Severe Mental Illness (SMI) encompasses a range of chronic conditions including schizophrenia, bipolar disorder and psychoses. Patients with SMI often require inpatient psychiatric care. Despite equity being a key objective in the English National Health Service (NHS) and in many other health care systems worldwide, little is known about the socio-economic equity of hospital care utilisation for patients with SMI and how it has changed over time. This analysis seeks to address that gap in the evidence base. We exploit a five-year (2006-2010) panel dataset of admission rates at small area level (n = 162,410). The choice of control variables was informed by a systematic literature search. To assess changes in socio-economic equity of utilisation, OLS-based standardisation was first used to conduct analysis of discrete deprivation groups. Geographical inequity was then illustrated by plotting standardised and crude admission rates at local purchaser level. Lastly, formal statistical tests for changes in socio-economic equity of utilisation were applied to a continuous measure of deprivation using pooled negative binomial regression analysis, adjusting for a range of risk factors. Our results suggest that one additional percentage point of area income deprivation is associated with a 1.5% (p < 0.001) increase in admissions for SMI after controlling for population size, age, sex, prevalence of SMI in the local population, as well as other need and supply factors. This finding is robust to sensitivity analyses, suggesting that a pro-poor inequality in utilisation exists for SMI-related inpatient services. One possible explanation is that the supply or quality of primary, community or social care for people with mental health problems is suboptimal in deprived areas. Although there is some evidence that inequity has reduced over time, the changes are small and not always robust to sensitivity analyses.
Bibliographical noteCopyright © 2014. Published by Elsevier Ltd.
- Healthcare disparities/trends
- Mental health services
- Regression analysis
- Small-area analysis
- Socioeconomic factors
- State Medicine/Organization & Administration