Home Disparities in preconception health indicators in U.S. women: a cross-sectional analysis of the behavioral risk factor surveillance system 2019
Article
Licensed
Unlicensed Requires Authentication

Disparities in preconception health indicators in U.S. women: a cross-sectional analysis of the behavioral risk factor surveillance system 2019

  • Rachel Terry ORCID logo EMAIL logo , Ashton Gatewood , Covenant Elenwo , Abigail Long , Wendi Wu , Caroline Markey , Shawn Strain and Micah Hartwell
Published/Copyright: December 27, 2023
Journal of Perinatal Medicine
From the journal Journal of Perinatal Medicine Volume 52 Issue 2

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.

Keywords: preconception health; maternal mortality; pregnancy
Corresponding author: Rachel Terry, Oklahoma State University Center for Health Sciences, Office of Medical Student Research, 1111 W 17th St., Tulsa, OK 74107, USA, E-mail: , Phone: +405 315-8033

  1. Research ethics: Institutional Review Board approval was sought and obtained for the BRFSS survey administration through the CDC.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Competing interests: The authors state no conflict of interest.

  5. Research funding: None declared.

  6. Data availability: Not applicable.

References

1. Hoyert, D. Maternal mortality rates in the United States, 2020. NCHS Health E-Stats [Internet]. 2022 [cited 2023 March 22]. Available from: https://doi.org/10.15620/cdc:113967.10.15620/cdc:113967Search in Google Scholar

2. Health Organization W. Trends in maternal mortality 2000 to 2017: estimates by WHO, UNICEF, UNFPA. World Bank Group and the United Nations Population. Division. 2019 [cited 2023 March 19]. Available from: https://apps.who.int/iris/bitstream/handle/10665/327595/9789241516488-eng.pdf.Search in Google Scholar

3. Gunja, MZ, Seervai, S, Zephyrin, L, Williams, RDII. Health and health care for women of reproductive age: how the United States compares with other high-income countries [Internet]. Commonwealth Fund; 2022 [cited 2023 March 19]. Available from: https://www.commonwealthfund.org/publications/issue-briefs/2022/mar/health-and-health-care-women-reproductive-ageSearch in Google Scholar

4. Reduce maternal deaths — healthy people in action - healthy people 2030 [Internet]. [cited 2023 Mar 15]; 2019. Available from: https://health.gov/healthypeople/objectives-and-data/browse-objectives/pregnancy-and-childbirth/reduce-maternal-deaths-mich-04/healthy-people-in-actionSearch in Google Scholar

5. Hung, P, Liu, J, Norregaard, C, Shih, Y, Liang, C, Zhang, J, et al.. Analysis of residential segregation and racial and ethnic disparities in severe maternal morbidity before and during the COVID-19 pandemic. JAMA Netw Open 2022;5:e2237711. https://doi.org/10.1001/jamanetworkopen.2022.37711.Search in Google Scholar PubMed PubMed Central

6. Lu, MC. Reducing maternal mortality in the United States. JAMA 2018;320:1237–8. https://doi.org/10.1001/jama.2018.11652.Search in Google Scholar PubMed

7. Hoyert, D. Maternal mortality rates in the United States, 2021. [Internet]. [cited 2023 March 22]. National Center for Health Statistics (U.S.); 2023. Available from: https://stacks.cdc.gov/view/cdc/12467810.15620/cdc:124678Search in Google Scholar

8. Singh, GK. Trends and social inequalities in maternal mortality in the United States, 1969-2018. Int J MCH AIDS 2021;10:29–42. https://doi.org/10.21106/ijma.444.Search in Google Scholar PubMed PubMed Central

9. Kozhimannil, KB, Interrante, JD, Tofte, AN, Admon, LK. Severe maternal morbidity and mortality among indigenous women in the United States. Obstet Gynecol 2020;135:294–300. https://doi.org/10.1097/aog.0000000000003647.Search in Google Scholar

10. Maternal Mortality Review [Internet]. Health Department. [cited 2023 May 15]; 2023. Available from: https://oklahoma.gov/health/health-education/children---family-health/maternal-and-child-health-service/perinatal-and-reproductive-health-/maternal-mortality-review.htmlSearch in Google Scholar

11. Robbins, CL, D’Angelo, D, Zapata, L, Boulet, SL, Sharma, AJ, Adamski, A, et al.. Preconception health indicators for public health surveillance. J Womens Health 2018;27:430–43. https://doi.org/10.1089/jwh.2017.6531.Search in Google Scholar PubMed PubMed Central

