Initial Validation of a Computerized Adaptive Test for Substance Use Disorder Identification in Adolescents.

PURPOSE: Computerized adaptive tests (CATs) are highly efficient assessment tools that couple low patient and clinician time burden with high diagnostic accuracy. A CAT for substance use disorders (CAT-SUD-E) has been validated in adult populations but has yet to be tested in adolescents. The purpose of this study was to perform initial evaluation of the K-CAT-SUD-E (i.e., Kiddy-CAT-SUD-E) in an adolescent sample compared to a gold-standard diagnostic interview. METHODS: Adolescents (N = 156; aged 11-17) with diverse substance use histories completed the K-CAT-SUD-E electronically and the substance related disorders portion of a clinician-conducted diagnostic interview (K-SADS) via tele-videoconferencing platform. The K-CAT-SUD-E assessed both current and lifetime overall SUD and substance-specific diagnoses for nine substance classes. RESULTS: Using the K-CAT-SUD-E continuous severity score and diagnoses to predict the presence of any K-SADS SUD diagnosis, the classification accuracy ranged from excellent for current SUD (AUC = 0.89, 95% CI = 0.81, 0.95) to outstanding (AUC = 0.93, 95% CI = 0.82, 0.97) for lifetime SUD. Regarding current substance-specific diagnoses, the classification accuracy was excellent for alcohol (AUC = 0.82), cannabis (AUC = 0.83) and nicotine/tobacco (AUC = 0.90). For lifetime substance-specific diagnoses, the classification accuracy ranged from excellent (e.g., opioids, AUC = 0.84) to outstanding (e.g., stimulants, AUC = 0.96). K-CAT-SUD-E median completion time was 4 min 22 s compared to 45 min for the K-SADS. CONCLUSIONS: This study provides initial support for the K-CAT-SUD-E as a feasible accurate diagnostic tool for assessing SUDs in adolescents. Future studies should further validate the K-CAT-SUD-E in a larger sample of adolescents and examine its acceptability, feasibility, and scalability in youth-serving settings.

Pragmatic Clinical Trial of Population Health, Portal-Based Depression Screening: the PORTAL-Depression Study.

BACKGROUND: A population health approach to depression screening using patient portals may be a promising strategy to proactively engage and identify patients with depression. OBJECTIVE: To determine whether a population health approach to depression screening is more effective than screening during clinic appointments alone for identifying patients with depression. DESIGN: A pragmatic clinical trial at an adult outpatient internal medicine clinic at an urban, academic, tertiary care center. PATIENTS: Eligible patients (n = 2713) were adults due for depression screening with active portal accounts. Patients with documented depression or bipolar disorder and those who had been screened in the year prior to the study were excluded. INTERVENTION: Patients were randomly assigned to usual (n = 1372) or population healthcare (n = 1341). For usual care, patients were screened by medical assistants during clinic appointments. Population healthcare patients were sent letters through the portal inviting them to fill out an online screener regardless of whether they had a scheduled appointment. The same screening tool, the Computerized Adaptive Test for Mental Health (CAT-MH™), was used for clinic- and portal-based screening. MAIN MEASURES: The primary outcome was the depression screening rate. KEY RESULTS: The depression screening rate in the population healthcare arm was higher than that in the usual care arm (43% (n = 578) vs. 33% (n = 459), p < 0.0001). The rate of positive screens was also higher in the population healthcare arm compared to that in the usual care (10% (n = 58) vs. 4% (n = 17), p < 0.001). CONCLUSION: Findings suggest depression screening via a portal as part of a population health approach can increase screening and case identification, compared to usual care. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03832283.

Radiological risk assessment of the Hunters Point Naval Shipyard (HPNS).

