Robbery-Related Injury in Convenience Stores: Estimating Lifetime Risk and Identifying High-Risk Populations

Robbery-related injuries constitute a major risk for convenience store workers in the United States. Studies that focus on the injury outcomes associated with convenience store robbery are extremely limited in number. This is a prospective study of 1271 convenience stores in three metropolitan areas of Virginia between February 1, 1995 and September 30, 1996. The study quantifies the lifetime risk for an occupational robbery-related injury occurrence and determines the relative importance of various types of factors in the classification of high risk stores. Lifetime risk was estimated by calculating the probability in convenience stores for having one or more employee(s) sustain at least one robbery-related injury over a range of years that a store could be in operation. Results indicate that knowledge of the circumstances of the robbery are needed to maximize the identification of high risk stores. Estimated lifetime risk reaches 567 stores with an occupational robbery-related injury occurrence per 1,000 stores in operation after 45 years. This study addresses limitations of previous research by including information on clerk resistance and the number of robbers in its analysis. These two circumstantial characteristics of robbery have been previously hypothesized to be associated with robbery-related injury.     

Comparing Injury and Illness Risk Assessments for Occupational Hazards

One common framework for describing the evaluation and assessment of hazards in the workplace includes the four steps of hazard identification, exposure assessment, exposure-response modeling, and risk characterization (NAS, 1983). We discuss hazards for occupational injury and illness in light of this framework, and we contrast the evaluation of injury hazards with the evaluation of illness hazards. In particular, the nature of the hazards, typical exposure patterns, quantification of exposure, and the attribution of outcome to exposure are discussed. Finally, we discuss the management of occupational illness and injury hazards and issues encountered when evaluating efforts designed to mitigate the effects of occupational hazards.     

Estimating the Sizes of Populations at High Risk for HIV: A Comparison Study

Objectives

Behavioral interventions are effective strategies for HIV/AIDS prevention and control. However, implementation of such strategies relies heavily on the accurate estimation of the high-risk population size. The multiplier method and generalized network scale-up method were recommended to estimate the population size of those at high risk for HIV by UNAIDS/WHO in 2003 and 2010, respectively. This study aims to assess and compare the two methods for estimating the size of populations at high risk for HIV, and to provide practical guidelines and suggestions for implementing the two methods.

Methods

Studies of the multiplier method used to estimate the population prevalence of men who have sex with men in China published between July 1, 2003 and July 1, 2013 were reviewed. The generalized network scale-up method was applied to estimate the population prevalence of men who have sex with men in the urban district of Taiyuan, China.

Results

The median of studies using the multiplier method to estimate the population prevalence of men who have sex with men in China was 4–8 times lower than the national level estimate. Meanwhile, the estimate of the generalized network scale-up method fell within the range of national level estimate.

Conclusions

When high-quality existing data are not readily available, the multiplier method frequently yields underestimated results. We thus suggest that the generalized network scale-up method is preferred when sampling frames for the general population and accurate demographic information are available.     

Colony-Site Selection Drives Management Priorities for Yelkouan Shearwater Populations

Abstract: We tested whether colony-site availability could allow for an increase in the unusually small breeding populations of yelkouan shearwater (Puffinus yelkouan) on the islands of the Port-Cros National Park (France) if feral cat eradication were undertaken. Comparisons between colony and noncolony sites indicated yelkouan shearwaters preferred deep-soiled and low-outcrop-covered coastal sites. A substrate cover, light avoidance, and sea proximity model suggested that 17.5% of unoccupied sites are suitable for colony establishment. The low proportion of suitable sites currently used by yelkouan shearwaters suggests that these colonies could be refuges and that feral cat eradication will probably lead to a breeding population increase.     

200例先天性尿道下裂患儿的临床特征及其危险因素分析

目的:研究200例先天性尿道下裂患儿的临床特征及其危险因素.方法:选择2016年1月?2019年12月我院收治的先天性尿道下裂患儿200例进行研究,记作观察组,另取同期于我院接受体检的健康儿童200例作为对照组,分析观察组患儿的临床分型情况,比较两组儿童父母的一般情况、儿童出生情况,并采用多因素Logistic回归分析...     