12. Johnson, K, Posner, SF, Biermann, J, Cordero, JF, Atrash, HK, Parker, CS, et al.. Recommendations to improve preconception health and health care--United States: a report of the CDC/ATSDR preconception care work group and the Select Panel on preconception care. [Internet]. [cited 2023 March 22]. PsycEXTRA Dataset. 2006. Available from: https://doi.org/10.1037/e506902006-001.Search in Google Scholar

13. Broussard, DL, Sappenfield, WB, Fussman, C, Kroelinger, CD, Grigorescu, V. Core state preconception health indicators: a voluntary, multi-state selection process. Matern Child Health J 2011;15:158–68. https://doi.org/10.1007/s10995-010-0575-x.Search in Google Scholar PubMed

14. Farr, SL, Bish, CL. Preconception health among women with frequent mental distress: a population-based study. J Womens Health 2013;22:153–8. https://doi.org/10.1089/jwh.2012.3722.Search in Google Scholar PubMed PubMed Central

15. ACOG Committee Opinion No. 762. Prepregnancy counseling. Obstet Gynecol 2019;133:e78–89. https://doi.org/10.1097/AOG.0000000000003013.Search in Google Scholar PubMed

16. Hasin, DS, Sarvet, AL, Meyers, JL, Saha, TD, Ruan, WJ, Stohl, M, et al.. Epidemiology of adult DSM-5 major depressive disorder and its specifiers in the United States. JAMA Psychiatr 2018;75:336–46. https://doi.org/10.1001/jamapsychiatry.2017.4602.Search in Google Scholar PubMed PubMed Central

17. Wolla, SA, Sullivan, J. Education, Income, and Wealth. Page One Economics® [Internet]. [cited 2023 May 15]; 2017. Available from: https://files.stlouisfed.org/research/publications/page1-econ/2017-01-03/education-income-and-wealth_SE.pdfSearch in Google Scholar

18. Zamora-Kapoor, A, Sinclair, K, Nelson, L, Lee, H, Buchwald, D. Obesity risk factors in American Indians and Alaska Natives: a systematic review. Publ Health 2019;174:85–96. https://doi.org/10.1016/j.puhe.2019.05.021.Search in Google Scholar PubMed

19. Committee on Gynecologic Practice. Committee opinion No. 534: well-woman visit. Obstet Gynecol 2012;120:421–4. https://doi.org/10.1097/AOG.0b013e3182680517.Search in Google Scholar PubMed

20. Dawkins, B, Renwick, C, Ensor, T, Shinkins, B, Jayne, D, Meads, D. What factors affect patients’ ability to access healthcare? An overview of systematic reviews. Trop Med Int Health 2021;26:1177–88. https://doi.org/10.1111/tmi.13651.Search in Google Scholar PubMed

21. Funk, C, Lopez, MH. Hispanic Americans’ trust in and engagement with science [Internet]. Pew Research Center Science & Society. 2022 [cited 2023 May 16]. Available from: https://www.pewresearch.org/science/2022/06/14/hispanic-americans-trust-in-and-engagement-with-science/Search in Google Scholar

22. Daw, JR, Kolenic, GE, Dalton, VK, Zivin, K, Winkelman, T, Kozhimannil, KB, et al.. Racial and Ethnic Disparities in Perinatal Insurance Coverage. Obstet Gynecol [Internet]. 2020 [cited 2023 Aug 13];135. Available from: https://pubmed.ncbi.nlm.nih.gov/32168215/10.1097/AOG.0000000000003728Search in Google Scholar PubMed PubMed Central

23. Kruse, G, Lopez-Carmen, VA, Jensen, A, Hardie, L, Sequist, TD. The Indian health service and American Indian/Alaska native health outcomes. Annu Rev Public Health 2022;43:559–76. https://doi.org/10.1146/annurev-publhealth-052620-103633.Search in Google Scholar PubMed

24. Popova, S, Lange, S, Probst, C, Gmel, G, Rehm, J. Estimation of national, regional, and global prevalence of alcohol use during pregnancy and fetal alcohol syndrome: a systematic review and meta-analysis. Lancet Glob Health 2017;5:e290–9. https://doi.org/10.1016/s2214-109x(17)30021-9.Search in Google Scholar PubMed

25. Witbrodt, J, Mulia, N, Zemore, SE, Kerr, WC. Racial/ethnic disparities in alcohol-related problems: differences by gender and level of heavy drinking. Alcohol Clin Exp Res 2014;38:1662–70. https://doi.org/10.1111/acer.12398.Search in Google Scholar PubMed PubMed Central

26. Pittman, DM, Brooks, JJ, Kaur, P, Obasi, EM. The cost of minority stress: risky alcohol use and coping-motivated drinking behavior in African American college students. J Ethn Subst Abuse [Internet]. 2017 [cited 2023 May 14]; Available from: https://www.tandfonline.com/doi/abs/10.1080/15332640.2017.133695810.1080/15332640.2017.1336958Search in Google Scholar PubMed PubMed Central

