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01.12.2016 | Research article | Ausgabe 1/2016 Open Access

BMC Nephrology 1/2016

Cluster analysis and its application to healthcare claims data: a study of end-stage renal disease patients who initiated hemodialysis

Zeitschrift:
BMC Nephrology > Ausgabe 1/2016
Autoren:
Minlei Liao, Yunfeng Li, Farid Kianifard, Engels Obi, Stephen Arcona
Wichtige Hinweise

Competing interests

ML is an Analyst at KMK Consulting Inc. and works as a consultant for Novartis Pharmaceuticals Corporation. YL, FK, and SA are employees of Novartis Pharmaceuticals Corporation. EO is an Outcomes Research Fellow at Novartis Pharmaceuticals Corporation and a Post-Doctoral Research Associate at the Institute for Health Outcomes, Policy, and Economics, Rutgers University, Piscataway, NJ. ML, YL, FK, SA, and EO have made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data; have been involved in drafting the manuscript or revising it critically for important intellectual content; have given final approval of the version to be published; and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Funding for this project was provided by Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA. Publication of the study results was not contingent upon sponsor’s approval.

Authors’ contributions

All listed authors met the criteria for authorship set for by the International Committee for Medical Journal Editors (ICMJE). ML, YL, FK, EO, and SA participated in study’s conception and design; ML and YL handled the database, collected and analyzed the data. ML, YL, FK, EO, and SA drafted the article, and interpreted the data. ML, YL, FK, EO, and SA revised it critically for important intellectual content and gave final approval. All authors read and approved the final manuscript.

Abstract

Background

Cluster analysis (CA) is a frequently used applied statistical technique that helps to reveal hidden structures and “clusters” found in large data sets. However, this method has not been widely used in large healthcare claims databases where the distribution of expenditure data is commonly severely skewed. The purpose of this study was to identify cost change patterns of patients with end-stage renal disease (ESRD) who initiated hemodialysis (HD) by applying different clustering methods.

Methods

A retrospective, cross-sectional, observational study was conducted using the Truven Health MarketScan® Research Databases. Patients aged ≥18 years with ≥2 ESRD diagnoses who initiated HD between 2008 and 2010 were included. The K-means CA method and hierarchical CA with various linkage methods were applied to all-cause costs within baseline (12-months pre-HD) and follow-up periods (12-months post-HD) to identify clusters. Demographic, clinical, and cost information was extracted from both periods, and then examined by cluster.

Results

A total of 18,380 patients were identified. Meaningful all-cause cost clusters were generated using K-means CA and hierarchical CA with either flexible beta or Ward’s methods. Based on cluster sample sizes and change of cost patterns, the K-means CA method and 4 clusters were selected: Cluster 1: Average to High (n = 113); Cluster 2: Very High to High (n = 89); Cluster 3: Average to Average (n = 16,624); or Cluster 4: Increasing Costs, High at Both Points (n = 1554). Median cost changes in the 12-month pre-HD and post-HD periods increased from $185,070 to $884,605 for Cluster 1 (Average to High), decreased from $910,930 to $157,997 for Cluster 2 (Very High to High), were relatively stable and remained low from $15,168 to $13,026 for Cluster 3 (Average to Average), and increased from $57,909 to $193,140 for Cluster 4 (Increasing Costs, High at Both Points). Relatively stable costs after starting HD were associated with more stable scores on comorbidity index scores from the pre-and post-HD periods, while increasing costs were associated with more sharply increasing comorbidity scores.

Conclusions

The K-means CA method appeared to be the most appropriate in healthcare claims data with highly skewed cost information when taking into account both change of cost patterns and sample size in the smallest cluster.
Literatur
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