Using spatial analysis to predict health care use at the local level: a case study of type 2 diabetes medication use and its association with demographic change and socioeconomic status

• Published in: PloS one Vol. 8; no. 8; p. e72730
• Main Authors: Dijkstra, Aletta, Janssen, Fanny, De Bakker, Marinus, Bos, Jens, Lub, René, Van Wissen, Leo J G, Hak, Eelko
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 30.08.2013; Public Library of Science (PLoS)
• Subjects: Added value; Aging; Ambulatory care; Case reports; Case studies; Clustering; Delivery of Health Care - statistics & numerical data; Demographic variables; Demographics; Demography; Diabetes; Diabetes mellitus; Diabetes Mellitus, Type 2 - drug therapy; Diabetes Mellitus, Type 2 - epidemiology; Drug abuse; Drug stores; Drugs; Geography; Health care; Humans; Hypoglycemic Agents - therapeutic use; Medicine; Middle Aged; Netherlands - epidemiology; Obesity; Pharmacoeconomics; Pharmacy; Population decline; Prevalence; Public health; Regression Analysis; Regression models; Social and Behavioral Sciences; Social Class; Socio-economic aspects; Socioeconomic factors; Socioeconomic status; Socioeconomics; Spatial Analysis; Spatial variability; Statistical analysis; Variability; Netherlands
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0072730

Local health status and health care use may be negatively influenced by low local socio-economic profile, population decline and population ageing. To support the need for targeted local health care, we explored spatial patterns of type 2 diabetes mellitus (T2DM) drug use at local level and determined its association with local demographic, socio-economic and access to care variables. We assessed spatial variability in these associations. We estimated the five-year prevalence of T2DM drug use (2005-2009) in persons aged 45 years and older at four-digit postal code level using the University of Groningen pharmacy database IADB.nl. Statistics Netherlands supplied data on potential predictor variables. We assessed spatial clustering, correlations and estimated a multiple linear regression model and a geographically weighted regression (GWR) model. Prevalence of T2DM medicine use ranged from 2.0% to 25.4%. The regression model included the extent of population ageing, proportion of social welfare/benefits, proportion of low incomes and proportion of pensioners, all significant positive predictors of local T2DM drug use. The GWR model demonstrated considerable spatial variability in the association between T2DM drug use and above predictors and was more accurate. The findings demonstrate the added value of spatial analysis in predicting health care use at local level.