Unraveling the South Asian enigma: concurrent manifestations of child anthropometric failures and their determinants in selected South Asian countries
BMC Nutrition volume 9, Article number: 120 (2023)
Malnutrition among children is pervasive in South Asia and there are also reports of overnutrition. To better understand this phenomenon, we need a composite measure. However, the existing measures such as CIAF (Composite Index of Anthropometric Failure) and its revised version have ignored the overnutrition aspect of the phenomenon. This study proposes an extended version of CIAF which also considers overnutrition. This new measure was compared with the existing measures by using data from 1990 to 2018 for three selected South Asian countries including Pakistan, India and Bangladesh. We also examined the effects of socioeconomic and environmental variables on the outcome variable. The results reveal that the new measure (ECIAF) is better at measuring the phenomena. The burden of overall malnutrition has been decreased in the region. However, an increase in the concomitant prevalence of wasting and underweight is observed in both Pakistan and India and stunting and overweight is observed only in India. Besides, political stability, prevalence of undernourishment, anemia in children, mother’s education, household size, dependency ratio, air pollution and unimproved sanitation are significantly correlated with childhood malnutrition. The findings also testified to long-run cointegrating relationship among the variables.
Health is an engine of economic growth . The health of women, mothers and children in particular is fundamental to economic development . Healthy children are the linchpin for healthy and thriving societies as reflected by the agenda of Millennium and Sustainable Development Goals . Child health affects economic growth directly and indirectly in many ways. Directly it plays a pivotal role in establishing the foundations of human capital investment  and reduction in the economic burden of illnesses . Indirectly it impacts economic growth by affecting the future income of people through the impact, health has on education such as schooling and cognitive skills [6, 7].
Child malnutrition manifests in three broad forms: undernutrition, which includes stunting (height-for-age), wasting (weight-for-height), and underweight (weight-for-age), overnutrition (overweight and obesity) and micronutrient-related malnutrition . There is no such confirmatory test to measure undernutrition and overnutrition. Anthropometry is a pragmatic and immediately applicable technique for measuring children’s development patterns during the first five years of his/her life. It quantifies the nutritional situation at one point in time and allows comparisons over time .
How serious is malnutrition?
Child nutritional anthropometry is the most pressing problem in the world, damaging to both children and nations. According to the WHO global estimates (2021), around 149.2 million children under 5 years of age suffered from stunting, 45.4 million were affected by wasting, 20.5 million were underweight and 38.9 million were too heavy for their height. These statistics are staggering and confirm that malnutrition is a global health issue. Moreover around 89% of those stunted, 93% of wasted and 77% of overweight children are more likely to live in low and middle- income countries. Most of them are the sub-regions of Africa and Asia . Although some efforts are being made towards reducing malnutrition and as a result, stunting is declining steadily in all regions of East, Pacific, and South Asia except Africa . However, still, only one-quarter of all countries are ‘on track’ to halve the number of children affected by stunting by 2030. On the other hand, wasting persists at alarming rates and it seems quite impossible to achieve the 2030 wasting targets for nearly half of countries . Whereas, overweight prevalence is increasing in the majority of the countries, analysed in Southern Africa, Oceania, South-eastern Asia, South America and the Caribbean and therefore requires a reversal in trajectory to meet the required targets .
More than half of the malnourished children of South Asia live mainly in Pakistan, Bangladesh and India. This is the main reason that this study focused on these three countries for analysis. Children in the region, have some highest global rates of stunting and wasting, also known as South Asian enigma . The number of stunted children under the age of five is around 38% in Pakistan, 32% in Bangladesh and 30% in India. According to the global nutrition report, Pakistan and India was reported the home of almost half of all stunted children around the globe carrying 10.7 million and 46.6 million stunted children respectively . India is home to the world’s most wasted children (25.5 million). Bangladesh is ‘on course’ to prevent childhood wasting but still, 9.8% children are affected by wasting which is higher than the average for the Asia region (8.9%). Pakistan is also making some progress towards achieving the target for wasting but still, 7.1% children are affected. The report also highlights the worrying incidence and universality of malnutrition in all its forms the changing face of malnutrition in Asia. While, the prevalence of overweight among children under five is 2.5%, 1.6% and 2.4% in Pakistan, India and Bangladesh respectively . All this indicates that despite some achievements and partial success, the current pace of change is too slow to achieve the nutrition targets by 2030 in the majority of countries.
Moreover, South Asia has seen a large increase in the “double burden” of malnutrition in recent years. At the individual level, it was observed by the concurrent manifestations of multiple anthropometric failures, undernutrition represented by wasting, stunting, or underweight co-occurring with overnutrition overweight/obesity or subsequent overweight in a malnourished child under five. This complex phenomenon constitutes an unprecedented challenge to global public health and has been prioritized by international health organizations, prompting governments to accurately measure the coexisting forms of malnutrition and take swift action .
