title: Data completeness as an unmeasured confounder in dialysis facility performance comparison with 1-year follow-up .
abstract: ;Aims: Standardized mortality and hospitalization ratios (SMRs, SHRs) are used to measure dialysis facility performance in the US, with adjustment for demographics and comorbid conditions derived from the end-stage renal disease (ESRD) Medical Evidence (ME) Report. Sensitivities are low for ME-based comorbidity, and levels of under-reporting may differ among facilities. We aimed to assess the effect of data inaccuracy on performance comparison.
  
  
  METHODS
  Using the United States Renal Data System ESRD database, we included patients who initiated hemodialysis July 1 - December 31 in each of the years 2006 - 2010, had Medicare as primary payer, were aged ≥ 66 years, and had no prior transplant. Patients were followed from dialysis initiation to the earliest of death, transplant, modality change, or 1 year. SMRs and SHRs were calculated for for-profit/non-profit and rural/urban facilities for ME-based and claims-based comorbidity, separately. Cox models were used for expected number of deaths and piecewise Poison models for expected number of hospitalizations. Comorbidity agreement was measured by κ-statistic. Testing of differences between ME-based and claims-based SMRs/SHRs was performed by bootstrap.
  
  
  RESULTS
  In all, 73,950 incident hemodialysis patients were included. κ-values for comorbidity agreement were low, < 0.5, except for diabetes (0.77). Percentages of claims-based comorbidity were similar for for-profit and non-profit facilities; ME-based comorbidity was lower for for-profit facilities. Differences between ME-based and claims-based SMRs/SHRs were statistically significant. Compared with ME-based SMRs/SHRs, claims-based ratios decreased 0.9/0.6% for for-profit and 1/0.7% for urban facilities and increased 3.4/2.8% for non-profit and 5.9/4.1% for rural facilities.
  
  
  CONCLUSIONS
  Comorbidity data source may affect performance evaluation. The impact is larger for smaller groups. .
authors.forename: Jiannong
authors.initials: J
authors.lastname: Liu
authors.forename: Mahesh
authors.initials: M
authors.lastname: Krishnan
authors.forename: Jincheng
authors.initials: J
authors.lastname: Zhou
authors.forename: Kimberly M
authors.initials: KM
authors.lastname: Nieman
authors.forename: Yi
authors.initials: Y
authors.lastname: Peng
authors.forename: David T
authors.initials: DT
authors.lastname: Gilbertson
mesh_terms: D000328:Adult; D000368:Aged; D000554:Ambulatory Care Facilities / Q000592:standards / Q000706:statistics & numerical data*; D015897:Comorbidity; D015986:Confounding Factors, Epidemiologic; D000068598:Data Accuracy; D016208:Databases, Factual; D005260:Female; D005500:Follow-Up Studies; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D007676:Kidney Failure, Chronic / Q000401:mortality*; D008297:Male; D006278:Medicare; D008875:Middle Aged; D016016:Proportional Hazards Models; D006435:Renal Dialysis / Q000592:standards / Q000706:statistics & numerical data*; D019035:Rural Health Services / Q000706:statistics & numerical data; D014481:United States; D019037:Urban Health Services / Q000706:statistics & numerical data