Recycling Attitudes and Behavior among a Clinic-Based Sample of Low-Income Hispanic Women in Southeast Texas

We examined attitudes and behavior surrounding voluntary recycling in a population of low-income Hispanic women. Participants (N = 1,512) 18–55 years of age completed a self-report survey and responded to questions regarding household recycling behavior, recycling knowledge, recycling beliefs, potential barriers to recycling (transportation mode, time), acculturation, demographic characteristics (age, income, employment, marital status, education, number of children, birth country), and social desirability. Forty-six percent of participants (n = 810) indicated that they or someone else in their household recycled. In a logistic regression model controlling for social desirability, recycling behavior was related to increased age (P<0.05), lower acculturation (P<0.01), knowing what to recycle (P<0.01), knowing that recycling saves landfill space (P<0.05), and disagreeing that recycling takes too much time (P<0.001). A Sobel test revealed that acculturation mediated the relationship between recycling knowledge and recycling behavior (P<0.05). We offer new information on recycling behavior among Hispanic women and highlight the need for educational outreach and intervention strategies to increase recycling behavior within this understudied population

The Effect of Interpersonal Psychotherapy and other Psychodynamic Therapies versus ‘Treatment as Usual’ in Patients with Major Depressive Disorder

Major depressive disorder afflicts an estimated 17% of individuals during their lifetimes at tremendous suffering and costs. Interpersonal psychotherapy and other psychodynamic therapies may be effective interventions for major depressive disorder, but the effects have only had limited assessment in systematic reviews.Cochrane systematic review methodology with meta-analysis and trial sequential analysis of randomized trials comparing the effect of psychodynamic therapies versus ‘treatment as usual’ for major depressive disorder. To be included the participants had to be older than 17 years with a primary diagnosis of major depressive disorder. Altogether, we included six trials randomizing a total of 648 participants. Five trials assessed ‘interpersonal psychotherapy’ and only one trial assessed ‘psychodynamic psychotherapy’. All six trials had high risk of bias. Meta-analysis on all six trials showed that the psychodynamic interventions significantly reduced depressive symptoms on the 17-item Hamilton Rating Scale for Depression (mean difference −3.12 (95% confidence interval −4.39 to −1.86;P<0.00001), no heterogeneity) compared with ‘treatment as usual’. Trial sequential analysis confirmed this result.We did not find convincing evidence supporting or refuting the effect of interpersonal psychotherapy or psychodynamic therapy compared with ‘treatment as usual’ for patients with major depressive disorder. The potential beneficial effect seems small and effects on major outcomes are unknown. Randomized trials with low risk of systematic errors and low risk of random errors are needed

Modeling causes of death: an integrated approach using CODEm

Background: Data on causes of death by age and sex are a critical input into health decision-making. Priority setting in public health should be informed not only by the current magnitude of health problems but by trends in them. However, cause of death data are often not available or are subject to substantial problems of comparability. We propose five general principles for cause of death model development, validation, and reporting.Methods: We detail a specific implementation of these principles that is embodied in an analytical tool - the Cause of Death Ensemble model (CODEm) - which explores a large variety of possible models to estimate trends in causes of death. Possible models are identified using a covariate selection algorithm that yields many plausible combinations of covariates, which are then run through four model classes. The model classes include mixed effects linear models and spatial-temporal Gaussian Process Regression models for cause fractions and death rates. All models for each cause of death are then assessed using out-of-sample predictive validity and combined into an ensemble with optimal out-of-sample predictive performance.Results: Ensemble models for cause of death estimation outperform any single component model in tests of root mean square error, frequency of predicting correct temporal trends, and achieving 95% coverage of the prediction interval. We present detailed results for CODEm applied to maternal mortality and summary results for several other causes of death, including cardiovascular disease and several cancers.Conclusions: CODEm produces better estimates of cause of death trends than previous methods and is less susceptible to bias in model specification. We demonstrate the utility of CODEm for the estimation of several major causes of death

The CARTS study: Chemoradiation therapy for rectal cancer in the distal rectum followed by organ-sparing transanal endoscopic microsurgery