One way to achieve those nutrition targets is to accurately measure the overall burden of malnutrition among children under five (outcome variable) and find the factors that are responsible for this exceptional scale of undernutrition and overnutrition. Empirically, many studies have tried to fulfill the underlying objective, see, e.g. [17,18,19,20,21,22,23,24,25,26,27]. All of these studies have tried to determine child malnutrition status in South Asia by using conventional indices of anthropometric failures. Though these standard estimates mirror distinct biological processes but have the problem of overlapping. Thus they are unable to provide the correct and comprehensive measure of undernourishment among the study subjects. For instance, a number of stunted children will also have wasting; others might be experiencing the concurrent burden of stunting and underweight. Some children might face a concomitant prevalence of all three indicators. Consequently, these conventional indices can’t truly detect the overall burden of malnutrition . To fill this gap some studies have used CIAF as proposed by Peter Svedberg  and its revised version by Nandy . These include [31,32,33,34]. Firstly all those studies only focus on and estimate undernutrition and overlook overnutrition. In this way, did not address the issue of a concomitant coexistence of stunting with overweight among children under five. Over the last three decades, the high prevalence of overweight among children has been confirmed by many studies [35, 36]. Several recent studies have reported the presence of overweight and obesity simultaneously with stunting, i.e., low HAZ among children under five [37, 38]. Secondly, none of those studies have been done exclusively on South Asia using the CIAF model to identify the hidden vulnerabilities among children under five. In this context, the extended model of CIAF, employed by this study represents a better estimate of the overall burden of malnutrition among children as it measures both undernutrition and overnutrition simultaneously. Accordingly, this study would fill this gap by estimating the extent and pattern of malnutrition among children and by exploring further dimensions of the hidden malnutrition “iceberg”.
The second aim is to examine, what are the most proximate factors, especially environmental indicators that are responsible for this concomitant prevalence of anthropometric failures among children to contribute to the existing body of evidence needed for the formulation of effective interventions. Are they the same as in case of individual conventional indices? Besides, there is a dearth of evidence on potential confounders of malnutrition in children under five focusing in specific on socioeconomic and environmental factors utilizing the latest 2018 PDHS data set. Perceptibly, using a panel modeling allows us to see the impact of those indicators not reported in DHS. Moreover this study measures the interaction of sanitation and ambient air pollution on child malnutrition. Although literature is available on sanitation facilities and air pollution in Pakistan yet no study analyze the dual effect of sanitation and air pollution in the case of Pakistan. These are the gaps that this study tried to cover.
Variables used to decompose the change in the coincidence of growth disorders among children under five were demographic and socioeconomic characteristics of household (household size, dependency ratio, female literacy rate and political stability), nutritional characteristics (prevalence of anemia among children under five and prevalence of undernourishment), and environmental characteristics (unimproved sanitation facilities and ambient particulate matter pollution). These variables are correlated with child nutritional status in prior empirical studies. The theoretical justification of the variables used in the multivariate analysis is given below turn by turn.
Female literacy rate
Social determinants of child health such as female education are strongly associated to health-seeking behavior and improving overall health outcomes of their children. Education of women reflected as higher literacy rates are related to higher incomes and better health indicators such as lower infant mortality, child malnutrition and population growth rates. Women’s education has a ‘multiplier effect’ on the well-being of their children . Educated women tend to marry at a later age, have fewer children and be more informed about nutrition requirements and healthcare practices of their children . Therefore there exists a negative correlation between female literacy rate and child death . Thus, the coefficient α is expected to be negative.
Ecological quality indicators
This study used two indicators of ecological quality; one is air pollution and the other is unimproved sanitation facility. Emerging evidence suggests that adverse ecological conditions and pollution are major contributors to childhood malnutrition and mortality, particularly in developing countries. Children are particularly vulnerable to certain environmental risks, including air pollution and inadequate sanitation . Another study also found a potential relationship between ambient air pollution and child growth indicators . Similarly, Unimproved sanitation was also found to be a significant predictor of anthropometric failures among children under five [44,45,46]. Khan et al. (2021) assessed that sanitation in terms of the sanitation ladder frequently contributes to child growth failures and may bring the source of fecal contamination to the doorstep of the households . Therefore environmental risks have an impact on the health and development of children . Thus, the coefficient α is expected to be positive.
Malnutrition is often considered a political problem as the constant instability aggravates the food and sanitation situation in the country . Political stability rests on a government’s ability to carry out its proclaimed programs and provide reliable public services to the commons. It creates conditions that are conducive to the economic stability of the households, the functioning of markets for essential nutrition inputs such as food and keep food price at levels . Children are particularly vulnerable to food insecurity resulting from food price spikes. The effects are likely to be an increased incidence of stunting, wasting and other growth disorders among children . Therefore it is claimed that political stability is estimated to have large and permanent effects on nutrition status and plays a significant role in reducing childhood undernutrition along with other socio-demographic factors . Thus, the coefficient α is expected to take a positive sign.
Prevalence of undernourishment
Undernourishment refers to the condition of insufficient intake of food. It can lead to serious health issues, including impaired growth and obesity in children [51, 52]. Therefore the effect of complementary feeding practices is reflected in the severely jeopardized health of children under five. The coefficient of Prevalence of undernourishment is contemplated to be positive.
Anemia in children
Prieto-Patron et al. (2018) identified that severe chronic anemia may lead to child health variables such as stunting, wasting, underweight and overweight . The likely cause of childhood anemia is delayed growth problems among children under five . Thus, the coefficient is expected to take a positive sign.
Malnutrition is not only a health sector problem. Demographic factors like high household dependency ratios  and large household size , which are mainly the social determinants, are detrimental to children’s nutritional outcomes and inequalities. Thus, the coefficient is expected to take a negative sign.
As household size increases malnutrition status wasting, stunting, overweight and underweight increases, because with family size increase, resources become scarce and less nutrition and care focused on children. Thus, the coefficient is expected to take a negative sign.
