How Has the Pandemic Affected Career Satisfaction and Employment Perceptions of Faculty in Health Administration?

Employee satisfaction has been shown to affect productivity and turnover among faculty in higher education. The COVID-19 pandemic has required significant organizational changes in higher education, including hiring freezes, furloughs, and a rapid move to online teaching. Little is known about the effects of these changes on health administration faculty. Therefore, the current study utilizes data from a national survey of health administration faculty conducted in 2018 and 2021 to perform three analyses: quantification of the proportion of faculty respondents experiencing furloughs or whose depart ments implemented a hiring freeze brought upon by the pandemic;changes in career satisfaction and employment perceptions between the years 2018 and 2021;and a cross-sectional analysis of the relationship between furloughs and/ or hiring freezes and 2021 career satisfaction and employment perceptions. Overall, 17.9% experienced a furlough and 81.4% indicated their department had a hiring freeze. We observed no significant changes in career satisfaction or employment perceptions from 2018 to 2021. However, receipt of furloughs was negatively associated with multiple indicators of career satisfaction and employment perceptions. The results of this study will be of interest to health administration program administrators and faculty as well as leaders in higher education who would benefit from understanding the impact of the pandemic on faculty more broadly.

In-person classroom instruction and risk of SARS-CoV-2 infection among undergraduates at Indiana University, Fall 2020.

Objective: To examine how in-person classroom instruction was related to risk of SARS-CoV-2 infection in undergraduate students. Participants: Indiana University undergraduate students (n = 69,606) enrolled in Fall 2020, when courses with in-person and remote instruction options were available. Methods: Students participated weekly in mandatory SARS-CoV-2 RT-PCR asymptomatic testing by random selection, supplemented with symptomatic testing as needed. We used log-binomial regression models to estimate the association between number of in-person credit hours and the risk of SARS-CoV-2 infection over the course of the semester. Results: Overall 5,786 SARS-CoV-2 cases were observed. Increased in-person credit hour exposures were not associated with increased risk of SARS-CoV-2 overall [aRR (95% CI): 0.98 (0.97,0.99)], nor within specific subgroups (Greek affiliation and class). Conclusions: In-person instruction did not appear to increase SARS-CoV-2 transmission in a university setting with rigorous protective measures in place, prior to mass vaccine rollout and prior to delta variant emergence.

SARS-CoV-2 reinfections in a US university setting, Fall 2020 to Spring 2021.

BACKGROUND: SARS-CoV-2 reinfections are a public health concern because of the potential for transmission and clinical disease, and because of our limited understanding of whether and how well an infection confers protection against subsequent infections. Despite the public health importance, few studies have reported rigorous estimates of reinfection risk. METHODS: Leveraging Indiana University's comprehensive testing program to identify both asymptomatic and symptomatic SARS-CoV-2 cases, we estimated the incidence of SARS-CoV-2 reinfection among students, faculty, and staff across the 2020-2021 academic year. We contextualized the reinfection data with information on key covariates: age, sex, Greek organization membership, student vs faculty/staff affiliation, and testing type. RESULTS: Among 12,272 people with primary infections, we found a low level of SARS-CoV-2 reinfections (0.6%; 0.4 per 10,000 person-days). We observed higher risk for SARS-CoV-2 reinfections in Greek-affiliated students. CONCLUSIONS: We found evidence for low levels of SARS-CoV-2 reinfection in a large multi-campus university population during a time-period prior to widespread COVID-19 vaccination.

Successive Wave Analysis to Assess Nonresponse Bias in a Statewide Random Sample Testing Study for SARS-CoV-2.

INTRODUCTION: Nonresponse bias occurs when participants in a study differ from eligible nonparticipants in ways that can distort study conclusions. The current study uses successive wave analysis, an established but underutilized approach, to assess nonresponse bias in a large-scale SARS-CoV-2 prevalence study. Such an approach makes use of reminders to induce participation among individuals. Based on the response continuum theory, those requiring several reminders to participate are more like nonrespondents than those who participate in a study upon first invitation, thus allowing for an examination of factors affecting participation. METHODS: Study participants from the Indiana Population Prevalence SARS-CoV-2 Study were divided into 3 groups (eg, waves) based upon the number of reminders that were needed to induce participation. Independent variables were then used to determine whether key demographic characteristics as well as other variables hypothesized to influence study participation differed by wave using chi-square analyses. Specifically, we examined whether race, age, gender, education level, health status, tobacco behaviors, COVID-19-related symptoms, reasons for participating in the study, and SARS-CoV-2 positivity rates differed by wave. RESULTS: Respondents included 3658 individuals, including 1495 in wave 1 (40.9%), 1246 in wave 2 (34.1%), and 917 in wave 3 (25%), for an overall participation rate of 23.6%. No significant differences in any examined variables were observed across waves, suggesting similar characteristics among those needing additional reminders compared with early participants. CONCLUSIONS: Using established techniques, we found no evidence of nonresponse bias in a random sample with a relatively low response rate. A hypothetical additional wave of participants would be unlikely to change original study conclusions. Successive wave analysis is an effective and easy tool that can allow public health researchers to assess, and possibly adjust for, nonresponse in any epidemiological survey that uses reminders to encourage participation.

