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Our take —

The study directly measured the viral shedding in aerosolized particles during talking, singing, and breathing. Among 22 participants from Singapore, they found that 41% had no viral RNA in their aerosols, but that singing released more viral RNA than talking or breathing, among those with viral RNA in any of their aerosol samples. Having detectable viral particles was more likely if the sample was collected earlier post symptom onset. SARS-CoV-2 antibody levels were not associated with viral RNA in aerosolized particles. The study was limited by the small number of participants, with most viral RNA copies recorded among just 2 individuals. Therefore, it is unclear how well these findings represent all people infected with SARS-CoV-2. However, the study provides evidence that singing has greater risk for transmitting viral particles compared to talking or breathing, regardless of underlying characteristics of a patient.

Study design

Cross-Sectional

Study population and setting

The objective of the study was to characterize viral loads in coarse and fine respiratory aerosol particles among patients >=21 years of age and positive for SARS-CoV-2 infection by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Participants were recruited from February to April 2021 at the National Centre for Infectious Diseases in Singapore, regardless of clinical status. Participants were admitted for inpatient isolation and evaluation before being transferred to isolation facilities. No severely ill patients were included in the study. Expiratory samples were collected from breath, with participants facing a cone which draws in air continually. Participants completed three activities: 30 minutes of breathing, 15 minutes of talking, 15 minutes of singing. Aerosols were collected and two sizes were defined (coarse and fine using a 5-micrometer threshold) for analysis. Participants also completed demographic questionnaires and answered symptom checklists on 7 pre-specified symptoms. The day of diagnosis was also recorded with previously conducted SARS-CoV-2 serology noted in the medical records as well. Day of illness sampling was determined among symptomatic patients as from the day symptoms began, while for asymptomatic and presymptomatic patients, it was defined as the day of PCR-positive clinical sample diagnosis. Virus genome sequences were also obtained from the National Public Health Laboratory. They used Fisher’s exact test to compare categorical variables, Mann-Whitney U tests for continuous variables, and Kruskal-Wallis tests to compare median viral loads between participants with positive SARS-CoV-2 aerosol detection vs. negative aerosol detection.

Summary of Main Findings

The study used data from 23 participants, and 1 withdrew before sample collection. Of the 22 participants, 13 (59%) had detectable SARS-CoV-2 RNA in their aerosol samples. Of these13, 3 participants were asymptomatic and 1 was presymptomatic at the time of collection. There was no evidence of significant associations with age, sex, type of virus variant, clinical symptoms, or presence of SARS-CoV-2 antibodies at time of diagnosis with detectable SARS-CoV-2 RNA in their aerosol samples. However, patients with detectable viral RNA in aerosols were sampled a median of 3 days post-illness onset, compared to a median of 5 days for individuals without detectable viral RNA in aerosols (P-value: 0.025). Two participants whose samples were collected on day 3 of their illness accounted for 52% of the total viral load captured by the study overall. Six participants (27% of the 13) had detectable levels of RNA from all three activities. Two (9%) had detectable levels only from fine particles collected from speaking, while another 2 only had detectable levels from fine particles collected from singing. The amount of detected viral RNA also differed by activities, with the median number of viral N gene copies in singing was 713.6, compared to talking with 477.9, and breathing with 63.5 (p 0.026). Among participants overall, 16 (73%) were infected with a variant of concern or a variant of interest for SARS-CoV-2.

Study Strengths

The major study strength was the direct testing of aerosol particles, which they collected through standardized activities. This is in contrast to other environmental studies which conduct ambient testing after a potential outbreak has occurred. This method is likely more accurate in measurement. They also were able to conduct quantitative PCR, which put a continuous number to the viral RNA copies, as opposed to less granular assessments. They also conducted testing for variants of concern or variants of interest to see if different variant strains were more highly associated. They also were able to compare both fine aerosols and coarse aerosols that may be blocked at different rates by masking or air filtering methods.

Limitations

The primary limitation was the small sample size. There was not enough power in their statistical analyses to quantify the difference in the average amount of RNA between individuals given the high amount of person-to-person variation. Additionally, they could not disaggregate this further by symptom status (e.g., asymptomatic vs. symptomatic) to better describe potential viral shedding patterns between participants. Given the diversity of strains, and that only 4 study participants were infected with variants that were neither of concern nor interest, they were not able to determine differences between strains, or groups of strains, and associated aerosol particles. Additionally, 2 participants accounted for the majority of the viral load detected, which may mean these results are most reflective of those participants, rather than fully generalizable to the rest of the sample or the at-large population. The study also passively ascertained antibody serology testing, which was likely impacted by the time at which the antibody specimens were collected and may not reflect the antibody levels the day of the actual aerosol sample collection. It is not possible to predict how this may bias potential associations with viral shedding in aerosol specimens.

