Literature DB >> 25759144

Imputing Missing Race/Ethnicity in Pediatric Electronic Health Records: Reducing Bias with Use of U.S. Census Location and Surname Data.

Robert W Grundmeier1, Lihai Song1, Mark J Ramos1, Alexander G Fiks1, Marc N Elliott2, Allen Fremont2, Wilson Pace3, Richard C Wasserman4, Russell Localio5.   

Abstract

OBJECTIVE: To assess the utility of imputing race/ethnicity using U.S. Census race/ethnicity, residential address, and surname information compared to standard missing data methods in a pediatric cohort. DATA SOURCES/STUDY
SETTING: Electronic health record data from 30 pediatric practices with known race/ethnicity. STUDY
DESIGN: In a simulation experiment, we constructed dichotomous and continuous outcomes with pre-specified associations with known race/ethnicity. Bias was introduced by nonrandomly setting race/ethnicity to missing. We compared typical methods for handling missing race/ethnicity (multiple imputation alone with clinical factors, complete case analysis, indicator variables) to multiple imputation incorporating surname and address information. PRINCIPAL
FINDINGS: Imputation using U.S. Census information reduced bias for both continuous and dichotomous outcomes.
CONCLUSIONS: The new method reduces bias when race/ethnicity is partially, nonrandomly missing. © Health Research and Educational Trust.

Keywords:  Multiple imputation; U.S. Census location and surname data; health disparities; race and ethnicity

Mesh：

Year:  2015        PMID： 25759144      PMCID： PMC4545341          DOI： 10.1111/1475-6773.12295

Source DB:  PubMed          Journal:  Health Serv Res        ISSN： 0017-9124            Impact factor:   3.402


  19 in total

Review 1.  Multiple imputation: a primer.

Authors:  J L Schafer
Journal:  Stat Methods Med Res       Date:  1999-03       Impact factor: 3.021

2.  Unpredictable bias when using the missing indicator method or complete case analysis for missing confounder values: an empirical example.

Authors:  Mirjam J Knol; Kristel J M Janssen; A Rogier T Donders; Antoine C G Egberts; E Rob Heerdink; Diederick E Grobbee; Karel G M Moons; Mirjam I Geerlings
Journal:  J Clin Epidemiol       Date:  2010-03-25       Impact factor: 6.437

3.  Use of geocoding in managed care settings to identify quality disparities.

Authors:  Allen M Fremont; Arlene Bierman; Steve L Wickstrom; Chloe E Bird; Mona Shah; José J Escarce; Thomas Horstman; Thomas Rector
Journal:  Health Aff (Millwood)       Date:  2005 Mar-Apr       Impact factor: 6.301

4.  Multiple imputation of discrete and continuous data by fully conditional specification.

Authors:  Stef van Buuren
Journal:  Stat Methods Med Res       Date:  2007-06       Impact factor: 3.021

5.  Collecting adequate data on racial and ethnic disparities in health: the challenges continue.

Authors:  Linda T Bilheimer; Jane E Sisk
Journal:  Health Aff (Millwood)       Date:  2008 Mar-Apr       Impact factor: 6.301

6.  Power of tests for a dichotomous independent variable measured with error.

Authors:  Daniel F McCaffrey; Marc N Elliott
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7.  Collection of data on patients' race and ethnic group by physician practices.

Authors:  Matthew K Wynia; Susan L Ivey; Romana Hasnain-Wynia
Journal:  N Engl J Med       Date:  2010-03-04       Impact factor: 91.245

Review 8.  Use of geocoding and surname analysis to estimate race and ethnicity.

Authors:  Kevin Fiscella; Allen M Fremont
Journal:  Health Serv Res       Date:  2006-08       Impact factor: 3.402

9.  Multiple imputation using chained equations: Issues and guidance for practice.

Authors:  Ian R White; Patrick Royston; Angela M Wood
Journal:  Stat Med       Date:  2010-11-30       Impact factor: 2.373

10.  Health plan administrative records versus birth certificate records: quality of race and ethnicity information in children.

