Statistical Approaches for Investigating Periods of Susceptibility in Children’s Environmental Health Research
- 38 Downloads
Purpose of Review
Children’s environmental health researchers are increasingly interested in identifying time intervals during which individuals are most susceptible to adverse impacts of environmental exposures. We review recent advances in methods for assessing susceptible periods.
We identified three general classes of modeling approaches aimed at identifying susceptible periods in children’s environmental health research: multiple informant models, distributed lag models, and Bayesian approaches. Benefits over traditional regression modeling include the ability to formally test period effect differences, to incorporate highly time-resolved exposure data, or to address correlation among exposure periods or exposure mixtures.
Several statistical approaches exist for investigating periods of susceptibility. Assessment of susceptible periods would be advanced by additional basic biological research, further development of statistical methods to assess susceptibility to complex exposure mixtures, validation studies evaluating model assumptions, replication studies in different populations, and consideration of susceptible periods from before conception to disease onset.
KeywordsCritical windows Susceptibility Vulnerability Children’s health Environmental epidemiology Statistical methods
JPB and GBH: 5U24OD023382
JMB: R01 ES025214, R01 ES024381, R01 ES027408, and UG3 OD023313
Compliance with Ethical Standards
Conflict of Interest
Joseph M. Braun was financially compensated for serving as an expert witness for plaintiffs in litigation related to tobacco smoke exposures. Jessie P. Buckley and Ghassan B. Hamra report grants from NIH during the conduct of the study.
Human and Animal Rights and Informed Consent
This article does not contain any studies with human or animal subjects performed by any of the authors.
Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance
- 3.NIEHS (2012) Advancing science, improving health: a plan for environmental health research.Google Scholar
- 7.Larsen WJ. Human embryology. Philadelphia: Churchill Livingstone; 2001.Google Scholar
- 11.Diderichsen F, Hallqvist J, Whitehead M. Differential vulnerability and susceptibility: how to make use of recent development in our understanding of mediation and interaction to tackle health inequalities. Int J Epidemiol 2018.Google Scholar
- 12.Raz R, Roberts AL, Lyall K, Hart JE, Just AC, Laden F, et al. Autism spectrum disorder and particulate matter air pollution before, during, and after pregnancy: a nested case-control analysis within the Nurses’ Health Study II Cohort. Environ Health Perspect. 2015;123(3):264–70.CrossRefGoogle Scholar
- 14.•• Chen YH, Ferguson KK, Meeker JD, McElrath TF, Mukherjee B. Statistical methods for modeling repeated measures of maternal environmental exposure biomarkers during pregnancy in association with preterm birth. Environ Health. 2015;14:9 Compares several methods for examining susceptible periods with repeated biomarker measures in relation to a time-fixed binary outcome.CrossRefGoogle Scholar
- 18.•• Wilson A, Chiu YM, Hsu HL, Wright RO, Wright RJ, Coull BA. Bayesian distributed lag interaction models to identify perinatal windows of vulnerability in children’s health. Biostatistics (Oxford, England). 2017;18(3):537–52 Demonstrates bias when using trimester-averaged exposure that is not present when using distributed lag models to identify susceptible periods.CrossRefGoogle Scholar
- 19.• Liu SH, Bobb JF, Lee KH, Gennings C, Claus Henn B, Bellinger D, et al. Lagged kernel machine regression for identifying time windows of susceptibility to exposures of complex mixtures. Biostatistics. 2018;19(3):325–41 Examines periods of susceptibility to exposure mixtures using a highly flexible extension of distributed lag models based on Bayesian Kernel Machine Regression.CrossRefGoogle Scholar
- 33.Stacy SL, Papandonatos GD, Calafat AM, Chen A, Yolton K, Lanphear BP, et al. Early life bisphenol a exposure and neurobehavior at 8years of age: identifying windows of heightened vulnerability. Environ Int 2017.Google Scholar
- 34.Gasparrini A, Scheipl F, Armstrong B, Kenward MG. A penalized framework for distributed lag non-linear models. Biometrics. 2017.Google Scholar
- 42.Cox B, Vicedo-Cabrera AM, Gasparrini A, Roels HA, Martens E, Vangronsveld J, et al. Ambient temperature as a trigger of preterm delivery in a temperate climate. J Epidemiol Community Health. 2016.Google Scholar
- 44.• Bello GA, Arora M, Austin C, Horton MK, Wright RO, Gennings C. Extending the distributed lag model framework to handle chemical mixtures. Environ Res. 2017;156:253–64 Extends distributed lag models to address periods of susceptibility to exposure mixtures using weighted quantile sum or tree-based methods.CrossRefGoogle Scholar
- 54.• Chang HH, Warren JL, Darrow LA, Reich BJ, Waller LA. Assessment of critical exposure and outcome windows in time-to-event analysis with application to air pollution and preterm birth study. Biostatistics. 2015;16(3):509–21 Addresses susceptible periods when the outcome is time-varying.CrossRefGoogle Scholar