PMID- 34293557
OWN - NLM
STAT- MEDLINE
DCOM- 20211110
LR  - 20240404
IS  - 1879-1298 (Electronic)
IS  - 0045-6535 (Print)
IS  - 0045-6535 (Linking)
VI  - 286
IP  - Pt 1
DP  - 2022 Jan
TI  - Health effects of air pollutant mixtures on overall mortality among the elderly 
      population using Bayesian kernel machine regression (BKMR).
PG  - 131566
LID - S0045-6535(21)02038-5 [pii]
LID - 10.1016/j.chemosphere.2021.131566 [doi]
AB  - It is well documented that fine particles matter (PM(2.5)), ozone (O(3)), and 
      nitrogen dioxide (NO(2)) are associated with a range of adverse health outcomes. 
      However, most epidemiologic studies have focused on understanding their additive 
      effects, despite that individuals are exposed to multiple air pollutants 
      simultaneously that are likely correlated with each other. Therefore, we applied 
      a novel method - Bayesian Kernel machine regression (BKMR) and conducted a 
      population-based cohort study to assess the individual and joint effect of air 
      pollutant mixtures (PM(2.5), O(3), and NO(2)) on all-cause mortality among the 
      Medicare population in 15 cities with 656 different ZIP codes in the southeastern 
      US. The results suggest a strong association between pollutant mixture and 
      all-cause mortality, mainly driven by PM(2.5). The positive association of 
      PM(2.5) with mortality appears stronger at lower percentiles of other pollutants. 
      An interquartile range change in PM(2.5) concentration was associated with a 
      significant increase in mortality of 1.7 (95% CI: 0.5, 2.9), 1.6 (95% CI: 0.4, 
      2.7) and 1.4 (95% CI: 0.1, 2.6) standard deviations (SD) when O(3) and NO(2) were 
      set at the 25th, 50th, and 75th percentiles, respectively. BKMR analysis did not 
      identify statistically significant interactions among PM(2.5), O(3), and NO(2). 
      However, since the small sub-population might weaken the study power, additional 
      studies (in larger sample size and other regions in the US) are in need to 
      reinforce the current finding.
CI  - Copyright (c) 2021 Elsevier Ltd. All rights reserved.
FAU - Li, Haomin
AU  - Li H
AD  - Department of Epidemiology, Rollins School of Public Health, Emory University, 
      Atlanta, GA, USA.
FAU - Deng, Wenying
AU  - Deng W
AD  - Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, 
      MA, USA.
FAU - Small, Raphael
AU  - Small R
AD  - Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, 
      MA, USA.
FAU - Schwartz, Joel
AU  - Schwartz J
AD  - Department of Environmental Health, Harvard T.H. Chan School of Public Health, 
      Boston, MA, USA.
FAU - Liu, Jeremiah
AU  - Liu J
AD  - Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, 
      MA, USA.
FAU - Shi, Liuhua
AU  - Shi L
AD  - Gangarosa Department of Environmental Health, Rollins School of Public Health, 
      Emory University, Atlanta, GA, USA. Electronic address: liuhua.shi@emory.edu.
LA  - eng
GR  - P30 ES019776/ES/NIEHS NIH HHS/United States
GR  - R01 AG074357/AG/NIA NIH HHS/United States
GR  - R21 ES032606/ES/NIEHS NIH HHS/United States
PT  - Journal Article
DEP - 20210717
PL  - England
TA  - Chemosphere
JT  - Chemosphere
JID - 0320657
RN  - 0 (Air Pollutants)
RN  - 0 (Particulate Matter)
RN  - 66H7ZZK23N (Ozone)
RN  - S7G510RUBH (Nitrogen Dioxide)
SB  - IM
MH  - Aged
MH  - *Air Pollutants/analysis/toxicity
MH  - *Air Pollution/analysis/statistics & numerical data
MH  - Bayes Theorem
MH  - Cohort Studies
MH  - Environmental Exposure/analysis/statistics & numerical data
MH  - Humans
MH  - Nitrogen Dioxide/analysis/toxicity
MH  - *Ozone/analysis/toxicity
MH  - Particulate Matter/analysis/toxicity
PMC - PMC8578302
MID - NIHMS1726565
OTO - NOTNLM
OT  - Air pollution
OT  - Bayesian kernel machine regression (BKMR)
OT  - Fine particles matter (PM(2.5))
OT  - Mortality
OT  - Nitrogen dioxide (NO(2))
OT  - Ozone (O(3))
COIS- Declaration of interests The authors declare that they have no known competing 
      financial interests or personal relationships that could have appeared to 
      influence the work reported in this paper.
EDAT- 2021/07/23 06:00
MHDA- 2021/11/11 06:00
PMCR- 2023/01/01
CRDT- 2021/07/22 20:16
PHST- 2021/02/09 00:00 [received]
PHST- 2021/05/02 00:00 [revised]
PHST- 2021/07/14 00:00 [accepted]
PHST- 2021/07/23 06:00 [pubmed]
PHST- 2021/11/11 06:00 [medline]
PHST- 2021/07/22 20:16 [entrez]
PHST- 2023/01/01 00:00 [pmc-release]
AID - S0045-6535(21)02038-5 [pii]
AID - 10.1016/j.chemosphere.2021.131566 [doi]
PST - ppublish
SO  - Chemosphere. 2022 Jan;286(Pt 1):131566. doi: 10.1016/j.chemosphere.2021.131566. 
      Epub 2021 Jul 17.