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Abstract Background Underweight prevalence continues to be major public health challenge worldwide, particularly in developing countries like Pakistan. This study is focused on socio-economic and demographic aspects of underweight prevalence among children under-five in Punjab. Methods In this study, several socioeconomic and demographic factors are considered using MICS-4 data-set. Only those variables which are usually described in the nutritional studies of children were picked. Covariates include: the age of children, sex of the children, age of mother, total number of children born to women, family wealth index quintile, source of drinking water, type of sanitation, place of residence, parents’ education and occupation. All Categorical variables are effect coded. The child’s age and the mother’s age are assumed to be nonlinear, geographical region is spatial effect, while other variables are parametric in nature. Since, the response is binary, covariate comprises linear terms, nonlinear effects of continues covariates and geographic effects, so we have use Geo-additive models (based on Fully Bayesian approach) with binomial family under logit link. Statistical analysis is performed on Statistical package R using Bayes X and R2 Bayes X Libraries. Results Underweight status of children was found to be positively associated with number of under-five children in household, total number of children ever born to women and age of mother when the child was born. Whereas, it negatively associated with place of residence, parent’s education and family wealth index quintile. On the regional effect, the Southern Punjab has higher prevalence of underweight compared to Central and Northern Punjab. Conclusion Similarity of our results with several other studies demonstrate that the Geo-additive models are an ideal substitute of other statistical models to analyze the underweight prevalence among children. Moreover, our findings suggest the Punjab Government, to introduce target-oriented programs such as poverty reduction and enhancement of education and health facilities for poor population and disadvantaged regions, especially Southern Punjab.

Background: Underweight prevalence continues to be major public health challenge worldwide, particularly in developing countries like Pakistan. This study is focused on socio-economic and demographic aspects of underweight prevalence among children under-five in Punjab.Methods: In this study, several socioeconomic and demographic factors are considered using MICS-4 data-set. Only those variables which are usually described in the nutritional studies of children were picked. Covariates include: the age of children, sex of the children, age of mother, total number of children born to women, family wealth index quintile, source of drinking water, type of sanitation, place of residence, parents' education and occupation. All Categorical variables are effect coded. The child's age and the mother's age are assumed to be nonlinear, geographical region is spatial effect, while other variables are parametric in nature. Since, the response is binary, covariate comprises linear terms, nonlinear effects of continues covariates and geographic effects, so we have use Geo-additive models (based on Fully Bayesian approach) with binomial family under logit link. Statistical analysis is performed on Statistical package R using Bayes X and R2 Bayes X Libraries.Results: Underweight status of children was found to be positively associated with number of under-five children in household, total number of children ever born to women and age of mother when the child was born. Whereas, it negatively associated with place of residence, parent's education and family wealth index quintile. On the regional effect, the Southern Punjab has higher prevalence of underweight compared to Central and Northern Punjab.Conclusion: Similarity of our results with several other studies demonstrate that the Geo-additive models are an ideal substitute of other statistical models to analyze the underweight prevalence among children. Moreover, our findings suggest the Punjab Government, to introduce target-oriented programs such as poverty reduction and enhancement of education and health facilities for poor population and disadvantaged regions, especially Southern Punjab. [ABSTRACT FROM AUTHOR]

Objective: To estimate risk factors of urban malaria in Blantyre, Malawi, with the goal of understanding the epidemiology and ecology of the disease, and informing malaria elimination policies for African urban cities that have markedly low prevalence of malaria. Methods: We used a case-control study design, with cases being children under the age of five years diagnosed with malaria, and matched controls obtained at hospital and communities. The data were obtained from Ndirande health facility catchment area. We then fitted a multivariate spatial logistic model of malaria risk. Covariate and risk factors in the model included child-specific, household and environmental risk factor (nearness to garden, standing water, river and swamps). The spatial component was assumed to follow a Gaussian process and model fitted using Bayesian inference. Results: Our findings showed that children who visited rural areas were 6 times more likely to have malaria than those who did not [odds ratio (OR) = 6.66, 95% confidence interval (CI): 4.79–9.61]. The risk of malaria increased with age of the child (OR = 1.01, 95% CI: 1.003–1.020), but reduced with high socio-economic status compared to lower status (OR = 0.39, 95% CI: 0.25–0.54 for the highest level and OR = 0.67, 95% CI: 0.47–0.94 for the medium level). Although nearness to a garden, river and standing water showed increased risk, these effects were not significant. Furthermore, significant spatial clusters of risk emerged, which does suggest other factors do explain malaria risk variability apart from those established above. Conclusions: As malaria in urban areas is highly fuelled by rural-urban migration, emphasis should be to optimize information, education and communication prevention strategies, particularly targeting children from lower socio-economic position.

