# DATA COORDINATING CENTER FOR THE PRENATAL AND CHILDHOOD MECHANISMS OF HEALTH DISPARITIES: START-UP AND RECRUITMENT OF THE FIRST SUBCOHORT OF PREGNANT WOMEN AND THEIR PARTNERS

> **NIH NIH N01** · THE EMMES COMPANY, LLC · 2020 · $387,978

## Abstract

BACKGROUND
Health disparities in the United States have their origins as early as the prenatal period. Early life conditions including poverty and discrimination generate disparities in health over the life course that become further entrenched in the population through their transmission across generations. Parental mental health, which is strongly linked with social and economic disadvantage as well as child development, may play a key mediating role in the transmission of disparities across generation, but a persistent gap in the disparities literature is that both maternal and paternal psychopathology have not been fully considered as mechanisms nor measured using
phenotypically validated approaches.
As a result, though disparities in health are well documented, the developmental mechanisms that impact disparities at the very beginning of life are not, particularly those which lead to developmental deficits that emerge long before disease states. The National Institutes of Health (NIH) strategic plan (2016-2020) highlights the need for research to improve “understanding mechanisms that lead to disparities by race/ethnicity and socioeconomic status.” Such enhanced understanding is needed to clarify the etiology of disparities – particularly the specific exposures linked with social or economic disadvantage that impact early development. Advancing knowledge of the developmental mechanisms that generate disparities requires a more thorough understanding of how socioeconomic and race/ethnic status influence the determinants of development from gestation onward. To accomplish this, more in-depth measurement of potential causes of disparities is needed from more diverse samples starting earlier in the life course are needed than is currently available from existing studies.
A. Study Research Aims
The Division of Intramural Population Health Research (DIPHR) is examining evidence-and theory-based risk and protective factors for maternal health during pregnancy and child development that are expected to be affected by inequalities at the individual, family, and neighborhood levels. The results of this study will expand the knowledge base concerning the impacts of disparities on parental health and early child development that may account for the establishment of life-long disparities in health. This knowledge base will, in turn, inform interventional research on effective approaches to the
alleviation of disparities.
Accordingly, this prospective observational cohort study will enroll a socioeconomically and racially and ethnically diverse cohort totaling approximately 2,000 women and their partners during the first trimester of pregnancy and conduct follow-up assessments through pregnancy, delivery and the first year of their offspring’s life. The specific aims
of the overall study are the following:
(1) To investigate disparities in parents’ health and behaviors during pregnancy
(2) To investigate the mechanisms generating disparities in birth outc...

## Key facts

- **NIH application ID:** 10261309
- **Project number:** 275201800006I-P00001-759401900126-1
- **Recipient organization:** THE EMMES COMPANY, LLC
- **Principal Investigator:** Seth Sherman
- **Activity code:** N01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $387,978
- **Award type:** —
- **Project period:** 2019-09-23 → 2021-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10261309

## Citation

> US National Institutes of Health, RePORTER application 10261309, DATA COORDINATING CENTER FOR THE PRENATAL AND CHILDHOOD MECHANISMS OF HEALTH DISPARITIES: START-UP AND RECRUITMENT OF THE FIRST SUBCOHORT OF PREGNANT WOMEN AND THEIR PARTNERS (275201800006I-P00001-759401900126-1). Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nih/10261309. Licensed CC0.

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