Abstract
Purpose To estimate the prevalence of current and past COVID-19 in Ohio adults.
Methods We used stratified, probability-proportionate-to-size cluster sampling. During July 2020, we enrolled 727 randomly-sampled adult English- and Spanish-speaking participants through a household survey. Participants provided nasopharyngeal swabs and blood samples to detect current and past COVID-19. We used Bayesian latent class models with multilevel regression and poststratification to calculate the adjusted prevalence of current and past COVID-19. We accounted for the potential effects of non–ignorable non–response bias.
Results The estimated statewide prevalence of current COVID-19 was 0.9% (95% credible interval: 0.1%–2.0%), corresponding to ∼85,000 prevalent infections (95% credible interval: 6,300–177,000) in Ohio adults during the study period. The estimated statewide prevalence of past COVID-19 was 1.3% (95% credible interval: 0.2%–2.7%), corresponding to ∼118,000 Ohio adults (95% credible interval: 22,000–240,000). Estimates did not change meaningfully due to non–response bias.
Conclusions Total COVID-19 cases in Ohio in July 2020 were approximately 3.5 times as high as diagnosed cases. The lack of broad COVID-19 screening in the United States early in the pandemic resulted in a paucity of population-representative prevalence data, limiting the ability to measure the effects of statewide control efforts.
Keywords: Sars-cov-2, Covid-19, Population-representative, probability-proportional-to-size cluster sampling, Bayesian, Ohio
Abbreviation: CDC, Centers for Disease Control and Prevention; CI, confidence interval; COVID-19, coronavirus disease 2019; IgG, Immunoglobulin G; IgM, Immunoglobulin M; IRB, Institutional Review Board; MASS-C, MASS Commercial Laboratory Survey; ODH, Ohio Department of Health; OSU, Ohio State University; PPS-CS, probability-proportional-to-size cluster sampling; REDCap, Research Electronic Data Capture; RT-PCR, real-time polymerase chain reaction; SARS-CoV-2, Severe acute respiratory syndrome coronavirus 2
Introduction
SARS-CoV-2, the cause of COVID-19, emerged in late 2019 following a zoonotic transfer event[1, 2]. Since that time, the United States has had more documented cases and deaths than any other country[3]. The limited availability of COVID-19 testing in the United States early in the pandemic led to a paucity of population-representative prevalence data; only Indiana, Rhode Island, and Connecticut reported statewide estimates of COVID-19 prevalence from 2020 [4], [5], [6].
The first three cases of COVID-19 in the United States state of Ohio were reported on March 9, 2020. More than twenty months later, Ohio has recorded more than 1.6 million cases, and more than 26,000 deaths [7]. Although public health restrictions were adopted early [8], COVID-19 testing was largely limited to symptomatic or exposed individuals for the first half of 2020. The lack of reliable prevalence data undermined the ability of the state to make evidence-informed health policy decisions. To address this, in July 2020, we undertook a population-representative household survey to estimate statewide prevalence of both past and current SARS-CoV-2 infections in Ohio adults.
Materials and methods
Sample
We used stratified, probability-proportional-to-size cluster sampling (PPS-CS) [9] to select 240 geographically-representative census tracts: 30 census tracts from within each of Ohio's eight planning regions (Figure 1 ). We planned to enroll five households within each selected tract. Because of expected non–response, we randomly sampled 50 residential addresses from each census tract. Because of the household recruitment design, we excluded post office box addresses. After removing duplicate addresses, 11,974 households were notified of their selection for recruitment.
Figure 1.
Sampled census tracts and dates of data collection across Ohio's eight administrative regions.
Recruitment
The study took place from July 9 to 28, 2020. In early July, we mailed postcards to all selected households (Supplementary Figure 1). Approximately three days before a trained team was expected to visit, each household received a second mailer describing privacy and safety measures, procedures for biosample collection, testing to be performed, the expected time required of the participant, the date when the team expected to visit, and other details.
At each address, the team used a random-number generator to randomly select one adult from among all eligible adults in the household. Eligible adults were non–institutionalized, aged 18 or older, slept in the home at least four of the last seven days, English or Spanish speakers, willing to provide blood and nasopharyngeal swab samples for COVID-19 testing, and able to consent for themselves. Individuals who agreed to only partial participation (e.g., only the survey but not the biosamples) were ineligible. If the randomly-selected individual declined participation, the team moved to the next targeted household.
If no adult was home, the team left an informational flier. In the first region, staff revisited each household up to three times to maximize recruitment. Analysis of the recruitment visits from that region revealed that repeated visits had not led to increased recruitment (that is, almost everyone who enrolled was recruited during the first home visit). For subsequent regions, staff left fliers requesting that an adult contact the study if the household wanted to participate. Households were not re-visited if no one contacted the study.
If the selected adult was not present, the team established a time to return. If the team returned at the specified time and the selected adult was still not present, the team randomly selected a second adult from among eligible adults present in the household. This approach balanced the goal of minimizing selection bias (by allowing multiple opportunities to enroll the selected adult) against the labor of making repeated visits to the household.
Consent
Staff explained the project procedures to the selected adult, and consenting individuals signed a paper consent form. Testing was free and participants were not compensated.
Survey and sample collection
Staff administered a 10-minute survey capturing demographics, symptoms, COVID-19 prevention behaviors, and other data.
