Population-level hypertension control rate in India: A systematic review and meta-analysis of community based non-interventional studies, 2001-2020
Bibliographic record
Abstract
Abstract Background Hypertension is a significant contributor to mortality in India. Adequate control of hypertension is important to prevent cardiovascular morbidity and mortality. Methods We conducted a systematic review and meta-analysis of community-based, non-interventional studies published between 2001 and 2020. We screened records from PubMed, Embase, and Web of Science databases, extracted data, and assessed risk of bias. We conducted random-effects meta-analysis to provide overall summary estimates and subgroup estimates, and mixed-effects meta-regression with sex, region, and study period as covariates. The risk of bias was assessed using modified New Castle-Ottawa scales. This study is registered with PROSPERO, CRD42021267973. Results The systematic review included 37 studies (n=170,631 hypertensive patients). Twelve studies (32%) reported poorer control rates among males than females, four studies (11%) reported poorer control rates among rural patients, while very few studies reported differences across socioeconomic variables. The overall control rate was 33.2% (n=84,485, 95% CI=27.9,38.6) with substantial heterogeneity (I2=99.1%, \chi^2= 3003.91, 95% CI=98.9,99.2; p <0.001). Unadjusted sub-group analysis showed significantly different hypertension control rates across regions (n=12,938, p=0.003) but not across study periods (n= 84,485, p=0.22), or sex (n= 81,197, p=0.22). Meta-regression showed that control rates increased by 14.7% during 2011-2020 compared to 2001-2010 (95%CI=5.8, 23.5, p=0.0021), and was 26.3% higher in the south (95%CI=12.6, 39.9, p=0.0005) and 15.9% higher in the west (95%CI=3.4, 31.4, p=0.0456) compared to the east. The control rates did not differ by sex. Conclusion Hypertension is adequately controlled only among one-third of patients in India. The control rate has improved during 2011-2020 compared to 2001-2010, but substantial differences exist across regions. Very few studies examined relevant socioeconomic factors relevant to hypertension control. India needs more studies at the community level to understand the health system and socioeconomic factors that determine uncontrolled hypertension in India.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".