Minimum Area Required for Local Populations of Japanese Macaques Estimated from the Relationship Between Habitat Area and Population Extinction

I estimated the minimum area required (MAR) for local populations of Japanese macaques (Macaca fuscata) from empirical data on habitat area and population viability. I used logistic regression analysis to examine the relationship between habitat area and survival/extinction among 50 populations over 50 yr. Estimated habitat areas satisfying 95–99% probability of a population persisting for 100–1000 yr range from 525 to 975 km². However, confidence limits of parameters in the logistic regression equation are very large. Moreover, the number of extinct population might be underestimated in the empirical data. Consequently, a much wider habitat area (>1000 km²) should be considered for actual conservation planning for local populations of Japanese macaques. The method involves fewer variables and assumptions than previous methods of MAR estimation, and therefore may be a more useful way to estimate MAR for various species and regions.     

Occupational Injury Risk Assessment Using Injury Severity Odds Ratios: Male Falls from Heights in the Danish Construction Industry, 1993-1999

To allow for an improved targeted approach to occupational injury research and prevention, detailed risk analyses of fatal, serious, and minor occupational injuries were completed with a focus on male falls from heights in construction. Reported lost-time injuries in Denmark (1993 to 1999) were analyzed for proportions, relative rates, and an injury severity odds ratio to assess relative hazards and reporting. Different types of injury risks, such as elevation falls through surfaces, were identified by contrasting fatal, serious and minor injuries. Trade-specific analyses provided evidence that the carpentry-trade group merits increased attention, as this group has excessively high proportions, rates and hazards for falls from heights, compared to the entire construction industry. Age-specific analyses of workers aged 20 to 59 revealed that the rates of serious-injury falls from heights increase with increasing age. This relationship was inverted for elevation fall injuries from roof surfaces. A focus on construction and falls from heights is necessary not only in terms of fatal injuries, but also for serious injuries. The use of an injury severity odds ratio to assess relative hazards and reporting is a useful supplement to proportions and injury rates in contributing to more precise targeting of subgroups for primary injury prevention.     

Y-Chromosome Evidence for Common Ancestry of Three Chinese Populations with a High Risk of Esophageal Cancer

High rates of esophageal cancer (EC) are found in people of the Henan Taihang Mountain, Fujian Minnan, and Chaoshan regions of China. Historical records describe great waves of populations migrating from north-central China (the Henan and Shanxi Hans) through coastal Fujian Province to the Chaoshan plain. Although these regions are geographically distant, we hypothesized that EC high-risk populations in these three areas could share a common ancestry. Accordingly, we used 16 East Asian-specific Y-chromosome biallelic markers (single nucleotide polymorphisms; Y-SNPs) and six Y-chromosome short tandem repeat (Y-STR) loci to infer the origin of the EC high-risk Chaoshan population (CSP) and the genetic relationship between the CSP and the EC high-risk Henan Taihang Mountain population (HTMP) and Fujian population (FJP). The predominant haplogroups in these three populations are O3*, O3e*, and O3e1, with no significant difference between the populations in the frequency of these genotypes. Frequency distribution and principal component analysis revealed that the CSP is closely related to the HTMP and FJP, even though the former is geographically nearer to other populations (Guangfu and Hakka clans). The FJP is between the CSP and HTMP in the principal component plot. The CSP, FJP and HTMP are more closely related to Chinese Hans than to minorities, except Manchu Chinese, and are descendants of Sino-Tibetans, not Baiyues. Correlation analysis, hierarchical clustering analysis, and phylogenetic analysis (neighbor-joining tree) all support close genetic relatedness among the CSP, FJP and HTMP. The network for haplogroup O3 (including O3*, O3e* and O3e1) showed that the HTMP have highest STR haplotype diversity, suggesting that the HTMP may be a progenitor population for the CSP and FJP. These findings support the potentially important role of shared ancestry in understanding more about the genetic susceptibility in EC etiology in high-risk populations and have implications for determining the molecular basis of this disease.     

