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Data · dataset · 2027

Routine Distributional Analysis of Health Inequality Impact by Socioeconomic Group: Technical Feasibility Study in Queensland, Australia

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OBJECTIVES: This study aimed to evaluate the technical feasibility of routine distributional analyses of healthcare interventions in Australia, by adapting a Health Inequality Impact Calculator for Queensland.

Description

Building on methods established in a previous study in England, the calculator compares the magnitude of health inequality impact across interventions for different diseases. METHODS: The calculator was adapted for adults in Queensland using the Index of Relative Socio-economic Advantage and Disadvantage quintiles, Australian Bureau of Statistics population estimates, 2023 hospital admissions (International Classification of Diseases-10th edition 3-digit level) from Queensland Health, and published quality-adjusted life expectancy.

It was used to conduct aggregate distributional cost-effectiveness analysis of 5 interventions for diseases with relatively high prevalence in disadvantaged groups, based on existing cost-effectiveness results identified by a targeted review. The health inequality impact was summarized using the slope index of inequality in quality-adjusted life-years (QALYs). RESULTS: The selected interventions were 2 screening interventions for diabetes and hypertension, and 3 treatment interventions for chronic kidney disease.

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The calculator demonstrated that all 5 interventions would reduce health inequality. Population screening for diabetes had the greatest impact on reducing health inequality of 8562 QALYs. Among the 3 treatment interventions, intensive blood pressure control had the greatest impact, with a health inequality benefit of 514 QALYs.

CONCLUSIONS: Routine aggregate distributional cost-effectiveness analysis of interventions in Queensland is feasible, once standard cost-effectiveness estimates are available. The calculator for Queensland enables fast, transparent, and replicable analysis of health inequality impacts using minimal data, with visual outputs to explore trade-offs between health outcomes, inequality, and cost-effectiveness.

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Inferred from text
Disease 75%
Provenance · 1 source records, 13 field assertions
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