Diagnostic value of symptoms for pediatric SARS-CoV-2 infection in a primary care setting

Purpose To evaluate the diagnostic value of symptoms used by daycares and schools to screen children and adolescents for SARS-CoV-2 infection, we analyzed data from a primary care setting. Methods This cohort study included all patients ≤17 years old who were evaluated at Providence Community Health Centers (PCHC; Providence, U.S.), for COVID-19 symptoms and/or exposure, and received SARS-CoV-2 polymerase chain reaction (PCR) testing between March-June 2020. Participants were identified from PCHC electronic medical records. For three age groups– 0–4, 5–11, and 12–17 years–we estimated the sensitivity, specificity, and area under the receiver operating curve (AUC) of individual symptoms and three symptom combinations: a case definition published by the Rhode Island Department of Health (RIDOH), and two novel combinations generated by different statistical approaches to maximize sensitivity, specificity, and AUC. We evaluated symptom combinations both with and without consideration of COVID-19 exposure. Myalgia, headache, sore throat, abdominal pain, nausea, anosmia, and ageusia were not assessed in 0–4 year-olds due to the lower reliability of these symptoms in this group. Results Of 555 participants, 217 (39.1%) were SARS-CoV-2-infected. Fever was more common among 0–4 years-olds (p = 0.002); older children more frequently reported fatigue (p = 0.02). In children ≥5 years old, anosmia or ageusia had 94–98% specificity. In all ages, exposure history most accurately predicted infection. With respect to individual symptoms, cough most accurately predicted infection in <5 year-olds (AUC 0.69) and 12–17 year-olds (AUC 0.62), while headache was most accurate in 5–11 year-olds (AUC 0.62). In combination with exposure history, the novel symptom combinations generated statistically to maximize test characteristics had sensitivity >95% but specificity <30%. No symptom or symptom combination had AUC ≥0.70. Conclusions Anosmia or ageusia in children ≥5 years old should raise providers’ index of suspicion for COVID-19. However, our overall findings underscore the limited diagnostic value of symptoms.

Time-varying associations between an exposure history and a subsequent health outcome: a landmark approach to identify critical windows

Background Long-term behavioral and health risk factors constitute a primary focus of research on the etiology of chronic diseases. Yet, identifying critical time-windows during which risk factors have the strongest impact on disease risk is challenging. To assess the trajectory of association of an exposure history with an outcome, the weighted cumulative exposure index (WCIE) has been proposed, with weights reflecting the relative importance of exposures at different times. However, WCIE is restricted to a complete observed error-free exposure whereas exposures are often measured with intermittent missingness and error. Moreover, it rarely explores exposure history that is very distant from the outcome as usually sought in life-course epidemiology. Methods We extend the WCIE methodology to (i) exposures that are intermittently measured with error, and (ii) contexts where the exposure time-window precedes the outcome time-window using a landmark approach. First, the individual exposure history up to the landmark time is estimated using a mixed model that handles missing data and error in exposure measurement, and the predicted complete error-free exposure history is derived. Then the WCIE methodology is applied to assess the trajectory of association between the predicted exposure history and the health outcome collected after the landmark time. In our context, the health outcome is a longitudinal marker analyzed using a mixed model. Results A simulation study first demonstrates the correct inference obtained with this approach. Then, applied to the Nurses’ Health Study (19,415 women) to investigate the association between body mass index history (collected from midlife) and subsequent cognitive decline (evaluated after age 70), the method identified two major critical windows of association: long before the first cognitive evaluation (roughly 24 to 12 years), higher levels of BMI were associated with poorer cognition. In contrast, adjusted for the whole history, higher levels of BMI became associated with better cognition in the last years prior to the first cognitive interview, thus reflecting reverse causation (changes in exposure due to underlying disease). Conclusions This approach, easy to implement, provides a flexible tool for studying complex dynamic relationships and identifying critical time windows while accounting for exposure measurement errors.

Influence of temperature on spinosad toxicity in different populations of Plutella xylostella (Linnaeus)

Diamond back moth, Plutella xylostella (Linnaeus) is one of the major insect-pest of cole crops, causing high yield losses. In present study, larval populations of P. xylostella, collected from different regions of Punjab i.e. Amritsar, Kapurthala, Malerkotla and Ludhiana were exposed to different concentrations of spinosad at 15, 20, 25 and 30 °C for evaluation of LC values. The LC (Lethal 50 50 concentration) increased from lower (15 °C) to higher (30 °C) temperature for all populations of P. xylostella. Negative correlation was observed between the temperature coefficient and toxicity of spinosad towards P. xylostella populations, which decreased with increase in temperature. The LC 50 values varied among different populations of P. xylostella i.e. Amritsar populations with higher LC values 50 followed by Kapurthala, Malerkotla and Ludhiana populations. The temperature and insecticide exposure history both affected the toxicity of spinosad for P. xylostella. The information would be helpful in developing management strategies for P. xylostella according to prevailing environment conditions.

