Prevalence and associated factors of stunting, wasting and underweight of children below five using quintile regression analysis (PDHS 2017–2018)

Abstract

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.

Knowledge and practice of breastfeeding among lactating mothers in AIIMS, Bhubaneswar – A hospital-based cross-sectional study

“…Also, not following properly breastfeeding practices was found to have a negative effect on different measures of malnutrition and this effect had significantly raised across different quantiles of stunting, wasting, and underweight. [ 17 ] Awareness about the breastfeeding practice in the early stages of a woman's life forms the most cost-effective intervention for creating an economically productive yet healthy country.…”

Nutritional status of children and its association with parental smoking in Uttar Pradesh

“…21 After the year 2000, there has been a significant decrease in stunting globally, but more progress needs to be made to reach the target for 2030. 7 Best et al, 16 in their analysis has found that when the prevalence of paternal smoking was 73.7% in Indonesia, stunting was found to be 31.4%, and severe stunting was 9.1%. According to an analysis by Bonu and colleagues from the National Family Health Survey-II, children belonging to households where at least one family member used tobacco were more likely to have a child with severe stunting.…”

“…Malnutrition has been an unspoken and unseen emergency, as it has been the cause of about 5.2 million deaths of children under the age of five each year in developing. 7 Malnutrition has been defined as a state of poor health brought on by nutritional deficiencies in calories, protein, vitamins, and minerals, which can also interact with infections, other health issues, and other psychological and social issues. 8 There are various interrelated variables that cause malnutrition, which also has negative short-and long-term implications on health.…”

A new Topp-Leone Kumaraswamy Marshall-Olkin generated family of distributions with applications

“…12 ), we deduce that the TLKMOIEx model is a better fit for the three datasets than the EMIEEx, TIR, IWIEx, AIW, LIEx and IEx models. For more reading about distributions and statistical inferences and modeling see [42] , [43] , [44] , [45] , [46] , [47] .…”