Cost-effectiveness of intervention combinations towards the elimination of vertical transmission of HIV in limited-resource settings: a mathematical modelling study.

BACKGROUND: Since 2000, there has been a substantial global reduction in the vertical transmission of HIV. Despite effective interventions, gaps still remain in progress towards elimination in many low-income and middle-income countries. We developed a mathematical model to determine the most cost-effective combinations of interventions to prevent vertical transmission. METHODS: We developed a 12-month Markov model to follow a cohort of women of childbearing age (aged 15-49 years) in Zambia (n=1 107 255) who were either pregnant, in delivery, or breastfeeding; the population included in the model reflects the estimated number of pregnant women in Zambia from the 2018 Zambia Demographic and Health Survey. The model incorporated nine interventions: infant prophylaxis; three different HIV retesting schedule options; oral pre-exposure prophylaxis; maternal peer-support groups; regimen shift; tracing of loss to follow-up; and point-of-care viral load testing. We analysed incident HIV infections among mothers and infants, intervention costs, and evaluated 190 scenarios of different combinations of inventions to calculate the incremental cost-effectiveness ratios (ICERs) over 1 year. FINDINGS: Three interventions with the greatest reduction in vertical transmission, individually, were support groups for 80% of those in need (35% reduction in infant infections), HIV retesting schedules (6·5% reduction), and infant prophylaxis (4·5% reduction). Of all 190 scenarios evaluated, eight were on the cost-effectiveness frontier (ie, were considered to be cost-effective); all eight included increasing infant prophylaxis, regimen shift, and use of support groups. Excluding the highest-cost scenarios, for a 1-22% increase in total budget, 23-43% of infant infections could be prevented, producing ICERs between US$244 and $16 242. INTERPRETATION: Using the interventions modelled, it is possible to reduce vertical transmission and to cost-effectively prevent up to 1734 infant HIV infections (43% reduction) in Zambia over a period of 1 year. To optimise their effect, these interventions must be scaled with fidelity. Future work is needed to incorporate evidence on additional innovative interventions and HIV risk factors, and to apply the model to other country contexts to support targeted implementation and resource use. FUNDING: The ELMA Foundation.

Burden of Chronic Obstructive Pulmonary Disease Attributable to Tuberculosis: A Microsimulation Study.

Tuberculosis (TB) is a risk factor for chronic obstructive pulmonary disease (COPD), but COPD is also a predictor of TB. The excess life-years lost to COPD caused by TB can potentially be saved by screening for and treating TB infection. We examined the number of life-years that could be saved by preventing TB and TB-attributable COPD. We compared the observed (no intervention) and counterfactual microsimulation models constructed from observed rates in the Danish National Patient Registry (covering all Danish hospitals between 1995 and 2014). In the Danish population of TB and COPD-naive individuals (n = 5,206,922), 27,783 persons (0.5%) developed TB. Among those who developed TB, 14,438 (52.0%) developed TB with COPD. Preventing TB saved 186,469 life-years overall. The excess number of life-years lost to TB alone was 7.07 years per person, and the additional number of life-years lost among persons who developed COPD after TB was 4.86 years per person. The life-years lost to TB-associated COPD are substantial, even in regions where TB can be expected to be identified and treated promptly. Prevention of TB could prevent a substantial amount of COPD-related morbidity; the benefit of screening and treatment for TB infection is underestimated by considering morbidity from TB alone.

Spatial heterogeneity of extensively drug resistant-tuberculosis in Western Cape Province, South Africa.

Tuberculosis (TB) remains a leading infectious disease killer globally. Treatment outcomes are especially poor among people with extensively drug-resistant (XDR) TB, until recently defined as rifampicin-resistant (RR) TB with resistance to an aminoglycoside (amikacin) and a fluoroquinolone (ofloxacin). We used laboratory TB test results from Western Cape province, South Africa between 2012 and 2015 to identify XDR-TB and pre-XDR-TB (RR-TB with resistance to one second-line drug) spatial hotspots. We mapped the percentage and count of individuals with RR-TB that had XDR-TB and pre-XDR-TB across the province and in Cape Town, as well as amikacin-resistant and ofloxacin-resistant TB. We found the percentage of pre-XDR-TB and the count of XDR-TB/pre-XDR-TB highly heterogeneous with geographic hotspots within RR-TB high burden areas, and found hotspots in both percentage and count of amikacin-resistant and ofloxacin-resistant TB. The spatial distribution of percentage ofloxacin-resistant TB hotspots was similar to XDR-TB hotspots, suggesting that fluoroquinolone-resistace is often the first step to additional resistance. Our work shows that interventions used to reduce XDR-TB incidence may need to be targeted within spatial locations of RR-TB, and further research is required to understand underlying drivers of XDR-TB transmission in these locations.

