Prediction Models for Adverse Drug Reactions During Tuberculosis Treatment in Brazil.

BACKGROUND: Tuberculosis (TB) treatment-related adverse drug reactions (TB-ADRs) can negatively affect adherence and treatment success rates. METHODS: We developed prediction models for TB-ADRs, considering participants with drug-susceptible pulmonary TB who initiated standard TB therapy. TB-ADRs were determined by the physician attending the participant, assessing causality to TB drugs, the affected organ system, and grade. Potential baseline predictors of TB-ADR included concomitant medication (CM) use, human immunodeficiency virus (HIV) status, glycated hemoglobin (HbA1c), age, body mass index (BMI), sex, substance use, and TB drug metabolism variables (NAT2 acetylator profiles). The models were developed through bootstrapped backward selection. Cox regression was used to evaluate TB-ADR risk. RESULTS: There were 156 TB-ADRs among 102 of the 945 (11%) participants included. Most TB-ADRs were hepatic (n = 82 [53%]), of moderate severity (grade 2; n = 121 [78%]), and occurred in NAT2 slow acetylators (n = 62 [61%]). The main prediction model included CM use, HbA1c, alcohol use, HIV seropositivity, BMI, and age, with robust performance (c-statistic = 0.79 [95% confidence interval {CI}, .74-.83) and fit (optimism-corrected slope and intercept of -0.09 and 0.94, respectively). An alternative model replacing BMI with NAT2 had similar performance. HIV seropositivity (hazard ratio [HR], 2.68 [95% CI, 1.75-4.09]) and CM use (HR, 5.26 [95% CI, 2.63-10.52]) increased TB-ADR risk. CONCLUSIONS: The models, with clinical variables and with NAT2, were highly predictive of TB-ADRs.

Effectiveness of BNT162b2 COVID-19 primary series vaccination in children aged 5-17 years in the United States: a cohort study.

BACKGROUND: COVID-19 vaccines are authorized for use in children in the United States; real-world assessment of vaccine effectiveness in children is needed. This study's objective was to estimate the effectiveness of receiving a complete primary series of monovalent BNT162b2 (Pfizer-BioNTech) COVID-19 vaccine in US children. METHODS: This cohort study identified children aged 5-17 years vaccinated with BNT162b2 matched with unvaccinated children. Participants and BNT162b2 vaccinations were identified in Optum and CVS Health insurance administrative claims databases linked with Immunization Information System (IIS) COVID-19 vaccination records from 16 US jurisdictions between December 11, 2020, and May 31, 2022 (end date varied by database and IIS). Vaccinated children were followed from their first BNT162b2 dose and matched to unvaccinated children on calendar date, US county of residence, and demographic and clinical factors. Censoring occurred if vaccinated children failed to receive a timely dose 2 or if unvaccinated children received any dose. Two COVID-19 outcome definitions were evaluated: COVID-19 diagnosis in any medical setting and COVID-19 diagnosis in hospitals/emergency departments (EDs). Propensity score-weighted hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated with Cox proportional hazards models, and vaccine effectiveness (VE) was estimated as 1 minus HR. VE was estimated overall, within age subgroups, and within variant-specific eras. Sensitivity, negative control, and quantitative bias analyses evaluated various potential biases. RESULTS: There were 453,655 eligible vaccinated children one-to-one matched to unvaccinated comparators (mean age 12 years; 50% female). COVID-19 hospitalizations/ED visits were rare in children, regardless of vaccination status (Optum, 41.2 per 10,000 person-years; CVS Health, 44.1 per 10,000 person-years). Overall, vaccination was associated with reduced incidence of any medically diagnosed COVID-19 (meta-analyzed VE = 38% [95% CI, 36-40%]) and hospital/ED-diagnosed COVID-19 (meta-analyzed VE = 61% [95% CI, 56-65%]). VE estimates were lowest among children 5-11 years and during the Omicron-variant era. CONCLUSIONS: Receipt of a complete BNT162b2 vaccine primary series was associated with overall reduced medically diagnosed COVID-19 and hospital/ED-diagnosed COVID-19 in children; observed VE estimates differed by age group and variant era. REGISTRATION: The study protocol was publicly posted on the BEST Initiative website ( https://bestinitiative.org/wp-content/uploads/2022/03/C19-VX-Effectiveness-Protocol_2022_508.pdf ).

