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Background: Medical outcomes of interest to clinicians may have multiple categories. Researchers face several options for risk prediction of such outcomes, including dichotomized logistic regression and multinomial logit regression modeling. We aimed to compare these methods and provide guidance needed for practice. Methods: We described dichotomized logistic regression, multinomial continuation-ratio logit regression, which is an alternative to standard multinomial logit regression for ordinal outcomes, and logistic competing risks regression. We then applied these methods to develop prediction models of survival and neurodevelopmental outcomes based on the NICHD Extremely Preterm Birth Outcome Tool model. The statistical and practical advantages and flaws of these methods were examined. Both discrimination and calibration of the estimated logistic models of dichotomized outcomes and continuation-ratio logit model were assessed. Results: The dichotomized logistic models and multinomial continuation-ratio logit model had similar discrimination and calibration in predicting death and survival without neurodevelopmental impairment. But the continuation-ratio logit model had better discrimination and calibration in predicting neurodevelopmental impairment. The sum of predicted probabilities of outcome categories from the dichotomized logistic models could deviate from 100% substantially, ranging from 87.7 to 124.0%, and the dichotomized logistic model of neurodevelopmental impairment greatly overpredicted low risks and underpredicted high risks. Conclusions: Estimating multiple logistic regression models of dichotomized outcomes may result in poorly calibrated predictions for an outcome with multiple ordinal categories. Multinomial continuation-ratio logit regression produces better calibrated predictions, constrains the sum of predicted probabilities to 100%, and has the advantages of simplicity in model interpretation, flexibility to include outcome category-specific predictors and random-effect terms for patient heterogeneity by hospital. It also accounts for mutual dependence among multiple categories and accommodates competing risks. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic presented unique challenges to the U.S. healthcare system, particularly for nonprofit U.S. hospitals that are obligated to provide community benefits in exchange for federal tax exemptions. We sought to examine how hospitals initiated, modified, or disbanded community benefits programming in response to the COVID-19 pandemic. We used the free-response text in Part IV of Internal Revenue Service (IRS) Form 990 Schedule H (F990H) to assess health equity and disparities. We combined traditional key term frequency and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) clustering approaches with a novel Generative Pre-trained Transformer (GPT) 3.5 summarization approach. Our research reveals shifts in community benefits programming. We observed an increase in COVID-related terms starting in the 2019 tax year, indicating a pivot in community focus and efforts toward pandemic-related activities such as telehealth services and COVID-19 testing and prevention. The clustering analysis identified themes related to COVID-19 and community benefits. Generative Artificial Intelligence (GenAI) summarization with GPT3.5 contextualized these changes, revealing examples of healthcare system adaptations and program cancellations. However, GPT3.5 also encountered some accuracy and validation challenges. This multifaceted text analysis underscores the adaptability of hospitals in maintaining community health support during crises and suggests the potential of advanced AI tools in evaluating large-scale qualitative data for policy and public health research. [ABSTRACT FROM AUTHOR]

Background: Accurate prevalence estimates of drug use and its harms are important to characterize burden and develop interventions to reduce negative health outcomes and disparities. Lack of a sampling frame for marginalized/stigmatized populations, including persons who use drugs (PWUD) in rural settings, makes this challenging. Respondent-driven sampling (RDS) is frequently used to recruit PWUD. However, the validity of RDS-generated population-level prevalence estimates relies on assumptions that should be evaluated. Methods: RDS was used to recruit PWUD across seven Rural Opioid Initiative studies between 2018-2020. To evaluate RDS assumptions, we computed recruitment homophily and design effects, generated convergence and bottleneck plots, and tested for recruitment and degree differences. We compared sample proportions with three RDS-adjusted estimators (two variations of RDS-I and RDS-II) for five variables of interest (past 30-day use of heroin, fentanyl, and methamphetamine; past 6-month homelessness; and being positive for hepatitis C virus (HCV) antibody) using linear regression with robust confidence intervals. We compared regression estimates for the associations between HCV positive antibody status and (a) heroin use, (b) fentanyl use, and (c) age using RDS-1 and RDS-II probability weights and no weights using logistic and modified Poisson regression and random-effects meta-analyses. Results: Among 2,842 PWUD, median age was 34 years and 43% were female. Most participants (54%) reported opioids as their drug of choice, however regional differences were present (e.g., methamphetamine range: 4-52%). Many recruitment chains were not long enough to achieve sample equilibrium. Recruitment homophily was present for some variables. Differences with respect to recruitment and degree varied across studies. Prevalence estimates varied only slightly with different RDS weighting approaches, most confidence intervals overlapped. Variations in measures of association varied little based on weighting approach. Conclusions: RDS was a useful recruitment tool for PWUD in rural settings. However, several violations of key RDS assumptions were observed which slightly impacts estimation of proportion although not associations. [ABSTRACT FROM AUTHOR]

Key Points: Question: Which pharmacotherapies are associated with improved outcomes for people with alcohol use disorder? Findings: In this systematic review and meta-analysis that included 118 clinical trials and 20 976 participants, 50 mg/d of oral naltrexone and acamprosate were each associated with significantly improved alcohol consumption-related outcomes compared with placebo. Meaning: These findings support oral naltrexone at 50 mg/d and acamprosate as first-line therapies for alcohol use disorder. Importance: Alcohol use disorder affects more than 28.3 million people in the United States and is associated with increased rates of morbidity and mortality. Objective: To compare efficacy and comparative efficacy of therapies for alcohol use disorder. Data Sources: PubMed, the Cochrane Library, the Cochrane Central Trials Registry, PsycINFO, CINAHL, and EMBASE were searched from November 2012 to September 9, 2022 Literature was subsequently systematically monitored to identify relevant articles up to August 14, 2023, and the PubMed search was updated on August 14, 2023. Study Selection: For efficacy outcomes, randomized clinical trials of at least 12 weeks' duration were included. For adverse effects, randomized clinical trials and prospective cohort studies that compared drug therapies and reported health outcomes or harms were included. Data Extraction and Synthesis: Two reviewers evaluated each study, assessed risk of bias, and graded strength of evidence. Meta-analyses used random-effects models. Numbers needed to treat were calculated for medications with at least moderate strength of evidence for benefit. Main Outcomes and Measures: The primary outcome was alcohol consumption. Secondary outcomes were motor vehicle crashes, injuries, quality of life, function, mortality, and harms. Results: Data from 118 clinical trials and 20 976 participants were included. The numbers needed to treat to prevent 1 person from returning to any drinking were 11 (95% CI, 1-32) for acamprosate and 18 (95% CI, 4-32) for oral naltrexone at a dose of 50 mg/d. Compared with placebo, oral naltrexone (50 mg/d) was associated with lower rates of return to heavy drinking, with a number needed to treat of 11 (95% CI, 5-41). Injectable naltrexone was associated with fewer drinking days over the 30-day treatment period (weighted mean difference, −4.99 days; 95% CI, −9.49 to −0.49 days) Adverse effects included higher gastrointestinal distress for acamprosate (diarrhea: risk ratio, 1.58; 95% CI, 1.27-1.97) and naltrexone (nausea: risk ratio, 1.73; 95% CI, 1.51-1.98; vomiting: risk ratio, 1.53; 95% CI, 1.23-1.91) compared with placebo. Conclusions and Relevance: In conjunction with psychosocial interventions, these findings support the use of oral naltrexone at 50 mg/d and acamprosate as first-line pharmacotherapies for alcohol use disorder. This systematic review and meta-analysis evaluates efficacy and comparative efficacy of 9 therapies for alcohol use disorder (AUD). [ABSTRACT FROM AUTHOR]

