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 US Children

BACKGROUND

It is unclear whether there are racial/ethnic disparities in the risk of upper respiratory viral infection acquisition and/or lower respiratory manifestations.

METHODS

We studied all children and children with asthma aged 6 to 17 years in the National Health and Nutrition Examination Survey (2007-2012) to evaluate (1) the association between race/ethnicity and upper respiratory infection (URI) and (2) whether race/ethnicity is a risk factor for URI-associated pulmonary eosinophilic inflammation or decreased lung function.

RESULTS

Children who identified as Black (adjusted odds ratio [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.

CONCLUSIONS

Findings suggest that there may be racial and ethnic disparities in acquiring a URI but not in the severity of infection. Given that upper respiratory viral infection 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.

Evaluating traumatic event scoring schemas for their predictive value to concurrent diagnostic profiles: Texas Childhood Trauma Research Network

BACKGROUND

To prospectively chart pathways of risk and resiliency following childhood trauma studies need to address three limitations of prior work: 1) recruit beyond social service/ treatment settings; 2) include broad spectrum of trauma types and 3) cast a broad longitudinal measurement framework of both clinical diagnoses and traumatic exposures. The Texas-Childhood Trauma Research Network (TX-CTRN) is a multi-site collaboration that addresses these limitations. In this baseline-only report, we examined domains of trauma and evaluated the concurrent predictive validity of various traumatic event scoring schemas for clinical diagnoses.

METHODS

Broad-base recruitment of 8-20 year-olds (N = 1289) included trauma centers, emergency departments, pediatric and primary care clinics, and other community settings. Assessments were comprehensive and based on clinical interviews by trained research interviewers.

RESULTS

Factor analyses supported a five-factor solution of trauma domains including unintentional/acute, intentional/interpersonal, bullying, in-home versus community witnessed interpersonal harms. Trauma burden scoring schemas were examined for their predictive superiority. Domain-specific counts of traumas that met DSM-5 post-traumatic-stress disorder (PTSD) Criterion-A was the best overall schema in distinguishing among youth with no diagnosis, comorbidities, those with depression, suicidality, substance misuse, and PTSD.

LIMITATIONS

There were no assessments of neglect.

CONCLUSIONS

Findings largely aligned with earlier studies on the relative importance of intentional interpersonal traumas and showed bullying may be an important source of traumatic stress that independently adds to prediction of several diagnoses and should be considered in clinical practice.

A - 53 A Digital Health Solution for Early Detection of Cognitive Impairment in Primary Care

OBJECTIVE

To determine which task or combination of tasks provided the most effective way to differentiate cognitively impaired (CI) from cognitively normal (CN) participants in under 5 minutes and to ensure that classification accuracy was equal to or better than a traditional brief cognitive screening task, the Quick Mild Cognitive Impairment (Qmci) screen.

METHOD

CN (n = 53) and CI (n = 51) participants completed a risk assessment task, a symbol matching (SM) task, and four speech-language tasks, followed by a second administration of SM to examine utility of practice effects administered on an iPad. Participants also completed the Qmci. Eleven models were tested using Bayesian adaptive regression trees.

RESULTS

The top three models all included the two SM variables: the one with SM by itself (estimated c = 0.91), one with SM and features from a personal narrative task (c = 0.94), and one with SM and a counting backwards task (c = 0.90). Models with picture description and procedural discourse tasks performed the worst. For comparison, the QMCI-only model yielded c = 0.91.

CONCLUSIONS

A combination of working memory/processing speed and acoustic and linguistic variables from recalling a personal story achieved a high level of classification accuracy, slightly exceeding that of a traditional cognitive screening task. The inclusion of both verbal and nonverbal tasks may be an important feature, allowing for cognitive screening of individuals who are not able to do one type of task or the other. Future work is planned to examine this shortened tool in a pragmatic clinical trial in two primary care clinics.

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.

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.

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.

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.

Coping Behaviors and Incident Kidney Disease

Key Points

Adaptive coping behaviors are associated with lower odds of incident CKD. Coping behaviors could represent a target to prevent CKD.

Background

How someone copes may alter the trajectory of their kidney function. We aimed to evaluate whether coping behaviors were associated with incident CKD or rapid kidney function decline.

Methods

We used data from the Healthy Aging in Neighborhoods of Diversity across the Life Span study (Baltimore, MD) for this longitudinal analysis. Adaptive and maladaptive coping behavioral constructs were measured using the Brief COPE Inventory at visit 1. We used multiple logistic regression to assess the odds of incident CKD and rapid kidney function decline per point increase in coping scales and adjusted for baseline demographics and clinical variables.

Results

Of 1935 participants, mean age was 48 years, 44% were male, 56% were Black persons, and baseline mean (SD) eGFR was 91 (16) ml/min per 1.73 m2. After a median of 8.2 years, 113 participants developed incident CKD and 341 had rapid kidney function decline. Compared with those who reported they usually did not use adaptive coping behaviors at all (such as emotional support), those with the highest use of adaptive coping had lower odds of incident CKD. Every 1-unit increase in adaptive coping corresponded with a 2% lower adjusted odds of incident CKD (odds ratio, 0.98; 95% confidence interval, 0.95 to 0.99). There was no association between maladaptive coping behaviors and incident CKD. Coping behaviors were not associated with rapid kidney function decline.

Conclusions

Adaptive coping behaviors were associated with lower odds of incident CKD and could represent a target to facilitate CKD prevention. The role of medical care in this association is an area worthy of further investigation.

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 (β = 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 (β = 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..

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.

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.

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.

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.

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.

