Factors associated with elevated SARS-CoV-2 immune response in children and adolescents.

Background: Understanding the distinct immunologic responses to SARS-CoV-2 infection among pediatric populations is pivotal in navigating the COVID-19 pandemic and informing future public health strategies. This study aimed to identify factors associated with heightened antibody responses in children and adolescents to identify potential unique immune dynamics in this population. Methods: Data collected between July and December 2023 from the Texas Coronavirus Antibody REsponse Survey (Texas CARES), a statewide prospective population-based antibody survey among 1-to-19-year-old participants, were analyzed. Each participant had the following data available for analysis: (1) Roche Elecsys® Anti-SARS-CoV-2 Immunoassay for Nucleocapsid protein antibodies (Roche N-test), (2) qualitative and semi-quantitative detection of antibodies to the SARS CoV-2 spike protein receptor binding domain (Roche S-test), and (3) self-reported antigen/PCR COVID-19 test results, vaccination, and health status. Statistical analysis identified associations between participant characteristics and spike antibody quartile group. Results: The analytical sample consisted of 411 participants (mean age 12.2 years, 50.6% female). Spike antibody values ranged from a low of 6.3 U/ml in the lowest quartile to a maximum of 203,132.0 U/ml in the highest quartile in the aggregate sample. Older age at test date (OR = 1.22, 95% CI: 1.12, 1.35, p < .001) and vaccination status (primary series/partially vaccinated, one or multiple boosters) showed significantly higher odds of being in the highest spike antibody quartile compared to younger age and unvaccinated status. Conversely, fewer days since the last immunity challenge showed decreased odds (OR = 0.98, 95% CI: 0.96, 0.99, p = 0.002) of being in the highest spike antibody quartile vs. more days since last immunity challenge. Additionally, one out of every three COVID-19 infections were asymptomatic. Conclusions: Older age, duration since the last immunity challenge (vaccine or infection), and vaccination status were associated with heightened spike antibody responses, highlighting the nuanced immune dynamics in the pediatric population. A significant proportion of children/adolescents continue to have asymptomatic infection, which has important public health implications.

Baseline characteristics of SARS-CoV-2 vaccine non-responders in a large population-based sample.

INTRODUCTION: Studies indicate that individuals with chronic conditions and specific baseline characteristics may not mount a robust humoral antibody response to SARS-CoV-2 vaccines. In this paper, we used data from the Texas Coronavirus Antibody REsponse Survey (Texas CARES), a longitudinal state-wide seroprevalence program that has enrolled more than 90,000 participants, to evaluate the role of chronic diseases as the potential risk factors of non-response to SARS-CoV-2 vaccines in a large epidemiologic cohort. METHODS: A participant needed to complete an online survey and a blood draw to test for SARS-CoV-2 circulating plasma antibodies at four-time points spaced at least three months apart. Chronic disease predictors of vaccine non-response are evaluated using logistic regression with non-response as the outcome and each chronic disease + age as the predictors. RESULTS: As of April 24, 2023, 18,240 participants met the inclusion criteria; 0.58% (N = 105) of these are non-responders. Adjusting for age, our results show that participants with self-reported immunocompromised status, kidney disease, cancer, and "other" non-specified comorbidity were 15.43, 5.11, 2.59, and 3.13 times more likely to fail to mount a complete response to a vaccine, respectively. Furthermore, having two or more chronic diseases doubled the prevalence of non-response. CONCLUSION: Consistent with smaller targeted studies, a large epidemiologic cohort bears the same conclusion and demonstrates immunocompromised, cancer, kidney disease, and the number of diseases are associated with vaccine non-response. This study suggests that those individuals, with chronic diseases with the potential to affect their immune system response, may need increased doses or repeated doses of COVID-19 vaccines to develop a protective antibody level.

Long-term immune response to SARS-CoV-2 infection and vaccination in children and adolescents.