Hunters Point Naval Shipyard in San Francisco, California was deemed a Superfund site by the USEPA in 1989 due to chemical and radiological contamination resulting from U.S. Navy operations from 1939 to 1974. During characterization and remediation efforts, over 50,000 radiological soil samples and 19,000 air samples were collected. This risk assessment, conducted in accordance with federal guidelines, represents the first comprehensive evaluation of past, present, and future health risks associated with radionuclides present at the site. The assessment indicated that before site remediation, most radionuclide soil concentrations were at or near local background concentrations. Had such low remedial goals not been established, significant remediation of surface soils would not have been necessary to protect human health. The pre-remediation lifetime incremental cancer morbidity risks for on-site workers and theoretical on-site residents due to radionuclide contamination were found to be 1.3 × 10-6 and 3.2 × 10-6, respectively. The post-remediation risks to future on-site residents were found to be 6.3 × 10-8 (without durable cover) and 3.7 × 10-8 (with durable cover), while post-remediation risks to on-site workers were found to be 2.6 × 10-8 (without durable cover) and 1.6 × 10-8 (with durable cover). Risk estimates for all scenarios were found to be significantly below the acceptable risk of 3 × 10-4 approved by regulatory agencies. Upwind and downwind air samples collected during remediation indicate that remediation activities never posed a measurable risk to off-site residents. This risk assessment emphasizes the importance of establishing clear and scientifically rigorous soil remedial goals at sites as well as understanding local radionuclide background concentrations.

Preoperative Assessment of Anxiety and Depression Using Computerized Adaptive Screening Tools: A Pilot Prospective Cohort Study.

BACKGROUND: Serious mental health conditions (eg, anxiety and depression) are common in surgical patients, yet likely underassessed due to the time-consuming and cumbersome traditional screening process. A recently developed computerized adaptive mental health assessment tool (computerized adaptive test-mental health [CAT-MH]) allows rapid, precise, and accurate assessment of numerous mental health disorders, including anxiety and depression, without the need for a trained interviewer. The goal of this investigation was to determine the feasibility of administering CAT-MH for anxiety and depression in the preoperative setting and to obtain preliminary evidence of the prevalence of anxiety and depression in preoperative patients. METHODS: In this prospective cohort study, 100 adult patients scheduled for elective surgery were enrolled and asked to complete the CAT-MH in the preoperative clinic. Urgent and emergency surgeries were excluded as were pregnant patients. Primary feasibility outcomes were completion rate and time to completion. Secondary outcomes were prevalence estimates of anxiety and depression. RESULTS: All 100 enrolled patients completed the study. All patients were able to complete the mental health assessment (mean time: 3.6 ± 1.8 minutes standard deviation). Sixteen patients (16%) screened positive for anxiety (severity: mild, n = 7 [7%]; moderate, n = 7 [7%]); severe, n = 2 [2%]); 12 of 16 (75%) did not have a previous diagnosis of anxiety disorder. Twenty-eight (28%) patients screened positive for depression (severity: mild, n = 26 [26%]; moderate and severe, n = 1 each [1%]); 23 of 28 (82%) had no previous diagnosis of depressive disorder. Nineteen patients (19%) met the screening criteria for major depressive disorder; 14 of 19 (74%) of which had no previous diagnosis of major depressive disorder. CONCLUSIONS: The results of this pilot study support the feasibility of using CAT-MH in a preoperative evaluation and indicate that there is a substantial prevalence of undiagnosed anxiety and depression in surgical patients.

Development and External Validation of a Machine Learning Model for Prediction of Potential Transfer to the PICU.

OBJECTIVES: Unrecognized clinical deterioration during illness requiring hospitalization is associated with high risk of mortality and long-term morbidity among children. Our objective was to develop and externally validate machine learning algorithms using electronic health records for identifying ICU transfer within 12 hours indicative of a child's condition. DESIGN: Observational cohort study. SETTING: Two urban, tertiary-care, academic hospitals (sites 1 and 2). PATIENTS: Pediatric inpatients (age <18 yr). INTERVENTIONS: None. MEASUREMENT AND MAIN RESULTS: Our primary outcome was direct ward to ICU transfer. Using age, vital signs, and laboratory results, we derived logistic regression with regularization, restricted cubic spline regression, random forest, and gradient boosted machine learning models. Among 50,830 admissions at site 1 and 88,970 admissions at site 2, 1,993 (3.92%) and 2,317 (2.60%) experienced the primary outcome, respectively. Site 1 data were split longitudinally into derivation (2009-2017) and validation (2018-2019), whereas site 2 constituted the external test cohort. Across both sites, the gradient boosted machine was the most accurate model and outperformed a modified version of the Bedside Pediatric Early Warning Score that only used physiologic variables in terms of discrimination ( C -statistic site 1: 0.84 vs 0.71, p < 0.001; site 2: 0.80 vs 0.74, p < 0.001), sensitivity, specificity, and number needed to alert. CONCLUSIONS: We developed and externally validated a novel machine learning model that identifies ICU transfers in hospitalized children more accurately than current tools. Our model enables early detection of children at risk for deterioration, thereby creating opportunities for intervention and improvement in outcomes.

