Lifting Statewide Mask Mandates and COVID-19 Cases: A Synthetic Control Study.

IMPORTANCE: As states reopened their economies state and local officials made decisions on policies and restrictions that had an impact on the evolution of the pandemic and the health of the citizens. Some states made the decision to lift mask mandates starting spring 2021. Data-driven methods help evaluate the appropriateness and consequences of such decisions. OBJECTIVE: To investigate the association of lifting the mask mandate with changes in the cumulative coronavirus case rate. DESIGN: Synthetic control study design on lifting mask mandate in the state of Iowa implemented on February 7, 2021. SETTING: Daily state-level data from the COVID-19 Community Profile Report published by the US Department of Health & Human Services, COVIDcast dataset of the Delphi Research Group, and Google Community Mobility Reports. EXPOSURES AND OUTCOME: Mask mandate policy lift at the state level. State-day observations of the cumulative case rate measured as the cumulative number of new cases per 100,000 people in the previous 7 days. RESULTS: The cumulative case rate in Iowa increased by 20%-30% within 3 weeks of lifting the mask mandate as compared with a synthetic control unit. This association appeared to be related to people, in fact, reducing their mask-wearing habits. CONCLUSIONS: Lifting the mask mandate in Iowa was associated with an increase in new COVID-19 cases. Caution should be applied when making this type of policy decision before having achieved a more stable control of the pandemic.

Impact of COVID-19 on an infection prevention and control program, Iowa 2020-2021.

BACKGROUND: The COVID-19 pandemic has affected infection prevention and control (IPC) programs worldwide. We evaluated the impact of COVID-19 on the University of Iowa Hospitals & Clinics IPC program by measuring the volume of calls to the program, changes in healthcare-associated infection rates, and team member perceptions. METHODS: We retrieved the IPC call log and healthcare-associated infection trends for 2018-2020. We defined 2 periods: pre-COVID-19 (2018-2019) and COVID-19 (January-December 2020). We also conducted one-on-one interviews and focus group interviews with members of the IPC program and describe changes in their working conditions during the COVID-19 period. RESULTS: A total of 6,564 calls were recorded during 2018-2020. The pre-COVID-19 period had a median of 71 calls and/or month (range: 50-119). During the COVID-19 period, the median call volume increased to 368/month (range: 149-829), and most calls were related to isolation precautions (50%). During the COVID-19 period, the central line-associated bloodstream infection incidence increased significantly. Infection preventionists reported that the ambiguity and conflicting guidance during the pandemic were major challenges. CONCLUSIONS: Our IPC program experienced a 500% increase in consultation requests. Planning for future bio-emergencies should include creative strategies to increase response capacity within IPC programs.

Evaluation of a Parsimonious COVID-19 Outbreak Prediction Model: Heuristic Modeling Approach Using Publicly Available Data Sets.

BACKGROUND: The COVID-19 pandemic has changed public health policies and human and community behaviors through lockdowns and mandates. Governments are rapidly evolving policies to increase hospital capacity and supply personal protective equipment and other equipment to mitigate disease spread in affected regions. Current models that predict COVID-19 case counts and spread are complex by nature and offer limited explainability and generalizability. This has highlighted the need for accurate and robust outbreak prediction models that balance model parsimony and performance. OBJECTIVE: We sought to leverage readily accessible data sets extracted from multiple states to train and evaluate a parsimonious predictive model capable of identifying county-level risk of COVID-19 outbreaks on a day-to-day basis. METHODS: Our modeling approach leveraged the following data inputs: COVID-19 case counts per county per day and county populations. We developed an outbreak gold standard across California, Indiana, and Iowa. The model utilized a per capita running 7-day sum of the case counts per county per day and the mean cumulative case count to develop baseline values. The model was trained with data recorded between March 1 and August 31, 2020, and tested on data recorded between September 1 and October 31, 2020. RESULTS: The model reported sensitivities of 81%, 92%, and 90% for California, Indiana, and Iowa, respectively. The precision in each state was above 85% while specificity and accuracy scores were generally >95%. CONCLUSIONS: Our parsimonious model provides a generalizable and simple alternative approach to outbreak prediction. This methodology can be applied to diverse regions to help state officials and hospitals with resource allocation and to guide risk management, community education, and mitigation strategies.

Coronavirus disease 2019 (COVID-19) admission screening and assessment of infectiousness at an academic medical center in Iowa, 2020.

