Using electronic health record data to link families: An illustrative example using intergenerational patterns of obesity Journal Article
| Authors: | Krefman, A. E.; Ghamsari, F.; Turner, D. R.; Lu, A.; Borsje, M.; Wood, C. W.; Petito, L. C.; Polubriaginof, F. C. G.; Schneider, D.; Ahmad, F.; Allen, N. B. |
| Article Title: | Using electronic health record data to link families: An illustrative example using intergenerational patterns of obesity |
| Abstract: | OBJECTIVE: Electronic health record (EHR) data are a valuable resource for population health research but lack critical information such as relationships between individuals. Emergency contacts in EHRs can be used to link family members, creating a population that is more representative of a community than traditional family cohorts. MATERIALS AND METHODS: We revised a published algorithm: relationship inference from the electronic health record (RIFTEHR). Our version, Pythonic RIFTEHR (P-RIFTEHR), identifies a patient's emergency contacts, matches them to existing patients (when available) using network graphs, checks for conflicts, and infers new relationships. P-RIFTEHR was run on December 15, 2021 in the Northwestern Medicine Electronic Data Warehouse (NMEDW) on approximately 2.95 million individuals and was validated using the existing link between children born at NM hospitals and their mothers. As proof-of-concept, we modeled the association between parent and child obesity using logistic regression. RESULTS: The P-RIFTEHR algorithm matched 1 157 454 individuals in 448 278 families. The median family size was 2, the largest was 32 persons, and 247 families spanned 4 generations or more. Validation of the mother-child pairs resulted in 95.1% sensitivity. Children were 2 times more likely to be obese if a parent is obese (OR: 2.30; 95% CI, 2.23-2.37). CONCLUSION: P-RIFTEHR can identify familiar relationships in a large, diverse population in an integrated health system. Estimates of parent-child inheritability of obesity using family structures identified by the algorithm were consistent with previously published estimates from traditional cohort studies. © The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com. |
| Keywords: | cohort studies; cohort analysis; childhood obesity; obesity; family; parents; family characteristics; child parent relation; electronic health records; humans; human; electronic health record; population health; pediatric obesity |
| Journal Title: | Journal of the American Medical Informatics Association |
| Volume: | 30 |
| Issue: | 5 |
| ISSN: | 1067-5027 |
| Publisher: | Oxford University Press |
| Date Published: | 2023-05-01 |
| Start Page: | 915 |
| End Page: | 922 |
| Language: | English |
| DOI: | 10.1093/jamia/ocad028 |
| PUBMED: | 36857086 |
| PROVIDER: | scopus |
| PMCID: | PMC10114127 |
| DOI/URL: | |
| Notes: | The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PDF -- Source: Scopus |
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