Value added

This is the first study to directly measure the viral shedding in aerosolized particles during common activities.

Our take —

In this small study, available as a preprint and thus not yet peer reviewed, researchers found that vaccinated individuals from a single county in Wisconsin may have the same level of SARS-CoV-2 RNA in nasal secretions than unvaccinated individuals based on comparisons of cycle threshold (Ct) values. Information as to the vaccine manufacturer in the case of vaccinated individuals was not included in this preprint. However, this study cannot be used to conclude the infectiousness of the Delta variant from vaccinated individuals without further microbiological testing to compare amount of viable virus being shed, as other studies have suggested that vaccinated individuals shed less viable virus. In addition, this study cannot be used to inform the rate of breakthrough infections from the delta variant given no denominator of all vaccinated individuals was included.

Study design

Cross-Sectional

Study population and setting

This study compared SARS-CoV-2 threshold cycle (Ct) values by self-reported vaccination status from a convenience sample of 83 individuals with COVID-19 between June 28, 2021 and July 24, 2021 in Dane County, Wisconsin. Information as to the vaccine manufacturer in the case of vaccinated individuals was not reported in this preprint. An additional 208 samples from COVID-19 positive individuals from outside Dane County were considered in secondary analyses. For all samples, Ct values were determined by a semi-quantitative PCR assay in the same commercial laboratory. A subset of the of the specimens (50/291) were sequenced to identify whether individuals were infected with the delta variant. 

Summary of Main Findings

In the primary sample (n=83), 32 individuals reported being fully vaccinated and 51 individuals reported they had not received any vaccine. There was no significant difference in the Ct values between fully vaccinated individuals and those who reported receiving no vaccination. Of these specimens, only 16 were able to generate sequence data and 14/16 were of the delta lineage. In the entire expanded sample (n=291), there was also no difference in Ct values by vaccination status and 42 of the 50 sequenced specimens were of the delta lineage. Notably, there was a high proportion of vaccinated individuals who had a low Ct value (<30; 84% [66/79]) and an extremely low Ct value (<20; 33% [26/79]).

Study Strengths

It is a strength that all laboratory testing was conducted in the same commercial laboratory, which enables direct comparison of Ct values, and individuals were sampled from the same area and time.

Limitations

The sample size of the study was limited, and only a fraction of the included samples were able to be sequenced. It is a limitation that vaccination status was ascertained by self-report, which may be subject to social desirability bias. The study also did not clearly characterize the individuals included in the study, so it is unclear if vaccinated individuals were similar to unvaccinated individuals by demographics, such as age, comorbidities, timing during infection when samples were collected, and symptoms. In addition, it is probable that symptomatic vaccinated individuals were more likely to get tested than asymptomatic vaccinated individuals resulting in potential selection bias. Furthermore, while Ct values crudely correlate with viral load in an inverse manner, there was no microbiological testing to confirm whether detectable virus was infectious. Finally, the study cannot make conclusions about the rate of breakthrough infections without considering all vaccinated individuals in the population.

As a note, this ESS refers to the version of the study published at medRxiv on July 31, 2021. Subsequently, a revised version, entitled “Shedding of Infectious SARS-CoV-2 Despite Vaccination” was published on August 24, 2021— NCRC has not yet reviewed this version.

Value added

This investigation demonstrated that some vaccinated individuals who have a breakthrough infection may carry SARS-CoV-2 RNA in nasal secretions at levels that may contribute to transmission.

Our take —

This study, available as a preprint and thus not yet peer-reviewed, used cross-sectional data from three rounds of a national population-based study in England to estimate the prevalence and correlates of persistent COVID-19 symptoms, and patterns of symptom occurrence. Of the 76,155 participants with self-reported COVID-19 symptom onset 12+ weeks before their survey date, 37.7% reported at least one persistent symptom 12+ weeks from COVID-19 diagnosis. Among all participants, the weighted population prevalence of at least one persistent symptom 12+ weeks from symptom onset was 5.75% (95% CI: 5.68, 5.82) in England. They also identified that among those with persistent symptoms, symptoms tended to cluster as “tiredness” (fatigue, muscle aches, and difficulty sleeping) or “respiratory” (shortness of breath, chest tightness, and chest pain). The “respiratory cluster” was more common among those with more severe initial disease. While it’s possible that this persistent symptom prevalence is an overestimate given a low survey response rate and potential for pre-existing symptoms to be attributed to COVID-19, this paper has important implications for clinicians treating individuals with a history of COVID-19 and adds to our collective understanding about COVID-19’s long-term sequelae.