Awareness of illness moderates self-assessment of psychotic symptoms.

OBJECTIVE: Self-reports or patient-reported outcome measures are seldom used in psychosis due to concerns about the ability of patients to accurately report their symptomatology, particularly in cases of low awareness of illness. The aim of this study was to assess the effect of insight on the accuracy of self-reported psychotic symptoms using a computerized adaptive testing tool (CAT-Psychosis). METHODS: A secondary analysis of data drawn from the CAT-Psychosis development and validation study was performed. The Brief Psychiatric Rating Scale and the Scale of Unawareness of Mental Disorders were administered by clinicians. Patients completed the self-reported version of the CAT-Psychosis. Patients were median-split regarding their insight level to compare the correlation between the two psychosis severity measures. A subgroup sensitivity analysis was performed only on patients with schizophrenia spectrum disorders. RESULTS: A total of 159 patients with a psychotic disorder who completed both CAT-Psychosis and Scale of Unawareness of Mental Disorders were included. For the whole sample, CAT-Psychosis scores showed convergent validity with Brief Psychiatric Rating Scale ratings (r = 0.517, 95% confidence interval = [0.392, 0.622], p < 0.001). Insight was found to moderate this correlation (ß = -0.511, p = 0.005), yet agreement between both measures remained statistically significant for both high (r = 0.621, 95% confidence interval = [0.476, 0.733], p < 0.001) and low insight patients (r = 0.408, 95% confidence interval = [0.187, 0.589], p < 0.001), while psychosis severity was comparable between these groups (for Brief Psychiatric Rating Scale: U = 3057, z = -0.129, p = 0.897; disorganization: U = 2986.5, z = -0.274, p = 0.784 and for CAT-Psychosis: U = 2800.5, z = -1.022, p = 0.307). Subgroup of patients with schizophrenia spectrum disorders showed very similar results. CONCLUSIONS: Insight moderates the correlation between self-reported and clinician-rated severity of psychosis, yet CAT-Psychosis remains valid in patients with both high and low awareness of illness.

Accounting for Context in Randomized Trials after Assignment.

Many preventive trials randomize individuals to intervention condition which is then delivered in a group setting. Other trials randomize higher levels, say organizations, and then use learning collaboratives comprised of multiple organizations to support improved implementation or sustainment. Other trials randomize or expand existing social networks and use key opinion leaders to deliver interventions through these networks. We use the term contextually driven to refer generally to such trials (traditionally referred to as clustering, where groups are formed either pre-randomization or post-randomization - i.e., a cluster-randomized trial), as these groupings or networks provide fixed or time-varying contexts that matter both theoretically and practically in the delivery of interventions. While such contextually driven trials can provide efficient and effective ways to deliver and evaluate prevention programs, they all require analytical procedures that take appropriate account of non-independence, something not always appreciated. Published analyses of many prevention trials have failed to take this into account. We discuss different types of contextually driven designs and then show that even small amounts of non-independence can inflate actual Type I error rates. This inflation leads to rejecting the null hypotheses too often, and erroneously leading us to conclude that there are significant differences between interventions when they do not exist. We describe a procedure to account for non-independence in the important case of a two-arm trial that randomizes units of individuals or organizations in both arms and then provides the active treatment in one arm through groups formed after assignment. We provide sample code in multiple programming languages to guide the analyst, distinguish diverse contextually driven designs, and summarize implications for multiple audiences.

Between-center variation in high-priority listing status under the new heart allocation policy.

Under the new US heart allocation policy, transplant centers listed significantly more candidates at high priority statuses (Status 1 and 2) with mechanical circulatory support devices than expected. We determined whether the practice change was widespread or concentrated among certain transplant centers. Using data from the Scientific Registry of Transplant Recipients, we used mixed-effect logistic regression to compare the observed listings of adult, heart-alone transplant candidates post-policy (December 2018 to February 2020) to seasonally matched pre-policy cohort (December 2016 to February 2018). US transplant centers (N = 96) listed similar number of candidates in each policy period (4472 vs. 4498) but listed significantly more at high priority status (25.5% vs. 7.0%, p < .001) than expected. Adjusted for candidate characteristics, 91 of 96 (94.8%) centers listed significantly more candidates at high-priority status than expected, with the unexpected increase varying from 4.8% to 50.4% (interquartile range [IQR]: 14.0%-23.3%). Centers in OPOs with highest Status 1A transplant rate pre-policy were significantly more likely to utilize high-priority status under the new policy (OR: 9.73, p = .01). The new heart allocation policy was associated with widespread and significantly variable changes in transplant center practice that may undermine the effectiveness of the new system.

