Maintaining Health Insurance During a Recession: Likely COBRA Eligibility

Assesses laid-off workers' eligibility and financial ability to extend employer-sponsored insurance through COBRA. Recommends extending COBRA and providing premium assistance, as well as expanding Medicaid and State Children's Health Insurance Programs

How Health Care Reform Can Lower the Costs of Insurance Administration

Examines the sources of U.S. insurance administration costs and estimates potential cost savings from the creation of a national insurance exchange to replace the individual and small-group markets as part of a public-private approach to reform

Rite of Passage? Why Young Adults Become Uninsured and How New Policies Can Help, 2009 Update

Provides an annual assessment of the uninsurance of 19- to 29-year-olds and their demographics, incomes, and health status. Outlines federal and state actions to expand access to coverage and suggests policy options to address the impact of the recession

Patch size effects on plant species decline in an experimentally fragmented landscape

This is the publisher's version, also available electronically from http://www.esa.org/esa.Understanding local and global extinction is a fundamental objective of both basic and applied ecology. Island biogeography theory (IBT) and succession theory provide frameworks for understanding extinction in changing landscapes. We explore the relative contribution of fragment size vs. succession on species' declines by examining distributions of abundances for 18 plant species declining over time in an experimentally fragmented landscape in northeast Kansas, USA. If patch size effects dominate, early-successional species should persist longer on large patches, but if successional processes dominate, the reverse should hold, because in our system woody plant colonization is accelerated on large patches. To compare the patterns in abundance among patch sizes, we characterize joint shifts in local abundance and occupancy with a new metric: rank occupancy–abundance profiles (ROAPs). As succession progressed, statistically significant patch size effects emerged for 11 of 18 species. More early-successional species persisted longer on large patches, despite the fact that woody encroachment (succession) progressed faster in these patches. Clonal perennial species persisted longer on large patches compared to small patches. All species that persisted longer on small patches were annuals that recruit from the seed bank each year. The degree to which species declined in occupancy vs. abundance varied dramatically among species: some species declined first in occupancy, others remained widespread or even expanded their distribution, even as they declined in local abundance. Consequently, species exhibited various types of rarity as succession progressed. Understanding the effect of fragmentation on extinction trajectories requires a species-by-species approach encompassing both occupancy and local abundance. We propose that ROAPs provide a useful tool for comparing the distribution of local abundances among landscape types, years, and species

Community-level consequences of mycorrhizae depend on phosphorus availability

This is the publisher's version, also available electronically from http://www.esa.org/esa.In grasslands, arbuscular mycorrhizal fungi (AMF) mediate plant diversity; whether AMF increase or decrease diversity depends on the relative mycotrophy in dominant vs. subordinate plants. In this study we investigated whether soil nutrient levels also influence the ability of AMF to mediate plant species coexistence. First, we developed a conceptual model that predicts the influence of AMF on diversity along a soil nutrient gradient for plant communities dominated by mycotrophic and non-mycotrophic species. To test these predictions, we manipulated phosphorus to create a soil nutrient gradient for mesocosm communities composed of native prairie grasses and then compared community properties for mesocosms with and without AMF. We found that, where P was limiting, AMF increased plant diversity and productivity, and also altered community structure; however, at high P, AMF had little influence on aboveground communities. Compositional differences among treatments were due largely to a trade-off in the relative abundance of C3 vs. C4 species. Our study emphasizes how environmental constraints on mutualisms may govern community- and ecosystem-level properties

Front and Center: Ensuring That Health Reform Puts People First

Outlines the failures of the healthcare system and the benefits of the Commonwealth Fund's comprehensive reform plan for the uninsured, the underinsured, those who cannot afford out-of-pocket costs or premiums, and others without adequate access to care

Poor handling of continuous predictors in clinical prediction models using logistic regression: a systematic review

Background and Objectives When developing a clinical prediction model, assuming a linear relationship between the continuous predictors and outcome is not recommended. Incorrect specification of the functional form of continuous predictors could reduce predictive accuracy. We examine how continuous predictors are handled in studies developing a clinical prediction model. Methods We searched PubMed for clinical prediction model studies developing a logistic regression model for a binary outcome, published between July 01, 2020, and July 30, 2020. Results In total, 118 studies were included in the review (18 studies (15%) assessed the linearity assumption or used methods to handle nonlinearity, and 100 studies (85%) did not). Transformation and splines were commonly used to handle nonlinearity, used in 7 (n = 7/18, 39%) and 6 (n = 6/18, 33%) studies, respectively. Categorization was most often used method to handle continuous predictors (n = 67/118, 56.8%) where most studies used dichotomization (n = 40/67, 60%). Only ten models included nonlinear terms in the final model (n = 10/18, 56%). Conclusion Though widely recommended not to categorize continuous predictors or assume a linear relationship between outcome and continuous predictors, most studies categorize continuous predictors, few studies assess the linearity assumption, and even fewer use methodology to account for nonlinearity. Methodological guidance is provided to guide researchers on how to handle continuous predictors when developing a clinical prediction model

