The aim of this study was to examine the impact of maternal education on child immunization uptake in Pakistan, both at individual and community levels. Pakistan Demographic and Health Survey data were used for analysis. Multilevel logistic regression was used to access the individual- and community-level factors associated with childhood immunization coverage. Out of 6765 children 2659 (39.3%) were fully immunized. Parents education, access to media, and wealth status have positive while ethnicity and working status of mother have a negative impact on the immunization uptake. In the community with a high percentage of educated mothers, the odds of immunized children were high (odds ratio = 1.43, 95% confidence interval = 1.14-1.80) as compared with communities with lower percentage of educated mothers. Moreover, significant variation was found in the likelihood of full immunization across communities. Both community- and individual-level factors have substantial impact on children immunization status. There is a need of improvement in maternal education, poverty alleviation, and removal of rural-urban disparities.

In this paper, the modelling of extreme rainfall is carried out in Pakistan by analysing annual daily maximum rainfall data via frequentist and Bayesian approaches. In frequentist settings, the parameters and return levels of the best fitted probabilistic model (i.e., generalized extreme value) are estimated using maximum likelihood and linear moments method. On the other side, under the Bayesian framework, the parameters and return levels are calculated both for noninformative and informative priors. This task is completed with the help of the Markov Chain Monte Carlo method using the Metropolis-Hasting algorithm. This study also highlights a procedure to build an informative prior through historical records of the underlying processes from other nearby weather stations. The findings attained from the Bayesian paradigm demonstrate that the posterior inference could be affected by the choice of past knowledge used for the construction of informative priors. Additionally, the best method for the modelling of extreme rainfall over the country is decided with the support of assessment measures. In general, the Bayesian paradigm linked with the informative priors offers an adequate estimations scheme in terms of accuracy as compared to frequentist methods, accounting for ambiguity in parameters and return levels. Hence, these findings are very helpful in adopting accurate flood protection measures and designing infrastructures over the country.

SEC has a significant impact on the prevalence of malaria fever in the context of different regions in Pakistan.

Pakistan experiences the southwest monsoon normally from July to September every year. In this paper, we analyse the effect of monthly rainfall for the months of July, August and September on overall seasonal monsoon for the period of 1960–2006 across 37 different meteorological stations of Pakistan. The statistical results are based on the notion of conditional probability. We find conditional probabilities of seasonal monsoon to be in excess, deficient and normal when the monthly rainfall of July, August and September is considered to be in excess and deficient. When rainfall in the months of July and August is in excess the seasonal monsoon behaviour is also likely to be in excess and probabilities of seasonal monsoon to be deficient are zero for most of the meteorological stations. Similarly, when rainfall in the months of July and August is considered to be deficient the seasonal monsoon behaviour is also likely to be deficient and probabilities of seasonal monsoon to be in excess are zero for most of the meteorological stations. © 2013 Royal Meteorological Society

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