Comparing diagnostic criteria for posttraumatic stress disorder in a diverse sample of trauma-exposed youth.

Divergent conceptualization of posttraumatic stress disorder (PTSD) within the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5) and International Statistical Classification of Diseases and Related Health Problems (11th ed..; ICD-11) significantly confounds both research and practice. Using a diverse sample of trauma-exposed youth (N = 1,542, age range: 8-20 years), we compared these two diagnostic approaches along with an expanded version of the ICD-11 PTSD criteria that included three additional reexperiencing symptoms (ICD-11+). Within the sample, PTSD was more prevalent using the DSM-5 criteria (25.7%) compared to the ICD-11 criteria (16.0%), with moderate agreement between these diagnostic systems, κ = .57. The inclusion of additional reexperiencing symptoms (i.e., ICD-11+) reduced this discrepancy in prevalence (24.7%) and increased concordance with DSM-5 criteria, κ = .73. All three PTSD classification systems exhibited similar comorbidity rates with major depressive episode (MDE) or generalized anxiety disorder (GAD; 78.0%-83.6%). Most youths who met the DSM-5 PTSD criteria also met the criteria for ICD-11 PTSD, MDE, or GAD (88.4%), and this proportion increased when applying the ICD-11+ criteria (95.5%). Symptom-level analyses identified reexperiencing/intrusions and negative alterations in cognition and mood symptoms as primary sources of discrepancy between the DSM-5 and ICD-11 PTSD diagnostic systems. Overall, these results challenge assertions that nonspecific distress and diagnostically overlapping symptoms within DSM-5 PTSD inflate comorbidity with depressive and anxiety disorders. Further, they support the argument that the DSM-5 PTSD criteria can be refined and simplified without reducing the overall prevalence of psychiatric diagnoses in youth.

Comparative effectiveness of dexamethasone in treatment of hospitalized COVID-19 patients in the United States during the first year of the pandemic: Findings from the National COVID Cohort Collaborative (N3C) data repository.

BACKGROUND: Dexamethasone was approved for use in hospitalized COVID-19 patients early in the pandemic based on the RECOVERY trial, but evidence is still needed to support its real-world effectiveness in heterogeneous populations of patients with a wide range of comorbidities. METHODS: COVID-19 inpatients represented within the National COVID Cohort Collaborative (N3C) Data Enclave, prior to vaccine availability, were studied. Primary outcome was in-hospital death; secondary outcome was combined in-hospital death and severe outcome defined by use of ECMO or mechanical ventilation. Missing data were imputed with single imputation. Dexamethasone-treated patients were propensity score (PS) matched to non-dexamethasone-treated controls, stratified by remdesivir treatment and based on demographics, baseline laboratory values, comorbidities, and amount of missing data before imputation. Treatment benefit was quantified using logistic regression. Further sensitivity analyses were performed using clinical adjusters in matched groups and in strata defined by quartiles of PS. RESULTS: Dexamethasone treatment was associated with reduced risk of in-hospital mortality for n = 1,263 treated, matched 1:3 to untreated, patients not receiving remdesivir (OR = 0.77, 95% CI: 0.62 to 0.95, p = 0.017), and for n = 804 treated, matched 1:1 to untreated, patients receiving remdesivir (OR = 0.74, 95% CI: 0.53 to 1.02, p = 0.054). Treatment showed secondary outcome benefit. In sensitivity analyses, treatment effect generally remained similar with some heterogeneity of benefit across quartiles of PS, possibly reflecting concentration of benefit among the more severely affected. CONCLUSIONS: We add evidence that dexamethasone provides benefit with respect to mortality and severe outcomes in a diverse, national hospitalized sample, prior to vaccine availability.

Racial and ethnic identity and vulnerability to upper respiratory viral infections among children in the US.