Study design

Cross-Sectional

Study population and setting

This study used cross-sectional data from rounds three to five of the Real-Time Assessment of Community Transmission-2 (REACT-2) study in England between September 2020 and February 2021. The REACT-2 study used a cluster sampling approach to randomly select individuals from the National Health Service patient list within each of the 315 lower-tier local authority areas (LTLA). The study collected self-reported PCR testing history, as well as demographic characteristics, medical comorbidities, and current symptoms that may be related to COVID-19. This analysis included individuals who reported a history of symptomatic COVID-19 with symptom onset 12-weeks or more before the survey date. They weighted symptom prevalence estimates by sex, age, ethnicity, LTLA-area, and an index of multiple deprivation to estimate prevalence across England. They then investigated the relationship between demographic and lifestyle factors with any symptom persistence at 12 weeks or more via logistic regression, gradient boosted tree models, and generalized additive models. Finally, they used CLustering LARge Applications (CLARA) to identify symptom clusters among participants with lingering symptoms 12 or more weeks from their COVID-19 onset.

Summary of Main Findings

Of the 508,707 participants in REACT-2 rounds three to five (26-29% response rate across rounds), 76,155 reported a valid date of symptomatic COVID-19 symptom onset 12 or more weeks before their survey date. A large percentage (37.7%) reported at least one persistent symptom at 12+ weeks from symptom onset, with 14.8% reporting at least three persistent symptoms. The predominant symptom at 12 weeks was tiredness, followed by shortness of breath, difficulty sleeping, and muscle aches. They calculated the weighted population prevalence in England of at least one persistent symptom of 5.75% (95% CI: 5.68, 5.82) and three or more persistent symptoms of 2.22% (95% CI: 2.18, 2.26). Overall, female participants reported more persistent symptoms than male participants (age-adjusted OR: 1.51, 95% CI: 1.46, 1.55 for 12+ weeks of symptoms), which increased with age. After adjustment for age and sex, comorbidities, weight, smoking, vaping, living in deprived areas, being low income, and being a healthcare worker were each associated with increased reports of symptom persistence at 12+ weeks. In the clustering analysis (N=53,309), they identified a “tiredness cluster” (N=15,799, 30%) — which  included fatigue, muscle aches, and difficulty sleeping — and a “respiratory cluster” (N=4,441, 9%)— which included shortness of breath, chest tightness, and chest pain — of persistent symptoms, with the “respiratory cluster” more common among those with a history of more severe COVID-19 initially.

Study Strengths

This national study included a large number of randomly selected participants across England drawn from NHS patient lists. The study sample therefore included individuals who tested positive for SARS-CoV-2 in the community regardless of their initial disease severity or whether or not they were hospitalized. The study used multivariable analyses to identify factors with independent associations with COVID-19 sequelae.

Limitations

This survey had a relatively low response rate (<30%), which raises questions about how representative respondents were of the initial sampling frame or the national population. Without data comparing respondents and non-respondents, it is difficult to estimate the magnitude or direction of bias, but it is plausible that individuals with prolonged COVID-19 symptoms may be more likely to participate than those without sequelae, potentially overestimating the prevalence of long COVID-19 among those with symptomatic acute infections. It is not clear when symptom onset occurred (i.e. whether it was caused by COVID-19 or whether it was prevalent before COVID-19 onset, both of which could be complicated by initial symptomatic COVID-19 duration). By focusing on “any” symptom prevalence at 12+ weeks, they may be overestimating symptom persistence, especially considering many symptoms reported could be influenced by non-COVID-19 conditions and lifestyle factors (such as fatigue and headaches).

Value added

This population-based study estimated the national prevalence of COVID-19 symptoms that persist 12+ weeks from initial COVID-19 onset, highlighting the long-term implications of SARS-CoV-2 infection. The large sample size and random sampling methodology of this study have likely captured a more representative range of post-COVID-19 experiences than studies relying on samples who were hospitalized with COVID-19 or those who responded to surveys on social media.

Our take —

A population-based serosurvey was conducted using random household-based sampling in Kinshasa, Democratic Republic of Congo from October 2020 to November 2020. Of those participating in the study, 16.6% tested positive for SARS-CoV-2 antibodies, suggesting a substantial undercounting of the true number of SARS-CoV-2 infections compared to the number of cases reported by the national health surveillance system. Findings should be interpreted with some caution as close to half of those eligible were not included in the final analyses, potentially under- or overestimating the prevalence, depending on whether those at home were more or less likely to be previously infected.

