Clinical outcomes in borderline and locally advanced pancreatic cancer with the addition of low-dose-rate brachytherapy to standard of care therapy.

PURPOSE: Surgical resection remains the only curative therapy for pancreatic cancer. Unfortunately, many patients have borderline or unresectable disease at diagnosis due to proximity of major abdominal vessels. Neoadjuvant chemotherapy and radiation are used to down-stage, however, there is a risk that there will be a positive/close surgical margin. The CivaSheet is a low-dose-rate (LDR) brachytherapy device placed at the time of surgery to target the area of highest risk of margin positivity. The purpose of this study is to assess the clinical value of brachytherapy in addition to standard-of-care therapy in pancreatic therapy. METHODS AND MATERIALS: Between 2017 and 2022 patients with borderline and locally advanced pancreatic cancer treated with neoadjuvant chemotherapy and radiation followed by surgical resection were included. There were 2 cohorts of patients: (1) Those who had the LDR brachytherapy device placed at the time of surgery and (2) those who did not. Sixteen of 19 (84%) patients who had brachytherapy were enrolled in a prospective clinical trial (NCT02843945). Patients were matched for comorbidities, cancer staging, and treatment details. The primary outcome was progression-free survival (PFS). RESULTS: Thirty-five patients were included in this analysis, 19 in the LDR brachytherapy group and 16 in the comparison cohort. The 2-year PFS was 21% vs. 0% (p = 0.11), 2-year OS was 26% vs. 13% (p = 0.43), and the pancreatic cancer cause-specific survival was 84% vs. 56% (p = 0.13) in favor of the brachytherapy patients. CONCLUSIONS: Use of LDR brachytherapy at the time of resection shows a trend towards improved progression free and overall survival for patients with borderline or locally advanced pancreatic cancer treated with neoadjuvant chemoradiation.

Disparities Among Pediatric Firearm Suicides in the United States: An Analysis of the National Violent Death Reporting System, 2014 to 2018.

Suicide is a leading cause of death in the United States (U.S.), with firearms being the predominant method. This study examines the racial disparity and disproportionality of pediatric firearm suicide from 2014 to 2018 in 17 U.S. states. We used the National Violent Death Reporting System to quantify the burden of pediatric firearm suicide by race/ethnicity and gender and assessed themes among decedents aged 10 to 17 years. Racial disparity and disproportionality were measured using the Disparity Index and Disproportionality Representation Index, respectively. Decedents were primarily non-Hispanic White (NHW, 77.5%) and male (84.0%). NHW children died at a rate that was 1.3 times greater than expected based on their proportion in the general population and were 2.6 times more likely to die by firearm suicide than non-Hispanic Black (NHB) children. NHB children were less likely to disclose suicide intention, suggesting that this group may require more active screening intervention. Qualitative analysis revealed unsafe firearm storage as a common theme among these deaths. Differences in age with respect to social media use and precipitating factors such as bullying and arguments with parents were also identified as contributing factors. Results of this study support the expansion of interventions such as lethal means restriction counseling and implementation of safer firearm storage laws.

Documented Penicillin Allergies on Antibiotic Selection at Pediatric Emergency Department Visits.

BACKGROUND: Penicillin or amoxicillin are the recommended treatments for the most common pediatric bacterial illnesses. Allergies to penicillin are commonly reported among children but rarely true. We evaluated the impact of reported penicillin allergies on broad-spectrum antibiotic use overall and for the treatment of common respiratory infections among treat-and-release pediatric emergency department (ED) visits. METHODS: Retrospective cohort study of pediatric patients receiving antibiotics during a treat-and-release visit at a large, pediatric ED in the northeast from 2014 to 2016. Study exposure was a reported allergy to penicillin in the electronic medical record. Study outcomes were the selection of broad-spectrum antibiotics and alternative (second-line) antibiotic therapy for the treatment of acute otitis media (AOM) and group A streptococcus (GAS) pharyngitis. We used unadjusted and adjusted generalized estimating equation models to analyze the impact of reported penicillin allergies on the selection of broad-spectrum antibiotics. We used unadjusted and adjusted logistic regression models to determine the probability of children with a documented penicillin allergy receiving alternative antibiotic treatments for AOM and GAS. RESULTS: Among 12,987 pediatric patients, 810 (6.2%) had a documented penicillin allergy. Penicillin allergies increased the odds of children receiving a broad spectrum versus narrow spectrum antibiotic (adjusted odds ratio, 13.55; 95% confidence interval (CI), 11.34-16.18). In our adjusted logistic regression model, the probability of children with a documented penicillin allergy receiving alternative antibiotic treatment for AOM was 0.97 (95% CI, 0.94-0.99) and for GAS was 0.97 (95% CI, 0.92-0.99). CONCLUSIONS: Antibiotic stewardship efforts in pediatric EDs may consider the delabeling of penicillin allergies particularly among children receiving antibiotics for an acute respiratory infection as a target for intervention.

