Using Bayesian imputation to assess racial and ethnic disparities in pediatric performance measures
Open accessAbstract
Objective: To analyze health care disparities in pediatric quality of care measures and determine the impact of data imputation. Data Sources: Five HEDIS measures are calculated based on 2012 administrative data for 145,652 children in two public insurance programs in Florida. Methods: The Bayesian Improved Surname and Geocoding (BISG) imputation method is used to impute missing race and ethnicity data for 42 percent of the sample (61,954 children). Models are estimated with and without the imputed race and ethnicity data. Principal Findings: Dropping individuals with missing race and ethnicity data biases quality of care measures for minorities downward relative to nonminority children for several measures. Conclusions: These results provide further support for the importance of appropriately accounting for missing race and ethnicity data through imputation methods.
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Ten of these (66%) identified that the incomplete data led to validity issues, most commonly that these data were not missing at random and had the potential to introduce bias.
“Ten of these (66%) identified that the incomplete data led to validity issues, most commonly that these data were not missing at random and had the potential to introduced bias.12,34,43–50”
From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [50]
The Discussion reports that dropping records with missing race and ethnicity can bias disparity estimates, supporting the concern about validity and bias. The Summary Statistics section also finds differences between children with and without reported race and ethnicity, but the paper does not establish that the data were ‘not missing at random.’ The ten-study (66%) figure is the citing authors’ tally, not a result attributed to this individual paper. (LLM, conf=0.83)
Experimental — accuracy not yet measuredThree studies in the review found that casewise deletion decreased the quality of race/ethnicity data.
“However, 3 studies in our review found that casewise deletion decreased the quality of race/ethnicity data.23,50,65”
From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [50]
The HEDIS Analysis and Discussion report that dropping cases with missing race or ethnicity data biased disparity estimates and sometimes reduced their statistical significance. That supports the concern about casewise deletion, but the paper does not show that deletion decreased the quality of the race/ethnicity data themselves. The count of three studies describes the citing authors’ review, not a finding attributed to this paper. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe most widely researched imputation method was Bayesian Improved Surname Geocoding (BISG).
“The most widely researched imputation method was Bayesian Improved Surname Geocoding (BISG).22,45,46,50,65”
From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [50]
The Abstract and “Imputation of Race and Ethnicity” section show that this paper studies and uses BISG, so it supports citing the paper as an example of BISG research. Neither section establishes that BISG was the most widely researched imputation method relative to others. (LLM, conf=0.84)
Experimental — accuracy not yet measuredBrown et al found that race and ethnicity coefficient estimates are often biased downwards either toward zero or more negative when data with missing race and ethnicity is dropped.
“Brown et al50 found that their “race and ethnicity coefficient estimates are often biased downwards either toward zero or more negative when data with missing race and ethnicity is dropped.””
From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [50]
Several studies looked at the use of imputation to increase the completeness of datasets and avoid casewise deletion.
“Several studies looked at the use of imputation, also referred to as indirect estimation, to increase the completeness of datasets and avoid casewise deletion.14,22,30,45,46,50,61,65,67–70”
From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [50]
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Cites
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Research works printed but not linked: 11
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Cited by
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The quality of social determinants data in the electronic health record: a systematic review
Cook, L. A., Sachs, J., & Weiskopf, N. G. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab199 · Cited by 153 · see citing works
Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination
Kallus, N., Mao, X., & Zhou, A. (2019) · arXiv · doi:10.48550/arxiv.1906.00285 · Cited by 28 · see citing works
- Authors
- Brown, D. P., Knapp, C., Baker, K., & Kaufmann, M.
- Affiliations
- University of Alberta (CA)
- Pennsylvania State University (US)
- University of Florida Health (US)
- Corresponding author
- Kaufmann, Meggen
- ORCID
- Brown, David P.
- Venue
- Health Services Research
- Publisher
- Wiley
- Published
- 2015-10-20
- Volume
- 51
- Issue
- 3
- Pages
- 1095-1108
- ISSN
- 0017-9124 (ISSN-L)
- 1475-6773
- PMC
- PMC4874818
- OpenAlex
- W2153186701
- Cited by
- 27
- References
- 1
- Citations by year
- 2025: 2 · 2024: 4 · 2023: 8 · 2022: 4 · 2021: 3 · 2018: 4 · 2016: 1 · 2014: 1
- FWCI
- 3.18
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Health Services Research (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- ERA: Education and Research Archive (University of Alberta) (cc-by)
- Funders
- Institute for Child Health Policy at the University of Florida
- Topics
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Social Sciences › Health › Health disparities and outcomes
- Health Sciences › Health Professions › Health Information Management › Healthcare Quality and Management
- Keywords
- racial and ethnic disparities, HEDIS measures, missing data imputation, children
- MeSH terms
- Adolescent, Bayes Theorem, Child, Child, Preschool, Ethnicity, Florida, Humans, Infant, Insurance Claim Review, Pediatrics, Quality of Health Care, United States, Quality Indicators, Health Care, Benchmarking, Racial Groups, Healthcare Disparities, Young Adult, Geographic Mapping
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