Vivian H. Lyons

Strategies to Address Unmet Needs and Facilitate Return to Learn Guideline Adoption Following Concussion.

BACKGROUND Many students do not receive return to learn (RTL) services upon return to academics following a concussion. METHODS Using a mixed-methods approach, we conducted a survey of RTL practices and experiences in Washington State schools between January 2015 and June 2015. We then held a statewide summit of RTL stakeholders and used a modified Delphi process to develop a consensus-based RTL implementation model and process. RESULTS Survey participants included 83 educators, 57 school nurses, 14 administrators, and 30 parents, representing 144 schools in rural and urban areas. Unmet need domains and recommendations identified were (1) a current lack of school policies; (2) barriers to providing or receiving accommodations; (3) wide variability in communication patterns; and (4) recommendations shared by all stakeholder groups (including desire for readily available best practices, development of a formal school RTL policy for easy adoption and more training). Using stakeholder input from RTL summit participants and survey responses, we developed an RTL implementation model and checklist for RTL guideline adoption. CONCLUSIONS Washington State children have unmet needs upon returning to public schools after concussion. The student-centered RTL model and checklist for implementing RTL guidelines can help schools provide timely RTL services following concussion.

Violence perpetration among patients hospitalized for unintentional and assault-related firearm injury: a case-control study and a cohort study