Understanding the Burden of 30-Day Readmission in Patients With Both Primary and Secondary Diagnoses of Heart Failure: Causes, Timing, and Impact of Co-Morbidities.

Although efforts to reduce 30-day readmission rates have mainly focused on patients with heart failure (HF) as a primary diagnosis at index hospitalization, patients with HF as a secondary diagnosis remain common, costly, and understudied. This study aimed to determine the incidence, etiology, and patterns of 30-day readmissions after discharge for HF as a primary and secondary diagnosis and investigate the impact of co-morbidities on HF readmission. The National Readmission Database from 2014 to 2016 was used to identify HF patients with a linked 30-day readmission. Patient and hospital characteristics, admission features, and Elixhauser-related co-morbidities were compared between the 2 groups. Readmitted patients in both groups were younger, male, with lower household income, higher mortality risk, and higher hospitalization costs. Over 60% of readmissions were for reasons other than HF, and greater than 1/3 had more than 2 readmissions within 30 days, with a median time to readmission of 12 days. Both cohorts had high readmission rates and high rates of readmission for causes other than HF. Our findings suggest that efforts to reduce 30-day readmission rates should be extended to patients with secondary HF diagnosis, with surveillance extending to 2 weeks postdischarge to identify patients at risk.

Predictors of 30-Day Readmission in Patients Hospitalized With Heart Failure as a Primary Versus Secondary Diagnosis.

Short-term rehospitalizations are common, costly, and detrimental to patients with heart failure (HF). Current research and policy have focused primarily on 30-day readmissions for patients with HF as a primary diagnosis at index hospitalization, whereas a much larger population of patients are admitted with HF as a secondary diagnosis. This study aims to compare patients initially hospitalized for HF as either a primary or a secondary diagnosis, and to identify the most important factors in predicting 30-day readmission. Patients admitted with HF between 2014 and 2016 in the Nationwide Readmissions Database were included and divided into 2 cohorts: those admitted with a primary and secondary diagnosis of HF. Multivariable logistic regression was performed to predict 30-day readmission. Statistically significant predictors in multivariable logistic regression were used for dominance analysis to rank these factors by relative importance. Co-morbidities were the major driver of increased risk of 30-day readmission in both groups. Individual Elixhauser co-morbidities and the Elixhauser co-morbidity indexes were significantly associated with an increase in 30-day readmission. The 5 most important predictors of 30-day readmission according to dominance analysis were age, Elixhauser co-morbidity indexes of co-morbidity complications and readmission, number of diagnoses, and renal failure. These 5 factors accounted for 68% of the 30-day readmission risk. Measures of patient co-morbidities were among the strongest predictors of readmission risk. This study highlights the importance of expanding predictive models to include a broader set of clinical measures to create better-performing models of readmission risk for HF patients.

A Survey on Cone-beam Computed Tomography Usage Among Endodontists in the United States.

