DOI: https://doi.org/10.21203/rs.3.rs-2070713/v1
Stunting is a measure of health inequalities between children with implications that extend into adulthood. Sudan is one of 14 countries carrying 80% of the global burden of stunting. Stunting is reversible if addressed in the first 1000 days of life, so it is essential to identify associated factors in order to target them through government policy. This study aimed to identify factors associated with stunting in children under 5 in Sudan. A secondary analysis of the Sudan Household Health Survey 2010 was done following the UNICEF framework for child under-nutrition which identifies immediate, underlying, and basic factors. We used the chi-square test and multiple regression to adjust for potential confounders. We found that 33.4% (n = 3734) of the children in our study were stunted. Stunting was more common among males, children living in rural areas, born to or household heads with no education, living in households without toilets and had suffered from diarrhea in the past two weeks. Poverty, rurality, poor education and poor sanitation are reversible socioeconomic factors significantly associated with childhood stunting. Government policies aiming to promote child health in Sudan should include poverty-reduction strategies, proper housing, rural development and improving girls’ and women’s education.
Stunting, or failure to reach the appropriate height for age, is a chronic condition reflecting poor nutrition and health [1]. Children who are less than 2 standard deviations below the median height for their age in the WHO growth chart are classified as moderately to severely stunted, while those below 3 standard deviations (SD) are considered severely stunted [2]. The importance of stunting lies in the fact that it captures several dimensions of child health beyond simple under-nutrition, as it has a positive correlation with social and economic deprivation and reflects several determinants directly and indirectly related to socioeconomic status, making it an acknowledged proxy to child health inequities and socioeconomic differences [3–5].
While around 16% and 45% of all children live in low-income and lower-middle income countries, respectively, the former is home to 25% of stunted children and the latter has 59% of all children affected by stunting [6]. Even within countries, stunting follows socioeconomic lines in distribution, with poorer populations faring worse than richer populations, and studies show that countries implementing redistributive policies addressing elimination of hunger and poverty have reduced their stunting prevalence and narrowed this gap [7–9].
Stunting is an important public health problem particularly in developing countries with several short and long-term complications, including increased under-5 mortality, attenuated cognitive abilities, increased behavioural problems in childhood, impaired motor and cognitive development and increased risk for obesity and non-communicable diseases (NCDs) in childhood and as an adult [10–14]. Growth during the first two years or 1000 days since conception has the most impact on height and brain development and is identified as the period during which interventions are needed the most and investments in reducing stunting have the highest return [15–18]. It was estimated that there were around 149.2 million stunted children worldwide in 2020–22% of all children under the age of 5 [6]. The majority of stunted children live in Asia and Sub Saharan Africa, and while global prevalence is slowly declining, the number of stunted children has actually risen in Africa from 54.4 million to 61.4 million children [6, 12, 19]. And of the 67 countries that had had a very high stunting prevalence (> 30%) in 2000, 33 countries remained in 2020 – one of which is Sudan [6].
Sudan has recently been demoted from a lower-middle income country to a low-income country, and seems to be facing endless political, economic and social difficulties translating into high levels of food insecurity and persistently low health and nutritional indicators [20]. Malnutrition cost the country an estimated 11.6 billion Sudanese pounds, 2.6% of the GDP in 2014 [21].
The UNICEF framework for under-nutrition and stunting (Fig. 1) is one of several frameworks found in the literature examining the factors associated with stunting. It provides a holistic and pragmatic approach to studying under-nutrition in children in developing countries, and follows the triple A approach: assessment, analysis and action [22]. The framework is an example of an integrated conceptual framework that considers the different interrelated social, economic and environmental determinants and their interactions [1]. It classifies the causes of undernutrition into 3 levels that influence each other: the immediate causes are disease and inadequate dietary intake; the underlying causes are those related to the household in terms of food insecurity, inadequate care, unhealthy household environment and lack of health services, and income poverty; while the basic causes include lack of capital and the social, economic and political context [23].
Existing literature on stunting in Sudan examines the issue mainly within the context of undernutrition in general, its prevalence in specific areas in the country, nutritional factors associated with stunting and those associated with reversal of stunting, and the economic impact of undernutrition as a whole [9, 21, 24–30]. This study aimed to examine factors associated with stunting in children under the age of five in Sudan with a focus on socioeconomic factors utilizing the UNICEF framework for childhood undernutrition.