BACKGROUND: It is unclear whether there are racial and/or ethnic disparities in the risk of acquiring an upper respiratory infection (URI) and/or the risk of lower respiratory manifestations of a URI. METHODS: We studied all children and children with asthma, 6-17 years of age in the National Health and Nutritional Examination Survey (2007-2012), to evaluate the association between race/ethnicity and URI and to evaluate whether race/ethnicity is a risk factor for URI-associated pulmonary eosinophilic inflammation or decreased lung function. RESULTS: Children who identified as Black (aOR, 1.38; 95% CI, 1.10-1.75) and Mexican American (aOR, 1.50; 95% CI, 1.16-1.94) were more likely to report a URI than those who identified as White. Among those with asthma, Black children were more than twice as likely to report a URI than White children (aOR, 2.28; 95% CI, 1.31-3.95). Associations between URI and pulmonary eosinophilic inflammation or lung function did not differ by race/ethnicity. DISCUSSION: Findings suggest that there may be racial and ethnic disparities in acquiring a URI but not in the severity of infection. Given that URI is tightly linked to asthma exacerbations in children, differences in the risk of infection among children with asthma may contribute to disparities in asthma exacerbations.

Characterizing patterns of substance use in trauma exposed youth.

OBJECTIVE: Previous work investigating the impact of childhood trauma on substance use and co-occurring psychiatric disorders has primarily been conducted in adults or on specific trauma types. This limits understanding of traumas impact in childhood and how different types of traumas play a role. We sought to characterize substance use in a sample of trauma-exposed youth in the context of psychiatric comorbidities. METHOD: 1152 youth from the Texas Childhood Trauma Research Network (TX-CTRN) that were exposed to at least one trauma meeting DSM-5 Criterion A were assessed for current substance use and psychiatric diagnoses. Latent class analysis was used to identify patterns of substance use. To characterize these patterns, we examined if demographics, number of trauma types experienced, or childhood psychiatric disorders predicted class membership. RESULTS: We identified four primary patterns of substance use: Non-use (66.1%), predominantly alcohol use (19.7%), predominantly cannabis use (4.5%), and polysubstance use (9.7%). Compared to the non-users, polysubstance users tended to be older, Non-Hispanic White, have experienced more types of trauma. They were also more likely to have fulfilled diagnostic criteria for suicidality and ADHD. Comparisons among the substance using classes were more nuanced. CONCLUSION: The findings highlight the need for universal assessments of trauma, substance misuse, and mental health symptoms in youth as the presence or absence of their co-occurrence has implications for treatment.

Generalizing the information content for stepped wedge designs: A marginal modeling approach.

Stepped wedge trials are increasingly adopted because practical constraints necessitate staggered roll-out. While a complete design requires clusters to collect data in all periods, resource and patient-centered considerations may call for an incomplete stepped wedge design to minimize data collection burden. To study incomplete designs, we expand the metric of information content to discrete outcomes. We operate under a marginal model with general link and variance functions, and derive information content expressions when data elements (cells, sequences, periods) are omitted. We show that the centrosymmetric patterns of information content can hold for discrete outcomes with the variance-stabilizing link function. We perform numerical studies under the canonical link function, and find that while the patterns of information content for cells are approximately centrosymmetric for all examined underlying secular trends, the patterns of information content for sequences or periods are more sensitive to the secular trend, and may be far from centrosymmetric.

Intelligibility in Context Scale: Growth Curves for Typically Developing English-Speaking Children Between Ages 2;6 and 9;11.

PURPOSE: The aim of this study was to quantify the clinical utility of the Intelligibility in Context Scale (ICS) English version by characterizing the growth patterns of the ICS composite scores and seven ICS individual item scores of typically developing American English-speaking children. METHOD: Parents of 545 typically developing children aged 2;6-9;11 (years;months) completed the ICS. Using a proportional odds model, we regressed ICS composite scores on age and computed for model-estimated mean and lower quantile ICS composite scores. Logistic regression and proportional odds modeling were utilized to quantify the relationship of individual ICS items and age. RESULTS: ICS composite scores of typically developing children changed with age, but change was small and incremental, with scores compressed between 3 and 5 across the range of ages. An average child (i.e., on the 50th percentile) is expected to have an ICS composite score of 4 beginning at 3;0 and an ICS composite score of 5 by 6;6. On average, parents gave different intelligibility ratings based on communicative partners, and the rating differences between communicative partners decreased with age. CONCLUSIONS: Given that ICS scores increase with age, the expected score for average children also increases. A child's age is a main factor for interpreting ICS scores.