The study also attempts to examine the impact of economic growth and health expenditures as a percentage of GDP on child undernutrition in South Asia but finds no synergistic effects and the value of the coefficient of economic growth and public health expenditure will remain insignificant in the equation of child’s malnutrition status. The role of economic growth in reducing child undernutrition remains an open and highly debated question. Economic growth does not necessarily help countries to decrease undernourishment ; this outcome is only found significant for South Asia. Economic growth is indispensable but not enough factors require combating undernourishment. The reason could be the substantial disparity in the share of poor people in the aggregate economic growth . Similarly, the actual relationship between health spending and child health is still unclear, particularly at the macro level as most of the researchers found an insignificant association between health expenditure and under-five malnutrition. Healthcare expenditures as a percentage of GDP are not a dominant driver of childhood malnutrition . Household water supply facilities were also not significantly associated with the concurrent prevalence of children’s anthropometric failures.
Data and methodology
Data sources and description
Nationwide cross-sectionals have been employed to capture the health status of children under five over the past three decades in Pakistan, India and Bangladesh. There are five available cross-sections for Pakistan from 1990 to 2018. These include PDHS (1990–1991), NNS (2001–2002), NNS (2011), PDHS (2012–2013) and PDHS (2017–2018). PDHS (2006–2007) is not utilized for the current analysis as it doesn’t consist of anthropometric indicators. For this reason, the study used NNS (2001) to fill the huge time gap between 1991 and 2012. Similarly Indian available data sets for the same time period are IDHS (1992-93), (1998–1999), (2005–2006) and (2015–2016). Seven data sets are available for Bangladesh. These include BDHS (1990), (1996–1997), (2004), (2007), (2011), (2014) and (2017–2018).
Data for exposure variables such as female literacy rate, household size, dependency ratio, the prevalence of Anemia in children and prevalence of undernourishment is taken from (WDI) and World Bank data indicators for Pakistan, Bangladesh and India for the period of 1990 to 2018. Data for political stability was derived from (ICRG) as one of the six dimensions of good governance. Data for household unimproved sanitation facilities was extracted from the WASH data source. Data for ambient particulate matter pollution was downloaded from GBD data visualizations and the Global Health Data Exchange (GHDx), IHME’s catalog of the world’s health and demographic data.
The methodological approach consists of zooming in on this data through several interrelated steps. The first step involves the construction of an extended version of a composite index of anthropometric failures (ECIAF) which represents our main contribution to a better operationalization of malnutrition among children under five within the selected region. We identified the vulnerable distinct subgroup as pointed out in Table 1 and compared the ECIAF index with other conventional indices. The next step incorporates the shifting conditions of undernutrition and overnutrition concerning different periods within the selected region. The third step involves different econometrics steps to investigate the association between child anthropometric failures and socioeconomic and environmental determinants. Firstly we check the data stationarity through the unit root test. After confirming that the data is stationary at first difference, we will check the cointegration. This test is used to find the long- run association between the dependent variable and explanatory variables. For this purpose, Kao and Johansen’s - Fisher Panel cointegration tests have been employed. These tests are very effective and suitable for Panel study. The last step is to employ the advanced panel data econometric techniques known as fully modified ordinary least squares (FMOLS) along with descriptive statistics. The FMOLS model is best to employ because it directly evaluates the long-run impact of the independent variables on the dependent variable after correcting for the endogeneity bias and small sample bias in the time series by taking the leads and lags of the first-differenced regressors. FMOLS estimator also takes into account the nuisance parameters and possible autocorrelation and heteroscedasticity phenomena of the residues.
The study would examine the short-run affiliation and long-run association between nominated potential confounders and child anthropometric failures for selected South Asian countries (Pakistan, Bangladesh and India).
Panel econometric equation
ECIAF f (Dependency ratio, Household size, Female literacy rate, Prevalence of undernourishment, Prevalence of anemia in children under five, Air pollution, unimproved sanitation facilities and political stability).
ECIAF = β0 + β1 (Dependency ratio) +β2 (Household size) + β3 (Female literacy rate) + β4 (Prevalence of undernourishment) + β5 (Prevalence of anemia) + β6 (Air pollution) + β7 (Unimproved sanitation facilities) + β8 (Political stability) + µ it ECIAF = β0 + β1 (DR) +β2 (HHS) + β3 (FLR) + β4 (POU) + β5 (POA) + β6 (AP) + β7 (USF) + β8 (PS) + µ it.
This study has calculated WHZ, WAZ and HAZ scores by using ENA for smart software concerning WHO standards. Descriptive analysis and other computations have been done utilizing SPSS version 20. Panel econometric analysis has been done by using EViews 12.
Operationalization of ECIAF, the outcome variable
The first step involves estimating the anthropometric indices. Anthropometric indices are constructed using the information on children’s weight, recumbent length, (< 24 months or child unable to stand without support) stature (> 24 months), age in months, and gender. Four key anthropometric indicators are calculated, these include height for age (for stunting), weight for age (for underweight), weight for height (for wasting) and overweight. Stunting refers to impaired growth and development, experienced by children less than five years of age, used as the marker of chronic malnutrition. In more logical terms, stunting can be defined as height for age z scores (<-2 SD), below the average according to the WHO child growth standards. Wasting signifies a severe course of weight loss and is defined as weight for age z scores (<-2 SD), below the average according to the WHO child growth standards. Underweight for age mirrors body mass corresponding to chronological age and a diagnostic of weight for age z scores (<-2 SD) concerning the WHO standards. Whereas overweight can be examined as a combination of two terms, high weight for height and high weight for age. It is defined as weight for height z scores and weight for age z scores (> 2 SD) above the average according to the WHO reference values [59, 60].
There are three generally accepted procedures for assessing child growth statuses. Among these, the study used the method of creating z scores. In the first step, we took the difference between the child’s height or weight (relative to the age and gender) and the mean/median values for the reference population. Then in the second step Z score is computed by dividing this difference by the standard deviation of the reference group. This can be written as follows in the case of calculating height for age z scores.