Background: Recent policies have lessened restrictions around prescribing buprenorphine-naloxone (buprenorphine) for the treatment of opioid use disorder (OUD). The primary concern expressed by critics of these policies is the potential for buprenorphine diversion. However, the population-level effects of increased buprenorphine diversion are unclear. If replacing the use of heroin or fentanyl, use of diverted buprenorphine could be protective. Methods: Our study aim was to estimate the impact of buprenorphine diversion on opioid overdose using an agent-based model calibrated to North Carolina. We simulated the progression of opioid misuse and opioid-related outcomes over a 5-year period. Our status quo scenario assumed that 50% of those prescribed buprenorphine diverted at least one dose per week to other individuals with OUD and 10% of individuals with OUD used diverted buprenorphine at least once per week. A controlled prescription only scenario assumed that no buprenorphine would be diverted, while an increased diversion scenario assumed that 95% of those prescribed buprenorphine diverted and 50% of individuals with OUD used diverted buprenorphine. We assumed that use of diverted buprenorphine replaced the use of other opioids for that day. Sensitivity analyses increased the risk of overdose when using diverted buprenorphine, increased the frequency of diverted buprenorphine use, and simulated use of diverted buprenorphine by opioid-naïve individuals. Scenarios were compared on opioid overdose-related outcomes over the 5-year period. Results: Our status quo scenario predicted 10,658 (credible interval [CI]: 9699–11,679) fatal opioid overdoses. A scenario simulating controlled prescription only of buprenorphine (i.e., no diversion) resulted in 10,741 (9895–11,650) fatal opioid overdoses versus 10,301 (9439–11,244) within a scenario simulating increased diversion. Compared to the status quo, the controlled prescription only scenario resulted in a similar number of fatal overdoses, while the scenario with increased diversion of buprenorphine resulted in 357 (3.35%) fewer fatal overdoses. Even when increasing overdose risk while using diverted buprenorphine and incorporating use by opioid naïve individuals, increased diversion did not increase overdoses compared to a scenario with no buprenorphine diversion. Conclusions: A similar number of opioid overdoses occurred under modeling conditions with increased rates of buprenorphine diversion among persons with OUD, with non-statistical trends toward lower opioid overdoses. These results support existing calls for low- to no-barrier access to buprenorphine for persons with OUD. [ABSTRACT FROM AUTHOR]

We sought to examine how the impact of revocable behavioral interventions, e.g., shelterin-place, varies throughout an epidemic, as well as the role that the proportion of susceptible individuals had on an intervention's impact. We estimated the theoretical impacts of start day, length, and intensity of interventions on disease transmission and illustrated them on COVID-19 dynamics inWake County, North Carolina, to inform how interventions can be most effective. We used a Susceptible, Exposed, Infectious, and Recovered (SEIR) model to estimate epidemic curves with modifications to the disease transmission parameter (b). We designed modifications to simulate events likely to increase transmission (e.g., long weekends, holiday seasons) or behavioral interventions likely to decrease it (e.g., shelter-in-place, masking). We compared the resultant curves' shape, timing, and cumulative case count to baseline and across other modified curves. Interventions led to changes in COVID-19 dynamics, including moving the peak's location, height, and width. The proportion susceptible, at the start day, strongly influenced their impact. Early interventions shifted the curve, while interventions near the peak modified shape and case count. For some scenarios, in which the transmission parameter was decreased, the final cumulative count increased over baseline. We showed that the timing of revocable interventions has a strong impact on their effect. The same intervention applied at different time points, corresponding to different proportions of susceptibility, resulted in qualitatively differential effects. Accurate estimation of the proportion susceptible is critical for understanding an intervention's impact. The findings presented here provide evidence of the importance of estimating the proportion of the population that is susceptible when predicting the impact of behavioral infection control interventions. Greater emphasis should be placed on the estimation of this epidemic component in intervention design and decision-making. Our results are generic and are applicable to other infectious disease epidemics, as well as to future waves of the current COVID-19 epidemic. Developed into a publicly available tool that allows users to modify the parameters to estimate impacts of different interventions, these models could aid in evaluating behavioral intervention options prior to their use and in predicting case increases from specific events. [ABSTRACT FROM AUTHOR]

Aims We tested the hypothesis that high novelty seeking (NS—an externalizing trait), sweet-liking (SL—a phenotype that may reflect processing of hedonic stimuli) and initial insensitivity to the impairing effects of alcohol (SRE-A) act independently and synergistically to increase the likelihood of having alcohol-related problems in young adults. Methods A sample of 145 young adults, ages 18–26, balanced for gender and alcohol use disorders identification test (AUDIT) scores <8 or ≥8 were selected from a prior sample. NS, SL and SRE-A were assessed along with AUDIT score and family history of alcoholism (FH). The effect of phenotypes and their interaction on the likelihood of alcohol problems was assessed. Results All three phenotypes contribute to the total AUDIT score. The best-fitting model explaining 35.8% of AUDIT variance includes all three phenotypes and an interaction between NS and SL/sweet-disliking (SDL) status. The addition of FH to the model explains an additional 4% of variance in both models. Classification and regression tree analysis showed that the main phenotype influencing AUDIT score is NS. The SL/SDL phenotype is a strong modifying factor for high NS. SRE-A was shown to be a weak modifier for individuals with low NS. Conclusion The evidence supports the hypothesis that the presence of multiple alcohol use disorders (AUD) risk phenotypes with different underlying neurobiological mechanisms within an individual (SL, NS and SRE-A) represents a higher likelihood for developing alcohol-related problems and may allow for a graded assessment of risk for AUD and offer the possibility for early intervention strategies. [ABSTRACT FROM AUTHOR]

Agent-based models (ABMs) have become a common tool for estimating demand for hospital beds during the COVID-19 pandemic. A key parameter in these ABMs is the probability of hospitalization for agents with COVID-19. Many published COVID-19 ABMs use either single point or age-specific estimates of the probability of hospitalization for agents with COVID-19, omitting key factors: comorbidities and testing status (i.e., received vs. did not receive COVID-19 test). These omissions can inhibit interpretability, particularly by stakeholders seeking to use an ABM for transparent decision-making. We introduce a straightforward yet novel application of Bayes' theorem with inputs from aggregated hospital data to better incorporate these factors in an ABM. We update input parameters for a North Carolina COVID-19 ABM using this approach, demonstrate sensitivity to input data selections, and highlight the enhanced interpretability and accuracy of the method and the predictions. We propose that even in tumultuous scenarios with limited information like the early months of the COVID-19 pandemic, straightforward approaches like this one with discrete, attainable inputs can improve ABMs to better support stakeholders. [ABSTRACT FROM AUTHOR]