Sensor-Controlled Digital Game for Heart Failure Self-management: Protocol for a Randomized Controlled Trial.

BACKGROUND: Heart failure (HF) is the leading cause of hospitalization among older adults in the United States. There are substantial racial and geographic disparities in HF outcomes, with patients living in southern US states having a mortality rate 69% higher than the national average. Self-management behaviors, particularly daily weight monitoring and physical activity, are extremely important in improving HF outcomes; however, patients typically have particularly low adherence to these behaviors. With the rise of digital technologies to improve health outcomes and motivate health behaviors, sensor-controlled digital games (SCDGs) have become a promising approach. SCDGs, which leverage sensor-connected technologies, offer the benefits of being portable and scalable and allowing for continuous observation and motivation of health behaviors in their real-world contexts. They are also becoming increasingly popular among older adults and offer an immersive and accessible way to measure self-management behaviors and improve adherence. No SCDGs have been designed for older adults or evaluated to test their outcomes. OBJECTIVE: This randomized clinical trial aims to assess the efficacy of a SCDG in integrating the behavioral data of participants with HF from weight scale and activity tracker sensors to activate game progress, rewards, and feedback and, ultimately, to improve adherence to important self-management behaviors. METHODS: A total of 200 participants with HF, aged ≥45 years, will be recruited and randomized into 2 groups: the SCDG playing group (intervention group) and sensor-only group (control group). Both groups will receive a weight scale, physical activity tracker, and accompanying app, whereas only the intervention group will play the SCDG. This design, thereby, assesses the contributions of the game. All participants will complete a baseline survey as well as posttests at 6 and 12 weeks to assess the immediate effect of the intervention. They will also complete a third posttest at 24 weeks to assess the maintenance of behavioral changes. Efficacy and benefits will be assessed by measuring improvements in HF-related proximal outcomes (self-management behaviors of daily weight monitoring and physical activity) and distal outcomes (HF hospitalization, quality of life, and functional status) between baseline and weeks 6, 12, and 24. The primary outcome measured will be days with weight monitoring, for which this design provides at least 80% power to detect differences between the 2 groups. RESULTS: Recruitment began in the fall of 2022, and the first patient was enrolled in the study on November 7, 2022. Recruitment of the last participant is expected in quarter 1 of 2025. Publication of complete results and data from this study is expected in 2026. CONCLUSIONS: This project will generate insight and guidance for scalable and easy-to-use digital gaming solutions to motivate persistent adherence to HF self-management behaviors and improve health outcomes among individuals with HF. TRIAL REGISTRATION: ClinicalTrials.gov NCT05056129; https://clinicaltrials.gov/ct2/show/NCT05056129. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/45801.

Prenatal Weight Change Trajectories and Perinatal Outcomes among Twin Gestations.

