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: Q_0.25: − 0.625; Q_0.50: − 0.623; Q_0.75: − 0.426 and wasting: Q_0.50: − 0.513); having uneducated mother (underweight: Q_0.25: − 0.387; Q_0.50: − 0.247; Q_0.75: − 0.328), belonged to a poor household (underweight: Q_0.50: − 0.251),residing in rural areas (underweight: Q_0.25: − 0.443), not following properly breastfeeding practices (underweight: Q_0.50: − 0.439; Q_0.75: − 0.438) have negative effect on different measures of malnutrition and this effect is significantly raises across different quantiles of stunting , wasting and underweight (atp value < 0.01 and < 0.05). Older age mother (stunting: Q_0.50: 0.777; Q_0.75: 1.078; underweight Q_0.20: 0.568; Q_0.50: 0.429; Q_0.75: 0.524) and higher birth order number (stunting: Q_0.50: 0.415; Q_0.75: 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.

Child malnutrition persists in low-resource countries such as Pakistan, indicating an urgent need for interventions and policies aimed to address this critical population health issue. The World Health Organization Global Target 2025 includes the reduction of malnourishment in the form of stunting, wasting, and low weight. This study aims to examine the prevalence of factors associated with three measures of child malnutrition, i.e., stunting, wasting, and low weight in Pakistan. This study uses a secondary data analysis design based on data from Pakistan Demographic and Health Survey (2017-18) that used a two-stage cluster sampling approach. National level data covering urban and rural areas were used for this study consisting of 4,226 children less than 5 years of age. Univariate and multivariable analyses using logistic regression models were conducted. Over 23% of the children were underweight, 8.0% suffered wasting, and 37.7% were stunted. Children with small size at birth (60 cm) were also less likely to be stunted (AOR, 0.288) and underweight (AOR, 0.538). Children who consumed fresh milk were less likely to be classified as wasted (AOR, 0.524) than those children who did not consume fresh milk. The children in high- and middle-economic status families were less likely to be stunted, underweight, or wasted. Children of mothers who had secondary and higher education were less likely to be stunted (AOR, 0.584) and were less likely to be underweight (AOR, 0.668) than illiterate mothers’ children. Children of working mothers were less likely to be wasted compared to children of nonworking mothers (AOR, 0.287). Maternal BMI is also inversely associated with being underweight because overweight and obese mothers were less likely to have underweight children (AOR, 0.585). Our findings reflect a need to design targeted public health policies and community-based education that emphasize the mother’s education on nutrition health and provide socioeconomic resources that enable mothers to provide dietary needs that prevent malnutrition.

Background: This study examines the levels and predictors of malnutrition in Indian children under 5 years of age. Methods: Composite Index of Anthropometric Failure was applied to data from the India National Family Health Survey 2019–2021. A multivariable logistic regression model was used to assess the predictors. Results: 52.59% of children experienced anthropometric failure. Child predictors of lower malnutrition risk included female gender (adjusted odds ratio (AOR) = 0.881) and average or large size at birth (AOR = 0.729 and 0.715, respectively, compared to small size). Higher birth order increased malnutrition odds (2nd-4th: AOR = 1.211; 5th or higher: AOR = 1.449) compared to firstborn. Maternal predictors of lower malnutrition risk included age 20–34 years (AOR = 0.806), age 35–49 years (AOR = 0.714) compared to 15–19 years, normal BMI (AOR = 0.752), overweight and obese BMI (AOR = 0.504) compared to underweight, and secondary or higher education vs. no education (AOR = 0.865). Maternal predictors of higher malnutrition risk included severe anemia vs. no anemia (AOR = 1.232). Protective socioeconomic factors included middle (AOR = 0.903) and rich wealth index (AOR = 0.717) compared to poor, and toilet access (AOR = 0.803). Children’s malnutrition risk also declined with paternal education (primary: AOR = 0.901; secondary or higher: AOR = 0.822) vs. no education. Conversely, malnutrition risk increased with Hindu (AOR = 1.258) or Islam religion (AOR = 1.369) vs. other religions. Conclusions: Child malnutrition remains a critical issue in India, necessitating concerted efforts from both private and public sectors. A ‘Health in All Policies’ approach should guide public health leadership in influencing policies that impact children’s nutritional status.

