Open Access Coronavirus-Related Works

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This collection includes works by IUPUI and IU School of Medicine authors that address topics relevant to the COVID-19 pandemic. If you know of other works that should be included in this collection or, if you have questions regarding the inclusion criteria, please contact the University Library Center for Digital Scholarship: digschol@iupui.edu.

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Small businesses, startups will lead economy after COVID
(2021) Saxton, M. Kim; Saxton, Todd; Kelley School of Business - Indianapolis
Effects of COVID-19 Pandemic on the Professional Roles and Responsibilities of Health Educators
(Sage, 2021) Hancher-Rauch, Heidi L.; Bishop, Charity; Campbell, Alli; Cecil, Kara; Yazel, Lisa; Kinesiology, School of Health and Human Sciences
Public health professionals are at the forefront of the COVID-19 pandemic response. However, the roles and responsibilities of health educators in pandemic response are unknown. Researchers examined multiple factors that described how health educators' work priorities and lives have been affected by COVID-19. An electronic questionnaire was administered nationally to health educators to assess the effect of the pandemic on their professional responsibilities, the challenges they are facing, and their fears about the future. Of the 913 respondents, 487 (43%) reported changing work priorities, with 80% of that group (389) sharing that their work priorities shifted focus to COVID-19. Most felt qualified to take on the new job responsibilities, but many feared the inability to get back to previous work roles or for their organizations to financially withstand the pandemic. Regardless of workplace setting or job priorities, health educators are prepared in the skills outlined in the Responsibilities and Competencies for Health Education Specialists, which may have led to their abilities in shifting roles so quickly and effectively. Findings from this study may prepare public health agencies to better use and train health educators for their roles in rapidly shifting public health priorities.
Corrigendum to: Human Challenge Studies Are Unlikely to Accelerate Coronavirus Vaccine Licensure due to Ethical and Practical Issues
(Oxford University Press, 2021-05-28) Spinola, Stanley M.; Zimet, Gregory D.; Ott, Mary A.; Katz, Barry P.; Microbiology and Immunology, School of Medicine
This corrects the article "Human Challenge Studies Are Unlikely to Accelerate Coronavirus Vaccine Licensure Due to Ethical and Practical Issues" in volume 222 on page 1572; https://doi.org/10.1093/infdis/jiaa457
238 Adolescent Sleep Variability, Social Jetlag, and Mental Health during COVID-19: Findings from a Large Nationwide Study
(Oxford University Press, 2021-05) Wong, Patricia; Wolfson, Amy; Honaker, Sarah; Owens, Judith; Wahlstrom, Kyla; Saletin, Jared; Seixas, Azizi; Meltzer, Lisa; Carskadon, Mary; Pediatrics, School of Medicine
Introduction: Adolescents are vulnerable to short, insufficient sleep stemming from a combined preference for late bedtimes and early school start times, and also circadian disruptions from frequent shifts in sleep schedules (i.e., social jetlag). These sleep disruptions are associated with poor mental health. The COVID-19 pandemic has impacted education nationwide, including changes in instructional formats and school schedules. With data from the Nationwide Education and Sleep in TEens During COVID (NESTED) study, we examined whether sleep variability and social jetlag (SJL) during the pandemic associate with mental health. Methods: Analyses included online survey data from 4767 students (grades 6-12, 46% female, 36% non-White, 87% high school). For each weekday, participants identified if they attended school in person (IP), online-scheduled synchronous classes (O/S), online-no scheduled classes (asynchronous, O/A), or no school. Students reported bedtimes (BT) and wake times (WT) for each instructional format and for weekends/no school days. Sleep opportunity (SlpOpp) was calculated from BT and WT. Weekday night-to-night SlpOpp variability was calculated with mean square successive differences. SJL was calculated as the difference between the average sleep midpoint on free days (O/A, no school, weekends) versus scheduled days (IP, O/S). Participants also completed the PROMIS Pediatric Anxiety and Depressive Symptoms Short Form. Data were analyzed with hierarchical linear regressions controlling for average SlpOpp, gender, and school-level (middle vs high school). Results: Mean reported symptoms of anxiety (60.0 ±9.1; 14%≧70) and depression (63.4 ±10.2; 22%≧70) fell in the at-risk range. Shorter average SlpOpp (mean=8.3±1.2hrs) was correlated with higher anxiety (r=-.10) and depression (r=-.11; p’s<.001) T-scores. Greater SlpOpp variability was associated with higher anxiety (B=.71 [95%CI=.41-1.01, p<.001) and depression (B=.67 [.33-1.00], p<.001) T-scores. Greater SJL (mean=1.8±1.2hrs; 94% showed a delay in midpoint) was associated with higher anxiety (B=.36 [.12-.60], p<.001) and depression (B=.77 [.50-1.03], p<.001) T-scores. Conclusion: In the context of system-wide education changes during COVID-19, students on average reported at-risk levels of anxiety and depression symptoms which were associated with greater variability in sleep opportunity across school days and greater social jetlag. Our findings suggest educators and policymakers should consider these sleep-mental health associations when developing instructional formats and school schedules during and post-pandemic.
