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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

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  • Aletta Dijkstra
  • Fanny Janssen
  • Marinus De Bakker
  • Jens Bos
  • René Lub
  • Leo J G Van Wissen
  • Eelko Hak

Abstract

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.

Suggested Citation

  • Aletta Dijkstra & Fanny Janssen & Marinus De Bakker & Jens Bos & René Lub & Leo J G Van Wissen & Eelko Hak, 2013. "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," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-9, August.
    • Handle: RePEc:plo:pone00:0072730
    DOI: 10.1371/journal.pone.0072730
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    References listed on IDEAS

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    1. Michael de Looper & Gaétan Lafortune, 2009. "Measuring Disparities in Health Status and in Access and Use of Health Care in OECD Countries," OECD Health Working Papers 43, OECD Publishing.
    2. Green, Chris & Hoppa, Robert D. & Young, T. Kue & Blanchard, J. F., 2003. "Geographic analysis of diabetes prevalence in an urban area," Social Science & Medicine, Elsevier, vol. 57(3), pages 551-560, August.
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    2. Hsin-Ling Yeh & Shang-Wei Hsu & Yu-Chia Chang & Ta-Chien Chan & Hui-Chen Tsou & Yen-Chen Chang & Po-Huang Chiang, 2017. "Spatial Analysis of Ambient PM 2.5 Exposure and Bladder Cancer Mortality in Taiwan," IJERPH, MDPI, vol. 14(5), pages 1-14, May.
    3. Pablo Villalobos Dintrans, 2018. "Do long-term care services match population needs? A spatial analysis of nursing homes in Chile," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-15, June.
    4. Jie Gao & Zhijie Zhang & Yi Hu & Jianchao Bian & Wen Jiang & Xiaoming Wang & Liqian Sun & Qingwu Jiang, 2014. "Geographical Distribution Patterns of Iodine in Drinking-Water and Its Associations with Geological Factors in Shandong Province, China," IJERPH, MDPI, vol. 11(5), pages 1-14, May.
    5. Kashnitsky, Ilya, 2020. "Changing regional inequalities in ageing across Europe," OSF Preprints 6m4kg, Center for Open Science.
    6. Boris Kauhl & Werner Maier & Jürgen Schweikart & Andrea Keste & Marita Moskwyn, 2018. "Who is where at risk for Chronic Obstructive Pulmonary Disease? A spatial epidemiological analysis of health insurance claims for COPD in Northeastern Germany," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-18, February.

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