Evaluation and pilot implementation of essential interventions for the management of hypertension and prevention of cardiovascular diseases in primary health care in the Republic of Tajikistan
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to determine the feasibility of implementing and evaluating essential interventions for the management of hypertension and prevention of cardiovascular disease in primary healthcare in Tajikistan. METHODS: The study protocol was published a priori. A pragmatic, sequential, mixed methods explanatory design was piloted. The quantitative strand is reported here. All primary health care facilities that met inclusion criteria in Shahrinav district were included and computer randomized to either usual care or intervention. The intervention consisted of: adaptation of WHO PEN/HEARTS clinical algorithms for hypertension and diabetes, a two-day training of doctors and nurses, supportive supervision visits, clinical decision support tools, and quality improvement support. Data were collected from paper-based clinical records at baseline and 12 months follow-up. The primary outcome was blood pressure control among patients with hypertension, in addition to several secondary process indicators along the care pathway. Age and sex adjusted logistic regression models were used for intervention and control clinics to determine changes between baseline and follow-up and to assess interactions between allocation group and time. For continuous variables, multivariate linear regression models were used. RESULTS: 19 primary health care centres were included of which ten were randomized to intervention and nine to control. 120 clinicians received training. The records of all registered hypertensive patients were reviewed at baseline and follow-up for a total of 1,085 patient records. Blood pressure control significantly improved in the intervention clinics (OR 3.556, 95 % CI 2.219, 5.696) but not the control clinics (OR 0.644, 95 % CI 0.370, 1.121) (p < 0.001 for interaction). Smoking assessment, statin prescribing, triple therapy prescribing, and blood pressure measurement significantly improved in intervention clinics relative to control, whereas cholesterol and glucose testing, and aspirin prescribing did not. CONCLUSIONS: It is feasible to use routine, paper-based, clinical records to evaluate essential CVD interventions in primary health care in Tajikistan. Adapted WHO PEN/HEARTS guidelines in the context of a complex intervention significantly improved blood pressure control after 12 months.
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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 | 0.024 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".