The Magic Number 63 - Redefining the Geriatric Age for Massive Transfusion in Trauma.

INTRODUCTION: The arbitrary geriatric age cutoff of 65 may not accurately define older adults at higher risk of mortality following massive transfusion (MT). We sought to redefine a new geriatric age threshold for MT and understand its association with outcomes. MATERIAL AND METHODS: The 2013-2018 Trauma Quality Improvement Program database was queried for all adults who received ≥10 units of packed red blood cells (pRBCs) within 24 h of admission. A bootstrap analysis using multiple logistic regression established transfusion futility thresholds (TTs), where additional pRBCs no longer improved mortality for various age cutoffs. The age cutoff at which the TT for those relatively older and relatively younger was statistically significant was used to define the new "geriatric" age for MT. Outcomes were then compared between the newly defined geriatric and nongeriatric patients. RESULTS: The difference in TT first became significant when the age cutoff was 63 y. The TT for patients aged ≥63 y (new geriatric, n = 2870) versus <63 y (nongeriatric, n = 17,302) was 34 and 40 units of pRBCs, respectively (P = 0.04). Although geriatric patients had a higher Glasgow coma scale score (9 versus 6, P < 0.01) and lower abbreviated injury score-abdomen (3 versus 4, P < 0.01) than the nongeriatric, they suffered higher overall mortality (62% versus 45%, P < 0.01). A lower percentage of geriatric patients were discharged to home (7% versus 35%, P < 0.01). CONCLUSIONS: The new geriatric age for MT is 63 y, with a TT of 34 units. Despite suffering less severe injuries, physiologically "geriatric" patients have worse outcomes following MT.

Trusted Information Sources About the COVID-19 Vaccine Vary in Underserved Communities.

The COVID-19 pandemic was one of the deadliest global public health events. In the United States, over 1.1 million individuals have died, and now COVID-19 is the third leading cause of death (CDC, 2023). Vaccine uptake has stalled among different demographics. Vaccine hesitancy, a delay in accepting or refusing vaccines, poses a significant challenge regardless of the availability of safe and effective COVID-19 vaccines. This study aimed to identify disparate COVID-19 vaccine uptake among individuals in Western New York. The primary objective was to identify the factors contributing to lower rates of COVID-19 vaccination within this population.Data were collected from 585 adults recruited from 20 Niagara and Erie Counties sites using a self-administered survey on vaccine hesitancy, vaccination status, and COVID-19-related characteristics. The survey included the adult Vaccine Hesitancy Scale (aVHS) and acquired information on demographic characteristics and COVID-19 impact, knowledge, and information sources. Data were analyzed using descriptive statistics, a chi-squared test, a Wilcoxon rank-sum test, and a logistic regression model.Findings suggest that unvaccinated participants (n = 35) were concerned about vaccine side effects (48.6%). For vaccinated/unboosted participants (n = 52), they (40.0%) reported clinical concerns. After adjusting for gender and age, healthcare provider guidance and family guidance remained significant predictors of vaccination status, while clinical research studies were significant predictors of booster status. Findings from this study suggest public health interventions that target vaccine education and facilitate well-informed decisions about COVID-19 vaccines lead to less vaccine hesitancy.

Validation of the predictive model for operative intervention after blunt abdominal trauma in children with equivocal computed tomography findings: a multi-institutional study.

BACKGROUND: We recently developed a preliminary predictive model identifying clinical and radiologic factors associated with the need for surgery following blunt abdominal trauma (BAT) in children. Our aim in this study was to further validate the factors in this predictive model in a multi-institutional study. METHODS: A retrospective chart review of pediatric patients from five pediatric trauma centers who experienced BAT between 2011 and 2020 was performed. Patients under 18 years of age who had BAT and computed tomography (CT) abdomen imaging were included. Children with evidence of pneumoperitoneum, and hemodynamic instability were excluded. Fisher's exact test was used for statistical analysis of the association between the following risk factors and need for laparotomy: abdominal wall bruising (AWB), abdominal pain/tenderness (APT), thoracolumbar fracture (TLF), presence of free fluid (FF), presence of solid organ injury (SOI). A predictive logistic regression model was then estimated employing these factors. FINDINGS: Seven hundred thirty-four patients were identified in this multi-institutional dataset with BAT and abdominal CT imaging, and 726 were included. Of those, 59 underwent surgical intervention (8.8%). Univariate analysis of association between the studied factors and need for surgical management showed that the presence of TLF (p < 0.01), APT (p < 0.01), FF (p < 0.01), and SOI (p < 0.01) were significantly associated. A predictive model was created using the 5 factors resulting in an area under the curve (AUC) of 0.80. For the motor vehicle collisions (MVC) group, only FF, SOI, and TLF are significantly associated with the need for surgical intervention. The AUC for the MVC group was 0.87. CONCLUSIONS: A clinical and radiologic prediction rule was validated using a large multi-institutional dataset of pediatric BAT patients, demonstrating a high degree of accuracy in identifying children who underwent surgery. FF, SOI, and TLF are the most important factors associated with the need for surgical intervention. LEVEL OF EVIDENCE: Level III.