396. Disparities in SARS-CoV-2 Antibody Prevalence: Findings from a Citywide Serosurvey in Holyoke, Massachusetts, November 2020–January 2021

Background Seroprevalence studies are important tools to estimate the prevalence of prior or recent SARS-CoV-2 infections. This information is critical for identifying hotspots and high-risk groups and informing public health responses to the COVID-19 pandemic. We conducted a city-level seroprevalence study in Holyoke, Massachusetts to estimate the seroprevalence of SARS-CoV-2 antibodies and risk factors for seropositivity. Methods We invited inhabitants of 2,000 randomly sampled addresses to participate between November 5 and December 31, 2020. Participants completed questionnaires measuring sociodemographic and health characteristics, and COVID-19 exposure history, and provided dried blood spots for measurement of SARS-CoV-2 IgG and IgM antibodies. To calculate total and group seroprevalence estimates, inverse probability of response weights were constructed based on age, gender, race/ethnicity and census tract to ensure estimates represented the city’s population. Results We enrolled 280 households including 472 individuals. 328 underwent antibody testing. The citywide weighted seroprevalence of SARS-CoV-2 IgG or IgM was 13.9% (95%CI 7.8 - 21.8) compared to 9.8% based on publicly reported case counts. Seroprevalence was 16.8% (95%CI 5.7 – 28.0) among individuals identifying as Hispanic compared to 8.9% (95%CI 3.0 - 14.7) among those identifying as White. Seroprevalence was 20.7% (95%CI 2.2 – 39.2) for ages 0-19; 13.8% (95%CI 5.6 – 22) for ages 20 – 44; 9.6% (95%CI 0 – 20.5) for ages 45 – 59; 4.8% (95%CI 0 – 10.2) for ages 60 – 84; and 42.9% (95%CI 0 – 100) for ages &gt;85. Table 1. Seroprevalence by antibody positivity profile Table 2. Unweighted and weighted seroprevalence by sociodemographic characteristics Figure 1. Seroprevalence by Medical, Symptom, Testing and Exposure History. Conclusion The measured SARS-CoV-2 seroprevalence in Holyoke was only 13.9% during the second surge of SARS-CoV-2 in this region, far from accepted thresholds for “herd immunity” and highlighting the need for expanding vaccination. Individuals identifying as Hispanic were at high risk of prior infection. Subsequent community-level serosurveys are necessary to guide local responses to the SARS-CoV-2 pandemic. Disclosures All Authors: No reported disclosures

480. Alternate Diagnoses in Children Evaluated for Multisystem Inflammatory Syndrome in Children (MIS-C)

Background SARS-CoV-2 infection is typically a mild illness in children. Multisystem inflammatory syndrome in children (MIS-C) is a rare, post-infectious, hyperinflammatory condition associated with SARS-CoV-2 infection. The presentation of MIS-C is nonspecific and diagnostic criteria is broad. The Centers for Disease Control (CDC) defines MIS-C as a hospitalized patient &lt; 21 years presenting with fever, laboratory evidence of inflammation, no alternative plausible diagnosis, and with positive exposure history or testing for current or recent SARS-CoV-2 infection. Since there is no single diagnostic test for MIS-C, there are other disease processes that can mimic its presentation and delay prompt diagnosis and management. Methods Between March 2020 and February 2021, we reviewed 282 charts of patients admitted for evaluation of MIS-C at our institution. Results 101 were found to have MIS-C, 45 found to have Kawasaki Disease (KD), and 129 were ruled out. Of the ruled-out group, the most common final diagnoses were viral infection, urinary tract infection, and acute SARS-CoV-2 infection. Other diagnoses included rickettsial infections, pneumonia, rheumatologic conditions, and bloodstream infection. Rhinovirus/enterovirus, adenovirus, Epstein-Barr virus (EBV), and Herpes Simplex Virus (HSV) were the most common viruses other than SARS-CoV-2 identified. Conclusion These findings highlight the importance of maintaining a broad differential when evaluating a patient for MIS-C, especially as community seroprevalence rises, making antibody presence less predictive of MIS-C. Disclosures Susan Wu, MD, Eli Lilly (Shareholder)