Retrospective versus prospective score tests for genetic association with case‐control data

Since the seminal work of Prentice and Pyke, the prospective logistic likelihood has become the standard method of analysis for retrospectively collected case‐control data, in particular for testing the association between a single genetic marker and a disease outcome in genetic case‐control studies. In the study of multiple genetic markers with relatively small effects, especially those with rare variants, various aggregated approaches based on the same prospective likelihood have been developed to integrate subtle association evidence among all the markers considered. Many of the commonly used tests are derived from the prospective likelihood under a common‐random‐effect assumption, which assumes a common random effect for all subjects. We develop the locally most powerful aggregation test based on the retrospective likelihood under an independent‐random‐effect assumption, which allows the genetic effect to vary among subjects. In contrast to the fact that disease prevalence information cannot be used to improve efficiency for the estimation of odds ratio parameters in logistic regression models, we show that it can be utilized to enhance the testing power in genetic association studies. Extensive simulations demonstrate the advantages of the proposed method over the existing ones. A real genome‐wide association study is analyzed for illustration.     

Colon cancer prediction with genetic profiles using intelligent techniques

Micro array data provides information of expression levels of thousands of genes in a cell in a single experiment. Numerous efforts have been made to use gene expression profiles to improve precision of tumor classification. In our present study we have used the benchmark colon cancer data set for analysis. Feature selection is done using t‐statistic. Comparative study of class prediction accuracy of 3 different classifiers viz., support vector machine (SVM), neural nets and logistic regression was performed using the top 10 genes ranked by the t‐statistic. SVM turned out to be the best classifier for this dataset based on area under the receiver operating characteristic curve (AUC) and total accuracy. Logistic Regression ranks as the next best classifier followed by Multi Layer Perceptron (MLP). The top 10 genes selected by us for classification are all well documented for their variable expression in colon cancer. We conclude that SVM together with t-statistic based feature selection is an efficient and viable alternative to popular techniques.     

A comparison of statistical approaches for modelling fish species distributions

SUMMARY 1. The prediction of species distributions is of primary importance in ecology and conservation biology. Statistical models play an important role in this regard; however, researchers have little guidance when choosing between competing methodologies because few comparative studies have been conducted. 2. We provide a comprehensive comparison of traditional and alternative techniques for predicting species distributions using logistic regression analysis, linear discriminant analysis, classification trees and artificial neural networks to model: (1) the presence/absence of 27 fish species as a function of habitat conditions in 286 temperate lakes located in south‐central Ontario, Canada and (2) simulated data sets exhibiting deterministic, linear and non‐linear species response curves. 3. Detailed evaluation of model predictive power showed that approaches produced species models that differed in overall correct classification, specificity (i.e. ability to correctly predict species absence) and sensitivity (i.e. ability to correctly predict speciespresence) and in terms of which of the study lakes they correctly classified. Onaverage, neural networks outperformed the other modelling approaches, although all approaches predicted species presence/absence with moderate to excellent success. 4. Based on simulated non‐linear data, classification trees and neural networks greatly outperformed traditional approaches, whereas all approaches exhibited similar correct classification rates when modelling simulated linear data. 5. Detailed evaluation of model explanatory insight showed that the relative importance of the habitat variables in the species models varied among the approaches, where habitat variable importance was similar among approaches for some species and very different for others. 6. In general, differences in predictive power (both correct classification rate and identity of the lakes correctly classified) among the approaches corresponded with differences in habitat variable importance, suggesting that non‐linear modelling approaches (i.e. classification trees and neural networks) are better able to capture and model complex, non‐linear patterns found in ecological data. The results from the comparisons using simulated data further support this notion. 7. By employing parallel modelling approaches with the same set of data and focusing on comparing multiple metrics of predictive performance, researchers can begin to choose predictive models that not only provide the greatest predictive power, but also best fit the proposed application.     

