Woodhead, C;
Martin, P;
Osborn, D;
Barratt, H;
Raine, R;
(2021)
Health system influences on potentially avoidable hospital admissions by secondary mental health service use: A national ecological study.
Journal of Health Services Research & Policy
10.1177/13558196211036739.
(In press).
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Abstract
OBJECTIVES: Potentially avoidable hospital admissions (PAAs) are costly to health services and potentially harmful for patients. This study aimed to compare area-level PAA rates among people using and not using secondary mental health services in England and to identify health system features that may influence between-area PAA variation. METHODS: National ecological study using linked English hospital admissions and secondary mental health services data (2016–2018). We calculated two-year average age-sex standardised area-level PAA rates according to primary admission diagnoses for 12 physical conditions, among, first, secondary mental health service users with any non-organic diagnosis, and, second, people not in contact with secondary mental health services. We used penalised regression analyses to identify predictors of area-level variation in PAA rates. RESULTS: Area-level PAA rates were over four times greater in the mental health group, at 7,594 per 100,000 population compared to 1,819 per 100,000 in the comparator group. Common predictors of variation were greater density of older age groups (lower PAA rates), higher underlying population morbidity of chronic obstructive pulmonary disease and, to a lesser extent, urbanity (higher PAA rates). For both groups, health system factors such as the number of general practitioners per capita or ambulance despatch rates were significant but weak predictors of variation. Mental health diagnosis data were available for half of secondary mental health care records only and sensitivity analyses found that urbanity remained the sole significant predictor for PAAs in this group. CONCLUSIONS: Findings support the need for improved management of physical conditions for secondary mental health service users. Understanding and predicting variation in PAAs among mental health service users is constrained by availability of data on mental health diagnosis, physical health care and needs.
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