Effective coverage of diabetes and hypertension: an analysis of Thailand's national insurance database 2016-2019.

Nattadhanai Rajatanavin ORCID logo; Woranan Witthayapipopsakul; Vuthiphan Vongmongkol; Nithiwat Saengruang; Yaowaluk Wanwong; Aniqa Islam Marshall ORCID logo; Walaiporn Patcharanarumol; Viroj Tangcharoensathien; (2022) Effective coverage of diabetes and hypertension: an analysis of Thailand's national insurance database 2016-2019. BMJ open, 12 (12). e066289. ISSN 2044-6055 DOI: 10.1136/bmjopen-2022-066289
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OBJECTIVES: This study assesses effective coverage of diabetes and hypertension in Thailand during 2016-2019. DESIGN: Mixed method, analysis of National health insurance database 2016-2019 and in-depth interviews. SETTING: Beneficiaries of Universal Coverage Scheme residing outside Bangkok. PARTICIPANTS: Quantitative analysis was performed by acquiring individual patient data of diabetes and hypertension cases in the Universal Coverage Scheme residing outside bangkok in 2016-2019. Qualitative analysis was conducted by in-depth interview of 85 multi-stakeholder key informants to identify challenges. OUTCOMES: Estimate three indicators: detected need (diagnosed/total estimated cases), crude coverage (received health services/total estimated cases) and effective coverage (controlled/total estimated cases) were compared. Controlled diabetes was defined as haemoglobin A1C (HbA1C) below 7% and controlled hypertension as blood pressure below 140/90 mm Hg. RESULTS: Estimated cases were 3.1-3.2 million for diabetes and 8.7-9.2 million for hypertension. For diabetes, all indicators have shown slow improvement between 2016 and 2019 (67.4%, 69.9%, 71.9% and 74.7% for detected need; 38.7%, 43.1%, 45.1% and 49.8% for crude coverage and 8.1%, 10.5%, 11.8% and 11.7% for effective coverage). For hypertension, the performance was poorer for detection (48.9%, 50.3%, 51.8% and 53.3%) and crude coverage (22.3%, 24.7%, 26.5% and 29.2%) but was better for effective coverage (11.3%, 13.2%, 15.1% and 15.7%) than diabetes. Results were better for the women and older age groups in both diseases. Complex interplays between supply and demand side were a key challenge. Database challenges also hamper regular assessment of effective coverage. Sensitivity analysis when using at least three annual visits shows slight improvement of effective coverage. CONCLUSION: Effective coverage was low for both diseases, though improving in 2016-2019, especially among men and ัyounger populations. The increasing rate of effective coverage was significantly smaller than crude coverage. Health information systems limitation is a major barrier to comprehensive measurement. To maximise effective coverage, long-term actions should address primary prevention of non-communicable disease risk factors, while short-term actions focus on improving Chronic Care Model.


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