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.

power swgee: GEE-based power calculations in stepped wedge cluster randomized trials

Stepped wedge cluster randomized trials are increasingly being used to evaluate interventions in medical, public health, educational, and social science contexts. With the longitudinal and crossover nature of a SW-CRT, complex analysis techniques are often needed which makes appropriately powering SW-CRTs challenging. In this paper, we introduce a newly-developed SW-CRT power calculator, embedded within the power command in Stata. The power calculator assumes a marginal model (i.e., generalized estimating equations [GEE]) for the primary analysis of SW-CRTs, for which other currently available SW-CRT power calculators may not be suitable. The program accommodates complete cross-sectional and closed-cohort designs, and includes multilevel correlation structures appropriate for such designs. We discuss the methods and formulae underlying our SW-CRT calculator, and provide illustrative examples of the use of power swgee. We provide suggestions about the choice of parameters in power swgee, and conclude by discussing areas of future research which may improve the program.

Increasing socioeconomically disadvantaged patients' engagement in breast cancer surgery decision-making through a shared decision-making intervention (A231701CD): protocol for a cluster randomised clinical trial

INTRODUCTION

Socioeconomic disparities for breast cancer surgical care exist. Although the aetiology of the observed socioeconomic disparities is likely multifactorial, patient engagement during the surgical consult is critical. Shared decision-making may reduce health disparities by addressing barriers to patient engagement in decision-making that disproportionately impact socioeconomically disadvantaged patients. In this trial, we test the impact of a decision aid on increasing socioeconomically disadvantaged patients' engagement in breast cancer surgery decision-making.

METHODS AND ANALYSIS

This multisite randomised trial is conducted through 10 surgical clinics within the National Cancer Institute Community Oncology Research Program (NCORP). We plan a stepped-wedge design with clinics randomised to the time of transition from usual care to the decision aid arm. Study participants are female patients, aged ≥18 years, with newly diagnosed stage 0-III breast cancer who are planning breast surgery. Data collection includes a baseline surgeon survey, baseline patient survey, audio-recording of the surgeon-patient consultation, a follow-up patient survey and medical record data review. Interviews and focus groups are conducted with a subset of patients, surgeons and clinic stakeholders. The effectiveness of the decision aid at increasing patient engagement (primary outcome) is evaluated using generalised linear mixed-effects models. The extent to which the effect of the decision aid intervention on patient engagement is mediated through the mitigation of barriers is tested in joint linear structural equation models. Qualitative interviews explore how barriers impact engagement, especially for socioeconomically disadvantaged women.

ETHICS AND DISSEMINATION

This protocol has been approved by the National Cancer Institute Central Institutional Review Board, and Certificate of Confidentiality has been obtained. We plan to disseminate the findings through journal publications and national meetings, including the NCORP network. Our findings will advance the science of medical decision-making with the potential to reduce socioeconomic health disparities.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov Registry (NCT03766009).

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.

Longitudinal change in speech classification between 4 and 10 years in children with cerebral palsy

AIM

To examine speech impairment severity classification over time in a longitudinal cohort of children with cerebral palsy (CP).

METHOD

A total of 101 children (58 males, 43 females) between the ages of 4 and 10 years with CP participated in this longitudinal study. Speech severity was rated using the Viking Speech Scale (VSS), a four-level classification rating scale, at 4, 6, 8, and 10 years (age 4 years: mean = 52 months [3 SD]; age 6 years: mean = 75 months [2 SD]; age 8 years: mean = 100 months [4 SD]; age 10 years: mean = 125 months [5 SD]). We used Bayesian mixed-effects ordinal logistic regression to model (1) the extent to which speech severity changed over time and (2) patterns of change across age groups and classification rating group levels.

RESULTS

VSS ratings decreased (speech severity became less severe) between 4 and 10 years of age. Children who were first classified in VSS levels I, II, or III at age 4 years had a high probability of staying at, or improving to, VSS level I by 10 years. Children who were first classified in VSS level IV at 4 years had a high probability of remaining in VSS level IV at 10 years.

INTERPRETATION

Early speech performance is highly predictive of later childhood speech abilities. Children with any level of speech impairment at age 4 years should be receiving speech therapy. Those with more severe speech impairments should be introduced to augmentative and alternative communication as soon as possible.

WHAT THIS PAPER ADDS

Children with early Viking Speech Scale (VSS) ratings below level IV have a good prognosis for speech improvement. Children with early VSS level IV ratings are likely to remain at VSS level IV over time. Children did not show worsening of VSS level between the ages of 4 and 10 years.

A biobehavioral intervention to enhance recovery following hematopoietic cell transplantation: Protocol for a feasibility and acceptability randomized control trial

Background

Insomnia, fatigue, and depression are among the most persistent and distressing concerns for hematologic cancer patients recovering from hematopoietic cell transplantation (HCT). This study will evaluate a novel behavioral intervention, Restoring Sleep and Energy after Transplant (ReSET), designed to alleviate insomnia, fatigue, and depression by improving rest-activity patterns. Evidence-based behavioral strategies to improve nighttime sleep and increase non-sedentary daytime activity will be combined to optimize 24-h rest-activity patterns.

Methods

The protocol herein evaluates the feasibility and acceptability of ReSET by conducting a pilot randomized controlled trial to compare the intervention with usual care. Adults undergoing HCT will be randomly assigned to ReSET or usual care. The ReSET arm will receive 3 face-to-face sessions and telephone coaching delivered in an individual format tailored to each patient. Patient-reported insomnia, fatigue, and depression will be the primary outcome measures. Actigraphy will be used to objectively quantify rest-activity patterns. Semi-structured interviews will evaluate participant satisfaction with ReSET. The goals are to determine: (1) participant satisfaction with and acceptability of the behavioral techniques; (2) facilitator fidelity and participant uptake of key intervention components; (3) ability to recruit, retain, and collect complete data from participants; (4) participant willingness to be randomized and acceptability of the control condition; and (5) validity and acceptability of the assessment strategy.