Poor handling of continuous predictors in clinical prediction models using logistic regression:a systematic review

Background and Objectives When developing a clinical prediction model, assuming a linear relationship between the continuous predictors and outcome is not recommended. Incorrect specification of the functional form of continuous predictors could reduce predictive accuracy. We examine how continuous predictors are handled in studies developing a clinical prediction model. Methods We searched PubMed for clinical prediction model studies developing a logistic regression model for a binary outcome, published between July 01, 2020, and July 30, 2020. Results In total, 118 studies were included in the review (18 studies (15%) assessed the linearity assumption or used methods to handle nonlinearity, and 100 studies (85%) did not). Transformation and splines were commonly used to handle nonlinearity, used in 7 (n = 7/18, 39%) and 6 (n = 6/18, 33%) studies, respectively. Categorization was most often used method to handle continuous predictors (n = 67/118, 56.8%) where most studies used dichotomization (n = 40/67, 60%). Only ten models included nonlinear terms in the final model (n = 10/18, 56%). Conclusion Though widely recommended not to categorize continuous predictors or assume a linear relationship between outcome and continuous predictors, most studies categorize continuous predictors, few studies assess the linearity assumption, and even fewer use methodology to account for nonlinearity. Methodological guidance is provided to guide researchers on how to handle continuous predictors when developing a clinical prediction model

Sample size requirements are not being considered in studies developing prediction models for binary outcomes: a systematic review

Background Having an appropriate sample size is important when developing a clinical prediction model. We aimed to review how sample size is considered in studies developing a prediction model for a binary outcome. Methods We searched PubMed for studies published between 01/07/2020 and 30/07/2020 and reviewed the sample size calculations used to develop the prediction models. Using the available information, we calculated the minimum sample size that would be needed to estimate overall risk and minimise overfitting in each study and summarised the difference between the calculated and used sample size. Results A total of 119 studies were included, of which nine studies provided sample size justification (8%). The recommended minimum sample size could be calculated for 94 studies: 73% (95% CI: 63–82%) used sample sizes lower than required to estimate overall risk and minimise overfitting including 26% studies that used sample sizes lower than required to estimate overall risk only. A similar number of studies did not meet the ≥ 10EPV criteria (75%, 95% CI: 66–84%). The median deficit of the number of events used to develop a model was 75 [IQR: 234 lower to 7 higher]) which reduced to 63 if the total available data (before any data splitting) was used [IQR:225 lower to 7 higher]. Studies that met the minimum required sample size had a median c-statistic of 0.84 (IQR:0.80 to 0.9) and studies where the minimum sample size was not met had a median c-statistic of 0.83 (IQR: 0.75 to 0.9). Studies that met the ≥ 10 EPP criteria had a median c-statistic of 0.80 (IQR: 0.73 to 0.84). Conclusions Prediction models are often developed with no sample size calculation, as a consequence many are too small to precisely estimate the overall risk. We encourage researchers to justify, perform and report sample size calculations when developing a prediction model

Candida albicans Is Resistant to Polyglutamine Aggregation and Toxicity

Acknowledgments We thank the Donnelly Sequencing Centre for sequencing, and Jonathan Krieger at the SikKids Proteomics, Analytics, Robotics & Chemical Biology Centre at The Hospital for Sick Children for mass spectrometry analysis. M.D.L. is supported by a Sir Henry Wellcome Postdoctoral Fellowship (Wellcome Trust grant 096072), T.K. is supported by a Queen Elizabeth II Graduate Scholarship in Science and Technology (University of Toronto), M.L.D. is supported by a Canadian Institutes of Health Research (CIHR) Operating grant 325538, L.E.C. is supported by a Canada Research Chair in Microbial Genomics and Infectious Disease, by CIHR grants MOP-119520 and MOP-86452, and by the Natural Sciences and Engineering Research Council (NSERC) of Canada (grants 06261 and 462167).Peer reviewedPublisher PD