Optimal use of COVID-19 Ag-RDT screening at border crossings to prevent community transmission: A modeling analysis.

Countries around the world have implemented restrictions on mobility, especially cross-border travel to reduce or prevent SARS-CoV-2 community transmission. Rapid antigen testing (Ag-RDT), with on-site administration and rapid turnaround time may provide a valuable screening measure to ease cross-border travel while minimizing risk of local transmission. To maximize impact, we developed an optimal Ag-RDT screening algorithm for cross-border entry. Using a previously developed mathematical model, we determined the daily number of imported COVID-19 cases that would generate no more than a relative 1% increase in cases over one month for different effective reproductive numbers (Rt) and COVID-19 prevalence within the recipient country. We then developed an algorithm-for differing levels of Rt, arrivals per day, mode of travel, and SARS-CoV-2 prevalence amongst travelers-to determine the minimum proportion of people that would need Ag-RDT testing at border crossings to ensure no greater than the relative 1% community spread increase. When daily international arrivals and/or COVID-19 prevalence amongst arrivals increases, the proportion of arrivals required to test using Ag-RDT increases. At very high numbers of international arrivals/COVID-19 prevalence, Ag-RDT testing is not sufficient to prevent increased community spread, especially when recipient country prevalence and Rt are low. In these cases, Ag-RDT screening would need to be supplemented with other measures to prevent an increase in community transmission. An efficient Ag-RDT algorithm for SARS-CoV-2 testing depends strongly on the epidemic status within the recipient country, volume of travel, proportion of land and air arrivals, test sensitivity, and COVID-19 prevalence among travelers.

Reproducible Science Is Vital for a Stronger Evidence Base During the COVID-19 Pandemic.

Reproducible research becomes even more imperative as we build the evidence base on SARS-CoV-2 epidemiology, diagnosis, prevention, and treatment. In his study, Paez assessed the reproducibility of COVID-19 research during the pandemic, using a case study of population density. He found that most articles that assess the relationship of population density and COVID-19 outcomes do not publicly share data and code, except for a few, including our paper, which he stated "illustrates the importance of good reproducibility practices". Paez recreated our analysis using our code and data from the perspective of spatial analysis, and his new model came to a different conclusion. The disparity between our and Paez's findings, as well as other existing literature on the topic, give greater impetus to the need for further research. As there has been near exponential growth of COVID-19 research across a wide range of scientific disciplines, reproducible science is a vital component to produce reliable, rigorous, and robust evidence on COVID-19, which will be essential to inform clinical practice and policy in order to effectively eliminate the pandemic.

Changes in HIV treatment differentiated care uptake during the COVID-19 pandemic in Zambia: interrupted time series analysis.

INTRODUCTION: Differentiated service delivery (DSD) models aim to improve the access of human immunodeficiency virus treatment on clients and reduce requirements for facility visits by extending dispensing intervals. With the advent of the COVID-19 pandemic, minimising client contact with healthcare facilities and other clients, while maintaining treatment continuity and avoiding loss to care, has become more urgent, resulting in efforts to increase DSD uptake. We assessed the extent to which DSD coverage and antiretroviral treatment (ART) dispensing intervals have changed during the COVID-19 pandemic in Zambia. METHODS: We used client data from Zambia's electronic medical record system (SmartCare) for 737 health facilities, representing about three-fourths of all ART clients nationally. We compared the numbers and proportional distributions of clients enrolled in DSD models in the 6 months before and 6 months after the first case of COVID-19 was diagnosed in Zambia in March 2020. Segmented linear regression was used to determine whether the outbreak of COVID-19 in Zambia further accelerated the increase in DSD scale-up. RESULTS AND DISCUSSION: Between September 2019 and August 2020, 181,317 clients aged 15 or older (81,520 and 99,797 from 1 September 2019 to 1 March 2020 and from 1 March to 31 August 2020, respectively) enrolled in DSD models in Zambia. Overall participation in all DSD models increased over the study period, but uptake varied by model. The rate of acceleration increased in the second period for home ART delivery (152%), ≤ 2-month fast-track (143%) and 3-month MMD (139%). There was a significant reduction in the enrolment rates for 4- to 6-month fast-track (-28%) and "other" models (-19%). CONCLUSIONS: Participation in DSD models for stable ART clients in Zambia increased after the advent of COVID-19, but dispensing intervals diminished. Eliminating obstacles to longer dispensing intervals, including those related to supply chain management, should be prioritized to achieve the expected benefits of DSD models and minimize COVID-19 risk.

