Prevalence of childhood stunting and determinants in low and lower-middle income African countries: Evidence from standard demographic and health survey

Tadesse Tarik Tamir; Soliyana Adisu Gezhegn; Dejen Tegegne Dagnew; Abebe Tilahun Mekonenne; Genetu Tadese Aweke; Ayenew Molla Lakew

doi:10.1371/journal.pone.0302212

. 2024 Apr 25;19(4):e0302212. doi: 10.1371/journal.pone.0302212

Prevalence of childhood stunting and determinants in low and lower-middle income African countries: Evidence from standard demographic and health survey

Tadesse Tarik Tamir ^1,^*, Soliyana Adisu Gezhegn ², Dejen Tegegne Dagnew ², Abebe Tilahun Mekonenne ², Genetu Tadese Aweke ², Ayenew Molla Lakew ³

Editor: Satyajit Kundu⁴

PMCID: PMC11045052 PMID: 38662745

Abstract

Introduction

Undernutrition poses a significant global public health challenge, adversely affecting childhood cognitive and physical development while increasing the risk of disease and mortality. Stunting, characterized by impaired growth and development in children due to insufficient psychological stimulation, frequent infections, and inadequate nutrition, remains a critical issue. Although economic growth alone cannot fully address the prevalence of stunting, there exists a robust correlation between a country’s income level and childhood stunting rates. Countries with higher incomes tend to have lower rates of childhood stunting. Notably, while childhood stunting is declining worldwide, it remains persistent in Africa. Consequently, this study aims to assess the prevalence of childhood stunting and its determinants in low- and lower-middle-income African countries

Method

This study conducted a secondary analysis of standard demographic and health surveys in low- and lower-middle-income African countries spanning the period from 2010 to 2022. The analysis included a total sample of 204,214 weighted children under the age of five years. To identify the determinants of stunting, we employed a multilevel mixed-effect model, considering the three levels of variables. The measures of association (fixed effect) were determined using the adjusted odds ratio at a 95% confidence interval. Significance was declared when the association between the outcome variable and the explanatory variable had a p-value less than 0.05.

Result

In low and lower-middle-income African countries, 31.28% of children under five years old experience stunting, with a 95% confidence interval ranging from 31.08% to 31.48%. The results from a multilevel mixed-effect analysis revealed that 24 months or more of age of child, male gender, low and high birth weight, low and high maternal BMI, no and low maternal education, low household wealth index, multiple (twin or triplet) births, rural residence, and low income of countries were significantly associated with childhood stunting.

Conclusion

Stunting among children under five years of age in low- and lower-middle-income African countries was relatively high. Individual, community, and country-level factors were statistically associated with childhood stunting. Equally importantly, with child, maternal, and community factors of stunting, the income of countries needs to be considered in providing nutritional interventions to mitigate childhood stunting in Africa.

Introduction

Undernutrition is a major global public health concern affecting children under the age of five, since it delays their cognitive and physical development and raises their risk of disease and mortality [1]. Stunting is impaired growth and development that children experience due to inadequate psychological stimulation, frequent infections, and poor nutrition [2]. A child is deemed stunted if his or her height for age is more than two standard deviations below the WHO Child Growth Standards median [2]. The prevalence of stunting among children under 5 years of age is defined as the percentage of children whose height-for-age is more than two standard deviations below the median of the World Health Organization’s (WHO) Child Growth Standards [3].

In 2020, there were 149.2 million stunted children under the age of five worldwide [4]. Stunting has decreased, from 40% of children under five worldwide in 1990 to 22% (144 million children) in 2021 [4]. It accounts for 45% of mortality in children under the age of five in low and middle income countries including in Africa [5]. According to the WHO, the current prevalence of stunting among children under five years of age in Africa is estimated to be 30.80% [3].

The Sustainable Development Goal (SDG) 2.2 aimed to end all forms of malnutrition, including stunting and wasting in children under 5 years of age, and address the nutritional needs of adolescent girls, pregnant and lactating women, and older persons by 2030 [6]. However, it appears the target will be hardly attained in economically disadvantaged countries in Africa unless a bunch of strategies and interventions are implemented [4]. Various policies and strategies have been implemented in Africa to address the SDGs. Notably, the World Health Organization (WHO) member states have adopted a strategic plan to reduce malnutrition in Africa [7–9]. This plan prioritizes several key interventions, including reinforcing legislation and food safety standards, using fiscal measures to promote healthy food choices, and integrating essential nutrition actions into health service delivery platforms. Additionally, the Africa Regional Nutrition Strategy 2015–2025, developed by the African Union (AU), underscores the significance of combating chronic undernutrition (stunting) as a primary objective for nutrition interventions [10–12]. The strategy acknowledges the crucial connection between childhood stunting and the increasing prevalence of obesity, hypertension, and other non-communicable diseases across many African nations. It advocates evidence-based interventions and cost-effective development approaches.