Vitamin B-6 Intake Is Modestly Associated with Arsenic Methylation in Uruguayan Children with Low-Level Arsenic Exposure.

BACKGROUND: Detoxification of inorganic arsenic (iAs) occurs when it methylates to form monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA). Lower proportions of urinary iAs and MMA, and higher proportions of DMA indicate efficient methylation. The role of B-vitamins in iAs methylation in children with low-level arsenic exposure is understudied. OBJECTIVES: Our study objective was to assess the association between B-vitamin intake and iAs methylation in children with low-level arsenic exposure (<50 µg/L in water; urinary arsenic 5-50 µg/L). METHODS: We conducted a cross-sectional study in 290 ∼7-y-old children in Montevideo. Intake of thiamin, riboflavin, niacin, vitamin B-6, and vitamin B-12 was calculated by averaging 2 nonconsecutive 24-h recalls. Total urinary arsenic concentration was measured as the sum of urinary iAs, MMA, and DMA, and adjusted for urinary specific gravity; iAs methylation was measured as urinary percentage As, percentage MMA, and percentage DMA. Arsenic concentrations from household water sources were assessed. Linear regressions tested the relationships between individual energy-adjusted B-vitamins and iAs methylation. RESULTS: Median (range) arsenic concentrations in urine and water were 9.9 (2.2-48.7) and 0.45 (0.1-18.9) µg/L, respectively. The median (range) of urinary percentage iAs, percentage MMA, and percentage DMA was 10.6% (0.0-33.8), 9.7% (2.6-24.8), and 79.1% (58.5-95.4), respectively. The median (range) intake levels of thiamin, riboflavin, niacin, and vitamin B-6 were 0.81 (0.19-2.56), 1.0 (0.30-2.24), 8.6 (3.5-23.3), and 0.67 (0.25-1.73) mg/1000 kcal, respectively, whereas those of folate and vitamin B-12 were 216 (75-466) and 1.7 (0.34-8.3) µg/1000 kcal, respectively. Vitamin B-6 intake was inversely associated with urinary percentage MMA (ß = -1.60; 95% CI: -3.07, -0.15). No other statistically significant associations were observed. CONCLUSIONS: Although vitamin B-6 intake was inversely associated with urinary percentage MMA, our findings suggest limited support for a relation between B-vitamin intake and iAs methylation in children exposed to low-level arsenic.

Associations of dietary intakes and serum levels of folate and vitamin B-12 with methylation of inorganic arsenic in Uruguayan children: Comparison of findings and implications for future research.

