Racial and ethnic disparities in long-term contraception use among the birthing population at an academic hospital in the Southeastern United States.
Tosin AkintundeJeffrey HowardDulaney WilsonAmartha GoreChristine MortonLatha HebbarChris GoodierMyrtede C AlfredPublished in: Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting (2023)
Ensuring women and birthing people have access to the contraceptive of their choice is essential for patient-centered care, health equity, and reproductive justice. While trends in national data in the United States reveal racial disparities in long-term contraceptive use, health-system and hospital-level investigations are essential to understand disparities and encourage interventions. We used data from 5011 patients who delivered at a large academic hospital to determine the effect of race/ethnicity and social vulnerability index (SVI) on the odds of undergoing a long-term contraceptive procedure. Results indicate that SVI substantially affects the odds of long-term contraception for non-Hispanic White women and birthing people. In contrast, Hispanic and non-Hispanic Black women and birthing people have significantly higher odds of undergoing a long-term contraceptive procedure due to race/ethnicity. Contributions to these disparities may be based on factors including healthcare providers, organizational and external policies. Interventions at all levels of care are essential to address disparities in contraceptive care, outcomes, and patient experience.
Keyphrases
- gene expression
- healthcare
- affordable care act
- dna methylation
- polycystic ovary syndrome
- quality improvement
- palliative care
- public health
- mental health
- physical activity
- pregnancy outcomes
- climate change
- health insurance
- magnetic resonance
- type diabetes
- risk assessment
- minimally invasive
- pregnant women
- emergency department
- genome wide
- pain management
- machine learning
- magnetic resonance imaging
- acute care
- metabolic syndrome
- health information
- insulin resistance
- artificial intelligence
- deep learning
- global health
- mental illness