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.

Indoor Public Mask-Wearing Behavior Changes in Response to National, State, and Local COVID-19 Policies.

OBJECTIVE: To estimate changes in public mask-wearing behavior in response to public health policies during COVID-19. DESIGN: Panel of observed public mask-wearing. SETTING: Counts of adult behavior in Marion County, Indiana, between November 15, 2020, and May 31, 2021. DETERMINANTS OF INTEREST: (1) Removal of state masking requirement; (2) introduction of the National Strategy for the COVID-19 Response and Pandemic Preparedness; (3) the Centers for Disease Control and Prevention (CDC) recommendation that vaccinated individuals did not need to wear masks in public; and (4) COVID-19 vaccine availability. OUTCOME: Percent observed with correct mask-wearing. ANALYSES: Fixed-effects models estimated the association between policies and mask-wearing. RESULTS: Ending Indiana's mask requirement was not associated with changes in correct mask-wearing. The CDC's recommendation was associated with a decrease of 12.3 percentage points in correct mask-wearing (95% CI, -23.47 to -1.05; P = .032). CONCLUSIONS: Behavior encouraged by local mask requirements appeared to be resilient to changes in state policy. CDC recommendations appeared influential.

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.

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.

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.