Cross-sectional analysis of place-based and racial disparities in hospitalisation rates by disease category in California in 2001 and 2011

• Authors: Eva Raphael, University of California, San Francisco; Robert Gaynes, Emory University; Rita Hamad, University of California, San Francisco
• Journal Title: BMJ Open
• Volume: Volume 9, Number 10
• Publisher: BMJ Publishing Group: Open Access | 2019-10-01, Pages e031556-e031556
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1136/bmjopen-2019-031556
• Subjects: Health Sciences, Medicine and Surgery; Health Sciences, Public Health
• Keywords: disease burden; hospitalisations; zip code median household income

Abstract

Objectives: To study the association of place-based socioeconomic factors with disease distribution by comparing hospitalisation rates in California in 2001 and 2011 by zip code median household income. Design: Serial cross-sectional study testing the association between hospitalisation rates and zip code-level median income, with subgroup analyses by zip code income and race. Participants/setting: Our study included all hospitalised adults over 18 years old living in California in 2001 and 2011 who were not pregnant or incarcerated. This included all acute-care hospitalisations in California including 1632 zip codes in 2001 and 1672 zip codes in 2011. Primary and secondary outcomes: We compared age-standardised hospitalisations per 100 000 persons, overall and for several disease categories. Results: There were 1.58 and 1.78 million hospitalisations in California in 2001 and 2011, respectively. Spatial analysis showed the highest hospitalisation rates in urban inner cities and rural areas, with more than 5000 hospitalisations per 100 000 persons. Hospitalisations per 100 000 persons were consistently highest in the lowest zip code income quintile and particularly among black patients. Conclusion: Hospitalisation rates rose from 2001 to 2011 among Californians living in low-income and middle-income zip codes. Integrating spatially defined state hospital discharge and federal zip code income data provided a granular description of disease burden. This method may help identify high-risk areas and evaluate public health interventions targeting health disparities.