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Wang KH, Hendrickson ZM, Miller ML, Abel EA, Skanderson M, Erdos J, Womack JA, Brandt CA, Desai M, Han L. Leveraging Electronic Health Records to Assess Residential Mobility Among Veterans in the Veterans Health Administration. Med Care 2024; 62:458-463. [PMID: 38848139 DOI: 10.1097/mlr.0000000000002017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2024]
Abstract
BACKGROUND Residential mobility, or a change in residence, can influence health care utilization and outcomes. Health systems can leverage their patients' residential addresses stored in their electronic health records (EHRs) to better understand the relationships among patients' residences, mobility, and health. The Veteran Health Administration (VHA), with a unique nationwide network of health care systems and integrated EHR, holds greater potential for examining these relationships. METHODS We conducted a cross-sectional analysis to examine the association of sociodemographics, clinical conditions, and residential mobility. We defined residential mobility by the number of VHA EHR residential addresses identified for each patient in a 1-year period (1/1-12/31/2018), with 2 different addresses indicating one move. We used generalized logistic regression to model the relationship between a priori selected correlates and residential mobility as a multinomial outcome (0, 1, ≥2 moves). RESULTS In our sample, 84.4% (n=3,803,475) veterans had no move, 13.0% (n=587,765) had 1 move, and 2.6% (n=117,680) had ≥2 moves. In the multivariable analyses, women had greater odds of moving [aOR=1.11 (95% CI: 1.10,1.12) 1 move; 1.27 (1.25,1.30) ≥2 moves] than men. Veterans with substance use disorders also had greater odds of moving [aOR=1.26 (1.24,1.28) 1 move; 1.77 (1.72,1.81) ≥2 moves]. DISCUSSION Our study suggests about 16% of veterans seen at VHA had at least 1 residential move in 2018. VHA data can be a resource to examine relationships between place, residential mobility, and health.
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Affiliation(s)
| | | | | | - Erica A Abel
- Yale School of Medicine, New Haven
- VA Connecticut Healthcare System, West Haven, CT
| | | | - Joseph Erdos
- Yale School of Medicine, New Haven
- VA Connecticut Healthcare System, West Haven, CT
| | - Julie A Womack
- VA Connecticut Healthcare System, West Haven, CT
- Yale School of Nursing, Orange, CT
| | - Cynthia A Brandt
- Yale School of Medicine, New Haven
- VA Connecticut Healthcare System, West Haven, CT
| | - Mayur Desai
- Yale University School of Public Health, Yale University, New Haven
| | - Ling Han
- Yale School of Medicine, New Haven
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Manning ER, Duan Q, Taylor S, Ray S, Corley AMS, Michael J, Gillette R, Unaka N, Hartley D, Beck AF, Brokamp C. Development of a multimodal geomarker pipeline to assess the impact of social, economic, and environmental factors on pediatric health outcomes. J Am Med Inform Assoc 2024; 31:1471-1478. [PMID: 38733117 PMCID: PMC11187418 DOI: 10.1093/jamia/ocae093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 03/05/2024] [Accepted: 04/15/2024] [Indexed: 05/13/2024] Open
Abstract
OBJECTIVES We sought to create a computational pipeline for attaching geomarkers, contextual or geographic measures that influence or predict health, to electronic health records at scale, including developing a tool for matching addresses to parcels to assess the impact of housing characteristics on pediatric health. MATERIALS AND METHODS We created a geomarker pipeline to link residential addresses from hospital admissions at Cincinnati Children's Hospital Medical Center (CCHMC) between July 2016 and June 2022 to place-based data. Linkage methods included by date of admission, geocoding to census tract, street range geocoding, and probabilistic address matching. We assessed 4 methods for probabilistic address matching. RESULTS We characterized 124 244 hospitalizations experienced by 69 842 children admitted to CCHMC. Of the 55 684 hospitalizations with residential addresses in Hamilton County, Ohio, all were matched to 7 temporal geomarkers, 97% were matched to 79 census tract-level geomarkers and 13 point-level geomarkers, and 75% were matched to 16 parcel-level geomarkers. Parcel-level geomarkers were linked using our exact address matching tool developed using the best-performing linkage method. DISCUSSION Our multimodal geomarker pipeline provides a reproducible framework for attaching place-based data to health data while maintaining data privacy. This framework can be applied to other populations and in other regions. We also created a tool for address matching that democratizes parcel-level data to advance precision population health efforts. CONCLUSION We created an open framework for multimodal geomarker assessment by harmonizing and linking a set of over 100 geomarkers to hospitalization data, enabling assessment of links between geomarkers and hospital admissions.
