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Disparities in preconception health indicators in U.S. women: a cross-sectional analysis of the behavioral risk factor surveillance system 2019.
Terry, Rachel; Gatewood, Ashton; Elenwo, Covenant; Long, Abigail; Wu, Wendi; Markey, Caroline; Strain, Shawn; Hartwell, Micah.
Afiliação
  • Terry R; Oklahoma State University Center for Health Sciences, Office of Medical Student Research, Tulsa, OK, USA.
  • Gatewood A; Oklahoma State University Center for Health Sciences, Office of Medical Student Research, Tulsa, OK, USA.
  • Elenwo C; Oklahoma State University Center for Health Sciences, Office of Medical Student Research, Tulsa, OK, USA.
  • Long A; Department of Obstetrics and Gynecology, SSM Health St. Anthony Hospital, Oklahoma City, OK, USA.
  • Wu W; Department of Obstetrics and Gynecology, SSM Health St. Anthony Hospital, Oklahoma City, OK, USA.
  • Markey C; Department of Obstetrics and Gynecology, University of Oklahoma School of Community Medicine, Tulsa, OK, USA.
  • Strain S; Department of Obstetrics and Gynecology, John Peter Smith Hospital, Fort Worth, TX, USA.
  • Hartwell M; Oklahoma State University Center for Health Sciences, Office of Medical Student Research, Tulsa, OK, USA.
J Perinat Med ; 52(2): 192-201, 2024 Feb 26.
Article em En | MEDLINE | ID: mdl-38146265
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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Vigilância da População / Cuidado Pré-Concepcional Limite: Female / Humans / Pregnancy Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Vigilância da População / Cuidado Pré-Concepcional Limite: Female / Humans / Pregnancy Idioma: En Ano de publicação: 2024 Tipo de documento: Article