Arizona Reopening Phase 3 and COVID-19: Returning to Normal

2021 ◽  
Vol 6 ◽  

Arizona is about the same size as Italy, and the sixth largest in size of the United States 50 states. The state’s Reopening Phase 3 began on March 5, 2021. There were declines in the weekly COVID-19 cases from March through June. In July and August, the cases rose as the Delta variant spread throughout the United States. Arizona had passed one million case milestone. This six-month longitudinal study examined the changes in the numbers of vaccinations given, new COVID-19 cases, hospitalized cases, deaths, testing given, and the weekly positively percentages during Reopening Phase 3. The data source used was from the Arizona Department of Health Services COVID-19 dashboard database. Even with the case surges, the new normal was low number of severe cases, manageable hospitalization numbers, and very low number of deaths.

2021 ◽  
Vol 6 (5) ◽  

Of the United States 50 states, Arizona is the sixth largest in size. It is about the same size as Italy. After three months of Arizona Reopening Phase 2, the COVID-19 cases had surged. In early January 2021, ABC and NBC News reported that Arizona has the highest new cases per capital in the world. This longitudinal study examined the Arizona’s Reopening Phase 2 surge in cases. The study examined the changes in the numbers of testing given, new COVID-19 cases, cases that required hospitalizations, deaths, and vaccines given. The data source used was from the Arizona Department of Health Services COVID-19 dashboard database. During the last third of seven-month study period, Arizona’s case numbers declined as the number of those infected recovered and acquired immunity and the state residents became fully vaccinated increased.


2020 ◽  
Vol 5 (Special) ◽  

Of the United States 50 states, Arizona is the sixth largest in size. It is about the same size as Italy. After six weeks of reopening the state, the COVID-19 cases had spiked. Arizona’s state COVID-19 ranking had rose from one of the states with the lowest number of reported cases to the top 7th in the total reported cases. The state took aggressive actions to address the rising cases. This longitudinal study examined the impacts of the actions taken. The study examined the changes in the numbers of new reported COVID-19 cases, the number of cases that required hospitalization, and the number of deaths. The data source used was from the Arizona Department of Health Services COVID-19 dashboard database. During the two-month study period, Arizona aggressive actions had slowed down the overall state rates of new COVID-19 cases and number of deaths.


1984 ◽  
Vol 39 (12) ◽  
pp. 1424-1434 ◽  
Author(s):  
David J. Knesper ◽  
John R. Wheeler ◽  
David J. Pagnucco

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S819-S820
Author(s):  
Jonathan Todd ◽  
Jon Puro ◽  
Matthew Jones ◽  
Jee Oakley ◽  
Laura A Vonnahme ◽  
...  

Abstract Background Over 80% of tuberculosis (TB) cases in the United States are attributed to reactivation of latent TB infection (LTBI). Eliminating TB in the United States requires expanding identification and treatment of LTBI. Centralized electronic health records (EHRs) are an unexplored data source to identify persons with LTBI. We explored EHR data to evaluate TB and LTBI screening and diagnoses within OCHIN, Inc., a U.S. practice-based research network with a high proportion of Federally Qualified Health Centers. Methods From the EHRs of patients who had an encounter at an OCHIN member clinic between January 1, 2012 and December 31, 2016, we extracted demographic variables, TB risk factors, TB screening tests, International Classification of Diseases (ICD) 9 and 10 codes, and treatment regimens. Based on test results, ICD codes, and treatment regimens, we developed a novel algorithm to classify patient records into LTBI categories: definite, probable or possible. We used multivariable logistic regression, with a referent group of all cohort patients not classified as having LTBI or TB, to identify associations between TB risk factors and LTBI. Results Among 2,190,686 patients, 6.9% (n=151,195) had a TB screening test; among those, 8% tested positive. Non-U.S. –born or non-English–speaking persons comprised 24% of our cohort; 11% were tested for TB infection, and 14% had a positive test. Risk factors in the multivariable model significantly associated with being classified as having LTBI included preferring non-English language (adjusted odds ratio [aOR] 4.20, 95% confidence interval [CI] 4.09–4.32); non-Hispanic Asian (aOR 5.17, 95% CI 4.94–5.40), non-Hispanic black (aOR 3.02, 95% CI 2.91–3.13), or Native Hawaiian/other Pacific Islander (aOR 3.35, 95% CI 2.92–3.84) race; and HIV infection (aOR 3.09, 95% CI 2.84–3.35). Conclusion This study demonstrates the utility of EHR data for understanding TB screening practices and as an important data source that can be used to enhance public health surveillance of LTBI prevalence. Increasing screening among high-risk populations remains an important step toward eliminating TB in the United States. These results underscore the importance of offering TB screening in non-U.S.–born populations. Disclosures All Authors: No reported disclosures


BMC Medicine ◽  
2017 ◽  
Vol 15 (1) ◽  
Author(s):  
Mary A. M. Rogers ◽  
Catherine Kim ◽  
Tanima Banerjee ◽  
Joyce M. Lee

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