Immigrant fertility: an examination of social characteristics and assimilation

"This study compares the fertility patterns of foreign-born and native-born women in Canada and examines whether [the] same set of social characteristics accounts for differential fertility among both the groups. The study also assesses the importance of social characteristics and assimilation on immigrant fertility behavior. Two generations of currently married/cohabiting women with spouse present are analyzed using multiple regressions. The results reveal similar effects on fertility of social characteristics for foreign-born and native-born, while in the case of younger generations the effects are stronger."     

A generalization of Petersen's typology of migration

A typology of migration is presented that extends the one originally developed by William Petersen, which was based on ecological push, migration policy, people's aspirations, and social momentum. The proposed typology considers both the migrant's state of mind and two points in time. Examples of 36 migrant types are provided using data for Canada and selected other countries. (SUMMARY IN FRE AND SPA)     

A Bayesian Approach for Analyzing Results of Vehicle Collision Tests

Certain motor vehicle safety standards stipulate a collision test speed and a set of performance criteria that vehicles must satisfy during or after the collision test. For example, Federal Motor Vehicle Safety Standard 301 requires a 30 mile per hour (mph) barrier collision and specifies a certain maximum allowable limit on the total spillage of fuel. Vehicle designs are required to meet this standard; however, when collision tests are conducted at speeds higher than the standard, vehicles do not always satisfy the performance criteria. This paper develops a mathematical model for estimating the probability of meeting the standard by using a Bayesian framework to incorporate engineering judgment with collision test results. The model is based on the idea that there are random features to a vehicle's ability to meet performance standards in a collision, especially at such elevated speeds. Example calculations are included to illustrate the estimation of the probability of meeting the standard and to compare it with a maximum likelihood approach.     

A fig tree in a concrete jungle: fine-scale population genetic structure of the cluster fig Ficus racemosa in an urban environment

Urban Ecosystems - Urban vegetation is an essential requirement in cities for mitigating pollution, heat island effects and providing food and shelter to urban fauna. Efforts to conserve and...     

Second‐Generation Immigrants? The “2.5 Generation” in the United States*

Objective. This article takes issue with the way that second‐generation immigrants have been traditionally defined. In most studies, respondents are considered to be “second generation” if they are born in the United States and if at least one of their parents was born outside the United States. This article considers whether the experiences and outcomes of those with one U.S.‐born parent and one foreign‐born parent (the “2.5 generation”) are different from those with no U.S.‐born parents (the “2.0 generation”) and those with two native‐born parents (the “third generation”). Methods. The article analyzes data from the March Current Population Survey (CPS) from 1999 to 2001. Results. The evidence indicates that the 2.5 generation is a numerically significant population, and that it varies from other groups in age structure, racial identification, educational attainment, and income. Conclusions. In studying the U.S.‐born children of immigrants, scholars should avoid lumping together the 2.5 generation with those who have no native‐born parents. The members of the 2.5 generation also should be treated as distinct from those born in the United States to two native‐born parents.     

Civic Spaces: Mexican Hometown Associations and Immigrant Participation

The study of civic participation and social capital in the United States has, until recently, been silent on the role of immigrant-serving organizations. There is a new line of scholarship, which indicates that ethnic organizations are generally disadvantaged in relation to White mainstream organizations on factors such as resources and political visibility. Our fieldwork on Mexican hometown associations (HTAs) in Los Angeles shows that transnational associations are even more disadvantaged than ethnic organizations that primarily serve the native born. However, this marginality leaves some counterintuitive advantages, namely the creation of safe spaces where undocumented immigrants, recent immigrants, and those with limited English proficiency can get involved in civic and political activities. We explore the extent to which these dynamics vary by gender and immigrant generation, and over time as Mexican hometown associations increasingly turn their attention to political issues in the United States.     

Tissue Dosimetry, Pharmacokinetic Modeling, and Interspecies Scaling Factors

Interspecies scaling factors (ISFs) are numbers used to adjust the potency factor (for example, the q1* for carcinogens or reference doses for compounds eliciting other toxic endpoints) determined in experimental animals to account for expected differences in potency between test animals and people. ISFs have been developed for both cancer and non-cancer risk assessments in response to a common issue: toxicologists often determine adverse effects of chemicals in test animals and then they, or more commonly risk assessors and risk managers, have to draw inferences about what these observations mean for the human population. This perspective briefly reviews the development of ISFs and their applications in health risk assessments over the past 20 years, examining the impact of pharmacokinetic principles in altering current perceptions of the ISFs applied in these health risk assessments, and assessing future directions in applying both pharmacokinetic and pharmacodynamic principles for developing ISFs.     

Application of Multiple Imputation in Analysis of Data from Clinical Trials with Treatment Related Dropouts

This article develops a functional form of the generalized Poisson regression model that parametrically nests the Poisson and the two well known generalized Poisson regression models (GP-1 and GP-2). The proposed model is applied on the Malaysian motor insurance claim count data.     

A Numerical Method for Estimating the Variance of Age at Maximum Growth Rate in Growth Models

Studies on maturation and body composition mention age at peak height velocity (PHV) as an important measure that could predict adulthood outcome. The age at PHV is often derived from growth models such as the triple logistic fitted to the stature (height) data. Theoretically, for a well-behaved growth function, age at PHV could be obtained by setting the second derivative of the growth function to zero and solving for age. Such a solution obviously depends on the parameters of the growth function. Therefore, the uncertainty in the estimation of age at PHV resulting from the uncertainty in the estimation of the growth model, need to be accounted for in the models in which it is used as a predictor. Explicit expressions for the age at PHV and, consequently the variance of the estimate of the age at PHV, do not exist for some of the commonly used nonlinear growth functions, such as the triple logistic function. Once an estimate of this variance is obtained, it could be incorporated in subsequent modeling either through measurement error models or by using the inverse variances as weights. A numerical method for estimating the variance is implemented. The accuracy of this method is demonstrated through comparisons in models where explicit solution for the variance exists. The method of estimating the variance is illustrated by applying to growth data from the Fels study and subsequently used as weights in modeling two adulthood outcomes from the same study.     

An improved comorbidity summary score for measuring disease burden and predicting mortality with applications to two national cohorts

Abstract

Research involving administrative healthcare data to study patient outcomes requires the investigator to account for the patient’s disease burden in order to reduce the potential for biased results. Here we develop a comorbidity summary score based on variable importance measures derived from several statistical and machine learning methods and show it has superior predictive performance to the Elixhauser and Charlson indices when used to predict 1-year, 5-year, and 10-year mortality. We used two large Veterans Administration cohorts to develop and validate the summary score and compared predictive performance using the area under ROC curve (AUC) and the Brier score.