Infants’ Biological Sensitivity to the Effects of Maternal Social Support: Evidence Among Mexican American Families

The identification of infants who are most susceptible to both negative and positive social environments is critical for understanding early behavioral development. This study longitudinally assessed the interactive effects of infant vagal tone (respiratory sinus arrhythmia; RSA) and maternal social support on behavioral problems and competence among 322 low‐income Mexican American mother–infant dyads (infants: 54.1% female) and explored sex differences. Infant RSA was calculated from resting HR data at 6 weeks of age. Mothers reported on general social support, partner support, and family support at 6 months, and infant behavioral problems and competence at 1 year. Two‐way interactions (RSA × support source) were evaluated to predict behavioral problems and competence, adjusting for covariates. Results indicated higher competence among infants with lower RSA whose mothers reported higher general support or higher partner support. Interactive effects on behavior problems of RSA with maternal partner or family support were only found for female infants: Girls with higher RSA showed more behavior problems when mothers reported low support, but fewer problem levels in the context of high support. Our results suggest that infant RSA is an important moderator of the effects of the early social environment on early development.     

Transmission mechanisms of financial stress into economic activity in Turkey

Measuring, analyzing and understanding systemic risk in financial system have become very important in the light of the recent global crisis. In this study, we follow Holló, Kremer, and Lo Duca (2012) and evaluate systemic stress of financial system of Turkey with a high frequency (daily) financial stress index which consists of daily 13 financial market indicators. Dynamics of the financial stress index indicate that the index creates proper signals to the well-known financial stress events. The dynamic interaction between financial stress and real economic activity is investigated with application of structural VAR (SVAR) model. Results of the study suggest that deterioration of financial conditions impacts real economic activity significantly and adversely.     

Prevalence of musculoskeletal disorders among staffs in specialized healthcare centre

This study was conducted to investigate pervasiveness of the musculoskeletal disorder (MSD) among staffs in a specialized healthcare centre. Sixty-eight staffs from three departments namely Cardiovascular Lab (CVL), Nuclear Radiology, and General Radiography were recruited in this study. A modified Nordic Musculoskeletal Questionnaire (NMQ) was distributed among study population. The result shows that the prevalence of MSD was highest in lower back (88.2%), neck (76.5%) and shoulder (60.3%) for the past 12 months followed by lower back and elbow (44.1%), and wrist (39.1%) correspondingly, for the past 7 days. Present results suggest that healthcare professionals - radiographers, patient assistants and nurses incurs MSD risks through work tasks as well as psychosocial factors. These include awkward posture during patient handling, workload, work stress and time pressure. Therefore, an ergonomics improvement on the job design and workspace are needed in order to reduce the MSD risks.     

Aging: physical difficulties and safety in cooking tasks

It is known that many older people have difficulties in performing daily living activities such as cooking. These are due to the demands of the tasks and the changes in functional capabilities of the older people. This study examines cooking tasks performed by the aged that includes preparing and cooking meals, and storing kitchen tools in the kitchen. The objectives are to investigate the cooking difficulties encounter by older people and the safety concerning cooking tasks. This study focuses on individuals of age 65 years and above who can cook for themselves and/or family. Data were collected through observation, interviews, questionnaires and role play methods. The findings revealed that the common problems were due to the awkward body position where subjects had to bend down to take things from lower shelves, taking/storing things on higher shelves and cleaning the cooker. Moreover, the safety concerns were the layout of work centres (storage, cooker and sink), the use of cooker and opening packaging. It can be concluded that cooking difficulties are caused by inappropriate kitchen design and the decline of functional capabilities in older people.     

Missing covariates in generalized linear models when the missing data mechanism is non-ignorable

We propose a method for estimating parameters in generalized linear models with missing covariates and a non-ignorable missing data mechanism. We use a multinomial model for the missing data indicators and propose a joint distribution for them which can be written as a sequence of one-dimensional conditional distributions, with each one-dimensional conditional distribution consisting of a logistic regression. We allow the covariates to be either categorical or continuous. The joint covariate distribution is also modelled via a sequence of one-dimensional conditional distributions, and the response variable is assumed to be completely observed. We derive the E- and M-steps of the EM algorithm with non-ignorable missing covariate data. For categorical covariates, we derive a closed form expression for the E- and M-steps of the EM algorithm for obtaining the maximum likelihood estimates (MLEs). For continuous covariates, we use a Monte Carlo version of the EM algorithm to obtain the MLEs via the Gibbs sampler. Computational techniques for Gibbs sampling are proposed and implemented. The parametric form of the assumed missing data mechanism itself is not `testable' from the data, and thus the non-ignorable modelling considered here can be viewed as a sensitivity analysis concerning a more complicated model. Therefore, although a model may have `passed' the tests for a certain missing data mechanism, this does not mean that we have captured, even approximately, the correct missing data mechanism. Hence, model checking for the missing data mechanism and sensitivity analyses play an important role in this problem and are discussed in detail. Several simulations are given to demonstrate the methodology. In addition, a real data set from a melanoma cancer clinical trial is presented to illustrate the methods proposed.     