27. Wickström, R. Effects of nicotine during pregnancy: human and experimental evidence. Curr Neuropharmacol 2007;5:213–22. https://doi.org/10.2174/157015907781695955.Search in Google Scholar PubMed PubMed Central

28. Tobacco, Nicotine Cessation Duri, ng Pregnancy: ACOG Committee Opinion. Number 807. Obstet Gynecol 2020;135:e221–9.10.1097/AOG.0000000000003822Search in Google Scholar PubMed

29. Odani, S, Armour, BS, Graffunder, CM, Garrett, BE, Agaku, IT. Prevalence and disparities in tobacco product use among American Indians/Alaska Natives – United States, 2010–2015. MMWR Morb Mortal Wkly Rep 2017;66:1374–8. https://doi.org/10.15585/mmwr.mm6650a2.Search in Google Scholar PubMed PubMed Central

30. National Research Council, Institute of Medicine. Board on children, youth, and families, committee on improving the health, safety, and well-being of young adults. In: Investing in the health and well-being of young adults. Washington, D.C.: National Academies Press; 2015.Search in Google Scholar

31. Dahlen, HM “Aging out” of dependent coverage and the effects on US labor market and health insurance choices. Am J Public Health [Internet]. 2015 [cited 2023 May 14];105 Suppl 5. Available from: https://pubmed.ncbi.nlm.nih.gov/26447916/10.2105/AJPH.2015.302791Search in Google Scholar PubMed PubMed Central

32. McWilliams, JM. Health consequences of uninsurance among adults in the United States: recent evidence and implications. Milbank Q 2009;87:443–94. https://doi.org/10.1111/j.1468-0009.2009.00564.x.Search in Google Scholar PubMed PubMed Central

33. Duggan, M, Ellison, NB, Lampe, C, Lenhart, A, Madden, M. Social Media Update 2014 [Internet]. Pew Research Center: Internet, Science & Tech. 2015 [cited 2023 May 14]. Available from: http://www.pewresearch.org/internet/2015/01/09/social-media-update-2014/Search in Google Scholar

34. The HealthCare Partnership at The University of Arizona offers accredited evidence-based continuing education/certification programs for the treatment of Nicotine Addiction [Internet]. The University of Arizona Healthcare Partnership. University of Arizona Healthcare Parternship; 2018 [cited 2023 May 15]. Available from: https://www.healthcarepartnership.org/Search in Google Scholar

35. Broussard, BA, Johnson, A, Himes, JH, Story, M, Fichtner, R, Hauck, F, et al.. Prevalence of obesity in American Indians and Alaska natives. Am J Clin Nutr 1991;53:1535S–1542S. https://doi.org/10.1093/ajcn/53.6.1535s.Search in Google Scholar

36. Zajacova, A, Lawrence, EM. The relationship between education and health: reducing disparities through a contextual approach. Annu Rev Public Health 2018;39:273–89. https://doi.org/10.1146/annurev-publhealth-031816-044628.Search in Google Scholar PubMed PubMed Central

37. Okobi, OE, Ajayi, OO, Okobi, TJ, Anaya, IC, Fasehun, OO, Diala, CS, et al.. The burden of obesity in the rural adult population of America. Cureus 2021;13:e15770. https://doi.org/10.7759/cureus.15770.Search in Google Scholar PubMed PubMed Central

38. Buettner-Schmidt, K, Miller, DR, Maack, B. Disparities in rural tobacco use, smoke-free policies, and tobacco taxes. West J Nurs Res 2019;41:1184–202. https://doi.org/10.1177/0193945919828061.Search in Google Scholar PubMed PubMed Central

39. Behavioral Risk Factor Surveillance System. (2020). Data User Guide [Internet]. [cited 2023 March 15]. Center for Disease Control and Prevention; 2022. Available from: https://www.cdc.gov/brfss/index.htmlSearch in Google Scholar
Received: 2023-06-15
Accepted: 2023-11-04
Published Online: 2023-12-27
Published in Print: 2024-02-26

