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.

Generalized linear models (GLMs) are used in understanding the impact of predictors on a dependent variable. The aim of this study is to fitGLMsto daily rainfall totals using potential predictors. First, the appropriate probability distributions within a specific family, the Tweedie family, were determined for daily rainfall totals from four stations of Peninsular Malaysia from 1983 to 2012. Within the Tweedie family, the Poisson Gamma (PG) distribution was found appropriate to model both components: occurrence (dry/wet days) and amount (rainfall totals on wet days) of rainfall simultaneously. Then, thePG‐GLMswere fitted to rainfall data with a sine term, a cosine term, lagged rainfall,NINO3.4 and Southern oscillation index (SOI) as predictors. Finally, the models were compared using the Likelihood ratio test and the Akaike information criterion. Initially, considering the cyclic pattern of rainfall data, models with only sine and cosine terms (the base model) were fitted. Then the lagged rainfall and climatological variables were added each time to the base model. DiagnosticQQplots indicate that the models fit the data well. The models were fitted using the first 60% of data and validated using the remainder. The models capture the various characteristics of observed datasets reasonably well. Including single climatological variables in the model significantly improves the fit compared to the base model with lagged rainfall (except for the south‐east coastal station, Mersing), however, including both climatological predictors in the same model does not improve the model significantly. The model withSOIis only favoured for the east coastal station, Kuala Terengganu, and the model withNINO3.4 fits better to the inland and west coastal stations. The models are useful in understanding the impact of the studied climatological variables and to predict the amount and probability of rainfall.