Social disorganization and history of child sexual abuse against girls in sub-Saharan Africa : a multilevel analysis

Background: Child sexual abuse (CSA) is a considerable public health problem. Less focus has been paid to the role of community level factors associated with CSA. The aim of this study was to examine the association between neighbourhood-level measures of social disorganization and CSA. Methods: We applied multiple multilevel logistic regression analysis on Demographic and Health Survey data for 6,351 adolescents from six countries in sub-Saharan Africa between 2006 and 2008. Results: The percentage of adolescents that had experienced CSA ranged from 1.04% to 5.84%. There was a significant variation in the odds of reporting CSA across the communities, suggesting 18% of the variation in CSA could be attributed to community level factors. Respondents currently employed were more likely to have reported CSA than those who were unemployed (odds ratio [OR] = 2.05, 95% confidence interval [CI] 1.48 to 2.83). Respondents from communities with a high family disruption rate were 57% more likely to have reported CSA (OR=1.57, 95% CI 1.14 to 2.16). Conclusion: We found that exposure to CSA was associated with high community level of family disruption, thus suggesting that neighbourhoods may indeed have significant important effects on exposure to CSA. Further studies are needed to explore pathways that connect the individual and neighbourhood levels, that is, means through which deleterious neighbourhood effects are transmitted to individuals

Climate Vulnerability and the Cost of Debt

We use indices from the Notre Dame Global Adaptation Initiative to investigate the impact of climate vulnerability on bond yields. Our methodology invokes panel ordinary least squares with robust standard errors and principal component analysis. The latter serves to address the multicollinearity between a set of vulnerability measures. We find that countries with higher exposure to climate vulnerability, such as the member countries of the V20 climate vulnerable forum, exhibit 1.174 percent higher cost of debt on average. This effect is significant after accounting for a set of macroeconomic controls. Specifically, we estimate the incremental debt cost due to higher climate vulnerability, for the V20 countries, to have exceeded USD 62 billion over the last ten years. In other words, for every ten dollars they pay in interest cost, they pay another dollar for being climate vulnerable. We also find that a measure of social readiness, which includes education and infrastructure, has a negative and significant effect on bond yields, implying that social and physical investments can mitigate climate risk related debt costs and help to stabilize the cost of debt for vulnerable countries

Weather, the Forgotten Factor in Business Cycle Analyses

In periods of unusual weather, forecasters face a problem of interpreting economic data: Which part goes back to the underlying economic trend and which part arises from a special weather effect? In this paper, we discuss ways to disentangle weather-related from business cycle-related influences on economic indicators. We find a significant influence of weather variables at least on a number of monthly indicators. Controlling for weather effects within these indicators should thus create opportunities to increase the accuracy of indicator-based forecasts. Focusing on quarterly GDP growth in Germany, we find that the accuracy of the RWI short term forecasting model improves but advances are small and not significant

The Determinants of young Adult Social well-being and Health (DASH) study: diversity, psychosocial determinants and health.

Purpose: The Determinants of young Adult Social well-being and Health longitudinal study draws on life-course models to understand ethnic differences in health. A key hypothesis relates to the role of psychosocial factors in nurturing the health and well-being of ethnic minorities growing up in the UK. We report the effects of culturally patterned exposures in childhood. Methods: In 2002/2003, 6643 11–13 year olds in London, ~80 % ethnic minorities, participated in the baseline survey. In 2005/2006, 4782 were followed-up. In 2012–2014, 665 took part in a pilot follow-up aged 21–23 years, including 42 qualitative interviews. Measures of socioeconomic and psychosocial factors and health were collected. Results: Ethnic minority adolescents reported better mental health than White British, despite more adversity (e.g. economic disadvantage, racism). It is unclear what explains this resilience but findings support a role for cultural factors. Racism was an adverse influence on mental health, while family care and connectedness, religious involvement and ethnic diversity of friendships were protective. While mental health resilience was a feature throughout adolescence, a less positive picture emerged for cardio-respiratory health. Both, mental health and cultural factors played a role. These patterns largely endured in early 20s with family support reducing stressful transitions to adulthood. Education levels, however, signal potential for socio-economic parity across ethnic groups

Family Resources in Two Generations and School Readiness Among Children of Teen Parents

Overall, children born to teen parents experience disadvantaged cognitive achievement at school entry compared with children born to older parents. However, within this population, there is variation, with a significant fraction of teen parents’ children acquiring adequate preparation for school entry during early childhood. We ask whether the family background of teen parents explains this variation. We use data on children born to teen mothers from three waves of the Early Childhood Longitudinal Study-Birth Cohort (N ~ 700) to study the association of family background with children’s standardized reading and mathematics achievement scores at kindergarten entry. When neither maternal grandparent has completed high school, children’s scores on standardized assessments of math and reading achievement are one-quarter to one-third of a standard deviation lower compared with families where at least one grandparent finished high school. This association is net of teen mothers’ own socioeconomic status in the year prior to children’s school entry

LGBTQ parenting post heterosexual relationship dissolution

The chapter examines parenting among sexual and gender minorities post heterosexual relationship dissolution (PHRD). Reviewing the literature around intersecting identities of LGBTQ parents, we consider how religion, race, and socioeconomic status are associated with routes into and out of heterosexual relationships and variation in the lived experience of sexual and gender identity minorities, in particular how LGBTQ parents PHRD feel about being out. Further consideration is given to examining how family relationships change and develop as parental sexual and/or gender identity changes. We also explore the impact of PHRD identity and parenthood on new partnerships and stepfamily experiences. The chapter addresses the reciprocal relationship between research on LGBTQ parenting and policy and legal influences that impact upon the experience of LGBTQ parenting PHRD when custody and access are disputed. Finally, the chapter includes future research directions and implications for practice in an area that has been revitalized in recent years

Investigating the Scalability of Algorithms, the Role of Similarity Metric and the List of Suggested Items Construction Scheme in Recommender Systems

The continuous increase in demand for new products and services on the market brought the need for systematic improvement of recommendation technologies. Recommender systems proved to be the answer to the data overload problem and an advantage for e-business. Nevertheless, challenges that recommender systems face, like sparsity and scalability, affect their performance in real-world situations where both the number of users and items are high and item rating is infrequent. In this article we propose a cluster based recommendation approach using genetic algorithms. Users are grouped into clusters based on their past choices and preferences and receive recommendations from the other cluster members with the aid of an innovative recommendation scheme called Top-Nvoted items. Similarity between users is computed using the max_norm Pearson coefficient. This is a modified form of the widely used Pearson coefficient and it is used to prevent very active users dominating recommendations. We compare our approach with five well established recommendation methods with the aid of three different datasets. These datasets vary in terms of the number of users, the number of items, and the sparsity of ratings. As a result important conclusions are drawn about the efficiency of each method with respect to scalability and dataset's sparsit