National Academy of Medicine

Methods for a National Birth Cohort Study

May 31, 2016 | Discussion Paper

As we outlined in the previous paper in this series, our nation needs foundational data in order to understand how social, physical, chemical, and nutritional environments interact to impact how Americans grow, live, and prosper. To satisfy this need, we propose a nationally representative birth cohort study beginning in the prenatal period and following the children through adulthood. Existing research efforts are inadequate because their data are not sufficiently comprehensive and representative to identify both positive and negative factors affecting children’s health or to fully understand health inequities in the United States.

A crucial element of the proposed study is a well-designed national probability sample from which conclusions can be drawn to the larger population from which the sample was randomly selected. In contrast, self-selection sampling consists of volunteers who elected to be part of a study. This technique introduces self-selection bias and can lead to a sample that is not representative of the population being studied. In fact, a report by the National Research Council and the Institute of Medicine, The National Children’s Study 2014: An Assessment, endorsed a probability sample design for a future national longitudinal birth cohort study. In this paper, we provide an overview of a feasible sample design, methods for stakeholder engagement, tools for data collection, and approaches for providing access to the data that would maximize its value.

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This paper is part of a two-part Perspectives series, Lifelong Impact: Why the United States Needs a National Birth Cohort Study, which discusses the reasons why a national birth cohort study is important for the future of health in the United States and how such a study could be designed in a way that is multidisciplinary, focuses on the main drivers of health, engages communities, employs a diverse set of data sources, and includes innovative techniques in data analysis. Learn more about the series >>


Note

Disclaimer: The views expressed in this paper are those of the authors and not necessarily of the authors’ organizations, the National Academy of Medicine (NAM), or the National Academies of Sciences, Engineering, and Medicine (the National Academies). The paper is intended to help inform and stimulate discussion. It is not a report of the NAM or the National Academies. Copyright by the National Academy of Sciences. All rights reserved.