After the survey, a trained nurse collected blood in three 5 mL serum-separator tubes. After 30–60 minutes, blood was centrifuged for 10 minutes at 1000 g. Some sera were refrigerated, and some maintained at room temperature, according to the intended diagnostic assay. The nurse also collected a nasopharyngeal swab sample following standard procedures. The nurse immediately placed the swab into viral transport media, and then into a cooler at 2–8°C. Sera and swabs were transported each evening to the laboratories for testing.
Laboratory testing
We used real-time reverse transcriptase polymerase chain reaction (RT-PCR) to diagnose current COVID-19 from nasopharyngeal swab samples. To identify people with past COVID-19, we used three assays to measure SARS-CoV-2 antibodies: Abbott SARS-CoV-2 IgG (targeting the nucleocapsid protein) [10], Diasorin Liaison S1/S2 IgG (targeting the spike protein) [11], and Epitope Diagnostics ELISA IgM (also targeting the nucleocapsid protein) [12]. Antibody results were qualitative (detected and/or not detected and/or equivocal).
Assay results
Per state law, positive PCR results were entered into the confidential Ohio Disease Reporting System (ODRS). ODRS automatically notified local health departments of new cases in their jurisdiction; local health departments conducted all case investigations. Participants received notice of positive swab PCR results from the local health department via telephone within four days of sample collection. We mailed negative swab PCR results, and all antibody results, to participants within two weeks.
Data management and statistical analysis
We used REDCap for data collection and management [13]. We used Stata (IC 16) and R (R Core Team) for all statistical analyses.
Using weights to account for the PPS-CS design, we calculated the frequencies and weighted proportions of selected variables for Ohio adults overall and in each of the eight administrative regions. Continuous variables are presented as means with linearized standard errors. We compared the characteristics of the enrolled sample to Ohio adults overall using 2018 population estimates (the most recent year available) from the American Community Survey [14].
To estimate the prevalence of current and past COVID-19, we used a Bayesian latent class approach with multilevel regression and poststratification [15], [16], [17], [18]. Detailed methods are presented elsewhere [19]; see also Supplement. We could not use a weighted approach because of the need to account for the sensitivities and specificities of the diagnostic assays and the relatively rare occurrence of positive results. We specified separate Bayesian models for current and past infection; both models adjusted for age and sex. We accounted for the underlying study design by including region as a fixed effect and the log of the census tract population as a covariate. Both models were fit using a Markov Chain Monte Carlo algorithm with 500,000 iterations, and the posterior distribution for each was summarized by the posterior mean and 95% credible interval.
For the model of current COVID-19, we specified an informative prior prevalence of 1% (based on confirmed cases in Ohio [7]) and informative prior distributions for the sensitivity (89%) and specificity (99.5%) of PCR testing of nasopharyngeal swab samples. For the model of past COVID-19, we specified an informative prior for the prevalence centered at 3% and informative prior distributions for the sensitivity and specificity of each of the antibody assays provided by each test's manufacturer: for Abbott SARS-CoV-2 IgG, 89% sensitivity and 99.6% specificity; for Diasorin Liaison S1/S2 IgG, 71% sensitivity and 98.5% specificity; and for Epitope Diagnostics ELISA IgM, 45% sensitivity and 99.8% specificity. Participants with at least one antibody result were included in the analysis of past COVID-19 prevalence. We used a flexible model for the observed data, so that individuals with different numbers of test results could contribute. Each participant's test results were incorporated into the model with the operating characteristics of each test, to infer their true underlying infection status. We used this inferred infection status to estimate prevalence in the population.
Because the estimated prevalence of both current and past COVID-19 may be biased due to non–ignorable non–response, we explored the robustness of the primary findings through sensitivity analyses that examined a range of simulated scenarios using the documented field response rate in each region. Separately for current and past infection, we reran each Bayesian model, using different ratios for the prevalence of COVID-19 for non–responders versus responders. Tested ratios ranged from 0.9 (non–responders had 10% lower COVID-19 prevalence compared to responders) to 3 (non–responders had COVID-19 prevalence that was three times as high as responders).
Ethical review
The Ohio Department of Health (ODH) Institutional Review Board (IRB) reviewed and approved the research. The Ohio State University IRB ceded review to the ODH IRB.
Safety
Before fieldwork initiation, all staff underwent PCR testing for COVID-19. Staff were supplied with personal protective equipment and underwent symptom checks every day. All procedures occurred outside whenever privacy and logistics permitted. Staff were offered a COVID-19 test at the end of data collection. No staff member tested positive during the mandatory pre-fieldwork testing, nor in the voluntary testing offered at the conclusion of data collection.
Results
Response rate
We calculated both an overall response rate (number of households participating out of all visited households that received a postcard, excluding from the denominator those that were ineligible because they were businesses, inaccessible, vacant or similar reasons), and a field response rate (number of households participating out of all visited households that received a postcard, again excluding from the denominator those that were ineligible; this rate also excludes from the denominator households that opted out prior to the study) (Figure 2 ). Across the full sample, the overall response rate was 13.4%, and the field response rate was 18.5%, with some variation by region (Supplementary Figure 2).
Figure 2.
Study flow.
Participant characteristics
The 727 participants were demographically similar across the eight geographic regions (Table 1 ). The sample skewed female (58.0% female vs. 40.8% male), and the mean age was 55.8 years. A majority (88.4%) reported White race, while 5.5% were Black. The sample was 3.2% Hispanic, and 1.1% of interviews were conducted in Spanish. Regarding education, 29.3% reported high school as their highest level of schooling, whereas 64.4% had attended at least some college. The mean number of adults living in enrolled households was 1.8, and the mean number of children was 0.5 ( Table 1).