As per studies [13–17], male gender, child age, multiple births, low birth weight, low maternal educational level, low maternal body mass index, poor household wealth, high illiteracy rate, maternal occupation, household income, antenatal care service utilization, and source of water were among the individual and community factors registered to have contributed to childhood stunting. This study included levels of factors at the country level, considering inter-country variation in stunting.

Though economic growth alone is not enough to reduce the prevalence of stunting, there is a strong association between the income of a country and childhood stunting [18, 19]. Countries with higher incomes have lower rates of childhood stunting [18, 19]. There is a decline in the number of stunted children throughout the globe but in Africa [4]. Hence, this study aimed to assess the prevalence of childhood stunting and its determinants in low and lower-middle income African countries.

Method

Data source, setting and sampling

This study conducted a secondary analysis of standard demographic and health surveys in low and lower-middle-income African countries spanning the period from 2010 to 2022. The study used the children’s recode datasets, which were accessed from the Monitoring and Evaluation to Assess and Use Results Demographic and Health Survey (MEASURE DHS) program and are available in the public domain at https://www.dhsprogram.com. The MEASURE DHS provides researchers with data for formal requests for registration and submission of the proposed projects. A total of 32 low- and lower-middle-income African countries were selected for the study based on the availability of the DHS between 2010 and 2022 and the availability of WHO height for the age of the child Growth Standard score in their DHS data. The DHS data are collected using two-stage stratified sampling. The first stage is the selection of enumeration areas, also called sampling clusters, and the second stage is the selection of a sample of households in the clusters, where actual data are collected. Hence, DHS data are hierarchical (at two levels). A total sample of 204,214 weighted children under the age of five years was included in the analysis of this study.

Variables

The outcome variable of the study was stunting among children under five years of age. Stunting was categorized using a binary code: 1 for stunted children and 0 for non-stunted children. Children were considered stunted if their height-for-age measurement fell more than two standard deviations below the WHO Child Growth Standards median [2].

The selection of explanatory variables for this study was guided by established principles and scholarly references [20]. Specifically, we conducted an extensive literature review, developed a theoretical framework, and considered data availability when choosing these variables [13–17, 20].

The explanatory variables of this study were grouped into three levels. At the first level, individual-level variables such as child age, gender, birth weight, maternal body mass index (BMI), maternal age, maternal education, maternal occupation, household wealth index, antenatal care (ANC) visit, place of delivery, media exposure, type of gestation, and source of water were included. At the second level, community-level variables such as distance to health facilities, residence, community women’s literacy, community media utilization, and community poverty were included. At the third level, country-level variables such as fertility rate, literacy rate, and income level were incorporated into the study (Fig 1). So the DHS data used for this study were of different years; the year of the surveys was used as a control variable to avoid the differential time effect.

Data cleaning and management

The archive of children’s recode data in Stata format for all countries was extracted from the Measure DHS website, https://www.dhsprogram.com. The data cleaning process of this study involved several key steps. First, we removed irrelevant data by identifying and excluding any variables or observations unrelated to stunting, maternal health, nutrition, and other relevant factors. Next, we checked for duplicate records within the dataset, eliminating any repeated observations to prevent bias in our analysis. Third, we followed the DHS guide to handle missing data. Fourth, we ensured consistency by inspecting variable names and maintaining uniformity across the dataset. With clean data, we proceeded to describe frequencies and rates using tables and charts. Finally, we performed statistical modeling, exploring associations between stunting and associated factors. These steps were essential to ensuring the reliability and validity of subsequent analyses. Weighted frequencies and percentages were presented in tables and charts.

The regression model

Considering the three levels of variables used, this study applied a multilevel mixed effect model to identify determinants of stunting among children under the age of five years. Accordingly, five mixed-effect models were fitted. First, the null model (model 0), a model with outcome and no explanatory variables; second, model I, a model with an outcome and individual-level explanatory variables; third, model II, a model with an outcome and community-level explanatory variables; fourth, model III, a model with an outcome and country-level explanatory variables; and finally, model IV, a model with an outcome variable and all three levels of variables.

