Lead Distribution in Urban Soils: Relationship Between Lead Sources and Children's Blood Lead Levels

Indiana University-Purdue University Indianapolis (IUPUI

Freedom Time: Rethinking Federal Democracy for a Postcolonial World

A review of Gary Wilder, Freedom Time: Negritude, Decolonization and the Future of The World (Duke University Press, 2015)

Patient preference as a predictor of outcomes in a pilot trial of person-centred counselling versus low-intensity cognitive behavioural therapy for persistent sub-threshold and mild depression

The aim of this analysis was to explore whether pre-treatment intervention preferences were related to outcomes for patients with persistent sub-threshold and mild depression who received one of two treatment types. Thirty-six patients took part in a two-arm, parallel group, pilot randomized controlled trial that compared short term (3 month and 6 month) outcomes of person-centred counselling (PCC) compared with low-intensity, CBT-based guided self-help (LICBT). Patient preferences for the two interventions were assessed at baseline assessment, and analysed as two independent linear variables (pro-PCC, pro-LICBT). Eight out of 30 interactions between baseline treatment preferences and treatment type were found to be significant at the p < .05 level. All were in the predicted direction, with patients who showed a stronger preference for a treatment achieving better outcomes in that treatment compared with the alternative. However, pro-LICBT was a stronger predictor of outcomes than pro-PCC. The findings provide preliminary support that treatment preferences should be taken into account when providing interventions for patients with persistent sub-threshold and mild depression. It is recommended that further research analyses preferences for different treatment types as independent variables, and examines preferences for format of treatment (e.g. guided self-help vs. face-to-face)

A randomized trial of an Asthma Internet Self-management Intervention (RAISIN): study protocol for a randomized controlled trial

<b>Background</b><p></p> The financial costs associated with asthma care continue to increase while care remains suboptimal. Promoting optimal self-management, including the use of asthma action plans, along with regular health professional review has been shown to be an effective strategy and is recommended in asthma guidelines internationally. Despite evidence of benefit, guided self-management remains underused, however the potential for online resources to promote self-management behaviors is gaining increasing recognition. The aim of this paper is to describe the protocol for a pilot evaluation of a website 'Living well with asthma' which has been developed with the aim of promoting self-management behaviors shown to improve outcomes.<p></p> <b>Methods</b><p></p> The study is a parallel randomized controlled trial, where adults with asthma are randomly assigned to either access to the website for 12 weeks, or usual asthma care for 12 weeks (followed by access to the website if desired). Individuals are included if they are over 16-years-old, have a diagnosis of asthma with an Asthma Control Questionnaire (ACQ) score of greater than, or equal to 1, and have access to the internet. Primary outcomes for this evaluation include recruitment and retention rates, changes at 12 weeks from baseline for both ACQ and Asthma Quality of Life Questionnaire (AQLQ) scores, and quantitative data describing website usage (number of times logged on, length of time logged on, number of times individual pages looked at, and for how long). Secondary outcomes include clinical outcomes (medication use, health services use, lung function) and patient reported outcomes (including adherence, patient activation measures, and health status).<p></p> <b>Discussion</b><p></p> Piloting of complex interventions is considered best practice and will maximise the potential of any future large-scale randomized controlled trial to successfully recruit and be able to report on necessary outcomes. Here we will provide results across a range of outcomes which will provide estimates of efficacy to inform the design of a future full-scale randomized controlled trial of the 'Living well with asthma' website

The value of source data verification in a cancer clinical trial

Background Source data verification (SDV) is a resource intensive method of quality assurance frequently used in clinical trials. There is no empirical evidence to suggest that SDV would impact on comparative treatment effect results from a clinical trial. Methods Data discrepancies and comparative treatment effects obtained following 100% SDV were compared to those based on data without SDV. Overall survival (OS) and Progression-free survival (PFS) were compared using Kaplan-Meier curves, log-rank tests and Cox models. Tumour response classifications and comparative treatment Odds Ratios (ORs) for the outcome objective response rate, and number of Serious Adverse Events (SAEs) were compared. OS estimates based on SDV data were compared against estimates obtained from centrally monitored data. Findings Data discrepancies were identified between different monitoring procedures for the majority of variables examined, with some variation in discrepancy rates. There were no systematic patterns to discrepancies and their impact was negligible on OS, the primary outcome of the trial (HR (95% CI): 1.18(0.99 to 1.41), p = 0.064 with 100% SDV; 1.18(0.99 to 1.42), p = 0.068 without SDV; 1.18(0.99 to 1.40), p = 0.073 with central monitoring). Results were similar for PFS. More extreme discrepancies were found for the subjective outcome overall objective response (OR (95% CI): 1.67(1.04 to 2.68), p = 0.03 with 100% SDV; 2.45(1.49 to 4.04), p = 0.0003 without any SDV) which was mostly due to differing CT scans. Interpretation Quality assurance methods used in clinical trials should be informed by empirical evidence. In this empirical comparison, SDV was expensive and identified random errors that made little impact on results and clinical conclusions of the trial. Central monitoring using an external data source was a more efficient approach for the primary outcome of OS. For the subjective outcome objective response, an independent blinded review committee and tracking system to monitor missing scan data could be more efficient than SDV

Sewage reflects the microbiomes of human populations

© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in mBio 6 (2015): e02574-14, doi:10.1128/mBio.02574-14.Molecular characterizations of the gut microbiome from individual human stool samples have identified community patterns that correlate with age, disease, diet, and other human characteristics, but resources for marker gene studies that consider microbiome trends among human populations scale with the number of individuals sampled from each population. As an alternative strategy for sampling populations, we examined whether sewage accurately reflects the microbial community of a mixture of stool samples. We used oligotyping of high-throughput 16S rRNA gene sequence data to compare the bacterial distribution in a stool data set to a sewage influent data set from 71 U.S. cities. On average, only 15% of sewage sample sequence reads were attributed to human fecal origin, but sewage recaptured most (97%) human fecal oligotypes. The most common oligotypes in stool matched the most common and abundant in sewage. After informatically separating sequences of human fecal origin, sewage samples exhibited ~3× greater diversity than stool samples. Comparisons among municipal sewage communities revealed the ubiquitous and abundant occurrence of 27 human fecal oligotypes, representing an apparent core set of organisms in U.S. populations. The fecal community variability among U.S. populations was significantly lower than among individuals. It clustered into three primary community structures distinguished by oligotypes from either: Bacteroidaceae, Prevotellaceae, or Lachnospiraceae/Ruminococcaceae. These distribution patterns reflected human population variation and predicted whether samples represented lean or obese populations with 81 to 89% accuracy. Our findings demonstrate that sewage represents the fecal microbial community of human populations and captures population-level traits of the human microbiome.Funding for this work was provided by the NIH grant R01AI091829-01A1 to S.L.M. and M.L.S