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.

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.

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.

Stagewise generalized estimating equations with grouped variables.

Forward stagewise estimation is a revived slow-brewing approach for model building that is particularly attractive in dealing with complex data structures for both its computational efficiency and its intrinsic connections with penalized estimation. Under the framework of generalized estimating equations, we study general stagewise estimation approaches that can handle clustered data and non-Gaussian/non-linear models in the presence of prior variable grouping structure. As the grouping structure is often not ideal in that even the important groups may contain irrelevant variables, the key is to simultaneously conduct group selection and within-group variable selection, that is, bi-level selection. We propose two approaches to address the challenge. The first is a bi-level stagewise estimating equations (BiSEE) approach, which is shown to correspond to the sparse group lasso penalized regression. The second is a hierarchical stagewise estimating equations (HiSEE) approach to handle more general hierarchical grouping structure, in which each stagewise estimation step itself is executed as a hierarchical selection process based on the grouping structure. Simulation studies show that BiSEE and HiSEE yield competitive model selection and predictive performance compared to existing approaches. We apply the proposed approaches to study the association between the suicide-related hospitalization rates of the 15-19 age group and the characteristics of the school districts in the State of Connecticut.

A Review of Statistical Disclosure Control Techniques Employed by Web-Based Data Query Systems.

We systematically reviewed the statistical disclosure control techniques employed for releasing aggregate data in Web-based data query systems listed in the National Association for Public Health Statistics and Information Systems (NAPHSIS). Each Web-based data query system was examined to see whether (1) it employed any type of cell suppression, (2) it used secondary cell suppression, and (3) suppressed cell counts could be calculated. No more than 30 minutes was spent on each system. Of the 35 systems reviewed, no suppression was observed in more than half (n = 18); observed counts below the threshold were observed in 2 sites; and suppressed values were recoverable in 9 sites. Six sites effectively suppressed small counts. This inquiry has revealed substantial weaknesses in the protective measures used in data query systems containing sensitive public health data. Many systems utilized no disclosure control whatsoever, and the vast majority of those that did deployed it inconsistently or inadequately.

Electronic Consultations to Improve the Primary Care-Specialty Care Interface for Cardiology in the Medically Underserved: A Cluster-Randomized Controlled Trial.

PURPOSE: Communication between specialists and primary care clinicians is suboptimal, and access to referrals is often limited, which can lead to lower quality, inefficiency, and errors. An electronic consultation (e-consultation) is an asynchronous, non-face-to-face consultation between a primary care clinician and a specialist using a secure electronic communication platform. The purpose of this study was to conduct a randomized controlled trial of e-consultations to test its efficacy and effectiveness in reducing wait times and improving access to specialty care. METHODS: Primary care clinicians were randomized into a control (9 traditional) or an intervention (17 e-consultation) arm for referrals to cardiologists. Primary care clinicians were recruited from 12 practice sites in a community health center in Connecticut with mainly medically underserved patients. Two end points were analyzed with a Cox proportional hazards model where the hazard of either a visit or an e-consultation was linked to study arm, sex, race, and age. RESULTS: Thirty-six primary care clinicians participated in the study, referring 590 patients. In total, 69% of e-consultations were resolved without a visit to a cardiologist. After adjusting for covariates, median days to a review for an electronic consultation vs a visit for control patients were 5 and 24, respectively. A review of 6-month follow-up data found fewer cardiac-related emergency department visits for the intervention group. CONCLUSION: E-consultation referrals improved access to and timeliness of care for an underserved population, reduced overall specialty utilization, and streamlined specialty referrals without any increase in adverse cardiovascular outcomes. e-consultations are a potential solution for improving access to specialty care.

The SOS Suicide Prevention Program: Further Evidence of Efficacy and Effectiveness.

This study replicated and extended previous evaluations of the Signs of Suicide (SOS) prevention program in a high school population using a more rigorous pre-test post-test randomized control design than used in previous SOS evaluations in high schools (Aseltine and DeMartino 2004; Aseltine et al. 2007). SOS was presented to an ethnically diverse group of ninth grade students in technical high schools in Connecticut. After controlling for the pre-test reports of suicide behaviors, exposure to the SOS program was associated with significantly fewer self-reported suicide attempts in the 3 months following the program. Ninth grade students in the intervention group were approximately 64% less likely to report a suicide attempt in the past 3 months compared with students in the control group. Similarly, exposure to the SOS program resulted in greater knowledge of depression and suicide and more favorable attitudes toward (1) intervening with friends who may be exhibiting signs of suicidal intent and (2) getting help for themselves if they were depressed or suicidal. In addition, high-risk SOS participants, defined as those with a lifetime history of suicide attempt, were significantly less likely to report planning a suicide in the 3 months following the program compared to lower-risk participants. Differential attrition is the most serious limitation of the study; participants in the intervention group who reported a suicide attempt in the previous 3 months at baseline were more likely to be missing at post-test than their counterparts in the control group.

Connecticut Hospital Readmissions Related to Chest Pain and Heart Failure: Differences by Race, Ethnicity, and Payer.

