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Home Research Research Library Disaggregating Latino nativity in equity research using electronic health records. Disaggregating Latino nativity in equity research using electronic health records. 2023 Author(s) Marino, Miguel, Fankhauser, Katie, Minnier, Jessica, Lucas, Jennifer A, Giebultowicz, Sophia, Kaufmann, Jorge, Hwang, Jun, Bailey, Steffani R, Crookes, Danielle M, Bazemore, Andrew W, Suglia, Shakira F, and Heintzman, John D Volume Health Services Research Source Health Services Research OBJECTIVE: To develop and validate prediction models for inference of Latino nativity to advance health equity research. DATA SOURCES/STUDY SETTING: This study used electronic health records (EHRs) from 19,985 Latino children with self-reported country of birth seeking care from January 1, 2012 to December 31, 2018 at 456 community health centers (CHCs) across 15 states along with census-tract geocoded neighborhood composition and surname data. STUDY DESIGN: We constructed and evaluated the performance of prediction models within a broad machine learning framework (Super Learner) for the estimation of Latino nativity. Outcomes included binary indicators denoting nativity (US vs. foreign-born) and Latino country of birth (Mexican, Cuban, Guatemalan). The performance of these models was compared using the area under the receiver operating characteristics curve (AUC) from an externally withheld patient sample. DATA COLLECTION/EXTRACTION METHODS: Census surname lists, census neighborhood composition, and Forebears administrative data were linked to EHR data. PRINCIPAL FINDINGS: Of the 19,985 Latino patients, 10.7% reported a non-US country of birth (5.1% Mexican, 4.7% Guatemalan, 0.8% Cuban). Overall, prediction models for nativity showed outstanding performance with external validation (US-born vs. foreign: AUC = 0.90; Mexican vs. non-Mexican: AUC = 0.89; Guatemalan vs. non-Guatemalan: AUC = 0.95; Cuban vs. non-Cuban: AUC = 0.99). CONCLUSIONS: Among challenges facing health equity researchers in health services is the absence of methods for data disaggregation, and the specific ability to determine Latino country of birth (nativity) to inform disparities. Recent interest in more robust health equity research has called attention to the importance of data disaggregation. In a multistate network of CHCs using multilevel inputs from EHR data linked to surname and community data, we developed and validated novel prediction models for the use of available EHR data to infer Latino nativity for health disparities research in primary care and health services research, which is a significant potential methodologic advance in studying this population. ABFM Research Read all 2026 Demonstrating the Reliability and Structural Validity of Creating Patient-Level and Clinician-Level Scores on the Person Centered Primary Care Measure Go to Demonstrating the Reliability and Structural Validity of Creating Patient-Level and Clinician-Level Scores on the Person Centered Primary Care Measure 2026 Reflections on Family Medicine’s First Year of Program Signals and Other New ERAS Features Go to Reflections on Family Medicine’s First Year of Program Signals and Other New ERAS Features 2026 Estimation of Mortality via the Neighborhood Atlas and Reproducible Area Deprivation Indices Go to Estimation of Mortality via the Neighborhood Atlas and Reproducible Area Deprivation Indices 2026 Primary Care Physician Continuity Is a Consistent Measure Associated with Lower Costs and Hospitalizations Go to Primary Care Physician Continuity Is a Consistent Measure Associated with Lower Costs and Hospitalizations
Author(s) Marino, Miguel, Fankhauser, Katie, Minnier, Jessica, Lucas, Jennifer A, Giebultowicz, Sophia, Kaufmann, Jorge, Hwang, Jun, Bailey, Steffani R, Crookes, Danielle M, Bazemore, Andrew W, Suglia, Shakira F, and Heintzman, John D Volume Health Services Research Source Health Services Research
ABFM Research Read all 2026 Demonstrating the Reliability and Structural Validity of Creating Patient-Level and Clinician-Level Scores on the Person Centered Primary Care Measure Go to Demonstrating the Reliability and Structural Validity of Creating Patient-Level and Clinician-Level Scores on the Person Centered Primary Care Measure 2026 Reflections on Family Medicine’s First Year of Program Signals and Other New ERAS Features Go to Reflections on Family Medicine’s First Year of Program Signals and Other New ERAS Features 2026 Estimation of Mortality via the Neighborhood Atlas and Reproducible Area Deprivation Indices Go to Estimation of Mortality via the Neighborhood Atlas and Reproducible Area Deprivation Indices 2026 Primary Care Physician Continuity Is a Consistent Measure Associated with Lower Costs and Hospitalizations Go to Primary Care Physician Continuity Is a Consistent Measure Associated with Lower Costs and Hospitalizations
2026 Demonstrating the Reliability and Structural Validity of Creating Patient-Level and Clinician-Level Scores on the Person Centered Primary Care Measure Go to Demonstrating the Reliability and Structural Validity of Creating Patient-Level and Clinician-Level Scores on the Person Centered Primary Care Measure
2026 Reflections on Family Medicine’s First Year of Program Signals and Other New ERAS Features Go to Reflections on Family Medicine’s First Year of Program Signals and Other New ERAS Features
2026 Estimation of Mortality via the Neighborhood Atlas and Reproducible Area Deprivation Indices Go to Estimation of Mortality via the Neighborhood Atlas and Reproducible Area Deprivation Indices
2026 Primary Care Physician Continuity Is a Consistent Measure Associated with Lower Costs and Hospitalizations Go to Primary Care Physician Continuity Is a Consistent Measure Associated with Lower Costs and Hospitalizations