BACKGROUND: This analysis examined the durability of antibodies present after SARS-CoV-2 infection and vaccination in children and adolescents. METHODS: Data were collected over 4 time points between October 2020-November 2022 as part of a prospective population-based cohort aged 5-to-19 years (N = 810). Results of the (1) Roche Elecsys® Anti-SARS-CoV-2 Immunoassay for detection of antibodies to the SARS-CoV-2 nucleocapsid protein (Roche N-test); and (2) qualitative and semi-quantitative detection of antibodies to the SARS CoV-2 spike protein receptor binding domain (Roche S-test); and (3) self-reported antigen/PCR COVID-19 test results, vaccination and symptom status were analyzed. RESULTS: N antibody levels reached a median of 84.10 U/ml (IQR: 20.2, 157.7) cutoff index (COI) ~ 6 months post-infection and increased slightly to a median of 85.25 (IQR: 28.0, 143.0) COI at 12 months post-infection. Peak S antibody levels were reached at a median of 2500 U/mL ~6 months post-vaccination and remained for ~12 months (mean 11.6 months, SD 1.20). CONCLUSIONS: This analysis provides evidence of robust durability of nucleocapsid and spike antibodies in a large pediatric sample up to 12 months post-infection/vaccination. This information can inform pediatric SARS-CoV-2 vaccination schedules. IMPACT: This study provided evidence of robust durability of both nucleocapsid and spike antibodies in a large pediatric sample up to 12 months after infection. Little is known about the long-term durability of natural and vaccine-induced SARS-CoV-2 antibodies in the pediatric population. Here, we determined the durability of anti-SARS-CoV-2 spike (S-test) and nucleocapsid protein (N-test) in children/adolescents after SARS-CoV-2 infection and/or vaccination lasts at least up to 12 months. This information can inform future SARS-CoV-2 vaccination schedules in this age group.

Integrating, Harmonizing, and Curating Studies With High-Frequency and Hourly Physiological Data: Proof of Concept from Seven Traumatic Brain Injury Data Sets.

Research in severe traumatic brain injury (TBI) has historically been limited by studies with relatively small sample sizes that result in low power to detect small, yet clinically meaningful outcomes. Data sharing and integration from existing sources hold promise to yield larger more robust sample sizes that improve the potential signal and generalizability of important research questions. However, curation and harmonization of data of different types and of disparate provenance is challenging. We report our approach and experience integrating multiple TBI data sets containing collected physiological data, including both expected and unexpected challenges encountered in the integration process. Our harmonized data set included data on 1536 patients from the Citicoline Brain Injury Treatment Trial (COBRIT), Effect of erythropoietin and transfusion threshold on neurological recovery after traumatic brain injury: a randomized clinical trial (EPO Severe TBI), BEST-TRIP, Progesterone for the Treatment of Traumatic Brain Injury III Clinical Trial (ProTECT III), Transforming Research and Clinical Knowledge in Traumatic brain Injury (TRACK-TBI), Brain Oxygen Optimization in Severe Traumatic Brain Injury Phase-II (BOOST-2), and Ben Taub General Hospital (BTGH) Research Database studies. We conclude with process recommendations for data acquisition for future prospective studies to aid integration of these data with existing studies. These recommendations include using common data elements whenever possible, a standardized recording system for labeling and timing of high-frequency physiological data, and secondary use of studies in systems such as Federal Interagency Traumatic Brain Injury Research Informatics System (FITBIR), to engage investigators who collected the original data.

SARS-CoV-2 Serostatus and COVID-19 Illness Characteristics by Variant Time Period in Non-Hospitalized Children and Adolescents.

OBJECTIVE: To describe COVID-19 illness characteristics, risk factors, and SARS-CoV-2 serostatus by variant time period in a large community-based pediatric sample. DESIGN: Data were collected prospectively over four timepoints between October 2020 and November 2022 from a population-based cohort ages 5 to 19 years old. SETTING: State of Texas, USA. PARTICIPANTS: Participants ages 5 to 19 years were recruited from large pediatric healthcare systems, Federally Qualified Healthcare Centers, urban and rural clinical practices, health insurance providers, and a social media campaign. EXPOSURE: SARS-CoV-2 infection. MAIN OUTCOME(S) AND MEASURE(S): SARS-CoV-2 antibody status was assessed by the Roche Elecsys® Anti-SARS-CoV-2 Immunoassay for detection of antibodies to the SARS-CoV-2 nucleocapsid protein (Roche N-test). Self-reported antigen or PCR COVID-19 test results and symptom status were also collected. RESULTS: Over half (57.2%) of the sample (N = 3911) was antibody positive. Symptomatic infection increased over time from 47.09% during the pre-Delta variant time period, to 76.95% during Delta, to 84.73% during Omicron, and to 94.79% during the Omicron BA.2. Those who were not vaccinated were more likely (OR 1.71, 95% CI 1.47, 2.00) to be infected versus those fully vaccinated. CONCLUSIONS: Results show an increase in symptomatic COVID-19 infection among non-hospitalized children with each progressive variant over the past two years. Findings here support the public health guidance that eligible children should remain up to date with COVID-19 vaccinations.

Incidence and Predictors of Breakthrough and Severe Breakthrough Infections of SARS-CoV-2 After Primary Series Vaccination in Adults: A Population-Based Survey of 22 575 Participants.