The equation for a three-level multilevel mixed effect regression, a statistical model that accounts for the hierarchical structure of data with three levels, can be written as [21, 22]:

y i j k ​ = β 0 k ​ + β 1 k ​ x i j k ​ + r i j k ​ + u i j k ​ + e i j k ​

Where yijk is the dependent variable for the i^th individual in the j^th community in the k^th country, β0k and β1k are the intercept and slope coefficients for the k^th country, xijk is the independent variable for the i^th individual in the j^th community in the k^th country, rijk is the random effect for the j^th community in the k^th country, uijk is the random effect for the i^th individual in the j^th community in the k^th country and eijk is the residual error for the i^th individual in the j^th community in the k^th country.

In our nested model with three levels, we operate under the following assumptions: Variations at different levels are intricately linked to the levels above them. At the foundation, Level 1, individual observations or measurements—variations in stunting among children—serve as the fundamental building blocks for our entire model. As we ascend to Level 2 (communities), we encounter community-level variations influenced by correlations between individuals within the same community. These community-level dynamics, in turn, reverberate upward to impact the highest level (Level 3), which represents countries. Importantly, our model explicitly accounts for the hierarchical structure inherent in the data: individuals nest within communities, and communities nest within countries.

The random effect and fixed effect components of the mixed effect model were assessed. The parameters variance, intra-class correlation coefficient, and median odds ratio were used to assess measures of variation (random effect). The MOR, and ICC were determined by the following equations [23]; $M O R = e^{0.95 \sqrt{V C}}$ and $I C C = \frac{V C}{V C + 3.29} \times 100 %$ , where; VC = variance of the cluster for respective model.

The measures of association (fixed effect) were determined using the adjusted odds ratio at a 95% confidence interval. The significance of the association between the outcome variable and the explanatory variable was declared at a p-value less than the level of significance (0.05). By considering the nested nature of multilevel mixed effects, model comparisons were made using deviance (-2LL).

Ethical consideration and approval

This study was based on analysis of existing survey datasets in the public domain that are freely available online with all the identifier information anonymized, no ethical approval was required. The first author was obtained authorization for the download and usage of the DHS dataset of all countries included in the analysis from MEASURE DHS.

Results

A total of 204,214 (50.54% males and 49.46% females) weighted children under the age of five years were included in the analysis of the study. More than half (57.27%) of children were aged 24–59 months. About half (51.67%) of the subjects were born low-birth-weighted. Importantly, 62.06% of the subjects were born to mothers with a normal body mass index. The majority (71.04%) of the subjects were born to mothers aged 20–34 years. More than two-thirds (69.24%) of children were residents of rural areas, and a bit more than half (51.56%) of the subjects were from low-income countries (Table 1).

Table 1. Description of childhood stunting by level of variables in low and lower-middle income African countries (N = 204,214).