OBJECTIVE: Patients admitted to the hospital may unknowingly carry severe acute respiratory coronavirus virus 2 (SARS-CoV-2), and hospitals have implemented SARS-CoV-2 admission screening. However, because SARS-CoV-2 reverse-transcription polymerase chain reaction (RT-PCR) assays may remain positive for months after infection, positive results may represent active or past infection. We determined the prevalence and infectiousness of patients who were admitted for reasons unrelated to COVID-19 but tested positive for SARS-CoV-2 on admission screening. METHODS: We conducted an observational study at the University of Iowa Hospitals & Clinics from July 7 to October 25, 2020. All patients admitted without suspicion of COVID-19 were included, and medical records of those with a positive admission screening test were reviewed. Infectiousness was determined using patient history, PCR cycle threshold (Ct) value, and serology. RESULTS: In total, 5,913 patients were screened and admitted for reasons unrelated to COVID-19. Of these, 101 had positive admission RT-PCR results; 36 of these patients were excluded because they had respiratory signs/symptoms on admission on chart review. Also, 65 patients (1.1%) did not have respiratory symptoms. Finally, 55 patients had Ct values available and were included in this analysis. The median age of the final cohort was 56 years and 51% were male. Our assessment revealed that 23 patients (42%) were likely infectious. The median duration of in-hospital isolation was 5 days for those likely infectious and 2 days for those deemed noninfectious. CONCLUSIONS: SARS-CoV-2 was infrequent among patients admitted for reasons unrelated to COVID-19. An assessment of the likelihood of infectiousness using clinical history, RT-PCR Ct values, and serology may help in making the determination to discontinue isolation and conserve resources.

Virtual VM4 Clinical Rotations: A COVID-19 Pandemic Response at Iowa State University College of Veterinary Medicine.

Policy changes in response to the coronavirus disease 2019 (COVID-19) pandemic at Iowa State University College of Veterinary Medicine (ISU-CVM) included the administrative directive that fourth-year (VM4) clinical rotations immediately transition from in-person to virtual format. This article summarizes the efforts, successes, and challenges experienced by ISU-CVM clinical faculty during this transition. Numerous data sources were reviewed, including college records and announcements, faculty survey results, and student rotation evaluations. Data were explored using quantitative and qualitative methods. Between March and July 2020, 36 faculty from 15 different clinical services invested approximately 5,000 hours in delivering virtual content to 165 VM4 students from ISU-CVM and Caribbean veterinary schools. With departmental, college, and university assistance, faculty effectively used educational technologies (Zoom, Canvas, Echo360) and developed adaptive and innovative methods for virtual content delivery. Virtual VM4 rotations were collectively well received and appreciated by students, and student evaluation scores for virtual rotations were statistically equivalent to or higher than those for the corresponding in-person rotations in the preceding year. Although certain hands-on skills could not be adequately acquired in a virtual environment, students gained theoretical knowledge and case-based problem-solving skills in the online format. Faculty reported satisfaction with their adaptability and resilience in these challenging circumstances. These findings demonstrate that ISU-CVM clinical faculty invested substantial time and effort to transition in-person clinical rotations to virtual format during the early COVID-19 pandemic. This is particularly noteworthy given that many of these same faculty simultaneously served as essential personnel managing clinical cases in the university's teaching hospital.

SARS-CoV-2 Infection during Pregnancy in a Rural Midwest All-delivery Cohort and Associated Maternal and Neonatal Outcomes.

OBJECTIVE: This study aimed to estimate the prevalence of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) among pregnant patients at the time of delivery in a rural Midwest tertiary care hospital and to examine demographics, clinical factors, and maternal and neonatal outcomes associated with SARS-CoV-2 infection during pregnancy. STUDY DESIGN: This prospective cohort study included all delivering patients between May 1 and September 22, 2020 at the University of Iowa Hospitals and Clinics. Plasma SARS-CoV-2 antibody testing was performed. SARS-CoV-2 viral reverse-transcription polymerase chain reaction (RT-PCR) results and maternal and neonatal outcomes were collected from the electronic medical record. Data were analyzed using univariate statistical methods with clustering for multiple births. RESULTS: In total, 1,000 patients delivered between May 1 and September 22, 2020. Fifty-eight (5.8%) were SARS-CoV-2 antibody positive. Twenty-three also tested viral positive during pregnancy. Three of 1,000 (0.3%) were viral positive on admission but antibody negative. The median age was 30 years (interquartile range [IQR]: 26-33 years) and body mass index was 31.75 kg/m2 (IQR 27.7-37.5 kg/m2). The cesarean delivery rate was 34.0%. The study population was primarily white (71.6%); however, 41.0% of SARS-CoV-2 infected patients identified as Black, 18.0% as Hispanic/Latino, 3.3% as Native Hawaiian/Pacific Islander, and only 27.9% as White (p < 0.0001). SARS-CoV-2 infection was more likely in patients without private insurance (p = 0.0243). Adverse maternal and/or neonatal outcomes were not more likely in patients with evidence of infection during pregnancy. Two SARS-CoV-2 infected patients were admitted to the intensive care unit. There were no maternal deaths during the study period. CONCLUSION: In this largely rural Midwest population, 6.1% of delivering patients had evidence of past or current SARS-CoV-2 infection. Rates of SARS-CoV-2 during pregnancy were higher among racial and ethnic minorities and patients without private insurance. The SARS-CoV-2 infected patients and their neonates were not found to be at increased risk for adverse outcomes. KEY POINTS: · SARS-CoV-2 seroprevalence rate in pregnant population in Iowa is 5.8%.. · Infections are higher among minorities, non-English speakers, and patients without private insurance.. · No increased adverse maternal/neonatal outcomes observed for SARS-CoV-2 infected mothers..