OBJECTIVE: Despite an increase in twin pregnancies in recent decades, the Institute of Medicine twin weight gain recommendations remain provisional and provide no guidance for the pattern or timing of weight change. We sought to characterize gestational weight change trajectory patterns and examine associations with birth outcomes in a cohort of twin pregnancies. STUDY DESIGN: Prenatal and delivery records were examined for 320 twin pregnancies from a maternal-fetal medicine practice in Austin, TX 2011-2019. Prenatal weights for those with >1 measured weight in the first trimester and ≥3 prenatal weights were included in analyses. Trajectories were estimated to 32 weeks (mean delivery: 33.7 ± 3.3 weeks) using flexible latent class mixed models with low-rank thin-plate splines. Associations between trajectory classes and infant outcomes were analyzed using multivariable Poisson or linear regression. RESULTS: Weight change from prepregnancy to delivery was 15.4 ± 6.3 kg for people with an underweight body mass index, 15.4 ± 5.8 kg for healthy weight, 14.7 ± 6.9 kg for overweight, and 12.5 ± 6.4 kg for obesity. Three trajectory classes were identified: low (Class 1), moderate (Class 2), or high gain (Class 3). Class 1 (24.7%) maintained weight for 15 weeks and then gained an estimated 6.6 kg at 32 weeks. Class 2 (60.9%) exhibited steady gain with 13.5 kg predicted total gain, and Class 3 (14.4%) showed rapid gain across pregnancy with 21.3 kg predicted gain. Compared to Class 1, Class 3 was associated with higher birth weight z-score (ß = 0.63, 95% confidence interval [CI]: 0.31,0.96), increased risk for large for gestational age (IRR = 5.60, 95% CI: 1.59, 19.67), and birth <32 weeks (IRR = 2.44, 95%CI: 1.10, 5.4) that was attenuated in sensitivity analyses. Class 2 was associated with moderately elevated birth weight z-score (ß = 0.24, 95%CI: 0.00, 0.48, p = 0.050). CONCLUSION: Gestational weight change followed a low, moderate, or high trajectory; both moderate and high gain patterns were associated with increased infant size outcomes. Optimal patterns of weight change that balance risk during the prenatal, perinatal, and neonatal periods require further investigation, particularly in high-risk twin pregnancies. KEY POINTS: · A majority gained weight below IOM twin recommendations.. · Three patterns of GWC across pregnancy were identified.. · Moderate or high GWC was associated with infant size..

ORTH.Ord: An R package for analyzing correlated ordinal outcomes using alternating logistic regressions with orthogonalized residuals.

BACKGROUND AND OBJECTIVES: Marginal models with generalized estimating equations (GEE) are usually recommended for analyzing correlated ordinal outcomes which are commonly seen in a longitudinal study or clustered randomized trial (CRT). Within-cluster association is often of interest in longitudinal studies or CRTs, and can be estimated with paired estimating equations. However, the estimators for within-cluster association parameters and variances may be subject to finite-sample biases when the number of clusters is small. The objective of this article is to introduce a newly developed R package ORTH.Ord for analyzing correlated ordinal outcomes using GEE models with finite-sample bias corrections. METHODS: The R package ORTH.Ord implements a modified version of alternating logistic regressions with estimation based on orthogonalized residuals (ORTH), which use paired estimating equations to jointly estimate parameters in marginal mean and association models. The within-cluster association between ordinal responses is modeled by global pairwise odds ratios (POR). The R package also provides a finite-sample bias correction to POR parameter estimates based on matrix multiplicative adjusted orthogonalized residuals (MMORTH) for correcting estimating equations, and bias-corrected sandwich estimators with different options for covariance estimation. RESULTS: A simulation study shows that MMORTH provides less biased global POR estimates and coverage of their 95% confidence intervals closer to the nominal level than uncorrected ORTH. An analysis of patient-reported outcomes from an orthognathic surgery clinical trial illustrates features of ORTH.Ord. CONCLUSIONS: This article provides an overview of the ORTH method with bias-correction on both estimating equations and sandwich estimators for analyzing correlated ordinal data, describes the features of the ORTH.Ord R package, evaluates the performance of the package using a simulation study, and finally illustrates its application in an analysis of a clinical trial.

Establishing a training plan and estimating inter-rater reliability across the multi-site Texas childhood trauma research network.

