In China, the doubling time of the coronavirus disease epidemic by province increased during January 20-February 9, 2020. Doubling time estimates ranged from 1.4 (95% CI 1.2-2.0) days for Hunan Province to 3.1 (95% CI 2.1-4.8) days for Xinjiang Province. The estimate for Hubei Province was 2.5 (95% CI 2.4-2.6) days.
A retrospective cohort study, using the electronic medical records of Kaiser Permanente Northern California (2011)(2012)(2013)(2014)(2015), included 560 robotic and 6785 conventional laparoscopic cases with 1836 "complex" patients (25%). The average operative time was 152 minutes (robotic) vs 157 minutes (conventional) laparoscopic hysterectomy. Complex surgical cases averaged 190 minutes and noncomplex cases averaged 144 minutes. For women with complex disease, the robotic approach, when used by a higher-volume surgeon, may be associated with shorter operative time and slightly less blood loss, but not with lower risk of complications.
Objective: Awareness and attentiveness have implications for the acceptance and adoption of disease prevention and control measures. Social media posts provide a record of the public's attention to an outbreak. To measure the attention of Chinese netizens to coronavirus disease 2019 (COVID-19), a pre-established nationally representative cohort of Weibo users was searched for COVID-19-related key words in their posts. Methods: COVID-19-related posts (N = 1101) were retrieved from a longitudinal cohort of 52 268 randomly sampled Weibo accounts (December 31, 2019-February 12, 2020). Results: Attention to COVID-19 was limited prior to China openly acknowledging human-to-human transmission on January 20. Following this date, attention quickly increased and has remained high over time.Particularly high levels of social media traffic appeared around when Wuhan was first placed in quarantine (January 23-24, 8-9% of the overall posts), when a scandal associated with the Red Cross Society of China occurred (February 1, 8%), and, following the death of Dr Li Wenliang (February 6-7, 11%), one of the whistleblowers who was reprimanded by the Chinese police in early January for discussing this outbreak online. Conclusion: Limited early warnings represent missed opportunities to engage citizens earlier in the outbreak. Governments should more proactively communicate early warnings to the public in a transparent manner.
To determine the transmission potential of severe acute respiratory syndrome coronavirus 2 in Iran in 2020, we estimated the reproduction number as 4.4 (95% CI 3.9-4.9) by using a generalized growth model and 3.5 (95% CI 1.3-8.1) by using epidemic doubling time. The reproduction number decreased to 1.55 after social distancing interventions were implemented.
Systemic inequity concerning the social determinants of health has been known to affect morbidity and mortality for decades. Significant attention has focused on the individual-level demographic and co-morbid factors associated with rates and mortality of COVID-19. However, less attention has been given to the county-level social determinants of health that are the main drivers of health inequities. To identify the degree to which social determinants of health predict COVID-19 cumulative case rates at the county-level in Georgia, we performed a sequential, cross-sectional ecologic analysis using a diverse set of socioeconomic and demographic variables. Lasso regression was used to identify variables from collinear groups. Twelve variables correlated to cumulative case rates (for cases reported by 1 August 2020) with an adjusted r squared of 0.4525. As time progressed in the pandemic, correlation of demographic and socioeconomic factors to cumulative case rates increased, as did number of variables selected. Findings indicate the social determinants of health and demographic factors continue to predict case rates of COVID-19 at the county-level as the pandemic evolves. This research contributes to the growing body of evidence that health disparities continue to widen, disproportionality affecting vulnerable populations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.