Costs of the HEARTS hypertension program in primary care in Lampang province, Thailand
Bibliographic record
Abstract
BACKGROUND: In 2020, a pilot program for hypertension control was initiated in primary care facilities in Lampang Province, Thailand. The program followed the framework of the HEARTS program for standardized hypertension treatment, but the financial costs of the program are not well understood. This study evaluates the costs of the HEARTS approach compared to usual care to inform future scale-up efforts of the program. METHODS: Cost data were collected and analyzed using the HEARTS costing tool, a Microsoft Excel-based tool that supports activity-based costing of the HEARTS program from the health system perspective. Three scenarios were considered: usual care, the HEARTS regimen using standardized hypertension treatment with single-agent pills, and a sub-scenario of the HEARTS regimen using single-pill dual-drug combination pills. Costs are estimated as annual costs from the health system perspective in all Lampang primary care facilities. RESULTS: For the usual care scenario, the HEARTS single-pill scenario, and the HEARTS combination-pill sub-scenario, the average annual medication cost per treated patient was USD 14.0 (THB 485), USD 13.8 (THB 479), and USD 14.3 (THB 497), respectively. Total program cost per primary care user was USD 13.6 (THB 472.7), THB USD 14.3 (494.5), and USD 14.4 (THB 499.9) across the three scenarios, respectively. The largest program cost driver (45-47% across the examined scenarios) was attributed to a comprehensive package of laboratory tests applied to all hypertension patients. Hypothetically, reducing test coverage from all hypertension patients (27% of primary care users) to 15% of primary care users (corresponding to the proportion of patients aged 65+) would reduce program cost per user from USD 14.3 to USD 12.0 in the HEARTS combination-pill scenario. CONCLUSIONS: Compared to usual care, HEARTS implementation costs include additional costs for staff training, which are balanced by lower medication expenditures using the HEARTS standardized regimen with single-agent pills. The HEARTS regimen using dual-drug combination pills was estimated to be slightly more costly due to the higher price of combination pills. Optimizing coverage of diagnostic tests and lowering the purchasing prices of combination-pill medicines are key areas for cost reduction in future scale-up efforts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".