Variables at respective levels		Frequency (Percent)	Childhood stunting
Variables at respective levels		N (%)	Stunted [N (%)]	Not stunted [N (%)]
Individual level variables
Child age	0–23 months	60,743 (42.73)	15,789 (25.99)	44,954 (74.01)
Child age	24–59 months	81,417 (57.27)	27,592 (33.89)	53,825 (66.11)
Gender	Male	103,216 (50.54)	34,366 (33.30)	68,850 (66.70)
Gender	Female	100,998 (49.46)	29,225 (28.94)	71,773 (71.06)
Birth weight	Low	97,110 (51.67)	36,406 (37.49)	60,705 (62.51)
	Normal	77,551 (41.26)	20,733 (26.73)	56,818 (73.27)
	High	13,273 (7.06)	3,277 (24.69)	9,996 (75.31)
Maternal BMI	Low	15,855 (9.48)	6,592 (41.58)	9,263 (58.42)
	Normal	103,781 (62.06)	36,142 (34.83)	67,639 (65.17)
	High	47,602 (28.46)	10,255 (21.54)	37,347 (78.46)
Maternal age	15–19	11,499 (5.63)	3,699 (32.17)	7,800 (67.83)
	20–34	145,079 (71.04)	44,891 (30.94)	100,188 (69.06)
	35–49	47,635 (23.33)	15,001 (31.49)	32,635 (68.51)
Maternal educational status	No education	80,533 (39.44)	30,006 (37.26)	50,527 (62.74)
	Primary	61,869 (30.30)	20,574 (33.25)	41,295 (66.75)
	Secondary	52,142 (25.53)	11,809 (22.65)	40,334 (77.35)
	Higher	9,669 (4.73)	1,203 (12.44)	8,466 (87.56)
Maternal occupation	Not working	92,225 (45.23)	27,609 (29.94)	64,615 (70.06)
Maternal occupation	Working	111,688 (54.77)	35,863 (32.11)	75,825 (67.89)
Wealth index	Poorest	45,457 (22.26)	17,516 (38.53)	27,940 (61.47)
	Poorer	43,168 (21.14)	15,540 (36.00)	27,628 (64.00)
	Middle	41,735 (20.44)	13,104 (31.40)	28,631 (68.60)
	Richer	39,734 (19.46)	11,038 (27.78)	28,695 (72.22)
	Richest	34,120 (16.71)	6,393 (18.74)	27,727 (81.26)
ANC visits	No visits	16,902 (11.92)	6,812 (40.31)	10,089 (59.69)
	1–3 visits	42,679 (30.1)	13,823 (32.39)	28,856 (67.61)
	4 or more	82,226 (57.98)	20,825 (25.33)	61,401 (74.67)
Place of delivery	Home	65,186 (33.85)	26,051 (39.96)	39,135 (60.04)
Place of delivery	Health facility	127,370 (66.15)	35,242 (27.67)	92,128 (72.33)
Media exposure	Yes	129,174 (63.38)	34188 (26.47)	94986 (73.53)
Media exposure	No	74,624 (36.62)	29235 (39.18)	45389 (60.82)
Type of gestation	Singleton	197793 (96.86)	60714 (30.70)	137079 (69.30)
Type of gestation	Multiple	6420 (3.14)	2877 (44.81)	3543 (55.19)
Source of water	Improved	68381 (33.49)	16975 (24.82)	51407 (75.18)
Source of water	Unimproved	135812 (66.51)	46,611 (34.32)	89,201 (65.68)
Community level variables
Distance to health facility	Not big problem	111,194 (62.65)	31,903 (28.69)	79,291 (71.31)
Distance to health facility	Big problem	66293 (37.35)	22754 (34.32)	43539 (65.68)
Residence	Urban	62,814 (30.76)	14378 (22.89)	48436 (77.11)
Residence	Rural	141,400 (69.24)	49,213 (34.80)	92,187 (65.20)
Community women literacy	Low	120,191 (58.86)	40389 (33.60)	79802 (66.40)
Community women literacy	High	84,023 (41.14)	23,202 (27.61)	60,821 (72.39)
Community media utilization	Low	112,913 (55.29)	38,181 (33.81)	74,732 (66.19)
Community media utilization	High	91,300 (44.71)	25,410 (27.83)	65,890 (72.17)
Community poverty	Low	98220 (48.15)	29053 (29.58)	69167 (70.42)
Community poverty	High	105,768 (51.85)	34488 (32.61)	71280 (67.39)
Country level variables
Fertility rate	Low	136223 (66.71)	39413 (28.93)	96810 (71.07)
Fertility rate	high	67990 (33.29)	24,178 (35.56)	43,812 (64.44)
Literacy rate	Low	98283 (48.13)	31639 (32.19)	66644 (67.81)
Literacy rate	High	105,930 (51.87)	31,952 (30.16)	73,978 (69.84)
Income	Low	105286 (51.56)	37398 (35.52)	67888 (64.48)
Income	Lower middle	98928 (48.44)	26193 (26.48)	72734 (73.52)

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ANC: Antenatal care, BMI: Body mass index

Out of 103,216 male children, one-third (33.30%) were stunted. By place of residence, 34.80% of rural and 22.89% of urban resident children were stunted. In addition, 35.52% of children with low income and 26.48% with lower-middle income were stunted (Table 1).

Prevalence of childhood stunting in low and lower-middle income African countries

The pooled prevalence of childhood stunting in low and lower-middle-income African countries was found to be 31.28% at 95% CI (31.08, 31.48). The prevalence was higher (54.51%) in Burundi and lower (17.92%) in Kenya (Fig 2).

Random effect and model comparison

The value of variance in the null model of the random effect shows that there was variation in childhood stunting among countries (τ = 0.51, p<0.001) and communities (τ = 0.58, p<0.001). About 13% and 15% of the total variation in childhood stunting was attributed to variation across countries (13.41%) and across communities (ICC = 14.99%), respectively. The variation of stunting among countries and communities (clusters) remains significant even after all levels of variables were fitted to it. The unexplained heterogeneity (MOR in the null model) in stunting among countries and communities was 1.97 and 2.06, respectively, without fitting any explanatory variables. The heterogeneity was reduced to 1.51 among countries and 1.54 among communities after all levels of the explanatory variables were fitted (Table 2).

