Disparities in preconception health indicators in U.S. women: a cross-sectional analysis of the behavioral risk factor surveillance system 2019
Author:
Terry Rachel1ORCID, Gatewood Ashton1, Elenwo Covenant1, Long Abigail2, Wu Wendi2, Markey Caroline3, Strain Shawn4, Hartwell Micah15
Affiliation:
1. Oklahoma State University Center for Health Sciences, Office of Medical Student Research , Tulsa , OK , USA 2. Department of Obstetrics and Gynecology , SSM Health St. Anthony Hospital , Oklahoma City , OK , USA 3. Department of Obstetrics and Gynecology , University of Oklahoma School of Community Medicine , Tulsa , OK , USA 4. Department of Obstetrics and Gynecology , John Peter Smith Hospital , Fort Worth , TX , USA 5. Department of Psychiatry and Behavioral Sciences , Oklahoma State University Center for Health Sciences, College of Osteopathic Medicine , Tulsa , OK , USA
Abstract
Abstract
Objectives
Optimized preconception care improves birth outcomes and women’s health. Yet, little research exists identifying inequities impacting preconception health. This study identifies age, race/ethnicity, education, urbanicity, and income inequities in preconception health.
Methods
We performed a cross-sectional analysis of the Center for Disease Control and Prevention’s (CDC) 2019 Behavioral Risk Factor Surveillance System (BRFSS). This study included women aged 18–49 years who (1) reported they were not using any type of contraceptive measure during their last sexual encounter (usage of condoms, birth control, etc.) and (2) reported wanting to become pregnant from the BRFSS Family Planning module. Sociodemographic variables included age, race/ethnicity, education, urbanicity, and annual household income. Preconception health indicators were subdivided into three categories of Physical/Mental Health, Healthcare Access, and Behavioral Health. Chi-squared statistical analysis was utilized to identify sociodemographic inequities in preconception health indicators.
Results
Within the Physical/Mental Health category, we found statistically significant differences among depressive disorder, obesity, high blood pressure, and diabetes. In the Healthcare Access category, we found statistically significant differences in health insurance status, having a primary care doctor, and being able to afford a medical visit. Within the Behavioral Health category, we found statistically significant differences in smoking tobacco, consuming alcohol, exercising in the past 30 days, and fruit and vegetable consumption.
Conclusions
Maternal mortality and poor maternal health outcomes are influenced by many factors. Further research efforts to identify contributing factors will improve the implementation of targeted preventative measures in directly affected populations to alleviate the current maternal health crisis.
Publisher
Walter de Gruyter GmbH
Subject
Obstetrics and Gynecology,Pediatrics, Perinatology and Child Health
Reference39 articles.
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