Public health decision makers rely on hospitalization forecasts to inform COVID-19 pandemic planning and resource allocation. Hospitalization forecasts are most relevant when they are accurate, made available quickly, and updated frequently. We rapidly adapted an agent-based model (ABM) to provide weekly 30-day hospitalization forecasts (i.e., demand for intensive care unit [ICU] beds and non-ICU beds) by state and region in North Carolina for public health decision makers. The ABM was based on a synthetic population of North Carolina residents and included movement of agents (i.e., patients) among North Carolina hospitals, nursing homes, and the community. We assigned SARS-CoV-2 infection to agents using county-level compartmental models and determined agents' COVID-19 severity and probability of hospitalization using synthetic population characteristics (e.g., age, comorbidities). We generated weekly 30-day hospitalization forecasts during May-December 2020 and evaluated the impact of major model updates on statewide forecast accuracy under a SARS-CoV-2 effective reproduction number range of 1.0-1.2. Of the 21 forecasts included in the assessment, the average mean absolute percentage error (MAPE) was 7.8% for non-ICU beds and 23.6% for ICU beds. Among the major model updates, integration of near-real-time hospital occupancy data into the model had the largest impact on improving forecast accuracy, reducing the average MAPE for non-ICU beds from 6.6% to 3.9% and for ICU beds from 33.4% to 6.5%. Our results suggest that future pandemic hospitalization forecasting efforts should prioritize early inclusion of hospital occupancy data to maximize accuracy. [ABSTRACT FROM AUTHOR]

Background: In this methodological paper, we use a novel, predictive approach to examine how demographics, substance use, mental and other health indicators predict multiple visits (≥3) to emergency departments (ED) within a year.Methods: State-of-the-art predictive methods were used to evaluate predictive ability and factors predicting multiple visits to ED within a year and to identify factors that influenced the strength of the prediction. The analysis used public-use datasets from the 2015-2018 National Surveys on Drug Use and Health (NSDUH), which used the same questionnaire on the variables of interest. Analysis focused on adults aged ≥18 years. Several predictive models (regressions, trees, and random forests) were validated and compared on independent datasets.Results: Predictive ability on a test set for multiple ED visits (≥3 times within a year) measured as the area under the receiver operating characteristic (ROC) reached 0.8, which is good for a national survey. Models revealed consistency in predictive factors across the 4 survey years. The most influential variables for predicting ≥3 ED visits per year were fair/poor self-rated health, being nervous or restless/fidgety, having a lower income, asthma, heart condition/disease, having chronic obstructive pulmonary disease (COPD), nicotine dependence, African-American race, female sex, having diabetes, and being of younger age (18-20).Conclusions: The findings reveal the need to address behavioral and mental health contributors to ED visits and reinforce the importance of developing integrated care models in primary care settings to improve mental health for medically vulnerable patients. The presented modeling approach can be broadly applied to national and other large surveys. [ABSTRACT FROM AUTHOR]

Antibiotic exposure can lead to unintended outcomes, including drug-drug interactions, adverse drug events, and healthcare-associated infections like Clostridioides difficile infection (CDI). Improving antibiotic use is critical to reduce an individual's CDI risk. Antibiotic stewardship initiatives can reduce inappropriate antibiotic prescribing (e.g., unnecessary antibiotic prescribing, inappropriate antibiotic selection), impacting both hospital (healthcare)-onset (HO)-CDI and community-associated (CA)-CDI. Previous computational and mathematical modeling studies have demonstrated a reduction in CDI incidence associated with antibiotic stewardship initiatives in hospital settings. Although the impact of antibiotic stewardship initiatives in long-term care facilities (LTCFs), including nursing homes, and in outpatient settings have been documented, the effects of specific interventions on CDI incidence are not well understood. We examined the relative effectiveness of antibiotic stewardship interventions on CDI incidence using a geospatially explicit agent-based model of a regional healthcare network in North Carolina. We simulated reductions in unnecessary antibiotic prescribing and inappropriate antibiotic selection with intervention scenarios at individual and network healthcare facilities, including short-term acute care hospitals (STACHs), nursing homes, and outpatient locations. Modeled antibiotic prescription rates were calculated using patient-level data on antibiotic length of therapy for the 10 modeled network STACHs. By simulating a 30% reduction in antibiotics prescribed across all inpatient and outpatient locations, we found the greatest reductions on network CDI incidence among tested scenarios, namely a 17% decrease in HO-CDI incidence and 7% decrease in CA-CDI. Among intervention scenarios of reducing inappropriate antibiotic selection, we found a greater impact on network CDI incidence when modeling this reduction in nursing homes alone compared to the same intervention in STACHs alone. These results support the potential importance of LTCF and outpatient antibiotic stewardship efforts on network CDI burden and add to the evidence that a coordinated approach to antibiotic stewardship across multiple facilities, including inpatient and outpatient settings, within a regional healthcare network could be an effective strategy to reduce network CDI burden. [ABSTRACT FROM AUTHOR]

Agent-based models (ABMs) describe and simulate complex systems comprising unique agents, or individuals, while accounting for geospatial and temporal variability among dynamic processes. ABMs are increasingly used to study healthcare-associated infections (ie, infections acquired during admission to a healthcare facility), including Clostridioides difficile infection, currently the most common healthcare-associated infection in the United States. The overall burden and transmission dynamics of healthcare-associated infections, including C difficile infection, may be influenced by community sources and movement of people among healthcare facilities and communities. These complex dynamics warrant geospatially explicit ABMs that extend beyond single healthcare facilities to include entire systems (eg, hospitals, nursing homes and extended care facilities, the community). The agents in ABMs can be built on a synthetic population, a model-generated representation of the actual population with associated spatial (eg, home residence), temporal (eg, change in location over time), and nonspatial (eg, sociodemographic features) attributes. We describe our methods to create a geospatially explicit ABM of a major regional healthcare network using a synthetic population as microdata input. We illustrate agent movement in the healthcare network and the community, informed by patient-level medical records, aggregate hospital discharge data, healthcare facility licensing data, and published literature. We apply the ABM output to visualize agent movement in the healthcare network and the community served by the network. We provide an application example of the ABM to C difficile infection using a natural history submodel. We discuss the ABM's potential to detect network areas where disease risk is high; simulate and evaluate interventions to protect public health; adapt to other geographic locations and healthcare-associated infections, including emerging pathogens; and meaningfully translate results to public health practitioners, healthcare providers, and policymakers. Agent-based models describe and simulate complex systems comprising unique agents, or individuals, while accounting for geospatial and temporal variability among dynamic processes. They are increasingly used to study healthcare-associated infections, including Clostridioides difficile infection. The authors describe their methods of creating a geospatially explicit model of a major regional healthcare network using a synthetic population as microdata input. [ABSTRACT FROM AUTHOR]

Ohio is leading the nation in an epidemic of overdose deaths, most of which are caused by opioids. Through this study we estimate associations between opioid drug overdoses measured as EMS calls and model-predicted drug misuse. The RTI-developed synthetic population statistically represents every household in Cincinnati and allows one to develop a geographically explicit model that links Cincinnati EMS data, and other datasets. From the publicly available National Survey on Drug Use and Health (NSDUH), we developed a model of opioid misuse and assigned probability of misuse to each synthetic individual. We then analyzed EMS overdose data in the context of local level misuse and demographic characteristics. The main results show locations where there is a dramatic variation in ratio values between overdose events and the number of misusers. We concluded that, for optimal efficacy, intervention strategies should consider the existence of exceptional geographic locations with extremely high or low values of this ratio. [ABSTRACT FROM AUTHOR]

The article discusses the increase demand of forensic accountants driven by the growing number of forensic accounting courses. It explores on the availability of forensic accounting education including undergraduate and graduate degrees, certificates and concentrations. It also cites the components in teaching forensic accounting which include service learning activities, coteaching with a practitioner and internships.

