Wild turkeys (Meleagris gallopavo) are a prolific species and valuable game animal throughout the United States. Stochastic simulations are commonly used to inform harvest management, and we used simulation to test performance of fall harvest management that included 1‐, 3‐, and 5‐year cycles of population assessment and updating of harvest targets, respectively. To assess robustness of our conclusions, we replicated analyses across 18 combinations of model parameters that included population productivity (3 levels), sex‐specific vulnerability to fall harvest (3 levels), and magnitude of spring harvest (2 levels). Performance of multi‐year cycles, measured using abundance of males and annual harvest, depended on the context of model parameters that interacted to determine responses of populations to harvest. One‐ and 3‐year cycles had similar performance so long as female harvests were less than or equal to male harvests. However, when harvest of females was greater than males, or when 5‐year regulation cycles were implemented, there was greater risk due to nonlinear population responses to increased harvest. For example, nonlinearity resulted in thresholds where declines to abundance and harvest could occur with small increases to harvest rates, and thus the sustainability of fall harvests was less robust for multi‐year cycles with time‐lagged assessment and decision making. Moreover, the harvest rate resulting in threshold responses depended on model parameters and often occurred within the range of harvest rates recommended by earlier modeling studies (7–15%). Our results imply that multi‐year cycles can be a viable approach to harvest management. Monitoring that provides information on sex‐specific harvest is recommended, however, to determine if nonlinear population responses should be anticipated. Ideally, information on population‐specific vital rates would also be available to allow managers to avoid harvest rates near thresholds that are expected to result in population declines. © The Wildlife Society, 2019
Background and Aims: Food insecurity combined with chronic disease conditions is a risk factor for Emergency Department (ED) utilization, an indicator of poor quality of care. However, such an association is not certain among school-age children with chronic conditions. Therefore, we aim to determine the association of food insecurity, chronic conditions, and ED utilization among school-age children in the United States.Methods: We analyzed the data from the 2017 Medical expenditure panel survey (MEPS) among children aged 6-17 years (N = 5518). MEPS data was released electronically by the Agency for Healthcare Research and Quality (AHRQ). We identified four groups of school-age children based on the presence of food security and chronic conditions: 1) with food insecurity and chronic conditions; 2) no food insecurity and chronic conditions; 3) with food insecurity and no chronic conditions; and 4) no food insecurity and no chronic conditions. We compared ED utilization among these four groups using incidence rate ratios (IRR) after adjusting children's age, sex, race and ethnicity, household income, insurance coverage, obesity, and geographic region using count data model, specifically multivariable Poison regression. We used SAS 9.4 and STATA 14.2 for all the data analyses.Results: There were unweighted 5518 school-age children who represented weighted 50,479,419 school-age children in the final analysis. Overall, 6.0% had food insecurity with chronic conditions. These children had higher ED utilization (19.7%) than the other three groups (13.3%, 8.8%, and 7.2%, p < 0.001). The adjusted IRR of ED utilization among school-age children with food insecurity and chronic conditions was 1.90 (95% confidence interval 1.20-3.01, p = 0.007) compared with those with food security and chronic conditions. Conclusion:One in 16 school-age children has both food insecurity and chronic conditions. Food insecurity was positively associated with frequent ED visits in the
Identifications of novel genetic signals conferring susceptibility to human complex diseases is pivotal to the disease diagnosis, prevention, and treatment. Genetic association study is a powerful tool to discover candidate genetic signals that contribute to diseases, through statistical tests for correlation between the disease status and genetic variations in study samples. In such studies with a case-control design, a standard practice is to perform the Cochran-Armitage (CA) trend test under an additive genetic model, which suffers from power loss when the model assumption is wrong. The Jonckheere-Terpstra (JT) trend test is an alternative method to evaluate association in a nonparametric way. This study compares the power of the JT trend test and the CA trend test in various scenarios, including different sample sizes (200–2000), minor allele frequencies (0.05–0.4), and underlying modes of inheritance (dominant genetic model to recessive genetic model). By simulation and real data analysis, it is shown that in general the JT trend test has higher, similar, and lower power than the CA trend test when the underlying mode of inheritance is dominant, additive, and recessive, respectively; when the sample size is small and the minor allele frequency is low, the JT trend test outperforms the CA trend test across the spectrum of genetic models. In sum, the JT trend test is a valuable alternative to the CA trend test under certain circumstances with higher statistical power, which could lead to better detection of genetic signals to human diseases and finer dissection of their genetic architecture.
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