675 COVID-19 Instruction Style (In-Person, Virtual, Hybrid), School Start Times, and Sleep in a Large Nationwide Sample of Adolescents
(Oxford University Press, 2021-05) Meltzer, Lisa; Wahlstrom, Kyla; Owens, Judith; Wolfson, Amy; Honaker, Sarah; Saletin, Jared; Seixas, Azizi; Wong, Patricia; Carskadon, Mary; Pediatrics, School of Medicine
Introduction: The COVID-19 pandemic significantly disrupted how and when adolescents attended school. This analysis used data from the Nationwide Education and Sleep in TEens During COVID (NESTED) study to examine the association of instructional format (in-person, virtual, hybrid), school start times, and sleep in a large diverse sample of adolescents from across the U.S. Methods: In October/November 2020, 5346 nationally representative students (grades 6–12, 49.8% female, 30.6% non-White) completed online surveys. For each weekday, participants identified if they attended school in person (IP), online-scheduled synchronous classes (O/S), online-no scheduled classes (asynchronous, O/A), or no school. Students reported school start times for IP or O/S days, and bedtimes (BT) and wake times (WT) for each applicable school type and weekends/no school days (WE). Sleep opportunity (SlpOpp, total sleep time proxy) was calculated from BT and WT. Night-to-night sleep variability was calculated with mean square successive differences. Results: Significant differences for teens’ sleep across instructional formats were found for all three sleep variables. With scheduled instructional formats (IP and O/S), students reported earlier BT (IP=10:54pm, O/S=11:24pm, O/A=11:36pm, WE=12:30am), earlier WT (IP=6:18am, O/S=7:36am, O/A=8:48am, WE=9:36am), and shorter SlpOpp (IP=7.4h, O/S=8.2h, O/A=9.2h, WE=9.2h). Small differences in BT, but large differences in WT were found, based on school start times, with significantly later wake times associated with later start times. Students also reported later WT on O/S days vs. IP days, even with the same start times. Overall, more students reported obtaining sufficient SlpOpp (>8h) for O/S vs. IP format (IP=40.0%, O/S=58.8%); when school started at/after 8:30am, sufficient SlpOpp was even more common (IP=52.7%, O/S=72.7%). Greater night-to-night variability was found for WT and SlpOpp for students with hybrid schedules with >1 day IP and >1 day online vs virtual schedules (O/S and O/A only), with no differences in BT variability reported between groups. Conclusion: This large study of diverse adolescents from across the U.S. found scheduled school start times were associated with early wake times and shorter sleep opportunity, with greatest variability for hybrid instruction. Study results may be useful for educators and policy makers who are considering what education will look like post-pandemic.
