ArticleBMC health services research2022
Profiles and predictors of healthcare utilization: using a cluster-analytic approach to identify typical users across conventional, allied and complementary medicine, and self-care.
Article in BMC health services research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Rehabilitation utilisation up to 24 months after stroke: a register-based cohort study in southern Sweden.European stroke journal · 2026Article
- Exploring online and in-person mental healthcare access and app use in a cohort of people living with disability: results from the 2019 and 2020 California Health Interview Survey.Internet interventions · 2026Article
- LASSO logistic regression and cluster analysis in predicting adherence and drug patterns among new users of monotherapy for antihypertensive drugs.Journal of hypertension · 2025Article
- Rising Demand for Fetoscopic Laser Therapy for Twin-to-Twin Transfusion Syndrome: Trends, Maternal Age Insights, and Future Challenges in Germany.Journal of clinical medicine · 2025Article
- Exploring network relations between healthcare access and utilisation in individuals with rare diseases.Public health in practice (Oxford, England) · 2025Article
- The impact of the COVID-19 pandemic on administrative eating disorder prevalence in the outpatient sector and on severity of anorexia nervosa.European child & adolescent psychiatry · 2025Article
- Self-perceived physical fitness and occurrences of individual levels of autistic traits in adolescents: a cluster association study.BMC psychiatry · 2025Article
- Complementary, alternative, and integrative medicine-specific COVID-19 misinformation on social media: A scoping review.Integrative medicine research · 2023Article
- The determinants of healthcare utilisation in regional, rural and remote South Australia: A cross-sectional study.Health & social care in the community · 2022Article
- Health information use by patients with systemic lupus erythematosus (SLE) pre and during the COVID-19 pandemic.Lupus science & medicine · 2022Article
- Subgroup analyses and patterns of multiple sclerosis health service utilisation: A cluster analysis.Multiple sclerosis journal - experimental, translational and clinicalArticle
- A clinical classification framework for identifying persons with high social and medical needs: The COMPLEXedex-SDH.Nursing outlookArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe 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.
methodsCross-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.
resultsNine 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.
conclusionsCluster 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.