An approach to identifying incident breast cancer cases using Medicare claims data

PURPOSE: There is little population-based information available on the use of chemotherapy in women with breast cancer. This study describes the use of chemotherapy through analysis of Medicare claims and determines the correlates of chemotherapy use. PATIENTS AND METHODS: We used the merged Surveillance, Epidemiology, and End Results–Medicare database and identified women ≥ 65 years of age diagnosed with breast cancer in 1991 and 1992. Chemotherapy was ascertained from Medicare claims through procedure codes for chemotherapy made within 24 months of the diagnosis. RESULTS: In women with stages I, II, III, and IV breast cancer, the percentage receiving chemotherapy within 24 months of diagnosis was 5.1%, 19.5%, 33.9%, and 35.2%, respectively. Most women receiving chemotherapy had two to 12 claims; the median number was eight. Use of chemotherapy decreased significantly with age across all tumor stages; eg, in women with stage III cancer, the use of chemotherapy declined from 49% in those aged 65 to 69 years to 10% in those ≥ 80 years old. In a multivariate analysis, there was little variation by ethnicity. Chemotherapy use was highest (70%) in women aged 65 to 69 years with node-positive and estrogen receptor–negative tumors and lowest (5%) in those with node-negative and estrogen receptor–positive tumors. Compared with those without comorbid diseases, patients with a comorbidity score of 2 had significantly lower use of chemotherapy. CONCLUSION: Medicare claims data seem to provide valuable information on the use of chemotherapy for breast cancer in older women. However, external validation of the accuracy and completeness of these data is required before any firm conclusion can be drawn.

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Medicare claims data identify hospital-acquired catheter-associated urinary tract infections with limited accuracy

PsycEXTRA Dataset ◽

10.1037/e504412010-006 ◽

2009 ◽

Keyword(s):

Urinary Tract ◽

Urinary Tract Infections ◽

Claims Data ◽

Medicare Claims ◽

Tract Infections ◽

Hospital Acquired ◽

Medicare Claims Data ◽

Limited Accuracy

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PC202. Patients More Likely to Die or Have a Stroke Following Carotid Artery Stenting vs Carotid Endarterectomy: Comparing 11-year Survival and Postoperative Stroke Using Vascular Quality Initiative Data Matched to Medicare Claims Data

Journal of Vascular Surgery ◽

10.1016/j.jvs.2016.03.360 ◽

2016 ◽

Vol 63 (6) ◽

pp. 215S

Author(s):

Adrienne Faerber ◽

Yunjie Song ◽

Andrew W. Hoel ◽

Philip P. Goodney

Keyword(s):

Carotid Artery ◽

Carotid Endarterectomy ◽

Carotid Artery Stenting ◽

Claims Data ◽

Medicare Claims ◽

Postoperative Stroke ◽

Medicare Claims Data

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PCN194 Comparing Complex Machine Learning Models to Predict Likelihood of Ovarian Cancer Using Medicare Claims DATA

Value in Health ◽

10.1016/j.jval.2021.04.285 ◽

2021 ◽

Vol 24 ◽

pp. S56

Author(s):

M. Zhang ◽

V. Mansfield

Keyword(s):

Machine Learning ◽

Ovarian Cancer ◽

Claims Data ◽

Learning Models ◽

Medicare Claims ◽

Medicare Claims Data ◽

Machine Learning Models

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Linking Provider Specialty and Outpatient Diagnoses in Medicare Claims Data: Data Quality Implications

Applied Clinical Informatics ◽

10.1055/s-0041-1732404 ◽

2021 ◽

Vol 12 (04) ◽

pp. 729-736

Author(s):

Vojtech Huser ◽

Nick D. Williams ◽

Craig S. Mayer

Keyword(s):

Data Quality ◽

Claims Data ◽

Medical Toxicology ◽

Health Care Research ◽

Real World Data ◽

Full Spectrum ◽

Data Completeness ◽

Medicare Claims ◽

Diagnosis Codes ◽

Medicare Claims Data

Abstract Background With increasing use of real world data in observational health care research, data quality assessment of these data is equally gaining in importance. Electronic health record (EHR) or claims datasets can differ significantly in the spectrum of care covered by the data. Objective In our study, we link provider specialty with diagnoses (encoded in International Classification of Diseases) with a motivation to characterize data completeness. Methods We develop a set of measures that determine diagnostic span of a specialty (how many distinct diagnosis codes are generated by a specialty) and specialty span of a diagnosis (how many specialties diagnose a given condition). We also analyze ranked lists for both measures. As use case, we apply these measures to outpatient Medicare claims data from 2016 (3.5 billion diagnosis–specialty pairs). We analyze 82 distinct specialties present in Medicare claims (using Medicare list of specialties derived from level III Healthcare Provider Taxonomy Codes). Results A typical specialty diagnoses on average 4,046 distinct diagnosis codes. It can range from 33 codes for medical toxicology to 25,475 codes for internal medicine. Specialties with large visit volume tend to have large diagnostic span. Median specialty span of a diagnosis code is 8 specialties with a range from 1 to 82 specialties. In total, 13.5% of all observed diagnoses are generated exclusively by a single specialty. Quantitative cumulative rankings reveal that some diagnosis codes can be dominated by few specialties. Using such diagnoses in cohort or outcome definitions may thus be vulnerable to incomplete specialty coverage of a given dataset. Conclusion We propose specialty fingerprinting as a method to assess data completeness component of data quality. Datasets covering a full spectrum of care can be used to generate reference benchmark data that can quantify relative importance of a specialty in constructing diagnostic history elements of computable phenotype definitions.

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