Association Between Nativity and Preventive Mammography Among Latina Community Health Center Patients

Abstract

Background: In the United States, Latina women have delayed mammography utilization, yet it is unknown how use varies among patients who receive primary care from community health centers who serve a disproportionately high number of Latina patients. Methods: We used data (2013–2022) from a nationwide network of primary care organizations. The main outcome of interest, mammography among females over the age of 50, was analyzed in two different ways: (1) being up to date on mammography and (2) time to first mammogram after age 50. We used covariate-adjusted generalized estimating equations logistic regression and Cox proportional hazard models to estimate the association between nativity (US-born versus Foreign-born, region of origin, and country of origin) and mammography. Results: Among the 24,579 included patients, being up to date with mammography varied by nativity, with US-born Latinas having 1.55 (95% CI: 1.27, 1.90) and Foreign-born Latinas having 1.85 (95% CI: 1.64, 2.10) times the odds of being up to date compared to Non-Hispanic White (NHW) females. Foreign-born Latinas, regardless of region or country of origin, had a higher prevalence of being up to date compared to US-born NHW and US-born Latinas. Latinas from the Caribbean and Central America had the highest prevalence of being up to date, with those from South America having the lowest. Finally, US-born Latinas and Foreign-born Latinas had a shorter time to first mammography. Conclusion: US-born and Foreign-born Latinas had greater mammography use than NHW patients highlighting the important role of community health centers in preventive cancer care for Latinas.

Publication
Journal of Racial and Ethnic Health Disparities
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Wyatt P. Bensken, PhD
Research Investigator & Adjunct Assistant Professor of Population and Quantitative Health Sciences

My expertise is in the use of complex health care data, paired with traditional statistical and novel machine learning approaches, to identify opportunities to improve health, health care, and health outcomes for all.