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Applying Multivariate Clustering Techniques to Health Data: The 4 Types of Healthcare Utilization in the Paris Metropolitan Area

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  • Thomas Lefèvre
  • Claire Rondet
  • Isabelle Parizot
  • Pierre Chauvin

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.

Suggested Citation

  • Thomas Lefèvre & Claire Rondet & Isabelle Parizot & Pierre Chauvin, 2014. "Applying Multivariate Clustering Techniques to Health Data: The 4 Types of Healthcare Utilization in the Paris Metropolitan Area," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-20, December.
    • Handle: RePEc:plo:pone00:0115064
    DOI: 10.1371/journal.pone.0115064
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    References listed on IDEAS

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    1. Liangwen Zhang & Yanbing Zeng & Lixia Wang & Ya Fang, 2020. "Urban–Rural Differences in Long-Term Care Service Status and Needs Among Home-Based Elderly People in China," IJERPH, MDPI, vol. 17(5), pages 1-18, March.
    2. Liangwen Zhang & Yanbing Zeng & Ya Fang, 2017. "The effect of health status and living arrangements on long term care models among older Chinese: A cross-sectional study," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-15, September.
    3. Achilleas Anastasiou & Peter Hatzopoulos & Alex Karagrigoriou & George Mavridoglou, 2021. "Causality Distance Measures for Multivariate Time Series with Applications," Mathematics, MDPI, vol. 9(21), pages 1-15, October.
    4. Annamaria Porreca & Salvador Cruz Rambaud & Francesca Scozzari & Marta Nicola, 2019. "A fuzzy approach for analysing equitable and sustainable well-being in Italian regions," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 64(6), pages 935-942, July.
    5. A Vanhaesebrouck & C Vuillermoz & S Robert & I Parizot & P Chauvin, 2018. "Who self-medicates? Results from structural equation modeling in the Greater Paris area, France," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-18, December.

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