Disparities in Telehealth Utilization in a Population of Publicly Insured Children During the COVID-19 Pandemic.

Telehealth became a crucial vehicle for health care delivery in the United States during the COVID-19 pandemic. However, little research exists on inequities in telehealth utilization among the pediatric population. This study examines disparities in telehealth utilization in a population of publicly insured children. This observational, retrospective study used administrative data from Alabama's stand-alone Children's Health Insurance Program, ALL Kids. Rates of any telehealth use for March to December 2020 were examined. In addition-to capture lack of health care utilization-rates of having no medical claims were examined and compared with March to December 2019 and 2018. Multinomial logit models were estimated to investigate how telehealth use and having no medical claims (reference category: having medical claims but no telehealth) were associated with race/ethnicity, rural-urban residence, and family income. Of the 106,478 enrollees over March to December 2020, 13.4% had any telehealth use and 24.7% had no medical claims. The latter was greater than no medical claims in 2019 (19.5%) and 2018 (20.7%). Black and Hispanic children had lower odds of any telehealth use (odds ratio [OR]: 0.81, P < 0.01; OR: 0.68, P < 0.01) and higher odds of no medical claims (OR: 1.11, P < 0.05; OR: 1.73, P < 0.05) than non-Hispanic White children. Rural residents had lower odds of telehealth use than urban residents. Those in the highest family income-based fee group had higher odds of telehealth use than the lowest family income-based fee group. As telehealth will likely continue to play an important role in health care delivery, additional efforts/investments are required to ensure telehealth does not further exacerbate inequities in pediatric health care access.

Evaluation of electronic recruitment efforts of primary care providers as research subjects during the COVID-19 pandemic.

BACKGROUND: Recruiting healthcare providers as research subjects often rely on in-person recruitment strategies. Little is known about recruiting provider participants via electronic recruitment methods. In this study, conducted during the COVID-19 pandemic, we describe and evaluate a primarily electronic approach to recruiting primary care providers (PCPs) as subjects in a pragmatic randomized controlled trial (RCT) of a decision support intervention. METHODS: We adapted an existing framework for healthcare provider research recruitment, employing an electronic consent form and a mix of brief synchronous video presentations, email, and phone calls to recruit PCPs into the RCT. To evaluate the success of each electronic strategy, we estimated the number of consented PCPs associated with each strategy, the number of days to recruit each PCP and recruitment costs. RESULTS: We recruited 45 of 63 eligible PCPs practicing at ten primary care clinic locations over 55 days. On average, it took 17 business days to recruit a PCP (range 0-48) and required three attempts (range 1-7). Email communication from the clinic leaders led to the most successful recruitments, followed by brief synchronous video presentations at regularly scheduled clinic meetings. We spent approximately $89 per recruited PCP. We faced challenges of low email responsiveness and limited opportunities to forge relationships. CONCLUSION: PCPs can be efficiently recruited at low costs as research subjects using primarily electronic communications, even during a time of high workload and stress. Electronic peer leader outreach and synchronous video presentations may be particularly useful recruitment strategies. TRIAL REGISTRATION: ClinicalTrials.gov , NCT04295135 . Registered 04 March 2020.

Factors that differentiate COVID-19 vaccine intentions among Indiana parents: Implications for targeted vaccine promotion.

Given low rates of uptake of the COVID-19 vaccine for children 12-17 and 5-11 years old, research is needed to understand parental behaviors and behavioral intentions related to COVID-19 vaccination for their children. In the state of Indiana, we conducted a non-random, online survey of parents or caregivers (N = 10,266) about their COVID-19 vaccine intentions or behaviors, demographic characteristics, and potential motivating reasons for getting the vaccine. In terms of behaviors/intentions, 44.8% of participants indicated they were vaccine acceptors (i.e., had already had their children vaccinated or would as soon as it was possible), 13.0% indicated they were vaccine hesitators (i.e., wanted to wait and see), and 42.2% indicated they were vaccine rejecters (i.e., would not vaccinate or only would if mandated). Compared to vaccine rejecters, vaccine hesitators were more likely to be motivated by perceptions of vaccine safety and efficacy, normative influences such as close friends/family who had been vaccinated and a recommendation from a provider, as well as if they were vaccinated themselves. These findings have implications for the development of targeted vaccine promotion strategies, such as social norms messaging and a focus on vaccine safety, in order to increase COVID-19 vaccination for eligible children.

