期刊
CURRENT OPINION IN PEDIATRICS
卷 32, 期 2, 页码 315-320出版社
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/MOP.0000000000000877
关键词
high-dimensional exposure data; mixture analysis; susceptible windows
类别
资金
- National Institute on Minority Health and Health Disparities (NIMHD) [R01MD013310]
- National Institute of Environmental Health Sciences (NIEHS) [R01ES028811, P30ES023515]
- National Heart, Lung, and Blood Institute (NHLBI) [K23HL135349]
Purpose of review Biomonitoring studies have shown that children are constantly exposed to complex patterns of chemical and nonchemical exposures. Here, we briefly summarize the rationale for studying multiple exposures, also called mixture, in relation to child health and key statistical approaches that can be used. We discuss advantages over traditional methods, limitations and appropriateness of the context. Recent findings New approaches allow pediatric researchers to answer increasingly complex questions related to environmental mixtures. We present methods to identify the most relevant exposures among a high-multitude of variables, via shrinkage and variable selection techniques, and identify the overall mixture effect, via Weighted Quantile Sum and Bayesian Kernel Machine regressions. We then describe novel extensions that handle high-dimensional exposure data and allow identification of critical exposure windows. Recent advances in statistics and machine learning enable researchers to identify important mixture components, estimate joint mixture effects and pinpoint critical windows of exposure. Despite many advantages over single chemical approaches, measurement error and biases may be amplified in mixtures research, requiring careful study planning and design. Future research requires increased collaboration between epidemiologists, statisticians and data scientists, and further integration with causal inference methods.
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