Dataset Information

Validation of Self-Reported Race in a Canadian Provincial Renal Administrative Database.

ABSTRACT: Background:Administrative 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. Objectives:Validation 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. Design:Prospective patient survey study to validate SRR in PROMIS. Setting:British Columbia, Canada. Patients:Adult patients registered in PROMIS. Measurements:Survey SRR was used as gold standard to validate SRR in PROMIS. Self-reported race in PROMIS was compared with census race categories. Methods:This 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). Results:A 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). Limitations:Analysis of minority groups and lower prevalence groups is limited by sample size. Results may not be generalizable to other administrative databases. Conclusions:We 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.

SUBMITTER: Waheed A

PROVIDER: S-EPMC6604118 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Validation of Self-Reported Race in a Canadian Provincial Renal Administrative Database.

Waheed Aiza A Djurdjev Ognjenka O Dong Jianghu J Gill Jagbir J Barbour Sean S

Canadian journal of kidney health and disease 20190701

<h4>Background</h4>Administrative 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.<h4>Objectives</h4>Validation of self-reported race (SRR) in a Canadian provincial renal administrative database (Patient Records and Outcome Management Information System [PROMIS]) and comparison w ...[more]

PMID: 31308951

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Project description:BackgroundStatutory State-based cancer registries are considered the 'gold standard' for researchers identifying cancer cases in Australia, but research using self-report or administrative health datasets (e.g. hospital records) may not have linkage to a Cancer Registry and need to identify cases. This study investigated the validity of administrative and self-reported data compared with records in a State-wide Cancer Registry in identifying invasive breast cancer cases.MethodsCases of invasive breast cancer recorded on the New South Wales (NSW) Cancer Registry between July 2004 and December 2008 (the study period) were identified for women in the 45 and Up Study. Registry cases were separately compared with suspected cases ascertained from: i) administrative hospital separations records; ii) outpatient medical service claims; iii) prescription medicines claims; and iv) the 45 and Up Study baseline survey. Ascertainment flags included diagnosis codes, surgeries (e.g. lumpectomy), services (e.g. radiotherapy), and medicines used for breast cancer, as well as self-reported diagnosis. Positive predictive value (PPV), sensitivity and specificity were calculated for flags within individual datasets, and for combinations of flags across multiple datasets.ResultsOf 143,010 women in the 45 and Up Study, 2039 (1.4%) had an invasive breast tumour recorded on the NSW Cancer Registry during the study period. All of the breast cancer flags examined had high specificity (>97.5%). Of the flags from individual datasets, hospital-derived 'lumpectomy and diagnosis of invasive breast cancer' and '(lumpectomy or mastectomy) and diagnosis of invasive breast cancer' had the greatest PPV (89% and 88%, respectively); the later having greater sensitivity (59% and 82%, respectively). The flag with the highest sensitivity and PPV ≥ 85% was 'diagnosis of invasive breast cancer' (both 86%). Self-reported breast cancer diagnosis had a PPV of 50% and sensitivity of 85%, and breast radiotherapy had a PPV of 73% and a sensitivity of 58% compared with Cancer Registry records. The combination of flags with the greatest PPV and sensitivity was '(lumpectomy or mastectomy) and (diagnosis of invasive breast cancer or breast radiotherapy)' (PPV and sensitivity 83%).ConclusionsIn the absence of Cancer Registry data, administrative and self-reported data can be used to accurately identify cases of invasive breast cancer for sample identification, removing cases from a sample, or risk adjustment. Invasive breast cancer can be accurately identified using hospital-derived diagnosis alone or in combination with surgeries and breast radiotherapy.

Dataset Information

Validation of Self-Reported Race in a Canadian Provincial Renal Administrative Database.

Publications

Validation of Self-Reported Race in a Canadian Provincial Renal Administrative Database.

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