A framework for modelling differences in regional mortality over time. Issue 5 (13th April 2004)

• Record Type: Journal Article
• Authors: Lix, L M; Ekuma, O; Brownell, M; Roos, L L
• Is Part Of: Journal of epidemiology and community health. Volume 58:Issue 5(2004)
• Page Start: 420
• Page End: 425
• Publication Date: 2004-04-13
• Subjects: small area analysis -- longitudinal data -- generalised linear model; GEE, generalised estimating equation -- GLM, generalised linear model -- RHA, regional health authority -- PMR, premature mortality rate; Public health -- Periodicals; Epidemiology -- Periodicals; 614.4
• DOI: 10.1136/jech.2003.009522
• Languages: English
• ISSNs: 0143-005X

Abstract

Abstract : Study objective: To present a conceptual framework for testing differences in mortality for small geographical areas over time using the generalised linear model with generalised estimating equations. This framework can be used to test whether the magnitude of regional inequalities in health status has changed over time. Design: A Poisson regression model for correlated data is used to investigate the relation of population health status to demographic, geographical, and temporal explanatory variables. Differences between regions at one or more points in time are tested with linear contrasts. Setting and participants: A case example shows the application of the framework. All cause mortality and cause specific mortality were compared for three rural regions of Manitoba, Canada between 1985 and 1999. The data were obtained from Vital Statistics records and the provincial health registry. Main results: Tests of linear contrasts on the regression coefficients for time and region show an increase in the magnitude of the difference in the risk of all cause mortality and heart disease mortality between northern and southern regions of the province for the 1985–1989 and 1995–1999 time periods. No significant differences are identified for cancer, injury, or respiratory disease mortality. Conclusions: The proposed framework enables testing of a variety of hypotheses about differences between regions and time periods and can be applied to other measures of population health status.