ArticleEnvironmental health : a global access science source2021
Assessing additive effects of air pollutants on mortality rate in Massachusetts.
Article in Environmental health : a global access science source, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Chronic effects of wildfire smoke and criteria air pollutants on cardiovascular hospitalization rates in the contiguous US.Environmental research · 2025Article
- Association between Long-term Exposure to Ambient Air Pollution and Myocardial Fibrosis Assessed with Cardiac MRI.Radiology · 2025Article
- Grouped mixtures of air pollutants and seasonal temperature anomalies and cardiovascular hospitalizations among U.S. Residents.Environment international · 2024Article
- Additive effects of 10-year exposures to PMEnvironmental epidemiology (Philadelphia, Pa.) · 2023Article
- Association of Long-term Exposure to Particulate Air Pollution With Cardiovascular Events in California.JAMA network open · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundWe previously found additive effects of long- and short-term exposures to fine particulate matter (PM
methodsWe proposed two new methods and reconducted the analysis using the same cohort of Medicare beneficiaries in Massachusetts during 2000-2012, which consisted of 1.5 million individuals with 3.8 billion person-days of follow-up. The first method, weighted least squares (WLS), leveraged large volume of data by aggregating person-days, which gave equivalent results to the linear probability model (LPM) method in the previous analysis but significantly reduced computational burden. The second method, m-out-of-n random forests (moonRF), implemented scaling random forests that captured all possible interactions and nonlinearities in the GPS model. To minimize confounding bias, we additionally controlled relative humidity and health care utilizations that were not included previously. Further, we performed low-level analysis by restricting to person-days with exposure levels below increasingly stringent thresholds.
resultsWe found consistent results between LPM/WLS and moonRF: all exposures were positively associated with mortality rate, even at low levels. For long-term PM
conclusionsThis study provides more rigorous causal evidence between PM
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Registered trials
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