Where, Hi stands for the estimated height of the child and Hr is the median height of the reference group.
The number of children whose z score is below minus 2SD is undernourished. The World Health Organization proposed a reference population. This reference is formed on the basis of the anthropometric indicators of the children of six countries [61, 62].
The ECIAF model with mutually exclusive categories is elaborated under.
Table 1 counts all children with wasting and/or stunting and/ or overweight and/ or underweight sub-grouped in eight different categories (Groups B-H and Y) and excludes the first category (group A), children with no anthropometric failure. Groups B, F, H and Y consisted of children vulnerable to only one kind of growth retardation problems while groups C, D, E and G were composed of children with concurrent manifestations of anthropometric failures.
ECIAF model equation
Lastly, the following formula has been proposed to detect normal, undernourished and overnourished children among the studied populations.
(Source: Kuiti & Bose, 2018) .
Definition and construction of predicted variables
The dependency ratio, household size, mother’s education, the prevalence of undernourishment within the population, anemia in children, household unimproved sanitation, air pollution and government stability are the selected as the potential risk factors. The dependency ratio is calculated by adding the percentage of children under the age of 15 years and older population above the age of 64 years divided by the percentage of independents in the household (15–64) year then multiplied by 100. Household size is the total number of members in a family. Mother’s education is the total number of years of schooling of a child’s mother. Prevalence of undernourishments is the percentage of the population whose habitual food intake is not enough to provide the dietary energy levels, required to maintain a normal active and healthy life. Child anemia is a condition referred to as low hemoglobin lack of enough healthy red blood cells, or high rates of red blood cell destruction among children under five. Environmental degradation is proxied by air pollution and household unimproved sanitation. Air pollution is the ambient particulate matter pollution (micrograms per cubic meter) and unimproved sanitation is the share of the population with access to unimproved sanitation facilities.
Child malnutrition trends within the selected region
An amalgam of anthropometric failures for children has been calculated by employing the above ECIAF model to examine variation in malnutrition over successive periods in selected South Asian regions. The results are as under.
Table 2 reveals that as the single growth retardation symptom, stunting is the largest among all the other problems of undernutrition over all the periods. Similarly, the greatest number of children is affected by the double burden of stunting and underweight among all the possible combinations. Wasting and overweight problems have been increased from (1991–2002) but reduced later while the co-occurrence of wasting and underweight increased in the same time cohort, reduced between 2002 and 2013 but rose again in 2018. The incidence of a double burden of stunting and overweight has also the same trend.
ECIAF aggregate detects more undernourished children as compared to stunting, wasting and underweight separately as it identifies 64.3%, 53.9%, 54.2% and 46% more malnourished proportions in children respectively from 1990 to 2018. The prevalence decreases periodically. However, a major percentage improvement has been observed between the periods of 1990 to 2002 (Table 3).
Table 4 expresses that Child malnutrition first increases and then decreases over successive periods. Malnutrition is highest within the time span of 1996-97 and lowest during 2017-18. Regarding single growth retardation problems, stunting is the highest and for double burden of malnutrition, the coexistence of stunting and underweight is highest among all.
ECIAF showed a higher prevalence of undernutrition in comparison to three traditional indicators that is stunting, wasting, overweight and underweight among all time spans. Therefore, it is established that ECIAF is a better indicator of child nutritional status than traditional measures because it determines overall anthropometric failure (Table 5).
Table 6 shows that child anthropometric failures are highest during 1992-92 and lowest during 2015-16. The stunting prevalence among children is highest within all-time spans and a coexistence of stunting and underweight is highest with respect to the double burden of growth retardation.
Table 7 depicts that measurement of underweight, stunting, wasting and overweight under-estimates the burden of malnourishment. Although conventional indices gives valuable information and must not be disregarded, ECIAF itself is constructed from the aggregation of these indices. Yet the Composite index of Anthropometric failure (ECIAF) better estimates the burden of undernutrition as it reveals additional dimensions of the malnutrition “iceberg”.
Figure 1. Trends of ECIAF over time among children in Pakistan, Bangladesh and India.
The following graph shows the different time trends in child malnutrition in Pakistan, Bangladesh and India from 1990 to 2018.
Panel econometric analysis
Panel unit root test
We cannot apply first- generation panel unit root tests to test the stationarity of the variables as cross-section units are not cross-sectionally independent. We will use the second-generation panel unit root test of Pesaran, (2007) that allows for cross-section dependence . The panel unit root test confirms that all the variables are stationary at first difference.
Table 8 shows the descriptive statistics of all the variables used in the current analysis, their central tendency, maximum and minimum values, variability and distribution.
Discussion of the FMOLS results
The study analysed pooled weighted FMOLS as proposed by Pedroni (2001) and Kao and Chiang (2000) [65, 66], to allow different long- run variances across the cross section for heterogeneous panels. The test statistics presented in Table 9 showed that all variables are significant and the t value of dependency ratio, household size, government stability, and female literacy rate, Prevalence of undernourishment among the studied population, access to unimproved sanitation, air pollution and anemia in children under five strongly affect child anthropometric failures and performing a significant role in reducing under-five child malnutrition.
The findings in the case of dependency ratio and household size depict positive and significant effects on the prevalence of composite growth retardation problems among children under five and unveil that one unit increase in both the variables positively influences undernourishment by 0.46 and 0.13 units respectively. Yaya et al. (2020), Ghimire et al. (2020) and Ahmad et al. (2020) are also of the view that household structure and family size a risk factors for child malnutrition [56, 67, 68]. Annim et al. (2013) explored the dual effects of household composition and dependency on nutritional outcomes of children under five and found that children of nucleated households with fewer dependents have better health outcomes compared with children in non-nucleated households . Likewise, Fentaw et al. (2013) revealed that households, characterized by more dependency ratio, are more likely to have undernourished children .