Association of Health Status and Nicotine Consumption with SARS-CoV-2 positivity rates.

BACKGROUND: Much of what is known about COVID-19 risk factors comes from patients with serious symptoms who test positive. While risk factors for hospitalization or death include chronic conditions and smoking; less is known about how health status or nicotine consumption is associated with risk of SARS-CoV-2 infection among individuals who do not present clinically. METHODS: Two community-based population samples (including individuals randomly and nonrandomly selected for statewide testing, n = 8214) underwent SARS-CoV-2 testing in nonclinical settings. Each participant was tested for current (viral PCR) and past (antibody) infection in either April or June of 2020. Before testing, participants provided demographic information and self-reported health status and nicotine and tobacco behaviors (smoking, chewing, vaping/e-cigarettes). Using descriptive statistics and a bivariate logistic regression model, we examined the association between health status and use of tobacco or nicotine with SARS-CoV-2 positivity on either PCR or antibody tests. RESULTS: Compared to people with self-identified "excellent" or very good health status, those reporting "good" or "fair" health status had a higher risk of past or current infections. Positive smoking status was inversely associated with SARS-CoV-2 infection. Chewing tobacco was associated with infection and the use of vaping/e-cigarettes was not associated with infection. CONCLUSIONS: In a statewide, community-based population drawn for SARS-CoV-2 testing, we find that overall health status was associated with infection rates. Unlike in studies of COVID-19 patients, smoking status was inversely associated with SARS-CoV-2 positivity. More research is needed to further understand the nature of this relationship.

Expanding the Curriculum in a School of Public Health.

Public health education has long been concentrated in a core set of public health disciplines such as epidemiology, biostatistics, and environmental health. Despite leaps forward in our understanding of the myriad influences on public health, little has changed in the organization of our educational systems. One issue brought to the forefront of public consciousness by the COVID-19 pandemic is the importance of leisure experiences, such as nature walks, to mental and physical well-being. In this descriptive best practice article, we discuss our approach to expanding the notion of a school of public health and provide examples of how disciplines and subjects outside of the "norms" of public health education, including leisure studies, can help better prepare students for their future in the field. Leisure studies is just one of many subject areas that can add value to public health pedagogy, and we envision many other subject areas and departments integrating into schools of public health in the future.

Population Point Prevalence of SARS-CoV-2 Infection Based on a Statewide Random Sample - Indiana, April 25-29, 2020.

Population prevalence of persons infected with SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), varies by subpopulation and locality. U.S. studies of SARS-CoV-2 infection have examined infections in nonrandom samples (1) or seroprevalence in specific populations* (2), which are limited in their generalizability and cannot be used to accurately calculate infection-fatality rates. During April 25-29, 2020, Indiana conducted statewide random sample testing of persons aged ≥12 years to assess prevalence of active infection and presence of antibodies to SARS-CoV-2; additional nonrandom sampling was conducted in racial and ethnic minority communities to better understand the impact of the virus in certain racial and ethnic minority populations. Estimates were adjusted for nonresponse to reflect state demographics using an iterative proportional fitting method. Among 3,658 noninstitutionalized participants in the random sample survey, the estimated statewide point prevalence of active SARS-CoV-2 infection confirmed by reverse transcription-polymerase chain reaction (RT-PCR) testing was 1.74% (95% confidence interval [CI] = 1.10-2.54); 44.2% of these persons reported no symptoms during the 2 weeks before testing. The prevalence of immunoglobulin G (IgG) seropositivity, indicating past infection, was 1.09% (95% CI = 0.76-1.45). The overall prevalence of current and previous infections of SARS-CoV-2 in Indiana was 2.79% (95% CI = 2.02-3.70). In the random sample, higher overall prevalences were observed among Hispanics and those who reported having a household contact who had previously been told by a health care provider that they had COVID-19. By late April, an estimated 187,802 Indiana residents were currently or previously infected with SARS-CoV-2 (9.6 times higher than the number of confirmed cases [17,792]) (3), and 1,099 residents died (infection-fatality ratio = 0.58%). The number of reported cases represents only a fraction of the estimated total number of infections. Given the large number of persons who remain susceptible in Indiana, adherence to evidence-based public health mitigation and containment measures (e.g., social distancing, consistent and correct use of face coverings, and hand hygiene) is needed to reduce surge in hospitalizations and prevent morbidity and mortality from COVID-19.

