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As of August 2020, the United States is the global epicenter of the COVID-19 pandemic. Emerging data suggests that \"essential\" workers, who are disproportionately more likely to be racial/ethnic minorities and immigrants, bear a disproportionate degree of risk. We used publicly available data to build a series of spatial autoregressive models assessing county level associations between COVID-19 mortality and (1) percentage of individuals engaged in farm work, (2) percentage of households without a fluent, adult English-speaker, (3) percentage of uninsured individuals under the age of 65, and (4) percentage of individuals living at or below the federal poverty line. We further adjusted these models for total population, population density, and number of days since the first reported case in a given county. We found that across all counties that had reported a case of COVID-19 as of July 12, 2020 (n = 3024), a higher percentage of farmworkers, a higher percentage of residents living in poverty, higher density, higher population, and a higher percentage of residents over the age of 65 were all independently and significantly associated with a higher number of deaths in a county. In urban counties (n = 115), a higher percentage of farmworkers, higher density, and larger population were all associated with a higher number of deaths, while lower rates of insurance coverage in a county was independently associated with fewer deaths. In non-urban counties (n = 2909), these same patterns held true, with higher percentages of residents living in poverty and senior residents also significantly associated with more deaths. Taken together, our findings suggest that farm workers may face unique risks of contracting and dying from COVID-19, and that these risks are independent of poverty, insurance, or linguistic accessibility of COVID-19 health campaigns.

Influenza vaccine effectiveness (VE) can vary by type and subtype. Over the past decade, the test-negative design has emerged as a valid method for estimation of VE. In this design, VE is calculated as 100% × (1 – odds ratio) for vaccine receipt in influenza cases versus test-negative controls. We did a systematic review and meta-analysis to estimate VE by type and subtype. In this systematic review and meta-analysis, we searched PubMed and Embase from Jan 1, 2004, to March 31, 2015. Test-negative design studies of influenza VE were eligible if they enrolled outpatients on the basis of predefined illness criteria, reported subtype-level VE by season, used PCR to confirm influenza, and adjusted for age. We excluded studies restricted to hospitalised patients or special populations, duplicate reports, interim reports superseded by a final report, studies of live-attenuated vaccine, and studies of prepandemic seasonal vaccine against H1N1pdm09. Two reviewers independently assessed titles and abstracts to identify articles for full review. Discrepancies in inclusion and exclusion criteria and VE estimates were adjudicated by consensus. Outcomes were VE against H3N2, H1N1pdm09, H1N1 (pre-2009), and type B. We calculated pooled VE using a random-effects model. We identified 3368 unduplicated publications, selected 142 for full review, and included 56 in the meta-analysis. Pooled VE was 33% (95% CI 26–39; I2=44·4) for H3N2, 54% (46–61; I2=61·3) for type B, 61% (57–65; I2=0·0) for H1N1pdm09, and 67% (29–85; I2=57·6) for H1N1; VE was 73% (61–81; I2=31·4) for monovalent vaccine against H1N1pdm09. VE against H3N2 for antigenically matched viruses was 33% (22–43; I2=56·1) and for variant viruses was 23% (2–40; I2=55·6). Among older adults (aged >60 years), pooled VE was 24% (−6 to 45; I2=17·6) for H3N2, 63% (33–79; I2=0·0) for type B, and 62% (36–78; I2=0·0) for H1N1pdm09. Influenza vaccines provided substantial protection against H1N1pdm09, H1N1 (pre-2009), and type B, and reduced protection against H3N2. Vaccine improvements are needed to generate greater protection against H3N2 than with current vaccines. None.