Associating Psychotic Symptoms with Altered Brain Anatomy in Psychotic Disorders Using Multidimensional Item Response Theory Models.

Reduced cortical thickness has been demonstrated in psychotic disorders, but its relationship to clinical symptoms has not been established. We aimed to identify the regions throughout neocortex where clinical psychosis manifestations correlate with cortical thickness. Rather than perform a traditional correlation analysis using total scores on psychiatric rating scales, we applied multidimensional item response theory to identify a profile of psychotic symptoms that was related to a region where cortical thickness was reduced. This analysis was performed using a large population of probands with psychotic disorders (N = 865), their family members (N = 678) and healthy volunteers (N = 347), from the 5-site Bipolar-Schizophrenia Network for Intermediate Phenotypes. Regional cortical thickness from structural magnetic resonance scans was measured using FreeSurfer; individual symptoms were rated using the Positive and Negative Syndrome Scale, Montgomery-Asberg Depression Rating Scale, and Young Mania Rating Scale. A cluster of cortical regions whose thickness was inversely related to severity of psychosis symptoms was identified. The regions turned out to be located contiguously in a large region of heteromodal association cortex including temporal, parietal and frontal lobe regions, suggesting a cluster of contiguous neocortical regions important to psychosis expression. When we tested the relationship between reduced cortical surface area and high psychotic symptoms we found no linked regions describing a related cortical set.

Depression and anxiety symptoms across pregnancy and the postpartum in low-income Black and Latina women.

Underserved women of color experience high rates of perinatal affective disorders, but most research to date on the natural history of these disorders has been conducted on White women. The present study investigated longitudinal changes in anxiety and depression in a sample of perinatal non-Hispanic Black and Latina women. Categorical (yes/no) measures of positive anxiety and depression screens, as well as total symptom scores, were measured longitudinally across the perinatal period in 178 women (115 non-Hispanic Black, 63 Latina) using the CAT-MH™, a computerized adaptive test. Time (up to 4 visits) and race/ethnicity effects were assessed in linear mixed effects models. Rates of positive anxiety screenings were 13.6%, 3.2%, 8.5%, and 0% in Latina women and 2.6%, 4.2%, 6.1%, and 5.8% in non-Hispanic Black women in the 1st, 2nd, and 3rd trimesters, and postpartum, respectively. Rates of positive anxiety screenings overall were highest in the first trimester (OR = 0.20; 95% CI 0.04-0.98), and there was a significant time-by-race/ethnicity interaction for positive anxiety screens (OR = 8.88; 95% CI 1.42-55.51), as positive screens were most frequent in the first trimester and sharply declined for Latina women, while rates were relatively consistent across the perinatal period in non-Hispanic Black women. Rates of positive depression screens did not change over time, but there was a trend (OR = 1.93; 95% CI 0.93-4.03) for a time-by-race/ethnicity interaction in a direction similar to that seen for anxiety. The odds of positive anxiety screens vary by race/ethnicity and trimester, suggesting that anxiety screening and anxiety interventions may be most resourcefully used in the first trimester for Latina women in particular.

A Vital Sign-Based Model to Predict Clinical Deterioration in Hospitalized Children.

