ABSTRACT
Wild meat hunting and trade across African savannas is widespread. We interviewed 299 people in rural settlements along the Kenya-Tanzania border to examine impacts of COVID-19 on wild meat consumption and perceptions about wild meat activities associated with zoonotic disease risks. Education level played a key part in understanding COVID-19 transmission. Information about the pandemic was mostly acquired from the media. Nearly all respondents recognized that COVID-19 originated in China. As many as 70% reported no impact of COVID-19 on wild meat consumption;some believed that there was an increase. Over half of the respondents believed that consumption of wild meat leads to food-borne illnesses. Respondents recognized disease risks such as anthrax and brucellosis and accepted that people slaughtering and handling wild meat with open cuts were at greater risk. Ungulates were the most consumed animals, followed by birds, rodents, and shrews. Respondents perceived that hyenas, monkeys, donkeys, and snakes were riskier to eat. More than 90% of the respondents understood that handwashing with soap reduces risks of disease transmission. Country level (11 answers), education and gender (three answers each) and household economy (158 answers) were significant. Country differences were linked to differences in nature legislation;50% of Kenyan respondents believed that wild meat should not be sold because of conservation concerns. Men were more worried about getting COVID-19 from live animals and perceived that wildlife should not be sold because of conservation reasons. Overall, there was a very strong inclination to stop buying wild meat if other meats were less expensive. Our results allow us to better understand the impact of the COVID-19 pandemic on wild meat-related activities. Differences between countries can frame the attitudes to wild meat since wild meat trade and consumption were found to be country specific.
ABSTRACT
Background: People who inject drugs (PWID) may be at a greater risk of SARS-CoV-2 infection and COVID-19 due to socio-structural inequities, high-risk behaviors and comorbidities;however, PWID have been underrepresented in case-based surveillance due to lower access to testing. We characterize temporal trends and correlates of SARS-CoV-2 seroprevalence among a community-based sample of current and former PWID. Method(s): A cross-sectional study was conducted among participants in the AIDS Linked to the IntraVenous Experience (ALIVE) study-a community-based cohort of adults with a history of injection drug use in Baltimore, Maryland. Participants' first serum sample collected at routine study visits between December 2020 and July 2022 was assayed for antibodies to the nucleocapsid (N) (past infection) and spike-1 (S) (past infection and/or vaccination) proteins using the MSD V-Plex Panel 2 IgG SARS-CoV-2 assay. For each correlate, we estimated adjusted prevalence ratios (PR) via separate Poisson regression models adjusted for calendar time, age, sex and race. Result(s): Of 561 participants, the median age was 59 years (range=28-77), 35% were female, 84% were Black, 36% were living with HIV (97% on ART), and 55% had received >=1 COVID-19 vaccine dose. Overall, anti-N and anti-S prevalence was 26% and 63%, respectively. Prevalence of anti-N increased from 23% to 40% between December 2020-May 2021 and December 2021-July 2022, with greater increases in the prevalence of anti-S from 34% to 86% over the same period (Figure). Being employed (PR=1.53 [95%CI=1.11-2.11]) and never being married (PR=1.40 [0.99-1.99]) were associated with a higher prevalence of anti-N, while female sex (PR=0.75 [0.55-1.02]) and a history of cancer (PR=0.40 [0.17-0.90]) were associated with a lower prevalence of anti-N. Younger age, female sex (PR=0.90 [0.80-1.02]), and homelessness (PR=0.78 [0.60-0.99]) were associated with a lower prevalence of anti-S. Although HIV infection was not associated with anti-N, it was associated with a higher prevalence of anti-S (PR=1.13 [1.02-1.27]). Substance use was not associated with anti-N or anti-S. Conclusion(s): Anti-N and anti-S levels increased over time, suggesting cumulative increases in SARS-CoV-2 incidence of infection and vaccination among PWID;however, disparities in seroprevalence remain. Younger and female PWID and those experiencing homelessness were less likely to be anti-S positive, suggesting programs should aim to improve vaccination coverage in such vulnerable populations.
