BackgroundSouth-Asian countries are considered to be a potential breeding ground for HIV epidemic. Although the prevalence of this incurable disease is low in Bangladesh, women still have been identified as more vulnerable group. The aim of this study is to assess the knowledge about HIV/AIDS: its trends and associated factors among the women in Bangladesh.MethodsWe analysed the nationally representative repeatedly cross-sectional Bangladesh Demographic and Health Surveys (BDHSs) data: 2007, 2011, and 2014. These data were clustered in nature due to the sampling design and the generalized mixed effects model is appropriate to examine the association between the outcome and the explanatory variables by adjusting for the cluster effect.ResultsOverall, women’s knowledge about HIV/AIDS has been decreasing over the years. Education plays the leading role and secondary-higher educated women are 6.6 times more likely to have HIV/AIDS knowledge. The likelihood of knowledge is higher among the women who had media exposure (OR: 1.6) and knowledge on family planning (OR: 2.3). A rural-urban gap is noticed in women’s knowledge about HIV/AIDS and significant improvement has been observed among the rural and media exposed women. Results reveal that age, region, religion, socio-economic status, education, contraceptive use have significant (p<0.01) effects on women’s knowledge about HIV/AIDS.ConclusionThis study recommends to emphasis more on women’s education, media exposure, and family planning knowledge in strengthening women’s knowledge about HIV/AIDS. In addition, residence specific programs regarding HIV/AIDS awareness also need to be prioritized.
ObjectiveThe availability of iodized salt in households remains low in Bangladesh, which calls for improving the salt iodization quality and its coverage. The present study assessed the socio-economic disparity in Bangladesh to characterize the availability of iodized salt at household level.DesignAssociations between different socio-economic factors and availability of iodized salt at household level were explored using Bayesian mixed-effects logistic models after adjusting the district- and cluster-level random effects.SettingBangladesh Multiple Indicator Cluster Survey (MICS), 2012–13.ParticipantsHouseholds (sample size, n 50981).ResultsResults showed that 73·15 % of household salt samples were iodized to some extent although iodization level varied. According to the regression model, houses with young (adjusted odds ratio of posterior mean (OR) = 1·31; 95 % credible interval (CI) 1·09, 1·64) and educated (OR = 3·66; 95 % CI 3·25, 4·23) household heads had significantly higher likelihood of availability of iodized salt. In addition, iodized salt was less likely be found in poor and rural households, as urban households were 2·88 times (95 % CI 2·41, 3·34) more likely have iodized salt. Moreover, the regional locations of the households were an important component that contributed to the local iodized salt coverage. As per the district-wise distribution, the north-west part of Bangladesh and Cox’s Bazar in the far south seemed to lack household-level iodized salt.ConclusionsOur findings suggest that iodized salt intervention should be promoted considering the area variations, which could potentially help policy makers to design interventions in the context of Bangladesh.
This research is motivated from the data from a large Selenium and Vitamin E Cancer Prevention Trial (SELECT). The prostate specific antigens (PSAs) were collected longitudinally, and the survival endpoint was the time to low-grade cancer or the time to high-grade cancer (competing risks). In this article, the goal is to model the longitudinal PSA data and the time-to-prostate cancer (PC) due to low- or high-grade. We consider the low-grade and high-grade as two competing causes of developing PC. A joint model for simultaneously analysing longitudinal and time-to-event data in the presence of multiple causes of failure (or competing risk) is proposed within the Bayesian framework. The proposed model allows for handling the missing causes of failure in the SELECT data and implementing an efficient Markov chain Monte Carlo sampling algorithm to sample from the posterior distribution via a novel reparameterization technique. Bayesian criteria, [Formula: see text]DIC[Formula: see text], and [Formula: see text]WAIC[Formula: see text], are introduced to quantify the gain in fit in the survival sub-model due to the inclusion of longitudinal data. A simulation study is conducted to examine the empirical performance of the posterior estimates as well as [Formula: see text]DIC[Formula: see text] and [Formula: see text]WAIC[Formula: see text] and a detailed analysis of the SELECT data is also carried out to further demonstrate the proposed methodology.
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