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Profiles and predictors of healthcare utilization: using a cluster-analytic approach to identify typical users across conventional, allied and complementary medicine, and self-care
Journal article   Open access   Peer reviewed

Profiles and predictors of healthcare utilization: using a cluster-analytic approach to identify typical users across conventional, allied and complementary medicine, and self-care

Daniela Rodrigues Recchia, Holger Cramer, Jon Wardle, David J Lee, Thomas Ostermann and Romy Lauche
BMC Health Services Research, Vol.22(1), 29
05/01/2022
DOI: https://doi.org/10.1186/s12913-021-07426-9
PMID: 34986866
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Abstract

Complementary Therapies Cross-Sectional Studies Health Services Accessibility Humans Patient Acceptance of Health Care Self Care Public health not elsewhere classified Traditional, complementary and integrative medicine not elsewhere classified Evaluation of health and support services not elsewhere classified Provision of health and support services not elsewhere classified Behaviour and health
Introduction: The identification of typologies of health care users and their specific characteristics can be performed using cluster analysis. This statistical approach aggregates similar users based on their common health-related behavior. This study aims to examine health care utilization patterns using cluster analysis; and the associations of health care user types with sociodemographic, health-related and health-system related factors. Methods: Cross-sectional data from the 2012 National Health Interview Survey were used. Health care utilization was measured by consultations with a variety of medical, allied and complementary health practitioners or the use of several interventions (exercise, diet, supplementation etc.) within the past 12 months (used vs. not used). A model-based clustering approach based on finite normal mixture modelling, and several indices of cluster fit were determined. Health care utilization within the cluster was analyzed descriptively, and independent predictors of belonging to the respective clusters were analyzed using logistic regression models including sociodemographic, health- and health insurance-related factors. Results: Nine distinct health care user types were identified, ranging from nearly non-use of health care modalities to over-utilization of medical, allied and complementary health care. Several sociodemographic and health-related characteristics were predictive of belonging to the respective health care user types, including age, gender, health status, education, income, ethnicity, and health care coverage. Conclusions: Cluster analysis can be used to identify typical health care utilization patterns based on empirical data; and those typologies are related to a variety of sociodemographic and health-related characteristics. These findings on individual differences regarding health care access and utilization can inform future health care research and policy regarding how to improve accessibility of different medical approaches.

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