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| Funder | EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT |
|---|---|
| Recipient Organization | University of California Los Angeles |
| Country | United States |
| Start Date | Sep 21, 2024 |
| End Date | Sep 20, 2026 |
| Duration | 729 days |
| Number of Grantees | 3 |
| Roles | Principal Investigator; Co-Investigator |
| Data Source | NIH (US) |
| Grant ID | 10905905 |
ABSTRACT Dobbs v. Jackson’s Women’s Health Organization, the U.S. Supreme Court’s 2022 reversal of Roe v. Wade, is the most consequential policy change for maternal and child health in a generation. At the time of submitting this grant, abortion had been banned or severely restricted in sixteen states, and efforts to impose additional bans
and restrictions are in process in other states. Existing data limit more detailed analysis of who has abortions and the potential consequences of Dobbs for maternal and child health. The overarching goal of this exploratory proposal is to use a unique and powerful data resource to characterize the underreporting of abortion, develop
new methods to improve estimates of abortion incidence, and facilitate more research on the consequences of Dobbs in national survey data. Our specific aims are to (1) document the individual-level determinants of abortion underreporting in surveys by comparing abortions measured in medical records to survey responses for the
same individuals using a unique sample of study participants; (2) examine how abortion reporting in surveys changed after the Dobbs decision; and (3) explore the potential to improve national and subgroup estimates regarding the incidence of abortion in national surveys using evidence from aims (1) and (2). Achieving these
aims will allow researchers to refine national survey estimates of abortion and understand the broader context in which these decisions occur. The proposed research will also inform a broader and deeper understanding of the disproportionate incidence of abortion bans or restrictions in the aftermath of Dobbs. The data underlying
this proposal have already been collected and cleaned, making the proposed research highly cost effective.
University of California Los Angeles
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