Conclusion

The overarching goal is to optimize recovery following HCT with a brief, non-invasive intervention that can be implemented as a part of routine clinical care.

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First intervention for cancer patients to optimize 24-h rest-activity rhythms.

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Non-invasive intervention designed for implementation in routine clinical care.

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Restoration of normal circadian rhythm targets insomnia, fatigue, and depression.

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Addresses the high need for efficacious, brief interventions for cancer patients.

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Innovative behavioral intervention approach applicable to a wide variety of cancers.

Magnetic Resonance Imaging Findings in Infants with Severe Traumatic Brain Injury and Associations with Abusive Head Trauma

Young children with severe traumatic brain injury (TBI) have frequently been excluded from studies due to age and/or mechanism of injury. Magnetic resonance imaging (MRI) is now frequently being utilized to detect parenchymal injuries and early cerebral edema. We sought to assess MRI findings in infants with severe TBI, and to determine the association between specific MRI findings and mechanisms of injury, including abusive head trauma (AHT). MRI scans performed within the first 30 days after injury were collected and coded according to NIH/NINDS Common Data Elements (CDEs) for Neuroimaging in subjects age < 2 years old with severe TBI enrolled in the Approaches and Decisions in Acute Pediatric Traumatic Brain Injury Trial. Demographics and injury characteristics were analyzed. A total of 81 children were included from ADAPT sites with MRI scans. Median age was 0.77 years and 57% were male. Most common MRI finding was ischemia, present in 57/81 subjects (70%), in a median of 7 brain regions per subject. Contusion 46/81 (57%) and diffuse axonal injury (DAI) 36/81 (44.4%) subjects followed. Children were dichotomized based on likelihood of AHT with 43/81 subjects classified as AHT. Ischemia was found to be significantly associated with AHT (p = 0.001) and “inflicted” injury mechanism (p = 0.0003). In conclusion, the most common intracerebral injury seen on MRI of infants with severe TBI was ischemia, followed by contusion and DAI. Ischemia was associated with AHT, and ischemia affecting > 4 brain regions was predictive of AHT.

Marginal modeling of cluster-period means and intraclass correlations in stepped wedge designs with binary outcomes

Stepped wedge cluster randomized trials (SW-CRTs) with binary outcomes are increasingly used in prevention and implementation studies. Marginal models represent a flexible tool for analyzing SW-CRTs with population-averaged interpretations, but the joint estimation of the mean and intraclass correlation coefficients (ICCs) can be computationally intensive due to large cluster-period sizes. Motivated by the need for marginal inference in SW-CRTs, we propose a simple and efficient estimating equations approach to analyze cluster-period means. We show that the quasi-score for the marginal mean defined from individual-level observations can be reformulated as the quasi-score for the same marginal mean defined from the cluster-period means. An additional mapping of the individual-level ICCs into correlations for the cluster-period means further provides a rigorous justification for the cluster-period approach. The proposed approach addresses a long-recognized computational burden associated with estimating equations defined based on individual-level observations, and enables fast point and interval estimation of the intervention effect and correlations. We further propose matrix-adjusted estimating equations to improve the finite-sample inference for ICCs. By providing a valid approach to estimate ICCs within the class of generalized linear models for correlated binary outcomes, this article operationalizes key recommendations from the CONSORT extension to SW-CRTs, including the reporting of ICCs.

Validation of remote atopic dermatitis severity assessment with the Eczema Area and Severity Ondex in children using caregiver-provided photos and videos

BACKGROUND/OBJECTIVES

We sought to quantify the reliability and validity of remote atopic dermatitis (AD) severity assessment using the Eczema Area and Severity Index (EASI) applied to caregiver-provided photos (p-EASI) and videos (v-EASI).

METHODS

Children (0-17 years) with a physician diagnosis of AD were recruited. Caregivers took photos and a video of their child's skin. A clinician scored in-person EASI on the same day, then p-EASI and v-EASI for each participant 10 days or more between ratings. Two additional clinicians scored p-EASI and v-EASI. Lin's concordance correlation coefficient (CCC) was employed to assess criterion validity using in-person EASI as the gold standard. Intraclass correlation coefficients (ICCs) were calculated to assess interrater reliability of p-EASI and v-EASI.

RESULTS

Fifty racially and ethnically diverse children (age [mean ± SD]: 4.3 ± 4.4 years; 42% female) with a range of AD severity (EASI: 6.3 ± 6.4) and Fitzpatrick skin types (1-2: 9%; 3-4: 60%; 5-6: 31%) were enrolled and received in-person EASI assessment. Fifty had p-EASI and 49 had v-EASI by the same in-person rater, and by two additional raters. The CCC and ICC for p-EASI were 0.89, 95% CI [0.83, 0.95] and 0.81, 95% CI [0.71, 0.89], respectively. The CCC and ICC for v-EASI were 0.75, 95% CI [0.63, 0.88] and 0.69, 95% CI [0.51, 0.81], respectively.

CONCLUSIONS

In this diverse population with a range of skin tones, p-EASI showed good criterion validity and good interrater reliability. v-EASI showed moderate to good criterion validity and moderate interrater reliability. Both may be reliable and valid options for remote AD severity assessment.

Caregiver Preferences and Barriers Toward Accessing Pediatric Dermatology Care for Childhood Atopic Dermatitis

INTRODUCTION

Atopic dermatitis (AD) is a common chronic childhood illness. It is often treated by primary care providers (PCPs) though it may require referral to a dermatology specialist.