An estimated 41024 persons aged 15 years or older were hospitalized because of nonfatal firearm injuries in 2014 in the United States (1). About 79% of those injuries were intentional and resulted from interpersonal violence (assault), and 11% were unintentional (accidental). The remaining 10% were self-inflicted, were due to legal interventions, or had an undetermined intent. Regardless of intent, many patients with nonfatal firearm injuries have short-term, long-term, or permanent physical and psychological sequelae (2). Illness associated with such trauma translates to a notable loss of healthy life-years and considerable societal costs (3, 4). Effective primary, secondary, and tertiary prevention strategies are critically needed to reduce the heavy burden of firearm injuries. Such strategies should preferably integrate pertinent elements of clinical care, public health, and/or the criminal justice systems. Previous investigations have highlighted overlapping risks between becoming a victim and perpetrator of violence (511). Population-based research to specifically examine violence perpetration before and after firearm injury can inform interventions in both community and health care settings. Because only about half of all violence victimizations (that is, becoming a victim of violence) are reported to police (12), hospital admission is an important sentinel event that could present a valuable opportunity for violence risk reduction. In 2009, the National Network of Hospital-based Violence Intervention Programs was formally established (13). These programs consider the in-hospital recovery period as a valuable opportunity or teachable moment during which patients can be connected with principal community services to help reduce retaliation and recidivism. Programs typically focus on patients whose injury was assault-related because of their presumed involvement in a cycle of interpersonal violence. Whether patients without assault-related injuries would also benefit from such programs is unclear. Of note, empirical evidence is lacking on prior involvement in, and subsequent risk for, violence perpetration among patients with unintentional injuries. We conducted 2 statewide studies to examine violent crime perpetration both before and after hospitalization for a firearm injury among patients aged 15 years or older. Injury and crime were studied together to add to the existing body of knowledge on gun violence by using data from both clinical and criminal justice system encounters. Injury intent was a central theme of both studies and was separated into 2 categories (assault vs. unintentional) to examine the association between intent-specific firearm injury and violence perpetration. We focused on these categories because they constitute most firearm injuries requiring hospitalization. Most patients with self-inflicted firearm injuries die before presenting to the hospital, and the number of patients in other injury intent categories (for example, legal interventions) is also relatively small within the hospitalized population. Methods Design, Setting, and Participants We conducted a casecontrol study and a retrospective cohort study. In the casecontrol study, we compared the odds of violence-related arrest before hospitalization between persons hospitalized for firearm injuries and those hospitalized for other reasons. In the cohort study, we compared rates of violence-related arrest after hospitalization between persons hospitalized for firearm injuries and those hospitalized for other reasons. We used data previously assembled in a larger investigation for both of these studies (14). In that investigation, we first identified all patients hospitalized for an injury by any mechanism from 2006 to 2007 in Washington by using International Classification of Diseases, Ninth Revision, codes. Then we chose a random sample of patients hospitalized for a noninjury reason (that is, the no injury group) and frequency-matched them with those in the injury group on age and year of hospitalization in a 2:1 ratio. For the analyses presented here, we separated the injury group into 2 mutually exclusive subgroups: patients hospitalized for a firearm injury (firearm injury group), and those hospitalized for an injury not caused by a firearm (other injury group), resulting in 3 groupsfirearm injury, other injury, and no injury. Figure 1 depicts the design of the 2 studies. In the casecontrol study, the firearm injury group served as the case population and the other injury and no injury groups served as 2 separate control populations. The exposure of interest was arrest for a violent crime before hospitalization. In the cohort study, the firearm injury group served as the exposed population and the other injury and no injury groups served as 2 separate unexposed populations. The outcome of interest was time to first arrest for a violent crime after hospital discharge. Figure 1. Design of the 2 studies. Information on hospitalizations was obtained from the Washington State Department of Health Comprehensive Hospital Abstract Reporting System (15). This system contains coded discharge information and is used to collect various data, such as age, sex, payer status, and diagnosis and procedure codes. Consistent with the literature, an injury-related hospitalization was defined as a discharge with a primary diagnosis of an acute injury (International Classification of Diseases, Ninth Revision, codes 800 to 959). Records containing injuries from medical and surgical misadventures (E870 to E879), late effects of injury (E929 or E999), and adverse effects of substances in therapeutic use (E930 to E949) were excluded (16). Codes for external causes of injury (that is, E codes) were used to determine the mechanism and intent of an injury. The Centers for Disease Control and Prevention recommended this framework of E-code groupings for presenting injury mortality and morbidity data (17). We used E codes to categorize hospitalizations by injury intent: assault, unintentional, self-inflicted, or undetermined. In these studies, we restricted intent-specific analyses to assault-related and unintentional injuries because of the small number of self-inflicted injuries and those with an undetermined intent; however, overall analyses included all injuries regardless of intent. Information on arrests was obtained from Washington State Patrol records. This database provided full arrest history, including juvenile criminal records, and contained information for persons as young as 10 years. We used specific codes in the Revised Code of Washington to identify violent crimes, including homicide, rape, robbery, and assault, according to the Uniform Crime Reporting program of the Federal Bureau of Investigation (18). All patients were aged 15 years or older at the time of hospital discharge. We excluded records for persons younger than 15 years at the time of discharge because they would not have had any criminal records before age 10. Probabilistic algorithms were used to link each patients hospitalization record to his or her arrest record from 2001 through 2011. A subset of identifiers, including the first 2 letters of the first name, first 2 letters of the last name, date of birth, sex, and first 3 digits of the ZIP code, was used for the linkage. Detailed information about data linkage procedures can be found elsewhere (14). The Human Subjects Division of the Washington State Department of Health approved the study protocol and procedures. Statistical Analysis In the casecontrol study, odds of prior violence-related arrest were compared between case and control patients. Odds ratios (ORs) and their corresponding 95% CIs were determined by using multivariable logistic regression models that included covariates for age; sex; payer status; hospital county; and history of diagnosis of a psychiatric disorder, including substance use disorders. In the cohort study, only patients who survived their hospitalization were included. Follow-up began on the day of discharge and ended on the day of the first violence-related arrest, death, or 31 December 2011whichever occurred first. The unadjusted absolute risk for violence-related arrest was estimated by using the cumulative incidence function, with death treated as a competing event. In regression analyses, we used the methods described by Fine and Gray (19) to model violence-related arrest with the subdistribution hazards regression. Subhazard ratios and their corresponding 95% CIs were determined by multivariable models that included the same set of covariates used in the casecontrol study plus history of violence-related arrest. Additional analyses were conducted to compare the firearm injury group with a subset of patients in the other injury group who had sustained injuries through cut or pierce mechanisms (for example, stab wounds) or struck-by or struck-against mechanisms. In terms of the social context in which the injury occurred, these individuals may have been more comparable with the firearm injury group than those who sustained injuries by such mechanisms as motor vehicle crashes or falls. In all analyses, an of 0.05 was used to denote statistical significance. All tests were 2-sided and conducted using SAS, version 10 (SAS Institute), and Stata, version 13 (StataCorp), with the stcrreg and stcurve package for Fine and Gray modeling. Role of the Funding Source This research was funded by the City of Seattle and the University of Washington Royalty Research Fund. The funding sources had no role in the design, conduct, and reporting of this research or the decision to submit the manuscript for publication. Results A total of 245343 hospitalized patients were included in this investigation. Of these, 658, 71855, and 172830 were in the firearm injury, other injury, and no injury groups, respectively. A greater proportion of patients in the firearm injury group than those in the other 2 groups