This study was a secondary analysis of data from the Sudan Household Health Survey 2 (SHHS2). The SHHS2 was carried out in all 16 Sudanese states between March and May of 2010 in a two-stage cluster design as a customized version of the Multiple Indicator Cluster Survey (MICS) and the Pan Arab Project for Family Health survey. It was estimated that a total of 900 households per state was needed to reach appropriate estimates, so 1,000 households (25 households in 40 clusters per state) were interviewed to allow for non-response. A total of 14,778 households were interviewed with a response rate of 96.8%, in which there were 13,587 children under the age of five. All measurements were performed in accordance with relevant guidelines and regulations.
In our secondary analysis of the data collected in the SHHS2, all children under the age of five enrolled in the original study were included in the analysis.
The data collection tool used in the SHHS2 consisted of five questionnaires on the household, women, children, men and food security. Our study used data collected from the household and children’s questionnaires only. The children’s questionnaire was administered to mothers of children under the age of five years, or the primary caretaker in case of the mother’s absence.
Heights and weights of the children were measured using standard equipment recommended by UNICEF and the reference population was based on the latest WHO standards. All data were collected by trained field workers and the information was double entered with internal consistency checks.
Variables The SHHS2 questionnaire worksheet for ‘child health’ originally had 325 variables, of which 10 variables were found relevant and were used in our study. The questionnaire worksheet for the ‘household’ originally had 100 variables, of which 4 relevant variables were used in our study. A total of 14 variables were included in our study, which after recoding were reduced to 11 independent variables and 1 dependent variable (see Table 1).
No |
Variable |
Description |
Type |
Dependent/ independent |
---|---|---|---|---|
1 |
Stunting |
This variable is modified from the variable for the WHO height for age z score, which measures the child’s linear growth. The original variable was a continuous scale, and was recoded into a new variable with 2 categories: stunted (z-score less than − 2 SD) and not stunted (z-score − 2 SD or higher). Values <-5SD and > + 3SD were considered outliers and coded as system missing. |
Nominal |
Dependent variable |
Immediate determinants |
||||
2 |
Diarrhea in the last 2 weeks |
Refers to the child having had diarrhoea in the last 2 weeks: yes/no, |
Nominal |
Independent variable |
3 |
Cough in the last 2 weeks |
Refers to the child having had cough in the last 2 weeks: yes/no |
Nominal |
|
4 |
Fever in the last 2 weeks |
Refers to the child having had fever in the last 2 weeks: yes/no |
Nominal |
|
Underlying determinants |
||||
5 |
Toilet |
Refers to the type of toilet used by household members: flushed, pit latrine, no facility/others |
Nominal |
Independent variable |
6 |
Water |
Refers to main source of drinking water: piped, brought from outside (well/river/spring/tanker), others |
Nominal |
|
7 |
Wealth index |
Measures the household’s cumulative living standard and is calculated based on data on the family’s ownership of selected assets, material for house construction, and types of water and sanitation access. It is classified into poorest, second, middle, third and richest quintiles. |
Ordinal |
|
8 |
Household head education |
Refers to the highest level of education the household head has reached: none, primary, secondary and higher |
Ordinal |
|
9 |
Mother’s education |
Refers to the highest level of education the mother has reached: none, primary or secondary and higher |
Ordinal |
|
Basic determinants |
||||
10 |
Age |
This measures the age of the child in months: 0 − 11, 12–23, 24–35, 36–47, 48–59 |
Ordinal |
Independent variable |
11 |
Sex |
Refers the child’s gender; either male or female |
Nominal |
|
12 |
Area |
Refers to the area of residence of the child and is either rural or urban |
Nominal |
A new dependent categorical variable was introduced called ‘StuntWHO’, in which the original variable for the WHO height for age z-score was re-coded into ‘stunted’ (z-score of less than − 2SD and lower) and ‘not stunted’ (-2SD and higher). Based on WHO recommendations, outliers of <-5 SD and > + 3SD were removed from analysis [31].
The variable for ‘main source of drinking water’ had 15 levels and was recoded into 3 levels: piped, brought from outside, and others, and the variable for ‘type of toilet facility’ had 14 levels and was recoded into 3: flushed, pit latrine, and no facility/others.
