Abstract
BackgroundCluster 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.MethodsA 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.ResultsA 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.ConclusionsThe 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.
Highlights
Cluster analysis (CA) is a frequently used applied statistical technique that helps to reveal hidden structures and “clusters” found in large data sets
CA has been widely used in varied applications including finding a true typology, prediction based on groups, hypothesis generation, data exploration, and data reductionor grouping similar entities into homogeneous classes, organizing large quantities of information and enabling labels that facilitate communication [1, 4, 5]
Numerous specific examples of the use of CA have been reported in the literature, such as characterizing psychiatric patients on the basis of clusters of symptoms [6]; finding a group of genes that have similar biological functions [7]; or identifying medical patient groups most in need of targeted interventions [4, 5]
Summary
Cluster analysis (CA) is a frequently used applied statistical technique that helps to reveal hidden structures and “clusters” found in large data sets. This method has not been widely used in large healthcare claims databases where the distribution of expenditure data is commonly severely skewed. Cluster analysis Cluster analysis (CA) is a statistical technique that helps reveal hidden structures by grouping entities or objects (e.g., individuals, products, locations) with similar characteristics into homogenous groups while maximizing heterogeneity across groups [1, 2]. Numerous specific examples of the use of CA have been reported in the literature, such as characterizing psychiatric patients on the basis of clusters of symptoms [6]; finding a group of genes that have similar biological functions [7]; or identifying medical patient groups most in need of targeted interventions [4, 5]
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