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10.1136/bmjopen-2020-041516

http://scihub22266oqcxt.onion/10.1136/bmjopen-2020-041516
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suck abstract from ncbi


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pmid33795292      BMJ+Open 2021 ; 11 (4): e041516
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  • Government management capacities and the containment of COVID-19: a repeated cross-sectional study across Chinese cities #MMPMID33795292
  • Li W; Li J; Yi J
  • BMJ Open 2021[Apr]; 11 (4): e041516 PMID33795292show ga
  • OBJECTIVES: Better understanding of the dynamics of the COVID-19 (2019 novel coronavirus disease) pandemic to curb its spread is now a global imperative. While travel restrictions and control measures have been shown to limit the spread of the disease, the effectiveness of the enforcement of those measures should depend on the strength of the government. Whether, and how, the government plays a role in fighting the disease, however, has not been investigated. Here, we show that government management capacities are critical to the containment of the disease. SETTING: We conducted a statistical analysis based on cross-city comparisons within China. China has undergone almost the entire cycle of the anticoronavirus campaign, which allows us to trace the full dynamics of the outbreak, with homogeneity in standards for statistics recording. PRIMARY AND SECONDARY OUTCOME MEASURES: Outcome measures include city-specific COVID-19 case incidence and recoveries in China. RESULTS: The containment of COVID-19 depends on the effectiveness of the enforcement of control measures, which in turn depends on the local government's management capacities. Specifically, government efficiency, capacity for law enforcement, and the transparency of laws and policies significantly reduce COVID-19 prevalence and increase the likelihood of recoveries. The organisation size of the government, which is not closely related to its capacity for management, has a limited role.
  • |*Government[MESH]
  • |COVID-19/*prevention & control[MESH]
  • |China/epidemiology[MESH]
  • |Cities[MESH]
  • |Communicable Disease Control/*legislation & jurisprudence[MESH]
  • |Cross-Sectional Studies[MESH]


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