INTRODUCTION: The aim of the study was to investigate the accessibility and frequency of cone-beam computed tomography (CBCT) usage and to assess the economic and logistical factors involved with its usage among active American Association of Endodontists (AAE) members, utilizing a web-based survey. METHODS: A survey of 19 questions was sent to 3,071 AAE members addressing participant access to, along with financial and logistical aspects of CBCT imaging. Descriptive analysis was performed and Fisher exact test utilized to test associations between groups (P < .05). RESULTS: The overall response rate was 14.7% (n = 544). Ninety-five percent of respondents (n = 486) had an in-office CBCT unit, with those graduating after the year 2000 statistically more likely to have one (P < .05). Utilization of CBCT imaging for every case was reported by 40% of providers. Eighty-nine percent reported taking the scan at the consultation visit and 20% included this charge with the consultation fee. For those who charged for the scan separately, 85% charged more than $100. Providers who paid off their unit did so within 1-2 years (41%), 3-4 years (36%), 4-5 years (12%), and 5+ years (11%). Limited field of view was utilized by 95% of respondents. Fifty-eight percent reported interpreting the scans themselves, 38% send only if pathology is expected, and 3% always send their scans to a radiologist. CONCLUSIONS: In conclusion, accessibility and utilization of CBCT imaging among United States endodontists has increased and acquisition of this equipment has not made a long lasting financial burden on providers.

Intentional Self-Harm Among US Veterans With Traumatic Brain Injury or Posttraumatic Stress Disorder: Retrospective Cohort Study From 2008 to 2017.

BACKGROUND: Veterans with a history of traumatic brain injury (TBI) and/or posttraumatic stress disorder (PTSD) may be at increased risk of suicide attempts and other forms of intentional self-harm as compared to veterans without TBI or PTSD. OBJECTIVE: Using administrative data from the US Veterans Health Administration (VHA), we studied associations between TBI and PTSD diagnoses, and subsequent diagnoses of intentional self-harm among US veterans who used VHA health care between 2008 and 2017. METHODS: All veterans with encounters or hospitalizations for intentional self-harm were assigned "index dates" corresponding to the date of the first related visit; among those without intentional self-harm, we randomly selected a date from among the veteran's health care encounters to match the distribution of case index dates over the 10-year period. We then examined the prevalence of TBI and PTSD diagnoses within the 5-year period prior to veterans' index dates. TBI, PTSD, and intentional self-harm were identified using International Classification of Diseases diagnosis and external cause of injury codes from inpatient and outpatient VHA encounters. We stratified analyses by veterans' average yearly VHA utilization in the 5-year period before their index date (low, medium, or high). Variations in prevalence and odds of intentional self-harm diagnoses were compared by veterans' prior TBI and PTSD diagnosis status (TBI only, PTSD only, and comorbid TBI/PTSD) for each VHA utilization stratum. Multivariable models adjusted for age, sex, race, ethnicity, marital status, Department of Veterans Affairs service-connection status, and Charlson Comorbidity Index scores. RESULTS: About 6.7 million veterans with at least two VHA visits in the 5-year period before their index dates were included in the analyses; 86,644 had at least one intentional self-harm diagnosis during the study period. During the periods prior to veterans' index dates, 93,866 were diagnosed with TBI only; 892,420 with PTSD only; and 102,549 with comorbid TBI/PTSD. Across all three VHA utilization strata, the prevalence of intentional self-harm diagnoses was higher among veterans diagnosed with TBI, PTSD, or TBI/PTSD than among veterans with neither diagnosis. The observed difference was most pronounced among veterans in the high VHA utilization stratum. The prevalence of intentional self-harm was six times higher among those with comorbid TBI/PTSD (6778/58,295, 11.63%) than among veterans with neither TBI nor PTSD (21,979/1,144,991, 1.92%). Adjusted odds ratios suggested that, after accounting for potential confounders, veterans with TBI, PTSD, or comorbid TBI/PTSD had higher odds of self-harm compared to veterans without these diagnoses. Among veterans with high VHA utilization, those with comorbid TBI/PTSD were 4.26 (95% CI 4.15-4.38) times more likely to receive diagnoses for intentional self-harm than veterans with neither diagnosis. This pattern was similar for veterans with low and medium VHA utilization. CONCLUSIONS: Veterans with TBI and/or PTSD diagnoses, compared to those with neither diagnosis, were substantially more likely to be subsequently diagnosed with intentional self-harm between 2008 and 2017. These associations were most pronounced among veterans who used VHA health care most frequently. These findings suggest a need for suicide prevention efforts targeted at veterans with these diagnoses.