AbstractObjectiveTo estimate the effectiveness of mRNA covid-19 vaccines against symptomatic infection and severe outcomes (hospital admission or death).DesignTest negative design study.SettingOntario, Canada between 14 December 2020 and 19 April 2021.Participants324 033 community dwelling people aged ≥16 years who had symptoms of covid-19 and were tested for SARS-CoV-2.InterventionsBNT162b2 (Pfizer-BioNTech) or mRNA-1273 (Moderna) vaccine.Main outcome measuresLaboratory confirmed SARS-CoV-2 by reverse transcription polymerase chain reaction (RT-PCR) and hospital admissions and deaths associated with SARS-CoV-2 infection. Multivariable logistic regression was adjusted for personal and clinical characteristics associated with SARS-CoV-2 and vaccine receipt to estimate vaccine effectiveness against symptomatic infection and severe outcomes.ResultsOf 324 033 people with symptoms, 53 270 (16.4%) were positive for SARS-CoV-2 and 21 272 (6.6%) received at least one dose of vaccine. Among participants who tested positive, 2479 (4.7%) were admitted to hospital or died. Vaccine effectiveness against symptomatic infection observed ≥14 days after one dose was 60% (95% confidence interval 57% to 64%), increasing from 48% (41% to 54%) at 14-20 days after one dose to 71% (63% to 78%) at 35-41 days. Vaccine effectiveness observed ≥7 days after two doses was 91% (89% to 93%). Vaccine effectiveness against hospital admission or death observed ≥14 days after one dose was 70% (60% to 77%), increasing from 62% (44% to 75%) at 14-20 days to 91% (73% to 97%) at ≥35 days, whereas vaccine effectiveness observed ≥7 days after two doses was 98% (88% to 100%). For adults aged ≥70 years, vaccine effectiveness estimates were observed to be lower for intervals shortly after one dose but were comparable to those for younger people for all intervals after 28 days. After two doses, high vaccine effectiveness was observed against variants with the E484K mutation.ConclusionsTwo doses of mRNA covid-19 vaccines were observed to be highly effective against symptomatic infection and severe outcomes. Vaccine effectiveness of one dose was observed to be lower, particularly for older adults shortly after the first dose.

Background. Recent studies suggest that influenza vaccination in the previous season may influence the effectiveness of current-season vaccination, but this has not been assessed in a single population over multiple years. Methods. Patients presenting with acute respiratory illness were prospectively enrolled during the 2004–2005 through 2012–2013 influenza seasons. Respiratory swabs were tested for influenza and vaccination dates obtained from a validated registry. Vaccination status was determined for the current, previous, and prior 5 seasons. Vaccine effectiveness (VE) was calculated for participants aged ≥9 years using logistic regression models with an interaction term for vaccination history. Results. There were 7315 enrollments during 8 seasons; 1056 (14%) and 650 (9%) were positive for influenza A (H3N2) and B, respectively. Vaccination during current only, previous only, or both seasons yielded similar protection against H3N2 (adjusted VE range, 31%–36%) and B (52%–66%). In the analysis using 5 years of historical vaccination data, current season VE against H3N2 was significantly higher among vaccinated individuals with no prior vaccination history (65%; 95% confidence interval [CI], 36%–80%) compared with vaccinated individuals with a frequent vaccination history (24%; 95% CI, 3%–41%; P = .01). VE against B was 75% (95% CI, 50%–87%) and 48% (95% CI, 29%–62%), respectively (P = .05). Similar findings were observed when analysis was restricted to adults 18–49 years. Conclusions. Current- and previous-season vaccination generated similar levels of protection, and vaccine-induced protection was greatest for individuals not vaccinated during the prior 5 years. Additional studies are needed to understand the long-term effects of annual vaccination.

ImportanceThe incidence of SARS-CoV-2 infection, including among individuals who have received 2 doses of COVID-19 vaccine, increased substantially following the emergence of the Omicron variant in Ontario, Canada. Understanding the estimated effectiveness of 2 or 3 doses of COVID-19 vaccine against outcomes associated with Omicron and Delta infections may aid decision-making at the individual and population levels.ObjectiveTo estimate vaccine effectiveness (VE) against symptomatic infections due to the Omicron and Delta variants and severe outcomes (hospitalization or death) associated with these infections.Design, Setting, and ParticipantsThis test-negative case-control study used linked provincial databases for SARS-CoV-2 laboratory testing, reportable disease, COVID-19 vaccination, and health administration in Ontario, Canada. Participants were individuals aged 18 years or older who had COVID-19 symptoms or severe outcomes (hospitalization or death) and were tested for SARS-CoV-2 between December 6 and 26, 2021.ExposuresReceipt of 2 or 3 doses of the COVID-19 vaccine and time since last dose.Main Outcomes and MeasuresThe main outcomes were symptomatic Omicron or Delta infection and severe outcomes (hospitalization or death) associated with infection. Multivariable logistic regression was used to estimate the effectiveness of 2 or 3 COVID-19 vaccine doses by time since the latest dose compared with no vaccination. Estimated VE was calculated using the formula VE = (1 – [adjusted odds ratio]) × 100%.ResultsOf 134 435 total participants, 16 087 were Omicron-positive cases (mean [SD] age, 36.0 [14.1] years; 8249 [51.3%] female), 4261 were Delta-positive cases (mean [SD] age, 44.2 [16.8] years; 2199 [51.6%] female), and 114 087 were test-negative controls (mean [SD] age, 42.0 [16.5] years; 67 884 [59.5%] female). Estimated VE against symptomatic Delta infection decreased from 89% (95% CI, 86%-92%) 7 to 59 days after a second dose to 80% (95% CI, 74%-84%) after 240 or more days but increased to 97% (95% CI, 96%-98%) 7 or more days after a third dose. Estimated VE against symptomatic Omicron infection was 36% (95% CI, 24%-45%) 7 to 59 days after a second dose and 1% (95% CI, –8% to 10%) after 180 days or longer, but 7 or more days after a third dose, it increased to 61% (95% CI, 56%-65%). Estimated VE against severe outcomes was high 7 or more days after a third dose for both Delta (99%; 95% CI, 98%-99%) and Omicron (95%; 95% CI, 87%-98%).Conclusions and RelevanceIn this study, in contrast to high estimated VE against symptomatic Delta infection and severe outcomes after 2 doses of COVID-19 vaccine, estimated VE was modest and short term against symptomatic Omicron infection but better maintained against severe outcomes. A third dose was associated with improved estimated VE against symptomatic infection and with high estimated VE against severe outcomes for both variants. Preventing infection due to Omicron and potential future variants may require tools beyond the currently available vaccines.

