Subjects: Medicine, Pharmacy >> Preventive Medicine and Hygienics submitted time 2023-11-20 Cooperative journals: 《中国全科医学》
Abstract： Background "Strengthening primary health care" is one of the focuses of China's health care reform，and the study of the efficiency and equity of rural medical and health resources allocation in China is of great significance in promoting the orderly development of primary health care services，but at present，there are few relevant literature based on the DEA-GIS methodology with both equity and efficiency. Objective To analyze the efficiency and equity of rural medical and health resources allocation in 29 provinces in China in 2020，in order to provide a reference for optimizing the allocation of rural medical and health resources and improving the rural medical and health service system in China. Methods The data for this study were collected from the Statistical Tables of Administrative Divisions of the People's Republic of China，the 2021 China Health Statistical Yearbook. After synthesizing the existing literature research，data availability and soliciting expert advice，the township hospitals and village clinics in 29 provinces in China（excluding Beijing，Shanghai，Hong Kong，Macao and Taiwan） were selected as the study objects，and the number of township hospitals and village clinics（hereinafter referred to as the number of health institutions），the number of beds in township hospitals（hereinafter referred to as the number of beds），and the number of health technicians in township hospitals and village clinics（hereinafter referred to as the number of health technicians） were used as input indicators; the number of consultations in township hospitals and village clinics（hereinafter referred to as the number of consultations），and the number of hospital admissions to township hospitals（hereinafter referred to as the number of admissions）were used as output indicators. The data envelopment analysis（DEA） model was used to assess the efficiency of rural medical and health resources allocation in China，and the agglomeration and geographic information system （GIS） technology were used to spatially map the rural medical and health resources allocation to analyze its equity. Results In 2020，China's rural medical and health resources had 4 provinces with effective DEA，7 provinces with weakly effective DEA，and 18 provinces with ineffective DEA. Among them，the DEA ineffective regions all had different degrees of excess inputs，and only Shandong Province and Tibet Autonomous Region had insufficient outputs. The results of regional analysis showed that rural medical and health resources were concentrated in the eastern region，followed by the central region，with the lowest degree of concentration in the western region. Conclusion The government needs to pay attention to improving the technical efficiency of rural medical and health resources allocation，and promote equity and efficiency by applying precise measures to each region based on optimizing the input and output structure，reducing resource redundancy，rationally coordinating the allocation of resources in the eastern，central and western regions.
Subjects: Medicine, Pharmacy >> Preventive Medicine and Hygienics submitted time 2023-08-17 Cooperative journals: 《中国全科医学》
Abstract： Background The prevalence of metabolic syndrome（MS）among the middle-aged and elderly population in China increases with age，serum uric acid to high density lipoprotein cholesterol ratio（UHR）is closely associated with various metabolic diseases，however，there are few studies on the relationship between UHR and MS in middle-aged and elderly people. Objective To explore the correlation between UHR and MS in middle-aged and elderly people in China，and evaluate the predictive value of UHR for MS. Methods This study used public data from the 2015 China Health and Retirement Longitudinal Survey（CHARLS）and included 9 233 subjects. General data，physical examination indexes and blood biochemical examination indexes were collected from the study subjects and their UHR was calculated. The subjects were divided into the non-MS group（n=7 006）and MS group（n=2 227）according to whether they had MS. At the same time，the subjects were divided into the Q1 group（UHR ≤ 7.32%，n=2 308），Q2 group（7.32%
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