Target-based fusion using social determinants of health to enhance suicide prediction with electronic health records.

OBJECTIVE: Preventing suicide in US youth is of paramount concern, with rates increasing over 50% between 2007 and 2018. Statistical modeling using electronic health records may help identify at-risk youth before a suicide attempt. While electronic health records contain diagnostic information, which are known risk factors, they generally lack or poorly document social determinants (e.g., social support), which are also known risk factors. If statistical models are built incorporating not only diagnostic records, but also social determinants measures, additional at-risk youth may be identified before a suicide attempt. METHODS: Suicide attempts were predicted in hospitalized patients, ages 10-24, from the State of Connecticut's Hospital Inpatient Discharge Database (HIDD; N = 38943). Predictors included demographic information, diagnosis codes, and using a data fusion framework, social determinants features transferred or fused from an external source of survey data, The National Longitudinal Study of Adolescent to Adult Health (Add Health). Social determinant information for each HIDD patient was generated by averaging values from their most similar Add Health individuals (e.g., top 10), based upon matching shared features between datasets (e.g., Pearson's r). Attempts were then modelled using an elastic net logistic regression with both HIDD features and fused Add Health features. RESULTS: The model including fused social determinants outperformed the conventional model (AUC = 0.83 v. 0.82). Sensitivity and positive predictive values at 90 and 95% specificity were almost 10% higher when including fused features (e.g., sensitivity at 90% specificity = 0.48 v. 0.44). Among social determinants variables, the perception that their mother cares and being non-religious appeared particularly important to performance improvement. DISCUSSION: This proof-of-concept study showed that incorporating social determinants measures from an external survey database could improve prediction of youth suicide risk from clinical data using a data fusion framework. While social determinant data directly from patients might be ideal, estimating these characteristics via data fusion avoids the task of data collection, which is generally time-consuming, expensive, and suffers from non-compliance.

Parent-reported penicillin allergies in children: A qualitative study.

BACKGROUND: Penicillin allergies are commonly reported in children. Most reported penicillin allergies are false, resulting in the unnecessary selection of alternative antibiotic treatments that promote antibiotic resistance. While formal allergy testing is encouraged to establish a diagnosis of penicillin allergy, children are rarely referred for allergy testing, and study of parents' experiences and perceptions of their child's reported penicillin allergy is limited. We aimed to describe parents' experiences and perceptions of their child's penicillin allergy and attitudes towards penicillin allergy testing to identify opportunities to engage parents in antimicrobial stewardship efforts. METHODS: This was a qualitative descriptive study. RESULTS: Eighteen parents participated in this study. Parents' children were on average 2 years old when the index reaction occurred, and 7 years had passed since the reaction. Transcripts revealed that participants were receptive to penicillin allergy testing for their child after learning the consequences of penicillin allergy and availability of allergy testing. Four major themes emerged from data (1) parents' making sense of allergy; (2) parents' impressions of allergy label, (3) parents' attitudes towards allergy testing, and (4) parents' desire to be informed of testing availability. CONCLUSIONS: Efforts are needed to engage parents in addressing spuriously reported penicillin allergies.

The association of prescription opioid use with suicide attempts: An analysis of statewide medical claims data.