The variables for ‘child ever been breastfed’ and ‘age of starting additional foods’ had missing data of more than 20%, and so were eventually excluded from the study.
Conceptual framework The UNICEF framework for undernutrition [10] divides causative factors for child under-nutrition into immediate, underlying and basic (see Fig. 1). The immediate independent variables in this study were childhood infections (diarrhea, fever or cough in the last 2 weeks) and nutrition (child ever been breastfed, later removed); the underlying independent variables were mother’s education (none, primary, secondary or higher), household head’s education (none, primary, secondary or higher), main source of drinking water, type of toilet facility, and household wealth index (by income quintile); and the basic independent variables were age in months (categorical), gender (male or female), and area of residence (rural or urban).
A descriptive analysis was conducted followed by cross tabulation and chi square test of association for each independent variable with the dependent variable for stunting StuntWHO. (Table 2) as the data was categorical. This was followed by multiple regression including the independent variables that had showed a significant association with the dependent variable to measure the strength of this association (Table 3).
Table 2: Results of crosstabulation and chi-square test between all variables and stunting
Variables |
Stunted N (%) |
Not stunted N (%) |
Total N (%) |
Pearson’s chi square |
Immediate determinants Diarrhea in the last 2 weeks Yes No Cough in the last 2 weeks Yes No Fever in the last 2 weeks Yes No Underlying determinants Type of toilet facility Flushed Pit latrine No facility/other Main source of drinking water Piped Brought from outside Other Wealth index Richest Fourth Middle Second Poorest Household head education Secondary and higher Primary None Mother’s education Secondary and higher Primary None Basic determinants Area Rural Urban Age in months 0 – 11 12 – 23 24 – 35 36 – 47 48 – 59 Sex Male Female |
1119 (30.0) 2614 (70.0)
1278 (34.2) 2454 (65.8)
333 (8.9) 3397 (91.9)
117 (3.2) 1775 (48.0) 1805 (48.8)
626 (16.8) 2773 (74.3) 335 (9.0)
214 (5.7) 538 (14.4) 963 (25.8) 1027 (27.5) 992 (26.6)
471 (12.7) 1003 (27.1) 2231 (60.2)
307 (8.3) 968 (26.1) 2435 (65.6)
2946 (78.9) 788 (21.1)
365 (9.8) 866 (23.2) 939 (25.1) 961 (29.7) 603 (16.1)
2014 (53.3) 1859 (46.7) |
1932 (26.0) 5499 (74.0)
2517 (33.9) 4909 (66.1)
639 (8.6) 6787 (91.4)
532 (7.2) 4233 (57.4) 2609 (35.4)
2119 (28.5) 4780 (64.4) 529 (7.1)
1255 (16.9) 1509 (20.3) 1670 (22.5) 1538 (20.7) 1462 (19.7)
1510 (20.4) 2228 (30.1) 3654 (49.4)
1168 (15.8) 2371 (32.1) 3846 (52.1)
5059 (68.1) 2375 (31.9)
2019 (27.2) 1390 (18.7) 1381 (18.6) 1433 (19.3) 1211 (16.3)
3643 (49.0) 3791 (51.0) |
3051 (27.3) 8113 (72.7)
3795 (34.0) 7363 (66.0)
972 (8.7) 10184 (91.3)
649 (5.9) 6008 (54.3) 4414 (39.9)
2745 (24.6) 7553 (67.7) 864 (7.7)
1469 (13.2) 2047 (18.3) 2633 (23.6) 2565 (23.0) 2454 (22.0)
1981 (17.9) 3231 (29.1) 5885 (53.0)
1475 (13.3) 3339 (30.1) 6281 (56.6)
8005 (71.7) 3163 (28.3)
2384 (21.3) 2256 (20.2) 2320 (29.8) 2394 (21.4) 1814 (16.2)
5767 (50.5) 5650 (49.5) |
19.785 (<0.001)
0.136 (0.713)
0.325 (0.569)
220.524 (<0.001)
186.862 (<0.001)
397.793 (<0.001)
144.393 (<0.001)
215.453 (<0.001)
143.988 (<0.001)
476.804 (<0.001)
19.381 (<0.001) |
Table 3: Results of multiple regression analysis
Characteristics |
Odds ratio |
95% Confidence Interval lower – upper |
P value |
Immediate determinants Diarrhea in the past 2 weeks No (reference) Yes Underlying determinants Type of toilet facility Flushed (reference) Pit latrine No facility/other Main source of drinking water Piped (reference) Brought from outside Other Wealth index Richest (reference) Fourth Middle Second Poorest Household head education Secondary or higher (reference) Primary None Mother’s education Secondary or higher (reference) Primary None Basic determinants Area Urban (reference) Rural Age in months 0 – 11 (Reference) 12 – 23 24 – 35 36 – 47 48 – 59 Sex Female (reference) Male |