A model for range expansion of an introduced species: the common waxbill Estrilda astrild in Portugal

Abstract. The common waxbill Estrilda astrild was first introduced to Portugal from Africa in 1964, and has spread across much of the country and into Spain. We modelled the expansion of the common waxbill on a 20 × 20 km UTM grid in 4‐year periods from 1964 to 1999. The time variation of the square root of the occupied area shows that this expansion process is stabilizing in Portugal, and reasons for this are discussed. Several methods used to model biological expansions are not appropriate for the present case, because little quantitative data are available on the species ecology and because this expansion has been spatially heterogeneous. Instead, colonization on a grid was modelled as a function of several biophysical and spatio‐temporal variables through the fitting of a multivariate autologistic equation. This approach allows examination of the underlying factors affecting the colonization process. In the case of the common waxbill it was associated positively with its occurrence in adjacent cells, and affected negatively by altitude and higher levels of solar radiation.     

Approximate maximum likelihood estimation for logistic regression with covariate measurement error

In nutritional epidemiology, dietary intake assessed with a food frequency questionnaire is prone to measurement error. Ignoring the measurement error in covariates causes estimates to be biased and leads to a loss of power. In this paper, we consider an additive error model according to the characteristics of the European Prospective Investigation into Cancer and Nutrition (EPIC)‐InterAct Study data, and derive an approximate maximum likelihood estimation (AMLE) for covariates with measurement error under logistic regression. This method can be regarded as an adjusted version of regression calibration and can provide an approximate consistent estimator. Asymptotic normality of this estimator is established under regularity conditions, and simulation studies are conducted to empirically examine the finite sample performance of the proposed method. We apply AMLE to deal with measurement errors in some interested nutrients of the EPIC‐InterAct Study under a sensitivity analysis framework.     

Measuring probabilistic reaction norms for age and size at maturation

We present a new probabilistic concept of reaction norms for age and size at maturation that is applicable when observations are carried out at discrete time intervals. This approach can also be used to estimate reaction norms for age and size at metamorphosis or at other ontogenetic transitions. Such estimations are critical for understanding phenotypic plasticity and life-history changes in variable environments, assessing genetic changes in the presence of phenotypic plasticity, and calibrating size- and age-structured population models. We show that previous approaches to this problem, based on regressing size against age at maturation, give results that are systematically biased when compared to the probabilistic reaction norms. The bias can be substantial and is likely to lead to qualitatively incorrect conclusions; it is caused by failing to account for the probabilistic nature of the maturation process. We explain why, instead, robust estimations of maturation reaction norms should be based on logistic regression or on other statistical models that treat the probability of maturing as a dependent variable. We demonstrate the utility of our approach with two examples. First, the analysis of data generated for a known reaction norm highlights some crucial limitations of previous approaches. Second, application to the northeast arctic cod (Gadus morhua) illustrates how our approach can be used to shed new light on existing real-world data.     

Application of Kinetic and Dynamic Data for Antihistamines to Risk Characterization in Sensitive Populations

A major goal of risk assessment is to protect the health of individuals who may be more sensitive than the general population. This study compared human phar-macokinetic and pharmacodynamic data in sensitive groups (i.e., children, the elderly, diseased states, and poor metabolizers) versus young, healthy adults for the antihistamines cetirizine, fexofenadine, loratadine, azelastine, ebastine, chlorpheniramine, and diphenhydramine. The default components (3.16 each for kinetic and dynamic aspects) of the intraspecies uncertainty factor were adjusted with compound specific data for the antihistamines. The majority (16 of 18) of the composite factors (kinetics X dynamics) for the sensitive groups were less than 10. Children had the lowest composite factors for antihistamines, ranging from 1.1 to 6.3. Application of kinetic and dynamic data for antihistamines to the Renwick/International Programme on Chemical Safety (IPCS) scheme can aid in characterizing the extent of variability in sensitive populations, thereby reducing the uncertainty associated with the risk assessment of sensitive populations.     

Synthesis of evidence from epidemiological studies with interval-censored exposure due to grouping

We describe a method for assessing dose-response effects from a series of case-control and cohort studies in which the exposure information is interval censored. The interval censoring of the exposure variable is dealt with through the use of retrospective models in which the exposure is treated as a multinomial response and disease status as a binary covariate. Polychotomous logistic regression models are adopted in which the dose-response relationship between exposure and disease may be modeled in a discrete or continuous fashion. Partial conditioning is possible to eliminate some of the nuisance parameters. The methods are applied to the motivating study of the relationship between chorionic villus sampling and the occurrence of terminal transverse limb reduction.     

