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The objective of the current study is to identify the risk factors for malnutrition among the age of under-five children’s in Pakistan. This is secondary data analysis for the data taken from Pakistan Demographic and Health Survey (PDHS 2017–18) and was analyzed by implementing quantile regression analysis. The sample size included 12,708 alive children in the study, for which the data collection period was from November 22, 2017, to April 30, 2018. The prevalence of malnutrition among boys is high (51.2%). Older age mother’s children have more prevalence of malnutrition (20.7%). A child born with small body size (underweight: Q0.25: − 0.625; Q0.50: − 0.623; Q0.75: − 0.426 and wasting: Q0.50: − 0.513); having uneducated mother (underweight: Q0.25: − 0.387; Q0.50: − 0.247; Q0.75: − 0.328), belonged to a poor household (underweight: Q0.50: − 0.251),residing in rural areas (underweight: Q0.25: − 0.443), not following properly breastfeeding practices (underweight: Q0.50: − 0.439; Q0.75: − 0.438) have negative effect on different measures of malnutrition and this effect is significantly raises across different quantiles of stunting , wasting and underweight (at p value < 0.01 and < 0.05). Older age mother (stunting: Q0.50: 0.777; Q0.75: 1.078; underweight Q0.20: 0.568; Q0.50: 0.429; Q0.75: 0.524) and higher birth order number (stunting: Q0.50: 0.415; Q0.75: 0.535), have a positive effect on three measures of under-nutrition and this effect is gradual raises at different quantile of stunting, wasting and underweight. Elder and smoker mothers were proved associated risk factors of both stunting and being underweight in Pakistan. Moreover, Proper breastfeeding practices, better economic status, average or above the average birth weight of the child, and milk consumption are found protective factors against stunting, wasting, and underweight children in Pakistan.

Objectives: To develop an externally validated multivariable prognostic model for an underprivileged dialysis population.Methods: This was a multicenter retrospective cohort study of 5 years duration from January 2013 to December 2017. A total of 758 patients (37.5% female; mean±SD age, 44.26±14.77 years) were enrolled for construction of the prognostic model. The data were analyzed using a proportional hazards model to identify predictors of survival. Three risk groups were identified at the 25th and 75th percentiles of the resultant prognostic index. The model was externally validated with another dataset of 622 dialysis patients. Original ArticleResults: The prognostic index included 5 predictor variables: hemoglobin, serum potassium, interdialytic weight gain, serum albumin, and duration of dialysis, which had good predictive performance on the calibration and discrimination aspects of the model (Harrell's c statistic: 0.748, Gonen and Heller k statistic: 0.647, Somers' D statistic: 0.496, calibration slope: 1.156). There were significant interaction effects between weight and hemoglobin, weight and albumin, albumin and potassium, and albumin and hemoglobin. Conclusions:We developed an externally validated model that contained 5 routinely collected prognosticators and confirmed its calibration and discrimination abilities in obtaining reliable prognostic estimates in developing countries. The model will assist clinicians in deciding the prognosis of dialysis patients. The application of this model in different clinical settings of developing countries can indicate interesting findings regarding public health.