OBJECTIVE: Minimal guidance is available in the literature to develop protocols for training non-clinician raters to administer semi-structured psychiatric interviews in large, multi-site studies. Previous work has not produced standardized methods for maintaining rater quality control or estimating interrater reliability (IRR) in such studies. Our objective is to describe the multi-site Texas Childhood Trauma Research Network (TX-CTRN) rater training protocol and activities used to maintain rater calibration and evaluate protocol effectiveness. METHODS: Rater training utilized synchronous and asynchronous didactic learning modules, and certification involved critique of videotaped mock scale administration. Certified raters attended monthly review meetings and completed ongoing scoring exercises for quality assurance purposes. Training protocol effectiveness was evaluated using individual measure and pooled estimated IRRs for three key study measures (TESI-C, CAPS-CA-5, MINI-KID [Major Depressive Episodes - MDE & Posttraumatic Stress Disorder - PTSD modules]). A random selection of video-recorded administrations of these measures was evaluated by three certified raters to estimate agreement statistics, with jackknife (on the videos) used for confidence interval estimation. Kappa, weighted kappa and intraclass correlations were calculated for study measure ratings. RESULTS: IRR agreement across all measures was strong (TESI-C median kappa 0.79, lower 95% CB 0.66; CAPS-CA-5 median weighted kappa 0.71 (0.62), MINI-MDE median kappa 0.71 (0.62), MINI-PTSD median kappa 0.91 (0.9). The combined estimated ICC was ≥0.86 (lower CBs ≥0.69). CONCLUSIONS: The protocol developed by TX-CTRN may serve as a model for other multi-site studies that require comprehensive non-clinician rater training, quality assurance guidelines, and a system for assessing and estimating IRR.

Developmental Cut-Points for Atypical Speech Intelligibility in Children With Cerebral Palsy.

PURPOSE: Early identification of speech motor involvement (SMI) in children with cerebral palsy (CP) is difficult because of overlapping features with many aspects of typical speech development. Quantitative measures of speech intelligibility have the potential to differentiate between children with SMI and those with no SMI (NSMI). We examined thresholds for speech intelligibility development in children with CP relative to the low end of age-specific typical developmental expectations. We sought to determine whether there were intelligibility differences between children with CP and NSMI versus typically developing (TD) age-mates across the range of development and whether there were differences between children with CP who have NSMI and those with CP who have SMI across the range of development based on speech intelligibility. METHOD: We used two large existing data sets that included speech samples from children between the ages of 2.5 and 8 years. One data set included 511 longitudinal speech samples from children with CP; the other included 505 cross-sectional speech samples from TD children. We examined receiver operating characteristic curves and sensitivity/specificity results by age for differentiating among groups of children. RESULTS: TD children versus those with CP and NSMI showed differentiation in their speech intelligibility across all ages, but the strength of differentiation was only marginally above chance. Children with CP and NSMI showed clear differentiation in their speech intelligibility from those with CP and SMI beginning at the earliest age point. Children with CP who have intelligibility below 40% at the age of 3 years have a very high probability of having SMI. CONCLUSIONS: Early intelligibility screening should be performed in children diagnosed with CP. Those with intelligibility below 40% at 3 years of age should be referred immediately for speech assessment and treatment.

Model misspecification and robust analysis for outcome-dependent sampling designs under generalized linear models.

Outcome-dependent sampling (ODS) is a commonly used class of sampling designs to increase estimation efficiency in settings where response information (and possibly adjuster covariates) is available, but the exposure is expensive and/or cumbersome to collect. We focus on ODS within the context of a two-phase study, where in Phase One the response and adjuster covariate information is collected on a large cohort that is representative of the target population, but the expensive exposure variable is not yet measured. In Phase Two, using response information from Phase One, we selectively oversample a subset of informative subjects in whom we collect expensive exposure information. Importantly, the Phase Two sample is no longer representative, and we must use ascertainment-correcting analysis procedures for valid inferences. In this paper, we focus on likelihood-based analysis procedures, particularly a conditional-likelihood approach and a full-likelihood approach. Whereas the full-likelihood retains incomplete Phase One data for subjects not selected into Phase Two, the conditional-likelihood explicitly conditions on Phase Two sample selection (ie, it is a "complete case" analysis procedure). These designs and analysis procedures are typically implemented assuming a known, parametric model for the response distribution. However, in this paper, we approach analyses implementing a novel semi-parametric extension to generalized linear models (SPGLM) to develop likelihood-based procedures with improved robustness to misspecification of distributional assumptions. We specifically focus on the common setting where standard GLM distributional assumptions are not satisfied (eg, misspecified mean/variance relationship). We aim to provide practical design guidance and flexible tools for practitioners in these settings.