Approaches for missing covariate data in logistic regression with MNAR sensitivity analyses

Data with missing covariate values but fully observed binary outcomes are an important subset of the missing data challenge. Common approaches are complete case analysis (CCA) and multiple imputation (MI). While CCA relies on missing completely at random (MCAR), MI usually relies on a missing at random (MAR) assumption to produce unbiased results. For MI involving logistic regression models, it is also important to consider several missing not at random (MNAR) conditions under which CCA is asymptotically unbiased and, as we show, MI is also valid in some cases. We use a data application and simulation study to compare the performance of several machine learning and parametric MI methods under a fully conditional specification framework (MI-FCS). Our simulation includes five scenarios involving MCAR, MAR, and MNAR under predictable and nonpredictable conditions, where “predictable” indicates missingness is not associated with the outcome. We build on previous results in the literature to show MI and CCA can both produce unbiased results under more conditions than some analysts may realize. When both approaches were valid, we found that MI-FCS was at least as good as CCA in terms of estimated bias and coverage, and was superior when missingness involved a categorical covariate. We also demonstrate how MNAR sensitivity analysis can build confidence that unbiased results were obtained, including under MNAR-predictable, when CCA and MI are both valid. Since the missingness mechanism cannot be identified from observed data, investigators should compare results from MI and CCA when both are plausibly valid, followed by MNAR sensitivity analysis.     

Testing alternative models for the conservation of koalas in fragmented rural–urban landscapes

Abstract Predicting the various responses of different species to changes in landscape structure is a formidable challenge to landscape ecology. Based on expert knowledge and landscape ecological theory, we develop five competing a priori models for predicting the presence/absence of the Koala (Phascolarctos cinereus) in Noosa Shire, south‐east Queensland (Australia). A priori predictions were nested within three levels of ecological organization: in situ (site level) habitat (<1 ha), patch level (100 ha) and landscape level (100–1000 ha). To test the models, Koala surveys and habitat surveys (n = 245) were conducted across the habitat mosaic. After taking into account tree species preferences, the patch and landscape context, and the neighbourhood effect of adjacent present sites, we applied logistic regression and hierarchical partitioning analyses to rank the alternative models and the explanatory variables. The strongest support was for a multilevel model, with Koala presence best predicted by the proportion of the landscape occupied by high quality habitat, the neighbourhood effect, the mean nearest neighbour distance between forest patches, the density of forest patches and the density of sealed roads. When tested against independent data (n = 105) using a receiver operator characteristic curve, the multilevel model performed moderately well. The study is consistent with recent assertions that habitat loss is the major driver of population decline, however, landscape configuration and roads have an important effect that needs to be incorporated into Koala conservation strategies.     

Fungal endophytes associated with three South American Myrtae (Myrtaceae) exhibit preferences in the colonization at leaf level

Fungal endophytes associated with Myrtaceae from Brazil and Argentina were isolated at three levels of nesting: leaf, individual host trees, and site collection. The alternating logistic regression (ALR) was used to model the data because it offers a computationally convenient method for fitting regression structures involving large clusters. The objectives of this study were to determine: (i) whether the colonization pattern is influenced by environmental variables, (ii) if there is some leaf part they prefer to colonize; (iii) if there is some fungal endophyte aggregation between hierarchical levels; (iv) what the distance effect is on the fungal association. The environmental variables were statistically significant only for Xylaria, i.e., when the elevation and water precipitation increase and the temperature decreases, the odds ratio of finding another fungal endophyte of that genus previously found increases. Sordariomycetes, Xylariales, and Xylaria exhibited leaf fragment preference to petiole and tip. Fungal endophytes showed association within leaf. The horizontal transmission mode and the dispersal limitation may explain this association at the leaf level. Moreover, our results suggest that when a fungal endophyte infects a leaf or host tree individual, the odds ratio of dispersal inside them is greater.