The Status of the United States Population of Night Shark, Carcharhinus signatus

Night sharks, Carcharhinus signatus, are an oceanic species generally occurring in outer continental shelf waters in the western North Atlantic Ocean including the Caribbean Sea and Gulf of Mexico. Although not targeted, night sharks make up a segment of the shark bycatch in the pelagic longline fishery. Historically, night sharks comprised a significant proportion of the artisanal Cuban shark fishery but today they are rarely caught. Although information from some fisheries has shown a decline in catches of night sharks, it is unclear whether this decline is due to changes in fishing tactics, market, or species identification. Despite the uncertainty in the decline, the night shark is currently listed as a species of concern due to alleged declines in abundance resulting from fishing effort, i.e. overutilization. To assess their relevance to the species of concern list, we collated available information on the night shark to provide an analysis of its status. Night shark landings were likely both over- and under-reported and thus probably did not reflect all commercial and recreational catches, and overall they have limited relevance to the current status of the species. Average size information has not changed considerably since the 1980’s based on information from the pelagic longline fishery when corrected for gear bias. Analysis of biological information indicates night sharks have intrinsic rates of increase (r) about 10% yr–1 and have moderate rebound potential and an intermediate generation time compared to other sharks. An analysis of trends in relative abundance from four data sources gave conflicting results, with one series in decline, two series increasing, and one series relatively flat. Based on the analysis of all currently available information, we believe the night shark does not qualify as a species of concern but should be retained on the prohibited species list as a precautionary approach to management until a more comprehensive stock assessment can be conducted

Examining the Relationship between Health-Related Need and the Receipt of Care by Participants Experiencing Homelessness and Mental Illness

Background People experiencing homelessness and mental illness face multiple barriers to care. The goal of this study was to examine the association between health service use and indicators of need among individuals experiencing homelessness and mental illness in Vancouver, Canada. We hypothesized that those with more severe mental illness would access greater levels of primary and specialist health services than those with less severe mental illness. Methods Participants met criteria for homelessness and current mental disorder using standardized criteria (n = 497). Interviews assessed current health status and involvement with a variety of health services including specialist, general practice, and emergency services. The 80th percentile was used to differentiate ‘low health service use’ and ‘high health service use’. Using multivariate logistic regression analysis, we analyzed associations between predisposing, enabling and need-related factors with levels of primary and specialist health service use. Results Twenty-one percent of participants had high primary care use, and 12% had high use of specialist services. Factors significantly (p ≤ 0.05) associated with high primary care use were: multiple physical illnesses [AOR 2.74 (1.12, 6.70]; poor general health [AOR 1.68 (1.01, 2.81)]; having a regular family physician [AOR 2.27 (1.27, 4.07)]; and negative social relationships [AOR 1.74 (1.01, 2.99)]. Conversely, having a more severe mental disorder (e.g. psychotic disorder) was significantly associated with lower odds of high service use [AOR 0.59 (0.35, 0.97)]. For specialist care, recent history of psychiatric hospitalization [AOR 2.53 (1.35, 4.75)] and major depressive episode [AOR 1.98 (1.11, 3.56)] were associated with high use, while having a blood borne infectious disease (i.e., HIV, HCV, HBV) was associated with lower odds of high service use. Conclusions Contrary to our hypotheses, we found that individuals with greater assessed need, including more severe mental disorders, and blood-borne infectious diseases had significantly lower odds of being high health service users than those with lower assessed needs. Our findings reveal an important gap between levels of need and service involvement for individuals who are both homeless and mentally ill and have implications for health service reform in relation to the unmet and complex needs of a marginalized sub-population. (Trial registration: ISRCTN57595077 and ISRCTN66721740)

Covariate balance in a Bayesian propensity score analysis of beta blocker therapy in heart failure patients