Table 2. Random effect and model comparison of childhood stunting in low and lower-middle income African countries.

Parameters		Null Model	Model I	Model II	Model III	Model IV
Random effect
Variance (τ)	Country	0.51 ^**	0.20 ^**	0.32^**	0.41^**	0.19 ^*
Variance (τ)	Community	0.58 ^**	0.22 ^**	0.38 ^**	0.35^**	0.21 ^*
ICC (%)	Country	13.42	5.73	8.86	11.08	5.45
ICC (%)	Community	14.99	6.27	10.35	9.61	6.00
MOR	Country	1.97	1.53	1.71	1.84	1.51
MOR	Community	2.06	1.56	1.80	1.75	1.54
Model comparison
Log likelihood		-127704.78	-41044.25	-108122.77	-126199.46	-40905.41
Deviance		255,409.56	82,088.50	216,245.54	252,398.92	81,810.82

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ICC: intra-class correlation coefficient, MOR: Median Odds Ratio

**: p<0.001

*: p<0.05.

Regarding the model comparison, model IV, the model with a large log likelihood and small deviance values was selected as the best fit (Table 2).

Fixed effect of childhood stunting in low and lower-middle income African countries

The multilevel mixed effect analysis of this study revealed that age of child, gender, birth weight, maternal BMI, maternal education, wealth index, type of gestation, place of residence, and income level of countries were significantly associated with childhood stunting.

At the individual level, At the individual level, the odds of stunting were 1.76 times as high among children aged 24–59 months (AOR = 1.76 at 95% CI: 1.70, 1.83) compared to children aged less than 24 months. The odds of stunting were 1.37 times as high among male children (AOR = 1.37 at 95% CI: 1.32, 1.42) compared to females. On the one hand, the odds of stunting were 1.38 times as high among children with low birth weight (AOR = 1.38 at 95% CI: 1.31, 1.43); on the other hand, the odds of stunting were reduced by 33% for children with high birth weight (AOR = 0.77 at 95% CI: 0.72, 0.83) when compared to children with normal birth weight. Similarly, the odds of stunting were 1.26 times as high among children born to mothers with low BMI (AOR = 1.26 at 95% CI: 1.19, 1.33) and reduced by 30% for children born to mothers with high BMI (AOR = 0.70 at 95% CI: 0.67, 0.73), compared to children born to mothers with normal BMI. The odds of stunting were 2.30, 2.08, and 1.68 times as high among children born to mothers with no formal education (AOR = 2.30 at 95% CI: 2.03, 2.60), primary schooling (AOR = 2.08 at 95% CI: 1.84, 2.35), and secondary schooling (AOR = 1.68 at 95% CI: 1.49, 1.90) as compared to children born to mothers with a higher level of education. Compared to children of households with the richest wealth index, the odds of stunting were 1.65, 1.64, 1.44, and 1.29 times as high for children of households with the poorest (AOR = 1.65 at 95% CI: 1.53, 1.77), poorer (AOR = 1.64 at 95% CI: 1.52, 1.76), middle (AOR = 1.44 at 95% CI: 1.34, 1.55), and richer (AOR = 1.29 at 95% CI: 1.21, 1.38) wealth indexes. The odds of stunting were 2.60 (AOR = 2.60 at 95% CI: 2.30, 2.93) times higher among children born multiple compared to children born singleton.

At the community level, the odds of stunting were increased by 10% among rural resident children (AOR = 1.10 at 95% CI: 1.04, 1.15) compared to urban resident children.

At the country level, the odds of childhood stunting were increased by 5% in low-income African countries (AOR = 1.05 at 95% CI: 1.01, 1.09) compared to lower-middle-income African countries (Table 3).

Table 3. Fixed effect of childhood stunting in low and lower-middle income African countries.

