Dataset Information

Using Record Linkage to Improve Race Data Quality for American Indians and Alaska Natives in Two Pacific Northwest State Hospital Discharge Databases.

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.

SUBMITTER: Bigback KM

PROVIDER: S-EPMC4545338 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Using Record Linkage to Improve Race Data Quality for American Indians and Alaska Natives in Two Pacific Northwest State Hospital Discharge Databases.

Bigback Kristyn M KM Hoopes Megan M Dankovchik Jenine J Knaster Elizabeth E Warren-Mears Victoria V Joshi Sujata S Weiser Thomas T

Health services research 20150701

<h4>Objective</h4>To evaluate and adjust for American Indian and Alaska Native (AI/AN) racial misclassification in two hospital discharge datasets in the Pacific Northwest.<h4>Data sources/study setting</h4>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.<h4>Study design</h4>Record linkage was used to match state hospital re ...[more]

PMID: 26133568

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Project description:Background:In the United States, the mortality burden of injury is higher among American Indians and Alaska Natives (AI/AN) than any other racial/ethnic group, and injury contributes to considerable medical costs, years of potential life lost (YPLL), and productivity loss among AI/AN.This study assessed the economic burden of injuries for AI/AN who are eligible for services through Indian Health Service, analyzing direct medical costs of injury for Indian Health Service's users and years of potential life lost (YPLL) and the value of productivity losses from injury deaths for AI/AN in the Indian Health Service population. Methods:Injury-related lifetime medical costs were estimated for Indian Health Service users with medically treated injuries using data from the 2011-2015 National Data Warehouse. Productivity costs and YPLL were estimated using data on injury-related deaths among AI/AN in Indian Health Service's 2008-2010 service population. Costs were reported in 2017?U.S. dollars. Results:The total estimated costs of injuries per year, including injuries among Indian Health Service users and productivity losses from injury-related deaths, were estimated at $4.5 billion. Lifetime medical costs to treat annual injuries among Indian Health Service users were estimated at $549 million, with the largest share ($131 million) going toward falls, the most frequent injury cause. Total estimated YPLL from AI/AN injury deaths in Indian Health Service's service population were 106,400. YPLL from injury deaths for men (74,000) were 2.2 times YPLL for women (33,000). Productivity losses from all injury-related deaths were $3.9 billion per year. The highest combined lifetime medical and mortality costs were for motor vehicle/traffic injuries, with an estimated cost of $1.6 billion per year. Conclusions:Findings suggest that targeted injury prevention efforts by Indian Health Service likely contributed to lower rates of injury among AI/AN, particularly for motor vehicle/traffic injuries. However, because of remaining disparities in injury-related outcomes between AI/AN and all races in the United States, Indian Health Service should continue to monitor changes in injury incidence and costs over time, evaluate the impacts of previous injury prevention investments on current incidence and costs, and identify additional injury prevention investment needs.

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Dataset Information

Using Record Linkage to Improve Race Data Quality for American Indians and Alaska Natives in Two Pacific Northwest State Hospital Discharge Databases.

Objective

Data sources/study setting

Study design

Data collection/extraction methods

Principal findings

Conclusion

Publications

Using Record Linkage to Improve Race Data Quality for American Indians and Alaska Natives in Two Pacific Northwest State Hospital Discharge Databases.

Similar Datasets

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