Symptoms and symptom clusters associated with SARS-CoV-2 infection in community-based populations: Results from a statewide epidemiological study.

BACKGROUND: Prior studies examining symptoms of COVID-19 are primarily descriptive and measured among hospitalized individuals. Understanding symptoms of SARS-CoV-2 infection in pre-clinical, community-based populations may improve clinical screening, particularly during flu season. We sought to identify key symptoms and symptom combinations in a community-based population using robust methods. METHODS: We pooled community-based cohorts of individuals aged 12 and older screened for SARS-CoV-2 infection in April and June 2020 for a statewide prevalence study. Main outcome was SARS-CoV-2 positivity. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for individual symptoms as well as symptom combinations. We further employed multivariable logistic regression and exploratory factor analysis (EFA) to examine symptoms and combinations associated with SARS-CoV-2 infection. RESULTS: Among 8214 individuals screened, 368 individuals (4.5%) were RT-PCR positive for SARS-CoV-2. Although two-thirds of symptoms were highly specific (>90.0%), most symptoms individually possessed a PPV <50.0%. The individual symptoms most greatly associated with SARS-CoV-2 positivity were fever (OR = 5.34, p<0.001), anosmia (OR = 4.08, p<0.001), ageusia (OR = 2.38, p = 0.006), and cough (OR = 2.86, p<0.001). Results from EFA identified two primary symptom clusters most associated with SARS-CoV-2 infection: (1) ageusia, anosmia, and fever; and (2) shortness of breath, cough, and chest pain. Moreover, being non-white (13.6% vs. 2.3%, p<0.001), Hispanic (27.9% vs. 2.5%, p<0.001), or living in an Urban area (5.4% vs. 3.8%, p<0.001) was associated with infection. CONCLUSIONS: Symptoms can help distinguish SARS-CoV-2 infection from other respiratory viruses, especially in community or urgent care settings where rapid testing may be limited. Symptoms should further be structured in clinical documentation to support identification of new cases and mitigation of disease spread by public health. These symptoms, derived from asymptomatic as well as mildly infected individuals, can also inform vaccine and therapeutic clinical trials.

How Many SARS-CoV-2-Infected People Require Hospitalization? Using Random Sample Testing to Better Inform Preparedness Efforts.

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 MEASURES: 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.

Bayesian estimation of SARS-CoV-2 prevalence in Indiana by random testing.

From 25 to 29 April 2020, the state of Indiana undertook testing of 3,658 randomly chosen state residents for the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, the agent causing COVID-19 disease. This was the first statewide randomized study of COVID-19 testing in the United States. Both PCR and serological tests were administered to all study participants. This paper describes statistical methods used to address nonresponse among various demographic groups and to adjust for testing errors to reduce bias in the estimates of the overall disease prevalence in Indiana. These adjustments were implemented through Bayesian methods, which incorporated all available information on disease prevalence and test performance, along with external data obtained from census of the Indiana statewide population. Both adjustments appeared to have significant impact on the unadjusted estimates, mainly due to upweighting data in study participants of non-White races and Hispanic ethnicity and anticipated false-positive and false-negative test results among both the PCR and antibody tests utilized in the study.

Community Coronavirus Disease 2019 Activity Level and Nursing Home Staff Testing for Active Severe Acute Respiratory Syndrome Coronavirus 2 Infection in Indiana.

OBJECTIVES: To assess whether using coronavirus disease 2019 (COVID-19) community activity level can accurately inform strategies for routine testing of facility staff for active severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. DESIGN: Cross-sectional study. SETTING AND PARTICIPANTS: In total, 59,930 nursing home staff tested for active SARS-CoV-2 infection in Indiana. MEASURES: Receiver operator characteristic curves and the area under the curve to compare the sensitivity and specificity of identifying positive cases of staff within facilities based on community COVID-19 activity level including county positivity rate and county cases per 10,000. RESULTS: The detection of any infected staff within a facility using county cases per 10,000 population or county positivity rate resulted in an area under the curve of 0.648 (95% confidence interval 0.601‒0.696) and 0.649 (95% confidence interval 0.601‒0.696), respectively. Of staff tested, 28.0% were certified nursing assistants, yet accounted for 36.9% of all staff testing positive. Similarly, licensed practical nurses were 1.4% of staff, but 4.7% of positive cases. CONCLUSIONS AND IMPLICATIONS: We failed to observe a meaningful threshold of community COVID-19 activity for the purpose of predicting nursing homes with any positive staff. Guidance issued by the Centers for Medicare and Medicaid Services in August 2020 sets the minimum frequency of routine testing for nursing home staff based on county positivity rates. Using the recommended 5% county positivity rate to require weekly testing may miss asymptomatic infections among nursing home staff. Further data on results of all-staff testing efforts, particularly with the implementation of new widespread strategies such as point-of-care testing, is needed to guide policy to protect high-risk nursing home residents and staff. If the goal is to identify all asymptomatic SARS-Cov-2 infected nursing home staff, comprehensive repeat testing may be needed regardless of community level activity.

