Social Capital, Ideology, and Health in the United States

Research from across disciplines has demonstrated that social and political contextual factors at the national and subnational levels can impact the health and health behavior risks of individuals. This paper examines the impact of state-level social capital and ideology on individual-level health out-comes in the United States. Leveraging the variation that exists across states in the United States, the results reveal that individuals report better health in states with higher levels of governmental liberalism and in states with higher levels of social capital. Critically, however, the effect of social capital was moderated by liberalism such that social capital was a stronger predictor of health in states with low levels of liberalism. We interpret this finding to mean that social capital within a political unit—as indicated by measures of interpersonal trust—can serve as a substitute for the beneficial impacts that might result from an active governmental structure

Systems biological and mechanistic modelling of radiation-induced cancer

This paper summarises the five presentations at the First International Workshop on Systems Radiation Biology that were concerned with mechanistic models for carcinogenesis. The mathematical description of various hypotheses about the carcinogenic process, and its comparison with available data is an example of systems biology. It promises better understanding of effects at the whole body level based on properties of cells and signalling mechanisms between them. Of these five presentations, three dealt with multistage carcinogenesis within the framework of stochastic multistage clonal expansion models, another presented a deterministic multistage model incorporating chromosomal aberrations and neoplastic transformation, and the last presented a model of DNA double-strand break repair pathways for second breast cancers following radiation therapy

Development and Testing of an Emergency Department Quality Measure for Pediatric Suicidal Ideation and Self-Harm

ObjectiveTo develop and test a new quality measure assessing timeliness of follow-up mental health care for youth presenting to the emergency department (ED) with suicidal ideation or self-harm.MethodsBased on a conceptual framework, evidence review, and a modified Delphi process, we developed a quality measure assessing whether youth 5 to 17 years old evaluated for suicidal ideation or self-harm in the ED and discharged to home had a follow-up mental health care visit within 7 days. The measure was tested in 4 geographically dispersed states (California, Pennsylvania, South Carolina, Tennessee) using Medicaid administrative data. We examined measure feasibility of implementation, variation, reliability, and validity. To test validity, adjusted regression models examined associations between quality measure scores and subsequent all-cause and same-cause hospital readmissions/ED return visits.ResultsOverall, there were 16,486 eligible ED visits between September 1, 2014 and July 31, 2016; 53.5% of eligible ED visits had an associated mental health care follow-up visit within 7 days. Measure scores varied by state, ranging from 26.3% to 66.5%, and by youth characteristics: visits by youth who were non-White, male, and living in an urban area were significantly less likely to be associated with a follow-up visit within 7 days. Better quality measure performance was not associated with decreased reutilization.ConclusionsThis new ED quality measure may be useful for monitoring and improving the quality of care for this vulnerable population; however, future work is needed to establish the measure's predictive validity using more prevalent outcomes such as recurrence of suicidal ideation or deliberate self-harm

Commonness and rarity in the marine biosphere

Explaining patterns of commonness and rarity is fundamental for understanding and managing biodiversity. Consequently, a key test of biodiversity theory has been how well ecological models reproduce empirical distributions of species abundances. However, ecological models with very different assumptions can predict similar species abundance distributions, whereas models with similar assumptions may generate very different predictions. This complicates inferring processes driving community structure from model fits to data. Here, we use an approximation that captures common features of “neutral” biodiversity models—which assume ecological equivalence of species—to test whether neutrality is consistent with patterns of commonness and rarity in the marine biosphere. We do this by analyzing 1,185 species abundance distributions from 14 marine ecosystems ranging from intertidal habitats to abyssal depths, and from the tropics to polar regions. Neutrality performs substantially worse than a classical nonneutral alternative: empirical data consistently show greater heterogeneity of species abundances than expected under neutrality. Poor performance of neutral theory is driven by its consistent inability to capture the dominance of the communities’ most-abundant species. Previous tests showing poor performance of a neutral model for a particular system often have been followed by controversy about whether an alternative formulation of neutral theory could explain the data after all. However, our approach focuses on common features of neutral models, revealing discrepancies with a broad range of empirical abundance distributions. These findings highlight the need for biodiversity theory in which ecological differences among species, such as niche differences and demographic trade-offs, play a central role

Antiviral Treatment among Older Adults Hospitalized with Influenza, 2006-2012

<div><p>Objective</p><p>To describe antiviral use among older, hospitalized adults during six influenza seasons (2006—2012) in Davidson County, Tennessee, USA.</p><p>Methods</p><p>Among adults ≥50 years old hospitalized with symptoms of respiratory illness or non-localizing fever, we collected information on provider-initiated influenza testing and nasal/throat swabs for influenza by RT-PCR in a research laboratory, and calculated the proportion treated with antivirals.</p><p>Results</p><p>We enrolled 1753 adults hospitalized with acute respiratory illness. Only 26% (457/1753) of enrolled patients had provider-initiated influenza testing. Thirty-eight patients had a positive clinical laboratory test, representing 2.2% of total patients and 8.3% of tested patients. Among the 38 subjects with clinical laboratory-confirmed influenza, 26.3% received antivirals compared to only 4.5% of those with negative clinical influenza tests and 0.7% of those not tested (p<0.001). There were 125 (7.1%) patients who tested positive for influenza in the research laboratory. Of those with research laboratory-confirmed influenza, 0.9%, 2.7%, and 2.8% received antivirals (p=.046) during pre-pandemic, pandemic, and post-pandemic influenza seasons, respectively. Both research laboratory-confirmed influenza (adjusted odds ratio [AOR] 3.04 95%CI 1.26-7.35) and clinical laboratory-confirmed influenza (AOR 3.05, 95%CI 1.07-8.71) were independently associated with antiviral treatment. Severity of disease, presence of a high-risk condition, and symptom duration were not associated with antiviral use.</p><p>Conclusions</p><p>In urban Tennessee, antiviral use was low in patients recognized to have influenza by the provider as well as those unrecognized to have influenza. The use of antivirals remained low despite recommendations to treat all hospitalized patients with confirmed or suspected influenza.</p></div

Antiviral treatment among adults ≥50 years of age hospitalized with acute respiratory illness, by season, 2006–2012.

<p>Antiviral treatment among adults ≥50 years of age hospitalized with acute respiratory illness, by season, 2006–2012.</p

Independent factors associated with antiviral treatment among adults, 50 years and older, hospitalized with symptoms of acute respiratory illness or non-localizing fever, and those with research laboratory-confirmed influenza, 2006–2012.

<p>¹ For all patients, the variables included in the model included 1) clinical laboratory influenza testing, 2) research laboratory influenza status, and 3) discharge diagnosis of influenza or pneumonia.</p><p>² For patient with research laboratory-confirmed patients, the variable included clinical laboratory influenza tests status.</p><p>Independent factors associated with antiviral treatment among adults, 50 years and older, hospitalized with symptoms of acute respiratory illness or non-localizing fever, and those with research laboratory-confirmed influenza, 2006–2012.</p