, Theil index), incidence rate ratios, and rate differences for the selected strategies under each approach.FindingsIn all cities, optimal combination strategies under the equity approach generated more QALYs than those with proportional services, ranging from a 3·1% increase (95% credible interval [CrI] 1·4-5·3) in New York to more than double (101·9% [75·4-134·6]) in Atlanta. Compared with proportional services, the equity approach delivered lower costs over 20 years in all cities except Los Angeles; cost reductions ranged from $22·9 million (95% CrI 5·3-55·7 million) in Seattle to $579·8 million (255·4-940·5 million) in Atlanta. The equity approach also reduced incidence disparities and health inequality measures in all cities except Los Angeles.InterpretationEquity-focused HIV combination implementation strategies that reduce disparities for Black and Hispanic or Latinx individuals can significantly improve population health, reduce costs, and drive progress towards Ending the HIV Epidemic goals in the USA.FundingNational Institute on Drug Abuse.

Project description:Background and objectiveDistributional cost-effectiveness analysis (DCEA) facilitates quantitative assessments of how health effects and costs are distributed among population subgroups, and of potential trade-offs between health maximisation and equity. Implementation of DCEA is currently explored by the National Institute for Health and Care Excellence (NICE) in England. Recent research conducted an aggregate DCEA on a selection of NICE appraisals; however, significant questions remain regarding the impact of the characteristics of the patient population (size, distribution by the equity measure of interest) and methodologic choices on DCEA outcomes. Cancer is the indication most appraised by NICE, and the relationship between lung cancer incidence and socioeconomic status is well established. We aimed to conduct an aggregate DCEA of two non-small cell lung cancer (NSCLC) treatments recommended by NICE, and identify key drivers of the analysis.MethodsSubgroups were defined according to socioeconomic deprivation. Data on health benefits, costs, and target populations were extracted from two NICE appraisals (atezolizumab versus docetaxel [second-line treatment following chemotherapy to represent a broad NSCLC population] and alectinib versus crizotinib [targeted first-line treatment to represent a rarer mutation-positive NSCLC population]). Data on disease incidence were derived from national statistics. Distributions of population health and health opportunity costs were taken from the literature. A societal welfare analysis was conducted to assess potential trade-offs between health maximisation and equity. Sensitivity analyses were conducted, varying a range of parameters.ResultsAt an opportunity cost threshold of £30,000 per quality-adjusted life-year (QALY), alectinib improved both health and equity, thereby increasing societal welfare. Second-line atezolizumab involved a trade-off between improving health equity and maximising health; it improved societal welfare at an opportunity cost threshold of £50,000/QALY. Increasing the value of the opportunity cost threshold improved the equity impact. The equity impact and societal welfare impact were small, driven by the size of the patient population and per-patient net health benefit. Other key drivers were the inequality aversion parameters and the distribution of patients by socioeconomic group; skewing the distribution to the most (least) deprived quintile improved (reduced) equity gains.ConclusionUsing two illustrative examples and varying model parameters to simulate alternative decision problems, this study suggests that key drivers of an aggregate DCEA are the opportunity cost threshold, the characteristics of the patient population, and the level of inequality aversion. These drivers raise important questions in terms of the implications for decision making. Further research is warranted to examine the value of the opportunity cost threshold, capture the public's views on unfair differences in health, and estimate robust distributional weights incorporating the public's preferences. Finally, guidance from health technology assessment organisations, such as NICE, is needed regarding methods for DCEA construction and how they would interpret and incorporate those results in their decision making.

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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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