237 Sleep disturbances, online instruction, and learning during COVID-19: evidence from 4148 adolescents in the NESTED study
(Oxford University Press, 2021-05) Saletin, Jared; Owens, Judith; Wahlstrom, Kyla; Honaker, Sarah; Wolfson, Amy; Seixas, Azizi; Wong, Patricia; Carskadon, Mary; Meltzer, Lisa; Pediatrics, School of Medicine
Introduction: COVID-19 fundamentally altered education in the United States. A variety of in-person, hybrid, and online instruction formats took hold in Fall 2020 as schools reopened. The Nationwide Education and School in TEens During COVID (NESTED) study assessed how these changes impacted sleep. Here we examined how instruction format was associated with sleep disruption and learning outcomes. Methods: Data from 4148 grade 6-12 students were included in the current analyses (61% non-male; 34% non-white; 13% middle-school). Each student’s instructional format was categorized as: (i) in-person; (ii) hybrid [≥1 day/week in-person]; (iii) online/synchronous (scheduled classes); (iv) online/asynchronous (unscheduled classes); (v) online-mixed; or (vi) no-school. Sleep disturbances (i.e., difficulty falling/staying asleep) were measured with validated PROMIS t-scores. A bootstrapped structural equation model examined how instructional format and sleep disturbances predict school/learning success (SLS), a latent variable loading onto 3 outcomes: (i) school engagement (ii) likert-rated school stress; and (iii) cognitive function (PROMIS t-scores). The model covaried for gender, race-ethnicity, and school-level Results: Our model fit well (RMSEA=.041). Examining total effects (direct + indirect), online and hybrid instruction were associated with lower SLS (b’s:-.06 to -.26; p’s<.01). The three online groups had the strongest effects (synchronous: b=-.15; 95%CI: [-.20, -.11]; asynchronous: b=-.17; [-.23, -.11]; mixed: b=-.14; [-.19, -.098]; p’s<.001). Sleep disturbance was also negatively associated with SLS (b=-.02; [-.02, -.02], p<.001). Monte-carlo simulations confirmed sleep disturbance mediated online instruction’s influence on SLS. The strongest effect was found for asynchronous instruction, with sleep disturbance mediating 24% of its effect (b = -.042; [-0.065, -.019]; p<.001). This sleep-mediated influence of asynchronous instruction propagated down to each SLS measure (p’s<.001), including a near 3-point difference on PROMIS cognitive scores (b = -2.86; [-3.73, -2.00]). Conclusion These analyses from the NESTED study indicate that sleep disruption may be one mechanism through which online instruction impacted learning during the pandemic. Sleep disturbances were unexpectedly influential for unscheduled instruction (i.e., asynchronous). Future analyses will examine specific sleep parameters (e.g., timing) and whether sleep’s influence differs in teens who self-report learning/behavior problems (e.g., ADHD). These nationwide data further underscore the importance of considering sleep as educators and policy makers determine school schedules.
How Many SARS-CoV-2–Infected People Require Hospitalization? Using Random Sample Testing to Better Inform Preparedness Efforts
(Wolters Kluwer, 2021) Menachemi, Nir; Dixon, Brian E.; Wools-Kaloustian, Kara K.; Yiannoutsos, Constantin T.; Halverson, Paul K.; Epidemiology, School of Public Health
Context: Existing hospitalization ratios for COVID-19 typically use case counts in the denominator, which problematically underestimates total infections because asymptomatic and mildly infected persons rarely get tested. As a result, surge models that rely on case counts to forecast hospital demand may be inaccurately influencing policy and decision-maker action. Objective: Based on SARS-CoV-2 prevalence data derived from a statewide random sample (as opposed to relying on reported case counts), we determine the infection-hospitalization ratio (IHR), defined as the percentage of infected individuals who are hospitalized, for various demographic groups in Indiana. Furthermore, for comparison, we show the extent to which case-based hospitalization ratios, compared with the IHR, overestimate the probability of hospitalization by demographic group. Design: Secondary analysis of statewide prevalence data from Indiana, COVID-19 hospitalization data extracted from a statewide health information exchange, and all reported COVID-19 cases to the state health department. Setting: State of Indiana as of April 30, 2020. Main Outcome Measure(s): Demographic-stratified IHRs and case-hospitalization ratios. Results: The overall IHR was 2.1% and varied more by age than by race or sex. Infection-hospitalization ratio estimates ranged from 0.4% for those younger than 40 years to 9.2% for those older than 60 years. Hospitalization rates based on case counts overestimated the IHR by a factor of 10, but this overestimation differed by demographic groups, especially age. Conclusions: In this first study of the IHR based on population prevalence, our results can improve forecasting models of hospital demand—especially in preparation for the upcoming winter period when an increase in SARS CoV-2 infections is expected.
