Pregnancy and Lactation

1F-01 - Comparison of Lactation Information from Electronic Health Records with Survey Data Across Five US Health Systems

Sunday, August 24, 2025

10:30 AM - 10:45 AM ET

Presenting Author(s)

GJ

Gregory Jansen

Division of Epidemiology and Community Health, University of Minnesota School of Public Health, United States

Co-Author(s)

ES

Elisabeth Seburg

Pregnancy and Child Health Research Center, HealthPartners Institute, United States
GV

Gabriela Vazquez Benitez

Pregnancy and Child Health Research Center, HealthPartners Institute, United States
KE

Kirsten Ehresmann

Pregnancy and Child Health Research Center, HealthPartners Institute, United States
HM

Hibo Mohamed

Pregnancy and Child Health Research Center, HealthPartners Institute, United States
LA

Lyndsay Avalos

Division of Research, Kaiser Permanente Northern California, United States
SN

Sonya Negriff

Department of Research and Evaluation, Kaiser Permanente Southern California, United States
AL

Amy Loree

Center for Health Policy & Health Services Research, Henry Ford Health, United States
CH

Connor Howick

Center for Integrated Health Care Research, Kaiser Permanente Hawaii, United States
YD

Yihe Daida

Center for Integrated Health Care Research, Kaiser Permanente Hawaii, United States
KP

Kristin Palmsten

Pregnancy and Child Health Research Center, HealthPartners Institute, United States

Background: Data on lactation status for research are often collected through surveys. Information on milk feeding collected at routine healthcare visits and stored in electronic health records (EHR) is an emerging source of data for pharmacoepidemiologic studies of lactation-related outcomes. However, validation is needed to ensure the data can be used for research.

Objectives: To compare information on lactation status obtained from structured EHR fields with survey data.

Methods: We included individuals from five US healthcare systems who participated in the Managing Our Mood survey. Information related to lactation is captured during routine healthcare visits within structured EHR fields at each site. Individuals had a live birth (March 2022-October 2023), depression diagnosis during pregnancy, and ≥1 record of milk feeding information in their or their infant’s EHR. We compared lactation status from EHR data up to 9 months after delivery with survey data collected 3-4 months after delivery as the reference. We assessed agreement on lactation status (human milk feeding ever and at survey completion) between data sources using percent agreement, Cohen’s kappa, sensitivity, specificity, positive predictive value (PPV), and negative predictive value with 95% confidence intervals (CIs) overall and by number of records available with milk feeding information.

Results: Among 281 eligible individuals, most were non-Hispanic White (43.3%) or Hispanic (24.7%), and 19.2% had public insurance. The prevalence of human milk feeding ever was 93.2% according to survey data and 91.5% according to EHR data. The median number of infant or birthing parent records with lactation information from EHR was 6. Prevalence of human milk feeding at survey completion was 73.0% and 77.6% according to EHR. Agreement between data sources for human milk feeding ever and at survey completion was ≥92% with kappas ≥0.77. For human milk feeding ever, sensitivity was 97.3% (95% CI: 94.6%, 98.7%) and PPV was 99.2% (95% CI: 97.2%, 99.8%). For human milk feeding at survey, sensitivity was 98.0% (95% CI: 95.1%, 99.2%) and PPV was 92.2% (95% CI: 87.9%, 95.1%). Among those with ≥6 records, sensitivity of human milk feeding at survey was 99.2% (95% CI: 95.4%, 99.9%); PPV was 96.7% (95% CI: 91.8%, 98.7%). Among those with < 6 records sensitivity was 96.6% (95% CI: 90.3%, 98.8%); PPV was 86.6% (95% CI: 78.4%, 92.0%).

Conclusions: There was substantial agreement on lactation status between EHR and survey data. Sensitivity and PPV were lower among those with fewer lactation records, suggesting the need for bias analysis according to number of lactation records available to mitigate misclassification. These findings suggest that lactation information from structured EHR may be used for pharmacoepidemiologic research.