The article focuses on the rise in demand for forensic accountants as universities increase the number of forensic accounting courses they offer and by offering undergraduate and graduate degrees, certificates in forensic accounting. It mentions differences in opinions regarding important content areas and teaching techniques in forensic accounting education. It also mentions opportunities with federal agencies such as the Federal Bureau of Investigation (FBI).

Background: When shared by people who inject drugs, needles and syringes with different dead space may affect the probability of HIV and hepatitis C virus (HCV) transmission differently. Methods: We measured dead space in 56 needle and syringe combinations obtained from needle and syringe programs across 17 countries in Europe and Asia. We also calculated the amounts of blood and HIV that would remain in different combinations following injection and rinsing. Results: Syringe barrel capacities ranged from 0.5 to 20 mL. Needles ranged in length from 8 to 38 mm. The average dead space was 3 μL in low dead space syringes with permanently attached needles, 13 μL in high dead space syringes with low dead space needles, 45 μL in low dead space syringes with high dead space needles, and 99 μL in high dead space syringes with high dead space needles. Among low dead space designs, calculated volumes of blood and HIV viral burden were lowest for low dead space syringes with permanently attached needles and highest for low dead space syringes with high dead space needles. Conclusion: The dead space in different low dead space needle and syringe combinations varied substantially. To reduce HIV transmission related to syringe sharing, needle and syringe programs need to combine this knowledge with the needs of their clients. [ABSTRACT FROM AUTHOR]

Background: Summary of findings tables in systematic reviews are highly informative but require epidemiological training to be interpreted correctly. The usage of fishbone diagrams as graphical displays could offer researchers an effective approach to simplify content for readers with limited epidemiological training. In this paper we demonstrate how fishbone diagrams can be applied to systematic reviews and present the results of an initial user testing.Methods: Findings from two systematic reviews were graphically depicted in the form of the fishbone diagram. To test the utility of fishbone diagrams compared with summary of findings tables, we developed and pilot-tested an online survey using Qualtrics. Respondents were randomized to the fishbone diagram or a summary of findings table presenting the same body of evidence. They answered questions in both open-ended and closed-answer formats; all responses were anonymous. Measures of interest focused on first and second impressions, the ability to find and interpret critical information, as well as user experience with both displays. We asked respondents about the perceived utility of fishbone diagrams compared to summary of findings tables. We analyzed quantitative data by conducting t-tests and comparing descriptive statistics.Results: Based on real world systematic reviews, we provide two different fishbone diagrams to show how they might be used to display complex information in a clear and succinct manner. User testing on 77 students with basic epidemiological training revealed that participants preferred summary of findings tables over fishbone diagrams. Significantly more participants liked the summary of findings table than the fishbone diagram (71.8% vs. 44.8%; p < .01); significantly more participants found the fishbone diagram confusing (63.2% vs. 35.9%, p < .05) or indicated that it was difficult to find information (65.8% vs. 45%; p < .01). However, more than half of the participants in both groups were unable to find critical information and answer three respective questions correctly (52.6% in the fishbone group; 51.3% in the summary of findings group).Conclusions: Fishbone diagrams are compact visualizations that, theoretically, may prove useful for summarizing the findings of systematic reviews. Initial user testing, however, did not support the utility of such graphical displays. [ABSTRACT FROM AUTHOR]

Computational neuroscience is a field that traces its origins to the efforts of Hodgkin and Huxley, who pioneered quantitative analysis of electrical activity in the nervous system. While also continuing as an independent field, computational neuroscience has combined with computational systems biology, and neural multiscale modeling arose as one offshoot. This consolidation has added electrical, graphical, dynamical system, learning theory, artificial intelligence and neural network viewpoints with the microscale of cellular biology (neuronal and glial), mesoscales of vascular, immunological and neuronal networks, on up to macroscales of cognition and behavior. The complexity of linkages that produces pathophysiology in neurological, neurosurgical and psychiatric disease will require multiscale modeling to provide understanding that exceeds what is possible with statistical analysis or highly simplified models: how to bring together pharmacotherapeutics with neurostimulation, how to personalize therapies, how to combine novel therapies with neurorehabilitation, how to interlace periodic diagnostic updates with frequent reevaluation of therapy, how to understand a physical disease that manifests as a disease of the mind. Multiscale modeling will also help to extend the usefulness of animal models of human diseases in neuroscience, where the disconnects between clinical and animal phenomenology are particularly pronounced. Here we cover areas of particular interest for clinical application of these new modeling neurotechnologies, including epilepsy, traumatic brain injury, ischemic disease, neurorehabilitation, drug addiction, schizophrenia and neurostimulation. [ABSTRACT FROM AUTHOR]

Purpose - This paper aims to determine current views of educators and practitioners regarding forensic accounting education, given the recent dramatic growth in the number of colleges and universities offering such education. Design/methodology/approach - Survey, with statistical analysis of responses. Findings - Results find that while both groups agree the demand for forensic accounting services will increase in the near future and that they prefer a separate course or degree be offered at the graduate and undergraduate levels, there are several significant differences between the educators' and practitioners' opinions on forensic accounting content and preferred teaching techniques. Practitioners consider topics outside traditional accounting as more important to include in forensic accounting education, and more highly value teaching techniques that add an experiential learning component. Practical implications - Results can be useful to educators who have not considered offering forensic accounting courses or who wish to refine or update their existing forensic accounting education. Students seeking a career in forensic accounting can also use these results to facilitate their course choices to develop skills that employers value. Social implications - These results find that practitioners value some nontraditional accounting skills, such as in forensic technology and interviewing, more highly than academics, which suggests accounting educators may need to develop interdisciplinary approaches to forensic accounting education. Originality/value - As the number of institutions offering forensic accounting education has dramatically increased, current views of forensic accounting educators and practitioners are needed. [ABSTRACT FROM AUTHOR]

Purpose: Tailored health care interventions are expected to transform clinical practice. The objective of this study was to develop an innovative model to assess the effectiveness, cost, and harms of risk stratified colorectal cancer screening.Methods: We updated a previously validated microsimulation model consisting of three interlinked components: risk assessment, natural history, and screening/treatment modules. We used data from representative national surveys and the literature to create a synthetic population that mimics the family history and genetic profile of the US population. We applied risk stratification based on published risk assessment tools to triage individuals into five risk categories: high, increased, medium, decreased, and low.Results: On average, the incremental cost of risk stratified screening for colorectal cancer compared to the current approach at 60% and 80% compliance rates is $18,342 and $23,961 per life year gained. The harms in terms of false positives and perforations are consistently lower for personalized scenarios across all compliance rates. False positives are reduced by more than 47.0% and perforations by at least 9.9%. There is considerable uncertainty in the life years gained, but the reduction in harms remains stable under all scenarios.Conclusion: A key finding is that risk stratified screening can reduce harms at all levels of compliance. Therefore, selection of screening scenarios should include comprehensive comparisons of mortality, harms from screening, and cost. This study provides guidance for evaluating risk stratified cancer screening and further research is required to identify optimal implementation approaches in the real-world setting. [ABSTRACT FROM AUTHOR]

