Dataset Information

Comparison of SEER Treatment Data With Medicare Claims.

ABSTRACT:

Background

The population-based Surveillance, Epidemiology, and End Results (SEER) registries collect information on first-course treatment, including surgery, chemotherapy, radiation therapy, and hormone therapy. However, the SEER program does not release data on chemotherapy or hormone therapy due to uncertainties regarding data completeness. Activities are ongoing to investigate the opportunity to supplement SEER treatment data with other data sources.

Methods

Using the linked SEER-Medicare data, we examined the validity of the SEER data to identify receipt of chemotherapy and radiation therapy among those aged 65 and older diagnosed from 2000 to 2006 with bladder, female breast, colorectal, lung, ovarian, pancreas, or prostate cancer and hormone therapy among men diagnosed with prostate cancer at age 65 or older. Treatment collected by SEER was compared with treatment as determined by Medicare claims, using Medicare claims as the gold standard. The ?, sensitivity, specificity, positive predictive values, and negative predictive values were calculated for the receipt of each treatment modality.

Results

The overall sensitivity of SEER data to identify chemotherapy, radiation, and hormone therapy receipt was moderate (68%, 80%, and 69%, respectively) and varied by cancer site, stage, and patient characteristics. The overall positive predictive value was high (>85%) for all treatment types and cancer sites except chemotherapy for prostate cancer.

Conclusions

SEER data should not generally be used for comparisons of treated and untreated individuals or to estimate the proportion of treated individuals in the population. Augmenting SEER data with other data sources will provide the most accurate treatment information.

SUBMITTER: Noone AM

PROVIDER: S-EPMC4981219 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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Publications

Comparison of SEER Treatment Data With Medicare Claims.

Noone Anne-Michelle AM Lund Jennifer L JL Mariotto Angela A Cronin Kathleen K McNeel Timothy T Deapen Dennis D Warren Joan L JL

Medical care 20160901 9

<h4>Background</h4>The population-based Surveillance, Epidemiology, and End Results (SEER) registries collect information on first-course treatment, including surgery, chemotherapy, radiation therapy, and hormone therapy. However, the SEER program does not release data on chemotherapy or hormone therapy due to uncertainties regarding data completeness. Activities are ongoing to investigate the opportunity to supplement SEER treatment data with other data sources.<h4>Methods</h4>Using the linked ...[more]

PMID: 24638121

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Project description:BackgroundWith 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.ObjectiveIn our study, we link provider specialty with diagnoses (encoded in International Classification of Diseases) with a motivation to characterize data completeness.MethodsWe 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).ResultsA 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.ConclusionWe 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.

Dataset Information

Comparison of SEER Treatment Data With Medicare Claims.

Background

Methods

Results

Conclusions

Publications

Comparison of SEER Treatment Data With Medicare Claims.

Similar Datasets

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