1. Spatial effects in hospital expenditures: A district level analysis
- Author
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Matteo Lippi Bruni, Irene Mammi, Lippi Bruni, Matteo, and Mammi, Irene
- Subjects
Economic growth ,health districts, hospital expenditure, spatial panel data models ,Contiguity ,General Practice ,jel:C23 ,Medical Overuse ,Settore SECS-P/02 - Politica Economica ,State Medicine ,Health services ,health districts ,Medicine ,spatial models, health expenditures, health districts ,Potential source ,050207 economics ,Settore SECS-P/01 - Economia Politica ,spatial panel data models ,Public economics ,I11 ,030503 health policy & services ,Health Policy ,05 social sciences ,Age Factors ,Hospital Charges ,jel:I11 ,Hospital care ,Hospitalization ,Geography ,Italy ,Quaderni - Working Paper DSE ,Settore SECS-P/03 - Scienza delle Finanze ,hospital expenditure ,0305 other medical science ,Knowledge transfer ,Models, Econometric ,C23 ,Hospital expenditures ,spatial effects ,panel data ,institutionally-clustered data ,Settore SECS-P/05 - Econometria ,Primary care ,03 medical and health sciences ,0502 economics and business ,ddc:330 ,Humans ,Diagnosis-Related Groups ,Spatial Analysis ,business.industry ,R12 ,Cross-Sectional Studies ,Geographical cluster ,jel:R12 ,SECS-P/03 Scienza delle finanze ,business ,District level ,Panel data - Abstract
Geographical clusters in health expenditures are well documented and accounting for spatial interactions may contribute to properly identify the factors affecting the use of health services the most. As for hospital care, spillovers may derive from strategic behaviour of hospitals and from patients’ preferences that may induce mobility across jurisdictions, as well as from geographically-concentrated risk factors, knowledge transfer and interactions between different layers of care. Our paper focuses on a largely overlooked potential source of spillovers in hospital expenditure: the heterogeneity of primary care providers’ behaviour. To do so, we analyse expenditures associated to avoidable hospitalisations separately from expenditures for highly complex treatments, as the former are most likely affected by General Practitioners, while the latter are not. We use administrative data for Italy’s Region Emilia Romagna between 2007 and 2010. Since neighbouring districts may belong to different Local Health Authorities (LHAs), we employ a spatial contiguity matrix that allows to investigate the effects of geographical and institutional proximity and use it to estimate Spatial Autoregressive and Spatial Durbin Models.
- Published
- 2017
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