Literature DB >> 27641053

Data completeness as an unmeasured confounder in dialysis facility performance comparison with 1-year follow-up
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Jiannong Liu, Mahesh Krishnan, Jincheng Zhou, Kimberly M Nieman, Yi Peng, David T Gilbertson.   

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.
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Year:  2016        PMID: 27641053     DOI: 10.5414/CN108816

Source DB:  PubMed          Journal:  Clin Nephrol        ISSN: 0301-0430            Impact factor:   0.975


  2 in total

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

Authors:  Yi Mu; Andrew I Chin; Abhijit V Kshirsagar; Heejung Bang
Journal:  PeerJ       Date:  2018-07-27       Impact factor: 2.984

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

Authors:  Yi Mu; Andrew I Chin; Abhijit V Kshirsagar; Heejung Bang
Journal:  Inquiry       Date:  2020 Jan-Dec       Impact factor: 1.730

  2 in total

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