The HCUP SID imputation project: improving statistical inferences for health disparities research by imputing missing race data
Open accessAbstract
Objective: To identify the most appropriate imputation method for missing data in the HCUP State Inpatient Databases (SID) and assess the impact of different missing data methods on racial disparities research. Data Sources/Study Setting: HCUP SID. Study Design: A novel simulation study compared four imputation methods (random draw, hot deck, joint multiple imputation [MI], conditional MI) for missing values for multiple variables, including race, gender, admission source, median household income, and total charges. The simulation was built on real data from the SID to retain their hierarchical data structures and missing data patterns. Additional predictive information from the U.S. Census and American Hospital Association (AHA) database was incorporated into the imputation. Principal Findings: Conditional MI prediction was equivalent or superior to the best performing alternatives for all missing data structures and substantially outperformed each of the alternatives in various scenarios. Conclusions: Conditional MI substantially improved statistical inferences for racial health disparities research with the SID.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Two additional studies looked at imputing race/ethnicity data with anonymized clinical datasets.
“Finally, 2 additional studies looked at imputing race/ethnicity data with anonymized clinical datasets,14,68”
From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [14]
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 [14]
Ma et al compared 4 imputation methods and found that conditional multiple imputation substantially improved statistical inferences for racial health disparities research.
“Ma et al14 compared 4 imputation methods and found that conditional multiple imputation, “substantially improved statistical inferences for racial health disparities research.””
From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [14]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
No references could be resolved yet.
Research works printed but not linked: 31
- Andridge, R. H., and R. J. A. Little. 2009. "The Use of Sample Weights in Hot Deck Imputation." Journal of Official Statistics 25: 21-36. Figure 6: Evaluation of Post-Imputation Performance: Root Mean Square Difference (RMSD) of Coefficient Estimates for Multinomial Logistic Regres- sion for Hospital TKA Volume
- Arnold, A. M., and R. A. Kronmal. 2003. "Multiple Imputation of Baseline Data in the Cardiovascular Health Study." American Journal of Epidemiology 157: 74-84.
- Chan, M. Y., S. Malik, B. R. Hallstrom, and R. E. Hughes. 2016. "Factors Affecting Readmission Cost after Primary Total Knee Arthroplasty in Michigan." Journal of Arthroplasty 31 (6): 1179-81. doi:10.1016/j.arth.2015.11.037 · DOI
- Deyo, R. A., D. C. Cherkin, and M. A. Ciol. 1992. "Adapting a Clinical Comorbidity Index for Use with ICD-9-CM Administrative Databases." Journal of Clinical Epi- demiology 45: 613-9.
- Elliott, M. N., K. Becker, M. K. Beckett, K. Hambarsoomian, P. Pantoja, and B. Karney. 2013. "Using Indirect Estimates Based on Name and Census Tract to Improve the Efficiency of Sampling Matched Ethnic Couples from Marriage License Data." Public Opinion Quarterly 77 (1): 375-84.
- Gelman, A., and J. Hill. 2006. Data Analysis Using Regression and Multilevel/Hierarchical Models. New York: Cambridge University Press.
- Hamlat, C. A., S. Arbabi, T. D. Koepsell, R. V. Maier, G. J. Jurkovich, and F. P. Rivara. 2012. "National Variation in Outcomes and Costs for Splenic Injury and the Impact of Trauma Systems: A Population-Based Cohort Study." Annals of Surgery 255 (1): 165-70.
- Held, U., A. Kessels, J. A. Aymerich, X. Basagaña, G. Riet, K. G. M. Moons, M. A. Puhan, and International COPD Cohorts Collaboration Working Group. 2016. "Methods for Handling Missing Variables in Risk Prediction Models." American Journal of Epidemiology 184 (7): 545-51. doi:10.1093/aje/kwv346 · DOI
- Hellinger, F. J. 2004. "HIV Patients in the HCUP Database: A Study of Hospital Utilization and Costs." Inquiry 41 (1): 95-105.
- Heltshe, S. L., J. H. Lubin, S. Koutros, J. B. Coble, B. T. Ji, M. C. Alavanja, A. Blair, D. P. Sandler, C. J. Hines, K. W. Thomas, J. Barker, G. Andreotti, J. A. Hoppin, and L. E. Beane Freeman. 2012. "Using Multiple Imputation to Assign Pesticide Use for Non-Responders in the Follow-up Questionnaire in the Agricultural Health Study." Journal of Exposure Science and Environmental Epidemiology 22: 409-16.
- Honaker, J., G. King, and M. Blackwell. 2011. "Amelia II: A Program for Missing Data." Journal of Statistical Software 45 (7): 1-47.
- Institute of Medicine (IOM). 2009. Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement. Washington, DC: The National Academies Press.
- Lee, K. J., and B. C. John. 2010. "Multiple Imputation for Missing Data: Fully Condi- tional Specification Versus Multivariate Normal Imputation." American Journal of Epidemiology 171 (5): 624-32.