Factors at respective levels		Model I	Model II	Model III	Model IV
Factors at respective levels		AOR (95% CI)	AOR (95% CI)	AOR (95% CI)	AOR (95% CI)
Survey year	2010–2014	1	1		1
	2015–2019	1.18 (1.10, 1.25)	1.07 (1.04, 1.09)	1.11 (1.09, 1.14)	1.08 (0.98, 1.16)
	2020–2022	0.86 (0.80, 0.92)	0.72 (0.70, 0.74)	0.72 (0.70, 0.74)	0.76 (0.71, 0.82)
Child age	0–23 months	1			1
Child age	24–59 months	1.79 (1.73, 1.85)			1.76 (1.70, 1.83)^*
Gender	Male	1.36 (1.32, 1.41)			1.37 (1.32, 1.42)^*
Gender	Female	1			1
Birth weight	Low	1.30 (1.24, 1.36)			1.38 (1.31, 1.43)^*
	Normal	1			1
	High	0.78 (0.73, 0.84)			0.77 (0.72, 0.83)^*
Maternal BMI	Low	1.27 (1.21, 1.35)			1.26 (1.19, 1.33)^*
	Normal	1			1
	High	0.67 (0.64, 0.70)			0.70 (0.67, 0.73)^*
Maternal age	15–19	1.09 (1.02, 1.16)			1.05 (0.93, 1.17)
	20–34	1			1
	35–49	0.99 (0.95, 1.03)			0.99 (0.94, 1.04)
Maternal educational status	No education	2.15 (1.90, 2.43)			2.30 (2.03, 2.60)^*
	Primary	2.20 (1.94, 2.48)			2.08 (1.84, 2.35)^*
	Secondary	1.71 (1.51, 1.93)			1.68 (1.49, 1.90)^*
	Higher	1			1
Maternal occupation	Not working	1			1
Maternal occupation	Working	1.16 (0.91, 1.20)			1.14 (0.90, 1.18)
Wealth index	Poorest	1.70 (1.59, 1.82)			1.65 (1.53, 1.77)^*
	Poorer	1.69 (1.58, 1.81)			1.64 (1.52, 1.76)^*
	Middle	1.47 (1.38, 1.57)			1.44 (1.34, 1.55)^*
	Richer	1.30 (1.22, 1.39)			1.29 (1.21, 1.38)^*
	Richest	1			1
ANC visits	No visits	1.01 (0.81, 1.25)			1.02 (0.79, 1.23)
	1–3 visits	1.03 (0.93, 1.18)			1.00 (0.92, 1.16)
	4 or more	1			1
Place of delivery	Home	1.01 (0.97, 1.06)			1.00 (0.96, 1.05)
Place of delivery	Health facility	1			1
Media exposure	Yes	1			1
Media exposure	No	1.12 (0.82, 1.17)			1.09 (0.79, 1.13)
Type of gestation	Singleton	1			1
Type of gestation	Multiple	2.56 (2.28, 2.89)			2.60 (2.30, 2.93)^*
Source of water	Improved	1			1
Source of water	Unimproved	0.99 (0.95, 1.03)			0.97 (0.93, 1.01)
Distance to health facility	Not big problem		1		1
Distance to health facility	Big problem		1.14 (1.11, 1.16)		0.95 (0.92, 1.02)
Residence	Urban		1		1
Residence	Rural		1.66 (1.62, 1.70)		1.10 (1.04, 1.15)^*
Community women literacy	Low		1.24 (1.18, 1.31)		1.03 (0.98, 1.07)
Community women literacy	High		1		1
Community media utilization	Low		1.36 (1.29, 1.43)		1.02 (0.98, 1.07)
Community media utilization	High		1		1
Community poverty	Low		1		1
Community poverty	High		0.94 (0.90, 1.02)		0.96 (0.92, 1.04)
Fertility rate	Low			1.26 (1.23, 1.28)	0.99 (0.95, 1.04)
Fertility rate	high
Literacy rate	Low			0.99 (0.97, 1.01)	0.85 (0.72, 1.08)
Literacy rate	High	1		1	1
Income	Low			1.39 (1.36, 1.42)	1.05 (1.01, 1.09)^*
Income	Lower middle			1	1

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ANC: Antenatal Care, AOR: Adjusted Odds Ration, BMI: Body mass index, CI: Confidence Interval

*: Statistically significant (p-value<0.05).

Discussion

Although economic growth alone is not enough to reduce the prevalence of stunting, there is a strong association between the income of a country and childhood stunting [18, 19]. Countries with higher incomes have lower rates of childhood stunting [18, 19]. There is a decline in the number of stunted children throughout the globe but in Africa [4]. Thus, this study revealed the prevalence of childhood stunting and its determinants in low and lower-middle income African countries.