Predictors of COVID-19 Infection: A Prevalence Study of Hospitalized Patients

Aim. To find the predictors of coronavirus disease 2019 (COVID-19) in hospitalized patients. Methods. A prevalence study compared the characteristics of COVID-19 patients with non-COVID-19 patients from January 19, 2020, to February 18, 2020, during the COVID-19 outbreak. Laboratory test results and pulmonary chest imaging of confirmed COVID-19 and non-COVID-19 patients were collected by retrieving medical records in our center. Results. 96 COVID-19 patients and 122 non-COVID-19 patients were enrolled in this study. COVID-19 patients were older (53 vs. 39; P < 0.001) and had higher body mass index (BMI) than non-COVID-19 group (24.21 ± 3.51 vs. 23.00 ± 3.27, P = 0.011); however, differences in gender were not observed between the two groups. Logistic regression analysis showed that exposure history (OR: 23.34, P < 0.001), rhinorrhea (odds radio (OR): 0.12, P = 0.006), alanine aminotransferase (ALT) (OR: 1.03, P = 0.049), lactate dehydrogenase (LDH) (OR: 1.01, P = 0.020), lymphocyte (OR: 0.27, P = 0.007), and bilateral involvement on chest CT imaging (OR: 23.01, P < 0.001) were independent risk factors for COVID-19. Moreover, bilateral involvement on chest CT imaging (AUC = 0.904, P < 0.001) had significantly higher AUC than others in predicting COVID-19. Conclusions. Exposure history, elevated ALT and LDH, absence of rhinorrhea, lymphopenia, and bilateral involvement on chest CT imaging provide robust evidence for the diagnosis of COVID-19, especially in resource-limited conditions where nucleic acid detection is not readily available.

Meta-analysis of risk factors associated with oxaliplatin hypersensitivity reactions in cancer patients

This study aimed to investigate risk factors associated with oxaliplatin hypersensitivity reactions in cancer patients through a meta-analysis. A comprehensive retrieve of Chinese databases China National Knowledge Infrastructure, Wanfang Data, VIP Database and English databases PubMed, ScienceDirect, Embase and Cochrane library was conducted. The studies that meet the requirements for meta-analysis according to inclusion and exclusion criteria were screened and assessed for eligibility. Odds ratio (OR) / Weighted mean difference (WMD) and 95% confidence intervals (95% CIs) or calculable dichotomous and continuous raw data were extracted to perform meta-analysis using random effect model or fixed effect model on the basis of heterogeneity between studies through Review Manager 5.4 software. A total of 14 cross-sectional studies and 3367 cancer patients were included. Meta-analysis results showed that platinum exposure history (OR value 3.13, 95% CI 2.19–4.48, heterogeneity P = 0.26), allergy history (OR value 1.76, 95% CI 1.09–2.85, heterogeneity P = 0.61), platinum free interval (OR value 3.75, 95% CI 2.00–7.06, heterogeneity P = 0.83), dexamethasone premedication dose (OR value 0.28, 95% CI 0.13–0.58, heterogeneity P = 0.21) were significantly correlated to oxaliplatin hypersensitivity reactions. Gender, age, metastasis, combination with bevacizumab, XELOX regimen and cancer types were detected to have no statistically significant effect on oxaliplatin hypersensitivity reactions. Platinum exposure history, allergy history and long platinum-free interval are risk factors of oxaliplatin hypersensitivity reactions. High dexamethasone premedication dose is a protective factor of oxaliplatin hypersensitivity reactions.

Seropositivity to Nucleoprotein to detect SARS-CoV-2 infections: a tool to detect breakthrough infections after COVID-19 vaccination

Background With COVID-19 vaccine roll-out ongoing in many countries globally, monitoring of breakthrough infections is of great importance. Antibodies persist in the blood after a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Since COVID-19 vaccines induce immune response to the Spike protein of the virus, which is the main serosurveillance target to date, alternative targets should be explored to distinguish infection from vaccination. Methods Multiplex immunoassay data from 1,513 SARS-CoV-2 RT-qPCR-tested individuals (352 positive and 1,161 negative) with a primary infection and no vaccination history were used to determine the accuracy of Nucleoprotein-specific immunoglobulin G (IgG) in detecting past SARS-CoV-2 infection. We also described Spike S1 and Nucleoprotein-specific IgG responses in 230 COVID-19 vaccinated individuals (Pfizer/BioNTech). Results The sensitivity of Nucleoprotein seropositivity was 85% (95% confidence interval: 80-90%) for mild COVID-19 in the first two months following symptom onset. Sensitivity was lower in asymptomatic individuals (67%, 50-81%). Participants who had experienced a SARS-CoV-2 infection up to 11 months preceding vaccination, as assessed by Spike S1 seropositivity or RT-qPCR, produced 2.7-fold higher median levels of IgG to Spike S1 ≥14 days after the first dose as compared to those unexposed to SARS-CoV-2 at ≥7 days after the second dose (p=0.011). Nucleoprotein-specific IgG concentrations were not affected by vaccination in naïve participants. Conclusions Serological responses to Nucleoprotein may prove helpful in identifying SARS-CoV-2 infections after vaccination. Furthermore, it can help interpret IgG to Spike S1 after COVID-19 vaccination as particularly high responses shortly after vaccination could be explained by prior exposure history.