Proposed variations of the stepped-wedge design can be used to accommodate multiple interventions

OBJECTIVES Stepped-wedge design (SWD) cluster-randomized trials have traditionally been used for evaluating a single intervention. We aimed to explore design variants suitable for evaluating multiple interventions in an SWD trial. STUDY DESIGN AND SETTING We identified four specific variants of the traditional SWD that would allow two interventions to be conducted within a single cluster-randomized trial: concurrent, replacement, supplementation, and factorial SWDs. These variants were chosen to flexibly accommodate study characteristics that limit a one-size-fits-all approach for multiple interventions. RESULTS In the concurrent SWD, each cluster receives only one intervention, unlike the other variants. The replacement SWD supports two interventions that will not or cannot be used at the same time. The supplementation SWD is appropriate when the second intervention requires the presence of the first intervention, and the factorial SWD supports the evaluation of intervention interactions. The precision for estimating intervention effects varies across the four variants. CONCLUSION Selection of the appropriate design variant should be driven by the research question while considering the trade-off between the number of steps, number of clusters, restrictions for concurrent implementation of the interventions, lingering effects of each intervention, and precision of the intervention effect estimates.

Critical Care Resource Utilization and Outcomes of Children With Moderate Traumatic Brain Injury

Objectives: To characterize admission patterns, critical care resource utilization, and outcomes in moderate pediatric traumatic brain injury. Design: Retrospective cohort study. Setting: National Trauma Data Bank. Patients: Children under 18 years old with a diagnosis of moderate traumatic brain injury (admission Glasgow Coma Scale score of 9–13) in the National Trauma Data Bank between 2007 and 2014. Measurement and Main Results: We examined clinical characteristics, critical care resource utilization, and discharge outcomes. Poor outcomes were defined as discharge to hospice, skilled nursing facility, long-term acute care, or death. We examined 20,010 patient records. Patients were 9 years old (interquartile range, 2–15 yr), male (64%) with isolated traumatic brain injury (81%), Glasgow Coma Scale score of 12, head Abbreviated Injury Scale score of 3, and Injury Severity Score of 10. Majority (34%) were admitted to nontrauma hospitals. Critical care utilization was 58.7% including 11.5% mechanical ventilation and 3.2% intracranial pressure monitoring. Compared to patients with Glasgow Coma Scale score of 13, admission Glasgow Coma Scale score of 9 was associated with greater critical care resource utilization, such as ICU admission (72% vs 50%), intracranial pressure monitoring (7% vs 1.8%), mechanical ventilation (21% vs 6%), and intracranial surgery (10% vs 5%). Most patients (70%) were discharged to home, but up to one third had poor outcomes. Older age group had a higher risk of poor outcomes (10–14 yr; adjusted relative risk, 1.32; 95% CI, 1.13–1.54; 15–17 yr; adjusted relative risk, 2.39; 95% CI, 2.12–2.70). Poor outcomes occurred with lower Glasgow Coma Scale (Glasgow Coma Scale score of 9 vs Glasgow Coma Scale score of 13: adjusted relative risk, 2.89; 95% CI, 2.47–3.38), higher Injury Severity Score (Injury Severity Score of ≥ 16 vs Injury Severity Score of < 9: adjusted relative risk, 8.10; 95% CI 6.27–10.45), and polytrauma (adjusted relative risk, 1.40; 95% CI, 1.22–1.61). Conclusions: Critical care resources are used in more than half of all moderate pediatric traumatic brain injury, and many receive care at nontrauma hospitals. Up to one third of moderate pediatric traumatic brain injury have poor outcomes, risk factors for which include age greater than 10 years, lower admission Glasgow Coma Scale, higher Injury Severity Score, and polytrauma. There is urgent need to optimize triage, care, and outcomes in this vulnerable population.