GEEMAEE: A SAS macro for the analysis of correlated outcomes based on GEE and finite-sample adjustments with application to cluster randomized trials.

BACKGROUND AND OBJECTIVES: Generalized estimating equations (GEE) are used to analyze correlated outcomes in marginal regression models with population-averaged interpretations of exposure effects. Limitations of popular software for GEE include: (i) user choice is restricted to a small set of within-cluster pairwise correlation (intra-class correlation; ICC) structures; and (ii) inference on ICC parameters is usually not possible because the precision of their estimates is not quantified. This is important because ICC values inform the design of cluster randomized trials. Beyond the standard GEE implementation, use of paired estimating equations (Prentice 1988) provides: (i) flexible specification of models for pairwise correlations and (ii) standard errors for ICC estimates. However, most GEEs give biased estimates of standard errors and correlations when the number of clusters is small (roughly, ≤40). Consequently, there is a need for software to provide GEE analysis with finite-sample bias-corrections. METHODS: The SAS macro GEEMAEE implements paired estimating equations to simultaneously estimate parameters in marginal mean and ICC models. It provides bias-corrected standard errors and uses matrix-adjusted estimating equations (MAEE) for bias-corrected estimation of correlations. Several built-in correlation matrix options, rarely found in software, are offered for multi-period, cluster randomized trials and similarly structured longitudinal observational data structures. Additional options include user-specified correlation structures and deletion diagnostics, namely Cooks' Distance and DBETA statistics that estimate the influence of observations, cluster-periods (when applicable) and clusters. RESULTS: GEEMAEE is illustrated for a binary and a count outcome in two stepped wedge cluster randomized trials and a binary outcome in a longitudinal study of disease surveillance. Use of MAEE resulted in larger values of correlation estimates compared to uncorrected estimating equations. Use of bias-corrected variance estimators resulted in (appropriately) larger values of standard errors compared to the usual sandwich estimators. Deletion diagnostics identified the clusters and cluster-periods having the most influence. CONCLUSIONS: The SAS macro GEEMAEE provides regression analysis for clustered or longitudinal responses, and is particularly useful when the number of clusters is small. Flexible specification and bias-corrected estimation of pairwise correlation parameters and standard errors are key features of the software to provide valid inference in real-world settings.

The utility of teledermatology in the evaluation of skin lesions.

INTRODUCTION: Past studies have shown mixed results about the accuracy of store-and-forward (SAF) teledermatology in the evaluation of skin lesions. The objective of this study is to determine the accuracy of SAF teledermatology in the diagnosis of skin lesions and biopsy decision compared to in-person clinical evaluation. METHODS: Histories and photographs of skin lesions gathered at clinic visits were sent as SAF consults to teledermatologists, whose diagnoses and biopsy decisions were recorded and compared statistically to the clinic data.Results and Discussion: We enrolled 206 patients with 308 lesions in the study. The study population was composed of 50% males (n = 104), and most patients were white (n = 179, 87%) and not Hispanic/Latino (n = 167, 81%). There was good concordance for biopsy decision between the clinic dermatologist (CD) and teledermatologist (TD) (Cohen's kappa (κ) = 0.51), which did not significantly differ when melanocytic lesions were excluded (κ = 0.54). The sensitivity and specificity of teledermatology based on biopsy decision was 0.71 and 0.85, respectively. Overall concordance in first diagnosis between the CD and TD was good (κ = 0.60). While there was no difference between CD and TD in proportion of correct diagnoses compared to histopathology, two skin cancers presentations were missed by TD. Study limitations included sample size, enrolment bias and differing amounts of teledermatologist case experience. Teledermatology has good concordance in diagnosis and biopsy decision when compared to clinic dermatology. Teledermatology may be utilized in the evaluation of skin lesions to expand access to dermatologic care.

