{"title":"Disparities in preconception health indicators in U.S. women: a cross-sectional analysis of the behavioral risk factor surveillance system 2019.","authors":"Rachel Terry, Ashton Gatewood, Covenant Elenwo, Abigail Long, Wendi Wu, Caroline Markey, Shawn Strain, Micah Hartwell","doi":"10.1515/jpm-2023-0249","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":16704,"journal":{"name":"Journal of Perinatal Medicine","volume":" ","pages":"192-201"},"PeriodicalIF":1.7000,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11190353/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Perinatal Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1515/jpm-2023-0249","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/2/26 0:00:00","PubModel":"Print","JCR":"Q3","JCRName":"OBSTETRICS & GYNECOLOGY","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
The Journal of Perinatal Medicine (JPM) is a truly international forum covering the entire field of perinatal medicine. It is an essential news source for all those obstetricians, neonatologists, perinatologists and allied health professionals who wish to keep abreast of progress in perinatal and related research. Ahead-of-print publishing ensures fastest possible knowledge transfer. The Journal provides statements on themes of topical interest as well as information and different views on controversial topics. It also informs about the academic, organisational and political aims and objectives of the World Association of Perinatal Medicine.