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Background: Sero-studies of SARS-CoV-2 have used antibody (Ab) responses to spike (S) and nucleocapsid (N) antigens to differentiate mRNA vaccinated (S+/N-) from infected (S+/N+) individuals. We performed testing on wellcharacterized subjects to determine how repeated vaccination or infection, and time from those exposures, influence these Ab levels. Method(s): Samples from individuals with known infection status: prepandemic negative controls n=462;first-time infected n=237 (~45 days post);vaccinated after infection n= 34 (~40 days post-vaccination and ~180 days post-infection);fully vaccinated n=158 (~50 days post);boosted n=31 (~30 days post);breakthrough n=18 (~14 days post-infection);reinfected n=10 (varied). Longitudinal samples (n=51) from subjects with evidence of reinfection (symptoms and/or positive rapid antigen test), were tested to determine the impact of the order of infection and/or vaccination on the magnitude of the anti-S and anti-N IgG Ab detected in the blood. Testing was performed with MesoScale Diagnostics (Gaithersburg, MD) assay. Outcomes are presented in WHO International Binding Antibody Units (BAU/mL). The cutoff for a positive result was 18 BAU for S and 12 BAU for N. Result(s): The median amount of Ab (IQR) in BAU for each group (Figure A) was: pre-pandemic negative controls S 0.53(0.27,1.03), N 0.55(0.18,1.67);first-time infected S 114(51,328), N 70(29,229);vaccinated after infection S 4367(2479,4837), N 15(7,35);fully vaccinated S 998(586,1529), N 0.31(0.16,0.68);boosted S 2988(1768,3522), N 0.59(0.32,1.03);breakthrough S 2429(2032,3413), N 2.5(0.93,8.6);reinfected S 1533(486,4643), N 7.8(2.6,62). For the breakthrough and second infections 17% and 40% were seropositive to N, respectively. Longitudinal analysis (Figure B) of those with multiple infections showed that all those with a positive rapid antigen test for their second infection had an increase in N Ab. Conclusion(s): The prevalence of antibodies to nucleocapsid cannot be used to determine the proportion of individuals infected to SARS-CoV-2 in a vaccinated population. Booster, repeated, and breakthrough infections are associated with IgG Ab levels to S >400 BAU/mL. A majority of breakthrough infections did not elicit an Ab response to N. For those with repeated infection, a minority elicited antibody responses to N. This could be related to misdiagnosis or the burden of infection, as only those who were positive by rapid antigen assay (indicative of a high viral load) had an increase in N Ab.
ABSTRACT
We study the effect of the COVID-19 pandemic on chronic disease drug adherence. Focusing on asthma, we use a database that tracks the vast majority of prescription drug claims in the U.S. from 2018 to 2020. Using a difference-in-differences empirical specification, we compare monthly drug adherence in 2019 and 2020 for the set of chronic patients taking asthma medication before the onset of the pandemic. We find that the pandemic increased adherence for asthmatic adults by 10 percent. However, we find a sustained decrease in pediatric drug adherence that is most severe for the youngest children. By the end of 2020, drug adherence fell by 30 percent for children aged 0 to 5, by 12 percent for children aged 6 to 12, and 5 percent for children aged 13 to 18. These negative effects are persistent regardless of changes in medical need, socioeconomic factors, insurance coverage and access to health services. We provide suggestive evidence that the observed pediatric changes are likely driven by parental inattention.
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.
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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.
