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Published in

SAGE Publications, Journal of Health Services Research and Policy, 1(27), p. 31-40, 2021

DOI: 10.1177/13558196211021618

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Reducing inequality in avoidable emergency admissions: Case studies of local health care systems in England using a realist approach

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Abstract

Objective People in disadvantaged areas are more likely to have an avoidable emergency hospital admission. Socio-economic inequality in avoidable emergency hospital admissions is monitored in England. Our aim was to inform local health care purchasing and planning by identifying recent health care system changes (or other factors), as reported by local health system leaders, that might explain narrowing or widening trends. Methods Case studies were undertaken in one pilot and at five geographically distinct local health care systems (Clinical Commissioning Groups, CCGs), identified as having consistently increasing or decreasing inequality. Local settings were explored through discussions with CCG officials and stakeholders to identify potential local determinants. Data were analysed using a realist evaluation approach to generate context-mechanism-outcome (CMO) configurations. Results Of the five geographically distinct CCGs, two had narrowing inequality, two widening, and one narrowing inequality, which widened during the project. None of the CCGs had designed a large-scale package of service changes with the explicit aim of reducing socio-economic inequality in avoidable emergency admissions, and local decision makers were unfamiliar with their own trends. Potential primary and community care determinants included: workforce, case finding and exclusion, proactive care co-ordination for patients with complex needs, and access and quality. Potential commissioning determinants included: data use and incentives, and targeting of services. Other potential determinants included changes in care home services, national A&E targets, and wider issues - such as public services financial constraints, residential gentrification, and health care expectations. Conclusions We did not find any bespoke initiatives that explained the inequality trends. The trends were more likely due to an interplay of multiple health care and wider system factors. Local decision makers need greater awareness, understanding and support to interpret, use and act upon inequality indicators. They are unlikely to find simple, cheap interventions to reduce inequalities in avoidable emergency admissions. Rather, long-term multifaceted interventions are required that embed inequality considerations into mainstream decision making.