OBJECTIVES: Clinical deterioration in hospitalized children is associated with increased risk of mortality and morbidity. A prediction model capable of accurate and early identification of pediatric patients at risk of deterioration can facilitate timely assessment and intervention, potentially improving survival and long-term outcomes. The objective of this study was to develop a model utilizing vital signs from electronic health record data for predicting clinical deterioration in pediatric ward patients. DESIGN: Observational cohort study. SETTING: An urban, tertiary-care medical center. PATIENTS: Patients less than 18 years admitted to the general ward during years 2009-2018. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The primary outcome of clinical deterioration was defined as a direct ward-to-ICU transfer. A discrete-time logistic regression model utilizing six vital signs along with patient characteristics was developed to predict ICU transfers several hours in advance. Among 31,899 pediatric admissions, 1,375 (3.7%) experienced the outcome. Data were split into independent derivation (yr 2009-2014) and prospective validation (yr 2015-2018) cohorts. In the prospective validation cohort, the vital sign model significantly outperformed a modified version of the Bedside Pediatric Early Warning System score in predicting ICU transfers 12 hours prior to the event (C-statistic 0.78 vs 0.72; p < 0.01). CONCLUSIONS: We developed a model utilizing six commonly used vital signs to predict risk of deterioration in hospitalized children. Our model demonstrated greater accuracy in predicting ICU transfers than the modified Bedside Pediatric Early Warning System. Our model may promote opportunities for timelier intervention and risk mitigation, thereby decreasing preventable death and improving long-term health.

Feasibility of perinatal mood screening and text messaging on patients' personal smartphones.

Screens and adjunctive treatments for perinatal mood are available, but barriers prevent many women from receiving them. Mobile technology may help bypass barriers. The purpose of this study was to evaluate the feasibility of screening and texting perinatal women via their personal smartphones. This prospective cohort study enrolled 203 pregnant and postpartum women receiving obstetric care at a Midwestern US academic medical center. Participants received one electronic mood screen and three text messages per week for two weeks. Texts were based on the Mothers and Babies Course, a CBT-based preventative program that addresses limited social support, lack of pleasant activities, and harmful thought patterns. Feasibility was defined as the ability to take the mood screen and receive texts without technical difficulties. Demographic variables were paired with results. Insurance type (private or public) was used as a proxy for socioeconomic status. Pearson chi-squared tests were used to analyze the data. A text-based satisfaction survey was also administered. The sample was 72% privately insured and 28% publicly insured. Sixty-seven percent completed electronic screening. Screen completion was significantly associated with private insurance (OR = 3.8, 95% CI 2.00-7.30) and "married" status (OR = 1.93, 95% CI 1.01-3.70). Most survey respondents (92%) found it easy to receive the texts, and 76% responded with very favorable comments about the texts. Smartphone mood screening and supportive texting were technically feasible. Screen completion was lower among single women with public insurance.

Imputing Gene Expression in Uncollected Tissues Within and Beyond GTEx.

Gene expression and its regulation can vary substantially across tissue types. In order to generate knowledge about gene expression in human tissues, the Genotype-Tissue Expression (GTEx) program has collected transcriptome data in a wide variety of tissue types from post-mortem donors. However, many tissue types are difficult to access and are not collected in every GTEx individual. Furthermore, in non-GTEx studies, the accessibility of certain tissue types greatly limits the feasibility and scale of studies of multi-tissue expression. In this work, we developed multi-tissue imputation methods to impute gene expression in uncollected or inaccessible tissues. Via simulation studies, we showed that the proposed methods outperform existing imputation methods in multi-tissue expression imputation and that incorporating imputed expression data can improve power to detect phenotype-expression correlations. By analyzing data from nine selected tissue types in the GTEx pilot project, we demonstrated that harnessing expression quantitative trait loci (eQTLs) and tissue-tissue expression-level correlations can aid imputation of transcriptome data from uncollected GTEx tissues. More importantly, we showed that by using GTEx data as a reference, one can impute expression levels in inaccessible tissues in non-GTEx expression studies.

Validation of the Computerized Adaptive Test for Mental Health in Primary Care.