Study design

Cross-Sectional

Study population and setting

Between October 22, 2020 and November 8, 2020, a cross-sectional, household-based serosurvey was conducted to assess the prevalence of SARS-CoV-2 IgG antibodies in Kinshasa, Democratic Republic of the Congo. A sampling frame was constructed using health divisions of the city; random sampling was done based on a three-stage probability proportional-to-size sampling strategy. Venous blood samples were collected from all available participants and a Luminex-based assay was used to detect IgG antibodies against both SARS-CoV-2 nucleocapsid and spike proteins. A positive serology was based on reactivity to both SARS-CoV-2 proteins. Using a smartphone application, participants answered questions regarding their household members, symptoms, socioeconomic status, and behaviors. The seroprevalence estimate was weighted and age-standardized based on demographic data.

Summary of Main Findings

Among 1,233 participants from 292 households, the weighted, age-standardized estimate of seroprevalence was 16.6% (95% CI: 14.0-19.5). An additional 17.1% were considered “indeterminate,” as their test was shown to be reactive to one of the two SARS-CoV-2 proteins. Nearly three of every four participants shared a common yard space (72.2%, 890/1,233), as opposed to living in a single family home, and over half did not have access to handwashing at home (54.2%, 668/1,233). Based on the measured prevalence, the authors estimated that there had been a total of 2,426,406 infections in Kinshasa , which would indicate that for every one case identified through the health surveillance system, there were 292 cases that were not diagnosed.

Study Strengths

The use of random probability proportional-to-size sampling is a strength because this study likely represents the population-based prevalence better than a study conducted in a specific population group.

Limitations

A total of 2,400 individuals were eligible for the study. Of these, 1,607 were present at the time of the enrollment and 1,233 were included in the final analysis. Therefore, the proportion of eligible participants included in the final analysis was 51%. By only enrolling such a limited proportion of all eligible individuals, this study may not be representative of the entire population of Kinshasa. As a result, the true population-based prevalence may be biased downward or upward, depending on whether those previously infected were more or likely to enroll; such bias would also influence the calculated ratio of reported cases to true infections.

Value added

This is one of the first studies to assess seroprevalence in the African context in the general population. Past serosurveys have primarily been conducted among specific population groups (e.g. healthcare workers). This study helps shed light on the discrepancy between reported cases and the true number of cases.

Our take —

The Quidel SARS-CoV-2 rapid antigen test has high specificity but lower sensitivity in both patients admitted to the hospital through the emergency department with COVID-19-compatible symptoms as the chief complaint (72%) and without (61%), enabling a quick way to detect most infected patients. However, negative antigen test results, especially in those with confirmed COVID-19 symptoms or other high prevalence conditions, should be followed by confirmatory RT-PCR testing to prevent false-negatives.

Study design

Cross-Sectional

Study population and setting

Between June 30 and August 31, 2020, matched samples (nasal, nasopharyngeal, and saliva) were collected from 307 patients with COVID-19-compatible symptoms listed as the chief complaint and 1,732 patients without (median age of total population: 56 years, 55% female) who were admitted to the hospital through the emergency department from a tertiary medical center in Los Angeles. The performance of the Quidel Sofia 2 SARS antigen fluorescent immunoassay was compared to a gold standard Fulgent COVID-19 RT-PCR test.

Summary of Main Findings

In those admitted to the hospital from the emergency department in a period of high COVID-19 community prevalence, 8% of patients without COVID-19-compatible symptoms as chief complaint and 39% of patients with COVID-19-compatible symptoms as chief complaint had a positive result from either test. Relative to the RT-PCR test, the Quidel rapid antigen test had a sensitivity of 61% in those without COVID-19-compatible symptoms as chief complaint and 72% in those with COVID-19-compatible symptoms as chief complaint. The specificity of the test was high (99.5% in patients without and 98.7% in patients with COVID-19-compatible symptoms as chief complaint). The results further confirm that rapid antigen tests produce results more promptly, are cost-effective, and highly specific, although they are less sensitive than RT-PCR tests.

Study Strengths

This study is one of the first and largest to report on the performance of the Quidel rapid antigen test in the clinical setting.

Limitations

In the study, patients were termed “symptomatic” or “asymptomatic” based on whether they had COVID-19-compatible symptoms listed as the chief complaint on admission to the emergency department which likely resulted in misclassification, as those with the chief complaint that was not suggestive of COVID-19 could still have had milder symptoms associated with the infection that were not recorded. Additionally, the study did not evaluate potential differences in rapid antigen performance by subgroups, such as by age (which ranged from 16-107 years). The study population was drawn from a single hospital in Los Angeles and only included those hospitalized from the emergency department, which may limit the generalizability of these findings to other clinical settings or the general population.

Value added

This study adds to the body of literature that shows rapid antigen detection tests have strong specificity yet modest sensitivity in COVID-19 patients, showing that positive results are generally correct while negative results may require further confirmatory RT-PCR testing, especially in high-COVID-19 prevalence conditions (such as patients entering the emergency department with COVID-19-compatible symptoms as their chief complaint).