Background: Nonmedical prescription opioid use has been linked to hepatitis C virus (HCV) infection among people who inject drugs and with using high dead space syringes that retain more blood and transfer more HIV if shared. Little is known regarding its effects on sex-risk behaviors.Objectives: This paper examines event-level associations between nonmedical prescription opioid use and sharing high dead space syringes (injection risk) and unprotected intercourse (sex risk) behaviors.Methods: We recruited 1,985 participants from two overlapping risk groups-drug users and men who have sex with men (MSM)-and their sex partners. Participants completed an interview that included event-level sex questions with recent sex partners and injection questions with recent injection partners. We used multivariable generalized estimating equations (GEE) to assess the associations between nonmedical prescription opioid use and unprotected intercourse during sexual encounters and sharing syringes during injection episodes, while adjusting for within-person correlations.Results: When both partners used nonmedical prescription opioids, its use was independently associated with unprotected intercourse in sexual encounters (OR = 2.24; 95% CI = 1.12, 4.49). The use of nonmedical prescription opioids was also associated with sharing high dead space syringes during injection episodes (OR = 6.57; 95% CI = 1.63, 26.51).Conclusion: Nonmedical prescription opioid use is associated with an increase in the risk of unprotected sex and sharing high dead space syringes. HIV and HCV prevention interventions for nonmedical prescription opioid users should address sex-risk behaviors and encourage the use of acceptable low dead space needles and syringes. [ABSTRACT FROM AUTHOR]

Proposed is a multicomponent model for the estimation of light-emitting diode (LED) lumen maintenance using test data that were acquired in accordance with the test standards of the Illumination Engineering Society of North America, i.e., LM-80-08. Lumen maintenance data acquired with this test do not always follow exponential decay, particularly data collected in the first 1000 h or under low-stress (e.g., low temperature) conditions. This deviation from true exponential behavior makes it difficult to use the full data set in models for the estimation of lumen maintenance decay coefficient. As a result, critical information that is relevant to the early life or low-stress operation of LED light sources may be missed. We present an efficiency-decay model approach, where all lumen maintenance data can be used to provide an alternative estimate of the decay rate constant. The approach considers a combined model wherein one part describes an initial “break-in” period and another part describes the decay in lumen maintenance. During the break-in period, several mechanisms within the LED can act to produce a small (typically < 10%) increase in luminous flux. The effect of the break-in period and its longevity is more likely to be present at low-ambient temperatures and currents, where the discrepancy between a standard TM-21 approach and our proposed model is the largest. For high temperatures and currents, the difference between the estimates becomes nonsubstantial. Our approach makes use of all the collected data and avoids producing unrealistic estimates of the decay coefficient. [ABSTRACT FROM PUBLISHER]

To impact population health, it is critical to collaborate across disciplinary and practice-based silos and integrate resources, experiences, and knowledge to exert positive change. Complex systems shape both the prevention outcomes researchers, practitioners, and policymakers seek to impact and how research is translated and can either impede or support movement from basic scientific discovery to impactful and scaled-up prevention practice. Systems science methods can be used to facilitate designing translation support that is grounded in a richer understanding of the many interacting forces affecting prevention outcomes across contexts. In this paper, we illustrate how one systems science method, system dynamics, could be used to advance research, practice, and policy initiatives in each stage of translation from discovery to translation of innovation into global communities (T0-T5), with tobacco prevention as an example. System dynamics can be applied to each translational stage to integrate disciplinary knowledge and document testable hypotheses to inform translation research and practice. [ABSTRACT FROM AUTHOR]

Background: While efficacy of Sudarshan Kriya Yoga (SKY) has been demonstrated in a number of prior studies, little is known about the effects of SKY taught as part of the Your Enlightened Side (YES+) workshop designed for college students and other young adults. Aims: This study aimed to assess the effects of YES+, a yogic breathing-based life skills workshop, on multiple measures of well-being and physiological stress response. Materials and Methods: Two nonrandomized open-trial pilot studies were conducted with a total of 74 young adults (age 25.4 ± 6.6 years; 55% female). Study 1 collected a variety of self-report questionnaires at baseline, postworkshop, and 1-month follow-up. Study 2 collected self-report questionnaires in addition to electrocardiography with a stationary cycling challenge at baseline and 1-month follow-up. Results: Study 1: Improvements in self-reported depression (P's ≤ 0.010), perceived stress (P's ≤ 0.002), life satisfaction (P's ≤ 0.002), social connectedness (P's ≤ 0.004), and gratitude (P's ≤ 0.090) were observed at postworkshop and 1-month after workshop relative to baseline. Study 2: Improvements in self-reported emotion regulation were observed at 1-month follow-up relative to baseline (P = 0.019). Positive and Negative Affect Schedule-Expanded Form positive affect increased (P = 0.021), while fatigue and sadness decreased (P's ≤ 0.005). During the stationary cycling challenge, rate to recovery of electrocardiography inter-beat interval also increased from baseline to 1-month follow-up (P = 0.077). Conclusions: These findings suggest that a life skills workshop integrating yogic breathing techniques may provide self-empowering tools for enhancing well-being in young adults. Future research is indicated to further explore these effects, particularly in regards to vagal tone and other aspects of stress physiology. [ABSTRACT FROM AUTHOR]

Background: "Low dead space" syringes with permanently attached needles retain less fluid, blood, and HIV after use than standard "high dead space" syringes. This reduces the probability of HIV transmission if they are shared by people who inject drugs (PWID). The World Health Organization recently recommended that needle and syringe programs (NSP) offer clients low dead space syringes. The success of this recommendation will depend on PWID switching to low dead space needles and syringes. This paper examines the needles and syringes that PWID in Tajikistan use and factors that influence their choices. Methods: In May 2014, we conducted six focus groups in Kulob and six in Khorog, Tajikistan, with a total of 100 participants. NSP staff members recruited participants. Focus group topics included the needles and syringes used and factors that influence choice of needles and syringes. Focus groups were conducted in Russian and Tajik, audio recorded, transcribed, and translated into English. The translated files were imported into NVivo 10 for coding and analysis. Results: All participants in both cities were male and reported injecting heroin. Everyone also reported using syringes with detachable needles almost exclusively. The most popular syringe sizes were 2 and 5 ml. Needles ranged in gauge from 25 to 21 g. Needle gauge was influenced by the size of the vein, the viscosity of drug solution to be injected, and problems with blood clotting. Needles ranged in length from 12 to 38 mm, with 25 and 32 mm being the most popular. Needle length was influenced by the depth of the vein being used. Many PWID inject volumes of fluid greater than 1 ml into deep veins that require needles at least 25 mm long and 25 g in diameter. Conclusion: Most low dead space syringes are 1-ml insulin syringes with 12 mm 28 g permanently attached needles. Findings from this project suggest that these will not be acceptable to PWID who need larger syringes and longer and thicker needles that are detachable. Low dead space detachable needles appear to be an acceptable option that could overcome barriers to the widespread use of low dead space equipment for reducing HIV and HCV transmission. [ABSTRACT FROM AUTHOR]