Development and Feasibility of a Real-Time Clinical Decision Support System for Traumatic Brain Injury Anesthesia Care

BACKGROUND Real-time clinical decision support (CDS) integrated with anesthesia information management systems (AIMS) can generate point of care reminders to improve quality of care. OBJECTIVE To develop, implement and evaluate a real-time clinical decision support system for anesthetic management of pediatric traumatic brain injury (TBI) patients undergoing urgent neurosurgery. METHODS We iteratively developed a CDS system for pediatric TBI patients undergoing urgent neurosurgery. The system automatically detects eligible cases and evidence-based key performance indicators (KPIs). Unwanted clinical events trigger and display real-time messages on the AIMS computer screen. Main outcomes were feasibility of detecting eligible cases and KPIs, and user acceptance. RESULTS The CDS system was triggered in 22 out of 28 (79%) patients. The sensitivity of detecting continuously sampled KPIs reached 93.8%. For intermittently sampled KPIs, sensitivity and specificity reached 90.9% and 100%, respectively. 88% of providers reported that CDS helped with TBI anesthesia care. CONCLUSIONS CDS implementation is feasible and acceptable with a high rate of case capture and appropriate generation of alert and guidance messages for TBI anesthesia care.

Loaded Handgun Carrying Among US Adults, 2015

Objectives To determine the frequency of loaded handgun carrying among US adult handgun owners, characterize those who carry, and examine concealed carrying by state concealed carry laws. Methods Using a nationally representative survey of US adults in 2015, we asked handgun owners (n = 1444) about their past-30-day carrying behavior. Results Among surveyed handgun owners, 24% (95% confidence interval[CI] = 21%, 26%) carried loaded handguns monthly, of whom 35% (95% CI = 29%, 41%) did so daily; 82% (95% CI = 77%, 86%) carried primarily for protection. The proportion of handgun owners who carried concealed loaded handguns in the past 30 days was 21% (95% CI = 12%, 35%) in unrestricted states, 25% (95% CI = 21%, 29%) in shall issue-no discretion states, 20% (95% CI = 16%, 24%) in shall issue-limited discretion states, and 9% (95% CI = 6%, 15%) in may-issue states. Conclusions We estimate that 9 million US adult handgun owners carry loaded handguns monthly, 3 million do so every day, and most report protection as the main carrying reason. Proportionally fewer handgun owners carry concealed loaded handguns in states that allow issuing authorities substantial discretion in granting carrying permits.

Formal firearm training among adults in the USA: results of a national survey

Despite broad support for policies requiring that prospective firearm owners receive training before acquiring a firearm, little is known about the scope and content of firearm training in the USA. Nationally representative surveys conducted in 1994 estimated that 56%-58% of the US firearm owners had received formal firearm training. We conducted a nationally representative survey in 2015 (n=3932; completion proportion=55%) to update those estimates and characterise training contents. 61% of firearm owners and 14% of non-owners living with a firearm owner reported having received formal firearm training. The most commonly reported combination of training topics was safe handling, safe storage and preventing accidents. 15% of firearm owners reported that their training included information about suicide prevention. The proportion of the US firearm owners with formal firearm training has not meaningfully changed since two decades ago. Training programme contents vary widely. Efforts to standardise and evaluate the effectiveness of firearm training are warranted.

