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Curr Environ Health Rep. 2019 Mar;6(1):1-7. doi: 10.1007/s40572-019-0224-5.

Statistical Approaches for Investigating Periods of Susceptibility in Children's Environmental Health Research.

Author information

1
Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD, 21205, USA. jessie.buckley@jhu.edu.
2
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD, 21205, USA. jessie.buckley@jhu.edu.
3
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD, 21205, USA.
4
Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA.

Abstract

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.

RECENT FINDINGS:

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.

KEYWORDS:

Children’s health; Critical windows; Environmental epidemiology; Statistical methods; Susceptibility; Vulnerability

PMID:
30684243
PMCID:
PMC6420841
[Available on 2020-03-01]
DOI:
10.1007/s40572-019-0224-5

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