PURPOSE: The US Preventive Services Task Force recommends screening for depression in the general adult population. Although screening questionnaires for depression and anxiety exist in primary care settings, electronic health tools such as computerized adaptive tests based on item response theory can advance screening practices. This study evaluated the validity of the Computerized Adaptive Test for Mental Health (CAT-MH) for screening for major depressive disorder (MDD) and assessing MDD and anxiety severity among adult primary care patients. METHODS: We approached 402 English-speaking adults for participation from a primary care clinic, of whom 271 adults (71% female, 65% black) participated. Participants completed modules from the CAT-MH (Computerized Adaptive Diagnostic Test for MDD, CAT-Depression Inventory, CAT-Anxiety Inventory); brief paper questionnaires (9-item Patient Health Questionnaire [PHQ-9], 2-item Patient Health Questionnaire [PHQ-2], Generalized Anxiety Disorder 7-item Scale [GAD-7]); and a reference-standard interview, the Structured Clinical Interview for DSM-5 (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition) Diagnoses. RESULTS: On the basis of the interview, 31 participants met criteria for MDD and 29 met criteria for GAD. The diagnostic accuracy of the Computerized Adaptive Diagnostic Test for MDD (area under curve [AUC] = 0.85) was similar to that of the PHQ-9 (AUC = 0.84) and higher than that of the PHQ-2 (AUC = 0.76) for MDD screening. Using the interview as the reference standard, the accuracy of the CAT-Anxiety Inventory (AUC = 0.93) was similar to that of the GAD-7 (AUC = 0.97) for assessing anxiety severity. The patient-preferred screening method was assessment via tablet/computer with audio. CONCLUSIONS: Computerized adaptive testing could be a valid and efficient patient-centered screening strategy for depression and anxiety screening in primary care settings.

Without Wasting a Word: Extreme Improvements in Efficiency and Accuracy Using Computerized Adaptive Testing for Mental Health Disorders (CAT-MH).

PURPOSE OF REVIEW: We review recent literature on the adaptive assessment of complex mental health disorders and provide a detailed comparison of classical test theory and adaptive testing based on multidimensional item response theory. RECENT FINDINGS: Adaptive tests for a wide variety of mental health traits (e.g., depression, anxiety, mania, substance misuse, suicidality) are now available in a cloud-based environment. These tests have been validated in a variety of settings against lengthy structured clinical interviews with excellent results and even higher reliability than fixed-length tests. Applications include screening and assessments in emergency departments, psychiatric and primary care clinics, student health clinics, perinatal medicine clinics, child welfare settings, and the judicial system. The future of mental health measurement will be based on automated screening and assessments. Adaptive tests will provide increased precision of measurement and decreased burden of measurement. Integration into the electronic health record is important and now easily accomplished.

Association of Transplant Center With Survival Benefit Among Adults Undergoing Heart Transplant in the United States.

Importance: In the United States, the number of deceased donor hearts available for transplant is limited. As a proxy for medical urgency, the US heart allocation system ranks heart transplant candidates largely according to the supportive therapy prescribed by transplant centers. Objective: To determine if there is a significant association between transplant center and survival benefit in the US heart allocation system. Design, Setting, and Participants: Observational study of 29 199 adult candidates for heart transplant listed on the national transplant registry from January 2006 through December 2015 with follow-up complete through August 2018. Exposures: Transplant center. Main Outcomes and Measures: The survival benefit associated with heart transplant as defined by the difference between survival after heart transplant and waiting list survival without transplant at 5 years. Each transplant center's mean survival benefit was estimated using a mixed-effects proportional hazards model with transplant as a time-dependent covariate, adjusted for year of transplant, donor quality, ischemic time, and candidate status. Results: Of 29 199 candidates (mean age, 52 years; 26% women) on the transplant waiting list at 113 centers, 19 815 (68%) underwent heart transplant. Among heart transplant recipients, 5389 (27%) died or underwent another transplant operation during the study period. Of the 9384 candidates who did not undergo heart transplant, 5669 (60%) died (2644 while on the waiting list and 3025 after being delisted). Estimated 5-year survival was 77% (interquartile range [IQR], 74% to 80%) among transplant recipients and 33% (IQR, 17% to 51%) among those who did not undergo heart transplant, which is a survival benefit of 44% (IQR, 27% to 59%). Survival benefit ranged from 30% to 55% across centers and 31 centers (27%) had significantly higher survival benefit than the mean and 30 centers (27%) had significantly lower survival benefit than the mean. Compared with low survival benefit centers, high survival benefit centers performed heart transplant for patients with lower estimated expected waiting list survival without transplant (29% at high survival benefit centers vs 39% at low survival benefit centers; survival difference, -10% [95% CI, -12% to -8.1%]), although the adjusted 5-year survival after transplant was not significantly different between high and low survival benefit centers (77.6% vs 77.1%, respectively; survival difference, 0.5% [95% CI, -1.3% to 2.3%]). Overall, for every 10% decrease in estimated transplant candidate waiting list survival at a given center, there was an increase of 6.2% (95% CI, 5.2% to 7.3%) in the 5-year survival benefit associated with heart transplant. Conclusions and Relevance: In this registry-based study of US heart transplant candidates, transplant center was associated with the survival benefit of transplant. Although the adjusted 5-year survival after transplant was not significantly different between high and low survival benefit centers, compared with centers with survival benefit significantly below the mean, centers with survival benefit significantly above the mean performed heart transplant for recipients who had significantly lower estimated expected 5-year waiting list survival without transplant.

