Effects of Household Air Pollution (HAP) on Cardiovascular Diseases in Low- and Middle-Income Countries (LMICs): A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Out of over 3 billion people exposed to household air pollution (HAP), approximately 4 million die prematurely, most from cardiorespiratory diseases. Although many recent studies have reported adverse effects of HAP on cardiovascular outcomes, the findings are inconsistent. OBJECTIVES: The primary aim of this systematic review is to critically appraise the published studies and report the pooled summary of the findings on the association between HAP and cardiovascular outcomes, particularly in LMICs. METHODS: During this systematic review and meta-analysis, six databases were searched systematically, and the protocol was published in PROSPERO (CRD 42021248800). Only peer-reviewed English-language studies published from 1980 to March 2021 were included. We extracted data for the population ≥ 18 years old. Newcastle-Ottawa Criteria were used to assess the quality of evidence. The heterogeneity and publication bias of the studies was evaluated. A meta-analysis was conducted using a random-effect model to pool the findings from published studies. RESULTS: In sixteen studies totaling 547,463 cases, 319,180 were exposed to HAP. The pooled estimate suggested an overall 13% higher risk of CVDs, and a 21% higher risk of CVD mortality in LMICs among those exposed to HAP. Similarly, the increased risk of stroke and cerebrovascular accidents, heart failure, and hypertension was statistically significant among those exposed to HAP but not with myocardial infarction, IHD, eclampsia/preeclampsia, and carotid intima-media thickness. CONCLUSIONS: Our findings suggest exposure to HAP increases the risk of cardiovascular outcomes.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.042 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".