1.219
1 1.081 1.341
1 0.906 0.937
1.990 2.897 2.721 2.518
1.110 1.272
1.130 1.435
1.232
3.670 4.097 4.083 2.928
1.259 |
1.108 – 1.340
- 0.857 - 1.046 1.363 - 1.720
- 0.783 - 0.768 1.048 - 1.144
1.636 - 2.330 2.146 - 1.967 2.421 - 3.601 3.451 - 3.225
0.966 - 1.111 1.275 - 1.456
0.964 - 1.223 1.325 - 1.684
1.102 - 1.378
3.714 - 3.546 3.535 - 2.508 4.243 - 4.733 4.715 - 3.419
1.158 - 1.369 |
<0.001
0.511 0.021
0.184 0.522
<0.001 <0.001 <0.001 <0.001
0.141 <0.001
0.133 <0.001
<0.001
<0.001 <0.001 <0.001 <0.001 <0.001
<0.001
|
The study utilized a 99% confidence interval and a p value of < 0.01 was considered significant. SPSS version 19 was used for all data analyses.
Ethical approval and consent to participate The original SHHS2010 study obtained ethical approval from the Federal Ministry of Health Research Committee and written informed consent was signed by all participants, and the parents or guardians of participants less than 18 years old. As this study was a secondary analysis of anonymized survey data no ethical clearance was deemed necessary.
Using SPSS version 19, the independent variables were crosstabulated with the dependent variable for stunting, followed by the chi square test of association. The variables found to be significantly associated with stunting (p < 0.01) were then entered into a multiple regression analysis with stunting as the dependent variable and the resulting odds ratios were interpreted with a confidence interval of 99% and significant p value of <0.01.
The results were interpreted based on the UNICEF framework for child under-nutrition and divided determining factors into basic, underlying and immediate.
Descriptive analysis
Basic determinants Around 71% (n = 3877) of the children in this study resided in rural areas. The highest number of them fell in the 0-11 months age group (22%, n = 2694) and the lowest number were in the 48 to 59 months age group (16%, n = 2118). Of the 13,587 children involved in the study, 49% (n = 6691) were girls and 51% (n = 6896) were boys.
Underlying determinants Only 6% (n = 798) lived in homes with toilets that flushed, while more than half had pit latrines, and 40% either had no toilet facility or another type. Almost 68% (n = 7553) brought their drinking water from outside the house. Most children lived in the 3 poorest quintiles, and only around 12% (n = 1718) lived in the richest. The majority of children lived in households whose head and mothers had no education (53.2%, n = 7234 and 57%, n = 7721, respectively).
Immediate determinants Twenty-six percent (n = 3577) of children had suffered from diarrhea in the preceding 2 weeks, 32.4% (n = 4408) had had a cough and 8.4% (n = 1142) had had a fever.
Thirty-three point four percent of the children in this study were stunted (n = 3734).
Crosstabulation and chi square test of association
Of the 11 independent variables included in our study, nine were found to be significantly associated with the dependent variable when conducting the chi square test of association and crosstabulation (see table 2).
Immediate determinants: twenty-nine point nine percent (n = 1189) of stunted children had had diarrhoea in the last two weeks, 34.3% (n = 1364) had a cough in the last two weeks and 9.2% (n = 366) had a fever in the last two weeks. Only having diarrhea in the last two weeks was found to be significantly associated with stunting (p <0.01).
Underlying determinants: Around 26.7% (n = 1062) of stunted children lived in the poorest quintile, 27.7% (n = 1104) in the second poorest, 25.3% (n = 1007) in the middle quintile, 14.4% (n = 575) in the fourth and 5.9% (n = 235) in the richest income quintile. The wealth index was found to be significantly associated with stunting (p < 0.01).
