ABSTRACT
Background: While the diversity in SARS-CoV-2 transmission across geographies and risk groups is well recognized, there has been limited investigation into spatial heterogeneity at a local scale, that is variability across a single city. Identifying patterns and factors associated with spatial variability requires population representative samples which are challenging to obtain but critical for mitigation strategies including vaccine distribution. Methods: From Jan to May 2021, we sampled 4,828 participants from 2,723 unique households across 100 spatial locations in Chennai, India using a probability proportional to population density sampling approach. All participants provided a blood sample and underwent a household and individual survey. 4,712 samples were tested for antibodies to the Spike protein (anti-Spike IgG) by the Abbott ARCHITECT. SARS-CoV-2 prevalence by spatial location was plotted using splines estimated by generalized additive models. Associations between seroprevalence and spatial attributes (zone, population density), study characteristics (date of sampling), household and individual-level covariates were estimated using Bayesian mixed effects logistic regression accounting for clustering within households and locations. Results: The median age was 38 and 49% self-identified as female. Overall, anti-S IgG prevalence was 61.9% (95% confidence interval [CI]: 60.5-63.3%) but ranged from 41.5% to 73.1% across the 12 zones. Splines indicated statistically significant variation in seroprevalence across the city (Panel A). Mixed effects regression including location and household effects indicated 31% of variance was attributable to location. In adjusted analysis, seroprevalence was significantly associated with population density (OR=1.46 per 100 people/100 sq meter [95%CI: 1.08-1.97];Panel B), age (OR=1.004 [95%CI: 1.0002-1.005]), having an air conditioner (OR=0.65 [95%CI: 0.43-0.98]) and sample timing but not with household crowding (OR=0.97 per person/room [95%CI: 0.75-1.26];Panel C). Significant spatial variation across locations remained after adjustment for these variables, accounting for 28% of variance. Conclusion: We observed substantial spatial heterogeneity of SARS-CoV-2 burden in this high prevalence setting not fully explained by individual, household or population factors. Such local variability in prevalence has implications not only for transmission but for scale-up of vaccines which remain in limited supply in low-and middle-income countries.
ABSTRACT
Background: With global vaccine scale-up, the utility of the more stable anti-S IgG assay in seroprevalence studies is limited. P population prevalence estimates of anti-N IgG SARS-CoV-2 using alternate targets (eg, anti-N IgG) will be critical for monitoring cumulative SARS-CoV-2 incidence., We demonstrate the utility of a Bayesian approach that accounts for heterogeneities in SARS-CoV-2 seroresponse (eg, must consider mild infections and/or antibody waning) to ensure anti-N IgG prevalence is not underestimated and correlates not misinterpreted. Methods: We sampled 4,828 participants from 2,723 households across 100 unique geospatial locations in Chennai, India, from Jan-May, 2021 when <1% of the general population was vaccinated. All samples were tested for SARS-CoV-2 IgG antibodies to S and N using the Abbott ARCHITECT. We calculated prevalence using manufacturer cut-offs and applied a Bayesian mixture model. In the mixture model, individuals were assigned a probability of being seropositive or seronegative based on their normalized index value, accounting for differential immune response by age and antibody waning. Regression analyses to identify correlates of infection defined seropositivity by manufacturer cut-offs and the mixture model. Results: The raw SARS-CoV-2 seroprevalence using IgG to S (cutoff=50) and N (cutoff=1.4) were 61.9% (95% confidence interval [CI]: 60.5-63.3%) and 13.7% (CI: 12.8-14.7%), respectively with a correlation of 0.33. With the mixture model, anti-N IgG prevalence was 65.4% (95% credible interval [CrI]: 61.8-68.9). Correlates of anti-N IgG positivity differed qualitatively by the two approaches (Table). Using the manufacturer cut-off, income loss during the pandemic, household crowding and lack of air conditioning were associated with significantly lower anti-N prevalence. By contrast, in the mixture model, many measures of lower socioeconomic status were associated with higher prevalence, associations that were comparable when anti-S was the outcome. The age pattern differed between approaches: the mixture model identified that individuals aged >50 had the lowest seroprevalence, but the highest immune response to infection. Conclusion: With global vaccine scale-up, population prevalence estimates of anti-N IgG will be critical for monitoring cumulative SARS-CoV-2 incidence. We demonstrate the utility of a Bayesian approach that accounts for heterogeneities in SARS-CoV-2 seroresponse to improve accuracy of anti-N IgG prevalence estimates and associated correlates.
ABSTRACT
Background: Rapid detection and isolation of SARS-CoV-2 infections is critical to mitigate the pandemic;however, testing access across the US has been uneven and data on barriers to testing are limited. Methods: We conducted serial cross-sectional assessments of experiences around SARS-CoV-2 PCR testing in Florida, Illinois, and Maryland. We sampled ∼1000/state using an online survey from Jul 15-31 and Sep 16-Oct 15, 2020, with additional waves planned at 6-8 week intervals. At the time of surveys, there were no systematic differences in testing availability (public, private and free testing options) across these states. Participants were recruited using on online panel;demographic targets were provided to match age, sex, race/ethnicity and income distributions of each state. Participants were ≥18 years, provided consent, and resided in the study state. The survey covered demographics, symptoms, and PCR testing in the prior 2 weeks. Results: Of 3,058 persons surveyed most recently (Sep 16-Oct 15), 316 (10%) reported wanting/needing a test in the prior two weeks. Median age of participants wanting/needing a test was 36 years and 46% were female;47% self-identified as White and 57% reported working outside home. Of 316 who wanted/needed a test in the prior 2 weeks, 53% were able to get tested, of whom, 94% received results, with no significant differences by state (Figure);this was not substantially different from the proportion able to get tested in July (51%). Among those wanting/needing a test, getting tested was significantly less common among men (aOR: 0.46) and those reporting black race (aOR: 0.53) and more common in those reporting recent travel (aOR: 3.35;all p<0.05). The primary reasons for testing were desire to know status (35%) and symptoms (28%). Among those tested, 53% had to wait ≥8 days to get a result from the time they wanted/needed a test. Of those tested, 71% reported quarantining while awaiting results. An additional 146 who wanted/needed a test did not get tested;the main reasons for not testing in this group were not knowing where to go (36%) and distance/waiting time (33%);an additional 21% reported fear of being tested. Conclusion: These data reflecting similar testing barriers across three US states underscore the importance of a unified national strategy with clear messaging on who, where, when, and how to get a test, as well as improved turn-aroundtimes. As demand rises borrowing strategies from HIV such as self-testing could help overcome logistical barriers.