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Aim: The high incidence of acute respiratory infection (ARI)-related morbidity and mortality is a major public health concern in developing countries. This study aimed to quantify regional inequalities and the degree of association between childhood ARI and background factors. Methods: This study utilised information of 238 945 children aged below five years extracted from the Fourth Indian National Family Health Survey conducted in 2015–16. Inter-state and regional inequality in the prevalence of ARI were quantified and presented using a map of India and forest plot. The association of background characteristics and ARI was quantified using bivariate and multivariable binary logistic regression models. Results: Significant inequalities in the prevalence of childhood ARI were observed across the six regions of India. Considering the children from north-east region as a reference, those from north, central and east regions were 0.68, 1.02 and 0.57 times more likely to suffer from ARI. Comorbidity, sex, age and nutritional status of children were significantly associated with the prevalence of ARI. Conclusions: ARI remains a significant public health concern among Indian children. The results of this study showed that significant regional disparities in the prevalence of ARI exist in India. This study adds value to the better understanding of inequality patterns and quantifies within- and intra-region inequalities in the prevalence of ARI in India. [ABSTRACT FROM AUTHOR]

This study aimed to quantify the inequalities and identify the associated factors of the UN sustainable development goal (SDG) targets in relation to safe drinking water. The concentration of the gut bacterium Escherichia coli in drinking water at the point of use (POU) and other information were extracted from the latest wave of the nationally representative Bangladesh Multiple Indicator Cluster Survey (MICS 2019). Bivariate and multivariable multinomial logistic regression models were used to identify potential predictors of contamination, whereas, classification trees were used to determine specific combinations of background characteristics with significantly higher rates of contamination. A higher risk of contamination from drinking water was observed for households categorized as middle or low wealth who collected water from sources with higher concentrations of E. coli. Treatment of drinking water significantly reduced the risk of higher levels of contamination, whereas owning a pet was significantly associated with recontamination. Regional differences in the concentrations of E. coli present in drinking water were also observed. Interventions in relation to water sources should emphasize reducing the level of E. coli contamination. Our results may help in developing effective policies for reducing diarrheal diseases by reducing water contamination risks. [ABSTRACT FROM AUTHOR]

ABSTRACT Theoretical probability distributions of seasonal rainfall totals have potential applications in fitting models and generating synthetic data. One of the major challenges in fitting distributions for seasonal rainfall from countries like Australia is that the data differ significantly for various seasons across stations. The wet stations may possess strictly positive rainfall amounts only, whereas dry stations may observe seasons with no rainfall. Furthermore the data ranges from approximately symmetric to highly skewed. We explore fitting distributions from the Tweedie family of distributions to model seasonal rainfall. The Tweedie family of distributions includes continuous-symmetric (normal), continuous-skewed (gamma) and mixture-type (Poisson-gamma, P-G) distributions. This study analysed seasonal rainfall totals from 989 Australian stations with data for about 100 years. For various seasons, within the Tweedie family the P-G distributions were optimal for about 50% of stations, the gamma distributions were near-optimal for approximately 40% and the normal distributions were near-optimal elsewhere. Most of the stations where the gamma distributions are near-optimal are concentrated on the Australian coastline. The quantile-quantile ( QQ) plots indicate that the models fit well to the seasonal rainfall totals of selected stations. Almost everywhere, various statistics of observed seasonal rainfall are within the empirical 95% confidence intervals of the respective statistics of simulated data using the Tweedie models. The model performs better in modelling extremely high rainfall events (95th percentiles) than the extremely low (5th percentiles) rainfall events. In addition, the P-G distribution within the Tweedie family models the probability of no rainfall and, for about 97% of stations, the observed probability of no rainfall is within the 95% confidence interval of the simulated probability of no rainfall. The ability of the Tweedie models to simulate the extreme rainfall amounts and the probability of no rainfall can be useful in drought monitoring and in water resources planning and management. [ABSTRACT FROM AUTHOR]

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. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Abstract: Modelling rainfall is important for prediction and simulation purposes in many areas of planning, agriculture, forestry, meteorology and hydrology. Usually two different models are needed to understand the two important features of rainfall: the occurrence and the amount. Here we use a single model, a Tweedie generalized linear model, to model the occurrence and amount of rainfall simultaneously. Choosing a simple model with only sine and cosine terms as predictors, the model is fitted for 220 Australian stations, with 6 rainfall stations are taken as case studies. The model fits well to monthly rainfall data based on studying the probability of no rain each month, and mean monthly rainfall amounts. Using the model, simulating monthly rainfall data for the stations with inadequate rainfall records is possible. The model also allows for a disaggregation of monthly rainfall amounts into the number of rainfall events in each month and the mean amount of rainfall per event. This information can then be used in agriculture production system simulators for agricultural planning and management. [ABSTRACT FROM AUTHOR]