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Background: Live virus micro-neutralization (MN) is the gold standard for quantifying the neutralizing titer (NT) of antibodies to SARS-CoV-2. However, performing MN is labor intensive and requires a biosafety level 3 laboratory. We assessed the performance of 8 immunoassays which measure SARS-CoV-2 NT and compared them to gold standard MN results. Methods: Samples from 269 individuals known to previously be SARS-CoV-2 PCR+ (i.e., convalescent individuals, <10% hospitalized) and 200 pre-pandemic individuals were evaluated on 3 lateral flow immunoassays (LFAs;Wondfo Colloidal Gold, Wondfo Colored Microsphere, Wondfo Finecare) and 5 enzyme-linked immunoassays (ELISAs;ImmunoRank, GenScript, Cusabio, Euroimmun NeutraLISA, Euroimmun QuantiVac). MN was performed on all samples from convalescent individuals;results were classified as undetectable vs any detection of MN NT (NT<20 vs. NT>20), as well as high and low MN NT (NT>80 vs. NT<80). Receiver operating curve analysis was used to assess accuracy for detecting levels of NT. The area under the curve (AUC) was calculated for the manufacturer's cut off and empirically to identify the best discriminatory cut off value. Cohen's kappa statistics were calculated to assess categorical agreement and Spearman's rank statistics were calculated to assess correlations. Results: Of the 269 convalescent plasma samples, 89 (33%) had MN NT values <20 (undetectable) and 117 (43%) >80 (high NT). Using the manufacturer's cutoffs, sensitivity for detection of samples with any NT ranged from 79% to 100%, and the false-positive rate (ie, classifying samples with undetectable NT as positive) was highest for LFAs (72% to 84%) and ranged from 14% to 69% for the ELISAs. For all assays except the ImmunoRank and NeutraLISA ELISAs, discrimination to identify samples with any NT was improved by raising the cut off values (Table). AUCs of ∼0.94 to discriminate high NT samples could be achieved for all quantifiable assays using an adjusted cut off value. Cohen's kappa statistic ranged from 0.20 to 0.69. Spearman's rank correlation between each assay and NT value ranged from 0.73 to 0.86. Using the manufacturer's cutoffs, specificity on pre-pandemic samples was ≥98% for all assays except for Cusabio which was 86%. Conclusion: The performance of immunoassays using manufacturer's cutoff to discriminate samples with any NT was accurate (AUC>0.83 for all assays), but could be improved by changing the cutoff. Identifying samples with high NT could be achieved using an alternative cutoff.
ABSTRACT
In the second wave of COVID-19 pandemic, there is another challenge to face: how to effectively dispense some medicines (like Remdesivir injection) amongst the multitudes to quickly achieve immunity against the corona infection. To overcome this situation, researchers and doctors are continuously active. This result into infected people get recovered. In this context, nations are now getting ready to face one more big challenge that is increases biomedical waste which causes carbon emissions. Since spoilage and deterioration results into a significant loss in medicines which hampers consumer’s satisfaction level as well affect the green environment. Keeping this in mind, the proposed article is addressed for an inventory model with carbon emissions sensitive demand which is a more realistic assumption and carbon tax policy is levied to diminish carbon emissions. A non-linear formulation is revealed with an objective to determine the optimum cycle length as to minimize total cost. The validity of the proposed model is demonstrated by presenting a numerical example. Sensitivity analysis is carried out to verify its factual practice. © 2021, International Forum of Management Scholars. All rights reserved.
ABSTRACT
Inventory model for vaccine of COVID-19 pandemic is the subject of analysis in the proposed article. The initial registration for vaccination and vaccination of registered individuals is taken during the period under consideration. The paper considers the utility of vaccine during storage, holding cost, purchase cost, manufacturing cost and inspection cost. A fraction of registered individuals who do not turn up for a vaccination is taken into account. All the actions by the player incur carbon emissions. During the whole procedure of vaccination starting from raw material to end user carbon emissions are observed. Carbon emissions in stocking raw material, during inspection, during purchase activity, during set-up and transportation phase and holding it at point of delivery. Maximum carbon emission of 28% occur during purchase activity followed by 21% during transportation at the point of delivery and stocking it at respective places. To follow green policy, carbon tax is levied. A non-linear formulation of the proposed problem is modelled to compute optimum cycle time without allowing shortages. The convexity of the objective function is established through the numerical data. Analysis of carbon emissions and carbon tax levied is carried out through the data. Research Objective: Carbon Emission is one of a cause for ozone layer depletion. Moreover, it causes many ecological disturbances resulting into several environmental temperature variations. These all problem affect an individual’s health. So, there arise a need to frame a mathematical model to decipher relationship between COVID-19 vaccine inventory and effect of carbon emissions. © 2021, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.