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Integrating Geographic Information into Diabetes Disease Management

Authors: Tsu-Yun Chiu, Tsung-Hsueh Lu, Tain-Junn Cheng


Background: Traditional chronic disease management did not pay attention to effects of geographic factors on the compliance of treatment regime, which resulted in geographic inequality in outcomes of chronic disease management. This study aims to examine the geographic distribution and clustering of quality indicators of diabetes care. Method: We first extracted address, demographic information and quality of care indicators (number of visits, complications, prescription and laboratory records) of patients with diabetes for 2014 from medical information system in a medical center in Tainan City, Taiwan, and the patients’ addresses were transformed into district- and village-level data. We then compared the differences of geographic distribution and clustering of quality of care indicators between districts and villages. Despite the descriptive results, rate ratios and 95% confidence intervals (CI) were estimated for indices of care in order to compare the quality of diabetes care among different areas. Results: A total of 23,588 patients with diabetes were extracted from the hospital data system; whereas 12,716 patients’ information and medical records were included to the following analysis. More than half of the subjects in this study were male and between 60-79 years old. Furthermore, the quality of diabetes care did indeed vary by geographical levels. Thru the smaller level, we could point out clustered areas more specifically. Fuguo Village (of Yongkang District) and Zhiyi Village (of Sinhua District) were found to be “hotspots” for nephropathy and cerebrovascular disease; while Wangliau Village and Erwang Village (of Yongkang District) would be “coldspots” for lowest proportion of ≥80% compliance to blood lipids examination. On the other hand, Yuping Village (in Anping District) was the area with the lowest proportion of ≥80% compliance to all laboratory examination. Conclusion: In spite of examining the geographic distribution, calculating rate ratios and their 95% CI could also be a useful and consistent method to test the association. This information is useful for health planners, diabetes case managers and other affiliate practitioners to organize care resources to the areas most needed.

Keywords: Geocoding, chronic disease management, quality of diabetes care, rate ratio.

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[1] P. Zhang et al., “Global healthcare expenditure on diabetes for 2010 and 2030,” Diabetes Res. Clin. Pract., vol. 87, no. 3, pp. 293-301, Mar. 2010.
[2] World Health Organization. Diabetes, available at (accessed 13 April 2017).
[3] Ministry of Health and Welfare, OPEN DATA, available at
[4] S.L. Norris, P.J. Nichols, “The effectiveness of disease and case management for people with diabetes. A systematic review,” Am J Med, vol. 22, no. 4 Suppl, pp. 15-38, May. 2002.
[5] Australian Government. The Department of Health. National service improvement framework for diabetes 2005, available at
[6] Ministry of Health and Welfare, Medical Services, Assistances & Cares, available at (accessed 13 April 2017).
[7] Ministry of Health and Welfare, Medical Services, Assistances & Cares, available at (accessed 13 April 2017).
[8] S. Macintyre, A. Ellaway, S. Cummins, “Place effects on health: how can we conceptualise operationalize and measure them?” Soc. Sci.& Med., vol. 55, pp. 125-139, 2002.
[9] R.J. Walker, B.L. Smalls, J.A. Campbell et al., “Impact of social determinants of health on outcomes for type 2 diabetes: a systemic review,” Endocrine, vol. 47, pp. 29-48, Sep. 2014.
[10] M. Jiwa, O. Gudes, R. Varhol, N. Mullan, “Impact of geography on the control of type 2 diabetes mellitus: a review of geocoded clinical data from general practice,” BMJ Open, vol. 5,2015.
[11] Ministry of the Interior, Statistical Area Access Services, available at
[12] WHO Collaborating Centre for Drug Statistics Methodology, ATC/DDD Index, available at
[13] Ministry of Health and Welfare, ICD & Range, available at (accessed 13 April 2017).
[14] B.A. Young, E. Lin, M.V. Korff et al., “Diabetes Complications Severity Index and Risk of Mortality, Hospitalization, and Healthcare Utilization,” Am. J. Manag. Care, vol. 14, no. 1, pp. 15-23, Jan. 2008.
[15] Epidemiology beyond the basics, 3rd Edition, M. Szklo, J. Nieto, 2014
[16] Ministry of the Interior, available at (accessed 13 April 2017).