ObjectivesThe present study aims to estimate the incidence of preventable infectious diseases or associated symptoms among young children in Bangladesh and also determine the factors affecting these conditions. The study hypothesised that various background characteristics of children as well as their parents influence the incidence of morbidity of children aged below 5 years.SettingThe study used data from the most recent nationally representative cross-sectional Bangladesh Demographic and Health Survey (BDHS) conducted in 2011.ParticipantsA total of 7550 children aged below 5 years during the survey from mothers aged between 12 and 49 years are the participants of the study.ResultsIn general, younger children were more likely to suffer from multiple health conditions than their older counterparts. Children belonging to households classified as poor (OR=1.425, 95% CI (1.130 to 1.796)) or middle (OR=1.349, 95% CI (1.113 to 1.636)) faced greater risk of illness than those from well-off households. A combination of source and treatment practices of drinking water showed a significant impact on incidence of childhood morbidity. Children from households using untreated non-piped water were 85.8% (OR=1.860, 95% CI (1.269 to 2.728)) more likely to suffer from comorbidity than those who treat their piped drinking water. However, we observed that water treatment alone has no impact unless the water itself was sourced from a pipe.ConclusionsAccelerated programmes promoting access to safe drinking water along with water treatment practices, and better household environment may prove effective in reducing the incidence of childhood morbidity in Bangladesh.

Statistical models for total monthly rainfall used for forecasting, risk management and agricultural simulations are usually based on gamma distributions and variations. In this study, we examine a family of distributions (called the Tweedie family of distributions) to determine if the choice of the gamma distribution is optimal within the family. We restrict ourselves to the exponential family of distributions as they are the response distributions used for generalized linear models (GLMs), which has numerous advantages. Further, we restrict ourselves to distributions where the variance is proportional to some power of the mean, as these distributions also have desirable properties. Under these restrictions, an infinite number of distributions exist for modelling positive continuous data and include the gamma distribution as a special case. Results show that for positive monthly rainfall totals in the data history for a particular station, monthly rainfall is optimally or near-optimally modelled using the gamma distribution by varying the parameters of the gamma distribution; using different distributions for each month cannot improve on this approach. In addition, under the same model restrictions, monthly rainfall totals that include zeros are also well modelled by the same family of distributions. Hence monthly rainfall can be suitably modelled using one of two Tweedie distributions depending on whether exact zeros appear in the rainfall history. We propose a slight variation of the gamma distribution for use in practice. This model fits the data almost as well as the gamma distribution but admits the possibility that future months may have zero rainfall.

Rainfall models are used to understand the effect of various climatological variables on rainfall amounts. The models also have potential uses in predicting and simulating rainfall. We use Tweedie generalized linear models to model monthly rainfall amounts and occurrence simultaneously with a set of predictors (sine term, cosine term, NINO 3.4, SOI and SOI phase). Models are fitted to the monthly rainfall data of 220 Australian stations with 4 stations as case studies. First, models with only sine and cosine terms (the base model) are fitted to model the cyclic pattern of rainfall data, and then one of the climatological variables is added each time in addition to the base model. On the basis of the BIC, the model with NINO 3.4 is preferred for most of the studied stations. Stations for which the model using the SOI is preferred appear in small clusters. Adding the climatological variables to the base model improves the fit of the model and makes substantial changes in the predicted mean monthly rainfall amount and probability of getting a dry month. The climatological variables have significant impacts on the amount of rainfall in most stations located on the eastern and northeastern regions of Australia. The models used lags one of the climatological covariates (i.e. value of the covariates of previous month with rainfall amount of a month) and are useful for one month lead rainfall prediction.

The wide availability of tracking devices has drastically increased the role of geolocation in social networks, resulting in new commercial applications; for example, marketers can identify current trending topics within a region of interest and focus their products accordingly. In this paper we study a basic analytics query on geotagged data, namely: given a spatiotemporal region, find the most frequent terms among the social posts in that region. While there has been prior work on keyword search on spatial data (find the objects nearest to the query point that contain the query keywords), and on group keyword search on spatial data (retrieving groups of objects), our problem is different in that it returns keywords and aggregated frequencies as output, instead of having the keyword as input. Moreover, we differ from works addressing the streamed version of this query in that we operate on large, disk resident data and we provide exact answers. We propose an index structure and algorithms to efficiently answer such top-k spatiotemporal range queries, which we refer as Top-k Frequent Spatiotemporal Terms (kFST) queries. Our index structure employs an R-tree augmented by top-k sorted term lists (STLs), where a key challenge is to balance the size of the index to achieve faster execution and smaller space requirements. We theoretically study and experimentally validate the ideal length of the stored term lists, and perform detailed experiments to evaluate the performance of the proposed methods compared to baselines on real datasets.

Background/Objectives:This study aims at examining the urban–rural differentials in the effects of socioeconomic predictors on underweight and obesity of ever-married women in Bangladesh. The effect of malnutrition and other risk factors on non-communicable diseases is also examined.Subjects/Methods:The information regarding nutritional status, socioeconomic and demographic background, and non-communicable diseases of ever-married women was extracted from the nationally representative, cross-sectional Bangladesh Demographic and Health Survey (BDHS 2011) data set. Both bivariate (χ2 test) and multivariate (multinomial logistic regression model) analyses were performed in determining the risk factors of malnutrition. The effect of malnutrition and associated risk factors on non-communicable diseases was determined using binary logistic regression models.Results:The overall prevalence as well as the effects of individual risk factors of malnutrition differ in urban and rural settings. Regional differentials in the prevalence of underweight were statistically significant only for rural areas. In rural and urban settings, women from households with poor economic status were 67% (odds ratio (OR) 0.33, 95% CI 0.26–0.43) and 81% (OR=0.19, 95% CI 0.13–0.29) less likely to be overweight, respectively, with respect to those from affluent households. Women from the Rangpur division were significantly more likely to suffer from anemia (OR=1.41, 95% CI 1.13–1.77) and hypertension (OR=1.67, 95% CI 1.19–2.34) than those from the Sylhet division (reference division). With respect to those considered as underweight, women who were categorized as overweight were 0.47 (OR=0.53, 95% CI 0.43–0.65) times less likely to suffer from anemia, and 1.83 (OR=2.83, 95% CI 1.99–4.02) and 1.70 (OR=2.70, 95% CI 2.09–3.50) times more likely to suffer from diabetes and hypertension, respectively.Conclusions:Rural–urban differentials in the effects of individual risk factors of malnutrition were observed. Wealth status of households and nutritional status of women showed significant effect on the prevalence of anemia, diabetes and hypertension.