Sixty-five point three percent (n = 2580) of stunted children were born to mothers with no education, 26.4% (n = 1045) had primary education and 8.3% (n = 329) had a secondary or higher education. The household head of stunted children had no education in 60% (n = 2231) of cases, a primary education in 27.1% (n = 1003) and secondary or higher in 12.7% (n = 471) of cases. The mother’s and household head’s education level were significantly associated with stunting (p < 0.01).
Basic determinants: Twenty five point six percentage of stunted children (n = 1021) fell in the 36 – 47 months age group and 25% (n = 995) in the 24 – 35 months age group, followed by 23.3% (n = 928) in the 12 – 23 months age group, 16.3% (n = 649) in the 48 – 59 months age group and 9.8% (n = 390) in the 0 – 11 months age group. Age was found to be significantly associated with stunting (p < 0.01)
Fifty-three point three percent (n = 2124) of stunted children were male and 46.7% (n = 1859) were female. Sex was significantly associated with stunting (p < 0.01).
Seventy-eight point seven percent (n = 3133) lived in rural areas and 21.3% (n = 850) in urban areas. Area was significantly associated with stunting (0.01).
Multiple regression analysis
The nine independent variables found to be significantly associated with stunting were entered into a multiple regression analysis, which showed that the wealth index, mother’s education and household head’s education levels, age, gender and rurality were significantly associated with children being stunted (see table 3).
Immediate determinants
Diarrhoea in the last two weeks The odds of children who had been suffering from diarrhoea in the last 2 weeks being stunted were 1.22 times as likely as those who had not had diarrhoea (95% CI 1.11, 1.34, p = 0.000).
Underlying determinants
Type of toilet facility Children living in homes with no toilet facilities or a type other than pit latrine and flushed were 1.34 times as likely to be stunted than those with flushed toilets (95% CI 1.04, 1.72, p = 0.02). Having a pit latrine was not a significant predictor of stunting (OR 1.08, 95% 0.85, 1.36, p = 0.51).
Main source of drinking water The main source of water was not a significant predictor of stunting: brought from outside compared to piped (OR 0.90, 95% CI 0.78, 1.05, p = 0.18), other sources compared to piped (OR 0.93, 95% CI 0.76, 1.14, p = 0.52).
Wealth index The odds of a child living in a household whose income falls in the middle wealth index quintile being stunted compared to the richest group was 2.89 times as likely (95% CI 2.33, 3.60, p = 0.000), closely followed by the second poorest quintile (OR 2.72, 95% CI 2.14, 3.45, p = 0.000) and the poorest quintile (OR 2.51, 95% CI 1.96, 3.22, p = 0.000). The odds of those in the fourth quintile were 1.99 times as likely as being stunted than the richest (95% CI 1.63, 2.42, p = 0.000).
Mother’s education The odds of a child born to a mother with no education being stunted was 1.43 as likely as that for a child born to a mother with secondary school education (95% CI 1.22, 1.68, p = 0.000). The odds of a child born to mother with primary school education was 1.13 times as likely as secondary school education (95% CI 0.96, 1.32, p = 0.13), but this category was not significant.
Household head’s education Children born in households whose head has no education were 1.27 times as likely to be stunted compared to those whose head had a secondary education or higher (95% CI 1.11, 1.45, p = 0.000). Having a primary education was not a significant predictor of stunting (OR 1.11, 95% CI 0.96, 1.32, p = 0.13).
Basic determinants
Area of residence Children living in rural areas were 1.23 times as likely as those living in urban areas of being stunted (95% CI 1.10, 1.37, p =0.000).
Age The highest odds of being stunted compared to the youngest age group was in both the 24 – 35 months age group (OR 4.09, 95% CI 3.54, 4.73, p = 0.000), and the 36 – 47m group (OR 4.08, 95% CI 3.53, 4.71, p = 0.000). Children aged 12 – 23 months had an odds of 3.67 times as likely as being stunted than the oldest group (95% CI 3.71, 4.24, p = 0.000). The odds then dropped in the 48 – 59m group to 2.92 (95% CI 2.50, 3.41, p = 0.000).