- Lewandowski, D., D. Kurowicka, and H. Joe. 2009. "Generating Random Correlation Matrices Based on Vines and Extended Onion Method." Journal of Multivariate Analysis 100 (9): 1989-2001.
- Little, R. J. A., and D. B. Rubin. 2003. Statistical Analysis with Missing Data. Hoboken, NJ: John Wiley & Son.
- Long, J. A., M. I. Bamba, B. Ling, and J. A. Shea. 2006. "Missing Race/Ethnicity Data in Veterans Health Administration Based Disparities Research: A Systematic Review." Journal of Health Care for the Poor and Underserved 17: 128-40.
- Madow, W. G., I. Olkin, and D. B. Rubin. 1983. Incomplete Data in Sample Surveys (Vol- ume 2): Theory and Bibliographies. New York: Academic Press.
- Mulugeta, G., Z. Yumin, A. Neal, E. G. Gregory, E. Carrae, and E. E. Leonard. 2012. "Lessons Learned in Dealing with Missing Race Data: An Empirical Investiga- tion." Journal of Biometrics & Biostatistics 3: 138.
- Pollard, D. 2002. A User's Guide to Measure Theoretic Probability. New York: Cambridge University Press.
- Raghunathan, T. E., J. M. Lepkowski, J. V. Hoewyk, and P. Solenberger. 2001. "A Mul- tivariate Technique for Multiply Imputing Missing Values Using a Sequence of Regression Models." Survey Methodology 27 (1): 85-96.
- Royston, P. 2004. "Multiple Imputation of Missing Values." Stata Journal 4 (3): 227-41.
- Rubin, D. 1987. Multiple Imputation for Nonresponse in Surveys. New York: John Wiley & Sons.
- Rubin, D. B., and R. J. A. Little. 2002. Statistical Analysis With Missing Data, 2d Edition. New York: John Wiley and Sons.
- Rubin, D. B., H. S. Stern, and V. Vehovar. 1995. "Handling 'Don't Know' Survey Responses: The Case of the Slovenian Plebiscite." Journal of the American Statisti- cal Association 90 (431): 822-8.
- Schenker, N., T. E. Raghunathan, P. L. Chiu, D. M. Makuc, G. Zhang, and A. J. Cohen. 2006. "Multiple Imputation of Missing Income Data in the National Health Interview Survey." Journal of the American Statistical Association 101 (475): 924-33.
- Su, Y. S., M. Yajima, A. E. Gelman, and J. Hill. 2011. "Multiple Imputation with Diag- nostics (mi) in R: Opening Windows into the Black box." Journal of Statistical Software 45 (2): 1-31.
- United States Department of Health and Human Services. Centers for Disease Control and Prevention. National Center for Health Statistics. 2008. National Hospital Ambulatory Medical Care Survey. ICPSR29922-v1. Available at http://doi.org/10. 3886/ICPSR29922.v1 · DOI
- Van Buuren, S., and C. G. Oudshoorn. 2011. "Multivariate Imputation by Chained Equations in R." Journal of Statistical Software 45 (3): 1-67.
- Verbeke, G., and G. Molenberghs. 2000. Linear Mixed Models for Longitudinal Data. New York: Springer.
- Vosseller, J. T., J. W. Karl, and J. K. Greisberg. 2014. "Incidence of Syndesmotic Injury." Orthopedics 37 (3): e226-9.
- Yu, L. M., A. Burton, and O. Rivero-Arias. 2007. "Evaluation of Software for Multiple Imputation of Semi-Continuous Data." Statistical Methods in Medical Research 16 (3): 243-58.
Cited by
Sorted by citations
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
- Authors
- Ma, Y., Zhang, W., Lyman, S., & Huang, Y.
- Affiliations
- George Washington University (US)
- University of Arkansas at Little Rock (US)
- Hospital for Special Surgery (US)
- Corresponding author
- Ma, Yan
- Venue
- Health Services Research
- Publisher
- Wiley
- Published
- 2017-05-04
- Volume
- 53
- Issue
- 3
- Pages
- 1870-1889
- ISSN
- 0017-9124 (ISSN-L)
- 1475-6773
- PMC
- PMC5980335
- OpenAlex
- W2611485240
- Cited by
- 29
- References
- 0
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 1 · 2023: 5 · 2022: 12 · 2021: 3 · 2020: 2 · 2019: 2
- FWCI
- 0.76
- 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)
- Funders
- Agency for Healthcare Research and Quality (R01HS21734)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- missing data imputation, racial disparities, hot deck imputation, hospital inpatient data, simulation study, statistical inference
- MeSH terms
- Computer Simulation, Data Interpretation, Statistical, Hospitals, Humans, Patient Admission, Research Design, Sex Factors, Models, Statistical, Racial Groups, Healthcare Disparities
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