Table 1.
Participant characteristics, overall and by region, Ohio, July 2020 (n = 727)
| Characteristic | Overall |
Central |
East Central |
Northeast |
Northwest |
Southeast |
Southeast Central |
Southwest |
West Central |
|||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
N = 727 |
N = 105 |
N = 97 |
N = 80 |
N = 162 |
N = 78 |
N = 61 |
N = 71 |
N = 73 |
||||||||||
| n | weighted% | n | weighted% | n | weighted% | n | weighted% | n | weighted% | n | weighted% | n | weighted% | n | weighted% | n | weighted% | |
| Gender | ||||||||||||||||||
| Male | 295 | 40.8 | 44 | 44.3 | 35 | 34.2 | 36 | 46.1 | 64 | 38.3 | 27 | 33.4 | 26 | 40.5 | 30 | 44.1 | 33 | 38.5 |
| Female | 425 | 58.0 | 57 | 52.0 | 61 | 64.2 | 44 | 53.9 | 98 | 61.7 | 51 | 66.6 | 34 | 58.6 | 41 | 55.9 | 39 | 60.7 |
| Missing | 7 | 1.2 | 4 | 3.7 | 1 | 1.7 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 1 | 1.0 | 0 | 0.0 | 1 | 0.8 |
| Race and/or ethnicity* | ||||||||||||||||||
| White | 659 | 88.4 | 89 | 80.1 | 90 | 94.4 | 74 | 90.6 | 148 | 87.7 | 75 | 97.1 | 58 | 96.5 | 61 | 86.7 | 64 | 86.6 |
| Black or African-American | 33 | 5.5 | 8 | 10.4 | 2 | 1.5 | 2 | 4.4 | 5 | 4.7 | 3 | 3.3 | 1 | 1.7 | 3 | 3.6 | 9 | 11.8 |
| Asian | 8 | 1.7 | 3 | 3.9 | 2 | 2.2 | 1 | 0.8 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 2 | 2.5 | 0 | 0.0 |
| Native American, American Indian, or Alaska Native | 4 | 0.3 | 1 | 0.8 | 0 | 0.0 | 0 | 0.0 | 1 | 0.7 | 0 | 0.0 | 1 | 1.3 | 1 | 1.1 | 0 | 0.0 |
| Hispanic, Latinx or Spanish | 20 | 3.2 | 3 | 3.6 | 0 | 0.0 | 4 | 4.2 | 8 | 7.0 | 1 | 0.0 | 0 | 0.0 | 4 | 6.1 | 0 | 0.0 |
| Other | 2 | 0.3 | 0 | 0.0 | 1 | 0.7 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 1.8 | 1 | 1.1 | 0 | 0.0 |
| Missing | 11 | 1.7 | 4 | 3.7 | 2 | 1.2 | 1 | 1.7 | 0 | 0.0 | 1 | 0.7 | 2 | 0.0 | 0 | 0.0 | 1 | 3.3 |
| Highest completed education | ||||||||||||||||||
| 1–8th grade | 6 | 0.9 | 1 | 1.7 | 1 | 0.7 | 0 | 0.0 | 1 | 0.5 | 1 | 3.3 | 1 | 3.3 | 1 | 1.1 | 0 | 0.0 |
| Some high school | 23 | 4.2 | 1 | 0.8 | 2 | 4.0 | 1 | 3.3 | 5 | 3.6 | 2 | 2.2 | 3 | 6.3 | 6 | 8.9 | 3 | 6.1 |
| High school graduate | 214 | 29.3 | 25 | 25.2 | 27 | 34.7 | 24 | 30.2 | 54 | 35.0 | 30 | 40.8 | 21 | 37.9 | 15 | 19.7 | 18 | 25.8 |
| Some college | 129 | 16.6 | 12 | 10.4 | 15 | 12.4 | 16 | 19.4 | 30 | 20.9 | 19 | 23.3 | 8 | 14.0 | 18 | 22.3 | 11 | 16.7 |
| Associates degree | 88 | 11.8 | 13 | 14.8 | 14 | 15.3 | 10 | 10.7 | 21 | 11.4 | 8 | 7.4 | 7 | 7.4 | 4 | 6.7 | 11 | 12.9 |
| Bachelor's degree | 149 | 20.7 | 29 | 23.4 | 22 | 19.3 | 13 | 19.7 | 32 | 18.4 | 9 | 13.2 | 9 | 13.8 | 18 | 25.2 | 17 | 21.5 |
| Higher than Bachelor's degree | 111 | 15.3 | 19 | 18.8 | 16 | 13.6 | 16 | 16.6 | 19 | 10.3 | 9 | 9.8 | 10 | 13.0 | 9 | 16.1 | 13 | 17.0 |
| Missing | 7 | 1.1 | 5 | 4.8 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 2 | 4.3 | 0 | 0.0 | 0 | 0.0 |
| Marital status | ||||||||||||||||||
| Married | 388 | 51.9 | 52 | 52.0 | 59 | 55.9 | 39 | 46.8 | 90 | 52.3 | 38 | 49.3 | 31 | 52.6 | 35 | 51.2 | 44 | 53.9 |
| Not married, living with partner | 53 | 7.1 | 6 | 7.4 | 3 | 4.7 | 4 | 3.9 | 17 | 12.7 | 7 | 10.8 | 8 | 14.6 | 6 | 7.9 | 2 | 4.2 |
