Using Record Linkage to Improve Race Data Quality for American Indians and Alaska Natives in Two Pacific Northwest State Hospital Discharge Databases. (1st July 2015) - Bigback, Kristyn M.; Hoopes, Megan; Dankovchik, Jenine; Knaster, Elizabeth; Warren‐Mears, Victoria; Joshi, Sujata; Weiser, Thomas
Abstract : Objective: To evaluate and adjust for American Indian and Alaska Native (AI/AN) racial misclassification in two hospital discharge datasets in the Pacific Northwest. Data Sources/Study Setting: Oregon (2010‐2011) and Washington (2011) hospital discharge datasets were linked with the Northwest Tribal Registry (NTR), a registry of AI/AN individuals who accessed services at Indian health facilities in the Northwest. Study Design: Record linkage was used to match state hospital records to the NTR. A state record was considered misclassified if it matched the NTR and was coded as non‐AI/AN or missing race data. Effect of misclassification was evaluated by comparing prelinkage and postlinkage, age‐adjusted hospital discharge rates. Data Collection/Extraction Methods: Researchers used Link Plus 2.0 software (Atlanta, GA, USA) for linkages and SAS 9.4 (Cary, NC, USA) for statistical analyses. Principal Findings: In Oregon, 55.4 percent of matching records were misclassified (66.5 percent miscoded white, and 22.1 percent were missing race information). In Washington, 44.9 percent of matching records were misclassified (61.8 percent miscoded white, and 32.7 percent were missing race information). Linkage increased ascertainment of AI/AN hospitalizations by 31.8 percent in Oregon and 33.9 percent in Washington. Linkage increased the rate ratio (RR) for AI/AN hospitalizations in comparison to non‐Hispanic whites (NHW) from 0.81 to 1.07 in Oregon, and from 1.21 to 1.62 in Washington. Conclusion: Correction of race in hospital discharge datasets through linkage with a reference file of known AI/AN individuals is an important first step before analytic research on AI/AN health care in the Pacific Northwest can be accomplished with administrative datasets.