Generalized case-control sampling under generalized linear models.

A generalized case-control (GCC) study, like the standard case-control study, leverages outcome-dependent sampling (ODS) to extend to nonbinary responses. We develop a novel, unifying approach for analyzing GCC study data using the recently developed semiparametric extension of the generalized linear model (GLM), which is substantially more robust to model misspecification than existing approaches based on parametric GLMs. For valid estimation and inference, we use a conditional likelihood to account for the biased sampling design. We describe analysis procedures for estimation and inference for the semiparametric GLM under a conditional likelihood, and we discuss problems with estimation and inference under a conditional likelihood when the response distribution is misspecified. We demonstrate the flexibility of our approach over existing ones through extensive simulation studies, and we apply the methodology to an analysis of the Asset and Health Dynamics Among the Oldest Old study, which motives our research. The proposed approach yields a simple yet versatile solution for handling ODS in a wide variety of possible response distributions and sampling schemes encountered in practice.

A general method for calculating power for GEE analysis of complete and incomplete stepped wedge cluster randomized trials.

Stepped wedge designs have uni-directional crossovers at randomly assigned time points (steps) where clusters switch from control to intervention condition. Incomplete stepped wedge designs are increasingly used in cluster randomized trials of health care interventions and have periods without data collection due to logistical, resource and patient-centered considerations. The development of sample size formulae for stepped wedge trials has primarily focused on complete designs and continuous responses. Addressing this gap, a general, fast, non-simulation based power procedure is proposed for generalized estimating equations analysis of complete and incomplete stepped wedge designs and its predicted power is compared to simulated power for binary and continuous responses. An extensive set of simulations for six and twelve clusters is based upon the Connect-Home trial with an incomplete stepped wedge design. Results show that empirical test size is well controlled using a t-test with bias-corrected sandwich variance estimator for as few as six clusters. Analytical power agrees well with a simulated power in scenarios with twelve clusters. For six clusters, analytical power is similar to simulated power with estimation using the correctly specified model-based variance estimator. To explore the impact of study design choice on power, the proposed fast GEE power method is applied to the Connect-Home trial design, four alternative incomplete stepped wedge designs and one complete design.

Do upper respiratory viruses contribute to racial and ethnic disparities in emergency department visits for asthma?

BACKGROUND: There are marked disparities in asthma-related emergency department (ED) visit rates among children by race and ethnicity. Following the implementation of coronavirus disease 2019 (COVID-19) prevention measures, asthma-related ED visits rates declined substantially. The decline has been attributed to the reduced circulation of upper respiratory viruses, a common trigger of asthma exacerbations in children. OBJECTIVES: To better understand the contribution of respiratory viruses to racial and ethnic disparities in ED visit rates, we investigated whether the reduction in ED visit rates affected Black, Latinx, and White children with asthma equally. METHODS: Asthma-related ED visits were extracted from electronic medical records at Dell Children's Medical Center in Travis County, Texas. ED visit rates among children with asthma were derived by race/ethnicity. Incidence rate ratios (IRRs) and 95% CIs were estimated by year (2019-2021) and season. RESULTS: In spring 2019, the ED visit IRRs comparing Black children with White children and Latinx children with White children were 6.67 (95% CI = 4.92-9.05) and 2.10 (95% CI = 1.57-2.80), respectively. In spring 2020, when infection prevention measures were implemented, the corresponding IRRs decreased to 1.73 (95% CI = 0.90-3.32) and 0.68 (95% CI = 0.38-1.23), respectively. CONCLUSIONS: The striking reduction of disparities in ED visits suggests that during nonpandemic periods, respiratory viruses contribute to the excess burden of asthma-related ED visits among Black and Latinx children with asthma. Although further investigation is needed to test this hypothesis, our findings raise the question of whether Black and Latinx children with asthma are more vulnerable to upper respiratory viral infections.

