TY - JOUR
T1 - Modeling the impact of exposure reductions using multi-stressor epidemiology, exposure models, and synthetic microdata
T2 - an application to birthweight in two environmental justice communities
AU - Milando, Chad W.
AU - Yitshak-Sade, Maayan
AU - Zanobetti, Antonella
AU - Levy, Jonathan I.
AU - Laden, Francine
AU - Fabian, M. Patricia
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Nature America, Inc.
PY - 2021/5/1
Y1 - 2021/5/1
N2 - Background: Many vulnerable populations experience elevated exposures to environmental and social stressors, with deleterious effects on health. Multi-stressor epidemiological models can be used to assess benefits of exposure reductions. However, requisite individual-level risk factor data are often unavailable at adequate spatial resolution. Objective: To leverage public data and novel simulation methods to estimate birthweight changes following simulated environmental interventions in two environmental justice communities in Massachusetts, USA. Methods: We gathered risk factor data from public sources (US Census, Behavioral Risk Factor Surveillance System, and Massachusetts Department of Health). We then created synthetic individual-level data sets using combinatorial optimization, and probabilistic and logistic modeling. Finally, we used coefficients from a multi-stressor epidemiological model to estimate birthweight and birthweight improvement associated with simulated environmental interventions. Results: We created geographically resolved synthetic microdata. Mothers with the lowest predicted birthweight were those identifying as Black or Hispanic, with parity > 1, utilization of government prenatal support, and lower educational attainment. Birthweight improvements following greenness and temperature improvements were similar for all high-risk groups and were larger than benefits from smoking cessation. Significance: Absent private health data, this methodology allows for assessment of cumulative risk and health inequities, and comparison of individual-level impacts of localized health interventions.
AB - Background: Many vulnerable populations experience elevated exposures to environmental and social stressors, with deleterious effects on health. Multi-stressor epidemiological models can be used to assess benefits of exposure reductions. However, requisite individual-level risk factor data are often unavailable at adequate spatial resolution. Objective: To leverage public data and novel simulation methods to estimate birthweight changes following simulated environmental interventions in two environmental justice communities in Massachusetts, USA. Methods: We gathered risk factor data from public sources (US Census, Behavioral Risk Factor Surveillance System, and Massachusetts Department of Health). We then created synthetic individual-level data sets using combinatorial optimization, and probabilistic and logistic modeling. Finally, we used coefficients from a multi-stressor epidemiological model to estimate birthweight and birthweight improvement associated with simulated environmental interventions. Results: We created geographically resolved synthetic microdata. Mothers with the lowest predicted birthweight were those identifying as Black or Hispanic, with parity > 1, utilization of government prenatal support, and lower educational attainment. Birthweight improvements following greenness and temperature improvements were similar for all high-risk groups and were larger than benefits from smoking cessation. Significance: Absent private health data, this methodology allows for assessment of cumulative risk and health inequities, and comparison of individual-level impacts of localized health interventions.
KW - Cumulative risk assessment
KW - Exposure modeling
KW - Multi-stressor epidemiology
KW - Public data
KW - Synthetic population microdata
KW - Vulnerable populations
UR - http://www.scopus.com/inward/record.url?scp=85103616060&partnerID=8YFLogxK
U2 - 10.1038/s41370-021-00318-4
DO - 10.1038/s41370-021-00318-4
M3 - Article
C2 - 33824415
AN - SCOPUS:85103616060
SN - 1559-0631
VL - 31
SP - 442
EP - 453
JO - Journal of Exposure Science and Environmental Epidemiology
JF - Journal of Exposure Science and Environmental Epidemiology
IS - 3
ER -