Design of a population-based longitudinal cohort study of SARS-CoV-2 incidence and prevalence among adults in the San Francisco Bay Area

Adjusting Incidence Estimates with Laboratory Test Performances: A Pragmatic Maximum Likelihood Estimation-Based Approach

Understanding the incidence of disease is often crucial for public policy decision-making, as observed during the COVID-19 pandemic. Estimating incidence is challenging, however, when the definition of incidence relies on tests that imperfectly measure disease, as in the case when assays with variable performance are used to detect the SARS-CoV-2 virus. To our knowledge, there are no pragmatic methods to address the bias introduced by the performance of labs in testing for the virus. In the setting of a longitudinal study, we developed a maximum likelihood estimation-based approach to estimate laboratory performance-adjusted incidence using the expectation-maximization algorithm. We constructed confidence intervals (CIs) using both bootstrapped-based and large-sample interval estimator approaches. We evaluated our methods through extensive simulation and applied them to a real-world study (TrackCOVID), where the primary goal was to determine the incidence of and risk factors for SARS-CoV-2 infection in the San Francisco Bay Area from July 2020 to March 2021. Our simulations demonstrated that our method converged rapidly with accurate estimates under a variety of scenarios. Bootstrapped-based CIs were comparable to the large-sample estimator CIs with a reasonable number of incident cases, shown via a simulation scenario based on the real TrackCOVID study. In more extreme simulated scenarios, the coverage of large-sample interval estimation outperformed the bootstrapped-based approach. Results from the application to the TrackCOVID study suggested that assuming perfect laboratory test performance can lead to an inaccurate inference of the incidence. Our flexible, pragmatic method can be extended to a variety of disease and study settings.

Estimating the incidence of COVID-19, influenza and respiratory syncytial virus infection in three regions of Queensland, Australia, winter 2022: findings from a novel longitudinal testing-based sentinel surveillance programme

OBJECTIVE

The 2022 Australian winter was the first time that COVID-19, influenza and respiratory syncytial virus (RSV) were circulating in the population together, after two winters of physical distancing, quarantine and borders closed to international travellers. We developed a novel surveillance system to estimate the incidence of COVID-19, influenza and RSV in three regions of Queensland, Australia.

DESIGN

We implemented a longitudinal testing-based sentinel surveillance programme. Participants were provided with self-collection nasal swabs to be dropped off at a safe location at their workplace each week. Swabs were tested for SARS-CoV-2 by PCR. Symptomatic participants attended COVID-19 respiratory clinics to be tested by multiplex PCR for SARS-CoV-2, influenza A and B and RSV. Rapid antigen test (RAT) results reported by participants were included in the analysis.

SETTING AND PARTICIPANTS

Between 4 April 2022 and 3 October 2022, 578 adults were recruited via their workplace. Due to rolling recruitment, withdrawals and completion due to positive COVID-19 results, the maximum number enrolled in any week was 423 people.

RESULTS

A total of 4290 tests were included. Participation rates varied across the period ranging from 25.9% to 72.1% of enrolled participants. The total positivity of COVID-19 was 3.3%, with few influenza or RSV cases detected. Widespread use of RAT may have resulted in few symptomatic participants attending respiratory clinics. The weekly positivity rate of SARS-CoV-2 detected during the programme correlated with the incidence of notified cases in the corresponding communities.

CONCLUSION

This testing-based surveillance programme could estimate disease trends and be a useful tool in settings where testing is less common or accessible. Difficulties with recruitment meant the study was underpowered. The frontline sentinel nature of workplaces meant participants were not representative of the general population but were high-risk groups providing early warning of disease.

Prevalence and determinants of post-acute sequelae of COVID-19 in Liberia

BACKGROUND

Evidence from resource-rich settings indicates that many people continue to have persistent symptoms following acute SARS-CoV-2 infection, called post-acute sequelae of COVID-19 (PASC). Only a few studies have described PASC in sub-Saharan Africa (SSA). We aimed to describe PASC in Liberia.

METHODS

We randomly sampled all people who were reported from the most populous county to the Liberian Ministry of Health (MOH) as having a laboratory-confirmed SARS-CoV-2 infection from June to August 2021. We interviewed individuals by phone 3 to 6 months later. Those with persistence of at least one symptom were considered to have PASC.

RESULTS

From among 2848 people reported to the MOH from Montserrado County during the period of interest, we randomly selected 650; of these, 548 (84.3%) were reached and 505 (92.2%) of those who were contacted were interviewed. The median age was 38 years (interquartile range (IQR), 30-49), and 43.6% were female. During acute infection, 40.2% were asymptomatic, 53.9% had mild/moderate disease and 6.9% had severe/critical disease. Among the 59.8% (n = 302) who were initially symptomatic, 50.2% (n = 152) reported at least one persistent symptom; the most common persistent symptoms were fatigue (21.2%), headache (16.2%) and cough (12.6%); 40.1% reported that PASC significantly affected their daily activities. Being hospitalized with moderate disease [adjusted prevalence ratio (aPR), 2.00 (95% CI, 1.59 to 2.80] or severe/critical disease [aPR, 2.11 (95% CI, 1.59 to 2.80)] was associated with PASC, compared with those not hospitalized. Females were more likely than males to report persistent fatigue [aPR, 1.67 (95% CI, 1.08 to 2.57)].

