Mortality Associated with Ambient PM2.5 Exposure in India: Results from the Million Death Study
Bibliographic record
Abstract
Background: Studies on the extent to which long-term exposure to ambient particulate matter (PM) with aerodynamic diameter ≤2.5μm (PM2.5) contributes to adult mortality in India are few, despite over 99% of Indians being exposed to levels that the World Health Organization (WHO) considers unsafe. Objective: We conducted a retrospective cohort study within the Million Death Study (MDS) to provide the first-ever quantification of national mortality from exposure to PM2.5 in India from 1999 to 2014. Methods: We calculated relative risks (RRs) by linking a total of ten 3-y intervals of satellite-based estimated PM2.5 exposure to deaths 3 to 5 y later in over 7,400 small villages or urban blocks covering a total population of 6.8 million. We applied using a model-based geostatistical model, adjusted for individual age, sex, and year of death; smoking prevalence, rural/urban residency, area-level female illiteracy, languages, and spatial clustering and unit-level variation. Results: PM2.5 exposure levels increased from 1999 to 2014, particularly in central and eastern India. Among 212,573 deaths at ages 15–69 y, after spatial adjustment, we found a significant RR of 1.09 [95% credible interval (CI): 1.04, 1.14] for stroke deaths per 10-μg/m3 increase in PM2.5 exposure, but no significant excess for deaths from chronic respiratory disease and ischemic heart disease (IHD), all nonaccidental causes, and total mortality (after excluding stroke). Spatial adjustment attenuated the RRs for chronic respiratory disease and IHD but raised those for stroke. The RRs were consistent in various sensitivity analyses with spatial adjustment, including stratifying by levels of solid fuel exposure, by sex, and by age group, addition of climatic variables, and in supplementary case–control analyses using injury deaths as controls. Discussion: Direct epidemiological measurements, despite inherent limitations, yielded associations between mortality and long-term PM2.5 inconsistent with those reported in earlier models used by the WHO to derive estimates of PM2.5 mortality in India. The modest RRs in our study are consistent with near or null mortality effects. They suggest suitable caution in estimating deaths from PM2.5 exposure based on MDS results and even more caution in extrapolating model-based associations of risk derived mostly from high-income countries to India. https://doi.org/10.1289/EHP9538
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".