BACKGROUND: Suicides and opioid overdose deaths are among the most pressing public health concerns in the US. However direct evidence for the association between opioid use and suicidal behavior is limited. The objective of this article is to examine the association between frequency and dose of prescription opioid use and subsequent suicide attempts. METHODS AND FINDINGS: This retrospective cohort study analyzed 4 years of statewide medical claims data from the Connecticut All-Payer Claims Database. Commercially insured adult patients in Connecticut (n = 842,773) who had any medical claims beginning in January 2012 were followed through December 2015. The primary outcome was suicide attempt identified using International Classification of Diseases (ICD 9) diagnosis codes. Primary predictor variables included frequency of opioid use, which was defined as the number of months with claims for prescription opioids per year, and strength of opioid dose, which was standardized using morphine milligram equivalent (MME) units. We also controlled for psychiatric and medical comorbidities using ICD 9 codes. We used Cox proportional hazards regression to examine the association between frequency, dose, and suicide attempts, adjusting for medical and psychiatric comorbid conditions. Interactions among measures of opioid use and comorbid conditions were analyzed. In this cohort study with follow-up time up to 4 years (range = 2-48 months, median = 46 months), the hazard ratios (HR) from the time-to-event analysis indicated that patients prescribed opioid medications for at least 6 months during the past year and at 20-50 MME levels or higher had 4.44 (95% CI: [3.71, 5.32]) to 7.23 (95% CI: [6.22, 8.41]) times the risk of attempted suicide compared to those not prescribed opioids. Risk of suicide attempt was sharply elevated among patients with psychiatric conditions other than anxiety who were prescribed more frequent and higher opioid doses. In contrast, more frequent and higher doses of prescription opioids were associated with lower risk of suicide attempts among patients with medical conditions necessitating pain management. This study is limited by its exclusive focus on commercially insured patients and does not include patients covered by public insurance. It is also limited to patients' receipt of prescription opioids and does not take into account opioids obtained through other means, nor does it include measures of actual patient opioid use. CONCLUSIONS: This analysis provides evidence of a complex relationship among prescription opioids, mental health, pain and other medical comorbidities, and suicide risk. Findings indicate the need for proactive suicide surveillance among individuals diagnosed with affective or psychotic disorders who are receiving frequent and high doses of opioids. However, appropriate opioid treatment may have significant value in reducing suicide risk for those without psychiatric comorbidities.

Dos and Don'ts in Designing School-Based Awareness Programs for Suicide Prevention.

Background: Despite the promising evidence for the effectiveness of school-based awareness programs in decreasing the rates of suicidal thoughts and suicide attempts in young people, no guidelines on the targets and methods of safe and effective awareness programs exist. Aims: This study intends to distill recommendations for school-based suicide awareness and prevention programs from experts. Method: A three-stage Delphi survey was administered to an expert panel between November 2018 and March 2019. A total of 214 items obtained from open-ended questions and the literature were rated in two rounds. Consensus and stability were used as assessment criteria. Results: The panel consisted of 19 participants in the first and 13 in the third stage. Recommended targets included the reduction of suicide attempts, the enhancement of help-seeking and peer support, as well as the promotion of mental health literacy and life skills. Program evaluation, facilitating access to healthcare, and long-term action plans across multiple levels were among the best strategies for the prevention of adverse effects. Limitations: The study is based on opinions of a rather small number of experts. Conclusion: The promotion of help-seeking and peer support as well as facilitating access to mental health-care utilities appear pivotal for the success of school-based awareness programs.

Improving suicide risk prediction via targeted data fusion: proof of concept using medical claims data.

OBJECTIVE: Reducing suicidal behavior among patients in the healthcare system requires accurate and explainable predictive models of suicide risk across diverse healthcare settings. MATERIALS AND METHODS: We proposed a general targeted fusion learning framework that can be used to build a tailored risk prediction model for any specific healthcare setting, drawing on information fusion from a separate more comprehensive dataset with indirect sample linkage through patient similarities. As a proof of concept, we predicted suicide-related hospitalizations for pediatric patients in a limited statewide Hospital Inpatient Discharge Dataset (HIDD) fused with a more comprehensive medical All-Payer Claims Database (APCD) from Connecticut. RESULTS: We built a suicide risk prediction model for the source data (APCD) and calculated patient risk scores. Patient similarity scores between patients in the source and target (HIDD) datasets using their demographic characteristics and diagnosis codes were assessed. A fused risk score was generated for each patient in the target dataset using our proposed targeted fusion framework. With this model, the averaged sensitivities at 90% and 95% specificity improved by 67% and 171%, and the positive predictive values for the combined fusion model improved 64% and 135% compared to the conventional model. DISCUSSION AND CONCLUSIONS: We proposed a general targeted fusion learning framework that can be used to build a tailored predictive model for any specific healthcare setting. Results from this study suggest we can improve the performance of predictive models in specific target settings without complete integration of the raw records from external data sources.

