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Xu Y, Yi L, Cabison J, Rosales M, O'Sharkey K, Chavez TA, Johnson M, Lurmann F, Pavlovic N, Bastain TM, Breton CV, Wilson JP, Habre R. The impact of GPS-derived activity spaces on personal PM 2.5 exposures in the MADRES cohort. ENVIRONMENTAL RESEARCH 2022; 214:114029. [PMID: 35932832 DOI: 10.1016/j.envres.2022.114029] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 07/22/2022] [Accepted: 07/30/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND In-utero exposure to particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5) is associated with low birth weight and health risks later in life. Pregnant women are mobile and locations they spend time in contribute to their personal PM2.5 exposures. Therefore, it is important to understand how mobility and exposures encountered within activity spaces contribute to personal PM2.5 exposures during pregnancy. METHODS We collected 48-h integrated personal PM2.5 samples and continuous geolocation (GPS) data for 213 predominantly Hispanic/Latina pregnant women in their 3rd trimester in Los Angeles, CA. We also collected questionnaires and modeled outdoor air pollution and meteorology in their residential neighborhood. We calculated three GPS-derived activity space measures of exposure to road networks, greenness (NDVI), parks, traffic volume, walkability, and outdoor PM2.5 and temperature. We used bivariate analyses to screen variables (GPS-extracted exposures in activity spaces, individual characteristics, and residential neighborhood exposures) based on their relationship with personal, 48-h integrated PM2.5 concentrations. We then built a generalized linear model to explain the variability in personal PM2.5 exposure and identify key contributing factors. RESULTS Indoor PM2.5 sources, parity, and home ventilation were significantly associated with personal exposure. Activity-space based exposure to roads was associated with significantly higher personal PM2.5 exposure, while greenness was associated with lower personal PM2.5 exposure (β = -3.09 μg/m3 per SD increase in NDVI, p-value = 0.018). The contribution of outdoor PM2.5 to personal exposure was positive but relatively lower (β = 2.05 μg/m3 per SD increase, p-value = 0.016) than exposures in activity spaces and the indoor environment. The final model explained 34% of the variability in personal PM2.5 concentrations. CONCLUSIONS Our findings highlight the importance of activity spaces and the indoor environment on personal PM2.5 exposures of pregnant women living in Los Angeles, CA. This work also showcases the multiple, complex factors that contribute to total personal PM2.5 exposure.
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Affiliation(s)
- Yan Xu
- Spatial Sciences Institute, University of Southern California, USA.
| | - Li Yi
- Spatial Sciences Institute, University of Southern California, USA.
| | - Jane Cabison
- Department of Population and Public Health Sciences, University of Southern California, USA.
| | - Marisela Rosales
- Department of Population and Public Health Sciences, University of Southern California, USA.
| | - Karl O'Sharkey
- Department of Population and Public Health Sciences, University of Southern California, USA.
| | - Thomas A Chavez
- Department of Population and Public Health Sciences, University of Southern California, USA.
| | - Mark Johnson
- Department of Population and Public Health Sciences, University of Southern California, USA.
| | | | | | - Theresa M Bastain
- Department of Population and Public Health Sciences, University of Southern California, USA.
| | - Carrie V Breton
- Department of Population and Public Health Sciences, University of Southern California, USA.
| | - John P Wilson
- Spatial Sciences Institute, University of Southern California, USA; Department of Population and Public Health Sciences, University of Southern California, USA; Department of Civil & Environmental Engineering, Computer Science, and Sociology, University of Southern California, USA.
| | - Rima Habre
- Spatial Sciences Institute, University of Southern California, USA; Department of Population and Public Health Sciences, University of Southern California, USA.
