ABSTRACT
Background: SARS-CoV-2 continues to change over time due to genetic mutations and viral recombination.1 Given the changing landscape of COVID-19 variants and availability of COVID-19 vaccinations, disease severity during acute infection has also been variable. However, most research related to COVID-19 to date has not focused on evaluating differences in outcomes by the dominant variant and the impact it might have on post-acute sequalae of COVID-19 (PASC). Method(s): We developed a data mart of electronic health record data pertaining to COVID-19 in a single North American metropolitan health system (RUSH University Medical Center). Patients were selected for analysis if they had at least one documented infection of COVID-19. Date ranges were established per dominant variant, and the date of diagnosis was matched to variant. Variants were determined by the most prominent variant of concern (VOC) circulating in the city of Chicago. Variants were categorized by the following by date ranges: Wildtype+D614G (3/7/20-3/20/21), Alpha (3/21/21-6/19/21), Delta (6/20/21-12/11/21), Omicron BA.1 (12/12/21-3/19/22), Omicron BA.2 (3/20/22- 6/18/22), and Omicron BA.4/BA.5 (6/19/22-present (9/30/22). Subsequent clinical outcomes were examined, including hospitalization, intensive care unit admission, or death. We characterized our sample by conducting descriptive statistics including frequency and percent of outcome by variant. Result(s): 44,499 patients were included in this analysis with 30.23% requiring hospitalization, 4.25% being admitted to intensive care unit (ICU), and 2.35% resulting in death. The greatest percentage of hospitalizations occurred with the Alpha variant at 41.88% (N=928), and the greatest percentage of ICU admissions (6.43%) and death (3.15%) occurred with the Delta variant. The latest Omicron variant (Wave 6) showed an increase in hospitalizations (35.18%), as compared to early Omicron waves (Wave 4 and 5) but maintained similar ICU rates. Death rates continued to decline during the Omicron waves (Table 1). Conclusion(s): Although Alpha and Delta variants seem to have more severe outcomes compared to other variants, it is important to note that COVID-19 prevention, treatment access, and management continues to change, potentially influencing how outcomes may differ over time. Future work should determine factors to adjust for when examining variant-level differences.
ABSTRACT
Background and aims: The country of Georgia launched its national Hepatitis C Virus (HCV) Elimination Program in 2015, and a serosurvey the same year showed prevalence of HCV antibody (anti-HCV) and HCV RNA among adults aged ≥18 years was 7.7%, and 5.4%, respectively. Since then, over 76, 000 people with chronic HCV have been treated, with a cure rate of 98.9%. To monitor progress, a second serosurvey was conducted in 2021 to estimate the prevalence of hepatitis C, hepatitis B, and anti-SARS-CoV-2. This analysis reports hepatitis C results of the serosurvey and progress towards elimination. Method: The serosurvey used a stratified, multi-stage cluster design with systematic sampling. Adults and children ≥5 years consenting (or assenting with parental consent) to the interviewand blood draw were eligible to participate. All blood samples were tested for anti- HCV and if positive, HCV RNA. Nationally representative weighted proportions and 95% confidence intervals (CI) were calculated and compared with 2015 age-adjusted estimates for adults. Results: A total of 7, 237 adults and 1, 473 children participated in the survey. For adults, the median age was 46 years (interquartile range: 32–60), and 53.3% (95% CI: 51.3–55.2)were female. The prevalence of anti-HCV was 6.8% (95% CI: 5.9–7.7), which was not significantly different from 2015 (7.7% [95% CI: 6.6–8.8];p = 0.20). The HCV RNA prevalencewas 1.8% (95% CI: 1.3–2.4), compared to 5.4% [95% CI: 4.5– 6.3] in 2015 (p < 0.001). This represents a 67% reduction in persons with chronic HCV infection, despite the program having treated 51% of the estimated 150, 000 infected. HCV RNA prevalence decreased among all age groups, most notably among those aged 40–59 years (9.3% in 2015 to 2.2% in 2021;p < 0.001). Substantial decreases were also observed among both males (9.0% to 3.1%;p < 0.001) and females (2.2% to 0.6%;p < 0.001). HCV RNA prevalence also decreased from 51.1% to 17.8% among persons who ever injected drugs, and 13.1% to 3.8% among those who received a blood transfusion (both p < 0.001). No children tested positive for anti-HCV or HCV RNA. Conclusion: These results demonstrate the substantial progress made since Georgia launched its HCV Elimination Program in 2015. The 67% reduction in chronic HCV infections during 2015–2021 also supports treatment as a means for prevention, as the reduction is larger than would be expected based on those treated alone. These findings can inform strategies to meet HCV elimination targets.
ABSTRACT
Background and aims: Georgia introduced routine infant hepatitis B (HepB) vaccination in 2001 with >90% coverage over the last decade. In 2015, a nationwide serosurvey demonstrated an anti-hepatitis B core antibody (anti-HBc) prevalence of 25.9% and hepatitis B surface antigen (HBsAg) prevalence of 2.9% among adults ≥18 years. No prevalence data were available for children. In 2021, we assessed hepatitis B virus (HBV) infection prevalence among children and updated estimates for adults in a combined COVID-19, hepatitis C and hepatitis B serosurvey of persons aged ≥5 years. Method: We used a stratified, multi-stage cluster design. We collected data on demographics, medical and exposure history;we tested blood samples for anti-HBc and, if positive, for HBsAg. Nationally representative weighted proportions and 95% confidence intervals (CI) for anti-HBc and HBsAg were calculated. Participants aged 5–20 years had been eligible for routine HepB vaccination as infants. Results: Among children aged 5–17 years, 0.7% were anti-HBc+ and 0.03%were HBsAg+ (Table). Among adults ≥18 years, 21.7%were anti- HBc+ and 2.7%were HBsAg+. Anti-HBc prevalence increased with age from 1.3% among 18–23-year-olds to 28.6% among ≥60 years. HBsAg prevalence was lowest (0.2%) among 18–23-year-olds and highest (8.6%) among 35–39-year-olds. Males had higher HBsAg prevalence than females (3.6% versus 2.0%;p = 0.003). Anti-HBc prevalence was highest in Samegrelo-Zemo Svaneti, Adjara, and Imereti regions. Higher education and income were associated with lower anti-HBc, and unemployment-with higher HBsAg prevalence. (Table Presented) Conclusion: The impact of HepB vaccination in Georgia is demonstrated by a low HBsAg prevalence among children that is below the 0.5% European regional hepatitis B control target and meets the ≤0 .1% seroprevalence target for elimination of mother-to-child transmission of HBV. Chronic HBV infection remains a problem among adults born before routine infant HepB vaccination. Focusing efforts on screening, treatment, and preventive interventions among adults, along with sustaining high immunization coverage among children, can help Georgia achieve elimination of hepatitis B as public health threat by 2030.
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.