BACKGROUND: In the human body, inorganic arsenic (iAs) is methylated via the one-carbon cycle to form monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA). Lower proportions of iAs and MMA, and higher proportions of DMA in urine indicate efficient methylation; formation of DMA is thought to detoxify iAs and MMA. Studies on folate, vitamin B-12 and iAs methylation yield mixed findings, depending on whether folate and vitamin B-12 were assessed from diet, supplements, or using a blood biomarker. OBJECTIVE: First, to compare the associations of serum concentrations and estimated intake of folate and vitamin B-12 with indicators of iAs methylation. Second, to highlight the implications of these different B-vitamin assessment techniques on the emerging evidence of the impact of dietary modifications on iAs methylation. METHODS: The study was conducted among ~7-year-old children from Montevideo, Uruguay. Serum folate and vitamin B-12 levels were measured on the Horiba ABX Pentra 400 analyzer; urinary arsenic was measured using High-Performance Liquid Chromatography on-line with Inductively Coupled Plasma Mass Spectrometry. Dietary intakes were assessed using the average of two 24-h dietary recalls. Linear regressions assessed the associations of serum levels, and dietary intakes of folate (n = 237) and vitamin B-12 (n = 217) with indicators of iAs methylation. Models were adjusted for age, sex, body mass index, total urinary arsenic, and rice intake. RESULTS: Serum folate and vitamin B-12 levels were above the adequacy threshold for 99% of the participants. No associations were observed between serum folate, serum vitamin B-12, or vitamin B-12 intake and iAs methylation. Folate intake was inversely associated with urinary %MMA [ß (95% confidence interval): -1.04 (-1.89, -0.18)]. CONCLUSION: Additional studies on the role of B-vitamins in iAs methylation are needed to develop a deeper understanding of the implications of assessing folate and vitamin B-12 intake compared to the use of biomarkers. Where possible, both methods should be employed because they reflect different exposure windows and inherent measurement error, and if used individually, will likely continue to contribute to lack of consensus.

Estimation of the area under a curve via several B-spline-based regression methods and applications.

Estimating the area under a curve (AUC) is an important subject in many fields of medicine and science. The regression model using B-spline functions provides flexibility in curve fitting, making it suitable for AUC estimation with various types of nonlinear trends. Despite the versatility of the B-spline approach, comprehensive discussions regarding relevant AUC estimation techniques using B-spline functions and their comparison with existing methods cannot be found in extant literature. In this paper, we investigate AUC estimation using B-spline regression and B-spline regression with several penalties, as well as discuss corresponding inferences. We carry out an extensive Monte Carlo study to evaluate the performance of the proposed methods in various realistic pharmacokinetics and analytical chemistry data settings. We show that the proposed methods provide robust and reliable AUC estimation regardless of different types of nonlinear models from scientific and medical research areas. Our proposed method is appropriate for general AUC estimation since it does not require nonlinear model specifications and inference techniques corresponding to the specified model.

A critical issue of using the variance of the total in the linearization method - In the context of unequal probability sampling.

Publicly available national survey data are useful for the evidence-based research to advance our understanding of important questions in the health and biomedical sciences. Appropriate variance estimation is a crucial step to evaluate the strength of evidence in the data analysis. In survey data analysis, the conventional linearization method for estimating the variance of a statistic of interest uses the variance estimator of the total based on linearized variables. We warn that this common practice may result in undesirable consequences such as susceptibility to data shift and severely inflated variance estimates, when unequal weights are incorporated into variance estimation. We propose to use the variance estimator of the mean (mean-approach) instead of the variance estimator of the total (total-approach). We show a superiority of the mean-approach through analytical investigations. A real data example (the National Comorbidity Survey Replication) and simulation-based studies strongly support our conclusion.

A sequential density-based empirical likelihood ratio test for treatment effects.

In health-related experiments, treatment effects can be identified using paired data that consist of pre- and posttreatment measurements. In this framework, sequential testing strategies are widely accepted statistical tools in practice. Since performances of parametric sequential testing procedures vitally depend on the validity of the parametric assumptions regarding underlying data distributions, we focus on distribution-free mechanisms for sequentially evaluating treatment effects. In fixed sample size designs, the density-based empirical likelihood (DBEL) methods provide powerful nonparametric approximations to optimal Neyman-Pearson-type statistics. In this article, we extend the DBEL methodology to develop a novel sequential DBEL testing procedure for detecting treatment effects based on paired data. The asymptotic consistency of the proposed test is shown. An extensive Monte Carlo study confirms that the proposed test outperforms the conventional sequential Wilcoxon signed-rank test across a variety of alternatives. The excellent applicability of the proposed method is exemplified using the ventilator-associated pneumonia study that evaluates the effect of Chlorhexidine Gluconate treatment in reducing oral colonization by pathogens in ventilated patients.

Effect of an equipment-behavior change intervention on handwashing behavior among primary school children in Kenya: the Povu Poa school pilot study.