Regression adjustment for the propensity score is a statistical method that reduces confounding from measured variables in observational data. A Bayesian propensity score analysis extends this idea by using simultaneous estimation of the propensity scores and the treatment effect. In this article, we conduct an empirical investigation of the performance of Bayesian propensity scores in the context of an observational study of the effectiveness of beta-blocker therapy in heart failure patients. We study the balancing properties of the estimated propensity scores. Traditional Frequentist propensity scores focus attention on balancing covariates that are strongly associated with treatment. In contrast, we demonstrate that Bayesian propensity scores can be used to balance the association between covariates and the outcome. This balancing property has the effect of reducing confounding bias because it reduces the degree to which covariates are outcome risk factors

Propensity Score Adjustment for Unmeasured Confounding in Observational Studies

Adjusting for several unmeasured confounders is a challenging problem in the analysis of observational data. Information about unmeasured confounders is sometimes available from external validation data, such as surveys or secondary samples drawn from the same source population. In principal, the validation permits us to recover information about the missing data, but the difficulty is in eliciting a valid model for nuisance distribution of the unmeasured confounders. Motivated by a British study of the effects of trihalomethane exposure on full-term low birthweight, we describe a flexible Bayesian procedure for adjusting for a vector of unmeasured confounders using external validation data. We summarize the unmeasured confounders with a scalar summary score using the propensity score methodology of Rosenbaum and Rubin. The score has the property that it breaks the dependence between the exposure and unmeasured confounders within levels of measured confounders. To adjust for unmeasured confounding in a Bayesian analysis, we need only update and adjust for the summary score during Markov chain Monte Carlo simulation. We demonstrate that trihalomethane exposure is associated with increased risk of full-term low birthweight, and this association persists even after adjusting for eight unmeasured confounders. Empirical results from simulation illustrate that our proposed method eliminates bias from several unmeasured confounders, even in small samples

Adjustment for Missing Confounders Using External Validation Data and Propensity Scores

Reducing bias from missing confounders is a challenging problem in the analysis of observational data. Information about missing variables is sometimes available from external validation data, such as surveys or secondary samples drawn from the same source population. In principle, the validation data permits us to recover information about the missing data, but the di�culty is in eliciting a valid model for nuisance distribution of the missing confounders. Motivated by a British study of the e�ects of trihalomethane exposure on risk of full-term low birthweight, we describe a exible Bayesian procedure for adjusting for a vector of missing confounders using external validation data. We summarize the missing confounders with a scalar summary score using the propensity score methodology of Rosenbaum and Rubin. The score has the property that it induces conditional independence between the exposure and the missing confounders given the measured confounders. It balances the unmeasured confounders across exposure groups, within levels of measured covariates. To adjust for bias, we need only model and adjust for the summary score during Markov chain Monte Carlo simulation. Simulation results illustrate that the proposed method reduces bias from several missing confounders over a range of di�erent sample sizes for the validation data

Bayesian Estimation of the Size of a Street-Dwelling Homeless Population

A novel Bayesian technique is proposed to calculate 95% interval estimates for the size of the homeless population in the city of Edmonton using plant-capture data from Toronto, Canada. The probabilities of capture in Edmonton and Toronto are modeled as exchangeable in a hierarchical Bayesian model, and Markov chain Monte Carlo is used to sample from the posterior distribution. Guidelines are recommended for applying the method to assess the accuracy of homeless counts in other cities

Probabilistic Approaches to Better Quantifying the Results of Epidemiologic Studies

Typical statistical analysis of epidemiologic data captures uncertainty due to random sampling variation, but ignores more systematic sources of variation such as selection bias, measurement error, and unobserved confounding. Such sources are often only mentioned via qualitative caveats, perhaps under the heading of ‘study limitations.’ Recently, however, there has been considerable interest and advancement in probabilistic methodologies for more integrated statistical analysis. Such techniques hold the promise of replacing a confidence interval reflecting only random sampling variation with an interval reflecting all, or at least more, sources of uncertainty. We survey and appraise the recent literature in this area, giving some prominence to the use of Bayesian statistical methodology