© 2023 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Mini Review
  3. Artificial intelligence in the NICU to predict extubation success in prematurely born infants
  4. Original Articles – Obstetrics
  5. Amniotic fluid embolism: a reappraisal
  6. A multivariable prediction model for intra-amniotic infection in patients with preterm labor and intact membranes including a point of care system that measures amniotic fluid MMP-8
  7. Analysis of gastric fluid in preterm newborns supports the view that the amniotic cavity is sterile before the onset of parturition: a retrospective cohort study
  8. The effect of uterine closure technique on cesarean scar niche development after multiple cesarean deliveries
  9. The association between obesity and the success of trial of labor after cesarean delivery (TOLAC) in women with past vaginal delivery
  10. Validation of an automated software (Smartpelvic™) in assessing hiatal area from three dimensional transperineal pelvic volumes of pregnant women: comparison with manual analysis
  11. Pathogenic recurrent copy number variants in 7,078 pregnancies via chromosomal microarray analysis
  12. Obstetric pulmonary embolism and long-term cardiovascular symptoms: a cross-sectional study in Western Mexico
  13. Clinical characteristics and outcomes of women with adenomyosis pain during pregnancy: a retrospective study
  14. Disparities in preconception health indicators in U.S. women: a cross-sectional analysis of the behavioral risk factor surveillance system 2019
  15. Vertical transmission of SARS-CoV-2 – data from the German COVID-19 related obstetric and neonatal outcome study (CRONOS)
  16. Effects of sildenafil on Doppler parameters, maternal and neonatal outcomes in the active labor phase of low-risk pregnancies: a randomized clinical trial
  17. Pregnancy and neonatal outcomes of SARS-CoV-2 infection discovered at the time of delivery: a tertiary center experience in North Italy
  18. The impact of the COVID-19 pandemic on antenatal care provision and associated mental health, obstetric and neonatal outcomes
  19. Original Articles – Fetus
  20. Left atrial strain in fetal echocardiography – could it be introduced to everyday clinical practice?
  21. The evaluation of fetal interventricular septum with M-mode and spectral tissue Doppler imaging in gestational diabetes mellitus: a case-control study
  22. Letters to the Editor
  23. ChatGPT, artificial intelligence and the Journal of Perinatal Medicine: correspondence
  24. Re: to the Letter to the Editor: “ChatGPT and artificial intelligence in the Journal of Perinatal Medicine”
  25. Retraction
  26. Clinical potential of human amniotic fluid stem cells
https://doi.org/10.1515/jpm-2023-0249
Keywords for this article
preconception health; maternal mortality; pregnancy

Articles in the same Issue

  1. Frontmatter
  2. Mini Review
  3. Artificial intelligence in the NICU to predict extubation success in prematurely born infants
  4. Original Articles – Obstetrics
  5. Amniotic fluid embolism: a reappraisal
  6. A multivariable prediction model for intra-amniotic infection in patients with preterm labor and intact membranes including a point of care system that measures amniotic fluid MMP-8
  7. Analysis of gastric fluid in preterm newborns supports the view that the amniotic cavity is sterile before the onset of parturition: a retrospective cohort study
  8. The effect of uterine closure technique on cesarean scar niche development after multiple cesarean deliveries
  9. The association between obesity and the success of trial of labor after cesarean delivery (TOLAC) in women with past vaginal delivery
  10. Validation of an automated software (Smartpelvic™) in assessing hiatal area from three dimensional transperineal pelvic volumes of pregnant women: comparison with manual analysis
  11. Pathogenic recurrent copy number variants in 7,078 pregnancies via chromosomal microarray analysis
  12. Obstetric pulmonary embolism and long-term cardiovascular symptoms: a cross-sectional study in Western Mexico
  13. Clinical characteristics and outcomes of women with adenomyosis pain during pregnancy: a retrospective study
  14. Disparities in preconception health indicators in U.S. women: a cross-sectional analysis of the behavioral risk factor surveillance system 2019
  15. Vertical transmission of SARS-CoV-2 – data from the German COVID-19 related obstetric and neonatal outcome study (CRONOS)
  16. Effects of sildenafil on Doppler parameters, maternal and neonatal outcomes in the active labor phase of low-risk pregnancies: a randomized clinical trial
  17. Pregnancy and neonatal outcomes of SARS-CoV-2 infection discovered at the time of delivery: a tertiary center experience in North Italy
  18. The impact of the COVID-19 pandemic on antenatal care provision and associated mental health, obstetric and neonatal outcomes
  19. Original Articles – Fetus
  20. Left atrial strain in fetal echocardiography – could it be introduced to everyday clinical practice?
  21. The evaluation of fetal interventricular septum with M-mode and spectral tissue Doppler imaging in gestational diabetes mellitus: a case-control study
  22. Letters to the Editor
  23. ChatGPT, artificial intelligence and the Journal of Perinatal Medicine: correspondence
  24. Re: to the Letter to the Editor: “ChatGPT and artificial intelligence in the Journal of Perinatal Medicine”
  25. Retraction
  26. Clinical potential of human amniotic fluid stem cells
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 19.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/jpm-2023-0249/html
Scroll to top button