BACKGROUND: Handwashing prevalence in schools in Kenya is low due to lack of access to water and soap and lack of drive for handwashing. Soapy water made from detergent powder is an inexpensive alternative to bar soap and disgust and social norms change can be powerful drivers of handwashing, but their effectiveness has not been assessed in school setting. In Kenyan public schools, we evaluated an equipment-behavior change intervention's effect on handwashing outcomes. We also monitored functionality of the Povu Poa prototypes to identify design improvements necessary for continued high usage in institutional settings. METHODS: The intervention included the "Povu Poa", a new type of handwashing station that dispensed foaming soap and rinse water, combined with school-wide behavior change promotion based on disgust and social norms. In this stepped-wedge cluster-randomized trial, we randomly selected 30 schools and divided them into 3 groups of 10. Following baseline data collection, we delivered the intervention sequentially (Group 1: 3-5 weeks after baseline; Group 2: 6-8 weeks; Group 3: 19-24 weeks). We observed outcomes [1] availability of handwashing materials at handwashing places, and; 2) observed handwashing behavior after toilet use among schoolchildren) at baseline and in three follow-up rounds. We compared the outcomes between schools that had received the intervention and schools that had not yet received the intervention. RESULTS: Water and soap/soapy water were available at 2% of school visits before intervention, and at 42% of school visits after intervention.. Before intervention, we observed handwashing with water after 11% of 461 toilet use events; no one was observed to wash hands with soap/soapy water. After intervention, we observed handwashing after 62% of 383 toilet use events (PR = 5.96, 95% CI = 3.02, 11.76) and handwashing with soap/soapy water after 26% of events (PR incalculable). Foaming soap dispenser caps were cracked in 31% of all observations, but were typically still functional. CONCLUSIONS: Our combined equipment-behavior intervention increased availability of handwashing materials and improved the compliance with handwashing after using the toilet, but handwashing with soap was still rare. Equipment durability must be improved for deployment in schools at scale. American Economic Association's Registry for Randomized Controlled Trials; Trial Registry Number (TRN): AEARCTR-0000662; Date of Registry: April 14, 2015.

Shared and Distinct Rupture Discriminants of Small and Large Intracranial Aneurysms.

BACKGROUND AND PURPOSE: Many ruptured intracranial aneurysms (IAs) are small. Clinical presentations suggest that small and large IAs could have different phenotypes. It is unknown if small and large IAs have different characteristics that discriminate rupture. METHODS: We analyzed morphological, hemodynamic, and clinical parameters of 413 retrospectively collected IAs (training cohort; 102 ruptured IAs). Hierarchal cluster analysis was performed to determine a size cutoff to dichotomize the IA population into small and large IAs. We applied multivariate logistic regression to build rupture discrimination models for small IAs, large IAs, and an aggregation of all IAs. We validated the ability of these 3 models to predict rupture status in a second, independently collected cohort of 129 IAs (testing cohort; 14 ruptured IAs). RESULTS: Hierarchal cluster analysis in the training cohort confirmed that small and large IAs are best separated at 5 mm based on morphological and hemodynamic features (area under the curve=0.81). For small IAs (<5 mm), the resulting rupture discrimination model included undulation index, oscillatory shear index, previous subarachnoid hemorrhage, and absence of multiple IAs (area under the curve=0.84; 95% confidence interval, 0.78-0.88), whereas for large IAs (≥5 mm), the model included undulation index, low wall shear stress, previous subarachnoid hemorrhage, and IA location (area under the curve=0.87; 95% confidence interval, 0.82-0.93). The model for the aggregated training cohort retained all the parameters in the size-dichotomized models. Results in the testing cohort showed that the size-dichotomized rupture discrimination model had higher sensitivity (64% versus 29%) and accuracy (77% versus 74%), marginally higher area under the curve (0.75; 95% confidence interval, 0.61-0.88 versus 0.67; 95% confidence interval, 0.52-0.82), and similar specificity (78% versus 80%) compared with the aggregate-based model. CONCLUSIONS: Small (<5 mm) and large (≥5 mm) IAs have different hemodynamic and clinical, but not morphological, rupture discriminants. Size-dichotomized rupture discrimination models performed better than the aggregate model.

Impact of Trauma Hospital Ransomware Attack on Surgical Residency Training.