| Widowed | 72 | 9.0 | 8 | 5.6 | 13 | 14.2 | 7 | 8.1 | 15 | 8.8 | 10 | 10.8 | 6 | 8.9 | 6 | 8.1 | 7 | 8.1 |
| Divorced | 98 | 13.3 | 15 | 13.7 | 12 | 11.7 | 11 | 14.7 | 18 | 12.5 | 14 | 19.0 | 9 | 15.0 | 8 | 8.9 | 11 | 17.8 |
| Separated | 8 | 1.3 | 1 | 0.8 | 1 | 0.6 | 3 | 5.3 | 1 | 0.7 | 2 | 1.7 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 |
| Never married | 102 | 16.6 | 19 | 16.7 | 9 | 13.0 | 16 | 21.2 | 21 | 13.0 | 7 | 8.4 | 6 | 8.0 | 15 | 22.8 | 9 | 16.1 |
| Missing | 6 | 0.9 | 4 | 3.7 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 1 | 1.0 | 1 | 1.1 | 0 | 0.0 |
| Employment status | ||||||||||||||||||
| Part-time | 53 | 6.9 | 5 | 4.6 | 5 | 4.8 | 8 | 9.0 | 15 | 11.0 | 4 | 3.7 | 4 | 6.6 | 3 | 4.2 | 9 | 12.8 |
| Full-time | 240 | 33.6 | 49 | 45.2 | 35 | 35.6 | 25 | 29.6 | 56 | 34.4 | 17 | 19.3 | 21 | 29.3 | 22 | 33.7 | 15 | 18.9 |
| Not employed | 420 | 58.0 | 45 | 45.0 | 56 | 58.7 | 46 | 60.6 | 90 | 54.1 | 56 | 75.8 | 32 | 60.3 | 46 | 62.1 | 49 | 68.3 |
| Do not know | 3 | 0.3 | 0 | 0.0 | 0 | 0.0 | 1 | 0.8 | 1 | 0.6 | 0 | 0.0 | 1 | 1.1 | 0 | 0.0 | 0 | 0.0 |
| Missing | 11 | 1.3 | 6 | 5.2 | 1 | 0.8 | 0 | 0.0 | 0 | 0.0 | 1 | 1.1 | 3 | 2.7 | 0 | 0.0 | 0 | 0.0 |
| Self-reported health | ||||||||||||||||||
| Excellent | 127 | 16.8 | 20 | 18.4 | 14 | 15.9 | 14 | 19.2 | 34 | 19.1 | 10 | 9.3 | 11 | 15.6 | 6 | 7.4 | 18 | 27.0 |
| Very good | 270 | 37.5 | 43 | 38.9 | 42 | 46.3 | 29 | 34.8 | 64 | 40.2 | 27 | 36.1 | 20 | 30.0 | 26 | 36.9 | 19 | 22.8 |
| Good | 214 | 29.8 | 27 | 27.3 | 29 | 25.6 | 34 | 31.4 | 40 | 24.7 | 24 | 31.0 | 19 | 30.7 | 25 | 37.3 | 26 | 33.8 |
| Fair | 82 | 11.3 | 9 | 9.0 | 10 | 10.9 | 10 | 11.2 | 20 | 13.7 | 11 | 12.8 | 6 | 13.7 | 8 | 10.0 | 8 | 14.4 |
| Poor | 27 | 3.6 | 2 | 2.8 | 1 | 0.7 | 2 | 2.5 | 4 | 2.3 | 6 | 10.8 | 4 | 9.0 | 6 | 8.3 | 2 | 1.9 |
| Do not know | 1 | 0.1 | 0 | 0 | 1 | 0.7 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 |
| Missing | 6 | 0.9 | 4 | 3.7 | 0 | 0.0 | 1 | 0.8 | 0 | 0.0 | 0 | 0.0 | 1 | 1.0 | 0 | 0.0 | 0 | 0.0 |
| Language | ||||||||||||||||||
| English | 714 | 98.0 | 99 | 93.8 | 97 | 100 | 79 | 99.3 | 162 | 100.0 | 78 | 100.0 | 59 | 97.4 | 69 | 97.2 | 71 | 98.3 |
| Spanish | 6 | 1.1 | 2 | 2.5 | 0 | 0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 1 | 1.7 | 2 | 2.8 | 1 | 0.8 |
| Missing | 7 | 1.0 | 4 | 3.7 | 0 | 0 | 1 | 0.7 | 0 | 0.0 | 0 | 0.0 | 1 | 1.0 | 0 | 0.0 | 1 | 0.8 |
| mean | SD | mean | SD | mean | SD | mean | SD | mean | SD | mean | SD | mean | SD | mean | SD | mean | SD | |
| Age (y) | 56.31 | 17.04 | 53.82 | 18.33 | 56.61 | 16.87 | 55.29 | 17.1 | 56.71 | 16.4 | 58.12 | 17.0 | 56.95 | 15.5 | 52.89 | 18.5 | 60.56 | 16.1 |
| # of adults living in the home | 1.84 | 0.83 | 1.84 | 0.83 | 1.92 | 0.9 | 1.81 | 0.9 | 1.86 | 0.9 | 1.68 | 0.7 | 1.77 | 0.7 | 1.9 | 0.8 | 1.9 | 0.8 |
| # children living in the home | 0.53 | 1.05 | 0.49 | 0.94 | 0.66 | 1.07 | 0.53 | 1.5 | 0.45 | 1.0 | 0.58 | 0.9 | 0.5 | 0.9 | 0.62 | 1.0 | 0.45 | 1.1 |
Since participants could select more than one category, percentages sum to more than 100%.
