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Modeling the impact of exposure reductions using multi-stressor epidemiology, exposure models, and synthetic microdata: an application to birthweight in two environmental justice communities

Journal of Exposure Science & Environmental Epidemiology volume 31, pages 442–453 (2021)Cite this article

Abstract

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.

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Fig. 1: Schematic of modeling methods used to create a synthetic population of mothers and newborns in Chelsea and Dorchester, Massachusetts.
Fig. 2: Annual average neighborhood-level economic and environmental exposures in Chelsea and Dorchester.
Fig. 3: Distributions of predicted birthweight (grams), separated by common risk factors.
Fig. 4: Census tracts for Chelsea and Dorchester with the number (#) of newborns in the lowest tertile of synthetic birthweight.
Fig. 5: Change in birthweight following environmental exposure improvements and smoking cessation for different high-risk population groups.

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References

  1. National Research Council. Science and decisions: advancing risk assessment. Washington, DC: National Academies Press; 2009.

  2. Sexton K. Cumulative health risk assessment: finding new ideas and escaping from the old ones. Hum Ecol Risk Assess. 2015;21:934–51.

    Article  CAS  Google Scholar 

  3. Institute of Medicine Committee on Environmental Justice. Toward environmental justice: research, education, and health policy needs. Washinton, DC: National Academy Press; 1999.

  4. Clougherty JE, Shmool JL, Kubzansky LD. The role of non-chemical stressors in mediating socioeconomic susceptibility to environmental chemicals. Curr Environ Health Rep. 2014;1:302–13.

    Article  CAS  Google Scholar 

  5. Hicken MT, Gee GC, Morenoff J, Connell CM, Snow RC, Hu H. A novel look at racial health disparities: the interaction between social disadvantage and environmental health. Am J Public Health. 2012;102:2344–51.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Gee GC, Payne-Sturges DC. Environmental health disparities: a framework integrating psychosocial and environmental concepts. Environ Health Perspect. 2004;112:1645–53.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Payne-Sturges D, Gee GC, Crowder K, Hurley BJ, Lee C, Morello-Frosch R, et al. Workshop summary: connecting social and environmental factors to measure and track environmental health disparities. Environ Res. 2006;102:146–53.

    Article  CAS  PubMed  Google Scholar 

  8. Morello-Frosch R, Shenassa ED. The environmental “riskscape” and social inequality: implications for explaining maternal and child health disparities. Environ Health Perspect. 2006;114:1150–3.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Burgard SA, Chen PV. Challenges of health measurement in studies of health disparities. Soc Sci Med. 2014;106:143–50.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Hermes K, Poulsen M. A review of current methods to generate synthetic spatial microdata using reweighting and future directions. Comput Environ Urban Syst. 2012;36:281–90.

    Article  Google Scholar 

  11. Ballas D, Clarke G, Turton I. Exploring microsimulation methodologies for the estimation of household attributes. International Conference on GeoComputation. Virginia: Mary Washington College; 1999.

    Google Scholar 

  12. Levy JI, Fabian MP, Peters JL. Community-wide health risk assessment using geographically resolved demographic data: a synthetic population approach. PLoS ONE. 2014;9:e87144.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Peters JL, Fabian MP, Levy JI. Epidemiologically-informed cumulative risk hypertension models simulating the impact of changes in metal, organochlorine, and non-chemical exposures in an environmental justice community. Environ Res. 2019;176:108544.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Basra K, Fabian MP, Holberger RR, French R, Levy JI. Community-engaged modeling of geographic and demographic patterns of multiple public health risk factors. Int J Environ Res Public Health. 2017;14:730.

    Article  PubMed Central  Google Scholar 

  15. Ncube CN, Enquobahrie DA, Albert SM, Herrick AL, Burke JG. Association of neighborhood context with offspring risk of preterm birth and low birthweight: a systematic review and meta-analysis of population-based studies. Soc Sci Med. 2016;153:156–64.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Zhu X, Liu Y, Chen Y, Yao C, Che Z, Cao J. Maternal exposure to fine particulate matter (PM2.5) and pregnancy outcomes: a meta-analysis. Environ Sci Pollut Res Int. 2015;22:3383–96.