Authors:  Ning Smith; Rajan L Iyer; Annette Langer-Gould; Darios T Getahun; Daniel Strickland; Steven J Jacobsen; Wansu Chen; Stephen F Derose; Corinna Koebnick
Journal:  BMC Health Serv Res       Date:  2010-11-23       Impact factor: 2.655
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  12 in total

1.  When Race/Ethnicity Data Are Lacking: Using Advanced Indirect Estimation Methods to Measure Disparities.

Authors:  Allen Fremont; Joel S Weissman; Emily Hoch; Marc N Elliott
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2.  Improving Disparity Research by Imputing Missing Data in Health Care Records.

Authors:  William Rhodes
Journal:  Health Serv Res       Date:  2015-08       Impact factor: 3.402

3.  Imputation of race/ethnicity to enable measurement of HEDIS performance by race/ethnicity.

Authors:  Ann Haas; Marc N Elliott; Jacob W Dembosky; John L Adams; Shondelle M Wilson-Frederick; Joshua S Mallett; Sarah Gaillot; Samuel C Haffer; Amelia M Haviland
Journal:  Health Serv Res       Date:  2018-12-03       Impact factor: 3.402

4.  Enrichment sampling for a multi-site patient survey using electronic health records and census data.

Authors:  Nathaniel D Mercaldo; Kyle B Brothers; David S Carrell; Ellen W Clayton; John J Connolly; Ingrid A Holm; Carol R Horowitz; Gail P Jarvik; Terrie E Kitchner; Rongling Li; Catherine A McCarty; Jennifer B McCormick; Valerie D McManus; Melanie F Myers; Joshua J Pankratz; Martha J Shrubsole; Maureen E Smith; Sarah C Stallings; Janet L Williams; Jonathan S Schildcrout
Journal:  J Am Med Inform Assoc       Date:  2019-03-01       Impact factor: 4.497

5.  Multiple Imputation of Missing Race and Ethnicity in CDC COVID-19 Case-Level Surveillance Data.

Authors:  Guangyu Zhang; Charles E Rose; Yujia Zhang; Rui Li; Florence C Lee; Greta Massetti; Laura E Adams
Journal:  Int J Stat Med Res       Date:  2022-01-28

6.  Diagnosis and Medication Treatment of Pediatric Hypertension: A Retrospective Cohort Study.

Authors:  David C Kaelber; Weiwei Liu; Michelle Ross; A Russell Localio; Janeen B Leon; Wilson D Pace; Richard C Wasserman; Alexander G Fiks
Journal:  Pediatrics       Date:  2016-12       Impact factor: 7.124

7.  Missing data in primary care research: importance, implications and approaches.

Authors:  Miguel Marino; Jennifer Lucas; Emile Latour; John D Heintzman
Journal:  Fam Pract       Date:  2021-03-29       Impact factor: 2.267

8.  From the clinic to the community: Can health system data accurately estimate population obesity prevalence?

Authors:  Stephen J Mooney; Lin Song; Adam Drewnowski; James Buskiewicz; Sean D Mooney; Brian E Saelens; David E Arterburn
Journal:  Obesity (Silver Spring)       Date:  2021-10-04       Impact factor: 5.002

9.  Assessment of Social Risk Factors and Interest in Receiving Health Care-Based Social Assistance Among Adult Patients and Adult Caregivers of Pediatric Patients.

Authors:  Emilia H De Marchis; Danielle Hessler; Caroline Fichtenberg; Eric W Fleegler; Amy G Huebschmann; Cheryl R Clark; Alicia J Cohen; Elena Byhoff; Mark J Ommerborn; Nancy Adler; Laura M Gottlieb
Journal:  JAMA Netw Open       Date:  2020-10-01

10.  RIDDLE: Race and ethnicity Imputation from Disease history with Deep LEarning.

Authors:  Ji-Sung Kim; Xin Gao; Andrey Rzhetsky
Journal:  PLoS Comput Biol       Date:  2018-04-26       Impact factor: 4.475
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