Characterization of different groups of elderly according to social engagement activity patterns

Croezen, Simone; Haveman‐Nies, Annemien; Alvarado, Víctor Jair Morales; Veer, P. van ‘t; Groot, C.P.G.M. de

doi:10.1007/s12603-009-0135-5

Cited by 12 publications

(19 citation statements)

References 18 publications

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“…Users need to pre-specify the desired number of segments K-means cluster analysis 60 1. Can deal with very large datasets [ 45 , 78 ] 2. Able to handle both continuous and categorical properties [ 79 , 80 ] 1.…”

Section: Resultsmentioning

confidence: 99%

“…Some, but not all included studies explicitly explained the choice of a specific segmentation methods over the others. For instance, Croezen et al explained K-means cluster analysis is the more suitable method when there are a large number of subjects as in their study [ 45 ]. The major advantages and disadvantages of commonly used statistical methods (used by more than 10 studies included in this systematic review) are listed in Table 3 .…”

Section: Resultsmentioning

confidence: 99%

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A systematic review of the clinical application of data-driven population segmentation analysis

Shi

Kwan

Tan

et al. 2018

BMC Med Res Methodol

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BackgroundData-driven population segmentation analysis utilizes data analytics to divide a heterogeneous population into parsimonious and relatively homogenous groups with similar healthcare characteristics. It is a promising patient-centric analysis that enables effective integrated healthcare interventions specific for each segment. Although widely applied, there is no systematic review on the clinical application of data-driven population segmentation analysis.MethodsWe carried out a systematic literature search using PubMed, Embase and Web of Science following PRISMA criteria. We included English peer-reviewed articles that applied data-driven population segmentation analysis on empirical health data. We summarized the clinical settings in which segmentation analysis was applied, compared and contrasted strengths, limitations, and practical considerations of different segmentation methods, and assessed the segmentation outcome of all included studies. The studies were assessed by two independent reviewers.ResultsWe retrieved 14,514 articles and included 216 articles. Data-driven population segmentation analysis was widely used in different clinical contexts. 163 studies examined the general population while 53 focused on specific population with certain diseases or conditions, including psychological, oncological, respiratory, cardiovascular, and gastrointestinal conditions. Variables used for segmentation in the studies are heterogeneous. Most studies (n = 170) utilized secondary data in community settings (n = 185). The most common segmentation method was latent class/profile/transition/growth analysis (n = 96) followed by K-means cluster analysis (n = 60) and hierarchical analysis (n = 50), each having its advantages, disadvantages, and practical considerations. We also identified key criteria to evaluate a segmentation framework: internal validity, external validity, identifiability/interpretability, substantiality, stability, actionability/accessibility, and parsimony.ConclusionsData-driven population segmentation has been widely applied and holds great potential in managing population health. The evaluations of segmentation outcome require the interplay of data analytics and subject matter expertise. The optimal framework for segmentation requires further research.Electronic supplementary materialThe online version of this article (10.1186/s12874-018-0584-9) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A systematic review of the clinical application of data-driven population segmentation analysis

Shi

Kwan

Tan

et al. 2018

BMC Med Res Methodol

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“…In a Dublin city sample, higher social engagement has been associated with enhanced mental and physical health, cognition and quality of life 37. Internationally, loneliness has been associated with higher mortality risk in a Finnish population sample38; while the less socially engaged were found to be less healthy in a large Dutch community sample 39. Strong cross-sectional association between social participation and disability were also found in a US community sample, but there was no evidence that participation was protective against functional decline, suggesting that findings may be more consistent with reciprocal causation, that is, functional decline causes lower participation levels 40…”

Section: Discussionmentioning

confidence: 97%

Modelling the association of disability according to the WHO International Classification of Functioning, Disability and Health (ICF) with mortality in the British Women's Heart and Health Study