Symptoms and symptom clusters associated with SARS-CoV-2 infection in community-based populations: Results from a statewide epidemiological study.

BACKGROUND: Prior studies examining symptoms of COVID-19 are primarily descriptive and measured among hospitalized individuals. Understanding symptoms of SARS-CoV-2 infection in pre-clinical, community-based populations may improve clinical screening, particularly during flu season. We sought to identify key symptoms and symptom combinations in a community-based population using robust methods. METHODS: We pooled community-based cohorts of individuals aged 12 and older screened for SARS-CoV-2 infection in April and June 2020 for a statewide seroprevalence study. Main outcome was SARS-CoV-2 positivity. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for individual symptoms as well as symptom combinations. We further employed multivariable logistic regression and exploratory factor analysis (EFA) to examine symptoms and combinations associated with SARS-CoV-2 infection. RESULTS: Among 8214 individuals screened, 368 individuals (4.5%) were RT-PCR positive for SARS-CoV-2. Although two-thirds of symptoms were highly specific (>90.0%), most symptoms individually possessed a PPV <50.0%. The individual symptoms most greatly associated with SARS-CoV-2 positivity were fever (OR=5.34, p<0.001), anosmia (OR=4.08, p<0.001), ageusia (OR=2.38, p=0.006), and cough (OR=2.86, p<0.001). Results from EFA identified two primary symptom clusters most associated with SARS-CoV-2 infection: (1) ageusia, anosmia, and fever; and (2) shortness of breath, cough, and chest pain. Moreover, being non-white (13.6% vs. 2.3%, p<0.001), Hispanic (27.9% vs. 2.5%, p<0.001), or living in an Urban area (5.4% vs. 3.8%, p<0.001) was associated with infection. CONCLUSIONS: Symptoms can help distinguish SARS-CoV-2 infection from other respiratory viruses, especially in community or urgent care settings where rapid testing may be limited. Symptoms should further be structured in clinical documentation to support identification of new cases and mitigation of disease spread by public health. These symptoms, derived from asymptomatic as well as mildly infected individuals, can also inform vaccine and therapeutic clinical trials. RESEARCH IN CONTEXT: Evidence before this study: Using multiple journal articles queried from MEDLINE as well as a Cochrane systematic review, we examined all studies that described symptoms known to be associated with COVID-19. We further examined the guidelines from WHO and CDC on the symptoms those public health authorities consider to be associated with COVID-19. Most of the evidence comes from China, Italy, and the United States. Collectively prior research and guidance suggests there are a dozen symptoms reported by individuals who tested positive for COVID-19 in multiple countries. Symptoms include fever, cough, fatigue, anosmia, ageusia, shortness of breath, chills, myalgias, headache, sore throat, chest pain, and gastrointestinal issues. The evidence is generally of low quality as it is descriptive in nature, and it is biased towards hospitalized patients. Most studies report the proportion of patients hospitalized or testing positive for infection who report one or more symptoms within 3-14 days prior to hospitalization or infection. There has been little validation of symptoms among hospitalized or non-hospitalized patients. Furthermore, according to a Cochrane review, no studies to date assess combinations of different signs and symptoms.Added value of this study: This study employs multiple, rigorous methods to examine the ability of specific symptoms as well as symptom combinations/groups to predict laboratory-confirmed (RT-PCR) infection of SARS-CoV-2. Furthermore, the study is unique in its large sample drawn exclusively from community-based populations rather than hospitalized patients.Implication of all the available evidence: Combining the evidence from this study with prior research suggests that anosmia and ageusia are key symptoms that differentiate COVID-19 from influenza-like symptoms. Clinical screening protocols for COVID-19 should look for these symptoms, which are not commonly asked of patients who present to urgent care or hospital with flu-like symptoms. KEY POINTS: Important symptoms specific to COVID-19 are fever, anosmia, ageusia, and cough. Two-thirds of symptoms were highly specific (>90.0%), yet most symptoms individually possessed a PPV <50.0%. This study confirms using robust methods the key symptoms associated with COVID-19 infection, and it also identifies combinations of symptoms strongly associated with positive infection.