BACKGROUND: A recent ransomware attack led to the shutdown of the electronic health information system (HIS) at our trauma center for 2 mo. We investigated its impact on residency training during the downtime. MATERIAL AND METHODS: General and orthopedic surgical residents who rotated at the hospital were invited to participate in a survey regarding their patient care and residency training experiences during the downtime. Attending surgeons from both the specialties were invited to participate in a semistructured interview regarding their attitude toward residency training during the downtime. RESULTS: Twenty-nine residents responded to the survey with a response rate of 78.4%. Residents acknowledged significant increases in face-to-face communication and decreases in use of online educational resources during the downtime (P < 0.01). Residents were significantly stressed by the dearth of online resources (P < 0.0001) and by paper-based orders and outpatient clinic (P < 0.05). A multivariate analysis demonstrated an inverse relationship between postgraduate year and stress from paper orders (P = 0.003). Attending surgeon's interviews revealed that they recognized residents' unpreparedness and strove harder to teach more effectively. CONCLUSIONS: Our study demonstrated that an unexpected shutdown of the hospital HIS imposed significant stress upon surgical residents providing trauma patient care and made attending surgeons take greater efforts to be more effective teachers. Residents who are digital natives lack adaptability to handle a paper-based workflow. With cyber security threats increasing in health care, preparedness should be included in the graduate medical education curriculum.

Household-level risk factors for secondary influenza-like illness in a rural area of Bangladesh.

OBJECTIVE: To describe household-level risk factors for secondary influenza-like illness (ILI), an important public health concern in the low-income population of Bangladesh. METHODS: Secondary analysis of control participants in a randomised controlled trial evaluating the effect of handwashing to prevent household ILI transmission. We recruited index-case patients with ILI - fever (<5 years); fever, cough or sore throat (≥5 years) - from health facilities, collected information on household factors and conducted syndromic surveillance among household contacts for 10 days after resolution of index-case patients' symptoms. We evaluated the associations between household factors at baseline and secondary ILI among household contacts using negative binomial regression, accounting for clustering by household. RESULTS: Our sample was 1491 household contacts of 184 index-case patients. Seventy-one percentage reported that smoking occurred in their home, 27% shared a latrine with one other household and 36% shared a latrine with >1 other household. A total of 114 household contacts (7.6%) had symptoms of ILI during follow-up. Smoking in the home (RRadj 1.9, 95% CI: 1.2, 3.0) and sharing a latrine with one household (RRadj 2.1, 95% CI: 1.2, 3.6) or >1 household (RRadj 3.1, 95% CI: 1.8-5.2) were independently associated with increased risk of secondary ILI. CONCLUSION: Tobacco use in homes could increase respiratory illness in Bangladesh. The mechanism between use of shared latrines and household ILI transmission is not clear. It is possible that respiratory pathogens could be transmitted through faecal contact or contaminated fomites in shared latrines.

Easy and accurate variance estimation of the nonparametric estimator of the partial area under the ROC curve and its application.

The receiver operating characteristic (ROC) curve is a popular technique with applications, for example, investigating an accuracy of a biomarker to delineate between disease and non-disease groups. A common measure of accuracy of a given diagnostic marker is the area under the ROC curve (AUC). In contrast with the AUC, the partial area under the ROC curve (pAUC) looks into the area with certain specificities (i.e., true negative rate) only, and it can be often clinically more relevant than examining the entire ROC curve. The pAUC is commonly estimated based on a U-statistic with the plug-in sample quantile, making the estimator a non-traditional U-statistic. In this article, we propose an accurate and easy method to obtain the variance of the nonparametric pAUC estimator. The proposed method is easy to implement for both one biomarker test and the comparison of two correlated biomarkers because it simply adapts the existing variance estimator of U-statistics. In this article, we show accuracy and other advantages of the proposed variance estimation method by broadly comparing it with previously existing methods. Further, we develop an empirical likelihood inference method based on the proposed variance estimator through a simple implementation. In an application, we demonstrate that, depending on the inferences by either the AUC or pAUC, we can make a different decision on a prognostic ability of a same set of biomarkers. Copyright © 2016 John Wiley & Sons, Ltd.

Analytical Problems and Suggestions in the Analysis of Behavioral Economic Demand Curves.