Contains fulltext : 96401.pdf (publisher's version ) (Open Access

The intellectual structure and substance of the knowledge utilization field: A longitudinal author co-citation analysis, 1945 to 2004

<p>Abstract</p> <p>Background</p> <p>It has been argued that science and society are in the midst of a far-reaching renegotiation of the social contract between science and society, with society becoming a far more active partner in the creation of knowledge. On the one hand, new forms of knowledge production are emerging, and on the other, both science and society are experiencing a rapid acceleration in new forms of knowledge utilization. Concomitantly since the Second World War, the science underpinning the knowledge utilization field has had exponential growth. Few in-depth examinations of this field exist, and no comprehensive analyses have used bibliometric methods.</p> <p>Methods</p> <p>Using bibliometric analysis, specifically first author co-citation analysis, our group undertook a domain analysis of the knowledge utilization field, tracing its historical development between 1945 and 2004. Our purposes were to map the historical development of knowledge utilization as a field, and to identify the changing intellectual structure of its scientific domains. We analyzed more than 5,000 articles using citation data drawn from the Web of Science^®. Search terms were combinations of knowledge, research, evidence, guidelines, ideas, science, innovation, technology, information theory and use, utilization, and uptake.</p> <p>Results</p> <p>We provide an overview of the intellectual structure and how it changed over six decades. The field does not become large enough to represent with a co-citation map until the mid-1960s. Our findings demonstrate vigorous growth from the mid-1960s through 2004, as well as the emergence of specialized domains reflecting distinct collectives of intellectual activity and thought. Until the mid-1980s, the major domains were focused on innovation diffusion, technology transfer, and knowledge utilization. Beginning slowly in the mid-1980s and then growing rapidly, a fourth scientific domain, evidence-based medicine, emerged. The field is dominated in all decades by one individual, Everett Rogers, and by one paradigm, innovation diffusion.</p> <p>Conclusion</p> <p>We conclude that the received view that social science disciplines are in a state where no accepted set of principles or theories guide research (<it>i.e.</it>, that they are pre-paradigmatic) could not be supported for this field. Second, we document the emergence of a new domain within the knowledge utilization field, evidence-based medicine. Third, we conclude that Everett Rogers was the dominant figure in the field and, until the emergence of evidence-based medicine, his representation of the general diffusion model was the dominant paradigm in the field.</p

The impact of surgical delay on resectability of colorectal cancer: An international prospective cohort study

AIM: The SARS-CoV-2 pandemic has provided a unique opportunity to explore the impact of surgical delays on cancer resectability. This study aimed to compare resectability for colorectal cancer patients undergoing delayed versus non-delayed surgery. METHODS: This was an international prospective cohort study of consecutive colorectal cancer patients with a decision for curative surgery (January-April 2020). Surgical delay was defined as an operation taking place more than 4 weeks after treatment decision, in a patient who did not receive neoadjuvant therapy. A subgroup analysis explored the effects of delay in elective patients only. The impact of longer delays was explored in a sensitivity analysis. The primary outcome was complete resection, defined as curative resection with an R0 margin. RESULTS: Overall, 5453 patients from 304 hospitals in 47 countries were included, of whom 6.6% (358/5453) did not receive their planned operation. Of the 4304 operated patients without neoadjuvant therapy, 40.5% (1744/4304) were delayed beyond 4 weeks. Delayed patients were more likely to be older, men, more comorbid, have higher body mass index and have rectal cancer and early stage disease. Delayed patients had higher unadjusted rates of complete resection (93.7% vs. 91.9%, P = 0.032) and lower rates of emergency surgery (4.5% vs. 22.5%, P < 0.001). After adjustment, delay was not associated with a lower rate of complete resection (OR 1.18, 95% CI 0.90-1.55, P = 0.224), which was consistent in elective patients only (OR 0.94, 95% CI 0.69-1.27, P = 0.672). Longer delays were not associated with poorer outcomes. CONCLUSION: One in 15 colorectal cancer patients did not receive their planned operation during the first wave of COVID-19. Surgical delay did not appear to compromise resectability, raising the hypothesis that any reduction in long-term survival attributable to delays is likely to be due to micro-metastatic disease