Brain, behavioral, affective, and sex correlates of recovery from alcohol use disorders.

BACKGROUND: Recovery from alcohol use disorders (AUDs) consists of salutary changes in behavior and affect. While evidence suggests that recovery-related behavioral changes, such as abstinence, emerge in tandem with both neural and affective changes, the precise relationships among these changes are unknown. To understand these relationships, we examined associations between the duration of abstinence (DOA), affective states, and neuroimaging-based structural measures of the brain reward system (BRS) in AUD men (AUDM ) and AUD women (AUDW ). METHODS: Participants were community respondents from the Boston area comprising right-handed abstinent individuals with AUD (n = 60; 30 men) and controls without AUD (NC; n = 60; 29 men). Multivariate linear regressions compared short-/mid-term abstainers (≤5 years), long-term abstainers (>5 years), and the NC group on measures of BRS volume (3T magnetic resonance imaging scans) and measures of affect (Profile of Mood States [POMS]; Multiple Affect Adjective Check List [MAACL]; Hamilton Rating Scale for Depression [HRSD]). Analyses contrasted sex differences and accounted for age, education, drinking severity, and verbal IQ. RESULTS: Compared to the NC group, short-/mid-term abstainers exhibited larger posterior insular volume (total (ß = 0.019, 95% CI: 0.004, 0.034)), higher negative affect (POMS Mood Disturbance (ß = 27.8, 95% CI: 11.56, 44.04), and lower positive affect (POMS Vigor (ß = -4.89, 95% CI: -9.06, -0.72)). Compared to the NC group, Long-term abstainers exhibited significantly smaller volumes of aggregate anterior cingulate cortex (ß = -0.06, 95% CI: -0.113, -0.008) and higher HRSD scores (ß = 1.56, 95% CI: 0.14, 2.98). Relative to AUDM , AUDW exhibited significantly larger right anterior insular volumes (ß = 0.03, 95% CI: 0.01, 0.06) and significantly greater MAACL Positive Affect scores (ß = 7.56, 95% CI: 0.59, 11.55) in association with DOA. CONCLUSIONS: We found that differences in abstinence from alcohol were correlated with differences in both neural recovery and affective dimensions of recovery from AUDs. The observed sex differences extend evidence of dimorphic effects of AUDs and recovery on brain structure and function. Future longitudinal research will test inferences concerning the directionality of these relationships.

Population density and basic reproductive number of COVID-19 across United States counties.

The basic reproductive number (R0) is a function of contact rates among individuals, transmission probability, and duration of infectiousness. We sought to determine the association between population density and R0 of SARS-CoV-2 across U.S. counties. We conducted a cross-sectional analysis using linear mixed models with random intercept and fixed slopes to assess the association of population density and R0, and controlled for state-level effects using random intercepts. We also assessed whether the association was differential across county-level main mode of transportation percentage as a proxy for transportation accessibility, and adjusted for median household income. The median R0 among the United States counties was 1.66 (IQR: 1.35-2.11). A population density threshold of 22 people/km2 was needed to sustain an outbreak. Counties with greater population density have greater rates of transmission of SARS-CoV-2, likely due to increased contact rates in areas with greater density. An increase in one unit of log population density increased R0 by 0.16 (95% CI: 0.13 to 0.19). This association remained when adjusted for main mode of transportation and household income. The effect of population density on R0 was not modified by transportation mode. Our findings suggest that dense areas increase contact rates necessary for disease transmission. SARS-CoV-2 R0 estimates need to consider this geographic variability for proper planning and resource allocation, particularly as epidemics newly emerge and old outbreaks resurge.

A clinician's primer on epidemiology for COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic has resulted in a concomitant deluge of medical, biological, and epidemiologic research. Clinicians are interested in incorporating the best new evidence-based practices when treating individuals with COVID-19 and instituting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission prevention protocols. However, without sufficient background knowledge, evaluating epidemiologic studies can be challenging, and failure to identify sources of bias could lead to poor treatment decisions. Here we provide a brief primer on key concepts and terms related to COVID-19 epidemiology to provide clinicians with a starting point for evaluating the emerging COVID-19 literature.

Predictors of antiretroviral therapy initiation in eThekwini (Durban), South Africa: Findings from a prospective cohort study.