Optimizing the public health response to reduce the burden of COVID-19 necessitates characterizing population-level heterogeneity of risks for the disease. However, heterogeneity in SARS-CoV-2 testing may introduce biased estimates depending on analytic design. We aimed to explore the potential for collider bias in a large study of disease determinants, and evaluate individual, environmental and social determinants associated with SARS-CoV-2 testing and diagnosis among residents of Ontario, Canada. We explored the potential for collider bias and characterized individual, environmental and social determinants of being tested and testing positive for SARS-CoV-2 infection using cross-sectional analyses among 14.7 million community-dwelling people in Ontario, Canada. Among those with a diagnosis, we used separate analytic designs to compare predictors of people testing positive versus negative; symptomatic people testing positive versus testing negative; and people testing positive versus people not testing positive (i.e., testing negative or not being tested). Our analyses included tests conducted between Mar. 1 and June 20, 2020. Of 14 695 579 people, we found that 758 691 were tested for SARS-CoV-2, of whom 25 030 (3.3%) had a positive test result. The further the odds of testing from the null, the more variability we generally observed in the odds of diagnosis across analytic design, particularly among individual factors. We found that there was less variability in testing by social determinants across analytic designs. Residing in areas with the highest household density (adjusted odds ratio [OR] 1.86, 95% confidence interval [CI] 1.75–1.98), highest proportion of essential workers (adjusted OR 1.58, 95% CI 1.48–1.69), lowest educational attainment (adjusted OR 1.33, 95% CI 1.26–1.41) and highest proportion of recent immigrants (adjusted OR 1.10, 95% CI 1.05–1.15) were consistently related to increased odds of SARS-CoV-2 diagnosis regardless of analytic design. Where testing is limited, our results suggest that risk factors may be better estimated using population comparators rather than test-negative comparators. Optimizing COVID-19 responses necessitates investment in and sufficient coverage of structural interventions tailored to heterogeneity in social determinants of risk, including household crowding, occupation and structural racism.

The effects of the COVID-19 pandemic have been more pronounced for socially disadvantaged populations. We sought to determine how access to SARS-CoV-2 testing and the likelihood of testing positive for COVID-19 were associated with demographic factors, socioeconomic status (SES) and social determinants of health (SDH) in three Canadian provinces. An observational population-based cross-sectional study was conducted for the provinces of Ontario, Manitoba and New Brunswick between March 1, 2020 and April 27, 2021, using provincial health administrative data. After excluding residents of long-term care homes, those without current provincial health insurance and those who were tested for COVID-19 out of province, records from provincial healthcare administrative databases were reviewed for 16,900,661 healthcare users. Data was modelled separately for each province in accordance to a prespecified protocol and follow-up consultations among provincial statisticians and collaborators. We employed univariate and multivariate regression models to examine determinants of testing and test results. After adjustment for other variables, female sex and urban residency were positively associated with testing, while female sex was negatively associated with test positivity. In New Brunswick and Ontario, individuals living in higher income areas were more likely to be tested, whereas in Manitoba higher income was negatively associated with both testing and positivity. High ethnocultural composition was associated with lower testing rates. Both high ethnocultural composition and high situational vulnerability increased the odds of testing positive for SARS-CoV-2. We observed that multiple demographic, income and SDH factors were associated with SARS-CoV-2 testing and test positivity. Barriers to healthcare access identified in this study specifically relate to COVID-19 testing but may reflect broader inequities for certain at-risk groups.

