Identify successful restrictions in suppressing the early outbreak of COVID-19 in Arizona, United States: Interrupted time series analysis

COVID-19 was responsible for many deaths and economic losses around the globe since its first case report. Governments implemented a variety of policies to combat the pandemic in order to protect their citizens and save lives. Early in 2020, the first cases were reported in Arizona State and continued to rise until the discovery of the vaccine in 2021. A variety of strategies and interventions to stop or decelerate the spread of the pandemic has been considered. It is recommended to define which strategy was successful for disease propagation prevention and could be used in further similar situations. This study aimed to evaluate the effect of people's contact interventions strategies which were implemented in Arizona State and their effect on reducing the daily new COVID-19 cases and deaths. Their effect on daily COVID-19 cases and deaths were evaluated using an interrupted time series analysis during the pandemic's first peaks to better understand the onward situation. Canceling the order of staying at home (95% CI, 1718.52 to 6218.79; p<0.001) and expiring large gatherings (95% CI, 1984.99 to 7060.26; p<0.001) on June 30 and August 17, 2020, respectively, had a significant effect on the pandemic, leading to the daily cases to grow rapidly. Moreover, canceling the stay at home orders led to an increase in the number of COVID-19 daily deaths by 67.68 cases (95% CI, 27.96 to 107.40; p<0.001) after about 21 days while prohibiting large gatherings significantly decreased 66.76 (95% CI: 20.56 to 112.96; p = 0.004) the number of daily deaths with about 21 days' lag. The results showed that strategies aimed at reducing people's contact with one another could successfully help fight the pandemic. Findings from this study provide important evidence to support state-level policies that require observance of social distancing by the general public for future pandemics. Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Mammography: interobserver variability in breast density assessment

Our objective was to determine the interobserver variability of breast density assessment according to the Breast Imaging Reporting and Data System (BI-RADS) and to examine potential associations between breast density and risk factors for breast cancer. Four experienced breast radiologists received instructions regarding the use of BI-RADS and they assessed 57 mammograms into BI-RADS density categories of 1-4. The weighted kappa values for breast density between pairs of observers were 0.84 (A, B) (almost perfect agreement); 0.75 (A, C), 0.74 (A, D), 0.71 (B, C), 0.77 (B, D), 0.65 (C, D) (substantial agreement). The weighted overall kappa, measured by the intraclass correlation coefficient (ICC), was 0.77 (95% CI: 0.69-0.85). Body mass index was inversely associated with high breast density. In conclusion, overall interobserver agreement in mammographic interpretation of breast density is substantial and therefore, the BI-RADS classification for breast density is useful for standardization in a multicentre stud