Pursuing sources of heterogeneity in modeling clustered population.

Researchers often have to deal with heterogeneous population with mixed regression relationships, increasingly so in the era of data explosion. In such problems, when there are many candidate predictors, it is not only of interest to identify the predictors that are associated with the outcome, but also to distinguish the true sources of heterogeneity, that is, to identify the predictors that have different effects among the clusters and thus are the true contributors to the formation of the clusters. We clarify the concepts of the source of heterogeneity that account for potential scale differences of the clusters and propose a regularized finite mixture effects regression to achieve heterogeneity pursuit and feature selection simultaneously. We develop an efficient algorithm and show that our approach can achieve both estimation and selection consistency. Simulation studies further demonstrate the effectiveness of our method under various practical scenarios. Three applications are presented, namely, an imaging genetics study for linking genetic factors and brain neuroimaging traits in Alzheimer's disease, a public health study for exploring the association between suicide risk among adolescents and their school district characteristics, and a sport analytics study for understanding how the salary levels of baseball players are associated with their performance and contractual status.

Barriers to Dissemination of Local Health Data Faced by US State Agencies: Survey Study of Behavioral Risk Factor Surveillance System Coordinators.

BACKGROUND: Advances in information technology have paved the way to facilitate accessibility to population-level health data through web-based data query systems (WDQSs). Despite these advances in technology, US state agencies face many challenges related to the dissemination of their local health data. It is essential for the public to have access to high-quality data that are easy to interpret, reliable, and trusted. These challenges have been at the forefront throughout the COVID-19 pandemic. OBJECTIVE: The purpose of this study is to identify the most significant challenges faced by state agencies, from the perspective of the Behavioral Risk Factor Surveillance System (BRFSS) coordinator from each state, and to assess if the coordinators from states with a WDQS perceive these challenges differently. METHODS: We surveyed BRFSS coordinators (N=43) across all 50 US states and the District of Columbia. We surveyed the participants about contextual factors and asked them to rate system aspects and challenges they faced with their health data system on a Likert scale. We used two-sample t tests to compare the means of the ratings by participants from states with and without a WDQS. RESULTS: Overall, 41/43 states (95%) make health data available over the internet, while 65% (28/43) employ a WDQS. States with a WDQS reported greater challenges (P=.01) related to the cost of hardware and software (mean score 3.44/4, 95% CI 3.09-3.78) than states without a WDQS (mean score 2.63/4, 95% CI 2.25-3.00). The system aspect of standardization of vocabulary scored more favorably (P=.01) in states with a WDQS (mean score 3.32/5, 95% CI 2.94-3.69) than in states without a WDQS (mean score 2.85/5, 95% CI 2.47-3.22). CONCLUSIONS: Securing of adequate resources and commitment to standardization are vital in the dissemination of local-level health data. Factors such as receiving data in a timely manner, privacy, and political opposition are less significant barriers than anticipated.

Machine learning for suicide risk prediction in children and adolescents with electronic health records.

Accurate prediction of suicide risk among children and adolescents within an actionable time frame is an important but challenging task. Very few studies have comprehensively considered the clinical risk factors available to produce quantifiable risk scores for estimation of short- and long-term suicide risk for pediatric population. In this paper, we built machine learning models for predicting suicidal behavior among children and adolescents based on their longitudinal clinical records, and determining short- and long-term risk factors. This retrospective study used deidentified structured electronic health records (EHR) from the Connecticut Children's Medical Center covering the period from 1 October 2011 to 30 September 2016. Clinical records of 41,721 young patients (10-18 years old) were included for analysis. Candidate predictors included demographics, diagnosis, laboratory tests, and medications. Different prediction windows ranging from 0 to 365 days were adopted. For each prediction window, candidate predictors were first screened by univariate statistical tests, and then a predictive model was built via a sequential forward feature selection procedure. We grouped the selected predictors and estimated their contributions to risk prediction at different prediction window lengths. The developed predictive models predicted suicidal behavior across all prediction windows with AUCs varying from 0.81 to 0.86. For all prediction windows, the models detected 53-62% of suicide-positive subjects with 90% specificity. The models performed better with shorter prediction windows and predictor importance varied across prediction windows, illustrating short- and long-term risks. Our findings demonstrated that routinely collected EHRs can be used to create accurate predictive models for suicide risk among children and adolescents.

