Validity of claims-based algorithms for selected cancers in Japan: Results from the VALIDATE-J study

Purpose

Real‐world data from large administrative claims databases in Japan have recently become available, but limited evidence exists to support their validity. VALIDATE‐J validated claims‐based algorithms for selected cancers in Japan.

Methods

VALIDATE‐J was a multicenter, cross‐sectional, retrospective study. Disease‐identifying algorithms were used to identify cancers diagnosed between January or March 2012 and December 2016 using claims data from two hospitals in Japan. Positive predictive values (PPVs), specificity, and sensitivity were calculated for prevalent (regardless of baseline cancer‐free period) and incident (12‐month cancer‐free period; with claims and registry periods in the same month) cases, using hospital cancer registry data as gold standard.

Results

22 108 cancers were identified in the hospital claims databases. PPVs (number of registry cases) for prevalent/incident cases were: any malignancy 79.0% (25 934)/73.1% (18 119); colorectal 84.4% (3519)/65.6% (2340); gastric 87.4% (3534)/76.8% (2279); lung 88.1% (2066)/79.9% (1636); breast 86.4% (4959)/59.9% (3185); pancreatic 87.1% (582)/80.4% (508); melanoma 48.7% (46)/42.9% (36); and lymphoma 83.6% (1457)/77.8% (1035). Specificity ranged from 98.3% to 100% (prevalent)/99.5% to 100% (incident); sensitivity ranged from 39.1% to 67.6% (prevalent)/12.5% to 31.4% (incident). PPVs of claims‐based algorithms for several cancers in patients ≥66 years of age were slightly higher than those in a US Medicare population.

Conclusions

VALIDATE‐J demonstrated high specificity and modest‐to‐moderate sensitivity for claims‐based algorithms of most malignancies using Japanese claims data. Use of claims‐based algorithms will enable identification of patient populations from claims databases, while avoiding direct patient identification. Further research is needed to confirm the generalizability of our results and applicability to specific subgroups of patient populations.

Improving medical record retrieval for validation studies in Medicare data

PURPOSE

The purpose of the study is to describe medical record retrieval for a study validating claims-based algorithms used to identify seven adverse events of special interest (AESI) in a Medicare population.

METHODS

We analyzed 2010-2011 Medicare claims of women with postmenopausal osteoporosis and men ≥65 years of age in the Medicare 5% national sample. The final cohorts included beneficiaries covered continuously for 12+ months by Medicare parts A, B, and D and not enrolled in Medicare Advantage before starting follow-up. We identified beneficiaries using each AESI algorithm and randomly selected 400 women and 100 men with each AESI for medical record retrieval. The Centers for Medicare and Medicaid Services provided beneficiary contact information, and we requested medical records directly from providers, without patient contact.

RESULTS

We selected 3331 beneficiaries (women: 2272; men: 559) for whom we requested 3625 medical records. Overall, we received 1738 [47.9% (95%CI 46.3%, 49.6%)] of the requested medical records. We observed small differences in the characteristics of the total population with AESIs compared with those randomly selected for retrieval; however, no differences were seen between those selected and those retrieved. We retrieved 54.7% of records requested from hospitals compared with 26.3% of records requested from physician offices (p < 0.001). Retrieval did not differ by sex or vital status of the beneficiaries.

CONCLUSIONS

Our national medical record validation study of claims-based algorithms produced a modest retrieval rate. The medical record procedures outlined in this paper could have led to the improved retrieval from our previous medical record retrieval study. Copyright © 2016 John Wiley & Sons, Ltd.

Predicting Disability among Community-Dwelling Medicare Beneficiaries Using Claims-Based Indicators

OBJECTIVES

To assess the feasibility of using existing claims-based algorithms to identify community-dwelling Medicare beneficiaries with disability based solely on the conditions for which they are being treated, and improving on these algorithms by combining them in predictive models.

DATA SOURCE

Data on 12,415 community-dwelling fee-for-service Medicare beneficiaries who first responded to the Medicare Current Beneficiary Survey (MCBS) in 2003-2006.

STUDY DESIGN

Logistic regression models in which six claims-based disability indicators are used to predict self-reported disability. Receiver operating characteristic (ROC) curves were used to assess the performance of the predictive models.

PRINCIPAL FINDINGS

The predictive performance of the regression-based models is better than that of the individual claims-based indicators. At a predicted probability threshold chosen to maximize the sum of sensitivity and specificity, sensitivity is 0.72 for beneficiaries age 65 or older and specificity is 0.65. For those under 65, sensitivity is 0.54 and specificity is 0.67. The findings also suggest ways to improve predictive performance for specific disability populations of interest to researchers.

CONCLUSIONS

Predictive models that incorporate multiple claims-based indicators provide an improved tool for researchers seeking to identify people with disabilities in claims data.