Association of exposure to air pollutants and risk of mortality among people living with HIV: a systematic review
Bibliographic record
Abstract
People living with HIV (PLWH) are more vulnerable to infectious and non-infectious comorbidities due to chronic inflammation and immune dysfunction. Air pollution is a major global health risk, contributing to millions of deaths annually, primarily from cardiovascular and respiratory diseases. However, the link between air pollution and mortality risk in PLWH is underexplored. This systematic review assesses the association between exposure to pollutants such as particulate matter (PM), nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ), ozone (O 3 ), and carbon monoxide (CO) and mortality risk in PLWH. A systematic search of PubMed, Web of Science, and Embase was conducted for studies published up to August 2024. Eligibility criteria included cohort, case-control, and cross-sectional studies assessing air pollution exposure and mortality in PLWH. Nested-Knowledge software was used for screening and data extraction. The Newcastle-Ottawa Scale was applied for quality assessment. A narrative approach and tabular summarization were used for data synthesis and presentation. Nine studies, mostly from China, demonstrated a significant association between long-term exposure to PM 1 , PM 2.5 , and PM 10 and increased risks of AIDS-related and all-cause mortality in PLWH. Hazard ratios for mortality increased by 2.38–5.13% per unit increase in PM concentrations, with older adults (> 60), females, and those with lower CD4 counts (< 500 cells/µL) being more vulnerable. Short-term exposure to ozone and sulfur dioxide also increased mortality risks, particularly during the warm season and in older populations. Specific pollutants like ammonium (NH4⁺) and sulfate (SO4²⁻) had the strongest links to elevated mortality. Air pollution, especially fine particulate matter and ozone, is associated with a higher risk of mortality in PLWH. Targeted interventions to reduce pollution exposure in vulnerable subgroups are crucial. Further research is needed to confirm these findings in diverse regions and develop effective mitigation strategies.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".