• Maternal anaemia (MA) and Haemoglobin (Hb) levels are analysed using NDHS data. • Semiparametric structured additive models are employed for analyses. • Spatial patterns and risk factors for MA and low Hb level are identified. • MA prevalence is highest and lowest in Imo and Cross River states respectively. • States in close proximity do not necessarily share similar spatial effects. Anaemia which is a condition that describes low haemoglobin (Hb) levels has been recognized as a major public health problem amongst pregnant women in many sub-Saharan African countries including Nigeria. The causes of maternal anaemia which are interconnected and complex vary between countries and can vary within a country. This study aimed to investigate the spatial pattern and identify demographic and socio-economic determinants associated with anaemia amongst Nigerian pregnant women aged 15–49 years using data from the 2018 Nigeria Demographic and Health Survey (NDHS). This study utilized chi-square tests of independence and semiparametric structured additive models to describe the relationship between the presumed factors and anaemia status or Hb level while also taking spatial effects at state level into account. The Gaussian and Binomial distributions were used for Hb level and anaemia status respectively. The overall observed prevalence of anaemia in pregnant women and average Hb level in Nigeria were 64% and 10.4 (SD = 1.6) g/dL respectively while the prevalence of mild, moderate and severe anaemia were 27.2%, 34.6% and 2.2% respectively. Higher education, older age, and currently breastfeeding were associated with higher Hb level. Low education, being unemployed and recently having a sexually transmitted infection were identified as risk factors for maternal anaemia. Body mass index (BMI) and household size had a nonlinear effect on Hb level while BMI and age were nonlinearly related to odds of anaemia. Bivariate analysis indicated that living in rural area, low wealth class, using unsafe water and non-usage of internet were significantly associated with increased risk of anaemia. Maternal anaemia prevalence was highest in the South Eastern part of Nigeria with Imo state producing the highest prevalence of maternal anaemia while Cross River state yielded the lowest prevalence of maternal anaemia. The spatial effects associated with states were significant but unstructured indicating that states in close proximity do not necessarily share similar spatial effects. Hence, unobserved characteristics shared by states in close proximity do not influence maternal anaemia and Hb level. The findings from this study can undoubtedly help in the planning and designing of anaemia interventions that match local conditions taking into consideration the aetiology of anaemia in Nigeria. [ABSTRACT FROM AUTHOR]

Objective To estimate risk factors of urban malaria in Blantyre, Malawi, with the goal of understanding the epidemiology and ecology of the disease, and informing malaria elimination policies for African urban cities that have markedly low prevalence of malaria. Methods We used a case-control study design, with cases being children under the age of five years diagnosed with malaria, and matched controls obtained at hospital and communities. The data were obtained from Ndirande health facility catchment area. We then fitted a multivariate spatial logistic model of malaria risk. Covariate and risk factors in the model included child-specific, household and environmental risk factor (nearness to garden, standing water, river and swamps). The spatial component was assumed to follow a Gaussian process and model fitted using Bayesian inference. Results Our findings showed that children who visited rural areas were 6 times more likely to have malaria than those who did not [odds ratio (OR) = 6.66, 95% confidence interval ( CI ): 4.79–9.61]. The risk of malaria increased with age of the child (OR = 1.01, 95% CI : 1.003–1.020), but reduced with high socio-economic status compared to lower status (OR = 0.39, 95% CI : 0.25–0.54 for the highest level and OR = 0.67, 95% CI : 0.47–0.94 for the medium level). Although nearness to a garden, river and standing water showed increased risk, these effects were not significant. Furthermore, significant spatial clusters of risk emerged, which does suggest other factors do explain malaria risk variability apart from those established above. Conclusions As malaria in urban areas is highly fuelled by rural-urban migration, emphasis should be to optimize information, education and communication prevention strategies, particularly targeting children from lower socio-economic position. [ABSTRACT FROM AUTHOR]

The article discusses a study which investigated the geographical and socioeconomic determinants of childhood undernutrition in Southern Africa. Researchers studied the three neighboring countries of Malawi, Tanzania and Zambia and estimated the models of undernutrition using the Markov chain Monte Carlo techniques. It mentions that distinct residual spatial patterns are found though they were not explained by the socioeconomic determinants.

We describe a flexible geo-additive Bayesian survival model that controls, simultaneously, for spatial dependence and possible nonlinear or time-varying effects of other variables. Inference is fully Bayesian and is based on recently developed Markov Chain Monte Carlo techniques. In illustrating the model we introduce a spatial dimension in modelling under-five mortality among Malawian children using data from Malawi Demographic and Health Survey of 2000. The results show that district-level socioeconomic characteristics are important determinants of childhood mortality. More importantly, a separate spatial process produces district clustering of childhood mortality indicating the importance of spatial effects. The visual nature of the maps presented in this paper highlights relationships that would, otherwise, be overlooked in standard methods. [ABSTRACT FROM AUTHOR]

This paper applies a geo-additive generalized linear mixed model to describe the spatial variation in the prevalence of cough among children under 5 years of age using the 2000 Demographic and Health survey (DHS) data from Malawi. Of particular interest in the analysis were the small area effect of geographical locations (districts) where the child lives in the time of the survey and the effect of the metrical covariate (child's age) which was assumed to be nonlinear and estimated nonparametrically. The model included other categorical covariates in the usual parametric form. We assign appropriate priors, within a Bayesian context, for the geographical location, vector of the unknown nonlinear smooth functions and a further vector of fixed effect parameters. For example, the spatial effects were modelled via Bayesian prior specifications reflecting spatial heterogeneity globally and relative homogeneity among neighbouring districts, thus a Markov random field prior is assumed. Inference is fully Bayesian and uses recent Markov chain Monte Carlo techniques. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]