Indoor environmental exposures and obstructive lung disease phenotypes among children with asthma living in poor urban neighborhoods.

BACKGROUND: Air trapping is an obstructive phenotype that has been associated with more severe and unstable asthma in children. Air trapping has been defined using pre- and postbronchodilator spirometry. The causes of air trapping are not completely understood. It is possible that environmental exposures could be implicated in air trapping in children with asthma. OBJECTIVE: We investigated the association between indoor exposures and air trapping in urban children with asthma. METHODS: Children with asthma aged 5 to 17 years living in Baltimore and enrolled onto the Environmental Control as Add-on Therapy for Childhood Asthma study were evaluated for air trapping using spirometry. Aeroallergen sensitization was assessed at baseline, and spirometry was performed at 0, 3, and 6 months. Air trapping was defined as an FVC z score of less than -1.64 or a change in FVC with bronchodilation of ≥10% predicted. Logistic normal random effects models were used to evaluate associations of air trapping and indoor exposures. RESULTS: Airborne and bedroom floor mouse allergen concentrations were associated with air trapping but not airflow limitation (odds ratio 1.19, 95% confidence interval 1.02-1.37, P = .02 per 2-fold increase in airborne mouse allergen; odds ratio 1.23, 95% confidence interval 1.07-1.41, P = .003 per 2-fold increase in bedroom floor mouse allergen). Other indoor exposures (cockroach, cat, dog, dust mite, particulate matter, and nicotine) were not associated with air trapping or airflow limitation. CONCLUSION: Mouse allergen exposure, but not other indoor exposure, was associated with air trapping in urban children with asthma.

Comparative Effectiveness of Dexamethasone in Treatment of Hospitalized COVID-19 Patients during the First Year of the Pandemic: The N3C Data Repository.

Background: Dexamethasone, a widely available glucocorticoid, was approved for use in hospitalized COVID-19 patients early in the pandemic based on the RECOVERY trial; however, evidence is still needed to support its real-world effectiveness in patients with a wide range of comorbidities and in diverse care settings. Objectives: To conduct a comparative effectiveness analysis of dexamethasone use with and without remdesivir in hospitalized COVID-19 patients using electronic health record data. Methods: We conducted a retrospective real-world effectiveness analysis using the harmonized, highly granular electronic health record data of the National COVID Cohort Collaborative (N3C) Data Enclave. Analysis was restricted to COVID-19 patients in an inpatient setting, prior to vaccine availability. Primary outcome was in-hospital death; secondary outcome was combined in-hospital death and severe outcome as defined by use of ECMO or mechanical ventilation during stay. Missing data were imputed with single imputation. Matching of dexamethasone-treated patients to non-dexamethasone-treated controls was accomplished using propensity score (PS) matching, stratified by remdesivir treatment and based on demographics, baseline laboratory values, and comorbidities. Treatment benefit was quantified using logistic regression. Further sensitivity analyses were performed using clinical adjusters in matched groups and in strata defined by quartiles of PS. Results: Regression analysis revealed a statistically significant association between dexamethasone use and reduced risk of in-hospital mortality for those not receiving remdesivir (OR=0.77, 95% CI:0.62 to 0.95, p=0.017), and a borderline statistically significant risk for those receiving remdesivir (OR=0.74, 95% CI: 0.53 to 1.02, p=0.054). Treatment also showed secondary outcome benefit. In sensitivity analyses, treatment effect size generally remained similar with some heterogeneity of benefit across strata of PS. Conclusions: We add evidence that dexamethasone provides benefit with respect to mortality and severe outcomes in a diverse, national hospitalized sample, prior to vaccine availability.