Identifying subgroups of complex patients with cluster analysis.
ABSTRACT To illustrate the use of cluster analysis for identifying sub-populations of complex patients who may benefit from targeted care management strategies.
Retrospective cohort analysis.
We identified a cohort of adult members of an integrated health maintenance organization who had 2 or more of 17 common chronic medical conditions and were categorized in the top 20% of total cost of care for 2 consecutive years (n = 15,480). We used agglomerative hierarchical clustering methods to identify clinically relevant subgroups based on groupings of coexisting conditions. Ward's minimum variance algorithm provided the most parsimonious solution.
Ward's algorithm identified 10 clinically relevant clusters grouped around single or multiple "anchoring conditions." The clusters revealed distinct groups of patients including: coexisting chronic pain and mental illness, obesity and mental illness, frail elderly, cancer, specific surgical procedures, cardiac disease, chronic lung disease, gastrointestinal bleeding, diabetes, and renal disease. These conditions co-occurred with multiple other chronic conditions. Mental health diagnoses were prevalent (range 28% to 100%) in all clusters.
Data mining procedures such as cluster analysis can be used to identify discrete groups of patients with specific combinations of comorbid conditions. These clusters suggest the need for a range of care management strategies. Although several of our clusters lend themselves to existing care and disease management protocols, care management for other subgroups is less well-defined. Cluster analysis methods can be leveraged to develop targeted care management interventions designed to improve health outcomes.
SourceAvailable from: Susanne Pettersson10/2012, Degree: PhD, Supervisor: Elisabet Welin Henriksson
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ABSTRACT: Background Cost containment policies and the need to satisfy patients’ health needs and care expectations provide major challenges to healthcare systems. Identification of homogeneous groups in terms of healthcare utilisation could lead to a better understanding of how to adjust healthcare provision to society and patient needs. Methods This study used data from the third wave of the SIRS cohort study, a representative, population-based, socio-epidemiological study set up in 2005 in the Paris metropolitan area, France. The data were analysed using a cross-sectional design. In 2010, 3000 individuals were interviewed in their homes. Non-conventional multivariate clustering techniques were used to determine homogeneous user groups in data. Multinomial models assessed a wide range of potential associations between user characteristics and their pattern of healthcare utilisation. Results We identified four distinct patterns of healthcare use. Patterns of consumption and the socio-demographic characteristics of users differed qualitatively and quantitatively between these four profiles. Extensive and intensive use by older, wealthier and unhealthier people contrasted with narrow and parsimonious use by younger, socially deprived people and immigrants. Rare, intermittent use by young healthy men contrasted with regular targeted use by healthy and wealthy women. Conclusion The use of an original technique of massive multivariate analysis allowed us to characterise different types of healthcare users, both in terms of resource utilisation and socio-demographic variables. This method would merit replication in different populations and healthcare systems.PLoS ONE 12/2014; 9:e115064. DOI:10.1371/journal.pone.0115064 · 3.53 Impact Factor
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ABSTRACT: Background Especially general practitioners are frequently confronted with complex health situations of patients, but knowledge of intensive forms of multimorbidity and their development during the life of patients is lacking.AimThis study explores the patterns and trajectories of chronic health problems of patients with multimorbidity particularly those with more than ten (11+) conditions and the type and variety of organ systems involved in these patterns during life.DesignObservational study.Method Life time prevalence patterns of chronic health problems were determined in patients with illness trajectories accumulating more than ten chronic health problems during life as registered by general practitioners in the South of the Netherlands in the Registration Network Family Practices (RNH).ResultsOverall 4,560 subjects (5%) were registered with more than ten chronic health problems during their life (MM11+), accounting for 61,653 (20%) of the 302,808 registered health problems in the RNH population (N¿=¿87,837 subjects). More than 30% of the patients accumulate 4 or more chronic health conditions during their lifetime (i.e. MM4-5: 4¿5 conditions (N¿=¿14,199; 16.2%); MM6-10: 6¿10 chronic conditions (N¿=¿14,365; 16.4%).Gastro-intestinal, cardiovascular, locomotor, respiratory and metabolic conditions occur more frequently in the MM11+ patients than in the other patients, while the nature and variety of body systems involved in lifetime accumulation of chronic health problem clusters is both generic and specific. Regarding chronic conditions possibly afflicting multiple sites throughout the body, the number of neoplasms seems low (N¿=¿3,592; 5.8%), but 2,461 (49%) of the 4,560 subjects have registered at least one neoplasm condition during life. A similar pattern can be noted for inflammation (N¿=¿3,537, 78%), infection (N¿=¿2,451, 54%) and injury (N¿=¿3,401, 75%).Conclusion There are many challenges facing multimorbidity research, including the implementation of a longitudinal, life-time approach from a family practice perspective. The present study, although exploratory by nature, shows that both general and specific mechanisms characterize the development of multimorbidity trajectories. A very small proportion of patients has a very high number of chronic health problems (MM11+) and keeps adding health problems in their life. However, GP¿s need to realise that more than one third of their patients accumulate four or more chronic health problems (MM4-5 and MM6-10) in their lifetime.BMC Family Practice 01/2015; 16(1):2. DOI:10.1186/s12875-014-0213-6 · 1.74 Impact Factor