Objective To understand the status of needs, demands and utilization of health services of urban and rural residents in Chongqing, so as to provide references for the evaluation of health services status and policy making and regulating. Methods The data from family health questionnaire of health service survey in Western China in 2008 were descriptively analyzed. Results The two-week prevalence rate was 216.9‰ and the two-week hospital visit rate was 211.5‰. The sick people who did not seek medical care accounted for 56.2% among the sick population. The chronic disease prevalence rate was 226.4‰. The annual hospitalization rate was 77.1‰. Conclusion During the past five-year from 2003 to 2008, the needs of health services in Chongqing have had no big change, but the chronic disease prevalence rate has been in uptrend, and the utilization has obviously increased. And the economic factor is still the major cause for impeding residents to seek medical care. So it’s necessary to strengthen the construction of primary health care institutions, to improve the level of health insurance system, and to decrease the disparity in urban and rural areas.
In colorectal cancer histopathology, the collaborative perception of microscopic and salient lesions is critical for effective diagnosis and improved patient prognosis. However, existing deep learning methods struggle to simultaneously capture microscopic glandular disorganization and salient tissue lesions. To address this limitation, a colorectal cancer diagnosis network based on dynamic gland-aware and tissue soft-clustering (DGTSNet) is proposed. The method employs dynamic gland-aware convolution to explicitly perceive gland boundaries and dynamically adjust sampling offsets, while incorporating a continuous-domain constraint to prevent out-of-bound sampling, thereby enhancing the perception of microscopic glandular disorders. Meanwhile, a tissue soft-clustering module is utilized to adaptively generate clustering prototypes and guide pixels toward relevant prototype centroids according to semantic similarity, suppressing irrelevant background interference and enhancing responses to significant tissue lesions. Finally, a cascaded sparse coupling module is introduced to collaboratively modulate cross-semantic feature representations along both the spatial and channel dimensions, while constructing differentiable masks to suppress low-contribution semantics, thereby achieving collaborative coupling of cross-semantic features. Experimental results demonstrated that the proposed method achieved superior performance on the Chaoyang, Kather-5K, and EBHI datasets, as well as a real-world clinical validation cohort, outperforming multiple baseline models. The study shows that the proposed method can effectively enhance the joint perception of microscopic glandular disorders and significant tissue lesions, providing an effective solution for colorectal cancer diagnosis.