Dale

Prieto‐Merino

Kuper

et al. 2011

J Epidemiol Community Health

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BackgroundThe WHO International Classification of Functioning, Disability and Health (ICF) is now the dominant model for exploring the social consequences of a health condition. This paper investigates the association of the different ICF disability domains with mortality. Methods Data are from the British Women Heart and Health Study, a large (n¼4157) prospectively studied cohort of women randomly selected from 23 towns aged 64e83 years in 2003. Scores were calculated to describe the cumulative load of impairments, limitations and restrictions within each ICF domain. Cox proportional hazards regression was used to calculate mortality HRs per unit score increase within each ICF domain. Adjustments were made for age, town, living status, socioeconomic status, lifestyle behaviours and health conditions. Results Each ICF domain was associated with mortality after controlling for lifestyle factors and health conditions. However, only complex activities (HR¼1.09, 95% CI 1.01 to 1.18) and participation (HR¼1.10, 95% CI 1.04 to 1.16) were independent predictors of mortality following adjustment for all other disability domains and potential confounders. Conclusions Results suggest that difficulties with complex activities or social participation could be used to identify and target women at high risk of dying. Interventions to facilitate complex activities or improve social participation may help to delay mortality in elderly women.

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“…This method is efficient and can handle large datasets. (12) Hierarchical cluster analysis was conducted by selecting 10 random population subsets of size 3,000 and calculating the pseudo F-statistic defined by Calinski and Harabasz. (13) This approach assessed the cluster tightness for increasing cluster size (2 to 20) by comparing the mean sum of squares between groups to that within groups.…”

Section: Cluster Analysesmentioning

confidence: 99%

Can cluster analyses of linked healthcare data identify unique population segments in a General Practice-registered population?

Nnoaham

Cann

2019

Preprint

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Background Population segmentation is useful for understanding the health needs of populations. Expert-driven segmentation has limitations which are theoretically overcome by more data-driven approaches. Previous explorations of utilisation-based cluster analysis for segmentation demonstrated feasibility but were limited in potential usefulness for local service planning. This study explores the potential for using utilisation-based cluster analyses to segment a local General Practice-registered population in the South Wales Valleys. Methods Primary and secondary care datasets were linked to create a database of 79,607 patients including socio-demographic variables, morbidities, care utilisation, cost and risk factor information. We used k-means cluster analysis to group the population into segments with distinct healthcare utilisation patterns based on seven utilisation variables: elective inpatient admissions, non-elective inpatient admissions, outpatient first & follow-up attendances, Emergency Department visits, GP practice visits and prescriptions. We analysed segments post-hoc to understand their morbidity, risk and demographic profiles Results Ten population segments were identified which had distinct profiles of healthcare use, morbidity, demographic characteristics and risk attributes. Although half of the study population were in segments characterised as ‘low need’ populations, there was heterogeneity in this group with respect to variables relevant to service planning – e.g. settings in which care was mostly consumed. Significant and complex healthcare need was a feature across age groups and was driven more by deprivation and behavioural risk factors than by age and functional limitation. Conclusions This analysis shows that cluster analysis of linked primary and secondary healthcare use data for a local GP-registered population can segment the population into distinct groups with unique health and care needs, providing useful intelligence to inform local population health service planning and care delivery. More detailed understanding of the health and care priorities of population groups could drive integration of health and care, reducing fragmentation and reducing healthcare costs. It could also inform a comprehensive approach to bridging population healthcare quality gaps and strengthening existing value-based healthcare frameworks.

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Characterization of different groups of elderly according to social engagement activity patterns

Cited by 12 publications

References 18 publications

A systematic review of the clinical application of data-driven population segmentation analysis

A systematic review of the clinical application of data-driven population segmentation analysis

Modelling the association of disability according to the WHO International Classification of Functioning, Disability and Health (ICF) with mortality in the British Women's Heart and Health Study

Can cluster analyses of linked healthcare data identify unique population segments in a General Practice-registered population?

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