    Article  CAS  PubMed  Google Scholar 

  17. Zhang Y, Yu C, Wang L. Temperature exposure during pregnancy and birth outcomes: an updated systematic review of epidemiological evidence. Environ Pollut. 2017;225:700–12.

    Article  CAS  PubMed  Google Scholar 

  18. Smith RB, Fecht D, Gulliver J, Beevers SD, Dajnak D, Blangiardo M, et al. Impact of London’s road traffic air and noise pollution on birth weight: retrospective population based cohort study. BMJ. 2017;359:j5299.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Ebisu K, Holford TR, Bell ML. Association between greenness, urbanicity, and birth weight. Sci Total Environ. 2016;542:750–6.

    Article  CAS  PubMed  Google Scholar 

  20. Barker M, Dombrowski SU, Colbourn T, Fall CHD, Kriznik NM, Lawrence WT, et al. Intervention strategies to improve nutrition and health behaviours before conception. Lancet. 2018;391:1853–64.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Yitshak-Sade M, Fabian MP, Lane KJ, Hart JE, Schwartz JD, Laden F, et al. Estimating the combined effects of natural and built environmental exposures on birthweight among urban residents in Massachusetts. Int J Environ Res Public Health. 2020;17:8805.

    Article  PubMed Central  Google Scholar 

  22. Massachusetts Department of Environmental Protection (MASS DEP). Environmental justice populations in Massachusetts. Available from: https://www.mass.gov/info-details/environmental-justice-populations-in-massachusetts.

  23. US Census Bureau. 2010 American Community Survey (ACS) 5-year estimates. Available from: data.census.gov. Accessed 15 Jan 2019.

  24. Williamson, P. CO Code and Documentation. Working Paper 2007/1 (v. 07.06.25) ed: Population Microdata Unit, Department of Geography, University of Liverpool 2007. Available from: http://pcwww.liv.ac.uk/~william/microdata/CO%20070615/CO_software.html. Accessed 15 Jan 2019.

  25. US Census Bureau. 2010-2014 American Community Survey 5-year Public Use Microdata Samples (PUMS). Available from: data.census.gov. Accessed 15 Jan 2019.

  26. Massachusetts Department of Public Health. Massachusetts Births 2010. 2013. Available from: https://www.mass.gov/doc/2010-report-2/download.

  27. Jones DR, Perttunen CD, Stuckman BE. Lipschitzian optimization without the Lipschitz constant. J Optim Theory Appl. 1993;79:157–81.

    Article  Google Scholar 

  28. Johnson, SG. The NLopt nonlinear-optimization package. Available from: http://github.com/stevengj/nlopt. Accessed January 2021.

  29. CDC. Behavioral Risk Factor Surveillance System Survey Data (BRFSS). Atlanta, GA: Centers for Disease Control and Prevention; 2011. Available from: https://www.cdc.gov/brfss/annual_data/annual_2011.htm.

  30. Kloog I, Chudnovsky AA, Just AC, Nordio F, Koutrakis P, Coull BA, et al. A new hybrid spatio-temporal model for estimating daily multi-year PM2.5 concentrations across northeastern USA using high resolution aerosol optical depth data. Atmos Environ. 2014;95:581–90.

    Article  CAS  Google Scholar 

  31. Kloog I, Nordio F, Coull BA, Schwartz J. Predicting spatiotemporal mean air temperature using MODIS satellite surface temperature measurements across the Northeastern USA. Remote Sens Environ. 2014;150:132–9.

    Article  Google Scholar 

  32. James P, Kioumourtzoglou MA, Hart JE, Banay RF, Kloog I, Laden F. Interrelationships between walkability, air pollution, greenness, and body mass index. Epidemiology. 2017;28:780–8.

    Article  PubMed  PubMed Central  Google Scholar 

  33. US Environmental Protection Agency. Smart location database. Washington, DC: Technical documentation and user guide; 2014.