Our take —

Using surveillance data paired with questionnaires from 622 individuals tested for SARS-CoV-2 infection at a research institute in Luanda, Angola, 14.3% tested positive. The odds of infection were higher among older individuals and those living in rural areas. Despite low data availability, this is one of the first studies to shed light on the characteristics associated with SARS-CoV-2 infection in sub-Saharan Africa, and Angola more specifically.

Study design

Cross-Sectional

Study population and setting

In this study, the associations between sociodemographic and SARS-CoV-2 test results were examined among 622 individuals at a research institute in Luanda, Angola (Instituto Nacional de Investigacão em Sau´de) between January and September 2020. Sociodemographic characteristics were collected using a surveillance questionnaire. Testing was performed because of suspected COVID-19, exposure to someone infected with SARS-CoV-2, or travel to a place with active transmission. Testing was performed using smears or swabs from the upper respiratory tract. Screening and confirmation were performed using RT-PCR assay.

Summary of Main Findings

Overall, 14.3% (89/622) tested positive for SARS-CoV-2. There was no difference between men and women, but older individuals and those from rural areas had a greater burden of disease. For those 60 years and older, the odds of infection was 23.3 times higher than that of those less than 10 years old (adjusted OR: 23.3; 95% CI: 4.83, 112), however estimates by age group were unstable due to small sample sizes within age sub-groups. Still, the odds of infection was higher for every age group compared with those less than 10 years old. Those living outside of Luanda had 7 times the odds of infection compared to those living in Luanda (aOR: 7.4; 95% CI:1.64, 33.4).

Study Strengths

This study was able to link sociodemographic results from a routine survey with regular testing from a research institute.

Limitations

It is noted that 622 individuals were analyzed as part of this study out of the 16,028 individuals tested during this time period, but there is no reference to how these individuals were sampled or to whom these results might apply. From the results, it appears as though 622 individuals completed the survey. There is no discussion on how these individuals may be different from those who did not complete the survey, potentially biasing the results of characteristics identified to be associated with a positive SARS-CoV-2 test.

Value added

This is one of the few studies to examine characteristics of those infected with SARS-CoV-2 in sub-Saharan Africa.

Our take —

This study, which included a prospective smartphone application cohort (United States, United Kingdom, and Sweden, total n = 400,750) and a cross-sectional Facebook survey (United States, n = 1,344,996), assessed SARS-CoV-2 testing, symptoms, and severity in 18 to 44 year-old women who also reported their pregnancy status from March to June 2020. Although they found that pregnant women were more likely to be hospitalized than non-pregnant women, they did not find a difference in self-reported symptom severity among hospitalized pregnant versus non-pregnant women. It is difficult to assess how clinical caution during pregnancy may have impacted the difference in reported hospitalization rates despite similar test positivity rates in women regardless of pregnancy status, especially without data on vitals or laboratory values on admission or outcomes after hospitalization (intensive care, death, etc.). Further, digital research recruitment tools may systematically leave out lower income women who may also be more likely to have more severe health outcomes due to underlying comorbidities and/or limited access to healthcare, making it difficult to generalize the findings to all reproductive aged women.

Study design

Cross-Sectional, Prospective Cohort

Study population and setting

This study, which included a prospective cohort and a cross-sectional survey to replicate cohort findings, assessed SARS-CoV-2 severity in reproductive age women (18-44 years old) who reported their pregnancy status from March to June 2020. The prospective cohort included women from the United States, United Kingdom, and Sweden (400,750 women in total) who used a smartphone application for a median of 18 days (Interquartile Range: 6, 34) between March 24 and June 7, 2020 to record symptoms associated with COVID-19. The cross-sectional replication dataset included 1,344,996 women surveyed through Facebook (participants were selected via a sampling procedure designed to achieve a representative sample of Facebook’s United States active user base), which asked about COVID-19 symptoms in the preceding 24 hours between April 6 and June 7, 2020. Participants in both prospective and cross-sectional samples were classified based on self-report according to their pregnancy status (pregnant vs. not pregnant), SARS-CoV-2 test results, COVID-19 symptoms, and hospitalization status. They used a bootstrapped train-test procedure in the prospective cohort to classify non-pregnant participants who declined to report their test results as suspected test positive based on self-reported symptoms. They assessed for differences in reported symptoms among women by pregnancy status, test positivity, and hospitalization status in both datasets. Finally, they created a severity index among hospitalized participants that was a weighted sum of symptoms present at hospital presentation.