BACKGROUND: Breakthrough infections of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are well documented. The current study estimates breakthrough incidence across pandemic waves, and evaluates predictors of breakthrough and severe breakthrough infections (defined as those requiring hospitalization). METHODS: In total, 89 762 participants underwent longitudinal antibody surveillance. Incidence rates were calculated using total person-days contributed. Bias-corrected and age-adjusted logistic regression determined multivariable predictors of breakthrough and severe breakthrough infection, respectively. RESULTS: The incidence was 0.45 (95% confidence interval [CI], .38-.50) during pre-Delta, 2.80 (95% CI, 2.25-3.14) during Delta, and 11.2 (95% CI, 8.80-12.95) during Omicron, per 10 000 person-days. Factors associated with elevated odds of breakthrough included Hispanic ethnicity (vs non-Hispanic white, OR = 1.243; 95% CI, 1.073-1.441), larger household size (OR = 1.251 [95% CI, 1.048-1.494] for 3-5 vs 1 and OR = 1.726 [95% CI, 1.317-2.262] for more than 5 vs 1 person), rural versus urban living (OR = 1.383; 95% CI, 1.122-1.704), receiving Pfizer or Johnson & Johnson versus Moderna, and multiple comorbidities. Of the 1700 breakthrough infections, 1665 reported on severity; 112 (6.73%) were severe. Higher body mass index, Hispanic ethnicity, vaccine type, asthma, and hypertension predicted severe breakthroughs. CONCLUSIONS: Breakthrough infection was 4-25 times more common during the Omicron-dominant wave versus earlier waves. Higher burden of severe breakthrough infections was identified in subgroups.

A novel NIH research grant recommender using BERT.

Research grants are important for researchers to sustain a good position in academia. There are many grant opportunities available from different funding agencies. However, finding relevant grant announcements is challenging and time-consuming for researchers. To resolve the problem, we proposed a grant announcements recommendation system for the National Institute of Health (NIH) grants using researchers' publications. We formulated the recommendation as a classification problem and proposed a recommender using state-of-the-art deep learning techniques: i.e. Bidirectional Encoder Representations from Transformers (BERT), to capture intrinsic, non-linear relationship between researchers' publications and grants announcements. Internal and external evaluations were conducted to assess the system's usefulness. During internal evaluations, the grant citations were used to establish grant-publication ground truth, and results were evaluated against Recall@k, Precision@k, Mean reciprocal rank (MRR) and Area under the Receiver Operating Characteristic curve (ROC-AUC). During external evaluations, researchers' publications were clustered using Dirichlet Process Mixture Model (DPMM), recommended grants by our model were then aggregated per cluster through Recency Weight, and finally researchers were invited to provide ratings to recommendations to calculate Precision@k. For comparison, baseline recommenders using Okapi Best Matching (BM25), Term-Frequency Inverse Document Frequency (TF-IDF), doc2vec, and Naïve Bayes (NB) were also developed. Both internal and external evaluations (all metrics) revealed favorable performances of our proposed BERT-based recommender.

Antibody Duration After Infection From SARS-CoV-2 in the Texas Coronavirus Antibody Response Survey.

Understanding the duration of antibodies to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus that causes COVID-19 is important to controlling the current pandemic. Participants from the Texas Coronavirus Antibody Response Survey (Texas CARES) with at least 1 nucleocapsid protein antibody test were selected for a longitudinal analysis of antibody duration. A linear mixed model was fit to data from participants (n = 4553) with 1 to 3 antibody tests over 11 months (1 October 2020 to 16 September 2021), and models fit showed that expected antibody response after COVID-19 infection robustly increases for 100 days postinfection, and predicts individuals may remain antibody positive from natural infection beyond 500 days depending on age, body mass index, smoking or vaping use, and disease severity (hospitalized or not; symptomatic or not).

Methodology to estimate natural- and vaccine-induced antibodies to SARS-CoV-2 in a large geographic region.

Accurate estimates of natural and/or vaccine-induced antibodies to SARS-CoV-2 are difficult to obtain. Although model-based estimates of seroprevalence have been proposed, they require inputting unknown parameters including viral reproduction number, longevity of immune response, and other dynamic factors. In contrast to a model-based approach, the current study presents a data-driven detailed statistical procedure for estimating total seroprevalence (defined as antibodies from natural infection or from full vaccination) in a region using prospectively collected serological data and state-level vaccination data. Specifically, we conducted a longitudinal statewide serological survey with 88,605 participants 5 years or older with 3 prospective blood draws beginning September 30, 2020. Along with state vaccination data, as of October 31, 2021, the estimated percentage of those 5 years or older with naturally occurring antibodies to SARS-CoV-2 in Texas is 35.0% (95% CI = (33.1%, 36.9%)). This is 3× higher than, state-confirmed COVID-19 cases (11.83%) for all ages. The percentage with naturally occurring or vaccine-induced antibodies (total seroprevalence) is 77.42%. This methodology is integral to pandemic preparedness as accurate estimates of seroprevalence can inform policy-making decisions relevant to SARS-CoV-2.