Advancing high quality longitudinal data collection: Implications for the HEALthy Brain and Child Development (HBCD) Study design and recruitment.

Autor:	Si Y; Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, United States. Electronic address: yajuan@umich.edu., Bandoli G; Department of Pediatrics, University of California San Diego, United States., Cole KM; Division of Extramural Research, the National Institute on Drug Abuse, United States., Daniele Fallin M; Rollins School of Public Health, Emory University, United States., Stuart EA; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, United States., Gurka KK; Department of Epidemiology, University of Florida, United States., Althoff KN; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, United States., Thompson WK; Laureate Institute for Brain Research, United States.
Jazyk:	angličtina
Zdroj:	Developmental cognitive neuroscience [Dev Cogn Neurosci] 2024 Oct; Vol. 69, pp. 101432. Date of Electronic Publication: 2024 Aug 10.
DOI:	10.1016/j.dcn.2024.101432
Abstrakt:	The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. The HBCD Study aims to reflect the sociodemographic diversity of pregnant individuals in the U.S. The study will also oversample individuals who use substances during pregnancy and enroll similar individuals who do not use to allow for generalizable inferences of the impact of prenatal substance use on trajectories of child development. Without probability sampling or a randomization-based design, the study requires innovation during enrollment, close monitoring of group differences, and rigorous evaluation of external and internal validity across the enrollment period. In this article, we discuss the HBCD Study recruitment and enrollment data collection processes and potential analytic strategies to account for sources of heterogeneity and potential bias. First, we introduce the adaptive design and enrollment monitoring indices to assess and enhance external and internal validity. Second, we describe the visit schedule for in-person and remote data collection where dyads are randomly assigned to visit windows based on a jittered design to optimize longitudinal trajectory estimation. Lastly, we provide an overview of analytic procedures planned for estimating trajectories. Competing Interests: Declaration of Competing Interest None. (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Databáze:	MEDLINE
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