Regression with Empirical Variable Selection: Description of a New Method and Application to Ecological Datasets

Despite recent papers on problems associated with full-model and stepwise regression, their use is still common throughout ecological and environmental disciplines. Alternative approaches, including generating multiple models and comparing them post-hoc using techniques such as Akaike's Information Criterion (AIC), are becoming more popular. However, these are problematic when there are numerous independent variables and interpretation is often difficult when competing models contain many different variables and combinations of variables. Here, we detail a new approach, REVS (Regression with Empirical Variable Selection), which uses all-subsets regression to quantify empirical support for every independent variable. A series of models is created; the first containing the variable with most empirical support, the second containing the first variable and the next most-supported, and so on. The comparatively small number of resultant models (n = the number of predictor variables) means that post-hoc comparison is comparatively quick and easy. When tested on a real dataset – habitat and offspring quality in the great tit (Parus major) – the optimal REVS model explained more variance (higher R2), was more parsimonious (lower AIC), and had greater significance (lower P values), than full, stepwise or all-subsets models; it also had higher predictive accuracy based on split-sample validation. Testing REVS on ten further datasets suggested that this is typical, with R2 values being higher than full or stepwise models (mean improvement = 31% and 7%, respectively). Results are ecologically intuitive as even when there are several competing models, they share a set of “core” variables and differ only in presence/absence of one or two additional variables. We conclude that REVS is useful for analysing complex datasets, including those in ecology and environmental disciplines

Identifying Sources of Health Care Underutilization Among California’s Immigrants

Many studies show that immigrants face significant barriers in accessing health care. These barriers may be particularly pronounced for newer immigrants, who may face additional obstacles in navigating the health care system. Understanding the sources of health care disparities between recent and non-recent immigrants may allow for better design of policies and interventions to address the vulnerabilities unique to different subgroups of immigrants defined by their length of residency. This study employs descriptive analyses and multivariate logistic regression to estimate the likelihood of accessing and utilizing health care services based on immigration-related factors after controlling for predisposing, enabling, and health care need factors. We also employ a regression-based decomposition method to determine whether health care differences between recent and non-recent immigrants are statistically significant and to identify the primary drivers of healthcare differences between recent and non-recent immigrants. The findings support the hypothesis that significant disparities in health care access and utilization exist between recent and non-recent immigrants. We found that health care access and utilization differences between recent and non-recent immigrants were driven primarily by enabling resources, including limited English proficiency (LEP), insurance status, public assistance usage, and poverty level. These results indicate that not only are newer immigrants more likely to underutilize health care, but also that their underutilization is driven primarily by their lack of insurance, lack of adequate financial resources, and inability to navigate the health care system due to LEP. The results further indicate that immigrants with prolonged LEP may be less likely to have a usual source of care and more likely to report delays in obtaining medical treatments, than even recent immigrants with LEP

Distance sampling as an effective method for monitoring feral pigeon (Columba livia f. domestica) urban populations

SUMMARY Current methods for estimating feral pigeon (Columba livia f. domestica) population size and for monitoring population trends are mainly based on indices, which according to the current literature on wildlife census methods often produce biased results. Distance Sampling techniques have never been used in this context, even though they could theoretically produce absolute abundance estimates at relatively low costs. The aim of this paper was to investigate the performance of Distance Sampling to census feral pigeons, and to compare these results with those obtained by using Quadrate Counts, a widespread method for monitoring these birds. Surveys were performed in Pisa (Italy) in two different periods of the year 2004 (end of January–beginning of February, and November), which correspond to minimum (January–February) and maximum (November) numbers for pigeon populations. We considered 40 line transects each about 250 m long for Distance Sampling, and 40 250×250 m cells for Quadrate Counts. In both cases, sampling units were randomized in a stratified design. In contrast to Quadrate Counts, Distance Sampling detected the predicted increase of abundance from January–February to November with an acceptable precision and no increase of costs per survey. Even though the possible biases (due to the not rigorously random distribution of transects and to the spiked nature of collected distance data) should be further investigated, results suggest that Distance Sampling is a viable and efficient alternative to the traditional methods used to estimate feral pigeons population size and to monitor trends