In addition to the conventional wisdom of categorizing countries based on nationally-averaged coverage in access to improved drinking water sources (IDWS), this study incorporated sub-national inequality into a novel categorisation framework. The association between access to IDWS and the incidence of childhood diarrhoea (ICD) was also quantified. Information from 1.63 million households obtained through nationally representative and cross-sectional demographic and health survey (DHS) and multiple indicator cluster survey (MICS) from 81 countries were analysed. Sub-national inequalities in the access to IDWS were measured using proportional variability (PV). Most studied countries with low coverage and high inequalities in accessing IDWS were from Sub-Saharan Africa (SSA). Wealth status of households showed a higher variability in the IDWS than the variability across locations. Significant negative associations between accessing IDWS and ICD were observed for regional models, except for SSA. This study adds knowledge towards understanding the state of sustainable development goal achievements in terms of accessing IDWS. The knowledge may be helpful in designing country-specific, achievable, short- and long-term strategies. The non-decisive relation between access to IDWS and ICD indicates the adoption of additional measures in the modelling mechanism. [ABSTRACT FROM AUTHOR]

Probabilistic models for sub-daily rainfall predictions are important tools for understanding catchment hydrology and estimating essential rainfall inputs for agricultural and ecological studies. This research aimed at achieving theoretical probability distribution to non-zero, sub-daily rainfall using data from 1467 rain gauges across the Australian continent. A framework was developed for estimating rainfall data at ungauged locations using the fitted model parameters from neighbouring gauges. The Lognormal, Gamma and Weibull distributions, as well as their mixed distributions were fitted to non-zero six-minutes rainfall data. The root mean square error was used to evaluate the goodness of fit for each of these distributions. To generate data at ungauged locations, parameters of well-fit models were interpolated from the four closest neighbours using inverse weighting distance method. Results show that the Gamma and Weibull distributions underestimate and lognormal distributions overestimate the high rainfall events. In general, a mixed model of two distributions was found better compared to the results of an individual model. Among the five models studied, the mixed Gamma and Lognormal (G-L) distribution produced the minimum root mean square error. The G-L model produced the best match to observed data for high rainfall events (e.g., 90th, 95th, 99th, 99.9th and 99.99th percentiles). [ABSTRACT FROM AUTHOR]

The North-West (NW) region of Bangladesh is pivotal for the country's agricultural development, mainly in producing irrigated Boro rice. However, increasing cost of irrigation water, fertilizers, labour and other inputs, and the spatio-temporal variation in actual yield, market price and profitability of rice, have added uncertainty to the sustainability of Boro rice cultivation. In this study, we evaluated the productivity, profitability, and prospect of Boro rice production using comprehensive field data collected directly from 420 farmers' fields over two consecutive seasons (2015–16 and 2016–17), across seven geographically distributed locations in the NW region. We also analyzed the risk and return trade of popular Boro rice cultivars using Monte-Carlo simulation. The results show that there were significant (p≤0.05) variations in rice yield between sites, irrigation pump-types, and rice varieties, with Hybrid rice and BRRI dhan29 producing highest yields (6.0–7.5 t/ha). Due to different pricing systems, the cost of irrigation water varied from site to site and from year to year, but always comprised the highest input cost (20–25% of total production). The total paid-out cost, gross benefit, and gross income of rice significantly (p≤0.05) differed between sites, type of irrigation pumps, rice varieties, transplanting dates, and two cropping years. The variations in observed yield and profitability reveal considerable scope to improve rice production systems. Market variation in the price of rice affected overall profitability significantly. Probability and risk analysis results show that Minikit and BRRI dhan29 are the most stable varieties for yield and profitability. Hybrid rice, which has the maximum attainable yield among the cultivated rice varieties, also has the risk of negative net income. Based on the analysis, we discussed ways to improve yield and profitability and the prospect of Boro rice cultivation in the region. The study provides valuable information for policy-makers to sustain irrigated rice cultivation in both the NW region and nationally. [ABSTRACT FROM AUTHOR]