Importance of Geospatial Heterogeneity in Chronic Disease Burden for Policy Planning in an Urban Setting Using a Case Study of Singapore

Ken Wei Tan, Joel R. Koo, Jue Tao Lim, Alex R. Cook and Borame L. Dickens
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Ken Wei Tan: Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore 117549, Singapore
Joel R. Koo: Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore 117549, Singapore
Jue Tao Lim: Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore 117549, Singapore
Alex R. Cook: Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore 117549, Singapore
Borame L. Dickens: Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore 117549, Singapore

IJERPH, 2021, vol. 18, issue 9, 1-12

Abstract: Chronic disease burdens continue to rise in highly dense urban environments where clustering of type II diabetes mellitus, acute myocardial infarction, stroke, or any combination of these three conditions is occurring. Many individuals suffering from these conditions will require longer-term care and access to clinics which specialize in managing their illness. With Singapore as a case study, we utilized census data in an agent-modeling approach at an individual level to estimate prevalence in 2020 and found high-risk clusters with >14,000 type II diabetes mellitus cases and 2000–2500 estimated stroke cases. For comorbidities, 10% of those with type II diabetes mellitus had a past acute myocardial infarction episode, while 6% had a past stroke. The western region of Singapore had the highest number of high-risk individuals at 173,000 with at least one chronic condition, followed by the east at 169,000 and the north with the least at 137,000. Such estimates can assist in healthcare resource planning, which requires these spatial distributions for evidence-based policymaking and to investigate why such heterogeneities exist. The methodologies presented can be utilized within any urban setting where census data exists.

Keywords: statistical modeling; chronic disease; spatial epidemiology; urbanization; environmental health (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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