Aim The goal of this systematic review was to identify moderators of naltrexone efficacy in the treatment of alcohol dependence. Methods We searched Pubmed, CINHAL, Embase, PsycINFO and the Cochrane Library from 1990 to April 2012 and reference lists of pertinent review articles, which yielded 622 trial, pooled analysis and review articles. Using pre-established eligibility criteria, two reviewers independently determined whether abstracts contained evidence of demographic or biological characteristics, i.e. moderators, influencing naltrexone response in alcohol dependence. We assessed each publication for risk of bias and evaluated the strength of the body of evidence for each moderator. Results Twenty-eight publications (on 20 studies) met criteria for data synthesis. These included 26 publications from 12 randomized, placebo-controlled trials, three non-randomized, non-placebo studies and one randomized, non-placebo study. In addition, there were two publications from pooled analyses of four randomized, placebo-controlled trials. Family history of alcohol problems and the Asn40 Asp polymorphism of the μ-opioid receptor gene showed a positive association with efficacy in four of five and three of five studies, respectively. Other moderators reported to be associated with efficacy included male sex (two of five studies), pre-treatment drinking (two of two studies) and high craving (two of five studies). However, the overall risk of bias in the published literature is high. Conclusions The identification of naltrexone-responsive alcohol-dependent patients is still in development. Studies to date point to two potential moderators-family history and presence of the OPRM1 Asn40 Asp polymorphism-as having the strongest evidence. However, the data to date is still insufficient to recommend that any moderator be used in determining clinical treatment. [ABSTRACT FROM AUTHOR]

Background We tested the hypothesis that high novelty seeking ( NS) (a trait that promotes experimentation) and sweet-liking ( SL) (a phenotype that may reflect processing of hedonic stimuli) act independently and synergistically to increase the risk of having alcohol-related problems in young adults. Methods A sample of 163 young adults, ages 18 to 26, was recruited and balanced for gender and evidence for presence of alcohol problems to yield 150 evaluable participants. NS was evaluated using the Tridimensional Personality Questionnaire. Pleasurable response to sweet taste was tested to identify sweet-likers and sweet-dislikers. Alcohol use and problems were assessed by the Alcohol Use Disorders Identification Test and the Rutgers Alcohol Problem Index. Results NS, but not SL, was positively and significantly associated with alcohol consumption and alcohol problems; however, the effect of NS on alcohol problems was significantly enhanced in the presence of the SL phenotype, thus showing a strong synergistic interaction. The combination of SL and high NS was associated with increased odds of having alcohol problems −20.64 (95% CI: −89.98, 4.74) compared to those with low NS and sweet-disliking. Other combinations did not produce such odds ratios. SL and low NS showed OR = 1.88 (95% CI 0.44, 7.99), and sweet-dislikers and high novelty seekers had OR = 4.07 (95%, CI 1.01, 16.46). Conclusions These results support and extend our hypothesis that as clinically distinct phenotypes, high NS and the SL phenotype are associated with risk of alcohol-related problems. High NS is associated with the use of alcohol, and the presence of the SL phenotype appears to bias an individual to alcohol problems once alcohol use is initiated. Understanding the biology and phenomenology of these phenotypes will allow a more complete picture of the processes that lead to alcohol problems. [ABSTRACT FROM AUTHOR]

This project explores techniques for reducing the complexity of an agent-based model (ABM). The analysis involved a model developed from the ethnographic research of Dr. Lee Hoffer in the Larimer area heroin market, which involved drug users, drug sellers, homeless individuals and police. The authors used statistical techniques to create a reduced version of the original model which maintained simulation fidelity while reducing computational complexity. This involved identifying key summary quantities of individual customer behavior as well as overall market activity and replacing some agents with probability distributions and regressions. The model was then extended to allow external market interventions in the form of police busts. Extensions of this research perspective, as well as its strengths and limitations, are discussed. [ABSTRACT FROM AUTHOR]

Importance: Alcohol use disorders cause substantial morbidity and early mortality yet remain greatly undertreated. Medications are considerably underused.Objective: To conduct a systematic review and meta-analysis of the benefits and harms of medications (US FDA-approved and others) for adults with alcohol use disorders.Data Sources: PubMed, Cochrane Library, PsycINFO, CINAHL, EMBASE, FDA website, and clinical trials registries (January 1, 1970, to March 1, 2014).Study Selection: Two reviewers selected randomized clinical trials (RCTs) with at least 12 weeks' duration that reported eligible outcomes and head-to-head prospective cohort studies reporting health outcomes or harms.Data Extraction and Synthesis: We conducted meta-analyses using random-effects models and calculated numbers needed to treat for benefit (NNTs) or harm (NNHs).Main Outcomes and Measures: Alcohol consumption, motor vehicle crashes, injuries, quality of life, function, mortality, and harms.Results: We included 122 RCTs and 1 cohort study (total 22,803 participants). Most assessed acamprosate (27 studies, n = 7519), naltrexone (53 studies, n = 9140), or both. The NNT to prevent return to any drinking for acamprosate was 12 (95% CI, 8 to 26; risk difference [RD], -0.09; 95% CI, -0.14 to -0.04) and was 20 (95% CI, 11 to 500; RD, -0.05; 95% CI, -0.10 to -0.002) for oral naltrexone (50 mg/d). The NNT to prevent return to heavy drinking was 12 (95% CI, 8 to 26; RD -0.09; 95% CI, -0.13 to -0.04) for oral naltrexone (50 mg/d). Meta-analyses of trials comparing acamprosate to naltrexone found no statistically significant difference between them for return to any drinking (RD, 0.02; 95% CI, -0.03 to 0.08) or heavy drinking (RD, 0.01; 95% CI, -0.05 to 0.06). For injectable naltrexone, meta-analyses found no association with return to any drinking (RD, -0.04; 95% CI, -0.10 to 0.03) or heavy drinking (RD, -0.01; 95% CI, -0.14 to 0.13) but found an association with reduction in heavy drinking days (weighted mean difference [WMD], -4.6%; 95% CI, -8.5% to -0.56%). Among medications used off-label, moderate evidence supports an association with improvement in some consumption outcomes for nalmefene (heavy drinking days per month: WMD, -2.0; 95% CI, -3.0 to -1.0; drinks per drinking day: WMD, -1.02; 95% CI, -1.77 to -0.28) and topiramate (% heavy drinking days: WMD, -9.0%; 95% CI, -15.3% to -2.7%; drinks per drinking day: WMD, -1.0; 95% CI, -1.6 to -0.48). For naltrexone and nalmefene, NNHs for withdrawal from trials due to adverse events were 48 (95% CI, 30 to 112) and 12 (95% CI, 7 to 50), respectively; risk was not significantly increased for acamprosate or topiramate.Conclusions and Relevance: Both acamprosate and oral naltrexone were associated with reduction in return to drinking. When directly compared with one another, no significant differences were found between acamprosate and naltrexone for controlling alcohol consumption. Factors such as dosing frequency, potential adverse events, and availability of treatments may guide medication choice. [ABSTRACT FROM AUTHOR]