Despite expanded antiretroviral therapy (ART) eligibility in South Africa, many people diagnosed with HIV do not initiate ART promptly, yet understanding of the reasons is limited. Using data from an 8-month prospective cohort interview study of women and men newly-diagnosed with HIV in three public-sector primary care clinics in the eThekwini (Durban) region, South Africa, 2010-2014, we examined if theoretically-relevant social-structural, social-cognitive, psychosocial, and health status indicators were associated with time to ART initiation. Of 459 diagnosed, 350 returned to the clinic for their CD4+ test results (linkage); 153 (33.3%) were ART-eligible according to treatment criteria at the time; 115 (75.2% of those eligible) initiated ART (median = 12.86 weeks [95% CI: 9.75, 15.97] after linkage). In adjusted Cox proportional hazard models, internalized stigma was associated with a 65% decrease in the rate of ART initiation (Adjusted hazard ratio [AHR] 0.35, 95% CI: 0.19-0.80) during the period less than four weeks after linkage to care, but not four or more weeks after linkage to care, suggesting that stigma-reduction interventions implemented shortly after diagnosis may accelerate ART uptake. As reported by others, older age was associated with more rapid ART initiation (AHR for 1-year age increase: 1.04, 95% CI: 1.01-1.07) and higher CD4+ cell count (≥300µL vs. <150µL) was associated with a lower rate of initiation (AHR 0.38, 95% CI: 0.19-0.80). Several other factors that were assessed prior to diagnosis, including stronger belief in traditional medicine, higher endorsement of stigma toward people living with HIV, food insecurity, and higher psychological distress, were found to be in the expected direction of association with ART initiation, but confidence intervals were wide and could not exclude a null finding.

Socioeconomic Disparities in Subway Use and COVID-19 Outcomes in New York City.

Using data from New York City from January 2020 to April 2020, we found an estimated 28-day lag between the onset of reduced subway use and the end of the exponential growth period of severe acute respiratory syndrome coronavirus 2 within New York City boroughs. We also conducted a cross-sectional analysis of the associations between human mobility (i.e., subway ridership) on the week of April 11, 2020, sociodemographic factors, and coronavirus disease 2019 (COVID-19) incidence as of April 26, 2020. Areas with lower median income, a greater percentage of individuals who identify as non-White and/or Hispanic/Latino, a greater percentage of essential workers, and a greater percentage of health-care essential workers had more mobility during the pandemic. When adjusted for the percentage of essential workers, these associations did not remain, suggesting essential work drives human movement in these areas. Increased mobility and all sociodemographic variables (except percentage of people older than 75 years old and percentage of health-care essential workers) were associated with a higher rate of COVID-19 cases per 100,000 people, when adjusted for testing effort. Our study demonstrates that the most socially disadvantaged not only are at an increased risk for COVID-19 infection, they lack the privilege to fully engage in social distancing interventions.

Bringing Data Analytics to the Design of Optimized Diagnostic Networks in Low- and Middle-Income Countries: Process, Terms and Definitions.

Diagnostics services are an essential component of healthcare systems, advancing universal health coverage and ensuring global health security, but are often unavailable or under-resourced in low- and middle-income (LMIC) countries. Typically, diagnostics are delivered at various tiers of the laboratory network based on population needs, and resource and infrastructure constraints. A diagnostic network additionally incorporates screening and includes point-of-care testing that may occur outside of a laboratory in the community and clinic settings; it also emphasizes the importance of supportive network elements, including specimen referral systems, as being critical for the functioning of the diagnostic network. To date, design and planning of diagnostic networks in LMICs has largely been driven by infectious diseases such as TB and HIV, relying on manual methods and expert consensus, with a limited application of data analytics. Recently, there have been efforts to improve diagnostic network planning, including diagnostic network optimization (DNO). The DNO process involves the collection, mapping, and spatial analysis of baseline data; selection and development of scenarios to model and optimize; and lastly, implementing changes and measuring impact. This review outlines the goals of DNO and steps in the process, and provides clarity on commonly used terms.

Previous and active tuberculosis increases risk of death and prolongs recovery in patients with COVID-19.