A note on marginalization of regression parameters from mixed models of binary outcomes.

This article discusses marginalization of the regression parameters in mixed models for correlated binary outcomes. As is well known, the regression parameters in such models have the "subject-specific" (SS) or conditional interpretation, in contrast to the "population-averaged" (PA) or marginal estimates that represent the unconditional covariate effects. We describe an approach using numerical quadrature to obtain PA estimates from their SS counterparts in models with multiple random effects. Standard errors for the PA estimates are derived using the delta method. We illustrate our proposed method using data from a smoking cessation study in which a binary outcome (smoking, Y/N) was measured longitudinally. We compare our estimates to those obtained using GEE and marginalized multilevel models, and present results from a simulation study.

Association between semi-quantitative microbial load and respiratory symptoms among Thai military recruits: a prospective cohort study.

BACKGROUND: Multiplex real-time polymerase chain reaction assays have improved diagnostic sensitivity for a wide range of pathogens. However, co-detection of multiple agents and bacterial colonization make it difficult to distinguish between asymptomatic infection or illness aetiology. We assessed whether semi-quantitative microbial load data can differentiate between symptomatic and asymptomatic states for common respiratory pathogens. METHODS: We obtained throat and nasal swab samples from military trainees at two Thai Army barracks. Specimens were collected at the start and end of 10-week training periods (non-acute samples), and from individuals who developed upper respiratory tract infection during training (acute samples). We analysed the samples using a commercial multiplex respiratory panel comprising 33 bacterial, viral and fungal targets. We used random effects tobit models to compare cycle threshold (Ct) value distributions from non-acute and acute samples. RESULTS: We analysed 341 non-acute and 145 acute swab samples from 274 participants. Haemophilus influenzae type B was the most commonly detected microbe (77.4% of non-acute and 64.8% of acute samples). In acute samples, nine specific microbe pairs were detected more frequently than expected by chance. Regression models indicated significantly lower microbial load in non-acute relative to acute samples for H. influenzae non-type B, Streptococcus pneumoniae and rhinovirus, although it was not possible to identify a Ct-value threshold indicating causal etiology for any of these organisms. CONCLUSIONS: Semi-quantitative measures of microbial concentration did not reliably differentiate between illness and asymptomatic colonization, suggesting that clinical symptoms may not always be directly related to microbial load for common respiratory infections.

Region-wide synchrony and traveling waves of dengue across eight countries in Southeast Asia.

Dengue is a mosquito-transmitted virus infection that causes epidemics of febrile illness and hemorrhagic fever across the tropics and subtropics worldwide. Annual epidemics are commonly observed, but there is substantial spatiotemporal heterogeneity in intensity. A better understanding of this heterogeneity in dengue transmission could lead to improved epidemic prediction and disease control. Time series decomposition methods enable the isolation and study of temporal epidemic dynamics with a specific periodicity (e.g., annual cycles related to climatic drivers and multiannual cycles caused by dynamics in population immunity). We collected and analyzed up to 18 y of monthly dengue surveillance reports on a total of 3.5 million reported dengue cases from 273 provinces in eight countries in Southeast Asia, covering ∼ 10(7) km(2). We detected strong patterns of synchronous dengue transmission across the entire region, most markedly during a period of high incidence in 1997-1998, which was followed by a period of extremely low incidence in 2001-2002. This synchrony in dengue incidence coincided with elevated temperatures throughout the region in 1997-1998 and the strongest El Niño episode of the century. Multiannual dengue cycles (2-5 y) were highly coherent with the Oceanic Niño Index, and synchrony of these cycles increased with temperature. We also detected localized traveling waves of multiannual dengue epidemic cycles in Thailand, Laos, and the Philippines that were dependent on temperature. This study reveals forcing mechanisms that drive synchronization of dengue epidemics on a continental scale across Southeast Asia.