Ausserhofer Dietmar, Barbieri Verena, Lombardo Stefano, Gärtner Timon, Eisendle Klaus, Piccoliori Giuliano, Engl Adolf, Wiedermann Christian J
Institute of General Practice and Public Health, Claudiana-College of Health Professions, 39100 Bolzano, Italy.
Claudiana Research, Claudiana-College of Health Professions, 39100 Bolzano, Italy.
Behav Sci (Basel). 2025 May 24;15(6):724. doi: 10.3390/bs15060724.
BACKGROUND/OBJECTIVES: Healthcare utilization is a behavioral phenomenon influenced by psychosocial factors. This study took place in South Tyrol, a culturally diverse autonomous province in northern Italy, and aimed to identify latent profiles of primary healthcare users based on health literacy, patient activation, sleep quality, and service use, and to examine the sociodemographic and health-related predictors of profile membership.
A cross-sectional survey was conducted with a representative adult sample ( = 2090). The participants completed the questionnaire in German or Italian. Latent profiles were identified via model-based clustering using Gaussian mixture modeling and four z-standardized indicators: total primary healthcare contacts (general practice and emergency room visits), HLS-EU-Q16 (health literacy), PAM-10 (patient activation), and B-PSQI (sleep quality). The optimal cluster solution was selected using the Bayesian Information Criterion (BIC). Kruskal-Wallis and chi-square tests were used for between-cluster comparisons of the data. Multinomial logistic regression was used to examine the predictors of cluster membership.
Among the 1645 respondents with complete data, a three-cluster solution showed a good model fit (BIC = 19,518; silhouette = 0.130). The identified profiles included 'Balanced Self-Regulators' (72.8%), 'Struggling Navigators' (25.8%), and 'Hyper-Engaged Users' (1.4%). Sleep quality could be used to differentiate between different levels of service use ( < 0.001), while low health literacy and patient activation were key features of the high-utilization groups. Poor sleep and inadequate health literacy were associated with increased healthcare contact.
The latent profiling revealed distinct patterns in health care engagement. Behavioral segmentation can inform more tailored and culturally sensitive public health interventions in diverse settings such as South Tyrol.
背景/目的：医疗保健利用是一种受社会心理因素影响的行为现象。本研究在意大利北部一个文化多元的自治省南蒂罗尔进行，旨在根据健康素养、患者激活度、睡眠质量和服务利用情况确定初级医疗保健使用者的潜在类型，并研究类型归属的社会人口统计学和健康相关预测因素。
对一个具有代表性的成年样本（n = 2090）进行了横断面调查。参与者用德语或意大利语完成问卷。通过基于模型的聚类分析，使用高斯混合模型和四个z标准化指标来确定潜在类型：初级医疗保健总接触次数（全科医疗和急诊室就诊）、HLS-EU-Q16（健康素养）、PAM-10（患者激活度）和B-PSQI（睡眠质量）。使用贝叶斯信息准则（BIC）选择最佳聚类解决方案。使用Kruskal-Wallis检验和卡方检验对数据进行聚类间比较。使用多项逻辑回归分析来研究聚类归属的预测因素。
在1645名有完整数据的受访者中，三聚类解决方案显示出良好的模型拟合（BIC = 19518；轮廓系数 = 0.130）。确定的类型包括“平衡自我调节者”（72.8%）、“艰难导航者”（25.8%）和“过度参与使用者”（1.4%）。睡眠质量可用于区分不同水平的服务利用情况（P < 0.001），而低健康素养和患者激活度是高利用组的关键特征。睡眠不佳和健康素养不足与医疗保健接触增加有关。
潜在类型分析揭示了医疗保健参与的不同模式。行为细分可为南蒂罗尔等不同环境中更具针对性和文化敏感性的公共卫生干预措施提供信息。