Participants were generally similar to the adult population of Ohio with respect to education, and ethnicity. Participants were not representative by gender, race, and age (Supplementary Table). While 11.7% of Ohio adults are Black, less than half that proportion of study participants reported Black race. The age distribution of Ohio adults overall is shifted younger than the age distribution of participants: 20.4% of Ohio adults are 18–29, compared to 9.7% of the study sample in that age range.
COVID-19 prevalence
Nearly all (n = 716, 98.5%) participants had valid nasopharyngeal swab PCR test results. Four individuals tested positive. From the Bayesian latent class model with multilevel regression and poststratification, the prevalence of current COVID-19 was 0.9% (95% credible interval: 0.1%–2.0%). Applying this prevalence to the 2018 adult population of Ohio (n = 9023,711) [14], we estimate ∼85,000 prevalent infections (95% credible interval: 6300–177,000) during the study period.
Slightly fewer participants (n = 667, 91.7%) had one or more antibody results. Of these, 39 tested positive on one, two or all three assays, with little observed agreement across tests: 36 tested positive on only one, one person tested positive on two, and two participants tested positive on all three assays. Again using a Bayesian latent class model, the prevalence of past COVID-19 was 1.3% (95% credible interval: 0.2%–2.7%). This past prevalence corresponds to ∼118,000 past infections (95% credible interval: 22,000–240,000) in Ohio adults.
In the sensitivity analysis accounting for the field response rate by region, the prevalence of current COVID-19 was 0.9% (95% credible interval: 0.1%–1.8%) when we assumed non–responders had 10% decreased COVID-19 prevalence compared to responders (e.g., prevalence ratio of 0.9), and 2.5% (95% credible interval: 0.2%–5.2%) when we assumed non–responders had 3-fold increased COVID-19 prevalence compared to responders (e.g. prevalence ratio of 3). For the prevalence of past infection accounting for non–response, corrected estimates ranged from 1.2% (95% credible interval: 0.2%–2.4%) to 3.5% (95% credible interval: 0.7%−7.1%) (Supplemental Figure 3) [19].
Symptoms
All participants were asked whether they had experienced non–specific symptoms associated with COVID-19 and other flu-like illnesses since March 1, 2020 (Figure 3 ). The most common mild symptoms were congestion (17.2%), headache (14.8%) and fatigue (14.1%), whereas the most common moderate and/or severe symptoms were fatigue (8.0%), joint pain (7.9%), and headache (6.8%). Overall, fever was reported by 5.9%, loss of taste and/or smell by 5.2%, and chills by 4.9%. More than half of respondents (54.1%) reported two or more symptoms; of these, 9.2% tested positive on at least one antibody assay. A quarter of participants (24.5%) reported at least one severe symptom, of whom 15.6% tested positive on at least one antibody assay.
Figure 3.
COVID-19 associated symptoms experienced since March 1, 2020, Ohio (n = 727). Notes. Fever measured as yes and/or no instead of mild versus moderate and/or severe. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Past COVID-19 testing and perception
Before study participation, 9.6% of respondents reported ever having been tested for COVID-19 (Table 2 ). Only one person reported a past positive swab test, and two people reported that a provider had told them they had been infected. However, despite not being diagnosed by a physician nor having a positive test, 93 participants (13.1%) reported that they had been infected previously; nearly all reported that the infection occurred in December 2019, January 2020 or February 2020. Eight of the 93 had detectable antibodies on one or more assays; none had a positive PCR test.
Table 2.