The prevalence of stunting among children under five years of age in low and lower-middle-income African countries was 31.28% at a 95% CI (31.08, 31.48). The prevalence of stunting in this study was higher than the WHO estimated prevalence of stunting among children under five years of age in Africa, which is 30.80% [3]. The higher prevalence of stunting in this study than the WHO estimate could be due to the fact that this study included only low- and lower-middle-income countries, while the WHO estimated stunting in Africa regardless of the level of income of the countries (in low-, lower-middle-, upper-middle-, and high-income countries). In addition, the confluence of instability, climate-related challenges, and economic hardships in various regions of Africa significantly contributes to the high prevalence of stunting among children in low and lower-middle income African countries [8, 24]. These intertwined factors collectively impact child growth and development, creating a formidable barrier to optimal health and well-being [8]. Thus, childhood stunting in low and lower-middle-income African countries is high, and the interventions that can mitigate the effects of stunting during pregnancy and early childhood are found to be critical.

Consistent with previous studies [25–27], the age of a child was significantly associated with stunting. The odds of stunting were high among babies aged 24 or more months compared to children aged less than 24 months. The reason behind the higher odds of stunting among older children than younger babies could be related to the longstanding feature of stunting. Stunting is a long-term condition that develops over a long period of time, and it is hard to reverse once it has happened [28]. Consequently, stunting is more common in older children who have experienced malnutrition for a longer period of time than in younger infants who have not [28].

This study found that the odds of stunting were higher among male children than females. Surprisingly, several previous studies have also witnessed the same [27, 29–31]. The reason behind gender-based inequalities in childhood undernutrition is not well understood. However, as a consequence of their higher birth weight than females, male children require more calories, which increases the risk that they will have undernutrition [32, 33]. Moreover, it has been suggested that male children are more prone to the three nutrient deficiencies (stunting, wasting, and underweight) due to their increased hunger than female children; therefore, breastfeeding alone may not be adequate for them [34, 35].

The abnormal birth weight was a significant determinant of childhood stunting. On the one hand, the odds of stunting were higher among children with low birth weight and on the other hand, the odds of stunting were lower among children with high birth weight. This was in agreement with previous findings [36–38]. Low birth weight is a common outcome of pregnancy-related malnutrition, which can impede the fetus’s growth and development [39]. This can lead to a child being born with a smaller size and lower weight than normal, which raises their risk of stunting later in life [37, 39]. High-birth-weight babies are more likely to be overnourished than undernourished, and it has been shown that high birth weight is progressive throughout childhood and is a possible cause of obesity in adulthood [40]. These findings show that certain characteristics of childhood undernutrition and subsequent linear development have antecedents in the prenatal period, and they emphasize the significance of maternal nutrition both preconceptionally and antenatally [39, 41–43].

According to this study, low and high maternal BMI were significantly associated with high and low odds of childhood stunting, respectively. This similar finding was also evidenced by previous studies [44, 45]. The reason for the consistent association between maternal BMI and child anthropometric failures may be the intrauterine intergenerational transmission of low maternal BMI during pregnancy, which puts infants at risk of low birth weight and small stature for gestational age and forms the fetal origins of subsequent childhood undernutrition [43, 46].

In line with previous scientific evidences [47–49], maternal education was significantly associated with childhood stunting. Accordingly, the odds of stunting were higher among children born to mothers with no formal education, primary schooling and secondary schooling as compared to children born to mothers with higher level of education. The association of maternal education with childhood stunting was attributable to the health and growth benefits that accompany increased resources and occupational standing among educated mothers [48]. In addition, maternal education improves the ability of the mother to foster a healthy environment for the child’s intrauterine growth [48].

Importantly, the odds of stunting increased with a decrease in the household wealth quintile. Compared to children of households with the richest wealth index, the odds of stunting were higher for children of households with the richer, middle, poorer, and poorest wealth indexes. This finding was in agreement with existing evidences [50–52]. It is plausible that children from low-income households are more likely to have growth failure due to poor nutrition, a higher risk of illness, and difficulties accessing basic health services [53].

Regarding the type of gestation, the odds of stunting were higher among children born multiple (twin or triplet) compared to children born singleton. This finding was consistent with previous evidence [52, 54]. This could be due to the fact that low birth weight and competition for nutrition, which is common among twins or triplets, lead to stunting later in childhood [55].

When compared to children in urban areas, the odds of stunting were high for children in rural areas. This was in agreement with previous findings [56–58]. Access to appropriate health care is challenging for people living in rural areas, and they lack immediate interventions for acute undernutrition [59] among their babies, which can progress to chronic growth failure (stunting). Poor household expenditure, unhealthy snacks, and poor sanitation, which are more common in rural areas, can contribute to stunting among children of rural residents compared to urban ones [56].

Equally importantly, this study found that low income level of countries was significantly associated with higher odds of childhood stunting compared to lower-middle income countries. This was in line with finding from another study [18]. The low income level of a country can lead to stunting among children due to a lack of resources and access to basic needs such as food, water, sanitation, and healthcare [19]. Investing in childhood nutrition and providing access to basic needs can help reduce stunting and expand the economic opportunities of children [2, 19].