BACKGROUND: There is a growing literature on the association of SARS-CoV-2 and other chronic conditions, such as noncommunicable diseases. However, little is known about the impact of coinfection with tuberculosis. We aimed to compare the risk of death and recovery, as well as time-to-death and time-to-recovery, in COVID-19 patients with and without tuberculosis. METHODS: We created a 4:1 propensity score matched sample of COVID-19 patients without and with tuberculosis, using COVID-19 surveillance data in the Philippines. We conducted a longitudinal cohort analysis of matched COVID-19 patients as of May 17, 2020, following them until June 15, 2020. The primary analysis estimated the risk ratios of death and recovery in patients with and without tuberculosis. Kaplan-Meier curves described time-to-death and time-to-recovery stratified by tuberculosis status, and differences in survival were assessed using the Wilcoxon test. RESULTS: The risk of death in COVID-19 patients with tuberculosis was 2.17 times higher than in those without (95% CI: 1.40-3.37). The risk of recovery in COVID-19 patients with tuberculosis was 25% lower than in those without (RR = 0.75,05% CI 0.63-0.91). Similarly, time-to-death was significantly shorter (p = .0031) and time-to-recovery significantly longer in patients with tuberculosis (p = .0046). CONCLUSIONS: Our findings show that coinfection with tuberculosis increased morbidity and mortality in COVID-19 patients. Our findings highlight the need to prioritize routine and testing services for tuberculosis, although health systems are disrupted by the heavy burden of the SARS-CoV-2 pandemic.

Socioeconomic disparities in subway use and COVID-19 outcomes in New York City.

BACKGROUND: The United States CDC has reported that racial and ethnic disparities in the COVID-19 pandemic may in part be due to socioeconomic disadvantages that require individuals to continue to work outside their home and a lack of paid sick leave. However, data-driven analyses of the socioeconomic determinants of COVID-19 burden are still needed. Using data from New York City (NYC), we aimed to determine how socioeconomic factors impact human mobility and COVID-19 burden. Methods/Summary: New York City has a large amount of heterogeneity in socioeconomic status (SES) and demographics among neighborhoods. We used this heterogeneity to conduct a cross-sectional spatial analysis of the associations between human mobility (i.e., subway ridership), sociodemographic factors, and COVID-19 incidence as of April 26, 2020. We also conducted a secondary analysis of NYC boroughs (which are equivalent to counties in the city) to assess the relationship between the decline in subway use and the time it took for each borough to end the exponential growth period of COVID-19 cases. FINDINGS: Areas with the lower median income, a greater percentage of individuals who identify as non-white and/or Hispanic/Latino, a greater percentage of essential workers, and a greater percentage of healthcare workers had more subway use during the pandemic. The positive associations between subway use and median income, and between subway use and percent non-white and/or Hispanic/Latino do not remain when adjusted for the percent of essential workers. This suggests essential work is what drives subway use in lower SES zip codes and communities of color. Increased subway use was associated with a higher rate of COVID-19 cases per 100,000 population when adjusted for testing effort (aRR=1.11; 95% CI: 1.03 - 1.19), but this association was weaker once we adjusted for median income (aRR=1.06; 95% CI: 1.00 - 1.12). All sociodemographic variables were significantly associated with the rate of positive cases per 100,000 population when adjusting for testing effort (except percent uninsured) and adjusting for both income and testing effort. The risk factor with the strongest association with COVID-19 was the percent of individuals in essential work (aRR = 1.59, 95% CI: 1.36 - 1.86). We found that subway use declined prior to any executive order, and there was an estimated 28-day lag between the onset of reduced subway use and the end of the exponential growth period of SARS-CoV-2 within New York City boroughs. INTERPRETATION: Our results suggest that the ability to stay home during the pandemic has been constrained by SES and work circumstances. Poorer neighborhoods are not afforded the same reductions in mobility as their richer counterparts. Furthermore, lower SES neighborhoods have higher disease burdens, which may be due to inequities in ability to shelter-in-place, and/or due to the plethora of other existing health disparities that increase vulnerability to COVID-19. Furthermore, the extended lag time between the dramatic fall in subway ridership and the end of the exponential growth phase for COVID-19 cases is important for future policy, because it demonstrates that if there is a resurgence, and stay-at-home orders are re-issued, then cities can expect to wait a month before reported cases will plateau.

Population density and basic reproductive number of COVID-19 across United States counties.

The basic reproductive number (R0) is a function of contact rates among individuals, transmission probability, and duration of infectiousness. We sought to determine the association between population density and R0 of SARS-CoV-2 across U.S. counties, and whether population density could be used as a proxy for contact rates. We conducted a cross-sectional analysis using linear mixed models with random intercept and fixed slopes to assess the association of population density and R0. We also assessed whether this association was differential across county-level main mode of transportation-to-work percentage. Counties with greater population density have greater rates of transmission of SARS-CoV-2, likely due to increased contact rates in areas with greater density. The effect of population density and R0 was not modified by private transportation use. Differential R0 by population density can assist in more accurate predictions of the rate of spread of SARS-CoV-2 in areas that do not yet have active cases.