Self-reported past COVID-19 testing and perceptions, overall and by region, Ohio, July 2020 (n = 727)
| Characteristic | Overall |
Central |
East Central |
Northeast |
Northwest |
Southeast |
Southeast Central |
Southwest |
West Central |
|||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
N = 727 |
N = 105 |
N = 97 |
N = 80 |
N = 162 |
N = 78 |
N = 61 |
N = 71 |
N = 73 |
||||||||||
| n | weighted% | n | weighted% | n | weighted% | n | weighted% | n | weighted% | n | weighted% | n | weighted% | n | weighted% | n | weighted% | |
| In-person appointment with a health care provider because of concerns about COVID-19 | ||||||||||||||||||
| Yes | 25 | 2.8 | 1 | 0.7 | 2 | 1.5 | 5 | 7.2 | 5 | 2.6 | 5 | 5.3 | 4 | 5.1 | 1 | 1.7 | 2 | 2.5 |
| No | 696 | 96.1 | 100 | 95.6 | 95 | 98.5 | 75 | 92.8 | 157 | 97.4 | 73 | 94.7 | 56 | 94.0 | 69 | 96.7 | 71 | 97.5 |
| Do not Know | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 |
| Missing | 6 | 1.0 | 4 | 3.7 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 1 | 1.0 | 1 | 1.7 | 0 | 0.0 |
| Telemedicine appointment with a health care provider because of concerns about COVID-19 | ||||||||||||||||||
| Yes | 36 | 4.7 | 6 | 7.3 | 4 | 3.9 | 7 | 7.8 | 7 | 3.9 | 6 | 6.9 | 3 | 4.7 | 0 | 0.0 | 3 | 3.3 |
| No | 683 | 94.1 | 95 | 88.9 | 91 | 94.3 | 73 | 92.2 | 155 | 96.1 | 71 | 91.4 | 57 | 94.4 | 71 | 100.0 | 70 | 96.7 |
| Do not Know | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 |
| Missing | 8 | 1.2 | 4 | 3.7 | 2 | 1.8 | 0 | 0.0 | 0 | 0.0 | 1 | 1.7 | 1 | 1.0 | 0 | 0.0 | 0 | 0.0 |
| Attended urgent care, emergency room, or hospital because of concerns about COVID-19 | ||||||||||||||||||
| Yes | 23 | 3.0 | 2 | 1.5 | 2 | 1.8 | 3 | 5.3 | 5 | 3.8 | 3 | 3.3 | 3 | 4.0 | 1 | 1.7 | 4 | 4.7 |
| No | 695 | 95.8 | 99 | 94.8 | 94 | 97.6 | 77 | 94.7 | 156 | 96.0 | 75 | 96.7 | 56 | 91.8 | 69 | 97.2 | 69 | 95.3 |
| Do not Know | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 |
| Missing | 9 | 1.2 | 4 | 3.7 | 1 | 0.7 | 0 | 0.0 | 1 | 0.3 | 0 | 0.0 | 2 | 4.3 | 1 | 1.1 | 0 | 0.0 |
| Went to a COVID-19 testing site | ||||||||||||||||||
| Yes | 38 | 4.8 | 8 | 6.9 | 1 | 0.7 | 5 | 7.5 | 9 | 5.0 | 6 | 10.8 | 2 | 3.9 | 1 | 1.7 | 6 | 7.2 |
| No | 683 | 94.3 | 93 | 89.4 | 96 | 99.3 | 75 | 92.5 | 152 | 94.5 | 72 | 89.2 | 58 | 95.2 | 70 | 98.3 | 67 | 92.8 |
| Do not Know | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 |
| Missing | 6 | 0.8 | 4 | 3.7 | 0 | 0.0 | 0 | 0.0 | 1 | 0.6 | 0 | 0.0 | 1 | 1.0 | 0 | 0.0 | 0 | 0.0 |
| Ever prior COVID-19 test | ||||||||||||||||||
| Yes | 66 | 9.6 | 9 | 11.2 | 6 | 5.8 | 8 | 10.0 | 18 | 14.0 | 5 | 9.4 | 6 | 9.1 | 7 | 9.3 | 7 | 8.6 |
| No | 655 | 89.6 | 92 | 85.1 | 91 | 94.2 | 72 | 90.0 | 144 | 86.0 | 72 | 89.4 | 54 | 90.0 | 64 | 90.7 | 66 | 91.4 |
| Do not Know | 1 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 1 | 1.1 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 |
| Missing | 5 | 0.8 | 4 | 3.7 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 1 | 1.0 | 0 | 0.0 | 0 | 0.0 |
| Result of most recent COVID-19 test* | ||||||||||||||||||
| Negative | 50 | 7.9 | 8 | 10.5 | 4 | 4.0 | 7 | 9.2 | 16 | 13.2 | 3 | 7.5 | 4 | 7.0 | 5 | 6.9 | 3 | 3.6 |
| Positive | 1 | 0.1 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 1 | 1.1 |
| Uncertain | 1 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 1 | 1.1 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 |
| No result yet | 6 | 0.6 | 0 | 0.0 | 2 | 1.8 | 0 | 0.0 | 2 | 0.9 | 0 | 0.0 | 1 | 1.1 | 0 | 0.0 | 1 | 1.1 |
| Do not know | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 |
| Not applicable | 669 | 91.4 | 97 | 89.5 | 91 | 94.2 | 73 | 90.8 | 144 | 86.0 | 74 | 91.4 | 56 | 91.9 | 66 | 93.1 | 68 | 94.2 |
| Participant thinks they currently have COVID-19 | ||||||||||||||||||
| Yes, a provider told me | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 |
| Yes, a provider did not tell me, but I think I have it | 3 | 0.7 | 0 | 0.0 | 1 | 1.1 | 1 | 1.1 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 1 | 1.7 | 0 | 0.0 |
| Maybe | 13 | 1.6 | 1 | 0.6 | 2 | 2.3 | 0 | 0.0 | 2 | 1.2 | 3 | 3.9 | 1 | 1.0 | 3 | 3.9 | 1 | 1.7 |
| No | 690 | 94.8 | 98 | 93.8 | 92 | 94.2 | 77 | 97.1 | 156 | 96.0 | 75 | 96.1 | 57 | 96.8 | 64 | 91.6 | 71 | 96.7 |
| Do not know | 14 | 2.0 | 2 | 1.9 | 2 | 2.3 | 1 | 1.1 | 4 | 2.8 | 0 | 0.0 | 1 | 0.7 | 3 | 2.9 | 1 | 1.7 |
| Missing | 7 | 0.9 | 4 | 3.7 | 0 | 0.0 | 1 | 0.7 | 0 | 0.0 | 0 | 0.0 | 2 | 1.6 | 0 | 0.0 | 0 | 0.0 |
| Participant thinks they have had COVID-19 in the past | ||||||||||||||||||
