Dataset Information

A new method for estimating race/ethnicity and associated disparities where administrative records lack self-reported race/ethnicity.

ABSTRACT:

Objective

To efficiently estimate race/ethnicity using administrative records to facilitate health care organizations' efforts to address disparities when self-reported race/ethnicity data are unavailable.

Data source

Surname, geocoded residential address, and self-reported race/ethnicity from 1,973,362 enrollees of a national health plan.

Study design

We compare the accuracy of a Bayesian approach to combining surname and geocoded information to estimate race/ethnicity to two other indirect methods: a non-Bayesian method that combines surname and geocoded information and geocoded information alone. We assess accuracy with respect to estimating (1) individual race/ethnicity and (2) overall racial/ethnic prevalence in a population.

Principal findings

The Bayesian approach was 74 percent more efficient than geocoding alone in estimating individual race/ethnicity and 56 percent more efficient in estimating the prevalence of racial/ethnic groups, outperforming the non-Bayesian hybrid on both measures. The non-Bayesian hybrid was more efficient than geocoding alone in estimating individual race/ethnicity but less efficient with respect to prevalence (p<.05 for all differences).

Conclusions

The Bayesian Surname and Geocoding (BSG) method presented here efficiently integrates administrative data, substantially improving upon what is possible with a single source or from other hybrid methods; it offers a powerful tool that can help health care organizations address disparities until self-reported race/ethnicity data are available.

SUBMITTER: Elliott MN

PROVIDER: S-EPMC2653886 | biostudies-literature | 2008 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A new method for estimating race/ethnicity and associated disparities where administrative records lack self-reported race/ethnicity.

Elliott Marc N MN Fremont Allen A Morrison Peter A PA Pantoja Philip P Lurie Nicole N

Health services research 20080512 5 Pt 1

<h4>Objective</h4>To efficiently estimate race/ethnicity using administrative records to facilitate health care organizations' efforts to address disparities when self-reported race/ethnicity data are unavailable.<h4>Data source</h4>Surname, geocoded residential address, and self-reported race/ethnicity from 1,973,362 enrollees of a national health plan.<h4>Study design</h4>We compare the accuracy of a Bayesian approach to combining surname and geocoded information to estimate race/ethnicity to ...[more]

PMID: 18479410

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Project description:BackgroundAdministrative data are commonly used to study clinical outcomes in renal disease. Race is an important determinant of renal health delivery and outcomes in Canada but is not validated in most administrative data, and the correlation with census-based definitions of race is unknown.ObjectivesValidation of self-reported race (SRR) in a Canadian provincial renal administrative database (Patient Records and Outcome Management Information System [PROMIS]) and comparison with the Canadian census categories of race.DesignProspective patient survey study to validate SRR in PROMIS.SettingBritish Columbia, Canada.PatientsAdult patients registered in PROMIS.MeasurementsSurvey SRR was used as gold standard to validate SRR in PROMIS. Self-reported race in PROMIS was compared with census race categories.MethodsThis is a cross-sectional telephone survey of a random sample of all adults in PROMIS conducted between February 2016 and November 2016. Responders selected a race category from PROMIS and from the Canadian census. Sensitivity (Sn) and specificity (Sp) were calculated with 95% confidence intervals (CIs).ResultsA total of 21 039 patients met inclusion criteria, 1677 were selected for the survey and 637 participated (38% response rate). There were no differences between the PROMIS, sampled, and responder populations. PROMIS SRR had an accuracy of 95.3% (95% CI: 94.2%-97.0%) when validated against the survey SRR with Sn and Sp ≥90% in all race groups except in Aboriginals (Sn 87.5%). The positive and negative predictive values were ≥95%, except in very low and high-prevalence groups, respectively. The Canadian census had an accuracy of 95.7% (95% CI: 94.4%-97.6%) when validated against PROMIS SRR with Sn and Sp ≥90%. The results did not differ in subgroups based on age, sex, birth outside Canada, or renal group (glomerulonephritis, chronic kidney disease, hemodialysis, peritoneal dialysis, transplant recipients, or live donors).LimitationsAnalysis of minority groups and lower prevalence groups is limited by sample size. Results may not be generalizable to other administrative databases.ConclusionsWe have shown high accuracy of PROMIS SRR that validates its use in the secondary analysis of administrative data for research. There is high correlation between PROMIS and census race categories which allows linkage with other data sources that use census-based definitions of race.

Project description:BackgroundFamily history and African-American race are important risk factors for both prostate cancer (CaP) incidence and aggressiveness. When studying complex diseases such as CaP that have a heritable component, chances of finding true disease susceptibility alleles can be increased by accounting for genetic ancestry within the population investigated. Race, ethnicity and ancestry were studied in a geographically diverse cohort of men with newly diagnosed CaP.MethodsIndividual ancestry (IA) was estimated in the population-based North Carolina and Louisiana Prostate Cancer Project (PCaP), a cohort of 2,106 incident CaP cases (2063 with complete ethnicity information) comprising roughly equal numbers of research subjects reporting as Black/African American (AA) or European American/Caucasian/Caucasian American/White (EA) from North Carolina or Louisiana. Mean genome wide individual ancestry estimates of percent African, European and Asian were obtained and tested for differences by state and ethnicity (Cajun and/or Creole and Hispanic/Latino) using multivariate analysis of variance models. Principal components (PC) were compared to assess differences in genetic composition by self-reported race and ethnicity between and within states.ResultsMean individual ancestries differed by state for self-reporting AA (p = 0.03) and EA (p = 0.001). This geographic difference attenuated for AAs who answered "no" to all ethnicity membership questions (non-ethnic research subjects; p = 0.78) but not EA research subjects, p = 0.002. Mean ancestry estimates of self-identified AA Louisiana research subjects for each ethnic group; Cajun only, Creole only and both Cajun and Creole differed significantly from self-identified non-ethnic AA Louisiana research subjects. These ethnicity differences were not seen in those who self-identified as EA.ConclusionsMean IA differed by race between states, elucidating a potential contributing factor to these differences in AA research participants: self-reported ethnicity. Accurately accounting for genetic admixture in this cohort is essential for future analyses of the genetic and environmental contributions to CaP.

Dataset Information

A new method for estimating race/ethnicity and associated disparities where administrative records lack self-reported race/ethnicity.

Objective

Data source

Study design

Principal findings

Conclusions

Publications

A new method for estimating race/ethnicity and associated disparities where administrative records lack self-reported race/ethnicity.

Similar Datasets

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