Acta psychologica · 2026
Spatiotemporal heterogeneity and drivers of health anxiety in China: Evidence from provincial big data search trends.
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Zhu Yan, Pan Xinyu, Song Tianchi, Yang Kai, Chen Jieting, Wu Xiangwei
Аннотация
OBJECTIVE: From the perspective of integrating health geography with cognitive behavior theory, this study explores the spatiotemporal heterogeneity of Chinese residents' health anxiety and its driving mechanisms, aiming to inform spatially targeted interventions for the "Healthy China" strategy and global health equity.
METHODS: A residents' health anxiety index model was constructed based on 151 Baidu search keywords. The entropy method was applied to evaluate health anxiety levels across 31 provincial-level administrative units in China from 2011 to 2023. The standard deviation ellipse method was used to characterize the evolutionary trends of spatial distribution. The Markov chain was employed to identify path dependence in regional state transitions, and the geographically and temporally weighted regression (GTWR) model was applied to reveal the spatial non-stationarity of the driving factors.
RESULTS: Health anxiety exhibited a pronounced east-west gradient, with high-value areas concentrated in Guangdong (0.260), Zhejiang (0.240), and Beijing (0.228), all located on the southeastern side of the Hu Huanyong Line. From 2011 to 2023, the spatial extent of anxiety expanded by 2.24%, and the spatial centroid shifted southwestward (111°28'E → 111°04'E; 37°58'N → 37°02'N). Driving factors, including unemployment rate, income disparity, environmental pollution, and public health events, exhibited significant spatial heterogeneity: a risk-amplification effect in eastern high-density areas (β > 3.0, p < 0.001) and an inhibitory effect in western low-sensitivity areas (β < -1.3, p < 0.001).
CONCLUSION: The spatial differentiation and path dependence of Chinese residents' health anxiety underscore the geographical imprint of regional developmental imbalances. Accordingly, a spatially hierarchical intervention framework is proposed, comprising eastern digital-ecological governance, western accessibility-oriented service supply, and community-based psychological hubs in central regions, which offers empirical evidence from China to inform the governance of global geographical health disparities.
METHODS: A residents' health anxiety index model was constructed based on 151 Baidu search keywords. The entropy method was applied to evaluate health anxiety levels across 31 provincial-level administrative units in China from 2011 to 2023. The standard deviation ellipse method was used to characterize the evolutionary trends of spatial distribution. The Markov chain was employed to identify path dependence in regional state transitions, and the geographically and temporally weighted regression (GTWR) model was applied to reveal the spatial non-stationarity of the driving factors.
RESULTS: Health anxiety exhibited a pronounced east-west gradient, with high-value areas concentrated in Guangdong (0.260), Zhejiang (0.240), and Beijing (0.228), all located on the southeastern side of the Hu Huanyong Line. From 2011 to 2023, the spatial extent of anxiety expanded by 2.24%, and the spatial centroid shifted southwestward (111°28'E → 111°04'E; 37°58'N → 37°02'N). Driving factors, including unemployment rate, income disparity, environmental pollution, and public health events, exhibited significant spatial heterogeneity: a risk-amplification effect in eastern high-density areas (β > 3.0, p < 0.001) and an inhibitory effect in western low-sensitivity areas (β < -1.3, p < 0.001).
CONCLUSION: The spatial differentiation and path dependence of Chinese residents' health anxiety underscore the geographical imprint of regional developmental imbalances. Accordingly, a spatially hierarchical intervention framework is proposed, comprising eastern digital-ecological governance, western accessibility-oriented service supply, and community-based psychological hubs in central regions, which offers empirical evidence from China to inform the governance of global geographical health disparities.