Tuberculosis Treatment Outcomes in Brazil: Different Predictors for Each Type of Unsuccessful Outcome.

BACKGROUND: Successful tuberculosis (TB) treatment is necessary for disease control. The World Health Organization (WHO) has a target TB treatment success rate of ≥90%. We assessed whether the different types of unfavorable TB treatment outcome had different predictors. METHODS: Using data from Regional Prospective Observational Research for Tuberculosis-Brazil, we evaluated biological and behavioral factors associated with each component of unsuccessful TB outcomes, recently updated by WHO (death, loss to follow-up [LTFU], and treatment failure). We included culture-confirmed, drug-susceptible, pulmonary TB participants receiving standard treatment in 2015-2019. Multinomial logistic regression models with inverse probability weighting were used to evaluate the distinct determinants of each unsuccessful outcome. RESULTS: Of 915 participants included, 727 (79%) were successfully treated, 118 (13%) were LTFU, 44 (5%) had treatment failure, and 26 (3%) died. LTFU was associated with current drug-use (adjusted odds ratio [aOR] = 5.3; 95% confidence interval [CI], 3.0-9.4), current tobacco use (aOR = 2.9; 95% CI, 1.7-4.9), and being a person with HIV (PWH) (aOR = 2.0; 95% CI, 1.1-3.5). Treatment failure was associated with PWH (aOR = 2.7; 95% CI, 1.2-6.2) and having diabetes (aOR = 2.2; 95% CI, 1.1-4.4). Death was associated with anemia (aOR = 5.3; 95% CI, 1.4-19.7), diabetes (aOR = 3.1; 95% CI, 1.4-6.7), and PWH (aOR = 3.9; 95% CI, 1.3-11.4). Direct observed therapy was protective for treatment failure (aOR = 0.5; 95% CI, .3-.9) and death (aOR = 0.5; 95% CI, .2-1.0). CONCLUSIONS: The treatment success rate was below the WHO target. Behavioral factors were most associated with LTFU, whereas clinical comorbidities were correlated with treatment failure and death. Because determinants of unsuccessful outcomes are distinct, different intervention strategies may be needed to improve TB outcomes.

A Clinical Prediction Model for Unsuccessful Pulmonary Tuberculosis Treatment Outcomes.

BACKGROUND: Despite widespread availability of curative therapy, tuberculosis (TB) treatment outcomes remain suboptimal. Clinical prediction models can inform treatment strategies to improve outcomes. Using baseline clinical data, we developed a prediction model for unsuccessful TB treatment outcome and evaluated the incremental value of human immunodeficiency virus (HIV)-related severity and isoniazid acetylator status. METHODS: Data originated from the Regional Prospective Observational Research for Tuberculosis Brazil cohort, which enrolled newly diagnosed TB patients in Brazil from 2015 through 2019. This analysis included participants with culture-confirmed, drug-susceptible pulmonary TB who started first-line anti-TB therapy and had ≥12 months of follow-up. The end point was unsuccessful TB treatment: composite of death, treatment failure, regimen switch, incomplete treatment, or not evaluated. Missing predictors were imputed. Predictors were chosen via bootstrapped backward selection. Discrimination and calibration were evaluated with c-statistics and calibration plots, respectively. Bootstrap internal validation estimated overfitting, and a shrinkage factor was applied to improve out-of-sample prediction. Incremental value was evaluated with likelihood ratio-based measures. RESULTS: Of 944 participants, 191 (20%) had unsuccessful treatment outcomes. The final model included 7 baseline predictors: hemoglobin, HIV infection, drug use, diabetes, age, education, and tobacco use. The model demonstrated good discrimination (c-statistic = 0.77; 95% confidence interval, .73-.80) and was well calibrated (optimism-corrected intercept and slope, -0.12 and 0.89, respectively). HIV-related factors and isoniazid acetylation status did not improve prediction of the final model. CONCLUSIONS: Using information readily available at treatment initiation, the prediction model performed well in this population. The findings may guide future work to allocate resources or inform targeted interventions for high-risk patients.