Women’s Health Care Utilization among Harder-to-Reach HIV-Infected Women ever on Antiretroviral Therapy in British Columbia

Background. HIV-infected women are disproportionately burdened by gynaecological complications, psychological disorders, and certain sexually transmitted infections that may not be adequately addressed by HIV-specific care. We estimate the prevalence and covariates of women’s health care (WHC) utilization among harder-to-reach, treatment-experienced HIV-infected women in British Columbia (BC), Canada. Methods. We used survey data from 231 HIV-infected, treatment-experienced women enrolled in the Longitudinal Investigations into Supportive and Ancillary Health Services (LISA) study, which recruited harder-to-reach populations, including aboriginal people and individuals using injection drugs. Independent covariates of interest included sociodemographic, psychosocial, behavioural, individual health status, structural factors, and HIV clinical variables. Logistic regression was used to generate adjusted estimates of associations between use of WHC and covariates of interest. Results. Overall, 77% of women reported regularly utilizing WHC. WHC utilization varied significantly by region of residence (P value <0.01). In addition, women with lower annual income (AOR (95% CI) = 0.14 (0.04–0.54)), who used illicit drugs (AOR (95% CI) = 0.42 (0.19–0.92)) and who had lower provider trust (AOR (95% CI) = 0.97 (0.95–0.99)), were significantly less likely to report using WHC. Conclusion. A health service gap exists along geographical and social axes for harder-to-reach HIV-infected women in BC. Women-centered WHC and HIV-specific care should be streamlined and integrated to better address women’s holistic health

Portable HEPA Filter Air Cleaner Use During Pregnancy and Children’s Behavior Problem Scores: A Secondary Analysis of the UGAAR Randomized Controlled Trial

Background Developmental exposure to particulate matter (PM) air pollution may impair children’s behaviors. Our objectives were to quantify the impact of reducing indoor PM using portable HEPA filter air cleaners during pregnancy on behavioral problems in children and to assess associations between indoor fine PM (PM2.5) concentrations during pregnancy and children’s behavior.   Methods This is a secondary analysis of a single-blind parallel-group randomized controlled trial in which we randomly assigned 540 non-smoking pregnant women to receive 1 or 2 HEPA filter air cleaners or no air cleaners. We administered the Behavior Assessment System for Children (BASC-3) to caregivers when children were a mean age of 23 months, and again at a mean age of 48 months. Primary outcomes were the four BASC-3 composite scales: externalizing problems, internalizing problems, adaptive skills, and the behavioral symptoms index. We imputed missing data using multiple imputation with chained equations. The primary analysis was by intention-to-treat. In a secondary analysis, we evaluated associations between BASC-3 composite indices and modeled trimester-specific PM2.5 concentrations inside residences.   Results We enrolled participants at a median of 11 weeks gestation. After excluding miscarriages, still births and neonatal deaths, our analysis included 478 children (233 control and 245 intervention). We observed no differences in the mean BASC-3 scores between treatment groups. An interquartile increase (20.1 µg/m3) in first trimester PM2.5 concentration was associated with higher externalizing problem scores (2.4 units, 95% CI: 0.7, 4.1), higher internalizing problem scores (2.4 units, 95% CI: 0.7, 4.0), lower adaptive skills scores (-1.5 units, 95% CI: -3.0, 0.0), and higher behavior symptoms index scores (2.3 units, 95% CI: 0.7, 3.9). Third trimester PM2.5 concentrations were also associated with some behavioral indices at age 4, but effect estimates were smaller. No significant associations were observed with PM2.5 concentrations during the second trimester or for any of the BASC indices when children were 2 years old.   Conclusion We found no benefit of reducing indoor particulate air pollution during pregnancy on parent-reported behaviors in children. Associations between indoor PM2.5 concentrations in the first trimester and behavioral scores among 4-year old children suggest that it may be necessary to intervene early in pregnancy to protect children, but these exploratory findings should be interpreted cautiously