Effective coverage of diabetes and hypertension: an analysis of Thailand's national insurance database 2016–2019. Issue 12 (1st December 2022)

• Record Type: Journal Article
• Authors: Rajatanavin, Nattadhanai; Witthayapipopsakul, Woranan; Vongmongkol, Vuthiphan; Saengruang, Nithiwat; Wanwong, Yaowaluk; Marshall, Aniqa Islam; Patcharanarumol, Walaiporn; Tangcharoensathien, Viroj
• Is Part Of: BMJ open. Volume 12:Issue 12(2022)
• Publication Date: 2022-12-01
• Subjects: hypertension -- public health -- health policy -- general diabetes; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2022-066289
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : 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.