The literacy rate among women of reproductive age is found to be negative and significant. Kundu et al. (2022), Sk et al. (2021) and Kousar et al. (2020) determined, a lack of maternal education is a common factor in coexisting prevalence of child anthropometric failures among children under five, in South Asia [71,72,73]. Prevalence of undernourishment within the studied population and anemia in children under five would also increase anthropometric failures among children. In the case of air pollution and unimproved sanitation facilities, our results show that one unit increase in both variables would increase child malnutrition by 0.26 and 0.21 respectively. A study by Gupta and Borkotoky (2016) also confirmed the higher percentage of children having multiple anthropometric failures in households that did not have improved toilet facilities . While few studies have addressed links between air pollution and child health. Bora, (2021), Malley et al. (2021) and Sinharoy et al. (2020) identified that ambient air pollution, slightly increased the risk of anthropometric failures among children under five [43, 75, 76].
The elasticity coefficient of government stability is found to be negative and significant. Thus, there is an inverse relationship between government stability and child malnutrition in South Asian countries and the higher the government stability, the lower would be the prevalence of growth retardation among children and vice versa. Aziz et al. (2021) also explored that the quality of governance is nuanced in declining the rate of undernourishment in South Asia .
Table 11 shows that as the probability of ADF test statistic is less than 0.05, we reject our null hypothesis and conclude that the variables are cointegrated and have a long- run association.
Limitations of the study
This study is based on DHS cross-sections that are usually conducted with a huge time gap. Reliable and timely data information on child health status is essential for public health interventions, policy making, monitoring progress and reaching the health- related MDGs. Therefore, lack of information and its influences may allow harmful exposures to go undetected, resulting in missed opportunities to improve prevention, health promotion, and treatment interventions. Secondly, there are various factors causing undernutrition in preschool-aged children like mother’s nutritional status, age at first pregnancy and some paternal characteristics that can’t be not examined due to the data’s unavailability. Thirdly, ECIAF does not determine the impact of micro nutrient deficiencies and clinical correlation is not possible as well.
We conclude that ECIAF accurately measures the overall burden of undernutrition and overnutrition than conventional indices. Since underestimating the size of hidden vulnerable subgroups might deprive a substantial number of children of getting the advantage of extra supplementation and care they urgently need. In this context, this malnutrition index reflects a wider view of the extent and pattern of malnutrition among children under five by exploring further dimensions of the hidden undernutrition “iceberg”. Thus it has potential implications to be used as a tool for screening systems of malnutrition, monitoring of nutritional interventions and tracking achievement of millennium development goals.
Moreover, all the associated risk factors significantly influence the under-five malnutrition problem in all three countries. The education of women of reproductive age (mothers) is a cornerstone for the betterment of child survival and health. An educated mother, through knowledge and awareness, can better deal with risk factors associated with child malnutrition. Thus improvement in mothers’ education improves the feeding and weaning practices of their children. Household demographics also play an important role in the healthy upbringing of their children. The findings of this research also implied that children less than 5 years of age living in houses with access to unimproved sanitation facilities and air pollution had increased the danger of child malnutrition. Children learn to crawl and walk at this stage of their age, and can experience more exposure to pathogens that are the prime cause of diarrhea from different environmental sources. Similarly, emerging environmental threats including air pollution have also been linked to an increased risk of childhood anthropometric failures. Political stability is also found to be an essential determinant of child health outcomes and growing political stability leads to strengthened social and health programs, that may reduce child malnutrition.
Unquestionably, the cumulative impact of the several underlying factors escalating the severity of undernutrition and overnutrition problems in South Asian countries is thought-provoking and requires considerable commitment and solemnity by the governments to address this issue. At first ECIAF approach should be adapted to accurately measure and recognize the size of all hidden vulnerable groups. This measurement model can accelerate the reduction in child mortality by expanding preventive and curative interventions that are more effective in addressing the significant causes of undernutrition.
Similar studies should be undertaken among other ethnic preschool children, especially in rural areas, to determine the extent of malnutrition using ECIAF. Such studies would help us to generate new data, that can be used for comparison with the pervasiveness of malnutrition in the regional, national and global context. Better health and nutritional policies can be formulated based on the findings of those investigations.
Although malnutrition can manifest in multiple ways the path to prevention is virtually identical. Adequate maternal nutrition before and during pregnancy and while breastfeeding should be ensured at any cost. Focus on the first crucial thousand days, and promote healthy feeding and weaning practices among the children to combat obesity. Give your child nutritious, diverse and safe foods in early childhood and a healthy environment, including access to basic health, water, hygiene and sanitation services and opportunities for safe physical activity. Improving overall household living conditions and increasing maternal nutritional awareness and knowledge can lead to reduced childhood malnutrition. Besides, an improved status for women should be prioritized.
At the national level, efforts should be made to improve the environment and air pollution conditions may need to be considered an integral part of the programmatic responses by governments and development partners for the prevention of under-five child health status. To combat the occurrence of undernourishment and anemia, the authorities should take steps to improve the quantity and quality of available food as well as food prices should also be controlled and managed at a pace to make food affordable for the commons. Enhance food and nutrition knowledge at the community level by initiating education programs and public awareness campaigns to maintain healthy lifestyles and dietary practices. The health ministry and the government should prioritize and initiate nutrition intervention programs of food fortification with multiple micronutrients and additional supplementation of vitamin A at the national, provincial and district levels with special emphasis on children as the builders of the nation. Last but not least, generating political commitment to ending all forms of malnutrition signifies a key challenge for the global nutrition community. The policies, programs, and resources needed to improve nutrition should be implausibly adopted, effectively implemented, or sustained, without commitment.
