JSM 2004 - Toronto

Abstract #301591

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Activity Number: 344
Type: Contributed
Date/Time: Wednesday, August 11, 2004 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #301591
Title: Modeling Small-area Variations in Mental Health Service Use with Geographically Weighted Regression
Author(s): Lisa Lix*+ and Geoffrey DeVerteuil
Companies: University of Manitoba and University of Manitoba
Address: Manitoba Centre for Health Policy, Winnipeg, MB, R3E 3P5, Canada
Keywords: small-area variations ; geography ; mental health services ; spatial correlation ; local regression ; neighborhoods
Abstract:

A spatial model for the relationship of health service use to socioeconomic indicators for small-area data was investigated. Geographically weighted regression (GWR) was used, in which estimates of location-specific (i.e., local) regression parameters are obtained by kernel smoothing, with a bandwidth that adapts to spatial locations. GWR was applied to models of mental health service use obtained from acute care in-patient hospitalizations and physician billing claims for 75 neighborhoods in Winnipeg, Canada, a city of approximately 650,000 residents. The independent variables were indicators of population distribution, economic risk, social isolation, social disorganization, and health care infrastructure. Based on model fit statistics, the GWR Poisson regression model captured spatial associations better than a conventional Poisson model for the in-patient data. However, there was a high degree of spatial smoothing across neighborhoods. Little improvement in model fit was observed for the physician data. Accounting for spatial correlation in models of health service use may be beneficial for identifying predictors of the need for mental health services.

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