Lack of Weight Gain During the First 2 Months of Treatment and Human Immunodeficiency Virus Independently Predict Unsuccessful Treatment Outcomes in Tuberculosis.

BACKGROUND: Weight change may inform tuberculosis treatment response, but its predictive power may be confounded by human immunodeficiency virus (HIV). METHODS: We prospectively followed up adults with culture-confirmed, drug-susceptible, pulmonary tuberculosis receiving standard 4-drug therapy (isoniazid, rifampin, pyrazinamide, and ethambutol) in Brazil. We examined median weight change 2 months after treatment initiation by HIV status, using quantile regression, and unsuccessful tuberculosis treatment outcome (treatment failure, tuberculosis recurrence, or death) by HIV and weight change status, using Cox regression. RESULTS: Among 547 participants, 102 (19%) were HIV positive, and 35 (6%) had an unsuccessful outcome. After adjustment for confounders, persons living with HIV (PLWH) gained a median of 1.3 kg (95% confidence interval [CI], -2.8 to .1) less than HIV-negative individuals during the first 2 months of tuberculosis treatment. PLWH were at increased risk of an unsuccessful outcome (adjusted hazard ratio, 4.8; 95% CI, 2.1-10.9). Weight change was independently associated with outcome, with risk of unsuccessful outcome decreasing by 12% (95% CI, .81%-.95%) per 1-kg increase. CONCLUSIONS: PLWH gained less weight during the first 2 months of tuberculosis treatment, and lack of weight gain and HIV independently predicted unsuccessful tuberculosis treatment outcomes. Weight, an easily collected biomarker, may identify patients who would benefit from alternative treatment strategies.

Knowledge and stigma of latent tuberculosis infection in Brazil: implications for tuberculosis prevention strategies.

BACKGROUND: Tuberculosis (TB) elimination requires treatment of millions of persons with latent M. tuberculosis infection (LTBI). LTBI treatment acceptance depends on population-wide TB knowledge and low stigma, but limited data are available on the relationship between stigma and knowledge. We assessed knowledge of TB disease and LTBI throughout Brazil and examined their association with TB stigma and incidence. METHODS: We performed a nationwide survey with multi-stage probability design through AmericasBarometer from April-May 2017; the sample was representative of Brazil at regional and national levels. Knowledge of and stigma toward TB were assessed by validated survey questions. RESULTS: Survey-weighted responses of 1532 individuals suggest that 57% of the population knew LTBI can occur, and 90% would seek treatment for it. Regarding active TB, 85% knew TB symptoms, 70% reported they should avoid contact with someone with active TB, and 24% had stigma toward persons with TB (i.e., thought persons with tuberculosis should feel ashamed, or deserved their illness). In regression models adjusting for clinical and demographic variables, knowledge of LTBI was associated with increased stigma toward persons with TB (adjusted odds ratio [OR] = 2.13, 95% confidence interval [CI]: 1·25-3.63, for "should feel ashamed"; OR = 1·82, 95% CI: 1·15-2·89, for "deserve illness"). Adjusting for regional TB incidence did not affect this association. CONCLUSIONS: High proportions of this representative Brazilian population had knowledge of LTBI and were willing to seek treatment for it. However, such knowledge was associated with TB-specific stigma. Strategies to educate and implement treatment of latent tuberculosis must include efforts to decrease TB stigma.

Clinical prediction model: Multisystem inflammatory syndrome in children versus Kawasaki disease.

BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) is a rare but serious complication of severe acute respiratory syndrome coronavirus 2 infection. Features of MIS-C overlap with those of Kawasaki disease (KD). OBJECTIVE: The study objective was to develop a prediction model to assist with this diagnostic dilemma. METHODS: Data from a retrospective cohort of children hospitalized with KD before the coronavirus disease 2019 pandemic were compared to a prospective cohort of children hospitalized with MIS-C. A bootstrapped backwards selection process was used to develop a logistic regression model predicting the probability of MIS-C diagnosis. A nomogram was created for application to individual patients. RESULTS: Compared to children with incomplete and complete KD (N = 602), children with MIS-C (N = 105) were older and had longer hospitalizations; more frequent intensive care unit admissions and vasopressor use; lower white blood cell count, lymphocyte count, erythrocyte sedimentation rate, platelet count, sodium, and alanine aminotransferase; and higher hemoglobin and C-reactive protein (CRP) at admission. Left ventricular dysfunction was more frequent in patients with MIS-C, whereas coronary abnormalities were more common in those with KD. The final prediction model included age, sodium, platelet count, alanine aminotransferase, reduction in left ventricular ejection fraction, and CRP. The model exhibited good discrimination with AUC 0.96 (95% confidence interval: [0.94-0.98]) and was well calibrated (optimism-corrected intercept of -0.020 and slope of 0.99). CONCLUSIONS: A diagnostic prediction model utilizing admission information provides excellent discrimination between MIS-C and KD. This model may be useful for diagnosis of MIS-C but requires external validation.

Effectiveness of monovalent COVID-19 booster/additional vaccine doses in the United States.

Background: Monovalent booster/additional doses of COVID-19 vaccines were first authorized in August 2021 in the United States. We evaluated the real-world effectiveness of receipt of a monovalent booster/additional dose of COVID-19 vaccine compared with receiving a primary vaccine series without a booster/additional dose. Methods: Cohorts of individuals receiving a COVID-19 booster/additional dose after receipt of a complete primary vaccine series were identified in 2 administrative insurance claims databases (Optum, CVS Health) supplemented with state immunization information system data between August 2021 and March 2022. Individuals with a complete primary series but without a booster/additional dose were one-to-one matched to boosted individuals on calendar date, geography, and clinical factors. COVID-19 diagnoses were identified in any medical setting, or specifically in hospitals/emergency departments (EDs). Propensity score-weighted hazards ratios (HRs) and 95% confidence intervals (CI) were estimated with Cox proportional hazards models; vaccine effectiveness (VE) was estimated as 1 minus the HR by vaccine brand overall and within subgroups of variant-specific eras, immunocompromised status, and homologous/heterologous booster status. Results: Across both data sources, we identified 752,165 matched pairs for BNT162b2, 410,501 for mRNA-1273, and 11,398 for JNJ-7836735. For any medically diagnosed COVID-19, meta-analyzed VE estimates for BNT162b2, mRNA-1273, and JNJ-7836735, respectively, were: BNT162b2, 54% (95% CI, 53%-56%); mRNA-1273, 58% (95% CI, 56%-59%); JNJ-7836735, 34% (95% CI, 23%-44%). For hospital/ED-diagnosed COVID-19, VE estimates ranged from 70% to 76%. VE was generally lower during the Omicron era than the Delta era and for immunocompromised individuals. There was little difference observed by homologous or heterologous booster status. Conclusion: The original, monovalent booster/additional doses were reasonably effective in real-world use among the populations for which they were indicated during the study period. Additional studies may be informative in the future as new variants emerge and new vaccines become available.Registration: The study protocol was publicly posted on the BEST Initiative website (https://bestinitiative.org/wp-content/uploads/2022/03/C19-VX-Effectiveness-Protocol_2022_508.pdf).

Diagnostic models predicting paediatric viral acute respiratory infections: a systematic review.