METHOD

We administered an exploratory survey to 50 caregivers of children aged 0-17 years with AD to assess their preferences and barriers toward accessing dermatology specialty care for their child's AD.

RESULTS

Caregivers felt PCPs and specialists equally listened to their child's AD concerns. However, many felt there was a difference in the care provided and control of the AD and preferred to see a specialist for ongoing management.

DISCUSSION

Caregivers may benefit from their children being referred to dermatology specialists earlier and more often for their AD. Further work must be done to characterize preferences and barriers toward AD care across race and ethnicity.

Model-assisted analyses of longitudinal, ordinal outcomes with absorbing states

Studies of critically ill, hospitalized patients often follow participants and characterize daily health status using an ordinal outcome variable. Statistically, longitudinal proportional odds models are a natural choice in these settings since such models can parsimoniously summarize differences across patient groups and over time. However, when one or more of the outcome states is absorbing, the proportional odds assumption for the follow-up time parameter will likely be violated, and more flexible longitudinal models are needed. Motivated by the VIOLET Study (Ginde et al), a parallel-arm, randomized clinical trial of Vitamin D 3 in critically ill patients, we discuss and contrast several treatment effect estimands based on time-dependent odds ratio parameters, and we detail contemporary modeling approaches. In VIOLET, the outcome is a four-level ordinal variable where the lowest "not alive" state is absorbing and the highest "at-home" state is nearly absorbing. We discuss flexible extensions of the proportional odds model for longitudinal data that can be used for either model-based inference, where the odds ratio estimator is taken directly from the model fit, or for model-assisted inferences, where heterogeneity across cumulative log odds dichotomizations is modeled and results are summarized to obtain an overall odds ratio estimator. We focus on direct estimation of cumulative probability model (CPM) parameters using likelihood-based analysis procedures that naturally handle absorbing states. We illustrate the modeling procedures, the relative precision of model-based and model-assisted estimators, and the possible differences in the values for which the estimators are consistent through simulations and analysis of the VIOLET Study data.

Association of Tumor Characteristics With Insurance Type Among Patients Undergoing Mohs Micrographic Surgery for Nonmelanoma Skin Cancer

Importance

Little is known about the association between insurance type and tumor or treatment characteristics among patients undergoing Mohs micrographic surgery (MMS) for nonmelanoma skin cancer (NMSC).

Objective

To investigate whether there are differences in tumor and treatment characteristics among patients undergoing MMS for NMSC by insurance type.

Design, Setting, and Participants

This retrospective cohort study included patients with NMSC who presented for surgery at an academic MMS practice between May 2017 and May 2019.

Main Outcomes and Measures

Preoperative and postoperative tumor diameters, number of MMS stages, type of closure, and number of high-risk tumors were compared based on insurance type among uninsured and underinsured patients and those with private insurance, Medicare, and Veterans Affairs (VA) insurance.

Results

A total of 1397 patients with NMSC (978 [70%] male; mean [SD] age, 68.5 [12.4] years) underwent 1916 MMS procedures. Of these patients, 868 (45%) had Medicare, 570 (30%) had private insurance, 299 (16%) had VA insurance, and 179 (9%) were treated at a safety net clinic or were uninsured. Compared with patients with private insurance, uninsured and underinsured patients had significantly larger preoperative tumor bed diameters (difference, 28%; 95% CI, 14%-43%; P < .001) and postoperative defect sizes (difference, 28%, 95% CI, 16%-41%; P < .001). Patients with Medicare and VA insurance did not have significantly different preoperative tumor bed diameters compared with patients with private insurance. Patients with VA insurance had larger postoperative defect sizes than patients with private insurance (difference, 12%; 95% CI, 2%-23%; P = .02). The number of MMS stages and type of closure did not significantly differ based on insurance type.

Conclusions and Relevance

In this cohort study of patients undergoing MMS for NMSC, larger preoperative tumor and postoperative defect sizes were associated with being uninsured or underinsured compared with privately insured. Future studies are required to determine why these differences exist to deliver optimal care to all patients.

Economic Evaluation of a Crisis Residential Program

This study assessed the cost savings to the local health care system from using a 16-bed crisis residential facility (the Inn) in Austin, Texas, instead of hospitalization, for individuals with acute psychiatric illness (N=1,364) during FY2017-FY2019. Health service utilization data were obtained from the provider and Central Texas's regional health information exchange. Unit cost data were obtained from the provider, Austin State Hospital, the Healthcare Cost and Utilization Project, and the Medical Expenditure Panel Survey. Results indicated that the Inn saved the health care system up to $2.8 million annually. Future work can use these findings to improve the efficiency and effectiveness of the mental health care system.

Incorporating patient-reported outcomes into shared decision-making in the management of patients with osteoarthritis of the knee: a hybrid effectiveness-implementation study protocol

INTRODUCTION

Osteoarthritis (OA) is a major clinical and public health concern. The primary surgical treatment of knee OA is total knee replacement (TKR), a procedure that aims to alleviate pain and restore physical function. TKR is expensive, however, and based on professional guidelines, inappropriately performed in up to a third of patients. Patient-reported outcome measures (PROMs) help evaluate treatment options by quantifying health outcomes that matter to patients and can thus inform shared decision-making (SDM) between patients and health professionals.

METHODS AND ANALYSIS

This is a US-based 2-year, two-site hybrid type 1 study to assess clinical effectiveness and implementation of a machine learning-based patient decision aid (PDA) integrating patient-reported outcomes and clinical variables to support SDM for patients with knee OA considering TKR. Substudy 1: At one study site, a randomised controlled trial is evaluating the clinical effectiveness of the PDA and SDM process on decision quality as measured after the baseline consultation and treatment choice measured 3 and 6 months after the baseline visit among 200 patients with knee OA. Substudy 2: At a second study site, a qualitative assessment using principles of behaviour design and intervention mapping is evaluating the feasibility and acceptability of the PROMs, PDA and SDM process by interviewing seven health professionals and 25 patients before and 25 patients after PDA implementation.