Spatiotemporal clustering of suicides in the US from 1999 to 2016: a spatial epidemiological approach.

PURPOSE: This study aims to describe and characterize the spatial and temporal clustering patterns of suicide in the ten states with the greatest suicide burden in the United States from 1999 to 2016. METHODS: All suicide deaths from January 1, 1999 to December 31, 2016 in the United States were identified using data from the Wide-ranging Online Data for Epidemiologic Research (WONDER) dataset. The ten states with the highest age-adjusted suicide rates were Montana, Alaska, Wyoming, New Mexico, Nevada, Utah, Idaho, Colorado, Arizona, and Oklahoma. A spatiotemporal scan statistic using a discrete Poisson model was employed to retrospectively detect spatiotemporal suicide clusters. RESULTS: From 1999 to 2016, a total of 649,843 suicides were recorded in the United States. Nineteen statistically significant spatiotemporal suicide mortality clusters were identified in the states with the greatest suicide rates, and 13.53% of the suicide cases within these states clustered spatiotemporally. The risk ratio of the clusters ranged from 1.45 to 3.64 (p < 0.001). All states had at least one cluster, with three clusters spanning multiple states, and four clusters were found in Arizona. While there was no clear secular trend in the average size of suicide clusters, the number of clusters increased from 1999 to 2016. CONCLUSIONS: Hot spots for suicidal behavior in the United States warrant public health intervention and continued surveillance. As suicide rates in the US continue to increase annually, public health efforts could be maximized by focusing on regions with substantial clustering.

The Feasibility of Telephone-Administered Cognitive Testing in Individuals 1 and 2 Years after Inpatient Rehabilitation for Traumatic Brain Injury.

Traumatic brain injury (TBI) often results in cognitive impairment, and trajectories of cognitive functioning can vary tremendously over time across survivors. Traditional approaches to measuring cognitive performance require face-to-face administration of a battery of objective neuropsychological tests, which can be time- and labor-intensive. There are numerous clinical and research contexts in which in-person testing is undesirable or unfeasible, including clinical monitoring of older adults or individuals with disability for whom travel is challenging, and epidemiological studies of geographically dispersed participants. A telephone-based method for measuring cognition could conserve resources and improve efficiency. The objective of this study is to examine the feasibility and usefulness of the Brief Test of Adult Cognition by Telephone (BTACT) among individuals who are 1 and 2 years post-moderate-to-severe TBI. A total of 463 individuals participated in the study at Year 1 post-injury, and 386 participated at Year 2. The sample was mostly male (73%) and white (59%), with an average age of (mean ± standard deviation) 47.9 ± 20.9 years, and 73% experienced a duration of post-traumatic amnesia (PTA) greater than 7 days. A majority of participants were able to complete the BTACT subtests (61-69% and 56-64% for Years 1 and 2 respectively); score imputation for those unable to complete a test due to severity of cognitive impairment yields complete data for 74-79% of the sample. BTACT subtests showed expected changes between Years 1-2, and summary scores demonstrated expected associations with injury severity, employment status, and cognitive status as measured by the Functional Independence Measure. Results indicate it is feasible, efficient, and useful to measure cognition over the telephone among individuals with moderate-severe TBI.

The Relationship Between Traumatic Brain Injury and Criminality in Juvenile Offenders.

OBJECTIVE: To examine the relationship between traumatic brain injury (TBI) and criminal behavior in youth who are incarcerated or on probation in Texas. SETTING: Seven juvenile justice facilities. PARTICIPANTS: Juvenile offenders in state or county correctional facilities or on probation. DESIGN: Screening for TBI was conducted among adolescents at 7 juvenile justice centers. MAIN MEASURES: Participants were administered the Brain Injury Screening Questionnaire, and results were linked to participants' offense history and psychiatric diagnoses. RESULTS: One in 4 juvenile offenders met criteria for TBI, and the majority of injuries occurred prior to the adolescents' criminal offenses. A history of TBI was related to more violent crimes, as well as more mental health diagnoses and symptoms. CONCLUSION: The high rates of TBI and levels of distress found in juvenile offenders suggest a need for preventive actions, interventions to compensate for challenges related to TBI, and programs to assist individuals' transitions into the community.