OBJECTIVES: To systematically review and evaluate diagnostic models used to predict viral acute respiratory infections (ARIs) in children. DESIGN: Systematic review. DATA SOURCES: PubMed and Embase were searched from 1 January 1975 to 3 February 2022. ELIGIBILITY CRITERIA: We included diagnostic models predicting viral ARIs in children (<18 years) who sought medical attention from a healthcare setting and were written in English. Prediction model studies specific to SARS-CoV-2, COVID-19 or multisystem inflammatory syndrome in children were excluded. DATA EXTRACTION AND SYNTHESIS: Study screening, data extraction and quality assessment were performed by two independent reviewers. Study characteristics, including population, methods and results, were extracted and evaluated for bias and applicability using the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and PROBAST (Prediction model Risk Of Bias Assessment Tool). RESULTS: Of 7049 unique studies screened, 196 underwent full text review and 18 were included. The most common outcome was viral-specific influenza (n=7; 58%). Internal validation was performed in 8 studies (44%), 10 studies (56%) reported discrimination measures, 4 studies (22%) reported calibration measures and none performed external validation. According to PROBAST, a high risk of bias was identified in the analytic aspects in all studies. However, the existing studies had minimal bias concerns related to the study populations, inclusion and modelling of predictors, and outcome ascertainment. CONCLUSIONS: Diagnostic prediction can aid clinicians in aetiological diagnoses of viral ARIs. External validation should be performed on rigorously internally validated models with populations intended for model application. PROSPERO REGISTRATION NUMBER: CRD42022308917.

Couples-based interventions and postpartum contraceptive uptake: A systematic review.

OBJECTIVE: Systematically review the existing evidence about couples-based interventions and postpartum contraceptive uptake and generate recommendations for future research. DATA SOURCES: PubMed, Web of Science, PsycINFO, Embase, and CINAHL through June 7, 2021. STUDY SELECTION AND DATA EXTRACTION: Studies with a couples-based intervention assessing postpartum contraceptive uptake. Two independent reviewers screened studies, extracted data, and assessed risk of bias with RoB-2 (Cochrane Risk of Bias 2) for randomized and ROBINS-I (Risk of Bias in Non-Randomized Studies - Interventions) for observational studies. Data were synthesized in tables, figures, and a narrative review. RESULTS: A total of 925 papers were identified, 66 underwent full text review, and 17 articles, which included 18 studies - 16 randomized, 2 observational - were included. The lack of intervention and outcome homogeneity precluded meta-analysis and isolating the effect of partner involvement. Four studies were partner-required, where partner involvement was a required component of the intervention, and 14 were partner-optional. Unadjusted risk differences ranged from 0.01 to 0.51 in favor of couples-based interventions increasing postpartum contraceptive uptake versus standard of care. Bias assessment of the 16 randomized studies classified 8, 3, and 5 studies as at a high, some concern, and low risk of bias. Common sources of bias included intervention non-adherence and missing outcome data. One observational study was at a high and the other at a low risk of bias. CONCLUSIONS: Future studies that assess couples-based interventions must clearly define and measure how partners are involved in the intervention and assess how intervention adherence impacts postpartum contraceptive uptake.

A Diagnostic Prediction Model to Distinguish Multisystem Inflammatory Syndrome in Children.

OBJECTIVE: Features of multisystem inflammatory syndrome in children (MIS-C) overlap with other syndromes, making the diagnosis difficult for clinicians. We aimed to compare clinical differences between patients with and without clinical MIS-C diagnosis and develop a diagnostic prediction model to assist clinicians in identification of patients with MIS-C within the first 24 hours of hospital presentation. METHODS: A cohort of 127 patients (<21 years) were admitted to an academic children's hospital and evaluated for MIS-C. The primary outcome measure was MIS-C diagnosis at Vanderbilt University Medical Center. Clinical, laboratory, and cardiac features were extracted from the medical record, compared among groups, and selected a priori to identify candidate predictors. Final predictors were identified through a logistic regression model with bootstrapped backward selection in which only variables selected in more than 80% of 500 bootstraps were included in the final model. RESULTS: Of 127 children admitted to our hospital with concern for MIS-C, 45 were clinically diagnosed with MIS-C and 82 were diagnosed with alternative diagnoses. We found a model with four variables-the presence of hypotension and/or fluid resuscitation, abdominal pain, new rash, and the value of serum sodium-showed excellent discrimination (concordance index 0.91; 95% confidence interval: 0.85-0.96) and good calibration in identifying patients with MIS-C. CONCLUSION: A diagnostic prediction model with early clinical and laboratory features shows excellent discrimination and may assist clinicians in distinguishing patients with MIS-C. This model will require external and prospective validation prior to widespread use.