BACKGROUND: Racial and ethnic disparities in hospital readmissions for several major illnesses and conditions are well-documented. However, due to the data typically used to assess readmission disparities little is known regarding the interplay between race/ethnicity and payer in fostering readmissions. This study used a statewide database of acute-care hospital admissions to examine 30-day readmission rates following hospitalization for chest pain and heart failure byrace/ethnicity and insurance status. METHODS: Connecticut hospital discharge data for patients admitted for Chest Pain-DRG 313 (n = 23,450) and Heart Failure and Shock-DRG 291 and 292 (n = 39,985) from 2008 - 2012 were analyzed using marginal logistic models for clustered data with generalized estimating equations. RESULTS: Results from logistic models indicated that Black patients were significantly more likely to be readmitted within 30 days of discharge following hospitalization for chest pain (OR = 1.19, CI = 1.04, 1.37) than were White patients. Hispanics, but not Blacks, were significantly more likely to be readmitted within 30 days of discharge following hospitalization for heart failure (OR = 1.30, CI = 1.15, 1.47). Rates of 30-day readmission across these conditions were between 50-100% higher among those covered by Medicaid compared to those covered by private payer. Controlling for patient socioeconomic status, patient comorbidities, and payer substantially reduced Black/White differences in the odds of readmission for chest pain but did not reduce Hispanic-White differences for heart failure. CONCLUSIONS: Racial and ethnic disparities were seen in hospital readmission rates for Chest Pain (DRG 313) and Heart Failure and Shock (DRG 291 and 292) when a statewide database that captures all acute care hospital admissions was analyzed. When controlling for patient socioeconomic status, comorbidities, and payer status, the difference in the odds of readmission for chest pain, but not heart failure, was reduced.

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.

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.

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.

Accuracy and transportability of machine learning models for adolescent suicide prediction with longitudinal clinical records.

Machine Learning models trained from real-world data have demonstrated promise in predicting suicide attempts in adolescents. However, their transportability, namely the performance of a model trained on one dataset and applied to different data, is largely unknown, hindering the clinical adoption of these models. Here we developed different machine learning-based suicide prediction models based on real-world data collected in different contexts (inpatient, outpatient, and all encounters) with varying purposes (administrative claims and electronic health records), and compared their cross-data performance. The three datasets used were the All-Payer Claims Database in Connecticut, the Hospital Inpatient Discharge Database in Connecticut, and the Electronic Health Records data provided by the Kansas Health Information Network. We included 285,320 patients among whom we identified 3389 (1.2%) suicide attempters and 66% of the suicide attempters were female. Different machine learning models were evaluated on source datasets where models were trained and then applied to target datasets. More complex models, particularly deep long short-term memory neural network models, did not outperform simpler regularized logistic regression models in terms of both local and transported performance. Transported models exhibited varying performance, showing drops or even improvements compared to their source performance. While they can achieve satisfactory transported performance, they are usually upper-bounded by the best performance of locally developed models, and they can identify additional new cases in target data. Our study uncovers complex transportability patterns and could facilitate the development of suicide prediction models with better performance and generalizability.

Assessing intervention efficacy on high-risk drinkers using generalized linear mixed models with a new class of link functions.

Unhealthy alcohol use is one of the leading causes of morbidity and mortality in the United States. Brief interventions with high-risk drinkers during an emergency department (ED) visit are of great interest due to their possible efficacy and low cost. In a collaborative study with patients recruited at 14 academic ED across the United States, we examined the self-reported number of drinks per week by each patient following the exposure to a brief intervention. Count data with overdispersion have been mostly analyzed with generalized linear mixed models (GLMMs), of which only a limited number of link functions are available. Different choices of link function provide different fit and predictive power for a particular dataset. We propose a class of link functions from an alternative way to incorporate random effects in a GLMM, which encompasses many existing link functions as special cases. The methodology is naturally implemented in a Bayesian framework, with competing links selected with Bayesian model selection criteria such as the conditional predictive ordinate (CPO). In application to the ED intervention study, all models suggest that the intervention was effective in reducing the number of drinks, but some new models are found to significantly outperform the traditional model as measured by CPO. The validity of CPO in link selection is confirmed in a simulation study that shared the same characteristics as the count data from high-risk drinkers. The dataset and the source code for the best fitting model are available in Supporting Information.

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.

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.

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.

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.

Efficient algorithms for fast integration on large data sets from multiple sources.

BACKGROUND: Recent large scale deployments of health information technology have created opportunities for the integration of patient medical records with disparate public health, human service, and educational databases to provide comprehensive information related to health and development. Data integration techniques, which identify records belonging to the same individual that reside in multiple data sets, are essential to these efforts. Several algorithms have been proposed in the literatures that are adept in integrating records from two different datasets. Our algorithms are aimed at integrating multiple (in particular more than two) datasets efficiently. METHODS: Hierarchical clustering based solutions are used to integrate multiple (in particular more than two) datasets. Edit distance is used as the basic distance calculation, while distance calculation of common input errors is also studied. Several techniques have been applied to improve the algorithms in terms of both time and space: 1) Partial Construction of the Dendrogram (PCD) that ignores the level above the threshold; 2) Ignoring the Dendrogram Structure (IDS); 3) Faster Computation of the Edit Distance (FCED) that predicts the distance with the threshold by upper bounds on edit distance; and 4) A pre-processing blocking phase that limits dynamic computation within each block. RESULTS: We have experimentally validated our algorithms on large simulated as well as real data. Accuracy and completeness are defined stringently to show the performance of our algorithms. In addition, we employ a four-category analysis. Comparison with FEBRL shows the robustness of our approach. CONCLUSIONS: In the experiments we conducted, the accuracy we observed exceeded 90% for the simulated data in most cases. 97.7% and 98.1% accuracy were achieved for the constant and proportional threshold, respectively, in a real dataset of 1,083,878 records.