Identifying risk factors for mortality among patients previously hospitalized for a suicide attempt.

Age-adjusted suicide rates in the US have increased over the past two decades across all age groups. The ability to identify risk factors for suicidal behavior is critical to selected and indicated prevention efforts among those at elevated risk of suicide. We used widely available statewide hospitalization data to identify and test the joint predictive power of clinical risk factors associated with death by suicide for patients previously hospitalized for a suicide attempt (N = 19,057). Twenty-eight clinical factors from the prior suicide attempt were found to be significantly associated with the hazard of subsequent suicide mortality. These risk factors and their two-way interactions were used to build a joint predictive model via stepwise regression, in which the predicted individual survival probability was found to be a valid measure of risk for later suicide death. A high-risk group with a four-fold increase in suicide mortality risk was identified based on the out-of-sample predicted survival probabilities. This study demonstrates that the combination of state-level hospital discharge and mortality data can be used to identify suicide attempters who are at high risk of subsequent suicide death.

Efficient interaction selection for clustered data via stagewise generalized estimating equations.

Model selection in the presence of interaction terms is challenging as the final model must maintain a hierarchy between main effects and interaction terms. This work presents two stagewise estimation approaches to appropriately select models with interaction terms that can utilize generalized estimating equations to model clustered data. The first proposed technique is a hierarchical lasso stagewise estimating equations approach, which is shown to directly correspond to the hierarchical lasso penalized regression. The second is a stagewise active set approach, which enforces the variable hierarchy by conforming the selection to a properly growing active set in each stagewise estimation step. The effectiveness in interaction selection and the superior computational efficiency of the proposed techniques are assessed in simulation studies. The new methods are applied to a study of hospitalization rates attributed to suicide attempts among 15 to 19 year old at the school district level in Connecticut.

Suicide prevention skills, confidence and training: Results from the Zero Suicide Workforce Survey of behavioral health care professionals.

INTRODUCTION: In behavioral health care settings, a workforce well trained in suicide prevention is critically important for behavioral health care professionals across different disciplines and service sectors who are likely to have considerable exposure to patients at risk for suicidal behavior. This study examined the types of training behavioral health care professionals received, their self-reported skills, comfort level and confidence related to suicide prevention, the association of types and length of training with skills, comfort level and confidence, and areas in which participants would like more training. METHODS: The Zero Suicide Workforce Survey was administered electronically to behavioral health care professionals at six behavioral health treatment centers with both inpatient and ambulatory programs in Connecticut, USA. Item numbers and percentages were calculated for 847 respondents with behavioral health care roles. The chi-square tests were performed to determine the statistical significance of group differences. Non-parametric sign tests were performed to determine the statistical significance of the collective differences in direction among items between groups. RESULTS: Suicide prevention training is associated with increased levels of behavioral health care professionals' skills and confidence, but one-third of behavioral health care professionals in the sample received no formal training in suicide prevention/intervention. Even brief training appears to have a positive impact on behavioral health care professionals' assessment of their skills and confidence. Prominent topics for additional training include suicide-specific treatment approaches, suicide prevention and awareness, and identification of risk factors and warning signs. CONCLUSION: Although behavioral health care professionals may often encounter patients at risk for suicide, many have not obtained any relevant training. The findings highlight the need to strengthen suicide identification, assessment and treatment within behavioral health care treatment settings as part of an effort to prevent suicide.

Age Differences in Racial/Ethnic Disparities in Preventable Hospitalizations for Heart Failure in Connecticut, 2009-2015: A Population-Based Longitudinal Study.