Concomitant Upper Extremity Fracture Worsens Outcomes in Elderly Patients With Hip Fracture

Background: Elderly patients with low-energy hip fractures have high rates of morbidity and mortality, but it is not well known how often concurrent upper extremity fractures occur and how this impacts outcomes. We used the National Trauma Databank (NTDB), the largest aggregation of US trauma registry data available, to determine whether patients with concurrent upper extremity and hip fractures have worse outcomes than patients with hip fractures alone. Methods: We accessed the NTDB to identify patients aged 65 to 100 who sustained a hip fracture. The cohort was then narrowed to include only patients who sustained their injury in a fall and had an injury severity score indicating hip fracture as the most severe injury. We then analyzed this group to assess the impact of a simultaneous upper extremity fracture on length of stay, in-hospital mortality, and discharge disposition. Results: From 2007 to 2014, a total of 231,299 patients aged 65 to 100 were identified as having a hip fracture. The narrowed cohort with fall as the mechanism and hip fracture as the most severe injury included 193,862 patients. Of these, 12,618 patients sustained a concomitant upper extremity fracture (6.5%). Compared to isolated hip fractures, patients with a concomitant upper extremity fracture had higher odds of death in the hospital (odds ratio [OR] = 1.3; 95% confidence interval = 1.2-1.4), were less likely to be discharged to home as compared to a skilled facility (OR = 0.73; 95% confidence interval = 0.68-0.78), and had a significantly longer average length of stay (7.1 vs 6.4 days, P < .001). Conclusions: We found a 6.5% prevalence of concomitant upper extremity fractures in patients aged 65 to 100 with a hip fracture sustained after a fall where the hip fracture was the most severe injury. These patients had a higher risk of in-hospital mortality, were less likely to be discharged to home, and had longer average length of stay.

Proceedings of the First International Conference on Stepped Wedge Trial Design: York, UK, 10 March 2016

CK is funded by a National Institute for Health Research (NIHR) Research Methods Fellowship.Table of contentsI1 IntroductionMona Kanaan, Noreen Dadirai Mdege, Ada KedingO1 The HiSTORIC trial: a hybrid before-and-after and stepped wedge designRA Parker, N Mills, A Shah, F Strachan, C Keerie, CJ WeirO2 Stepped wedge trials with non-uniform correlation structureAndrew Forbes, Karla HemmingO3 Challenges and solutions for the operationalisation of the ENHANCE study: a pilot stepped wedge trial within a general practice settingSarah A Lawton, Emma Healey, Martyn Lewis, Elaine Nicholls, Clare Jinks, Valerie Tan, Andrew Finney, Christian D Mallen, on behalf of the ENHANCE Study TeamO4 Early lessons from the implementation of a stepped wedge trial design investigating the effectiveness of a training intervention in busy health care settings: the Thistle studyErik Lenguerrand, Graeme MacLennan, John Norrie, Siladitya Bhattacharya, Tim Draycott, on behalf of the Thistle groupO5 Sample size calculation for longitudinal cluster randomised trials: a unified framework for closed cohort and repeated cross-section designsRichard Hooper, Steven Teerenstra, Esther de Hoop, Sandra EldridgeO6 Restricted randomisation schemes for stepped-wedge studies with a cluster-level covariateAlan Girling, Monica TaljaardO7 A flexible modelling of the time trend for the analysis of stepped wedge trials: results of a simulation studyGian Luca Di Tanna, Antonio GasparriniP1 Tackling acute kidney injury – a UK stepped wedge clinical trial of hospital-level quality improvement interventionsAnna Casula, Fergus Caskey, Erik Lenguerrand, Shona Methven, Stephanie MacNeill, Margaret May, Nicholas SelbyP2 Sample size considerations for quantifying secondary bacterial transmission in a stepped wedge trial of influenza vaccineLeon Danon, Hannah Christensen, Adam Finn, Margaret MayP3 Sample size calculation for time-to-event data in stepped wedge cluster randomised trialsFumihito Takanashi, Ada Keding, Simon Crouch, Mona KanaanP4 Sample size calculations for stepped-wedge cluster randomised trials with unequal cluster sizesCaroline A. Kristunas, Karen L. Smith, Laura J. GrayP5 The design of stepped wedge trials with unequal cluster sizesJohn N.S. MatthewsP6 Promoting Recruitment using Information Management Efficiently (PRIME): a stepped wedge SWAT (study-within-a-trial)R Al-Shahi Salman, RA Parker, A Maxwell, M Dennis, A Rudd, CJ WeirP7 Implications of misspecified mixed effect models in stepped wedge trial analysis: how wrong can it be?Jennifer A Thompson, Katherine L Fielding, Calum Davey, Alexander M Aiken, James R Hargreaves, Richard J HayesS1 Stepped Wedge Designs with Multiple InterventionsVivian H Lyons, Lingyu Li, James Hughes, Ali Rowhani-RahbarS2 Analysis of the cross-sectional stepped wedge cluster randomised trialKarla Hemming, Monica Taljaard, Andrew Forbes