All the data sets and materials used for this research are available on request from the corresponding author DHS data sets: https://www.dhsprogram.com/data/available-datasets.cfm?ctryid=31.
Composite Index of Anthropometric Failure
Fully Modified Ordinary Least Square
World Health Organization
Extended Composite Index of Anthropometric Failure
Gross Domestic Product
United Nations Children’s Fund
Mean Height for Age Z Scores
Mean Weight for Height Z Scores
Mean Weight for Age Z Scores
Pakistan Demographic and Health Survey
National Nutrition Survey
Indian Demographic and Health Survey
Bangladesh Demographic and Health Survey
World Development Indicators
Water, Sanitation and Hygiene
International Country Risk Guide
Global Burden of Disease
Institute for Health Metrics and Evaluation
Global Health Data Exchange
Emergency Nutrition Assessment
Statistical Package for Social Sciences
Nefiodow LA, Health. The economic growth engine of the 21st century. ICU Manage Pract. 2014;14(4):1–6.
Halkos G, Gkampoura EC. Where do we stand on the 17 Sustainable Development Goals? An overview on progress. Econ Anal Policy. 2021;70:94–122.
Amiri A, Gerdtham UG. Impact of maternal and child health on economic growth: new evidence based granger causality and DEA analysis. Newborn and child health, study commissioned by the partnership for maternal. Sweden: Lund University; 2013 Mar.
Attanasio O, Cattan S, Meghir C. Early Childhood Development, Human Capital, and poverty. Annual Rev Econ. 2021;14.
Esen E, Çelik Keçili M. Economic growth and health expenditure analysis for Turkey: evidence from time series. J Knowl Econ 2021 Apr 15:1–5.
Matingwina T. Health, academic achievement and school-based interventions. Volume 19. London: IntechOpen: Health and Academic Achievement; 2018 Sep. p. 143.
Lustig N. Investing in health for economic development: the case of Mexico. InAdvancing Development 2007 (pp. 168–82). Palgrave Macmillan, London.
Fanzo J, Davis C. The multiple Burdens of Malnutrition. InGlobal Food Systems, Diets, and Nutrition 2021 (pp. 51–69). Palgrave Macmillan, Cham.
Gorstein J, Akré J. The use of anthropometry to assess nutritional status. World health statistics quarterly 1988; 41 (2): 48–58. 1988.
World Health Organization. The state of Food Security and Nutrition in the World 2021: transforming food systems for food security, improved nutrition and affordable healthy diets for all. Volume Vol 2021. Food & Agriculture Org.; 2021.
Montenegro CR, Gomez G, Hincapie O, Dvoretskiy S, DeWitt T, Gracia D, Misas JD. (2022). The pediatric global burden of stunting: Focus on Latin America. Lifestyle Med, 3(3), e67.
World Health Organization. Levels and trends in child malnutrition. UNICEF; 2021.
Terasawa M. End poverty in South Asia: Tackling malnutrition in South Asia: 8 Years on. World Bank Blogs [Internet]. 2019 April 30. Available from: https://blogs.worldbank.org/endpovertyinsouthasia/tackling-malnutrition-south-asia-8-years.
Global Nutrition Report. 2020 Global Nutrition Report: Action on equity to end malnutrition.
Menon S, Peñalvo JL. Actions targeting the double burden of malnutrition: a scoping review. Nutrients. 2019;12(1):81.
Haque R, Alam K, Rahman SM, Mustafa MU, Ahammed B, Ahmad K, Hashmi R, Wubishet BL, Keramat SA. Nexus between maternal underweight and child anthropometric status in South and South-East Asian countries. Nutrition. 2022;98:111628.
Aziz N, He J, Raza A, Sui H, Yue W. Elucidating the macroeconomic determinants of undernourishment in south asian countries: building the framework for action. Front Public Health. 2021;9.
Wali N, Agho KE, Renzaho A. Wasting and Associated factors among children under 5 years in five south asian countries (2014–2018): analysis of demographic health surveys. Int J Environ Res Public Health. 2021;18(9):4578.
Ssentongo P, Ssentongo AE, Ba DM, Ericson JE, Na M, Gao X, Fronterre C, Chinchilli VM, Schiff SJ. Global, regional and national epidemiology and prevalence of child stunting, wasting and underweight in low-and middle-income countries, 2006–2018. Sci Rep. 2021;11(1):1–2.
Mohamed SF, Leng SK, Vanoh D. Malnutrition and its risk factors among children and adolescents with intellectual disability (ID) in asian countries: a scoping review. Malaysian J Nutr. 2021;27(1).
Li Z, Kim R, Vollmer S, Subramanian SV. Factors associated with child stunting, wasting, and underweight in 35 low-and middle-income countries. JAMA Netw open. 2020;3(4):e203386.
Smith LC, Haddad L. Reducing child undernutrition: past drivers and priorities for the post-MDG era. World Dev. 2015;68:180–204.
Vollmer S, Harttgen K, Subramanyam MA, Finlay J, Klasen S, Subramanian SV. Association between economic growth and early childhood undernutrition: evidence from 121 demographic and health surveys from 36 low-income and middle-income countries. The Lancet Global Health. 2014;2(4):e225–34.