CONCLUSIONS

Our findings suggest that persistent symptoms may have affected a large proportion of people with initially symptomatic COVID-19 in west Africa and highlight the need to create awareness among infected people and health care professionals.

Association of protective behaviors with SARS-CoV-2 infection: results from a longitudinal cohort study of adults in the San Francisco Bay Area

PURPOSE

In an effort to decrease transmission during the first years of the COVID-19 pandemic, public health officials encouraged masking, social distancing, and working from home, and restricted travel. However, many studies of the effectiveness of these measures had significant methodologic limitations. In this analysis, we used data from the TrackCOVID study, a longitudinal cohort study of a population-based sample of 3846 adults in the San Francisco Bay Area, to evaluate the association between self-reported protective behaviors and incidence of SARS-CoV-2 infection.

METHODS

Participants without SARS-CoV2 infection were enrolled from August to December 2020 and followed monthly with testing and surveys (median of four visits).

RESULTS

A total of 118 incident infections occurred (3.0% of participants). At baseline, 80.0% reported always wearing a mask; 56.0% avoided contact with nonhousehold members some/most of the time; 9.6% traveled outside the state; and 16.0% worked 20 or more hours per week outside the home. Factors associated with incident infection included being Black or Latinx, having less than a college education, and having more household residents. The only behavioral factor associated with incident infection was working outside the home (adjusted hazard ratio 1.62, 95% confidence interval 1.02-2.59).

CONCLUSIONS

Focusing on protecting people who cannot work from home could help prevent infections during future waves of COVID-19, or future pandemics from respiratory viruses. This focus must be balanced with the known importance of directing resources toward those at risk of severe infections.

Computational models predicting the early development of the COVID-19 pandemic in Sweden: systematic review, data synthesis, and secondary validation of accuracy

Computational models for predicting the early course of the COVID-19 pandemic played a central role in policy-making at regional and national levels. We performed a systematic review, data synthesis, and secondary validation of studies that reported on prediction models addressing the early stages of the COVID-19 pandemic in Sweden. A literature search in January 2021 based on the search triangle model identified 1672 peer-reviewed articles, preprints and reports. After applying inclusion criteria 52 studies remained out of which 12 passed a Risk of Bias Opinion Tool. When comparing model predictions with actual outcomes only 4 studies exhibited an acceptable forecast (mean absolute percentage error, MAPE < 20%). Models that predicted disease incidence could not be assessed due to the lack of reliable data during 2020. Drawing conclusions about the accuracy of the models with acceptable methodological quality was challenging because some models were published before the time period for the prediction, while other models were published during the prediction period or even afterwards. We conclude that the forecasting models involving Sweden developed during the early stages of the COVID-19 pandemic in 2020 had limited accuracy. The knowledge attained in this study can be used to improve the preparedness for coming pandemics.

Race-ethnicity and COVID-19 Vaccination Beliefs and Intentions: A Cross-Sectional Study among the General Population in the San Francisco Bay Area

OBJECTIVE

The study was designed to compare intentions to receive COVID-19 vaccination by race-ethnicity, to identify beliefs that may mediate the association between race-ethnicity and intention to receive the vaccine and to identify the demographic factors and beliefs most strongly predictive of intention to receive a vaccine.

DESIGN

Cross-sectional survey conducted from November 2020 to January 2021, nested within a longitudinal cohort study of the prevalence and incidence of SARS-CoV-2 among a general population-based sample of adults in six San Francisco Bay Area counties (called TrackCOVID). Study Cohort: In total, 3161 participants among the 3935 in the TrackCOVID parent cohort responded.

RESULTS

Rates of high vaccine willingness were significantly lower among Black (41%), Latinx (55%), Asian (58%), Multi-racial (59%), and Other race (58%) respondents than among White respondents (72%). Black, Latinx, and Asian respondents were significantly more likely than White respondents to endorse lack of trust of government and health agencies as a reason not to get vaccinated. Participants' motivations and concerns about COVID-19 vaccination only partially explained racial-ethnic differences in vaccination willingness. Concerns about a rushed government vaccine approval process and potential bad reactions to the vaccine were the two most important factors predicting vaccination intention.

CONCLUSIONS

Vaccine outreach campaigns must ensure that the disproportionate toll of COVID-19 on historically marginalized racial-ethnic communities is not compounded by inequities in vaccination. Efforts must emphasize messages that speak to the motivations and concerns of groups suffering most from health inequities to earn their trust to support informed decision making.