ETHICS AND DISSEMINATION

Ethics approval has been obtained from The University of Texas at Austin Institutional Review Board (protocol number: 2018-11-0042). Informed consent will be obtained from all participants. Study results will be disseminated through conference presentations, publications and professional societies.

TRIAL REGISTRATION NUMBER

NCT04805554.

Speech Development Between 30 and 119 Months in Typical Children II: Articulation Rate Growth Curves

Purpose We aimed to develop normative growth curves for articulation rate during sentence repetition for typically developing children. Our primary goal was the development of quantile/percentile growth curves so that typical variation in articulation rate with age could be estimated. We also estimated when children became adultlike in their articulation rate, and we examined the contributions of age and utterance length to articulation rate. Method This cross-sectional study involved collection of in-person speech samples from 570 typically developing children (297 girls; 273 boys) who passed speech, language, and hearing screening measures. Pauses greater than 150 ms in duration were removed from the samples, and articulation rate was measured in syllables per second (sps). Results Articulation rate reliably increased with age and utterance length. Rate in all key percentiles increased with age. The median rate (50th percentile) increased from 2.7 sps at 36 months to 3.3 sps at 96 months. The 5th percentile increased from 2.3 to 3.1 sps over the same age range. Using 3.2 sps as a benchmark for adultlike speech, we found the 25th, 50th, and 75th percentiles reached adultlike rates at 99, 75, and 53 months, respectively. Conclusions Articulation rate increases from early childhood into middle childhood, and it is generally adultlike by 10 years of age. Variability in articulation rate among typical children was substantial. Implications for prior research and for clinical usage are discussed.

Speech Development Between 30 and 119 Months in Typical Children I: Intelligibility Growth Curves for Single-Word and Multiword Productions

Purpose We extended our earlier study on normative growth curves for intelligibility development in typical children from 30 to 119 months of age. We also determined quantile-specific age of steepest growth and growth rates. A key goal was to establish age-specific benchmarks for single-word and multiword intelligibility. Method This cross-sectional study involved collection of in-person speech samples from 538 typically developing children (282 girls and 256 boys) who passed speech, language, and hearing screening measures. One thousand seventy-six normal-hearing naïve adult listeners (280 men and 796 women) orthographically transcribed children's speech. Speech intelligibility was measured as the percentage of words transcribed correctly by naive adults, with single-word and multiword intelligibility outcomes modeled separately. Results The age range for 50% single-word intelligibility was 31-47 months (50th-5th percentiles), the age range for 75% single-word intelligibility was 49-87 months, and the age range for 90% intelligibility for single words was 83-120+ months. The same milestones were attained for multiword intelligibility at 34-46, 46-61, and 62-87 months, respectively. The age of steepest growth for the 50th percentile was 30-31 months for both single-word and multiword intelligibility and was later for children in lower percentiles. The maximum growth rate was 1.7 intelligibility percentage points per month for single words and 2.5 intelligibility percentage points per month for multiword intelligibility. Conclusions There was considerable variability in intelligibility development among typical children. For children in median and lower percentiles, intelligibility growth continues through 9 years. Children should be at least 50% intelligible by 48 months. Supplemental Material https://doi.org/10.23641/asha.16583426.

Impact of unequal cluster sizes for GEE analyses of stepped wedge cluster randomized trials with binary outcomes

The stepped wedge (SW) design is a type of unidirectional crossover design where cluster units switch from control to intervention condition at different prespecified time points. While a convention in study planning is to assume the cluster‐period sizes are identical, SW cluster randomized trials (SW‐CRTs) involving repeated cross‐sectional designs frequently have unequal cluster‐period sizes, which can impact the efficiency of the treatment effect estimator. In this paper, we provide a comprehensive investigation of the efficiency impact of unequal cluster sizes for generalized estimating equation analyses of SW‐CRTs, with a focus on binary outcomes as in the Washington State Expedited Partner Therapy trial. Several major distinctions between our work and existing work include the following: (i) we consider multilevel correlation structures in marginal models with binary outcomes; (ii) we study the implications of both the between‐cluster and within‐cluster imbalances in sizes; and (iii) we provide a comparison between the independence working correlation versus the true working correlation and detail the consequences of ignoring correlation estimation in SW‐CRTs with unequal cluster sizes. We conclude that the working independence assumption can lead to substantial efficiency loss and a large sample size regardless of cluster‐period size variability in SW‐CRTs, and recommend accounting for correlations in the analysis. To improve study planning, we additionally provide a computationally efficient search algorithm to estimate the sample size in SW‐CRTs accounting for unequal cluster‐period sizes, and conclude by illustrating the proposed approach in the context of the Washington State study.

Sleep Disruption, Fatigue, and Depression as Predictors of 6-Year Clinical Outcomes Following Allogeneic Hematopoietic Cell Transplantation

BACKGROUND

Allogeneic hematopoietic cell transplantation (HCT) is a widely used treatment for hematologic cancers, with survival rates ranging from 25% to 78%. Known risk factors for chronic graft-versus-host disease (cGVHD), a serious and common long-term complication, disease relapse, and mortality following HCT have been identified, but much of the variability in HCT outcomes is unexplained. Biobehavioral symptoms including depression, sleep disruption, and fatigue are some of the most prevalent and distressing for patients; yet research on biobehavioral risk factors for HCT outcomes is limited. This study evaluated patient-reported depression, sleep disruption, and fatigue as risk factors for cGVHD, disease relapse, and mortality.

