BackgroundMalnutrition is one of the principal causes of child mortality in developing countries including Bangladesh. According to our knowledge, most of the available studies, that addressed the issue of malnutrition among under-five children, considered the categorical (dichotomous/polychotomous) outcome variables and applied logistic regression (binary/multinomial) to find their predictors. In this study malnutrition variable (i.e. outcome) is defined as the number of under-five malnourished children in a family, which is a non-negative count variable. The purposes of the study are (i) to demonstrate the applicability of the generalized Poisson regression (GPR) model as an alternative of other statistical methods and (ii) to find some predictors of this outcome variable.MethodsThe data is extracted from the Bangladesh Demographic and Health Survey (BDHS) 2007. Briefly, this survey employs a nationally representative sample which is based on a two-stage stratified sample of households. A total of 4,460 under-five children is analysed using various statistical techniques namely Chi-square test and GPR model.ResultsThe GPR model (as compared to the standard Poisson regression and negative Binomial regression) is found to be justified to study the above-mentioned outcome variable because of its under-dispersion (variance < mean) property. Our study also identify several significant predictors of the outcome variable namely mother’s education, father’s education, wealth index, sanitation status, source of drinking water, and total number of children ever born to a woman.ConclusionsConsistencies of our findings in light of many other studies suggest that the GPR model is an ideal alternative of other statistical models to analyse the number of under-five malnourished children in a family. Strategies based on significant predictors may improve the nutritional status of children in Bangladesh.

This article explores the correlates of intimate partner violence (IPV) among rural, southern Latinas. A sample of 1,212 women in blue-collar work sites in rural North Carolina completed a questionnaire assessing IPV and other social, demographic, and health-related variables. Social and demographic correlates of IPV were examined. Adult lifetime prevalence of IPV in Latinas was 19.5%, similar to that of non-Latinas. As compared to Latinas who did not experience IPV and non-Latinas who experienced IPV, Latinas who experienced IPV were more likely to lack social support and to have children in the home. Agencies that provide services to victims of IPV in the rural South need to be prepared to meet the unique needs of Latina immigrants.

Abstract:In response to the recent global financial crisis, the regulatory authorities in many countries have imposed stringent capital requirements in the form of the BASEL III Accord to ensure financial stability. On the other hand, bankers have criticized new regulation on the ground that it would enhance the cost of funds for bank borrowers and deteriorate the bank profitability. In this study, we examine the impact of capital requirements on the cost of financial intermediation and bank profitability using a panel dataset of 32 Bangladeshi banks over the period from 2000 to 2015. By employing a dynamic panel generalized method of moments (GMM) estimator, we find robust evidence that higher bank regulatory capital ratios reduce the cost of financial intermediation and increase bank profitability. The results hold when we use equity to total assets ratio as an alternative measure of bank capital. We also observe that switching from BASEL I to BASEL II has no measurable impact on the cost of financial intermediation and bank profitability in Bangladesh. In the empirical analysis, we further observe that higher bank management and cost efficiencies are associated with the lower cost of financial intermediation and higher bank profitability. These results have important implications for bank regulators, academicians, and bankers.

This study had three purposes, all related to evaluating energy expenditure (EE) prediction accuracy from body-worn accelerometers: (1) compare linear regression to linear mixed models, (2) compare linear models to artificial neural network models, and (3) compare accuracy of accelerometers placed on the hip, thigh, and wrists. Forty individuals performed 13 activities in a 90 min semi-structured, laboratory-based protocol. Participants wore accelerometers on the right hip, right thigh, and both wrists and a portable metabolic analyzer (EE criterion). Four EE prediction models were developed for each accelerometer: linear regression, linear mixed, and two ANN models. EE prediction accuracy was assessed using correlations, root mean square error (RMSE), and bias and was compared across models and accelerometers using repeated-measures analysis of variance. For all accelerometer placements, there were no significant differences for correlations or RMSE between linear regression and linear mixed models (correlations: r  =  0.71-0.88, RMSE: 1.11-1.61 METs; p > 0.05). For the thigh-worn accelerometer, there were no differences in correlations or RMSE between linear and ANN models (ANN-correlations: r  =  0.89, RMSE: 1.07-1.08 METs. Linear models-correlations: r  =  0.88, RMSE: 1.10-1.11 METs; p > 0.05). Conversely, one ANN had higher correlations and lower RMSE than both linear models for the hip (ANN-correlation: r  =  0.88, RMSE: 1.12 METs. Linear models-correlations: r  =  0.86, RMSE: 1.18-1.19 METs; p < 0.05), and both ANNs had higher correlations and lower RMSE than both linear models for the wrist-worn accelerometers (ANN-correlations: r  =  0.82-0.84, RMSE: 1.26-1.32 METs. Linear models-correlations: r  =  0.71-0.73, RMSE: 1.55-1.61 METs; p < 0.01). For studies using wrist-worn accelerometers, machine learning models offer a significant improvement in EE prediction accuracy over linear models. Conversely, linear models showed similar EE prediction accuracy to machine learning models for hip- and thigh-worn accelerometers and may be viable alternative modeling techniques for EE prediction for hip- or thigh-worn accelerometers.

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Over last two decades, emerging and developing nations have desperately endeavored for efficient banking sectors. In this study, we argue that bank efficiency generates incentives that can impact banks' capital holdings and the cost of financial intermediation. Analyzing a panel dataset of 1190 banks from BRICS (Brazil, Russia, India, China, South Africa) countries over the period 2007-2015, we find robust evidence that more efficient banks hold higher capital and charge lower financial intermediation costs. In an extended sample over the period 2000-2015, we observe that cost efficiency had a marginal positive impact on bank capital during the global financial crisis of [2007][2008][2009]. We also observe that on average, banks increased the cost of financial intermediation during the crisis, however, greater efficiency helped banks to not charge higher intermediation costs. Our results imply the beneficial impact of bank efficiency for bank stability and real economy.

We conducted a meta-analysis to explore dose-response relationships for bladder and lung cancers when people are chronically exposed to low doses of arsenic. We searched electronic databases for articles published through 2010. Ten studies on bladder cancer and ingested arsenic exposure and five studies on lung cancer and ingested arsenic exposure fit our selection criteria. We also investigate the sensitivity of the absolute risk of lung and bladder cancer under different underlying prevalence measures. Males have a higher risk of bladder cancer than do females at all maximum contamination levels. The absolute risk of bladder cancer and lung cancer from ingested arsenic correlates highly with smoking rates. For a maximum contamination level of 10 µg/L, we estimate that there are about 2.91 additional bladder cancer cases per 100,000 people and, considering studies since 2000, we estimate that there are about 4.51 additional lung cancer cases per 100,000 people.

In this paper, we examine the impact of trade openness on bank risk-taking behavior employing a panel dataset of 899 banks from the BRICS (i.e., Brazil, Russia, India, China, and South Africa) countries over the period 2000–2017. We find that higher trade openness lowers bank risk-taking. Our results are robust when we use alternative proxies of trade openness and bank risk-taking, estimate country-wise regressions, or use alternative estimation methods such as system Generalized Methods of Moments (GMM), fixed effects, pooled Ordinary Least Square (OLS), and Vector Error Correction Model (VECM) models. We also observe higher trade openness decreases bank risk-taking in both the short and long run. Moreover, banks in more open countries perform relatively better during the crisis period further signifying the diversification benefits of openness. Together, our findings imply the beneficial impact of trade openness for financial sector stability.