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Literature on the psychological effects on women survivors of violence (WSV) suggests there may be a relationship between the specific type of gender-based violence and patterns in the development of mental health consequences. Understanding these relationships would support early targeted (or early specialist) intervention. Since violence against women in families is a common social health problem in developing countries, the study attempted to explore the abuse specific reaction patterns within such a context. A total of 600 WSV (mean age = 26.86, SD = 7.47) were recruited from different social service organizations working for WSV in Bangladesh. To identify the type of gender-based violence (i.e., physical, sexual, emotional, psychological, and economic violence) experienced and psychiatric sequelae (i.e., post-traumatic stress disorder (PTSD), anxiety, depression, suicidal ideation, and substance abuse) in WSV, multiple reliable and valid measures were used. The results of the hierarchical multiple regression analysis showed that amongst the various different types of violence, only psychological, sexual, and economic violence have a significant independent predictive ability on PTSD, anxiety, and depression in the Bangladeshi WSV. Violence related factors such as witnessing violence in childhood among parents and history of childhood abuse had the unique predictive ability on suicidal ideation and substance abuse, respectively. Our result suggested that accepting physical and emotional abuse at any stage of life is very common for Bangladeshi women. This study suggested that non-physical forms of violence have the most significant independent predictive ability in the development of psychiatric symptoms. It is suggested that to develop appropriate support services for WSV within this socio-cultural context, further research is required which focuses on the psychological impact of non-physical forms of violence. [ABSTRACT FROM AUTHOR]

Women's experience of domestic violence has adverse consequences on women's health globally and is itself affected by several factors. This study aims to determine the factors that are associated with experience of any form of domestic violence in rural Bangladesh. Data were derived from the baseline survey database of Suchana, a large-scale nutrition program in Bangladesh. Data of 5,440 women from poor households in rural areas were analyzed. Descriptive statistics were used to summarize the data. Multilevel logistic regression analysis was conducted to determine the factors that were significantly associated with experiencing any form of domestic violence. The prevalence of women experiencing domestic violence was found to be 35% and household food insecurity was 86%. Of the women's characteristic domain, age at marriage, lack of any support from household members, visit from non-governmental organizations (NGO) health professionals, number of children, and decision-making power of women at the household level were significantly associated with reporting of domestic violence. Of the household characteristic domain, the associated factors were having a male household head, increased household size, low socio-economic status, having a loan, experience of at least one crisis event, and household food insecurity. The strength of association between household food insecurity and domestic violence was higher among poor families. Experience of domestic violence is inevitably associated with women's decision-making power and household food insecurity. Behavioral change communication interventions may help to positively affect decision-making power but a holistic approach needs to be undertaken to reduce food insecurity, for which the underlying mechanism of food insecurity in Bangladesh needs to be explored further. [ABSTRACT FROM AUTHOR]

When fitting marginal models to correlated outcomes, the so-called sandwich variance is commonly used. However, this is not the case when fitting mixed models. Using two data sets, we illustrate the problems that can be encountered. We show that the differences or the ratios between the naive and sandwich standard deviations of the fixed effects estimators provide convenient means of assessing the fit of the model, as both are consistent when the covariance structure is correctly specified, but only the latter is when that structure is misspecified. When the number of statistical units is not too small, the sandwich formula correctly estimates the variance of the fixed effects estimator even if the random effects are misspecified, and it can be used in a diagnostic tool for assessing the misspecification of the random effects. A simple comparison with the naive variance is sufficient and we propose considering a ratio of the naive and sandwich standard deviation out of the [3/4; 4/3] interval as signaling a risk of erroneous inference due to a model misspecification. We strongly advocate broader use of the sandwich variance for statistical inference about the fixed effects in mixed models. [ABSTRACT FROM AUTHOR]