METHODS

Adults receiving allogeneic HCT for a hematologic malignancy (N = 241) completed self-report measures of depression symptoms, sleep quality, and fatigue (severity, interference) pre-HCT and 100 days post-HCT. Clinical outcomes were monitored for up to 6 years.

RESULTS

Cox proportional hazard models (2-tailed) adjusting for patient demographic and medical characteristics revealed that high pre-HCT sleep disruption (Pittsburgh Sleep Quality Index >9; hazard ratio [HR] = 2.74, 95% confidence interval [CI] = 1.27 to 5.92) and greater post-HCT fatigue interference (HR = 1.32, 95% CI = 1.05 to 1.66) uniquely predicted increased risk of mortality. Moderate pre-HCT sleep disruption (Pittsburgh Sleep Quality Index 6-9) predicted increased risk of relapse (HR = 1.99, 95% CI = 1.02 to 3.87). Biobehavioral symptoms did not predict cGVHD incidence.

CONCLUSIONS

Biobehavioral symptoms, particularly sleep disruption and fatigue interference, predicted an increased risk for 6-year relapse and mortality after HCT. Because these symptoms are amenable to treatment, they offer specific targets for intervention to improve HCT outcomes.

Regularized Ordinal Regression and the ordinalNet R Package

Regularization techniques such as the lasso (Tibshirani 1996) and elastic net (Zou and Hastie 2005) can be used to improve regression model coefficient estimation and prediction accuracy, as well as to perform variable selection. Ordinal regression models are widely used in applications where the use of regularization could be beneficial; however, these models are not included in many popular software packages for regularized regression. We propose a coordinate descent algorithm to fit a broad class of ordinal regression models with an elastic net penalty. Furthermore, we demonstrate that each model in this class generalizes to a more flexible form, that can be used to model either ordered or unordered categorical response data. We call this the elementwise link multinomial-ordinal (ELMO) class, and it includes widely used models such as multinomial logistic regression (which also has an ordinal form) and ordinal logistic regression (which also has an unordered multinomial form). We introduce an elastic net penalty class that applies to either model form, and additionally, this penalty can be used to shrink a non-ordinal model toward its ordinal counterpart. Finally, we introduce the R package ordinalNet, which implements the algorithm for this model class.

Effectiveness of a Question Prompt List Intervention for Older Patients Considering Major Surgery: A Multisite Randomized Clinical Trial

Importance

Poor preoperative communication can have serious consequences, including unwanted treatment and postoperative conflict.

Objective

To compare the effectiveness of a question prompt list (QPL) intervention vs usual care on patient engagement and well-being among older patients considering major surgery.

Design, Setting, and Participants

This randomized clinical trial used a stepped-wedge design to randomly assign patients to a QPL intervention (n = 223) or usual care (n = 223) based on the timing of their visit with 1 of 40 surgeons at 5 US study sites. Patients were 60 years or older with at least 1 comorbidity and an oncologic or vascular (cardiac, neurosurgical, or peripheral vascular) problem that could be treated with major surgery. Family members were also enrolled (n = 263). The study dates were June 2016 to November 2018. Data analysis was by intent-to-treat.

Interventions

A brochure of 11 questions to ask a surgeon developed by patient and family stakeholders plus an endorsement letter from the surgeon were sent to patients before their outpatient visit.

Main Outcomes and Measures

Primary patient engagement outcomes included the number and type of questions asked during the surgical visit and patient-reported Perceived Efficacy in Patient-Physician Interactions scale assessed after the surgical visit. Primary well-being outcomes included (1) the difference between patient's Measure Yourself Concerns and Well-being (MYCaW) scores reported after surgery and scores reported after the surgical visit and (2) treatment-associated regret at 6 to 8 weeks after surgery.

Results

Of 1319 patients eligible for participation, 223 were randomized to the QPL intervention and 223 to usual care. Among 446 patients, the mean (SD) age was 71.8 (7.1) years, and 249 (55.8%) were male. On intent-to-treat analysis, there was no significant difference between the QPL intervention and usual care for all patient-reported primary outcomes. The difference in MYCaW scores for family members was greater in usual care (effect estimate, 1.51; 95% CI, 0.28-2.74; P = .008). When the QPL intervention group was restricted to patients with clear evidence they reviewed the QPL, a nonsignificant increase in the effect size was observed for questions about options (odds ratio, 1.88; 95% CI, 0.81-4.35; P = .16), expectations (odds ratio, 1.59; 95% CI, 0.67-3.80; P = .29), and risks (odds ratio, 2.41; 95% CI, 1.04-5.59; P = .04) (nominal α = .01).

Conclusions and Relevance

The results of this study were null related to primary patient engagement and well-being outcomes. Changing patient-physician communication may be difficult without addressing clinician communication directly.

Trial Registration

ClinicalTrials.gov identifier: NCT02623335.

Comparison of an Artificial Intelligence-Enabled Patient Decision Aid vs Educational Material on Decision Quality, Shared Decision-Making, Patient Experience, and Functional Outcomes in Adults With Knee Osteoarthritis: A Randomized Clinical Trial

IMPORTANCE

Decision aids can help inform appropriate selection of total knee replacement (TKR) for advanced knee osteoarthritis (OA). However, few decision aids combine patient education, preference assessment, and artificial intelligence (AI) using patient-reported outcome measurement data to generate personalized estimations of outcomes to augment shared decision-making (SDM).

OBJECTIVE

To assess the effect of an AI-enabled patient decision aid that includes education, preference assessment, and personalized outcome estimations (using patient-reported outcome measurements) on decision quality, patient experience, functional outcomes, and process-level outcomes among individuals with advanced knee OA considering TKR in comparison with education only.

