Dataset Information

Challenges with quality of race and ethnicity data in observational databases.

ABSTRACT:

Objective

We sought to assess the quality of race and ethnicity information in observational health databases, including electronic health records (EHRs), and to propose patient self-recording as an improvement strategy.

Materials and methods

We assessed completeness of race and ethnicity information in large observational health databases in the United States (Healthcare Cost and Utilization Project and Optum Labs), and at a single healthcare system in New York City serving a racially and ethnically diverse population. We compared race and ethnicity data collected via administrative processes with data recorded directly by respondents via paper surveys (National Health and Nutrition Examination Survey and Hospital Consumer Assessment of Healthcare Providers and Systems). Respondent-recorded data were considered the gold standard for the collection of race and ethnicity information.

Results

Among the 160 million patients from the Healthcare Cost and Utilization Project and Optum Labs datasets, race or ethnicity was unknown for 25%. Among the 2.4 million patients in the single New York City healthcare system's EHR, race or ethnicity was unknown for 57%. However, when patients directly recorded their race and ethnicity, 86% provided clinically meaningful information, and 66% of patients reported information that was discrepant with the EHR.

Discussion

Race and ethnicity data are critical to support precision medicine initiatives and to determine healthcare disparities; however, the quality of this information in observational databases is concerning. Patient self-recording through the use of patient-facing tools can substantially increase the quality of the information while engaging patients in their health.

Conclusions

Patient self-recording may improve the completeness of race and ethnicity information.

SUBMITTER: Polubriaginof FCG

PROVIDER: S-EPMC6696496 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Challenges with quality of race and ethnicity data in observational databases.

Polubriaginof Fernanda C G FCG Ryan Patrick P Salmasian Hojjat H Shapiro Andrea Wells AW Perotte Adler A Safford Monika M MM Hripcsak George G Smith Shaun S Tatonetti Nicholas P NP Vawdrey David K DK

Journal of the American Medical Informatics Association : JAMIA 20190801 8-9

<h4>Objective</h4>We sought to assess the quality of race and ethnicity information in observational health databases, including electronic health records (EHRs), and to propose patient self-recording as an improvement strategy.<h4>Materials and methods</h4>We assessed completeness of race and ethnicity information in large observational health databases in the United States (Healthcare Cost and Utilization Project and Optum Labs), and at a single healthcare system in New York City serving a rac ...[more]

PMID: 31365089

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Project description:BackgroundRacial and ethnic disparities in infant-feeding practices may negatively influence diet quality and health.ObjectivesThis study investigated the racial, ethnic, and language (English or Spanish) differences in infant diet quality, later diet quality, and weight status at 2-5 y, and whether these differences were explained through infant diet quality among participants in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC).MethodsUsing the WIC Infant and Toddler Feeding Practices Study-2 (unweighted n = 2663; weighted n = 362,712), relationships between the Infant Dietary Quality Index (IDQI; range 0-1) and Healthy Eating Index-2020 (HEI-2020; range 0-100) and BMI z-score (BMIz) at 2-5 y were analyzed by race, ethnicity, and language preference [Hispanic Spanish-speaking, Hispanic English-speaking, non-Hispanic (NH) White, and NH Black participants]. Statistical interaction between IDQI and each group was evaluated in multivariable models. The mediation of each group through the IDQI was assessed using causal mediation methods.ResultsDifferences in IDQI [mean (standard deviation)] were observed between Hispanic Spanish-speaking participants [0.41 (0.10)], Hispanic English-speaking participants [0.37 (0.10)], NH White participants [0.36 (0.10)], and NH Black participants [0.35 (0.09)], P < 0.001. Differences in HEI-2020 occurred at 2-5 y, with the Hispanic Spanish-speaking participants having consistently higher HEI-2020 scores. Differences in BMIz were observed at 5 y, with higher scores among Hispanic Spanish-speaking participants. Interaction between race, ethnicity, and IDQI was observed for all outcomes except for BMIz at 3 y. Through mediation, IDQI explained 13%-20% of the difference in HEI-2020 scores between Hispanic Spanish-speaking and NH White participants at 2-5 y. IDQI explained 22%-25% of the difference in HEI-2020 scores between the Hispanic Spanish-speaking and NH Black participants at 4 y and 5 y.ConclusionsHigher infant diet quality scores observed in Hispanic Spanish-speaking participants explain some of the racial and ethnic differences observed in later diet quality, suggesting that improving infant diet quality may help reduce diet disparities during early childhood.

Dataset Information

Challenges with quality of race and ethnicity data in observational databases.

Objective

Materials and methods

Results

Discussion

Conclusions

Publications

Challenges with quality of race and ethnicity data in observational databases.

Similar Datasets

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