Ijarotimi OS. Determinants of childhood malnutrition and consequences in developing countries. Curr Nutr Rep. 2013;2(3):129–33.
Gulati JK. Child malnutrition: trends and issues. The Anthropologist. 2010;12(2):131–40.
Khan AA, Bano N, Salam A. Child malnutrition in South Asia: a comparative perspective. South Asian Survey. 2007;14(1):129–45.
Ziba M, Kalimbira AA, Kalumikiza Z. Estimated burden of aggregate anthropometric failure among malawian children. South Afr J Clin Nutr. 2018;31(2):20–3.
Svedberg P. Poverty and undernutrition: theory, measurement, and policy. Clarendon press; 2000.
Nandy S, Irving M, Gordon D, Subramanian SV, Smith GD. Poverty, child undernutrition and morbidity: new evidence from India. Bull World Health Organ. 2005;83:210–6.
Permatasari TAE, Chadirin Y. Assessment of undernutrition using the composite index of anthropometric failure (CIAF) and its determinants: a cross-sectional study in the rural area of the Bogor District in Indonesia. BMC Nutr. 2022;8(1):133.
Islam MS, Biswas T. (2020). Prevalence and correlates of the composite index of anthropometric failure among children under 5 years old in Bangladesh. Matern Child Nutr, 16(2), e12930.
Shahid M, Cao Y, Shahzad M, Saheed R, Rauf U, Qureshi MG…, Ahmed F. Socio-economic and environmental determinants of malnutrition in under three children: evidence from PDHS-2018. Children. 2022;9(3):361.
Fenta HM, Zewotir T, Muluneh EK. (2021). Disparities in childhood composite index of anthropometric failure prevalence and determinants across ethiopian administrative zones. PLoS ONE, 16(9), e0256726.
Watchmaker B, Boyd B, Dugas LR. Newborn feeding recommendations and practices increase the risk of development of overweight and obesity. BMC Pediatr. 2020;20(1):1–6.
Butler ÉM, Fangupo LJ, Cutfield WS, Taylor RW. Systematic review of randomised controlled trials to improve dietary intake for the prevention of obesity in infants aged 0–24 months. Obesity Reviews.
Khaliq A, Wraith D, Miller Y, Nambiar-Mann S. Prevalence, trends, and socioeconomic determinants of coexisting forms of malnutrition amongst children under five years of age in Pakistan. Nutrients. 2021;13(12):4566.
Bejarano IF, Oyhenart EE, Torres MF, Cesani MF, Garraza M, Navazo B, Zonta ML, Luis MA, Quintero FA, Dipierri JE, Alfaro E. Extended composite index of anthropometric failure in argentinean preschool and school children. Public Health Nutr. 2019;22(18):3327–35.
Saurabh S, Sarkar S, Pandey DK. Female literacy rate is a better predictor of birth rate and infant mortality rate in India. J Family Med Prim care. 2013;2(4):349.
Kim J. Female education and its impact on fertility. IZA World of Labor. 2016 Feb 1.
Currie J. Healthy, wealthy, and wise: socioeconomic status, poor health in childhood, and human capital development. J Econ Lit. 2009;47(1):87–122.
World Health Organization. Environment, Climate Change and Health: Children’s environmental health. 2022. Available from: https://www.who.int/teams/environment-climate-change-and-health/settings-populations/children.
Sinharoy SS, Clasen T, Martorell R. Air pollution and stunting: a missing link? The Lancet Global Health. 2020;8(4):e472–5.
Bountogo M, Ouattara M, Sié A, Compaoré G, Dah C, Boudo V, Zakane A, Lebas E, Brogdon JM, Godwin WW, Lin Y. Access to improved sanitation and nutritional status among preschool children in Nouna District, Burkina Faso. Am J Trop Med Hyg. 2021;104(4):1540.
Rah JH, Sukotjo S, Badgaiyan N, Cronin AA, Torlesse H. Improved sanitation is associated with reduced child stunting amongst indonesian children under 3 years of age. Matern Child Nutr. 2020;16:e12741.
Bekele T, Rahman B, Rawstorne P. The effect of access to water, sanitation and handwashing facilities on child growth indicators: evidence from the Ethiopia demographic and health survey 2016. PLoS ONE. 2020;15(9):e0239313.
Khan AY, Fatima K, Ali M. Sanitation ladder and undernutrition among under-five children in Pakistan. Environ Sci Pollut Res. 2021;28(29):38749–63.
Tectonidis M. Malnutrition: a political problem. Medecins Sans Fronteirs [Internet]. 2004 Sep 23. Available from: https://www.msf.org/malnutrition-political-problem.
Lee S, Kim JY, Lee HH, Park CY. Food prices and population health in developing countries: An investigation of the effects of the food crisis using a panel analysis. Asian Development Bank Economics Working Paper Series. 2013 Sep 1(374).
Biadgilign S, Ayenew HY, Shumetie A, Chitekwe S, Tolla A, Haile D, Gebreyesus SH, Deribew A, Gebre B. Good governance, public health expenditures, urbanization and child undernutrition nexus in Ethiopia: an ecological analysis. BMC Health Serv Res. 2019;19(1):1–0.
Masuke R, Msuya SE, Mahande JM, Diarz EJ, Stray-Pedersen B, Jahanpour O, Mgongo M. Effect of inappropriate complementary feeding practices on the nutritional status of children aged 6–24 months in urban Moshi, Northern Tanzania: Cohort study. PLoS ONE. 2021;16(5):e0250562.
Casey PH, Szeto K, Lensing S, Bogle M, Weber J. Children in food-insufficient, low-income families: prevalence, health, and nutrition status. Arch Pediatr Adolesc Med. 2001;155(4):508–14.
