Implementing Single-Pill Combination Therapy for Hypertension: A Scoping Review of Key Health System Requirements in 30 Low- and Middle-Income Countries
Bibliographic record
Abstract
Objective: The World Health Organization (WHO) included single-pill combination (SPC) antihypertensive medications on their 2019 essential medicines list (EML) to encourage uptake and improved hypertension control. We documented key national-level facilitators (SPCs on national EMLs, recommendation for SPCs in national hypertension guidelines and availability of SPCs on the market) supporting uptake of SPCs in the 30 most populous low- and middle-income countries (LMICs). Methods: A hierarchical information gathering strategy was used including literature and web searches, the use of organisational databases and personal communications with colleagues to obtain information on (1) whether SPC antihypertensives are on national EMLs, (2) whether SPC antihypertensives are recommended in national hypertension guidelines and (3) whether SPCs are available on the market. Results: Eleven of 30 LMICs had all facilitators in place being Egypt, Kenya, Nigeria, Sudan, China, the Philippines, Thailand, Iran, Argentina, Colombia and Mexico. Twenty-six countries had national hypertension guidelines (or similar) in place with SPCs being recommended in 18 of these. Apart from Afghanistan, SPCs were available on the market in all countries. The facilitator least present was the inclusion of SPC antihypertensives on national EMLs at 12 of 29 (Turkey does not have an EML). Conclusion: This study demonstrated that many LMICs have made significant progress in their uptake of SPC antihypertensives and several had included SPCs on their EMLs and guidelines prior to their inclusion on the WHO EML. Despite this progress, the uptake of SPC antihypertensives in LMICs could be improved including through their further inclusion on EMLs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.046 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".