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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.

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.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.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.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.

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.

ObjectivesTo systematically review and evaluate diagnostic models used to predict viral acute respiratory infections (ARIs) in children.DesignSystematic review.Data sourcesPubMed and Embase were searched from 1 January 1975 to 3 February 2022.Eligibility criteriaWe included diagnostic models predicting viral ARIs in children (Data extraction and synthesisStudy 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).ResultsOf 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.ConclusionsDiagnostic 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 numberCRD42022308917.

BACKGROUNDThe 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.METHODSThe 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.RESULTSAmong 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.CONCLUSIONThese 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.

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: 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. REGISTRATION: PROSPERO (CRD42021250358).

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.

ObjectiveTo systematically review and critically evaluate prediction models developed to predict tuberculosis (TB) treatment outcomes among adults with pulmonary TB.DesignSystematic review.Data sourcesPubMed, Embase, Web of Science and Google Scholar were searched for studies published from 1 January 1995 to 9 January 2020.Study selection and data extractionStudies 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.Results14 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.ConclusionsTB 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 registrationThe study was registered on the international prospective register of systematic reviews PROSPERO (CRD42020155782)

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.

Background: Tuberculosis (TB) outcome prediction models are important for informing clinical practice and TB management policies, but existing models have not been systematically reviewed. Design/Methods: PubMed, Embase, Web of Science, and Google Scholar were searched for studies published January 1, 1995 - January 9, 2020. 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 (PROBAST). The study was pre-registered on OSF (https://osf.io/rz3wp). Findings: 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=29, 78%) 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 studies (54%) 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 (BMI), 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. Interpretation: 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. Funding Statement: This work was supported by the National Center for Advancing Translational Sciences [CTSA Award No. TL1TR000447 to L.S.P.]. Declaration of Interests: None declared.

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.

OBJECTIVES/GOALS: Many clinical prediction models have been developed to guide tuberculosis (TB) treatment, but their results and methods have not been formally evaluated. We aimed to identify and synthesize existing models for predicting TB treatment outcomes, including bias and applicability assessment. METHODS/STUDY POPULATION: Our review will adhere to methods that developed specifically for systematic reviews of prediction model studies. We will search PubMed, Embase, Web of Science, and Google Scholar (first 200 citations) to identify studies that internally and/or externally validate a model for TB treatment outcomes (defined as one or multiple of cure, treatment completion, death, treatment failure, relapse, default, and lost to follow-up). Study screening, data extraction, and bias assessment will be conducted independently by two reviewers with a third party to resolve discrepancies. Study quality will be assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). RESULTS/ANTICIPATED RESULTS: Our search strategy yielded 6,242 articles in PubMed, 10,585 in Embase, 10,511 in Web of Science, and 200 from Google Scholar, totaling 27,538 articles. After de-duplication, 14,029 articles remain. After screening titles, abstracts, and full-text, we will extract data from relevant studies, including publication details, study characteristics, methods, and results. Data will be summarized with narrative review and in detailed tables with descriptive statistics. We anticipate finding disparate outcome definitions, contrasting predictors across models, and high risk of bias in methods. Meta-analysis of performance measures for model validation studies will be performed if possible. DISCUSSION/SIGNIFICANCE OF IMPACT: TB outcome prediction models are important but existing ones have not been rigorously evaluated. This systematic review will synthesize TB outcome prediction models and serve as guidance to future studies that aim to use or develop TB outcome prediction models.