Dataset Information

Identifying chronic conditions in Medicare claims data: evaluating the Chronic Condition Data Warehouse algorithm.

ABSTRACT:

Objective

To examine the strengths and limitations of the Center for Medicare and Medicaid Services' Chronic Condition Data Warehouse (CCW) algorithm for identifying chronic conditions in older persons from Medicare beneficiary data.

Data sources

Records from participants of the NHANES I Epidemiologic Follow-up Study (NHEFS 1971-1992) linked to Medicare claims data from 1991 to 2000.

Study design

We estimated the percent of preexisting cases of chronic conditions correctly identified by the CCW algorithm during its reference period and the number of years of claims data necessary to find a preexisting condition.

Principal findings

The CCW algorithm identified 69 percent of preexisting diabetes cases but only 17 percent of preexisting arthritis cases. Cases identified by the CCW are a mix of preexisting and newly diagnosed conditions.

Conclusions

The prevalence of conditions needing less frequent health care utilization (e.g., arthritis) may be underestimated by the CCW algorithm. The CCW reference periods may not be sufficient for all analytic purposes.

SUBMITTER: Gorina Y

PROVIDER: S-EPMC3207195 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature

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Publications

Identifying chronic conditions in Medicare claims data: evaluating the Chronic Condition Data Warehouse algorithm.

Gorina Yelena Y Kramarow Ellen A EA

Health services research 20110607 5

<h4>Objective</h4>To examine the strengths and limitations of the Center for Medicare and Medicaid Services' Chronic Condition Data Warehouse (CCW) algorithm for identifying chronic conditions in older persons from Medicare beneficiary data.<h4>Data sources</h4>Records from participants of the NHANES I Epidemiologic Follow-up Study (NHEFS 1971-1992) linked to Medicare claims data from 1991 to 2000.<h4>Study design</h4>We estimated the percent of preexisting cases of chronic conditions correctly ...[more]

PMID: 21649659

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Project description:BackgroundThe linkage of registries to Medicare claims data can help extend follow-up for patients receiving medical devices. In the present study, we tested and validated an algorithm that does not require patient identifiers to link data from a national vascular registry and Medicare claims data.MethodsWe used data from the Vascular Quality Initiative (VQI), a registry capturing data from >600 centers for several different vascular procedures, and Medicare claims from 2003 to 2018. We restricted the study to patients aged ≥65 years who had fee-for-service entitlement at their procedure. We performed an indirect linkage to combine the VQI and Medicare data at the patient level using a sequential algorithm based on the patient's date of birth, sex, zip code, procedure date, and procedure facility. We compared the indirectly linked cohort against a reference standard of a cohort directly linked using Social Security numbers. We calculated the matching rate and accuracy overall and before and after October 2015 when the International Classification of Diseases, 10th revision (ICD-10) system was adopted in the United States.ResultsA total of 144,045 VQI-Medicare-linked patients were in the reference standard cohort. Using the indirect linking algorithm, we matched 133,966 of the 144,045 VQI patients to their Medicare claims with a matching rate of 93.0%. Of the 133,966 patients, 133,104 were correctly matched (matching accuracy, 99.4%). The matching rate was higher when the indirect linkage was implemented using the ICD-10 coded data than using the ICD-9 coded data (94.0% vs 92.2%). The accuracy of the indirect linkage remained high for all procedure modules after the ICD-10 coding change (overall, 99.4%; range, 99.0%-99.7%).ConclusionsIn the present study, we successfully used indirect identifiers to link the VQI data to Medicare claims with >90% success and >99% accuracy. When direct linkage of the registry claims data using Social Security numbers is not possible because of availability or confidentiality, or both, our algorithm for indirect linkage provides a suitable alternative. The matching rate and accuracy will help ensure the accuracy of long-term follow-up and the completeness and representativeness of linked databases for relevant research and quality improvement initiatives.

Dataset Information

Identifying chronic conditions in Medicare claims data: evaluating the Chronic Condition Data Warehouse algorithm.

Objective

Data sources

Study design

Principal findings

Conclusions

Publications

Identifying chronic conditions in Medicare claims data: evaluating the Chronic Condition Data Warehouse algorithm.

Similar Datasets

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