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

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 timeresolved exposure data, or to address correlation among exposure periods or exposure mixtures. Summary: 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.

Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods

“…For example, it has been well-documented that gestation can be a critical window of susceptibility for the developing fetus, and this level of susceptibility can differ from that during childhood and adolescence. Accordingly, longitudinal epidemiological cohorts such as birth cohort studies typically measure exposures of interest across multiple developmental windows [64]. There now exists a suite of methods to estimate critical windows of susceptibility from such data for single chemicals.…”

“…Because individuals are rarely exposed to single chemicals in isolation, the issue of critical windows of susceptibility extends to mixture epidemiology. Prior to the initiation of the PRIME program, most methods for evaluating the association between mixtures and an outcome were designed for studies with exposures measured at a single or discrete time points [64]. Notable exceptions were lagged weighted quantile sum (LWQS) regression [67], which uses a weighted quantile sum (WQS) regression within a DLM framework when a mixture is measured with high temporal resolution over time, and lagged kernel machine regression (LKMR) [68], which extends kernel machine regression to the setting in which a mixture is measured at a small number of discrete timepoints (e.g., trimesters during pregnancy).…”

Periconceptional and prenatal exposure to metal mixtures in relation to behavioral development at 3 years of age

“… 1 – 5 Timing of exposure is critical, as fetal development is an ordered and cumulative process, and exposures at different time points may have varying impacts. 1 , 2 , 6 Metals readily cross the placenta so that maternal exposures during pregnancy result in fetal exposure. 7 – 9 Such exposures have been linked to a variety of important health endpoints, including alterations in neurodevelopment.…”

Opportunities for evaluating chemical exposures and child health in the United States: the Environmental influences on Child Health Outcomes (ECHO) Program

“…While gestation is a highly susceptible period to environmental exposures, pre-conception and postnatal exposures are also critically important [ 229 ]. ECHO’s large biorepository with samples collected at key life stages will enable the repeated measurement of chemical exposures to facilitate well-powered analyses for assessing susceptible periods and effects of cumulative exposures from gestation through adolescence using state-of-the-art statistical approaches [ 230 ]. Characterizing chemical exposures in early childhood is a unique strength of the ECHO Program given that NHANES does not conduct biomonitoring of children under 3 years of age and has only recently begun measuring exposures among 3–5-year-olds.…”