Sex The odds of a male child being stunted were 1.26 times as likely as females (95% CI 1.16, 1.37, p = 0.000).
This study aimed to identify factors associated with stunting in children under the age of five in Sudan using the UNICEF framework for child under-nutrition as a conceptual framework. Our results support the findings of previous studies in that age and sex, as well as repeated infections are significantly associated with stunting, in addition to socioeconomic factors such as education, wealth index, rurality, drinking water and household toilet facilities. And based on the conceptual framework, all these factors affect one another and lead to the outcome of maternal and child undernutrition.
Children who had suffered from diarrhea in the last two weeks had odds of being stunted 1.2 times that of children who did not have diarrhea. Diarrhoea with or without fever is the most common illness associated with poor linear growth [19, 32], and there is evidence that repeated bouts of infection cumulatively increase risk of stunting, while stunting increases the risk of recurrent and persistent diarrhoea and other infections [12, 19, 32, 33]. Checkley et al’s 2008 pool analysis of nine longitudinal studies in five countries over 20 years showed a dose-response relationship between the cumulative incidence and longitudinal prevalence of diarrhea and stunting at 24 months of age, with the growth difference proving difficult to overcome with catch-up growth [33]. Recurrent infections as an immediate cause of stunting is closely linked to the unhealthy household environment and health seeking behaviour at the underlying causes level.
Poverty leads to diminished access to health care, exposure to contaminated environments from poor sanitation and hygiene leading to recurrent illnesses, poor child care practices and food insecurity, as well as being associated with inadequate nutrition and poor maternal education [1, 34–36]. Our study found that the odds of children living in poorer households being stunted were significantly higher than those from richer households, a finding supporting that found in the literature [3, 9, 12, 37–41]. Evidence shows inter-country differences in levels of stunting along socioeconomic lines, with obvious disadvantages for children in lower wealth quintiles [9, 12, 41]. Gender differences and the effect of maternal education are also more visible in poorer households and provinces [3, 12, 39]. The difference in stunting between the rich and the poor in urban and metropolitan areas is even more pronounced than between urban and rural areas [37, 38, 40]. Multisector rural development program interventions, improving the income of the poor and implementing income-generating projects for those most at need have shown much better results in reducing stunting than single separate nutritional interventions [37, 42].
The Sudan Household Health Utilization and Expenditure Survey of 2012 showed that 47% of households suffered a significant impact on their incomes because of healthcare expenditure, while 14% had to sell their assets to cover the costs [43]. The inability to finance healthcare meant that almost a third of the population did not get full treatment and 21% of people had to reduce non-health expenditures in favor of healthcare. A third of rural residents had to borrow money or sell their belongings to cover healthcare costs compared to only one fifth of urban residents. At the country level, almost 8% of households suffered catastrophic healthcare expenditures. Although Sudan has had a health insurance system since 1996, only 37% of the population is actually covered, the vast majority of which are employees of the public sector, while the informal sector – which forms a large percentage of the workforce – is both uninsured and unaccounted for [44].
Our study also found that children were more likely to be stunted when born to uneducated mothers or into a household whose head was uneducated. Maternal and paternal education have been linked to stunted growth in several studies, and improving girls’ education has been suggested as a means towards improving childhood health in general [8, 11, 22, 38, 45–47]. Bobak et al noted that maternal education was associated with improved nutritional practices, a reduction in parity, widening in birth intervals and use of contraceptives, as well as an increase in the duration of breastfeeding [47]. Also, home hygiene and health-seeking behaviour was likely to be influenced by the mother’s education. Educated mothers are more likely to have better health knowledge and health-seeking behaviours, recognize signs of illness and read medical instructions for treatment, and are generally more receptive to modern medicine [38]. They would also have more access to information on proper complementary feeding and nutritional practices. Maternal education also influences the socioeconomic status by having an increased likelihood of steady and higher paying jobs as well as the increased likelihood of being married to educated men with better incomes, and living in better neighbourhoods. Similarly, paternal education attenuates the effect of stunting due to improved health promotion activities, the higher possibility of having educated wives, higher incomes and being more interested in child nutrition [1]. Taking Brazil as an example, one of the main reasons that stunting had dropped by 80% between 1976 and 2006 was a combination of government policies that ensured universal access to primary education, improving the quality of schooling and reducing disparities between rich and poor municipalities [8, 16].