Proceedings of the First International Conference on Stepped Wedge Trial Design

CK is funded by a National Institute for Health Research (NIHR) Research Methods Fellowship.Table of contentsI1 IntroductionMona Kanaan, Noreen Dadirai Mdege, Ada KedingO1 The HiSTORIC trial: a hybrid before-and-after and stepped wedge designRA Parker, N Mills, A Shah, F Strachan, C Keerie, CJ WeirO2 Stepped wedge trials with non-uniform correlation structureAndrew Forbes, Karla HemmingO3 Challenges and solutions for the operationalisation of the ENHANCE study: a pilot stepped wedge trial within a general practice settingSarah A Lawton, Emma Healey, Martyn Lewis, Elaine Nicholls, Clare Jinks, Valerie Tan, Andrew Finney, Christian D Mallen, on behalf of the ENHANCE Study TeamO4 Early lessons from the implementation of a stepped wedge trial design investigating the effectiveness of a training intervention in busy health care settings: the Thistle studyErik Lenguerrand, Graeme MacLennan, John Norrie, Siladitya Bhattacharya, Tim Draycott, on behalf of the Thistle groupO5 Sample size calculation for longitudinal cluster randomised trials: a unified framework for closed cohort and repeated cross-section designsRichard Hooper, Steven Teerenstra, Esther de Hoop, Sandra EldridgeO6 Restricted randomisation schemes for stepped-wedge studies with a cluster-level covariateAlan Girling, Monica TaljaardO7 A flexible modelling of the time trend for the analysis of stepped wedge trials: results of a simulation studyGian Luca Di Tanna, Antonio GasparriniP1 Tackling acute kidney injury – a UK stepped wedge clinical trial of hospital-level quality improvement interventionsAnna Casula, Fergus Caskey, Erik Lenguerrand, Shona Methven, Stephanie MacNeill, Margaret May, Nicholas SelbyP2 Sample size considerations for quantifying secondary bacterial transmission in a stepped wedge trial of influenza vaccineLeon Danon, Hannah Christensen, Adam Finn, Margaret MayP3 Sample size calculation for time-to-event data in stepped wedge cluster randomised trialsFumihito Takanashi, Ada Keding, Simon Crouch, Mona KanaanP4 Sample size calculations for stepped-wedge cluster randomised trials with unequal cluster sizesCaroline A. Kristunas, Karen L. Smith, Laura J. GrayP5 The design of stepped wedge trials with unequal cluster sizesJohn N.S. MatthewsP6 Promoting Recruitment using Information Management Efficiently (PRIME): a stepped wedge SWAT (study-within-a-trial)R Al-Shahi Salman, RA Parker, A Maxwell, M Dennis, A Rudd, CJ WeirP7 Implications of misspecified mixed effect models in stepped wedge trial analysis: how wrong can it be?Jennifer A Thompson, Katherine L Fielding, Calum Davey, Alexander M Aiken, James R Hargreaves, Richard J HayesS1 Stepped Wedge Designs with Multiple InterventionsVivian H Lyons, Lingyu Li, James Hughes, Ali Rowhani-RahbarS2 Analysis of the cross-sectional stepped wedge cluster randomised trialKarla Hemming, Monica Taljaard, Andrew Forbes