Summary of Main Findings

In the prospective cohort, 14,049 (3.5%) of the 400,750 participants reported they were pregnant. While pregnant women were more likely to be tested (8% versus 6.1%), there were similar percentages of positive tests in pregnant and non-pregnant women (0.6% and 0.7%, respectively), positive tests and hospitalizations (0.07% and 0.09%, respectively), and suspected positive tests and hospitalizations (0.15% and 0.16%, respectively). Finally, non-pregnant women (6.7%) were more likely to have symptoms that classified them as suspected positive cases than pregnant women (4.5%). This pattern held in the cross-sectional replication dataset in receipt of testing (2.7% of pregnant women, 2.4% in non-pregnant women), and test positivity (0.4% for both groups), suspected positives (3% of pregnant women, 4% in non-pregnant women), which included 41,796 (3.1%) pregnant women of 1,344,966 participants. In the cross-sectional replication dataset, hospitalized pregnant women were more likely to report positive tests (0.09%) than hospitalized non-pregnant women (0.03% and 0.12%). Hospitalized pregnant women with COVID-19 were more likely to report abdominal pain and less likely to report delirium and skipped meals than hospitalized non-pregnant women with COVID-19 in both cohorts. Non-hospitalized pregnant women were less likely to report diarrhea than non-hospitalized non-pregnant women in both cohorts. Finally, symptom severity scores among hospitalized participants were not statistically different by pregnancy status in either cohort.

Study Strengths

This study employed large sample sizes and included participants in several countries.

Limitations

Both arms likely recruited participants who are more health conscious than the general population and are likely healthier and potentially at lower risk of adverse COVID-19 sequelae because of their baseline health status and therefore not representative to all women of reproductive age. Data from lower income women or other women who are less digitally connected are not represented; if their results differ due to underlying comorbidities that may affect COVID-19 severity, these results may not fully generalize to all pregnant women. Furthermore, the symptom severity score is difficult to interpret outside of the context of this study. It is also difficult to comment on whether differences in reported hospitalizations among pregnant and non-pregnant women was due to increased concern among physicians (who might be more likely to admit pregnant women with COVID-19) or concerning differences in vital signs or laboratory values.

Value added

This study included a large sample of women, including a substantial number of pregnant women, demonstrating the potential of using smartphone applications and social media to conduct broad-based symptom tracking during a pandemic and trying to understand COVID-19 severity in pregnant women.

Our take —

This study sought to examine COVID-19 incidence, case fatality, and testing capacity and positivity across the continent of Africa, with both by-country and by-region analyses. They found 2,763,421 cases across 55 countries between February 14 and December 31, 2020 and a case fatality of 2.4%. South Africa was particularly hard hit, with the highest percentage of cases (38.3%) and highest number of deaths (43.3%). The test per case ratio across the continent ranged from 9 to 12% throughout the pandemic suggesting insufficient testing capacity. The study was limited by what data were reported to the Africa CDC, and therefore countries that used rapid antigen tests or symptomatic-only testing regimens may had false negatives or reduced cases compared to those actually with the disease in the country. This study is the first to offer a high-level overview of COVID-19 with data from 55 countries in Africa.

Study design

Cross-Sectional

Study population and setting

The study sought to describe COVID-19 infections and mortality across Africa between February 14 and December 31, 2020. The study used data from the Africa CDC’s event-based surveillance data and publicly available public health data from 55 African Union member states and 5 regions. The outcomes of interest were country-specific and region-specific cumulative incidence per 100,000 people, weekly incidence per 100,000 people, case fatality ratio, testing ratio (defined as number of tests per 1 million population), and tests per case (defined as 3-week positive yield in tests conducted). Population estimates were taken from the UN Population Fund 2019 data. Active COVID-19 cases were estimated by subtracting reported deaths and recoveries from cumulative total cases reported.

Summary of Main Findings

The study identified 2,763,421 cases across 55 African Union member states. The majority (56%, N=31) of countries identified their first case between March 8 and 21, and 43% of cases were reported in the Southern region, followed by 34% in the Northern region, 12% in the Eastern region, 9% in the Western, and 3% in the Central region. Overall, 9 countries made up 82.6% of cases, with South Africa having the plurality of 38.3%, followed by Morocco (15.9%) and Tunisia (5.1%). Per 100,000 people, Cabo Verde had the highest incidence rate (1973.3/100,000), followed by South Africa (1819.6 per 100,000) and Libya (1526.4 per 100,000). 65,602 deaths were reported, with South Africa also having the most (43.3%, N=28,469). Across the continent the case fatality ratio was estimated at 2.4% and held steady since August 25. More than 26 million COVID-19 tests were conducted (19,956 tests per 1 million), with South Africa conducting the most. From March onwards, the tests per case ratio (3-week yield in positive tests) was between 9% and 12%, with heterogeneity between countries ranging from Algeria as 2.3% to Burundi with 94.1%.