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O’Sharkey K, Xu Y, Chavez T, Johnson M, Cabison J, Rosales M, Grubbs B, Toledo-Corral CM, Farzan SF, Bastain T, Breton CV, Habre R. In-utero personal exposure to PM 2.5 impacted by indoor and outdoor sources and birthweight in the MADRES cohort. ENVIRONMENTAL ADVANCES 2022; 9:100257. [PMID: 36778968 PMCID: PMC9912940 DOI: 10.1016/j.envadv.2022.100257] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
BACKGROUND In-utero exposure to outdoor particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5) is linked with low birthweight. However, previous results are mixed, likely due to measurement error introduced by estimating personal exposure from ambient data. This study investigated the effect of total personal PM2.5 exposure on birthweight and whether it differed when it was more heavily impacted by sources of indoor vs outdoor origin in the MADRES cohort study. METHODS Personal PM2.5 exposure was measured in 205 pregnant women in the 3rd trimester using 48 h integrated, filter-based sampling. Linear regression was used to test the association between personal PM2.5 exposure and birthweight, adjusting for key covariates. Interactions of PM2.5 with variables representing indoor sources of PM2.5, home ventilation, or time spent indoors tested whether the effect of total PM2.5 on birthweight varied when it was more impacted by sources of indoor vs outdoor origin. RESULTS In a sample of largely Hispanic (81%) pregnant women, total personal PM2.5 was not significantly associated with birthweight (β = 38.6 per 1SD increase in PM2.5; 95% CI:-21.1, 98.2). This association however, differed by home type (single family home: 156.9 (26.9, 287.0), 2-4 attached units:-16.6 (-111.9, 78.7), 5+ units:-62.6 (-184.9, 59.6), missing: 145.4 (-4.1, 294.9), interaction p = 0.028) and by household air conditioner use (none of the time: -27.6 (-101.5, 46.3) vs. some of the time: 139.9 (42.9, 237.0), interaction p = 0.008) Additionally, the effect of personal PM2.5 on birthweight varied by time spent indoors (none or little of the time: - 45.1 (-208.3, 118.1) vs. most or all of the time: 57.1 (-7.3, 121.6), interaction p = 0.255). CONCLUSIONS While no significant association between total personal PM2.5 exposure and birthweight was found, there was evidence that multi-unit housing (vs. single-family homes), candle and/or incense smoke, and greater outdoor source contributions to personal PM2.5 were more strongly associated with lower birthweight.
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Affiliation(s)
- Karl O’Sharkey
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 2001 N Soto St Rm 102M, Los Angeles, CA 90089, United States
| | - Yan Xu
- Spatial Sciences Institute, University of Southern California, Los Angeles, CA, United States
| | - Thomas Chavez
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 2001 N Soto St Rm 102M, Los Angeles, CA 90089, United States
| | - Mark Johnson
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 2001 N Soto St Rm 102M, Los Angeles, CA 90089, United States
| | - Jane Cabison
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 2001 N Soto St Rm 102M, Los Angeles, CA 90089, United States
| | - Marisela Rosales
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 2001 N Soto St Rm 102M, Los Angeles, CA 90089, United States
| | - Brendan Grubbs
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 2001 N Soto St Rm 102M, Los Angeles, CA 90089, United States
| | - Claudia M. Toledo-Corral
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 2001 N Soto St Rm 102M, Los Angeles, CA 90089, United States
- Department of Health Sciences, California State University Northridge, Northridge, CA, United States
| | - Shohreh F. Farzan
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 2001 N Soto St Rm 102M, Los Angeles, CA 90089, United States
| | - Theresa Bastain
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 2001 N Soto St Rm 102M, Los Angeles, CA 90089, United States
| | - Carrie V. Breton
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 2001 N Soto St Rm 102M, Los Angeles, CA 90089, United States
| | - Rima Habre
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 2001 N Soto St Rm 102M, Los Angeles, CA 90089, United States
- Spatial Sciences Institute, University of Southern California, Los Angeles, CA, United States
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Aregay M, Lawson AB, Faes C, Kirby RS, Carroll R, Watjou K. Multiscale measurement error models for aggregated small area health data. Stat Methods Med Res 2018; 25:1201-23. [PMID: 27566773 DOI: 10.1177/0962280216661094] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Spatial data are often aggregated from a finer (smaller) to a coarser (larger) geographical level. The process of data aggregation induces a scaling effect which smoothes the variation in the data. To address the scaling problem, multiscale models that link the convolution models at different scale levels via the shared random effect have been proposed. One of the main goals in aggregated health data is to investigate the relationship between predictors and an outcome at different geographical levels. In this paper, we extend multiscale models to examine whether a predictor effect at a finer level hold true at a coarser level. To adjust for predictor uncertainty due to aggregation, we applied measurement error models in the framework of multiscale approach. To assess the benefit of using multiscale measurement error models, we compare the performance of multiscale models with and without measurement error in both real and simulated data. We found that ignoring the measurement error in multiscale models underestimates the regression coefficient, while it overestimates the variance of the spatially structured random effect. On the other hand, accounting for the measurement error in multiscale models provides a better model fit and unbiased parameter estimates.
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Affiliation(s)
- Mehreteab Aregay
- Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, MUSC, Charleston, SC, USA
| | - Andrew B Lawson
- Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, MUSC, Charleston, SC, USA
| | - Christel Faes
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Hasselt, Belgium
| | - Russell S Kirby
- Department of Community and Family Health, University of South Florida, Tampa, FL, USA
| | - Rachel Carroll
- Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, MUSC, Charleston, SC, USA
| | - Kevin Watjou
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Hasselt, Belgium
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