Curating the Evidence About COVID-19 for Frontline Public Health and Clinical Care: The Novel Coronavirus Research Compendium.

The public health crisis created by the COVID-19 pandemic has spurred a deluge of scientific research aimed at informing the public health and medical response to the pandemic. However, early in the pandemic, those working in frontline public health and clinical care had insufficient time to parse the rapidly evolving evidence and use it for decision-making. Academics in public health and medicine were well-placed to translate the evidence for use by frontline clinicians and public health practitioners. The Novel Coronavirus Research Compendium (NCRC), a group of >60 faculty and trainees across the United States, formed in March 2020 with the goal to quickly triage and review the large volume of preprints and peer-reviewed publications on SARS-CoV-2 and COVID-19 and summarize the most important, novel evidence to inform pandemic response. From April 6 through December 31, 2020, NCRC teams screened 54 192 peer-reviewed articles and preprints, of which 527 were selected for review and uploaded to the NCRC website for public consumption. Most articles were peer-reviewed publications (n = 395, 75.0%), published in 102 journals; 25.1% (n = 132) of articles reviewed were preprints. The NCRC is a successful model of how academics translate scientific knowledge for practitioners and help build capacity for this work among students. This approach could be used for health problems beyond COVID-19, but the effort is resource intensive and may not be sustainable in the long term.

Minimal in-school SARS-CoV-2 transmission with strict mitigation protocols at two independent schools in Nashville, TN.

BACKGROUND: The COVID-19 pandemic has greatly impacted school operations. To better understand the role of schools in COVID-19 transmission, we evaluated infections at two independent schools in Nashville, TN during the 2020-2021 school year. METHODS: The cumulative incidence of COVID-19 within each school, age group, and exposure setting were estimated and compared to local incidence. Primary attack rates were estimated among students quarantined for in-school close contact. RESULTS: Among 1401 students who attended school during the study period, 98 cases of COVID-19 were reported, corresponding to cumulative incidence of 7.0% (95% confidence interval (CI): 5.7-8.5). Most cases were linked to household (58%) or community (31%) transmission, with few linked to in-school transmission (11%). Overall, 619 students were quarantined, corresponding to >5000 person-days of missed school, among whom only 5 tested positive for SARS-CoV-2 during quarantine (primary attack rate: 0.8%, 95% CI: 0.3, 1.9). Weekly case rates at school were not correlated with community transmission. CONCLUSION: These results suggest that transmission of COVID-19 in schools is minimal when strict mitigation measures are used, even during periods of extensive community transmission. Strict quarantine of contacts may lead to unnecessary missed school days with minimal benefit to in-school transmission.

Systematic review of prediction models for pulmonary tuberculosis treatment outcomes in adults.