Bayesian variable selection for proportional hazards models

The authors consider the problem of Bayesian variable selection for proportional hazards regression models with right censored data. They propose a semi-parametric approach in which a nonparametric prior is specified for the baseline hazard rate and a fully parametric prior is specified for the regression coefficients. For the baseline hazard, they use a discrete gamma process prior, and for the regression coefficients and the model space, they propose a semi-automatic parametric informative prior specification that focuses on the observables rather than the parameters. To implement the methodology, they propose a Markov chain Monte Carlo method to compute the posterior model probabilities. Examples using simulated and real data are given to demonstrate the methodology.     

Structured measurement error in nutritional epidemiology: applications in the Pregnancy, Infection, and Nutrition (PIN) Study

Preterm birth, defined as delivery before 37 completed weeks' gestation, is a leading cause of infant morbidity and mortality. Identifying factors related to preterm delivery is an important goal of public health professionals who wish to identify etiologic pathways to target for prevention. Validation studies are often conducted in nutritional epidemiology in order to study measurement error in instruments that are generally less invasive or less expensive than "gold standard" instruments. Data from such studies are then used in adjusting estimates based on the full study sample. However, measurement error in nutritional epidemiology has recently been shown to be complicated by correlated error structures in the study-wide and validation instruments. Investigators of a study of preterm birth and dietary intake designed a validation study to assess measurement error in a food frequency questionnaire (FFQ) administered during pregnancy and with the secondary goal of assessing whether a single administration of the FFQ could be used to describe intake over the relatively short pregnancy period, in which energy intake typically increases. Here, we describe a likelihood-based method via Markov Chain Monte Carlo to estimate the regression coefficients in a generalized linear model relating preterm birth to covariates, where one of the covariates is measured with error and the multivariate measurement error model has correlated errors among contemporaneous instruments (i.e. FFQs, 24-hour recalls, and/or biomarkers). Because of constraints on the covariance parameters in our likelihood, identifiability for all the variance and covariance parameters is not guaranteed and, therefore, we derive the necessary and suficient conditions to identify the variance and covariance parameters under our measurement error model and assumptions. We investigate the sensitivity of our likelihood-based model to distributional assumptions placed on the true folate intake by employing semi-parametric Bayesian methods through the mixture of Dirichlet process priors framework. We exemplify our methods in a recent prospective cohort study of risk factors for preterm birth. We use long-term folate as our error-prone predictor of interest, the food-frequency questionnaire (FFQ) and 24-hour recall as two biased instruments, and serum folate biomarker as the unbiased instrument. We found that folate intake, as measured by the FFQ, led to a conservative estimate of the estimated odds ratio of preterm birth (0.76) when compared to the odds ratio estimate from our likelihood-based approach, which adjusts for the measurement error (0.63). We found that our parametric model led to similar conclusions to the semi-parametric Bayesian model.     

Application of Bayesian analyses to doubly randomized delayed start,matched control designs to demonstrate disease modification

Disease modification is a primary therapeutic aim when developing treatments for most chronic progressive diseases. The best treatments do not simply affect disease symptoms but fundamentally improve disease course by slowing, halting, or reversing disease progression. One of many challenges for establishing disease modification relates to the identification of adequate analytic tools to show differences in a disease course following intervention. Traditional approaches rely on the comparisons of slopes or noninferiority margins. However, it has proven difficult to conclusively demonstrate disease modification using such approaches. To address these challenges, we propose a novel adaptation of the delayed start study design that incorporates posterior probabilities identified by hierarchical Bayesian inference approaches to establish evidence for disease modification. Our models compare the size of treatment differences at the end of the delayed start period with those at the end of the early start period. Simulations that compare several models are provided. These include general linear models, repeated measures models, spline models, and model averaging. Our work supports the superiority of model averaging for accurately characterizing complex data that arise in real world applications. This novel approach has been applied to the design of an ongoing, doubly randomized, matched control study that aims to show disease modification in young persons with schizophrenia (the Disease Recovery Evaluation and Modification (DREaM) study). The application of this Bayesian methodology to the DREaM study highlights the value of this approach and demonstrates many practical challenges that must be addressed when implementing this methodology in a real world trial.     

Impact of the External Debt on Standard of Living: A Case of Asian Countries

Social Indicators Research - This study discusses the basic purpose of external debt: to finance poverty alleviation and bring progress in a country in a time of crisis rather than increase the...     

Assessing the impact of measurement error in modeling change in the absence of auxiliary data

Measurement error is well known to cause bias in estimated regression coefficients and a loss of power for detecting associations. Methods commonly used to correct for bias often require auxiliary data. We develop a solution for investigating associations between the change in an imprecisely measured outcome and precisely measured predictors, adjusting for the baseline value of the outcome when auxiliary data are not available. We require the specification of ranges for the reliability or the measurement error variance. The solution allows one to investigate the associations for change and to assess the impact of the measurement error.