IMPORTANCE Alcohol use disorders cause substantial morbidity and early mortality yet remain greatly undertreated. Medications are considerably underused. OBJECTIVE To conduct a systematic review and meta-analysis of the benefits and harms of medications (US FDA-approved and others) for adults with alcohol use disorders. DATA SOURCES PubMed, Cochrane Library, PsyclNFO, CINAHL, EMBASE, FDA website, and clinical trials registries (January 1,1970, to March 1,2014). STUDY SELECTION Two reviewers selected randomized clinical trials (RCTs) with at least 12 weeks' duration that reported eligible outcomes and head-to-head prospective cohort studies reporting health outcomes or harms DATA EXTRACTION AND SYNTHESIS We conducted meta-analyses using random-effects models and calculated numbers needed to treat for benefit (NNTs) or harm (NNHs). MAIN OUTCOMES AND MEASURES Alcohol consumption, motor vehicle crashes, injuries, quality of life, function, mortality, and harms. RESULTS We included 122 RCTs and 1 cohort study (total 22 803 participants). Most assessed acamprosate (27 studies, n = 7519), naltrexone (53 studies, n = 9140), or both. The NNT to prevent return to any drinking for acamprosate was 12 (95% Ci, 8 to 26; risk difference [RD], -0.09; 95% CI, -0.14 to -0.04) and was 20 (95% CI, 11 to 500; RD, -0.05; 95% CI, -0.10 to -0.002) for oral naltrexone (50 mg/d). The NNT to prevent return to heavy drinking was 12 (95% CI, 8 to 26; RD -0.09; 95% CI, -0.13 to -0.04) for oral naltrexone (50 mg/d). Meta-analyses of trials comparing acamprosate to naltrexone found no statistically significant difference between them for return to any drinking (RD, 0.02; 95% CI, -0.03 to 0.08) or heavy drinking (RD, 0.01; 95% CI, -0.05 to 0.06). For injectable naltrexone, meta-analyses found no association with return to any drinking (RD, -0.04; 95% CI, -0.10 to 0.03) or heavy drinking (RD, -0.01; 95% CI, -0.14 to 0.13) but found an association with reduction in heavy drinking days (weighted mean difference [WMD], -4.6%; 95% CI, -8.5% to -0.56%). Among medications used off-label, moderate evidence supports an association with improvement in some consumption outcomes for nalmefene (heavy drinking days per month: WMD, -2.0; 95% CI, -3.0 to -1.0; drinks per drinking day: WMD, -1.02; 95% CI, -1.77 to -0.28) and topiramate (% heavy drinking days; WMD, -9.0%; 95% CI, -15.3% to -2.7%; drinks per drinking day: WMD, -1.0; 95% CI, -1.6 to -0.48). For naltrexone and nalmefene, NNHs for withdrawal from trials due to adverse events were 48 (95% CI, 30 to 112) and 12 (95% CI, 7 to 50), respectively; risk was not significantly increased for acamprosate or topiramate. CONCLUSIONS AND RELEVANCE Both acamprosate and oral naltrexone were associated with reduction in return to drinking. When directly compared with one another, no significant differences were found between acamprosate and naltrexone for controlling alcohol consumption. Factors such as dosing frequency, potential adverse events, and availability of treatments may guide medication choice. [ABSTRACT FROM AUTHOR]

A number of factors have been identified that are related to sexual and injecting HIV transmission. We developed a probabilistic mathematical model to put these factors together and interpret risks in the context of individual behavior among injecting drug-using (IDU) couples in St. Petersburg, Russia. Some HIV-discordant couples have unprotected sex and sometimes inject drugs together but stay discordant for a long time, while some individuals acquire HIV on the first encounter. We considered existing estimates of HIV transmission risks through injecting and sexual contacts to develop a predictive survival model for an individual who is exposed to HIV through intimate relationships. We computed simulated survival curves for a number of behavioral scenarios and discussed sources of simulated uncertainty. We then applied the model to a longitudinal study of HIV-discordant couples and validated the model’s forecast. Although individual prediction of seroconversion time appeared impossible, the ability to rank behavioral patterns in terms of HIV risk and to estimate the probability of survival HIV-free will be important to educators and counselors. [ABSTRACT FROM AUTHOR]

There is a need for brief HIV prevention interventions that can be disseminated and implemented widely. This article reports the results of a small randomized field experiment that compared the relative effects of a brief two-session counselor-delivered computer-tailored intervention and a control condition. The intervention is designed for use with African-American, non-Hispanic white and Hispanic males and females who may be at risk of HIV through unprotected sex, selling sex, male to male sex, injecting drug use or use of stimulants. Participants ( n = 120) were recruited using a quota sampling approach and randomized using block randomization, which resulted in ten male and ten female participants of each racial/ethnic group (i.e. African-American, non-Hispanic white and Hispanic) being assigned to either the intervention or a control arm. In logistic regression analyses using a generalized estimating equations approach, at 3-month followup, participants in the intervention arm were more likely than participants in the control arm to report condom use at last sex (Odds ratio [OR] = 4.75; 95 % Confidence interval [CI] = 1.70-13.26; p = 0.003). The findings suggest that a brief tailored intervention may increase condom use. Larger studies with longer followups are needed to determine if these results can be replicated. [ABSTRACT FROM AUTHOR]

Background: Recursive partitioning is a non-parametric modeling technique, widely used in regression and classification problems. Model-based recursive partitioning is used to identify groups of observations with similar values of parameters of the model of interest. The mob() function in the party package in R implements model-based recursive partitioning method. This method produces predictions based on single tree models. Predictions obtained through single tree models are very sensitive to small changes to the learning sample. We extend the model-based recursive partition method to produce predictions based on multiple tree models constructed on random samples achieved either through bootstrapping (random sampling with replacement) or subsampling (random sampling without replacement) on learning data. Results: Here we present an R package called "mobForest" that implements bagging and random forests methodology for model-based recursive partitioning. The mobForest package constructs large number of model-based trees and the predictions are aggregated across these trees resulting in more stable predictions. The package also includes functions for computing predictive accuracy estimates and plots, residuals plot, and variable importance plot. Conclusion: The mobForest package implements a random forest type approach for model-based recursive partitioning. The R package along with it source code is available at http://CRAN.R-project.org/package=mobForest. [ABSTRACT FROM AUTHOR]

Aims To illustrate the impact of different proportions of injecting drug users (IDUs) sharing high dead-space syringes (HDSS) or low dead-space syringes (LDSS) on the probability of human immunodeficiency virus (HIV) transmission; and thus the impact on injection-related HIV prevalence and incidence. Design A stochastic mathematical model was used to evaluate the impact of HDSS use in high- and low-risk IDU populations. Model parameters were obtained from peer-reviewed publications. Analytical solutions of a simplified deterministic model were obtained to explain the effect of HDSS on HIV endemic states. Findings Simulation analysis shows that the HIV epidemic could be sustained even when a small percentage of sharing (10%) involved HDSS. The effect is much stronger in high-risk compared with low-risk populations. Steady state HIV prevalence increases with the proportion of HDSS, and for high- and low-risk populations reaches around 80% and 20%, respectively. For low-risk populations, the use of LDSS could result in the virtual elimination of HIV. These results are dependent upon an evidence-supported assumption of a significant difference in HIV transmission risk associated with HDSS versus LDSS. Conclusions Our models suggest that injection-related HIV epidemics may not occur when most (e.g. 95% or more) IDUs use LDSS. While these results are based on indirect risk measures and a number of simplifying assumptions, the effect of blood retained in high dead-space syringes on HIV prevalence seems to be very strong, even using relatively conservative assumptions. The findings have potential implications for needle exchange programs and the types of syringes produced and distributed world-wide. [ABSTRACT FROM AUTHOR]