OBJECTIVE: To systematically review and critically evaluate prediction models developed to predict tuberculosis (TB) treatment outcomes among adults with pulmonary TB. DESIGN: Systematic review. DATA SOURCES: PubMed, Embase, Web of Science and Google Scholar were searched for studies published from 1 January 1995 to 9 January 2020. STUDY SELECTION AND DATA EXTRACTION: Studies that developed a model to predict pulmonary TB treatment outcomes were included. Study screening, data extraction and quality assessment were conducted independently by two reviewers. Study quality was evaluated using the Prediction model Risk Of Bias Assessment Tool. Data were synthesised with narrative review and in tables and figures. RESULTS: 14 739 articles were identified, 536 underwent full-text review and 33 studies presenting 37 prediction models were included. Model outcomes included death (n=16, 43%), treatment failure (n=6, 16%), default (n=6, 16%) or a composite outcome (n=9, 25%). Most models (n=30, 81%) measured discrimination (median c-statistic=0.75; IQR: 0.68-0.84), and 17 (46%) reported calibration, often the Hosmer-Lemeshow test (n=13). Nineteen (51%) models were internally validated, and six (16%) were externally validated. Eighteen (54%) studies mentioned missing data, and of those, half (n=9) used complete case analysis. The most common predictors included age, sex, extrapulmonary TB, body mass index, chest X-ray results, previous TB and HIV. Risk of bias varied across studies, but all studies had high risk of bias in their analysis. CONCLUSIONS: TB outcome prediction models are heterogeneous with disparate outcome definitions, predictors and methodology. We do not recommend applying any in clinical settings without external validation, and encourage future researchers adhere to guidelines for developing and reporting of prediction models. TRIAL REGISTRATION: The study was registered on the international prospective register of systematic reviews PROSPERO (CRD42020155782).

Curating and translating the evidence about SARS-CoV-2 and COVID-19 for frontline public health and clinical care: The Novel Coronavirus Research Compendium (NCRC).

The public health crisis created by the SARS-CoV-2 pandemic has spurred a deluge of scientific research aimed at informing public health and medical response to the COVID-19 pandemic. However, those working in frontline public health and clinical care had insufficient time to parse the rapidly evolving evidence and use it for decision making. Academics in public health and medicine were well-placed to translate the evidence for use by frontline clinicians and public health practitioners. The Novel Coronavirus Research Compendium (NCRC), a group of >50 faculty and trainees, began in March 2020 with the goal to quickly triage and review the large volume of preprints and peer-reviewed publications on SARS-CoV-2 and COVID-19, and to summarize the most important, novel evidence to inform pandemic response. From April 6, 2020 through January 1, 2021, 54,192 papers and preprints were screened by NCRC teams and 527 were selected for review and uploaded to the NCRC website for public consumption. The majority of papers reviewed were peer-reviewed publications (n=395, 75%), published in 102 journals; 25% (n=132) of papers reviewed were of preprints. The NCRC is a successful model of how academics can support practitioners by translating scientific knowledge into action and help to build capacity among students for this work. This approach could be used for health problems beyond COVID-19, but the effort is resource intensive and may not be sustainable over the long term.

Increased Neutrophil Count and Decreased Neutrophil CD15 Expression Correlate With TB Disease Severity and Treatment Response Irrespective of HIV Co-infection.

Tuberculosis remains a leading cause of death globally despite curative treatment, partly due to the difficulty of identifying patients who will not respond to therapy. Simple host biomarkers that correlate with response to drug treatment would facilitate improvement in outcomes and the evaluation of novel therapies. In a prospective longitudinal cohort study, we evaluated neutrophil count and phenotype at baseline, as well as during TB treatment in 79 patients [50 (63%) HIV-positive] with microbiologically confirmed drug susceptible TB undergoing standard treatment. At time of diagnosis, blood neutrophils were highly expanded and surface expression of the neutrophil marker CD15 greatly reduced compared to controls. Both measures changed rapidly with the commencement of drug treatment and returned to levels seen in healthy control by treatment completion. Additionally, at the time of diagnosis, high neutrophil count, and low CD15 expression was associated with higher sputum bacterial load and more severe lung damage on chest x-ray, two clinically relevant markers of disease severity. Furthermore, CD15 expression level at diagnosis was associated with TB culture conversion after 2 months of therapy (OR: 0.14, 95% CI: 0.02, 0.89), a standard measure of early TB treatment success. Importantly, our data was not significantly impacted by HIV co-infection. These data suggest that blood neutrophil metrics could potentially be exploited to develop a simple and rapid test to help determine TB disease severity, monitor drug treatment response, and identify subjects at diagnosis who may respond poorly to treatment.