Trends in dental insurance claims in the United States before and during the SARS-CoV-2 pandemic in 2020
(Wiley, 2022) Maupome, Gerardo; Scully, Allison C.; Yepes, Juan F.; Eckert, George J.; Downey, Timothy
Objectives: The SARS-CoV-2 pandemic disrupted health care services. Previous reports estimated reductions in demand and supply of dental care services, but actual changes have not been reported. The present report depicts a perspective of trends in claims from private dental practice in the United States during 2019 and 2020. Methods: Private dental insurance paid claims data from a data warehouse (encompassing 66+ carriers in the United States) were obtained for children and adults (treatments identified by their American Dental Association Code of Dental Procedures and Nomenclature [CDT]), encompassing a 5% random sample of all records between January 2019 and December 2020. A market-based treatment classification placed CDT codes into one of four categories based on the likelihood of being associated with urgent/emergency care. Results: Claims for 3.8 million patients constituted the 5% random sample for analyses. Substantial drops in the provision of treatment items were quantified for a large segment of private dental insurance plans at a national level, showing differential impacts in dental care categories. Conclusions: Week-by-week, detailed descriptions of demand/availability changes in dental care throughout the first year of the 2020 SARS-CoV-2 pandemic were obtained through contrasting perspectives in 2019. Provision of dental care and associated impacts fluctuated over time subject to treatment urgency, but also modified as the weeks/months of dental office lockdowns ebbed in and out of the dental market.
Supply-chain strategies for essential medicines in rural western Kenya during COVID-19
(WHO, 2021-05-01) Tran, Dan N.; Were, Phelix M.; Kangogo, Kibet; Amisi, James A.; Manji, Imran; Pastakia, Sonak D.; Vedanthan, Rajesh; Medicine, School of Medicine
Problem: The coronavirus disease 2019 (COVID-19) pandemic has disrupted health systems worldwide and threatened the supply of essential medicines. Especially affected are vulnerable patients in low- and middle-income countries who can only afford access to public health systems. Approach: Soon after physical distancing and curfew orders began on 15 March 2020 in Kenya, we rapidly implemented three supply-chain strategies to ensure a continuous supply of essential medicines while minimizing patients' COVID-19 exposure risks. We redistributed central stocks of medicines to peripheral health facilities to ensure local availability for several months. We equipped smaller, remote health facilities with medicine tackle boxes. We also made deliveries of medicines to patients with difficulty reaching facilities. Local setting: Τo implement these strategies we leveraged our 30-year partnership with local health authorities in rural western Kenya and the existing revolving fund pharmacy scheme serving 85 peripheral health centres. Relevant changes: In April 2020, stocks of essential chronic and non-chronic disease medicines redistributed to peripheral health facilities increased to 835 140 units, as compared with 316 330 units in April 2019. We provided medicine tackle boxes to an additional 46 health facilities. Our team successfully delivered medications to 264 out of 311 patients (84.9%) with noncommunicable diseases whom we were able to reach. Lessons learnt: Our revolving fund pharmacy model has ensured that patients' access to essential medicines has not been interrupted during the pandemic. Success was built on a community approach to extend pharmaceutical services, adapting our current supply-chain infrastructure and working quickly in partnership with local health authorities.
The Effect of In-Person Primary and Secondary School Instruction on County-Level Severe Acute Respiratory Syndrome Coronavirus 2 Spread in Indiana
(Oxford University Press, 2022-01-07) Bosslet, Gabriel T.; Pollak, Micah; Jang, Jeong Hoon; Roll, Rebekah; Sperling, Mark; Khan, Barbara; Medicine, School of Medicine
Background: Our goal was to determine the county-level effect of in-person primary and secondary school reopening on daily cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Indiana. Methods: In this county-level, population-based study, we used a panel data regression analysis of the proportion of in-person learning to evaluate an association with community-wide daily new SARS-CoV-2 cases. The study period was 12 July 2020-6 October 2020. We included 73 of 92 (79.3%) Indiana counties in the analysis, accounting for 85.7% of school corporations and 90.6% of student enrollment statewide. The primary exposure was the proportion of students returning to in-person instruction. The primary outcome was the daily new SARS-CoV-2 cases per 100 000 residents at the county level. Results: There is a statistically significant relationship between the proportion of students attending K-12 schools in-person and the county level daily cases of SARS-CoV-2 28 days later. For all ages, the coefficient of interest (β) is estimated at 3.36 (95% confidence interval, 1.91 to 4.81; P < .001). This coefficient represents the effect of a change in the proportion of students attending in-person on new daily cases 28 days later. For example, a 10 percentage point increase in K-12 students attending school in-person is associated with a daily increase in SARS-CoV-2 cases in the county equal to 0.336 cases/100 000 residents of all ages. Conclusions: In-person primary and secondary school is associated with a statistically significant but proportionally small increase in the spread of SARS-CoV-2 cases.

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