Although the source of drinking water was significantly associated with risk of stunting in the cross-tabulation and chi square analysis, this significance disappeared in the multiple regression. However, studies do support the finding that unsafe sources of drinking water are associated with stunting and increase the likelihood of recurrent infections and diarrheal diseases [34, 49]. The type of toilet facility, however, remained a significant predictor, similar to findings in the literature [34, 48]. Murray and Lopez [50] indicated that 6.8% of Disability Adjusted Life Years (DALYs) worldwide are attributed to poor water and sanitation and personal and household hygiene, as it is associated with increased risk of infections in children, and theories propose that poor hygiene causes malabsorption and intestinal loss of nutrients and their diversion to fight chronic infections rather than growth. In fact, poor sanitation practices have been identified as one of the main drivers behind the high burden of stunting in Southeast Asia [39].
Our study found that boys had 1.3 times the odds of being stunted compared to girls. The majority of the literature describes that males are indeed more prone to stunting than females [3, 12, 38, 46, 49, 52] and this is thought to be explained by natural selection and boys being more influenced by environmental stress and infections than girls [51, 52]. Another explanation is behavioural, as some authors suggested that local feeding habits give preference to females [53]; an explanation that is questionable in the Sudanese context. Grawert [54] notes that as part of the life-cycle of women in Kutum, Darfur, the female child is fed what is left over from the household males’ meal, while Ibnouf [55] calculated intra-household food allocation in her study that demonstrated that female children come third in line after the male adult and child members of the family when being fed. Some studies note that the disadvantage of males in linear growth is more prominent in lower socioeconomic levels, and also dispel the behavioural pattern preferring females, suggesting that the biological and evolutionary preference is more likely [3]. Bork and Diallo [52] noted that the sex differences in height for age varied widely according to the growth reference chosen, and so could actually be an artifact in part.
Stunting was more likely to occur after two years of age, a finding that is supported by previous studies and considered to be related to recurrent infections at this age, the introduction of inadequate complementary feeds, weaning and the unhygienic preparation of food that could further expose children to infections [22, 49, 51, 56]. Around the second year of life is when children are introduced to the family diet and become more responsible for their own feeding but may not find adequate amounts of solid food [51]. As the UNICEF framework suggests, these factors relate back to the household’s wealth index and caregivers’ education.
The study showed that the odds of a child living in a rural area being stunted were around 1.3 times higher than those of a child living in an urban area. This finding corroborates that of various studies that confirm higher prevalence of stunting in rural areas [12, 37, 28]. In Sudan, 28.7% of rural dwellers fall in the lowest wealth index quintile, compared to 9.9% of urban dwellers [43]. This is a stark contrast to the fact that 53% of out-of-pocket (OOP) expenditure is spent by rural dwellers on healthcare. Even so, urban dwellers use health services – mainly secondary and tertiary level – 60% more than rural dwellers.
Rural dwellers are also at a disadvantage in education compared to urban; the literacy rate is 58.4% in the former compared to 80.5% in the latter. Several factors impede improvement in areas of education and healthcare, mainly cultural beliefs; tribal chiefs and religious leaders play an important role in influencing demand and access, especially that of women and children. The Gender Parity Index for girls at secondary level living in rural areas is 0.82 compared to boys, as opposed to 1.08 overall [57].
Childhood stunting is a result of a number of intertwined factors, the most important of which is the socioeconomic environment the child is born into. These environmental factors can and should be reversed through integrated, cross-sectoral policy interventions rather than focusing solely on nutritional interventions. Efforts to combat childhood stunting should go hand in hand with poverty reduction strategies, improving access to education especially for girls and women, and improving housing, water and sanitation.
Acknowledgements
The authors would like to acknowledge Dr Maisa Elfadul and Ms Nahid Abdelgadir for their support in informing the data analysis.
Author contribution
RG conducted literature search, data analysis, manuscript preparation and final approval. AMM contributed to concept, data analysis, manuscript review and final approval. SMM contributed to manuscript review and final approval.
Data availability statement
The MICS data is publicly available online by request from UNICEF at https://mics.unicef.org/surveys
Competing Interests Statement
The authors declare no competing interests.