Background/Objectives: Early childhood anemia is a severe public health concern and the most common blood disorder worldwide, especially in emerging countries. This study examines the sources of childhood anemia in Ghana through various societal, parental, and child characteristics. Methods: This research used data from the 2022 Ghana Demographic and Health Survey (GDHS-2022), which comprised 9353 children. Using STATA 13 and R 4.4.2 software, we analyzed maternal, social, and child factors using a model-building procedure, logistic regression analysis, and machine learning (ML) algorithms. The analyses comprised machine learning methods including decision trees, K-nearest neighbor (KNN), logistic regression, and random forest (RF). We used discrimination and calibration parameters to evaluate the performance of each machine learning algorithm. Results: Key predictors of childhood anemia are the father’s education, socioeconomic status, iron intake during pregnancy, the mother’s education, and the baby’s postnatal checkup within two months. With accuracy (94.74%), sensitivity (82.5%), specificity (50.78%), and AUC (86.62%), the random forest model was proven to be the most effective machine learning predictive model. The logistic regression model appeared second with accuracy (67.35%), sensitivity (76.16%), specificity (56.05%), and AUC (72.47%). Conclusions: Machine learning can accurately predict childhood anemia based on child and paternal characteristics. Focused interventions to enhance maternal health, parental education, and family economic status could reduce the prevalence of early childhood anemia and improve long-term pediatric health in Ghana. Early intervention and identifying high-risk youngsters may be made easier with the application of machine learning techniques, which will eventually lead to a healthier generation in the future.

Burning velocity of different chemicals is estimated using a model from mixed population considering inverted Kumaraswamy (IKum) distribution for component parts. Two estimation techniques maximum likelihood estimation (MLE) and Bayesian analysis are applied for estimation purposes. BEs of a mixture model are obtained using gamma, inverse beta prior, and uniform prior distribution with two loss functions. Hyperparameters are determined through the empirical Bayesian method. An extensive simulation study is also a part of the study which is used to foresee the characteristics of the presented model. Application of the IKum mixture model is presented through a real dataset. We observed from the results that Linex loss performed better than squared error loss as it resulted in lower risks. And similarly gamma prior is preferred over other priors.

Background and Objective: This study examines the factors associated with child undernutrition among children under five in Ethiopia, Kenya, Madagascar, and Tanzania. It uses the Composite Index of Anthropometric Failure to measure the full burden of undernutrition, combining weight-for-height (WHZ), weight-for-age (WAZ), and height-for-age (HAZ) indicators. This approach captures children facing multiple forms of failure that single indicators miss. Methods: The study analyzed 37,570 children using nationally representative Demographic and Health Survey (DHS) data for Ethiopia 2019, Kenya 2022, Madagascar 2021, and Tanzania 2022. A binary logistic regression model identified key predictors of child undernutrition across countries. Results: The prevalence of anthropometric failure ranged from 24% to 44%. Higher parental education, child’s age, socioeconomic status, child’s sex, and a postnatal checkup within 2 months were associated with a lower odds of anthropometric failure. Children of educated mothers in Ethiopia (AOR = 0.547) and Tanzania (AOR = 0.606) had better outcomes. Educated fathers in Kenya (AOR = 0.589) and Madagascar (AOR = 0.369) reduced the risk of child undernutrition. Children aged 13–24 months had a higher risk in all countries. In Madagascar (AOR = 0.309), children who received a postnatal checkup had a decreased risk of malnutrition. Children from rich households in Ethiopia (AOR = 0.645) and from middle (AOR = 0.683) and rich (AOR = 0.535) households in Kenya had significantly lower odds of undernutrition. In comparison, female children had lower odds of anthropometric failure in all four countries. Conclusions: Viewed through a nutrition equity lens, these findings underscore the importance of recognizing how the intersectionality of anthropometric failures disproportionately affects children from poorer households and communities with limited access to education and postnatal care. This study advances existing knowledge by using the Composite Index of Anthropometric Failure to show overlapping and hidden forms of undernutrition. The findings identify child age, parental education, postnatal checkup, child sex, and socioeconomic status as shared priorities for reducing undernutrition. The results provide country-specific insights for designing integrated, evidence-based nutrition interventions in Eastern Africa.