Recent studies have provided insights into innate and adaptive immune dynamics in coronavirus disease 2019 (COVID-19). However, the exact features of antibody responses that govern COVID-19 disease outcomes remain unclear. In this study, we analyzed humoral immune responses in 229 patients with asymptomatic, mild, moderate and severe COVID-19 over time to probe the nature of antibody responses in disease severity and mortality. We observed a correlation between anti-spike (S) immunoglobulin G (IgG) levels, length of hospitalization and clinical parameters associated with worse clinical progression. Although high anti-S IgG levels correlated with worse disease severity, such correlation was time dependent. Deceased patients did not have higher overall humoral response than discharged patients. However, they mounted a robust, yet delayed, response, measured by anti-S, anti-receptor-binding domain IgG and neutralizing antibody (NAb) levels compared to survivors. Delayed seroconversion kinetics correlated with impaired viral control in deceased patients. Finally, although sera from 85% of patients displayed some neutralization capacity during their disease course, NAb generation before 14 d of disease onset emerged as a key factor for recovery. These data indicate that COVID-19 mortality does not correlate with the cross-sectional antiviral antibody levels per se but, rather, with the delayed kinetics of NAb production. A longitudinal analysis of humoral immune responses in patients with COVID-19 with varying disease severities reveals that mortality does not correlate with antiviral antibody levels but, instead, with slower seroconversion.

Before the development of COVID-19 vaccines, researchers in the UK developed QCOVID, a population-based prediction model that estimates the risk of hospitalisation with COVID-19.5 After the mass roll-out of COVID-19 vaccines, additional research determined that older age, immunosuppression, and specific underlying chronic conditions were associated with severe outcomes (ie, hospitalisation and death) resulting from COVID-19 breakthrough infections among those with the primary vaccine series.6,7 Investigations of risk factors for severe outcomes in boosted populations, however, are sparse. Characteristics identified as the greatest risk factors for severe COVID-19 outcomes included the presence of five or more comorbidities (≥5 comorbidities vs none; adjusted rate ratio 9·51 [95% CI 9·07–9·97]), immunosuppressed status (yes vs no; 5·80 [5·53–6·09]), history of neurological disorders (yes vs no; 5·30 [4·90–5·74]), presence of chronic kidney disease (chronic kidney disease stage 5 vs no; 3·71 [2·90–4·74]), and older age (aged ≥80 years vs 18–49 years; 3·60 [3·45–3·75]). The authorisation from the US Food and Drug Administration in August, 2022, and subsequent recommendation from the US Centers for Disease Control and Prevention for the use of bivalent COVID-19 vaccines is a laudable first step,9 as early research shows that these new COVID-19 vaccines induce relatively higher and broad-spectrum cross-neutralising activities compared with the original COVID-19 vaccines.10 Where supply of bivalent vaccine is limited, roll-out should be targeted to groups identified as at high risk of severe COVID-19 outcomes, especially older age groups and those with multimorbidity.

•Waning protection against influenza A (H3N2) occurred in a 2007–2008 community study.•Patients vaccinated earlier were more likely to develop H3N2 illness.•Effects were seen in young children and older adults, but not working age adults. Recent studies have suggested that vaccine-induced protection against influenza may decline within one season. We reanalyzed data from a study of influenza vaccine effectiveness to determine if time since vaccination was an independent predictor of influenza A (H3N2). Patients with acute respiratory illness were actively recruited during the 2007–2008 season. Respiratory swabs were tested for influenza, and vaccination dates were determined by a validated immunization registry. The association between influenza RT-PCR result and vaccination interval (days) was examined using multivariable logistic regression, adjusting for calendar time, age and other confounders. There were 629 vaccinated participants, including 177 influenza A (H3N2) cases and 452 test negative controls. The mean (SD) interval from vaccination to illness onset was 101.7 (25.9) days for influenza cases and 93.0 (29.9) days for controls. There was a significant association between vaccination interval and influenza result in the main effects model. The adjusted odds ratio (aOR) for influenza was 1.12 (CI 1.01, 1.26) for every 14 day increase in the vaccination interval. Age modified the association between vaccination interval and influenza (p=0.005 for interaction). Influenza was associated with increasing vaccination interval in young children and older adults, but not in adolescents or non-elderly adults. Similar results were found when calendar week of vaccine receipt was assessed as the primary exposure variable. Identification of influenza A (H3N2) was associated with increasing time since vaccination among young children and older adults during a single influenza season.