| Yes, a provider told me | 2 | 0.2 | 0 | 0.0 | 1 | 0.7 | 0 | 0.0 | 1 | 0.7 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 |
| Yes, a provider did not tell me, but I think I had it | 93 | 13.1 | 7 | 6.9 | 14 | 14.6 | 16 | 21.9 | 25 | 15.0 | 9 | 11.4 | 11 | 18.6 | 5 | 9.4 | 6 | 9.3 |
| Maybe | 69 | 8.4 | 8 | 7.1 | 7 | 6.7 | 7 | 7.3 | 20 | 11.6 | 8 | 14.0 | 5 | 10.4 | 5 | 5.8 | 9 | 13.6 |
| No | 507 | 71.4 | 79 | 74.2 | 74 | 77.4 | 54 | 67.1 | 106 | 66.6 | 54 | 66.2 | 36 | 55.2 | 54 | 75.7 | 50 | 68.2 |
| Do not know | 48 | 5.8 | 7 | 8.1 | 1 | 0.7 | 2 | 2.8 | 10 | 6.1 | 6 | 7.3 | 8 | 14.8 | 7 | 9.0 | 7 | 7.8 |
| Missing | 8 | 1.1 | 4 | 3.7 | 0 | 0.0 | 1 | 0.8 | 0 | 0.0 | 1 | 1.1 | 1 | 1.0 | 0 | 0.0 | 1 | 1.1 |
| Time period when participant thinks they had COVID-19 in the past† | ||||||||||||||||||
| December 2019 | 18 | 2.0 | 0 | 0.0 | 2 | 1.3 | 4 | 4.4 | 7 | 4.5 | 3 | 3.6 | 1 | 1.1 | 0 | 0.0 | 1 | 3.3 |
| January 2020 | 31 | 4.2 | 2 | 1.9 | 4 | 3.1 | 3 | 7.5 | 5 | 5.8 | 2 | 2.2 | 5 | 6.6 | 2 | 2.8 | 4 | 5.1 |
| February 2020 | 26 | 4.2 | 1 | 0.8 | 5 | 4.7 | 7 | 8.1 | 5 | 2.6 | 2 | 3.3 | 3 | 7.6 | 3 | 6.7 | 0 | 0.0 |
| March 2020 | 14 | 1.4 | 3 | 2.5 | 2 | 1.7 | 1 | 0.8 | 0 | 2.8 | 2 | 2.2 | 1 | 1.7 | 0 | 0.0 | 0 | 0.0 |
| April 2020 | 3 | 0.4 | 0 | 0.0 | 1 | 1.1 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 1 | 1.7 | 0 | 0.0 | 1 | 0.8 |
| May 2020 | 1 | 0.1 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 1 | 1.1 |
| June 2020 | 2 | 0.3 | 1 | 1.7 | 1 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 |
| July 2020 | 1 | 0.2 | 0 | 0.0 | 0 | 0.0 | 1 | 1.1 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 | 0.0 |
asked of the n = 58 participants who had been tested previously by nasopharyngeal swab.
asked of those who believed they had been infected in the past.
COVID-19 prevention behaviors
We asked participants about the extent to which they practiced seven prevention behaviors during the stay-at-home period (March 15–May 25, 2020) and in the last 30 days (Figure 4 ). During the stay-at-home period, between 75.6%, and 94.4% of respondents (depending on the question) reported practicing each behavior all or most of the time. In the last 30 days, the pattern was similar but the proportions endorsing each behavior were lower than during the stay-at-home period: 50.9% to 90.9% reported practicing each behavior most or all of the time (Figure 4).
Figure 4.
COVID-19 prevention strategies practiced “all or most of the time” during the stay-at-home period and in the last 30 days, Ohio, July 2020 (n = 727). *Asked only of individuals working outside their homes.
Discussion
Over 20 days in July 2020, 0.9% of adult Ohioans had COVID-19, corresponding to ∼85,000 prevalent COVID-19 cases among adults. In contrast, ∼23,000 Ohio adults were diagnosed with COVID-19 during the same 20 days, suggesting that total cases in the state were approximately 3.5 times as high as diagnosed cases.
The gap between total and diagnosed cases in Ohio is expected. In contrast to protocols limiting COVID-19 testing to symptomatic or exposed people, the present study tested adult Ohioans without restriction. Most participants would not have sought testing and thus the cases identified would not have been captured. Total infections in the United States may be 3–20 times as high as reported infections, with expected variation based on location and testing protocols [20]. A recent study reported a nationwide cumulative incidence of COVID-19 of 11.9% (95% credible interval: 10.5%–13.5%) as of the end of October 2020, and found that only 1 in 5 adult SARS-CoV-2 infections had been previously reported [21].
We estimated that 1.3% of adult Ohioans had been infected with COVID-19 as of July 2020, corresponding to ∼118,000 COVID-19 cases. A growing literature underscores the complexity of estimating past infection using serology. Some studies suggest that IgG declines rapidly within two or three months of infection [22, 23]. IgG levels appear to be correlated with severity of symptoms, with significantly lower levels both at baseline and after two months among asymptomatic individuals compared to those with COVID-19 symptoms [24, 25]. In contrast, IgG was detectable for more than three months in patients who experienced severe COVID-19 requiring hospitalization [26]. If IgG persists for three months, we would expect the present study to detect infections occurring from approximately mid-April through mid-July. Approximately 62,000 COVID-19 cases were diagnosed in Ohio adults during that time period, suggesting that total cases were approximately twice as high as diagnosed cases.
