Affiliations 

  • 1 Erasmus School of Health Policy and Management (ESHPM), Erasmus, University Rotterdam, the Netherlands; Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; Erasmus Center for Health Economics Rotterdam (EsCHER), the Netherlands. Electronic address: parramujica@eshpm.eur.nl
  • 2 Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
  • 3 Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
  • 4 Non-communicable Disease Section, Disease Control Division, Ministry of Health, Putrajaya, Malaysia
  • 5 Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
  • 6 Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia; Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
Soc Sci Med, 2024 Jan;340:116426.
PMID: 38016309 DOI: 10.1016/j.socscimed.2023.116426

Abstract

In the context of the escalating burden of diabetes in low and middle-income countries (LMICs), there is a pressing concern about the widening disparities in care and outcomes across socioeconomic groups. This paper estimates health poverty measures among individuals with type 2 diabetes mellitus (T2DM) in Malaysia. Using data from the National Diabetes Registry between 2009 and 2018, the study linked 932,855 people with T2DM aged 40-75 to death records. Cox proportional hazards models were used to estimate the 5-year survival probabilities for each patient, stratified by age and sex, while controlling for comorbidities and area-based indicators of socio-economic status (SES), such as district-level asset-based indices and night-time luminosity. Measures of health poverty, based on the Foster-Greer-Thorbecke (FGT) measures, were employed to capture excessive risk of premature mortality. Two poverty line thresholds were used, namely a 5% and 10% reduction in survival probability compared to age and sex-adjusted survival probability of the general population. Counterfactual simulations estimated the extent to which comorbidities contribute to health poverty. 43.5% of the sample experienced health poverty using the 5% threshold, and 8.9% were health poor using the 10% threshold. Comorbidities contribute 2.9% for males and 5.4% for females, at the 5% threshold. At the 10% threshold, they contribute 7.4% for males and 3.4% for females. If all patients lived in areas of highest night-light intensity, poverty would fall by 5.8% for males and 4.6% for females at the 5% threshold, and 4.1% for males and 0.8% for females at the 10% threshold. In Malaysia, there is a high incidence of health poverty among people with diabetes, and it is strongly associated with comorbidities and area-based measures of SES. Expanding the application of health poverty measurement, through a combination of clinical registries and open spatial data, can facilitate simulations for health poverty alleviation.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.