Study Strengths

The study used data across all African Union member countries to examine by-country and by-region differences. Given there was initial concern of expected high rates of COVID-19 in Africa, the study was able to examine heterogeneity between countries across different epidemiological metrics in order to provide a nuanced picture of the continent. The study had temporal data across the weeks, which allowed them to compare trends from early in the pandemic to later in the pandemic when restrictions and non-pharmaceutical intervention mandates had shifted or lifted. Additionally, by also recording changes in testing capacity and tests conducted, they were able to examine whether changes in incidence rates reflect the number of tests or actual spread of disease.

Limitations

The study used passive ascertainment which is subject to any limitations in the way data were collected in each country. For instance, if countries did not report testing data daily, there may be bias towards underestimating calculations of growth rate, case fatality ratio, active cases, and tests per case ratios. Different countries also may use different testing approaches, such as only symptomatic testing or asymptomatic testing as well. Other countries used rapid antigen tests which may have higher false negative results. There was also no way for the study to discern duplicate tests reported, and therefore the number of tests conducted may not reflect the number of people who received tests, and would deflate the test per case ratios. Finally, some of their numbers may be driven by specific countries, such as South Africa in the Southern region had the highest number of cases, while other countries in the region had much lower testing capacity as well as lower cases; therefore the region as a whole is reflective more of South Africa’s experience with COVID-19 than other countries.

Value added

This is the first continent-wide study of COVID-19 in Africa.

Our take —

The study, available as a preprint and thus not yet peer-reviewed, sought to describe the COVID-19-related mortality disparity among Native Americans in the US. The study found a standardized mortality ratio of 2.77 compared to white populations, and this was even higher in some states, with South Dakota having a mortality ratio of 9.7 as compared to the white population. They found that the standardized mortality ratio was highly correlated with cthe perent of Native Americans living on reservations. The study had many limitations due to its ecological study design, including use of data collected as far back as 2014, and potential underreporting of Native American race/ethnicity. Regardless, results show a high level of disparity in Native American mortality from COVID-19 compared to other racial/ethnic populations in the US.

Study design

Cross-Sectional, Ecological

Study population and setting

The study sought to describe risk factors for COVID-19 infection and related mortality among Native American/American Indian communities in the US. COVID-19-related death counts from the National Center for Health Statistics from January 1, 2020, through January 16, 2021 were used. Midyear population estimates of 2019 were drawn from the US Census Bureau data from 10 states: Arizona, California, Oklahoma, New Mexico, Washington, New York, South Dakota, Minnesota, Utah, and Mississippi. The American Community Survey (ACS) and the Behavioral Risk Factor Surveillance System (BRFSS) were used to estimate potential risk factors that may impact transmission and mortality. For the ACS, the analysis used 2014 – 2018 data to estimate the type of health insurance, income-poverty ratio, and household living arrangements. They also extracted data on frontline worker status using data from 2018. They used the BRFSS from 2011 to 2018 to estimate smoking status and health conditions including asthma, chronic obstructive pulmonary disease (COPD), kidney disease, cancer, heart disease, diabetes, and obesity. More recent ACS or BRFSS versions were not yet available. Finally, they utilized data from MultiState, which generates a rating of open-ness during the pandemic based on state policies and capacity/industry restrictions. They categorized race as non-Latino Native American (including American Indian and Alaskan Native), non-Latino white, non-Latino Black, and Latino, and using the My Tribal Area tool, integrated 2014 – 2018 ACS estimates of Native Americans living on- vs. off-reservation. They generated standardized mortality ratios compared to the 3 other racial categories overall and by state. They disaggregated this based on reservation living status, occupation, and chronic health conditions and behavioral risk factors, generating correlation estimates for each.

Summary of Main Findings

In this study, 2,789 COVID-19-related deaths were estimated from January 1, 2020 to January 16, 2021 among Native Americans. They estimated a crude death rate of 1.63 times that among the US white population, and a standardized mortality ratio of 2.77. This was greater than the standardized mortality ratio within the Black population (1.64) and the Latino population (1.81). Stratifying by state, they found geographic differences as well, with South Dakota having the highest standardized mortality ratio at 9.7 compared to the state’s White population, while California had the lowest at 1.6 times the mortality to the state’s White population. The standardized mortality ratios for the 10 states were correlated with increasing percentages of Native Americans living on reservations (correlation = 0.8). In their sociodemographic and behavioral correlations, they found the income-poverty ratio was highly negatively correlated with the standardized mortality ratio (-0.86).

