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Background <p>China’s health system faces widespread bypassing behavior, where residents prefer high-level hospitals, leading to inefficient primary resource use. This study aimed to identify distinct preference groups in urban residents’ choices of hospitals and to examine how predisposing, enabling, and need factors from Andersen’s Model differentially influence healthcare-seeking intention across groups.</p> Methods <p>A cross-sectional survey was conducted in Wuhan, China, from July 2023 to August 2024, involving 2,890 participants. Latent Class Analysis (LCA) was employed to segment the population based on seven consideration attributes for choosing hospitals. Multinomial logistic regression was then used to analyze the influence of predisposing, enabling, and need factors. Specifically, it examined how these factors affected healthcare-seeking intention within each identified latent class.</p> Results <p>LCA identified four distinct preference groups: the Core Resource Preference Group (29.9%), the Medical Technology Preference Group (35.6%), the High-Quality &amp; Low-Cost Preference Group (11.1%), and the Convenient Transportation Preference Group (23.5%). Healthcare-seeking intention varied significantly: the Medical Technology Group had the highest tertiary hospital selection rate (68.0%), while the Convenient Transportation Group had the highest combined rate for self-medication and primary care (57.6%). Regression analyses revealed heterogeneous influences: in the Core Resource and Convenient Transportation groups, higher confidence in family finances was associated with lower odds of choosing tertiary hospitals. In contrast, in the Medical Technology Group, worse past-year health status increased the likelihood of selecting secondary hospitals. Higher education consistently predicted tertiary hospital use across all groups.</p> Conclusion <p>Urban residents’ hospital-choice preferences are heterogeneous, and the factors influencing their healthcare-seeking intention vary significantly across different preference profiles. These findings suggest that “one-size-fits-all” policies are inadequate, and targeted interventions are needed. This study provides empirical evidence for designing more effective, population-specific strategies to promote a rational hierarchical healthcare system. Moreover, these findings indicate that the mechanisms underlying the healthcare-seeking intention of different preference groups require further exploration.</p>

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Urban residents’ choice preferences for hospitals at different tiers and influencing factors: a study based on latent class analysis and Andersen’s model of health service utilization

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  • 出版模式：

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  • 发表日期：

    2026年6月30日

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  • Ke Wang,
  • Hang Chen,
  • Yu Jin,
  • Rui Min

摘要

Background

China’s health system faces widespread bypassing behavior, where residents prefer high-level hospitals, leading to inefficient primary resource use. This study aimed to identify distinct preference groups in urban residents’ choices of hospitals and to examine how predisposing, enabling, and need factors from Andersen’s Model differentially influence healthcare-seeking intention across groups.

Methods

A cross-sectional survey was conducted in Wuhan, China, from July 2023 to August 2024, involving 2,890 participants. Latent Class Analysis (LCA) was employed to segment the population based on seven consideration attributes for choosing hospitals. Multinomial logistic regression was then used to analyze the influence of predisposing, enabling, and need factors. Specifically, it examined how these factors affected healthcare-seeking intention within each identified latent class.

Results

LCA identified four distinct preference groups: the Core Resource Preference Group (29.9%), the Medical Technology Preference Group (35.6%), the High-Quality & Low-Cost Preference Group (11.1%), and the Convenient Transportation Preference Group (23.5%). Healthcare-seeking intention varied significantly: the Medical Technology Group had the highest tertiary hospital selection rate (68.0%), while the Convenient Transportation Group had the highest combined rate for self-medication and primary care (57.6%). Regression analyses revealed heterogeneous influences: in the Core Resource and Convenient Transportation groups, higher confidence in family finances was associated with lower odds of choosing tertiary hospitals. In contrast, in the Medical Technology Group, worse past-year health status increased the likelihood of selecting secondary hospitals. Higher education consistently predicted tertiary hospital use across all groups.

Conclusion

Urban residents’ hospital-choice preferences are heterogeneous, and the factors influencing their healthcare-seeking intention vary significantly across different preference profiles. These findings suggest that “one-size-fits-all” policies are inadequate, and targeted interventions are needed. This study provides empirical evidence for designing more effective, population-specific strategies to promote a rational hierarchical healthcare system. Moreover, these findings indicate that the mechanisms underlying the healthcare-seeking intention of different preference groups require further exploration.