DESIGN, SETTING, AND PARTICIPANTS

This randomized clinical trial at a single US academic orthopedic practice included 129 new adult patients presenting for OA-related knee pain from March 2019 to January 2020. Data were analyzed from April to May 2020.

INTERVENTION

Patients were randomized into a group that received a decision aid including patient education, preference assessment, and personalized outcome estimations (intervention group) or a group receiving educational material only (control group) alongside usual care.

MAIN OUTCOMES AND MEASURES

The primary outcome was decision quality, measured using the Knee OA Decision Quality Instrument (K-DQI). Secondary outcomes were collaborative decision-making (assessed using the CollaboRATE survey), patient satisfaction with consultation (using a numerical rating scale), Knee Injury and Osteoarthritis Outcome Score Joint Replacement (KOOS JR) score, consultation time, TKR rate, and treatment concordance.

RESULTS

A total of 69 patients in the intervention group (46 [67%] women) and 60 patients in the control group (37 [62%] women) were included in the analysis. The intervention group showed better decisional quality (K-DQI mean difference, 20.0%; SE, 3.02; 95% CI, 14.2%-26.1%; P < .001), collaborative decision-making (CollaboRATE, 8 of 69 [12%] vs 28 of 60 [47%] patients below median; P < .001), satisfaction (numerical rating scale, 9 of 65 [14%] vs 19 of 58 [33%] patients below median; P = .01), and improved functional outcomes at 4 to 6 months (mean [SE] KOOS JR, 4.9 [2.24] points higher in intervention group; 95% CI, 0.8-9.0 points; P = .02). The intervention did not significantly affect consultation time (mean [SE] difference, 2.23 [2.18] minutes; P = .31), TKR rates (16 of 69 [23%] vs 7 of 60 [12%] patients; P = .11), or treatment concordance (58 of 69 [84%] vs 44 of 60 [73%] patients; P = .19).

CONCLUSIONS AND RELEVANCE

In this randomized clinical trial, an AI-enabled decision aid significantly improved decision quality, level of SDM, satisfaction, and physical limitations without significantly impacting consultation times, TKR rates, or treatment concordance in patients with knee OA considering TKR. Decision aids using a personalized, data-driven approach can enhance SDM in the management of knee OA.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03956004.

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.

Two-wave two-phase outcome-dependent sampling designs, with applications to longitudinal binary data

Two-phase outcome-dependent sampling (ODS) designs are useful when resource constraints prohibit expensive exposure ascertainment on all study subjects. One class of ODS designs for longitudinal binary data stratifies subjects into three strata according to those who experience the event at none, some, or all follow-up times. For time-varying covariate effects, exclusively selecting subjects with response variation can yield highly efficient estimates. However, if interest lies in the association of a time-invariant covariate, or the joint associations of time-varying and time-invariant covariates with the outcome, then the optimal design is unknown. Therefore, we propose a class of two-wave two-phase ODS designs for longitudinal binary data. We split the second-phase sample selection into two waves, between which an interim design evaluation analysis is conducted. The interim design evaluation analysis uses first-wave data to conduct a simulation-based search for the optimal second-wave design that will improve the likelihood of study success. Although we focus on longitudinal binary response data, the proposed design is general and can be applied to other response distributions. We believe that the proposed designs can be useful in settings where (1) the expected second-phase sample size is fixed and one must tailor stratum-specific sampling probabilities to maximize estimation efficiency, or (2) relative sampling probabilities are fixed across sampling strata and one must tailor sample size to achieve a desired precision. We describe the class of designs, examine finite sampling operating characteristics, and apply the designs to an exemplar longitudinal cohort study, the Lung Health Study.

A transdiagnostic approach to conceptualizing depression across the perinatal period in a high-risk sample

Clinical guidelines recommend assessing depression during pregnancy and postpartum but often overlook potential changes in symptoms across this developmental period. Such changes contribute to difficulties in conceptualizing maternal depression. This study aimed to situate depressive symptoms and related concerns (anxiety, stress, sleep) across the perinatal period within a transdiagnostic framework and to use this framework to better understand how depressive symptoms change across the perinatal period. First, items from seven symptom scales were a priori categorized into six transdiagnostic factors: four based on Research Domain Criteria (loss, potential threat, frustrative nonreward, and sleep-wakefulness) and two based on the depression literature (somatic and coping symptoms). Second, using prospective data from women with a history of an affective disorder (n = 657) in an observational study of neuropsychiatric illness, factor analyses were performed in seven periods (three trimesters of pregnancy and four quarters of first year postpartum). For each period, a bifactor model with six transdiagnostic factors and a general factor fit data better than models that combined or dropped a factor (p < .003). Except around delivery, item loadings and intercepts could be fixed between consecutive periods and still adequately fit data from both periods. Means of sleep-wakefulness and somatic factors increased significantly from second to third trimester (p < .01), with trends reversing early postpartum. In conclusion, depressive symptoms and related concerns exhibit factor structures that are only partly congruent across the perinatal period. This conclusion suggests that greater attention to specific life phases is warranted in the conceptualization of depression during this time in women's lives. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Longitudinal Growth in Intelligibility of Connected Speech From 2 to 8 Years in Children With Cerebral Palsy: A Novel Bayesian Approach

Aim The aim of the study was to examine longitudinal growth in intelligibility in connected speech from 2 to 8 years of age in children with cerebral palsy. Method Sixty-five children with cerebral palsy participated in the longitudinal study. Children were classified into speech-language profile groups using age-4 data: no speech motor impairment (SMI), SMI with typical language comprehension, and SMI with impaired language comprehension. We fit a Bayesian nonlinear mixed-effects model of intelligibility growth at the child and group levels. We compared groups by age of steepest growth, maximum growth rate, and predicted intelligibility at 8 years of age. Results The no SMI group showed earlier and steeper intelligibility growth and higher average outcomes compared to the SMI groups. The SMI groups had more variable growth trajectories, but the SMI with typical language comprehension group had higher age-8 outcomes and steeper rates of maximum growth than the SMI with impaired language comprehension group. Language comprehension impairment at age of 4 years predicted lower intelligibility outcomes at age of 8 years, compared to typical language at age of 4 years. Interpretation Children with SMI at age of 4 years show highly variable intelligibility growth trajectories, and comorbid language comprehension impairment predicts lower intelligibility outcomes. Supplemental Material https://doi.org/10.23641/asha.12777659.

