Maternal nutritional awareness might reduce the risk of malnutrition in children. This study assesses the impact of mothers' nutritional and health awareness (MNHA) on the nutritional status of pre-school children in rural South Punjab. Using a proportionate purposive simple random sampling technique we collect data with the help of a self-administered questionnaire on height, age, the weight of children, and socio-economic profile from 384 rural households in one of the marginalized districts of Punjab. The study applied the binary logistic regression model to compute the probability of malnutrition. The results indicate that malnutrition was high in the district (the prevalence rate for underweight is 46.1%, for stunting 34.83%, and for wasting is 15.49%). Around 91.84% of malnourished children belonged to the low MNHA category compared to medium (5.61%) and high (2.55%) MNHA categories. The results further show that the prevalence of moderate and severe stunting, wasting, and underweight in low MNHA categories was much higher with large differences compared to both medium and high MNHA categories. The binary logistic regression results depict that, across the household deprivation index (HDS), the odds of a child becoming malnourished were lower in households HDS-2 category (OR = 0.02, 95% CI: 0.01–0.89), and odds were also lower in households HDS-3 category (OR = 0.001, 95% CI: 0.001–0.16). Similarly, across the scores of MNHA index, the odds of malnutrition were lower among the children of those mothers who had medium MNHA (OR = 0.04, 95% CI: 0.002–1.24), and also the probability of child malnutrition was lower among the children of mothers who had high MNHA (OR = 0.008, 95% CI: 0.002–0.29). The study urges that well-resourced, targeted, and coordinated health and nutritional education and awareness programs are required to tackle malnutrition.

Objectives: This research investigates the association of malnutrition with social and economic factors in general and environmental factors in specific such as sanitation facilities and drinking water sources for Pakistan. Methods: Authors used the latest data of 1010 Under-Three children from Pakistan Demographic and Health Survey (PDHS) 2017–2018. Cumulative Index of Anthropometric Failure (CIAF) was developed to measure the malnutrition status among children based on z-scores of WHZ, WAZ, and HAZ, respectively. The study has applied the discrete-choice logistic methodology to find the relationship of malnutrition with socio-economic characteristics. The interaction terms of drinking water source and sanitation facility have been measured to see the impact of environmental factors on child malnutrition. Results: The study results depict that the likelihood of malnutrition increases when the child had diarrhea recently and the child belongs to the deprived region such as KPK, Sind, and Baluchistan. However, the chances of child malnutrition drop with (1) an escalation of mothers’ education, (2) a rise in wealth status of the household, and (3) the improved water source and sanitation facility in the household. The only water-improved sanitation category of the interaction term is significant in the model which depicts that households having both improved water and improved sanitation facilities had very fewer chances of malnutrition among their children. Conclusion: Authors conclude that malnutrition in younger children is associated with improved water as well as sanitation facilities, maternal education, and household wealth in Pakistan.

This paper aims to analyze the water, sanitation, and malnutrition situation in Pakistan and to evaluate the sustainable development goals situation. To find the association, this study applies a chi-square test utilizing a sample of 3,575 children of age less than five years, extracted from the data of Pakistan Demographic and Health Survey (PDHS) 2017-18. The results of chi-square show that underweight and stunting have a significant association with water and sanitation in Pakistan. Pakistan's progress in sustainable development goals is yet slow, especially targets of goal 3 and goal 6, which are far behind other countries of the region. The study concludes that there is a need to allocate more resources in programs such as water, sanitation, nutrition, and poverty reduction to uplift the socio-economic standard of the common folk.

Objectives: The proposed research studied the determinants of male and female child malnutrition in Pakistan. More specifically, it observed the role of the sanitation facility and drinking water source as important determinants of malnutrition in a gender analysis. Methods: Novel data of 1010 children under three years of age from PDHS 2017–18 were used. A CIAF (Cumulative Index for Anthropometric Failure) was established to assess malnourishment in the children. Discrete-choice logistic methodology was applied in this empirical research to study the likelihood of malnourishment in children. Results: The logistic regression results depicted that factors such as a child belonging to a deprived area, the status of home wealth, and the education of the mother were common determinants of malnutrition in children. Factors such as a child having diarrhea (OR = 1.55, CI = 0.96–2.50) and the drinking water source (OR = 0.62, CI = 0.37–1.03) were separate prominent predictors of malnutrition in male children whereas the sanitation facility was the main determinant of malnutrition in female children (OR = 0.64, CI = 0.43–0.95). Conclusion: This study concludes that important links exist between the drinking water source and male child malnutrition and between sanitation facilities and female child malnutrition.

The current study investigates income inequalitiesamong earners engaged in different occupationsand professions in Pakistan using HIES data for 2010-11 and2015-16, focusing on their yearly income. Income equation anddifferences of income between subgroups of the population areestimated by using the OLS method. The generalized Entropy(GE) Class method is employed to evaluate the contribution ofdifferent subgroups of household characteristics and differentincome sources in overall inequality. The regression-baseddecomposition method is used to assess decompose changes inincome inequality by various socio-economic factors. OLSestimates conclude that all variables play a significant role inexplaining the differences in income. All indices of GE methodindicate that inequality within the group is a greater problemthan inequality experiences between groups. The decompositionmethod shows a positive sign of inequality decomposition for mosthousehold characteristics and income sources which depicts thatthese determinants have greatly contributed to overall incomeinequality.