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Affiliation(s)
- Erika Rasnick Manning
- Division of Biostatistics and Epidemiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, United States
| | - Qing Duan
- Division of Biostatistics and Epidemiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, United States
| | - Stuart Taylor
- Office of Population Health, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, United States
| | - Sarah Ray
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45219, United States
| | - Alexandra M S Corley
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45219, United States
- Division of General and Community Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, United States
| | - Joseph Michael
- James M Anderson Center for Health Systems Excellence, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, United States
| | - Ryan Gillette
- Office of Population Health, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, United States
| | - Ndidi Unaka
- Office of Population Health, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45219, United States
- Division of Hospital Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, United States
| | - David Hartley
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45219, United States
- James M Anderson Center for Health Systems Excellence, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, United States
| | - Andrew F Beck
- Office of Population Health, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45219, United States
- Division of General and Community Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, United States
- James M Anderson Center for Health Systems Excellence, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, United States
- Division of Hospital Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, United States
- Michael Fisher Child Health Equity Center, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, United States
| | - Cole Brokamp
- Division of Biostatistics and Epidemiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45219, United States
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Meeker JR, Burris HH, Bai R, Levine LD, Boland MR. Neighborhood deprivation increases the risk of Post-induction cesarean delivery. J Am Med Inform Assoc 2022; 29:329-334. [PMID: 34921313 PMCID: PMC8757307 DOI: 10.1093/jamia/ocab258] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 09/24/2021] [Accepted: 11/03/2021] [Indexed: 01/14/2023] Open
Abstract
OBJECTIVE The purpose of this study was to measure the association between neighborhood deprivation and cesarean delivery following labor induction among people delivering at term (≥37 weeks of gestation). MATERIALS AND METHODS We conducted a retrospective cohort study of people ≥37 weeks of gestation, with a live, singleton gestation, who underwent labor induction from 2010 to 2017 at Penn Medicine. We excluded people with a prior cesarean delivery and those with missing geocoding information. Our primary exposure was a nationally validated Area Deprivation Index with scores ranging from 1 to 100 (least to most deprived). We used a generalized linear mixed model to calculate the odds of postinduction cesarean delivery among people in 4 equally-spaced levels of neighborhood deprivation. We also conducted a sensitivity analysis with residential mobility. RESULTS Our cohort contained 8672 people receiving an induction at Penn Medicine. After adjustment for confounders, we found that people living in the most deprived neighborhoods were at a 29% increased risk of post-induction cesarean delivery (adjusted odds ratio = 1.29, 95% confidence interval, 1.05-1.57) compared to the least deprived. In a sensitivity analysis, including residential mobility seemed to magnify the effect sizes of the association between neighborhood deprivation and postinduction cesarean delivery, but this information was only available for a subset of people. CONCLUSIONS People living in neighborhoods with higher deprivation had higher odds of postinduction cesarean delivery compared to people living in less deprived neighborhoods. This work represents an important first step in understanding the impact of disadvantaged neighborhoods on adverse delivery outcomes.
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Affiliation(s)
- Jessica R Meeker
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Center for Public Health Initiatives, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Leonard Davis Institute for Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Heather H Burris
- Center for Excellence in Environmental Toxicology, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Divsion of Neonatology, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Ray Bai
- Department of Statistics, University of South Carolina, Columbia, South Carolina, USA
| | - Lisa D Levine
- Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Mary Regina Boland
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Center for Public Health Initiatives, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Center for Excellence in Environmental Toxicology, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA,Corresponding Author: Mary Regina Boland, PhD, FAMIA, 423 Guardian Drive, 421 Blockley Hall, Philadelphia, PA 19104, USA;
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