Control of blood pressure and risk attenuation
Bibliographic record
Abstract
BACKGROUND: High blood pressure (BP) is the leading attributable risk for cardiovascular disease globally. There is little information on effective and sustainable public health system strategies for managing hypertension in South Asian countries. We conducted a feasibility study to gather preliminary data to optimize BP-lowering strategies for a public health intervention in rural communities in Bangladesh, Pakistan, and Sri Lanka. METHODS: A mixed method feasibility study comprised a 3-month pre and postevaluation of a multicomponent intervention (MCI), including BP screening and home health education by trained government community health worker (CHW); providers trained in hypertension management, and compensation of CHW for additional services. Checklists were used to document care. Stakeholder interviews were also conducted. Individuals aged 40 years and above with high BP (systolic ≥140 mmHg or diastolic ≥90 mmHg based on two readings from 2 separate days, or receiving antihypertensive medications) were enrolled from rural communities in Bangladesh, Pakistan, and Sri Lanka. BP was measured at baseline and 3 months postintervention. RESULTS: A total of 412 (90%) of the 454 eligible individuals were recruited. Of those recruited, 90% received home health education session by trained CHWs, 80% were referred to trained providers, of whom 83% completed the management checklist. A follow-up rate of 95.6% was achieved. The mean SBP declined significantly by 4.5 mmHg 95% confidence interval (2.3, 6.7) mmHg (P < 0.001) in the overall pooled analysis in three countries; however, it varied among countries. BP decline was 10.5 mmHg (8.1, 13.0 mmHg) (P < 0.001) in the pooled analysis of individuals with uncontrolled hypertension at baseline, and was also significant each of the three countries. All 98 stakeholders strongly supported upscaling the proposed MCI strategies. CONCLUSION: The proposed MCI is feasible for implementation and requires long-term, large-scale evaluation in the rural public health infrastructure in South Asian countries to determine sustainability of health system changes and BP control. If these long-term effects are confirmed, MCI may be a long-term strategy for tackling rising rates of cardiovascular disease in low-resourced countries.Clintrial.gov NCT02341651.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".