The pandemic setting presented implementation challenges that are described in depth elsewhere [27]. Some of these challenges likely contributed to the enrolled sample being older and whiter than the broader Ohio adult population. Because older age and white race are each associated with reduced COVID-19 risk in the US broadly and in Ohio [7, 28, 29], this study may underestimate COVID-19 prevalence. In contrast, people who chose to participate may have been motivated by symptoms or exposure, and thus this study may overestimate COVID-19 prevalence. The lack of financial incentive to participate, coupled with the invasive sample collection procedures, may have led to a reduced response rate [30], although this time period was also characterized by higher-than-usual engagement in research [31]. We do not know which phenomenon played a larger role in this study. Sensitivity analyses confirm the robustness of the primary findings to response bias: current and past prevalence estimates changed little under a range of plausible scenarios that correct for differential COVID-19 prevalence among non–responders and responders.
These findings are similar to the few existing estimates of statewide COVID-19 prevalence, especially those conducted at a similar time in the pandemic. In Indiana, a statewide study of people aged 12 and older conducted in April 2020 estimated current COVID-19 prevalence at 1.7% (95% CI: 1.1%–2.5%) and past COVID-19 at 1.1% (95% CI: 0.8%–1.5%) [4]. A statewide study in Rhode Island from May 2020, which also used a household sampling design and Bayesian estimation methods, reported overall antibody prevalence of 2.1% (95% credible interval: 0.6%–4.1%), with considerably higher prevalence of past infection in Hispanic (7.5%) and Black respondents (3.8%) [5]. Prevalence of current infection was estimated at 1.5% (95% CI: 0.5%–3.1%) [5]. Investigators in Connecticut, which experienced a significant COVID-19 surge in spring 2020, used random digit dial methods to recruit a representative sample of adults in June and July 2020. That project estimated the prevalence of past COVID-19 at 4.0% (95% CI: 2.0%–6.0%) [5]. For Ohio, the CDCs MASS Commercial Laboratory Survey (MASS-C) project reported that 2.2% of Ohio adults had SARS-CoV-2 antibodies (95% CI: 1.4%–3.9%) from late July to mid-August 2020 [32]. The Ohio Red Cross reported that 1.3%–1.8% of Ohio blood donors were antibody-positive during the study period (unpublished weekly data, Ohio Red Cross). The similarity across findings, despite differences in methodology, lends credibility to the generally low COVID-19 prevalence in Ohio at the time of the study.
The present study has important limitations. The low response rate led to a smaller than desired sample size, which reduced the precision of the estimates, and excluded the possibility of calculating regional subgroup-specific estimates. Also, as noted previously, the sample was not representative of Ohio adults in certain important ways, including by race and age. Children were not tested for logistical reasons, and recent epidemiologic studies have revealed the significant role of children in the transmission chain [33]. The sensitivities and specificities of the diagnostic assays were imperfect, although the Bayesian models corrected for test characteristics [19]. Finally, the analysis does not account quantitatively for waning antibody levels. As noted, antibody levels depend on the time since infection, and test sensitivity also varies with time since infection; we lacked the relevant data to account for these timing elements in our analysis. However, given the timing of data collection with respect to the emergence of SARS-CoV-2 in Ohio, we believe that waning antibody levels are unlikely to have led to substantial missed infections.
Conclusions
The prevalence of current and past COVID-19 in Ohio in July 2020 was relatively low, resulting in a large majority of the population remaining susceptible to future infection. Of course, 0.9% of Ohio adults with current infection and 1.3% with past infection nevertheless corresponds to hundreds of thousands of Ohioans who had been infected with SARS-CoV-2 as of July 2020. The continued embrace of prevention measures – even in the setting of rising vaccination rates – is essential to keeping Ohioans safe.
Funding
This study was funded by the Ohio Department of Health.
Supporting information
Supplementary Figure 1. Household notification postcard
Supplementary Figure 2. Field response rate by region, Ohio, July 2020
Supplementary Figure 3. Sensitivity analysis of the effect of non–ignorable non–response bias on the prevalence of current and past COVID-19, Ohio, July 2020 [19]
Supplementary Table. Comparison of characteristics of sample participants and all Ohio adults, as reported in the American Community Survey, 2018 [14]
Acknowledgments
The study was possible only through the extraordinary efforts of dozens of colleagues. They include Amy Acton, Katarina Bischof, Dan Brook, Morgan Brown, Quanta Brown, Adam Clay, Stacy Endres-Dighe, Jaime Erickson, Debbie Fadoju, Amy Fairchild, Erin Farrell, Alex Fraga, Lance Himes, Robert Hood, Rich Hudkins, Tracy Intihar, Dane Kirk, Anisa Kline, Susan Koletar, Kathryn Lancaster, Alicia Leatherman, Joseph Macisco, Courtney Maierhofer, Jennifer Matsui, Amiah Matthews, Margaret McDow, Mary McKay, Maeve McLoughlin, Jessica Miller, Peter Mohler, Dania Nixon, Reena Oza-Frank, Michael Para, Donald Perone, Kirtana Ramadugu, Socrates Tuch, Piers Turner, and Abbie Zewdu.
Footnotes
Declaration of interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
The authors declare the following financial interests and/or personal relationships which may be considered as potential competing interests: Abigail Norris Turner reports financial support was provided by Ohio Department of Health.
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.annepidem.2021.11.009.
Appendix. Supplementary materials
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