Data concordance between ESRD Medical Evidence Report and Medicare claims: is there any improvement?

Abstract

BackgroundMedicare is one of the world’s largest health insurance programs. It provides health insurance to nearly 44 million beneficiaries whose entitlements are based on age, disability, or end-stage renal disease (ESRD). Data of these ESRD beneficiaries are collected in the US Renal Data System (USRDS), which includes comorbidity information entered at the time of dialysis initiation (medical evidence data), and are used to shape health care policy. One limitation of USRDS data is the lack of validation of these medical evidence comorbidities against other comorbidity data sources, such as medical claims data.MethodsWe examined the potential for discordance between USRDS Medical Evidence and medical claims data for 11 comorbid conditions amongst Medicare beneficiaries in 2011–2013 via sensitivity, specificity, kappa and hierarchical logistic regression.ResultsAmong 61,280 patients, most comorbid conditions recorded on the Medical Evidence forms showed high specificity (>0.9), compared to prior medical claims as reference standard. However, both sensitivity and kappa statistics varied greatly and tended to be low (most <0.5). Only diabetes appeared accurate, whereas tobacco use and drug dependence showed the poorest quality (sensitivity and kappa <0.1). Institutionalization and patient region of residency were associated with data discordance for six and five comorbidities out of 11, respectively, after conservative adjustment of multiple testing. Discordance appeared to be non-informative for congestive heart failure but was most varied for drug dependence.ConclusionsWe conclude that there is no improvement in comorbidity data quality in incident ESRD patients over the last two decades. Since these data are used in case-mix adjustment for outcome and quality of care metrics, the findings in this study should press regulators to implement measures to improve the accuracy of comorbidity data collection.

Assessing the Impacts of Misclassified Case-Mix Factors on Health Care Provider Profiling: Performance of Dialysis Facilities

“…These could serve as good future research questions. 7,8,10,38,39 The limitations of our study should be noted. First, in the simulation study, we only considered simple scenarios with limited configurations; for example, misclassifications and size constant across providers, non-differential ME, and 2 covariates.…”

“…For case-mix, we adjusted the following factors: age, sex, body mass index, primary cause and years of ESRD, duration of index hospitalization, and a total of 11 comorbidities (alcohol dependence; drug dependence; tobacco use; diabetes; cancer; chronic obstructive pulmonary disease; and cardiovascular diseases including atherosclerotic heart disease, congestive heart failure [CHF], cerebrovascular disease, peripheral vascular disease, and other cardiac). 8,10 The dialysis facility profile that used claims data prior year to dialysis initiation was regarded as the reference standard. 10 We compared it against the 2 alternative approaches using comorbid conditions captured from CMS-2728: (1) using CHF as recorded on CMS-2728, while all other conditions from claims, and (2) using all of 11 comorbidities from CMS-2728.…”

“…We chose 11 comorbidities as in previous studies on concordance of data in CMS-2728 and claims. 8,10 These 11 comorbidities on CMS-2728 can be compared with those with ICD-9 codes. The other variables such as "institutionalization" does not have ICD-9 codes.…”

“…8,9 Earlier studies have attempted to validate comorbid conditions reported on CMS-2728 versus clinical data or claims data; the results showed sensitivity <0.6, specificity >0.9, agreement and Kappa statistics <0.5. 7,8,[10][11][12] On the other hand, case-mix adjustment based on administrative claims data (compared to more reliable medical records) is generally considered suitable for profiling hospital performance. 13 In other words, using information garnered from claims data in case-mix profile development models appear to have acceptable quality.…”

Primary causes of kidney disease and mortality in dialysis-dependent children

“…Because diagnoses in the primary causes of ESRD were more accurate after the CMS 2728 was revised in 1995 [37], we limited the patients to those who started dialysis after 1995 to minimize this potential information bias. In addition, information on comorbidities ascertained by the CMS 2728 form may be misclassified [38]. Second, analyses for specific causes of ESRD had a limited statistical power due to small sample size.…”