  34. Mennitt DJ, Fristrup KM. Influence factors and spatiotemporal patterns of environmental sound levels in the contiguous United States. Noise Control Eng J. 2016;64:342–53.

    Article  Google Scholar 

  35. Krieger N, Waterman PD, Batra N, Murphy JS, Dooley DP, Shah SN. Measures of local segregation for monitoring health inequities by local health departments. Am J Public Health. 2017;107:903–6.

    Article  PubMed  PubMed Central  Google Scholar 

  36. United States Department of Health and Human Services (US DHHS) Centers for Disease Control and Prevention (CDC) National Center for Health Statistics (NCHS) Division of Vital Statistics. Natality public-use data 2007-2018, on CDC WONDER Online Database. 2019. Available from: http://wonder.cdc.gov/natality-current.html.

  37. R Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2017. Available from: https://www.R-project.org/.

  38. Lumley J, Chamberlain C, Dowswell T, Oliver S, Oakley L, Watson L. Interventions for promoting smoking cessation during pregnancy. Cochrane Database Syst Rev. 2009;3:CD001055.

    Google Scholar 

  39. Fall CH, Fisher DJ, Osmond C, Margetts BM, Maternal Micronutrient Supplementation Study Group. Multiple micronutrient supplementation during pregnancy in low-income countries: a meta-analysis of effects on birth size and length of gestation. Food Nutr Bull. 2009;30:S533–46.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Ramakrishnan U, Grant FK, Goldenberg T, Bui V, Imdad A, Bhutta ZA. Effect of multiple micronutrient supplementation on pregnancy and infant outcomes: a systematic review. Paediatr Perinat Epidemiol. 2012;26:153–67.

    Article  PubMed  Google Scholar 

  41. Barber SL, Gertler PJ. The impact of Mexico’s conditional cash transfer programme, Oportunidades, on birthweight. Trop Med Int Health. 2008;13:1405–14.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Belbasis L, Savvidou MD, Kanu C, Evangelou E, Tzoulaki I. Birth weight in relation to health and disease in later life: an umbrella review of systematic reviews and meta-analyses. BMC Med. 2016;14:147.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Malin G, Morris R, Riley R, Teune M, Khan K. When is birthweight at term (≥37 weeks’ gestation) abnormally low? A systematic review and meta‐analysis of the prognostic and predictive ability of current birthweight standards for childhood and adult outcomes. BJOG. 2015;122:634–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Malin G, Morris R, Riley R, Teune M, Khan K. When is birthweight at term abnormally low? A systematic review and meta‐analysis of the association and predictive ability of current birthweight standards for neonatal outcomes. BJOG. 2014;121:515–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. James P, Banay RF, Hart JE, Laden F. A review of the health benefits of greenness. Curr Epidemiol Rep. 2015;2:131–42.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors appreciate feedback from the Center for Research on Social Stressors in Housing across the life course (CRESSH) advisory board, and acknowledge Fei Carnes for data processing.

Funding

This research is part of the Center for Research on Social Stressors in Housing across the life course (CRESSH); NIH/NIHMD Grant P50 MD010428 and USEPA RD83615601. The contents are solely the responsibility of the grantee and do not necessarily represent the official views of the USEPA. Furthermore, USEPA does not endorse the purchase of any commercial products or services mentioned in the publication.

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Authors and Affiliations

  1. Department of Environmental Health, Boston University School of Public Health, Boston, MA, USA

    Chad W. Milando, Jonathan I. Levy & M. Patricia Fabian

  2. Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Maayan Yitshak-Sade

  3. Exposure, Epidemiology, and Risk Program, Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Antonella Zanobetti & Francine Laden

  4. Department of Medicine, Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Francine Laden

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  1. Chad W. Milando
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Correspondence to Chad W. Milando.

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Milando, C.W., Yitshak-Sade, M., Zanobetti, A. et al. Modeling the impact of exposure reductions using multi-stressor epidemiology, exposure models, and synthetic microdata: an application to birthweight in two environmental justice communities. J Expo Sci Environ Epidemiol 31, 442–453 (2021). https://doi.org/10.1038/s41370-021-00318-4

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