An algorithm to identify residential mobility from electronic health-record data.

Background: Environmental, social and economic exposures can be inferred from address information recorded in an electronic health record. However, these data often contain administrative errors and misspellings. These issues make it challenging to determine whether a patient has moved, which is integral for accurate exposure assessment. We aim to develop an algorithm to identify residential mobility events and avoid exposure misclassification.

Methods: At Penn Medicine, we obtained a cohort of 12 147 pregnant patients who delivered between 2013 and 2017. From this cohort, we identified 9959 pregnant patients with address information at both time of delivery and one year prior. We developed an algorithm entitled REMAP (Relocation Event Moving Algorithm for Patients) to identify residential mobility during pregnancy and compared it to using ZIP code differences alone. We assigned an area-deprivation exposure score to each address and assessed how residential mobility changed the deprivation scores.

Results: To assess the accuracy of our REMAP algorithm, we manually reviewed 3362 addresses and found that REMAP was 95.7% accurate. In this large urban cohort, 41% of patients moved during pregnancy. REMAP outperformed the comparison of ZIP codes alone (82.9%). If residential mobility had not been taken into account, absolute area deprivation would have misclassified 39% of the patients. When setting a threshold of one quartile for misclassification, 24.4% of patients would have been misclassified.

Conclusions: Our study tackles an important characterization problem for exposures that are assigned based upon residential addresses. We demonstrate that methods using ZIP code alone are not adequate. REMAP allows address information from electronic health records to be used for accurate exposure assessment and the determination of residential mobility, giving researchers and policy makers more reliable information.