Prieto-Patron A, Van der Horst K, Hutton ZV, Detzel P. Association between anaemia in children 6 to 23 months old and child, mother, household and feeding indicators. Nutrients. 2018;10(9):1269.
Soliman A, De Sanctis V, Elalaily R. Nutrition and pubertal development. Indian J Endocrinol Metabol. 2014;18(Suppl 1):39.
Nwosu CO, Ataguba JE. Explaining changes in wealth inequalities in child health: the case of stunting and wasting in Nigeria. PLoS ONE. 2020;15(9):e0238191.
Ahmad D, Afzal M, Imtiaz A. Effect of socioeconomic factors on malnutrition among children in Pakistan. Future Bus J. 2020;6(1):1–1.
Warr P. Food insecurity and its determinants. Australian J Agricultural Resource Econ. 2014;58(4):519–37.
Baldacci E, Guin-Siu MT, Mello LD. More on the effectiveness of public spending on health care and education: a covariance structure model. J Int Development: J Dev Stud Association. 2003;15(6):709–25.
Titoria R, Ponnusamy P, Mehra S. Identification of undernutrition in under five children: Z score or a composite index of anthropometric failure? Int J Community Med Public Health. 2019;6(7):3150.
Croft TN, Marshall AM, Allen CK. Guide to DHS Statistics. DHS-7: the demographic and health surveys program. Rockville: ICF; 2018.
Sharaf MF, Mansour EI, Rashad AS. Child nutritional status in Egypt: a comprehensive analysis of socioeconomic determinants using a quantile regression approach. J Biosoc Sci. 2019;51(1):1–7.
Zere E, McIntyre D. Inequities in under-five child malnutrition in South Africa. Int J Equity Health. 2003;2(1):1–0.
Kuiti BK, Bose K. The concept of composite index of anthropometric failure (CIAF): revisited and revised. Anthropol Open J. 2018;3(1):32–5.
Pesaran MH. A simple panel unit root test in the presence of cross-section dependence. J Appl Econom. 2007;22(2):265–312.
Pedroni P. Fully modified OLS for heterogeneous cointegrated panels. InNonstationary panels, panel cointegration, and dynamic panels 2001 Feb 13. Emerald Group Publishing Limited.
Kao C, Chiang MH. On the estimation and inference of an Cointegrated regression in Panel Data, volume 15 of advances in Econometrics: Nonstationary Panels, Panel Cointegration, and Dynamic Panels.
Yaya S, Oladimeji O, Odusina EK, Bishwajit G. Household structure, maternal characteristics and children’s stunting in sub-saharan Africa: evidence from 35 countries. Int Health. 2022;14(4):381–9.
Ghimire U, Aryal BK, Gupta AK, Sapkota S. Severe acute malnutrition and its associated factors among children under-five years: a facility-based cross-sectional study. BMC Pediatr. 2020;20(1):1–9.
Annim SK, Awusabo-Asare K, Amo-Adjei J. Household nucleation, dependency and child health outcomes in Ghana. J Biosoc Sci. 2015;47(5):565–92.
Fentaw R, Bogale A, Abebaw D. Prevalence of child malnutrition in agro-pastoral households in Afar Regional State of Ethiopia. Nutr Res Pract. 2013;7(2):122–31.
Kundu, R. N., Hossain, M. G., Haque, M. A., Biswas, S., Huq, M. M., Pasa, M. K., …Bharati, P. (2022). Factor associated with anthropometric failure among under-five Bengali children: A comparative study between Bangladesh and India. Plos one, 17(8), e0272634.
Sk R, Banerjee A, Rana MJ. Nutritional status and concomitant factors of stunting among pre-school children in Malda, India: a micro-level study using a multilevel approach. BMC Public Health. 2021;21:1–13.
Kousar S, Shabbir A, Shafqat R. Investigation of socioeconomic determinants on child death in south asian countries: a panel cointegration analysis. OMEGA-Journal of Death and Dying. 2022;84(3):811–36.
Gupta AK, Borkotoky K. Exploring the multidimensional nature of anthropometric indicators for under-five children in India. Indian J Public Health. 2016;60(1):68–72.
Bora K. Air pollution as a determinant of undernutrition prevalence among under-five children in India: an exploratory study. J Trop Pediatr. 2021;67(5):fmab089.
Malley, C. S., Hicks, W. K., Kulyenstierna, J. C., Michalopoulou, E., Molotoks, A.,Slater, J., … Robinson, T. P. (2021). Integrated assessment of global climate, air pollution, and dietary, malnutrition and obesity health impacts of food production and consumption between 2014 and 2018. Environmental Research Communications, 3(7), 075001.
Fisher SR. Statistical Methods for Research Workers… Revised and Enlarged. Edinburgh,London; 1932.
I would like to express my deep gratitude to Professor Dr. Sofia Anwar, my research supervisor, for her exceptional support, patient guidance and useful critiques of this research work. Her willingness to give her time so generously has been very much appreciated.
I also wish to thank my parents and my daughter for their support, love and encouragement throughout my study.
The study is not supported by any ministry, organization or University. It’s the authors own contribution. The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
The authors have no relevant financial or non-financial interests to disclose.
Ethical Approval is not needed for this research.
Consent to participate
Not applicable for this research.
Consent to Publish
Not applicable for this research.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Saif, S., Anwar, S. Unraveling the South Asian enigma: concurrent manifestations of child anthropometric failures and their determinants in selected South Asian countries. BMC Nutr 9, 120 (2023). https://doi.org/10.1186/s40795-023-00771-4