Behavioral economic demand curves (Hursh, Raslear, Shurtleff, Bauman, & Simmons, 1988) are innovative approaches to characterize the relationships between consumption of a substance and its price. In this article, we investigate common analytical issues in the use of behavioral economic demand curves, which can cause inconsistent interpretations of demand curves, and then we provide methodological suggestions to address those analytical issues. We first demonstrate that log transformation with different added values for handling zeros changes model parameter estimates dramatically. Second, demand curves are often analyzed using an overparameterized model that results in an inefficient use of the available data and a lack of assessment of the variability among individuals. To address these issues, we apply a nonlinear mixed effects model based on multivariate error structures that has not been used previously to analyze behavioral economic demand curves in the literature. We also propose analytical formulas for the relevant standard errors of derived values such as P max, O max, and elasticity. The proposed model stabilizes the derived values regardless of using different added increments and provides substantially smaller standard errors. We illustrate the data analysis procedure using data from a relative reinforcement efficacy study of simulated marijuana purchasing.

Transfusion futility thresholds and mortality in geriatric trauma: Does frailty matter?

BACKGROUND: Data on massive transfusion (MT) in geriatric trauma patients is lacking. This study aims to determine geriatric transfusion futility thresholds (TT) and TT variations based on frailty. METHODS: Patients from 2013 to 2018 TQIP database receiving MT were stratified by age and frailty. TTs and outcomes were compared between geriatric and younger adults and among geriatric adults based on frailty status. RESULTS: The TT was lower for geriatric than younger adults (34 vs 39 units; p â€‹= â€‹0.03). There was no difference in TT between the non-frail, frail, and severely frail geriatric adults (37, 30 and 25 units, respectively, p â€‹> â€‹0.05). Geriatric adults had higher mortality than younger adults (63.1% vs 45.8%, p < 0.01). Non-frail geriatric adults had the highest mortality (69.4% vs 56.5% vs 56.2%, p < 0.01). CONCLUSIONS: Geriatric patients have a lower TT than younger adults, irrespective of frailty. This may help improve outcomes and optimize MT utilization.

A novel network architecture combining central-peripheral deviation with image-based convolutional neural networks for diffusion tensor imaging studies.

Brain imaging research is a very challenging topic due to complex structure and lack of explicitly identifiable features in the image. With the advancement of magnetic resonance imaging (MRI) technologies, such as diffusion tensor imaging (DTI), developing classification methods to improve clinical diagnosis is crucial. This paper proposes a classification method for DTI data based on a novel neural network strategy that combines a convolutional neural network (CNN) with a multilayer neural network using central-peripheral deviation (CPD), which reflects diffusion dynamics in the white matter by spatially evaluating the deviation of diffusion coefficients between the inner and outer parts of the brain. In our method, a multilayer perceptron (MLP) using CPD is combined with the final layers for classification after reducing the dimensions of images in the convolutional layers of the neural network architecture. In terms of training loss and the classification error, the proposed classification method improves the existing image classification with CNN. For real data analysis, we demonstrate how to process raw DTI image data sets obtained from a traumatic brain injury study (MagNeTS) and a brain atlas construction study (ICBM), and apply the proposed approach to the data, successfully improving classification performance with two age groups.

Comparing Pediatric Physical Trauma Outcomes by Special Health Care Needs Status.

BACKGROUND AND OBJECTIVES: Children and Youth with Special Health Care Needs (CYSHCN) have differing risk factors and injury characteristics compared with peers without special health care needs (SHCN). We examined the association between SHCN status and complications, mortality, and length of stay (LOS) after trauma hospitalization. METHODS: We conducted a cross-sectional study using 2018 data from the National Trauma Data Bank for patients aged 1 to 18 years (n = 108 062). We examined the following hospital outcomes: any complication reported, unplanned admission to the ICU, in-hospital mortality, and hospital and ICU LOS. Multivariate regression models estimated the effect of SHCN status on hospital outcomes after controlling for patient demographics, injury severity score, and Glasgow Coma Score. Subanalyses examined outcomes by age, SHCN, and injury severity score. RESULTS: CYSHCN encounters had a greater adjusted relative risk (ARR) of any hospital complications (ARR = 2.980) and unplanned admission to the ICU (ARR = 1.996) than encounters that did not report a SHCN (P < .001). CYSHCN had longer hospital (incidence rate ratio = 1.119) and ICU LOS (incidence rate ratio = 1.319, both P < .001). There were no statistically significant in-hospital mortality differences between CYSHCN and those without. Lower severity trauma was associated with a greater ARR of hospital complications for CYSHCN encounters versus non-CYSHCN encounters. CONCLUSIONS: CYSHCN, particularly those with lower-acuity injuries, are at greater risk for developing complications and requiring more care after trauma hospitalization. Future studies may examine mechanisms of hospital complications for traumatic injuries among CYSHCN to develop prevention and risk-minimization strategies.