Study Strengths

The study made use of a wide range of data to describe the health disparities impacting Native Americans, an often underreported population. They disaggregated by meaningful variables indicative of structural risks of disease, such as living on a reservation which may impact access to health services, and living in multigenerational or crowded households, and having insurance. They also examined individual-level factors, such as clinical risks through COPD and diabetes.

Limitations

The study’s primary limitation was that they used many different data sources which may have different reporting guidelines and criteria. Therefore, these results paint an overall picture of Native American health and health disparities, but do not generate individual-level estimates of risk factors and are limited to standardization by age and place alone.. They also limited their analysis to individuals reporting Native American as their only race, which likely underreports the true number of Native American people in the US. This standardization does not reflect differences in the underlying clinical health between white and Native American populations likely due to differences in access to health services and clinical care, and may be biased. They also used data from prior years going as far back as 2014 which may not reflect more recent trends in disease and social factors.

Value added

This is a large study of Native American people in the US, reflecting the health disparities they face compared to white and other racial/ethnic groups.

Our take —

This cross-sectional study examined the seroprevalence and symptom onset of SARS-CoV-2 infection among Orthodox Jewish communities in 5 US states. They found high prevalence of reported symptoms (61.0%) and high seroprevalence overall (30.1%) using antibody testing. Symptom onset was most frequent in March 2020, generally between March 9 and 31. The study’s primary limitation was the use of antibody testing which only reflects ever having been infected, and not whether onset of presumed COVID-19 symptoms actually corresponded to SARS-CoV-2 infection. Therefore, these estimates may not accurately reflect incident disease over the entire study period, but rather history of an illness and a SARS-CoV-2 infection. Further, estimates may under or overrepresent individuals based on infection severity or presumption, or community sub-group and thus should be interpreted with these caveats.

Study design

Cross Sectional

Study population and setting

The objective of this cross-sectional study was to understand the signs and symptoms, and seroprevalence, of SARS-CoV-2 in a cultural community with reported high rates of infection across 5 states in the US: New York, New Jersey, Connecticut, California, and Michigan. Participants were recruited in partnership with local non-profit and social service organizations serving Orthodox Jewish people 18 years and older. In the first stage of recruitment, which aimed to determine self-reported symptoms and infection, 12,626 individuals began the survey, 9,507 completed the it (75.3% completion) and 603 had obtained a positive PCR test (6.6%) during their illness. In the second stage of recruitment, a subset totaling 6,665 adults (70.1% response rate) had antibody testing following survey completion. Of the 6,665 in the antibody cohort, 422 (6.4%) obtained a positive PCR test during their illness and 2004 (30.1%) had a positive antibody test at the time of the study. The survey included patient demographics, symptoms of COVID-19, date of symptom onset, and whether they had been tested for SARS-CoV-2 by nasal swab.

Summary of Main Findings

In the full survey cohort, 61.0% (N=5803) of people in the survey cohort reported symptoms at any point in the study. The earliest date of symptom onset with a positive nasal swab test was on February 8, 2020 in Michigan. The median and mode dates of symptom onset occurred within the same 1-week period from March 13 to 20 across all sites. In the antibody cohort (N=6,665 individuals), 2004 individuals tested positive via antibody test (30.1%). The highest seroprevalence was in New Jersey (32.5%, N=1080), followed by New York (30.5%, N=671). As in the full cohort, most individuals within the antibody cohort reported symptom onset between March 9 and March 31, though the earliest reported date with an eventual positive antibody test was in New Jersey on December 18, 2019.

Study Strengths

The study’s main strength was the large number of Orthodox Jewish people who participated in the study, allowing the researchers to examine geographic differences and describe the temporal trends in symptom onset across multiple states. They additionally noted that the date of median and mode onset were approximately 7 to 10 days following a major Jewish festival (Purim) across all sites. They also were able to accurately describe prior infection using seroprevalence measures with their antibody testing.

Limitations

The primary limitation was that the researchers only had cross-sectional data available to them reflecting ever-infection through antibody testing, and then self-reported symptoms. For instance, for individuals reporting very early symptom onset in December and January, it was not possible to determine if this was SARS-CoV-2 infection or an unrelated respiratory illness and they later became infected with SARS-CoV-2 which was then captured by the antibody test. Additionally, the study only included cases of disease where participants could participate in the community, as opposed to hospital-based data collection. Therefore, it may not have reflected cases of severe disease. There also may have been volunteer bias, with individuals suspecting they had SARS-CoV-2 being more likely to participate than others, which would inflate their estimate of seroprevalence. Additionally, the population was largely Ashkenazi Jewish with limited racial diversity, thereby reflecting primarily white Orthodox Jewish people and not Orthodox Jewish people of color.

Value added

The study is the largest to date of a tight-knit religious and cultural community that experienced high prevalence of COVID-19 during the pandemic.