Dataset Information

Cancer diagnoses after living kidney donation: linking U.S. Registry data and administrative claims.

ABSTRACT: Mortality records identify cancer as the leading cause of death among living kidney donors, but information on the burden of cancer outside death records is limited in this population.We examined a database wherein U.S. Organ Procurement and Transplantation Network identifiers for 4,650 living kidney donors in 1987 to 2007 were linked to administrative data of a U.S. private health insurer (2000-2007 claims) to identify postdonation cancer diagnoses. Skin cancer and non-skin cancer diagnoses were ascertained from International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis codes on billing claims. Donors were also matched one-to-one with general insurance beneficiaries by sex and age when benefits began. Diagnosis rates within observation windows were compared as rate ratios.The median time from donation to the end of plan insurance enrollment was 7.7 years, with a median observation period of 2.1 years. Skin cancer rates were similar among prior living donors in the observation period and nondonor controls (rate ratio, 0.91; 95% confidence interval [CI], 0.59-1.40). In contrast, the rate of total non-skin cancers was significantly less common among donors than among controls (rate ratio, 0.74; 95% CI, 0.55-0.99), although reduced relative risk was limited to donors captured earlier in relation to donation. Several cases of cancer diagnosis (uterine, melanoma, "other") were identified within the first year after donation. Prostate cancer diagnosis was significantly more common among living donors compared with controls (rate ratio, 3.80; 95% CI, 1.42-10.2).Continued study of cancer after kidney donation is warranted to ensure that evaluation, selection, and long-term follow-up support overall good health of the donor.

SUBMITTER: Lentine KL

PROVIDER: S-EPMC4447490 | biostudies-literature | 2012 Jul

REPOSITORIES: biostudies-literature

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Cancer diagnoses after living kidney donation: linking U.S. Registry data and administrative claims.

Lentine Krista L KL Vijayan Anitha A Xiao Huiling H Schnitzler Mark A MA Davis Connie L CL Garg Amit X AX Axelrod David D Abbott Kevin C KC Brennan Daniel C DC

Transplantation 20120701 2

<h4>Background</h4>Mortality records identify cancer as the leading cause of death among living kidney donors, but information on the burden of cancer outside death records is limited in this population.<h4>Methods</h4>We examined a database wherein U.S. Organ Procurement and Transplantation Network identifiers for 4,650 living kidney donors in 1987 to 2007 were linked to administrative data of a U.S. private health insurer (2000-2007 claims) to identify postdonation cancer diagnoses. Skin cance ...[more]

PMID: 22825543

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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

Cancer diagnoses after living kidney donation: linking U.S. Registry data and administrative claims.

Publications

Cancer diagnoses after living kidney donation: linking U.S. Registry data and administrative claims.

Similar Datasets

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