This project applies agent-based modeling (ABM) techniques to better understand the operation, organization, and structure of a local heroin market. The simulation detailed was developed using data from an 18-month ethnographic case study. The original research, collected in Denver, CO during the 1990s, represents the historic account of users and dealers who operated in the Larimer area heroin market. Working together, the authors studied the behaviors of customers, private dealers, street-sellers, brokers, and the police, reflecting the core elements pertaining to how the market operated. After evaluating the logical consistency between the data and agent behaviors, simulations scaled-up interactions to observe their aggregated outcomes. While the concept and findings from this study remain experimental, these methods represent a novel way in which to understand illicit drug markets and the dynamic adaptations and outcomes they generate. Extensions of this research perspective, as well as its strengths and limitations, are discussed. [ABSTRACT FROM AUTHOR]

Gay and bisexual men are often treated as a homogenous group; however, there may be important differences between them. In addition, behaviorally bisexual men are a potential source of HIV infection for heterosexual women. In this study, we compared 97 men who have sex with men only (MSM) to 175 men who have sex with men and women (MSMW). We also compared the 175 MSMW to 772 men who have sex with women only (MSW). Bivariate and multiple logistic regression analyses were performed to assess correlates of MSMW risk behaviors with men and with women as well as whether MSMW, compared with MSW, engaged in more risky behaviors with women. Compared with MSM, MSMW were less likely to be HIV-positive or to engage in unprotected receptive anal intercourse. In contrast, MSMW were more likely than MSW to be HIV-positive and to engage in anal intercourse with their female partners; however, rates of unprotected anal intercourse were similar. The study findings suggest that there may be important differences in HIV risk behaviors and HIV prevalence between MSM and MSMW as well as between MSMW and MSW. [ABSTRACT FROM AUTHOR]

Transactional sex refers to selling sex (exchanging sex for money, drugs, food, shelter, or other items) or purchasing sex (exchanging money, drugs, food, shelter, or other items for sex). These activities have been associated with a higher risk for HIV and other sexually transmitted infections in a variety of populations and settings. This paper examines correlates of purchasing and selling sex in a large sample of drug users, men who have sex with men, and sex partners of these groups. Using respondent-driven sampling, participants were recruited between 2005 and 2008 in two urban and two rural counties in North Carolina. We used multiple logistic regressions to examine separate models for selling and purchasing sex in men and women. In addition, we estimated direct and indirect associations among independent variables in the logistic regression models and transactional sex using structural equation models. The analysis shows that factors associated with women selling and buying sex include being homeless, use of stimulants, bisexual behavior, and neighborhood disorder. There was also a significant difference by race. For men, the factors associated with selling and buying sex include being homeless, bisexual behavior, and not being in a relationship. Although neighborhood violence and disorder show significance in bivariate associations with the outcome, these associations disappear in the structural equation models. [ABSTRACT FROM AUTHOR]

Mathematical models that describe the global spread of infectious diseases such as influenza, severe acute respiratory syndrome (SARS), and tuberculosis (TB) often consider a sample of international airports as a network supporting disease spread. However, there is no consensus on how many cities should be selected or on how to select those cities. Using airport flight data that commercial airlines reported to the Official Airline Guide (OAG) in 2000, we have examined the network characteristics of network samples obtained under different selection rules. In addition, we have examined different size samples based on largest flight volume and largest metropolitan populations. We have shown that although the bias in network characteristics increases with the reduction of the sample size, a relatively small number of areas that includes the largest airports, the largest cities, the most-connected cities, and the most central cities is enough to describe the dynamics of the global spread of influenza. The analysis suggests that a relatively small number of cities (around 200 or 300 out of almost 3000) can capture enough network information to adequately describe the global spread of a disease such as influenza. Weak traffic flows between small airports can contribute to noise and mask other means of spread such as the ground transportation. [ABSTRACT FROM AUTHOR]

In the United States, a variety of programs have been developed to prevent substance use among youth. These programs often target youth directly, and may also have components that address the relational influence of families, schools, and communities. We discuss clustering of youth marijuana use within and between households and neighborhoods. As often discussed in the literature, we consider analyzing "components of variance" in a hierarchical sample design with two or more levels. With a continuous outcome variable, the estimated relative size of variance components at each level can be interpreted as its relative "importance." We estimate variance components when the outcome is dichotomous, and find that for the use of marijuana in the past year, the role of the individual (individual adolescent vs. role of household vs. role of neighborhood) is quite prominent (79% of variation). A similar result is observed for the continuous scale variable of individual positive attitudes toward drug use (83%). For continuous constructs related to either household (parental monitoring) or neighborhood (neighborhood disorganization) the majority of variation still occurs at the individual level (67% and 51%, respectively), although they reveal significant percent variation (about 30%) at the corresponding family or neighborhood levels as well. We discuss the use of variance component methodology and the relevance for prevention programs. [ABSTRACT FROM AUTHOR]

Background: The contribution of birth defects, including cleft lip and palate, to neonatal and infant mortality and morbidity is substantial. As other mortality and morbidity causes including infections, hygiene, prematurity, and nutrition are eradicated in less developed countries, the burden of birth defects will increase proportionally. Methods/Design: We are using cleft lip and palate as a sentinel birth defect to evaluate its burden on neonatal and infant health and to assess the effectiveness of systematic pediatric care during the first month and first two years of life in decreasing this burden. The neonatal intervention, consisting of weekly pediatric evaluation and referral to appropriate care, is delivered to about 696 infants born with cleft lip and/or palate in 47 hospitals in South America. Neonatal mortality in this group will be compared to that in a retrospective control group of about 464 infants born with cleft lip and/or palate in the same hospitals. The subgroup of infants with isolated clefts of both the lip and palate (about 264) is also randomized into two groups, intervened and non-intervened, and further followed up over 2 years. Intervened cases are evaluated by pediatricians every three months and referred for appropriate care. The intervened and non-intervened cases will be compared over study outcomes to evaluate the intervention effectiveness. Non-intervened cases are matched and compared to healthy controls to assess the burden of cleft lip and palate. Outcomes include child's neurological and physical development and family social and economic conditions. Discussion: Large-scale clinical trials to improve infant health in developing countries are commonly suggested, making it important to share the methods used in ongoing studies with other investigators implementing similar research. We describe here the content of our ongoing pediatric care study in South America. We hope that this may help researchers targeting this area to plan their studies more effectively and encourage the development of similar research efforts to target other birth defects or infant outcomes such as prematurity and low birth weight. [ABSTRACT FROM AUTHOR]