Two-sample density-based empirical likelihood ratio tests based on paired data, with application to a treatment study of attention-deficit/hyperactivity disorder and severe mood dysregulation.

It is a common practice to conduct medical trials to compare a new therapy with a standard-of-care based on paired data consisted of pre- and post-treatment measurements. In such cases, a great interest often lies in identifying treatment effects within each therapy group and detecting a between-group difference. In this article, we propose exact nonparametric tests for composite hypotheses related to treatment effects to provide efficient tools that compare study groups utilizing paired data. When correctly specified, parametric likelihood ratios can be applied, in an optimal manner, to detect a difference in distributions of two samples based on paired data. The recent statistical literature introduces density-based empirical likelihood methods to derive efficient nonparametric tests that approximate most powerful Neyman-Pearson decision rules. We adapt and extend these methods to deal with various testing scenarios involved in the two-sample comparisons based on paired data. We show that the proposed procedures outperform classical approaches. An extensive Monte Carlo study confirms that the proposed approach is powerful and can be easily applied to a variety of testing problems in practice. The proposed technique is applied for comparing two therapy strategies to treat children's attention deficit/hyperactivity disorder and severe mood dysregulation.

A waitlist-controlled trial of behavioral parent training for fathers of children with ADHD.

Fathers, in general, have been underrepresented in studies of parent training outcome for children with attention deficit hyperactivity disorder (ADHD), and the present study aimed to investigate the efficacy of a behavioral parent training program developed expressly for fathers. The present investigation randomly assigned 55 fathers of children ages 6 to 12 with ADHD to the Coaching Our Acting-out Children: Heightening Essential Skills (COACHES) program or a waitlist control group. Outcomes for the study included objective observations of parent behaviors and parent ratings of child behavior. Results indicated that fathers in the COACHES group reduced their rates of negative talk and increased rates of praise as measured in parent-child observations, and father ratings of the intensity of problem behaviors were reduced, relative to the waitlist condition. Groups did not differ on observations of use of commands or father ratings of child behavior problems. Untreated mothers did not significantly improve on observational measures or behavioral ratings. This study provides preliminary evidence for the efficacy of the COACHES parenting program for fathers of children with ADHD. Results are cast in light of the larger literature on behavioral parent training for ADHD as well as how to best work with fathers of children with ADHD in treatment contexts.

Influence function methods to assess the effectiveness of influenza vaccine with survey data.

OBJECTIVE: To examine a robust relative risk (RR) estimation for survey data analysis with ideal inferential properties under various model assumptions. DATA SOURCES: We employed secondary data from the Household Component of the 2000-2016 US Medical Expenditure Panel Survey (MEPS). STUDY DESIGN: We investigate a broad range of data-balancing techniques by implementing influence function (IF) methods, which allows us to easily estimate the variability for the RR estimates in the complex survey setting. We conduct a simulation study of seasonal influenza vaccine effectiveness to evaluate these approaches and discuss techniques that show robust inferential performance across model assumptions. DATA COLLECTION/EXTRACTION METHODS: Demographic information, vaccine status, and self-administered questionnaire surveys were obtained from the longitudinal data files. We linked this information with medical condition files and medical event to extract the disease type and associated expenditures for each medical visit. We excluded individuals who were 18 years or younger at the beginning of each panel. PRINCIPAL FINDINGS: Under various model assumptions, the IF methods show robust inferential performance when the data-balancing procedures are incorporated. Once IF methods and data-balancing techniques are implemented, contingency table-based RR estimation yields a comparable result to the generalized linear model approach. We demonstrate the applicability of the proposed methods for complex survey data using 2000-2016 MEPS data. When employing these methods, we find a significant, negative association between vaccine effectiveness (VE) estimates and influenza-incurred expenditures. CONCLUSIONS: We describe and demonstrate a robust method for RR estimation and relevant inferences for influenza vaccine effectiveness using MEPS data. The proposed method is flexible and can be extended to weighted data for survey data analysis. Hence, these methods have great potential for health services research, especially when data are nonexperimental and imbalanced.