OBJECTIVE: Preventable hospitalizations for heart failure result in a large proportion of hospitalizations. The primary objective of this study was to describe longitudinal trends in the association of race/ethnicity with preventable hospitalizations for heart failure in Connecticut and differences in disparities by age. METHODS: We analyzed data on hospitalizations in all civilian acute-care hospitals in Connecticut during a 7-year period, 2009 through 2015. We used raking methodology to weight the nonhospitalized population to create a reference population representative of the state's general population. Multivariate regression models examined racial/ethnic disparities among adults aged 35-64, controlling for age, sex, and type of health insurance. For adults aged ≥65, regression models controlled for age and sex. RESULTS: After controlling for age and sex, the non-Hispanic black to non-Hispanic white odds ratio for preventable hospitalizations for heart failure ranged from 5.2-6.4 during the study period among adults aged 35-64. Among adults aged ≥65, non-Hispanic black adults had significantly higher odds (range, 1.2-1.8) of preventable hospitalizations than non-Hispanic white adults. Rates among Hispanic adults were significantly higher than rates among non-Hispanic adults after controlling for age and sex among adults aged ≥65 in 2014 and 2015. CONCLUSIONS: This research provides information for clinical and population-based interventions targeting racial/ethnic gaps in heart failure hospitalizations. Demonstrating the persistent black-white disparity and age differences in racial/ethnic disparities, this study emphasizes the need for focused prevention among vulnerable populations. Raking methodology is an innovative approach to eliminating selection bias in hospital discharge data.

Reductions in Race and Ethnic Disparities in Hospital Readmissions Following Total Joint Arthroplasty from 2005 to 2015.

BACKGROUND: Racial and ethnic disparities in hospital readmissions following total joint arthroplasty present opportunities for reducing cost and improving health equity. Despite efforts to reduce readmissions following total joint arthroplasty in the general population, no studies have documented the impact of these efforts on racial and ethnic disparities in total joint arthroplasty readmissions. The purpose of this study was to determine whether comprehensive efforts to reduce hospital readmissions following total joint arthroplasty have impacted racial and ethnic disparities in readmission rates during the period from 2005 to 2015. METHODS: We conducted a retrospective analysis comparing patients readmitted and not readmitted to the hospital within 30 days of a total joint arthroplasty by estimating logistic regression models for clustered data using generalized estimating equations (GEEs) in R. Connecticut hospital discharge data for patients admitted for International Classification of Diseases, Ninth Revision (ICD-9) procedure codes 81.51 and 81.54 (Current Procedural Terminology [CPT] codes 27130 and 27447) during the 2005 to 2015 U.S. Centers for Medicare & Medicaid Services (CMS) fiscal years were analyzed. Models included quadratic terms to capture nonlinear time trends in readmissions, as well as terms for the statistical interaction between race or ethnicity and both the linear and quadratic time trends in predicting the odds of readmission. RESULTS: There were 102,510 total admissions to Connecticut hospitals for total joint arthroplasty from 2005 to 2015. The 30-day (all-cause) readmission rate declined from 5.1% in 2005 to 3.6% in 2015, with a steeper downward trend observed from 2009 to 2015. The results from logistic models indicated that black patients (odds ratio [OR], 1.68; p < 0.0001) and Hispanic patients (OR, 1.48; p < 0.0001) were significantly more likely to be readmitted within 30 days of discharge following a total joint arthroplasty than white patients over the study period. The significant interaction of black race and the quadratic time trend in models capturing nonlinear trends in readmission over time indicated that the readmission rates for black patients increased compared with those for white patients from 2005 through 2008 and decreased relative to those for white patients from 2009 to 2015 (OR, 0.24; p = 0.030). CONCLUSIONS: Data from Connecticut hospitals show that 30-day readmissions following a total joint arthroplasty declined by 1.5 percentage points from 2005 to 2015, and that this decline was much more pronounced among black patients, resulting in the narrowing of racial disparities in readmission following a surgical procedure. CLINICAL RELEVANCE: Racial and ethnic minorities have historically been at increased risk for complications and readmission following hospital-based surgical care. This analysis of readmission following total joint arthroplasty reveals that such disparities are remediable and should foster further research on the primary drivers of and remedies for readmission disparities.