Literature DB >> 35446610

Linking Electronic Health Records to the American Community Survey: Feasibility and Process.

Victoria Udalova1, Timothy S Carey1, Paul Roman Chelminski1, Lucinda Dalzell1, Patricia Knoepp1, Joanna Motro1, Barbara Entwisle1.   

Abstract

Objectives. To assess linkages of patient data from a health care system in the southeastern United States to microdata from the American Community Survey (ACS) with the goal of better understanding health disparities and social determinants of health in the population. Methods. Once a data use agreement was in place, a stratified random sample of approximately 200 000 was drawn of patients aged 25 to 74 years with at least 2 visits between January 1, 2016, and December 31, 2019. Information from the sampled electronic health records (EHRs) was transferred securely to the Census Bureau, put through the Census Person Identification Validation System to assign Protected Identification Keys (PIKs) as unique identifiers wherever possible. EHRs with PIKs assigned were then linked to 2001-2017 ACS records with a PIK. Results. PIKs were assigned to 94% of the sampled patients. Of patients with PIKs, 15.5% matched to persons sampled in the ACS. Conclusions. Linking data from EHRs to ACS records is feasible and, with adjustments for differential coverage, will advance understanding of social determinants and enhance the ability of integrated delivery systems to reflect and affect the health of the populations served. (Am J Public Health. 2022;112(6):923-930. https://doi.org/10.2105/AJPH.2022.306783).

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Year:  2022        PMID: 35446610      PMCID: PMC9137005          DOI: 10.2105/AJPH.2022.306783

Source DB:  PubMed          Journal:  Am J Public Health        ISSN: 0090-0036            Impact factor:   11.561


  14 in total

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3.  Using electronic health record data for environmental and place based population health research: a systematic review.

Authors:  Leah H Schinasi; Amy H Auchincloss; Christopher B Forrest; Ana V Diez Roux
Journal:  Ann Epidemiol       Date:  2018-03-21       Impact factor: 3.797

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Authors:  B G Link; J Phelan
Journal:  J Health Soc Behav       Date:  1995

5.  Linking electronic health records with community-level data to understand childhood obesity risk.

Authors:  E J Tomayko; T L Flood; A Tandias; L P Hanrahan
Journal:  Pediatr Obes       Date:  2015-01-05       Impact factor: 4.000

6.  Neighborhood Socioeconomic Status and Identification of Patients With CKD Using Electronic Health Records.

Authors:  Lama Ghazi; J Michael Oakes; Richard F MacLehose; Russell V Luepker; Theresa L Osypuk; Paul E Drawz
Journal:  Am J Kidney Dis       Date:  2020-12-22       Impact factor: 11.072

7.  The life course dynamics of affluence.

Authors:  Thomas A Hirschl; Mark R Rank
Journal:  PLoS One       Date:  2015-01-28       Impact factor: 3.240

8.  Personal and Neighborhood Attributes Associated with Cervical and Colorectal Cancer Screening in an Urban African American Population.

Authors:  James W Buehler; Juan C Castro; Suzanne Cohen; Yuzhe Zhao; Steven Melly; Kari Moore
Journal:  Prev Chronic Dis       Date:  2019-08-29       Impact factor: 2.830

9.  Identifying neighborhood characteristics associated with diabetes and hypertension control in an urban African-American population using geo-linked electronic health records.

Authors:  Félice Lê-Scherban; Lance Ballester; Juan C Castro; Suzanne Cohen; Steven Melly; Kari Moore; James W Buehler
Journal:  Prev Med Rep       Date:  2019-07-13

10.  Value of Neighborhood Socioeconomic Status in Predicting Risk of Outcomes in Studies That Use Electronic Health Record Data.

Authors:  Nrupen A Bhavsar; Aijing Gao; Matthew Phelan; Neha J Pagidipati; Benjamin A Goldstein
Journal:  JAMA Netw Open       Date:  2018-09-07
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