Universal coverage but unmet need: National and regional estimates of attrition across the diabetes care continuum in Thailand

• Main Authors: Lily D. Yan, Piya Hanvoravongchai, Wichai Aekplakorn, Suwat Chariyalertsak, Pattapong Kessomboon, Sawitri Assanangkornchai, Surasak Taneepanichskul, Nareemarn Neelapaichit, Andrew C. Stokes
• Format: Journal
• Published: 2020
• Subjects: Agricultural and Biological Sciences; Biochemistry, Genetics and Molecular Biology; Multidisciplinary
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85077941875&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/68220
• Institution: Chiang Mai University

© 2020 Yan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Background Diabetes is a growing challenge in Thailand. Data to assess health system response to diabetes is scarce. We assessed what factors influence diabetes care cascade retention, under universal health coverage. Methods We conducted a cross-sectional analysis of the 2014 Thai National Health Examination Survey. Diabetes was defined as fasting plasma glucose ≥126mg/dL or on treatment. National and regional care cascades were constructed across screening, diagnosis, treatment, and control. Unmet need was defined as the total loss across cascade levels. Logistic regression was used to examine the demographic and healthcare factors associated with cascade attrition. Findings We included 15,663 individuals. Among Thai adults aged 20+ with diabetes, 67.0% (95% CI 60.9% to 73.1%) were screened, 34.0% (95% CI 30.6% to 37.2%) were diagnosed, 33.3% (95% CI 29.9% to 36.7%) were treated, and 26.0% (95% CI 22.9% to 29.1%) were controlled. Total unmet need was 74.0% (95% CI 70.9% to 77.1%), with regional variation ranging from 58.4% (95% CI 45.0% to 71.8%) in South to 78.0% (95% CI 73.0% to 83.0%) in Northeast. Multivariable models indicated older age (OR 1.76), males (OR 0.65), and a higher density of medical staff (OR 2.40) and health centers (OR 1.58) were significantly associated with being diagnosed among people with diabetes. Older age (OR 1.80) and higher geographical density of medical staff (OR 1.82) and health centers (OR 1.56) were significantly associated with being controlled. Conclusions Substantial attrition in the diabetes care continuum was observed at diabetes screening and diagnosis, related to both individual and health system factors. Even with universal health insurance, Thailand still needs effective behavioral and structural interventions, especially in primary health care settings, to address unmet need in diabetes care for its population.