PMID- 36008583
OWN - NLM
STAT- MEDLINE
DCOM- 20230206
LR  - 20240328
IS  - 1614-7499 (Electronic)
IS  - 0944-1344 (Linking)
VI  - 30
IP  - 3
DP  - 2023 Jan
TI  - Are there joint effects of different air pollutants and meteorological factors on 
      mental disorders? A machine learning approach.
PG  - 6818-6827
LID - 10.1007/s11356-022-22662-0 [doi]
AB  - Exposure to air pollutants is considered to be associated with mental disorders 
      (MD). Few studies have addressed joint effect of multiple air pollutants and 
      meteorological factors on admissions of MD. We examined the association between 
      multiple air pollutants (PM(2.5), PM(10), O(3), SO(2), and NO(2)), meteorological 
      factors (temperature, precipitation, relative humidity, and sunshine time), and 
      MD risk in Yancheng, China. Associations were estimated by a generalized linear 
      regression model (GLM) adjusting for time trend, day of the week, and patients' 
      average age. Empirical weights of environmental exposures were judged by a 
      weighted quantile sum (WQS) model. A machine learning approach, Bayesian kernel 
      machine regression (BKMR), was used to assess the overall effect of mixed 
      exposures. We calculated excess risk (ER) and 95% confidence interval (CI) for 
      each exposure. According to the effect of temperature on MD, we divided the 
      exposure of all factors into different temperature groups. In the high 
      temperature group, GLM found that for every 10 mug/m(3) increase in O(3), PM(2.5) 
      and PM(10) exposure, the ERs were 1.926 (95%CI 0.345, 3.531), 1.038 (95%CI 0.024, 
      2.062), and 0.780 (95% CI 0.052, 1.512) after adjusting for covariates. 
      Temperature, relative humidity, and sunshine time also reported significant 
      results. The WQS identified O(3) and temperature (above the threshold) had the 
      highest weights among air pollutants and meteorological factors. BKMR found a 
      significant positive association between mixed exposure and MD risks. In the low 
      temperature group, only O(3) and temperature (below the threshold) showed 
      significant results. These findings provide policymakers and practitioners with 
      important scientific evidence for possible interventions. The association between 
      different exposures and MD risk warrants further study.
CI  - (c) 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Liang, Mingming
AU  - Liang M
AD  - Department of Epidemiology and Health Statistics, School of Public Health, Anhui 
      Medical University, No. 81 Meishan Road, Hefei, 230032, Anhui, China.
FAU - Min, Min
AU  - Min M
AD  - Anhui Institute of Medical Information (Anhui Medical Association), Hefei, 
      230061, Anhui, China.
FAU - Ye, Pengpeng
AU  - Ye P
AD  - National Center for Chronic and Noncommunicable Disease Control and Prevention, 
      Chinese Center for Disease Control and Prevention, Beijing, 100050, China.
FAU - Duan, Leilei
AU  - Duan L
AD  - National Center for Chronic and Noncommunicable Disease Control and Prevention, 
      Chinese Center for Disease Control and Prevention, Beijing, 100050, China.
FAU - Sun, Yehuan
AU  - Sun Y
AUID- ORCID: 0000-0002-8651-8059
AD  - Department of Epidemiology and Health Statistics, School of Public Health, Anhui 
      Medical University, No. 81 Meishan Road, Hefei, 230032, Anhui, China. 
      yhsun_ahmu_edu@yeah.net.
AD  - Chaohu Hospital, Anhui Medical University, Hefei, 238000, Anhui, China. 
      yhsun_ahmu_edu@yeah.net.
LA  - eng
GR  - No.2017FY101200/special foundation of basic science and technology resources 
      survey of ministry of science and technology of China/
PT  - Journal Article
DEP - 20220826
PL  - Germany
TA  - Environ Sci Pollut Res Int
JT  - Environmental science and pollution research international
JID - 9441769
RN  - 0 (Air Pollutants)
RN  - 0 (Particulate Matter)
SB  - IM
MH  - Humans
MH  - *Air Pollutants/analysis
MH  - *Air Pollution/analysis
MH  - Bayes Theorem
MH  - Meteorological Concepts
MH  - China
MH  - Particulate Matter/analysis
MH  - *Mental Disorders
OTO - NOTNLM
OT  - Air pollutants
OT  - Bayesian kernel machine regression
OT  - Generalized linear model
OT  - Mental disorders
OT  - Meteorological factor
OT  - Weighted quantile sum model
EDAT- 2022/08/26 06:00
MHDA- 2023/02/07 06:00
CRDT- 2022/08/25 23:33
PHST- 2022/06/09 00:00 [received]
PHST- 2022/08/18 00:00 [accepted]
PHST- 2022/08/26 06:00 [pubmed]
PHST- 2023/02/07 06:00 [medline]
PHST- 2022/08/25 23:33 [entrez]
AID - 10.1007/s11356-022-22662-0 [pii]
AID - 10.1007/s11356-022-22662-0 [doi]
PST - ppublish
SO  - Environ Sci Pollut Res Int. 2023 Jan;30(3):6818-6827. doi: 
      10.1007/s11356-022-22662-0. Epub 2022 Aug 26.