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

Abstract 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...

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Main Authors: Daniela Rodrigues Recchia, Holger Cramer, Jon Wardle, David J. Lee, Thomas Ostermann, Romy Lauche
Format: Article
Language:English
Published: BMC 2022-01-01
Series:BMC Health Services Research
Subjects:
Online Access:https://doi.org/10.1186/s12913-021-07426-9
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author Daniela Rodrigues Recchia
Holger Cramer
Jon Wardle
David J. Lee
Thomas Ostermann
Romy Lauche
author_facet Daniela Rodrigues Recchia
Holger Cramer
Jon Wardle
David J. Lee
Thomas Ostermann
Romy Lauche
author_sort Daniela Rodrigues Recchia
collection DOAJ
description Abstract 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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spelling doaj.art-394f28297e174874a0584cc67ea042ba2022-12-21T19:32:37ZengBMCBMC Health Services Research1472-69632022-01-0122111510.1186/s12913-021-07426-9Profiles and predictors of healthcare utilization: using a cluster-analytic approach to identify typical users across conventional, allied and complementary medicine, and self-careDaniela Rodrigues Recchia0Holger Cramer1Jon Wardle2David J. Lee3Thomas Ostermann4Romy Lauche5Chair of Research Methods and Statistics in Psychology, Department of Psychology and Psychotherapy, Witten/Herdecke UniversityDepartment of Internal and Integrative Medicine, Evang. Kliniken Essen-Mitte, Faculty of Medicine, University of Duisburg-EssenNational Centre for Naturopathic Medicine, Southern Cross UniversityChair of Department of Public Health Sciences, University of MiamiChair of Research Methods and Statistics in Psychology, Department of Psychology and Psychotherapy, Witten/Herdecke UniversityNational Centre for Naturopathic Medicine, Southern Cross UniversityAbstract 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.https://doi.org/10.1186/s12913-021-07426-9Complementary medicineCluster analysisHealthcare utilization pattern
spellingShingle Daniela Rodrigues Recchia
Holger Cramer
Jon Wardle
David J. Lee
Thomas Ostermann
Romy Lauche
Profiles and predictors of healthcare utilization: using a cluster-analytic approach to identify typical users across conventional, allied and complementary medicine, and self-care
BMC Health Services Research
Complementary medicine
Cluster analysis
Healthcare utilization pattern
title Profiles and predictors of healthcare utilization: using a cluster-analytic approach to identify typical users across conventional, allied and complementary medicine, and self-care
title_full Profiles and predictors of healthcare utilization: using a cluster-analytic approach to identify typical users across conventional, allied and complementary medicine, and self-care
title_fullStr Profiles and predictors of healthcare utilization: using a cluster-analytic approach to identify typical users across conventional, allied and complementary medicine, and self-care
title_full_unstemmed Profiles and predictors of healthcare utilization: using a cluster-analytic approach to identify typical users across conventional, allied and complementary medicine, and self-care
title_short Profiles and predictors of healthcare utilization: using a cluster-analytic approach to identify typical users across conventional, allied and complementary medicine, and self-care
title_sort profiles and predictors of healthcare utilization using a cluster analytic approach to identify typical users across conventional allied and complementary medicine and self care
topic Complementary medicine
Cluster analysis
Healthcare utilization pattern
url https://doi.org/10.1186/s12913-021-07426-9
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