Longitudinal Development of Receptive Vocabulary in Children with Cerebral Palsy and Anarthria: Use of the MacArthur-Bates CDI

OBJECTIVE

To examine receptive language growth in children with cerebral palsy (CP) and anarthria using a parent-reported measure of vocabulary.

METHOD

Scores from 47 children (29 males) with CP and anarthria were obtained from the vocabulary checklists on the MacArthur-Bates Communication Development Inventories (MCDI) and analyzed to examine the distribution of receptive language growth. Linear trajectories of word composite scores were created using a linear-mixed model, incorporating between two and ten data points per child.

RESULTS

Three different growth trajectories emerged: approximately 23% grew by 100 or more words per year, 13% grew by 50-100 words per year, and 64% grew by 50 words per year or less. Age-four vocabulary was strongly correlated with rate of increase in vocabulary.

CONCLUSION

Receptive vocabulary scores from the MCDI are increasing at a reduced pace for most children with CP and anarthria. More sensitive measures of language assessment are necessary to gain a complete picture of their language ability levels.

What Matters When It Comes to Trust in One's Physician: Race/Ethnicity, Sociodemographic Factors, and/or Access to and Experiences with Health Care?

Purpose: Interpersonal trust is linked to therapeutic factors of patient care, including adherence to treatment, continuity with a provider, perceived effectiveness of care, and clinical outcomes. Differences in interpersonal trust across groups may contribute to health disparities. We explored whether differences in interpersonal trust varied across three racial/ethnic groups. Additionally, we explored how different health care factors were associated with differences in trust.

Methods: We conducted a cross-sectional, computer-administered survey with 600 racially and ethnically diverse adults in Chicago, IL, from a wide variety of neighborhoods. We used staged ordinal logistic regression models to analyze the association between interpersonal trust and variables of interest.

Results: Interpersonal trust did not differ by racial or ethnic group. However, individuals with 0–2 annual doctor visits, those reporting having a “hard time” getting health care services, those answering “yes” to “Did you not follow advice or treatment plan because it cost too much?,” and those reporting waiting more than 6 days/never getting an appointment had significantly increased odds of low trust. We did not find differences across racial/ethnic groups.

Conclusion: Our study suggests that access to health care and interactions within the health care setting negatively impact individual's trust in their physician.

Development of Speech Intelligibility Between 30 and 47 Months in Typically Developing Children: A Cross-Sectional Study of Growth

Purpose We sought to establish normative growth curves for intelligibility development for the speech of typically developing children as revealed by objectively based orthographic transcription of elicited single-word and multiword utterances by naïve listeners. We also examined sex differences, and we compared differences between single-word and multiword intelligibility growth. Method One hundred sixty-four typically developing children (92 girls, 72 boys) contributed speech samples for this study. Children were between the ages of 30 and 47 months, and analyses examined 1-month age increments between these ages. Two different naïve listeners heard each child and made orthographic transcriptions of child-produced words and sentences (n = 328 listeners). Average intelligibility scores for single-word productions and multiword productions were modeled using linear regression, which estimated normal-model quantile age trajectories for single- and multiword utterances. Results We present growth curves showing steady linear change over time in 1-month increments from 30 to 47 months for 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles. Results showed that boys did not differ from girls and that, prior to 35 months of age, single words were more intelligible than multiword productions. Starting at 41 months of age, the reverse was true. Multiword intelligibility grew at a faster rate than single-word intelligibility. Conclusions Children make steady progress in intelligibility development through 47 months, and only a small number of children approach 100% intelligibility by this age. Intelligibility continues to develop past the fourth year of life. There is considerable variability among children with regard to intelligibility development. Supplemental Material https://doi.org/10.23641/asha.12330956.

Two-Phase, Generalized Case-Control Designs for the Study of Quantitative Longitudinal Outcomes

We propose a general class of 2-phase epidemiologic study designs for quantitative, longitudinal data that are useful when phase 1 longitudinal outcome and covariate data are available but data on the exposure (e.g., a biomarker) can only be collected on a subset of subjects during phase 2. To conduct a study using a design in the class, one first summarizes the longitudinal outcomes by fitting a simple linear regression of the response on a time-varying covariate for each subject. Sampling strata are defined by splitting the estimated regression intercept or slope distributions into distinct (low, medium, and high) regions. Stratified sampling is then conducted from strata defined by the intercepts, by the slopes, or from a mixture. In general, samples selected with extreme intercept values will yield low variances for associations of time-fixed exposures with the outcome and samples enriched with extreme slope values will yield low variances for associations of time-varying exposures with the outcome (including interactions with time-varying exposures). We describe ascertainment-corrected maximum likelihood and multiple-imputation estimation procedures that permit valid and efficient inferences. We embed all methodological developments within the framework of conducting a substudy that seeks to examine genetic associations with lung function among continuous smokers in the Lung Health Study (United States and Canada, 1986-1994).