The findings of this study should be regarded with the following strengths and limitations: The use of nationally representative DHS data and the application of an appropriate advanced model were virtues of this study. However, due to the cross-sectional nature of the surveys, the associations between independent variables and the outcome ascertained by this study cannot show causal associations.

Conclusion

Supporting information

S1 Checklist. Human participants research checklist.

(DOCX)

pone.0302212.s001.docx^{(52.6KB, docx)}

Data Availability

The data used for analysis of the current study are available publicly online at the MEASURE DHS program [https://www.dhsprogram.com/data/available-datasets.cfm].

Funding Statement

The author(s) received no specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0302212.r001

Decision Letter 0

Satyajit Kundu

17 Mar 2024

PONE-D-24-04373Prevalence of Childhood Stunting and Determinants in Low and Lower-Middle Income African Countries: Evidence from Standard Demographic and Health SurveyPLOS ONE

Dear Dr. Tamir,

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Reviewer #3: No

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Reviewer #1: This study aimed to assess the prevalence of childhood stunting and its determinants in low-and lower-middle-income African countries. Studying stunting in developing countries is very important and I encourage authors to continue their good work. I have a few suggestions, however, to help improve the manuscript.

1. In the introduction, I will suggest the authors add some policies, strategies and interventions done in Africa to meet the SDG 2.2 by 2030 for stunting and undernutrition.

2. In the variables section, i will suggest the authors clearly state how stunting was operationally defined in the DHS dataset and the responses and not just give how coding was done.

3. The authors should please provide references for what informed the explanatory variables.

4. In the discussion section, I will suggest the authors give reasons with references for the high prevalence of stunting in low-and lower-middle-income African countries and not just compare with the WHO findings.

Reviewer #2: The authors have done a comprehensive analysis of available standard demographic datasets to investigate the prevalence of childhood stunting throughout Africa and and its determinants in African countries. I think the findings will be really helpful to adopt continent-wide policy making by governments and international agencies. I hope this article will add value to the public health related literatures on low and middle income African countries.

Reviewer #3: I would like to thank the editor for giving me opportunity to review this article. Here are few points I am concerned about.

Methodology

1. The wording of second line in section data source, setting and sampling seems need correction.

2. Mentioning how researchers came with those potential predictors in section variables? Referencing again basically. I believe they are 7-11.

3. The description provided for the expression of the generic model presented in section The Regression Model need amendments? Basically, correcting subscripts.

4. In the regression model what assumptions were made? I see three random terms there. What distribution they have? Is r_jk independent of u_ik?

5. Since sample size is large will considering a 0.01 level make sense? So, 99% CI needs to be calculated instead of 95% CI.

6. Did authors do data cleaning? If so, what was their procedure?

Results

7. With table 2 researchers selected model IV. Is there a need for mentioning results from other models in table 3?

8. After considering 0.01 level, I believe there will more variables that will be insignificant. In that case will keeping only the significant variables in the model make sense?

9. If authors do not agree with 0.01 level, will keeping only significant variable in the model make sense?

10. “At the individual level, the odds of stunting were 1.76 times higher among children aged 24-59 months (AOR = 1.76 at 95% CI: 1.70, 1.83) compared to children aged less than 24 months.” In my opinion, this is not correct. The correct interpretation would be “At the individual level, the odds of stunting were 1.76 times as high among children aged 24-59 months (AOR = 1.76 at 95% CI: 1.70, 1.83) compared to children aged less than 24 months.” If authors agree, please change rest of the interpretations in this way.

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PLoS One. 2024 Apr 25;19(4):e0302212. doi: 10.1371/journal.pone.0302212.r002

Author response to Decision Letter 0

20 Mar 2024

Response to Comments

Manuscript ID: PONE-D-24-04373

Title: Prevalence of Childhood Stunting and Determinants in Low and Lower-Middle Income African Countries: Evidence from Standard Demographic and Health Survey

Journal: PLOS ONE

Subject: Submission of revised manuscript

I hope this letter finds you well. We appreciate the diligent efforts of the editorial team in facilitating the review process for our manuscript. Additionally, we extend our gratitude to the reviewers for their valuable time and thoughtful feedback, which significantly contributed to enhancing the quality of our work.

The constructive comments provided by the reviewers have been instrumental in refining our study. We are pleased to note that the reviewers share our assessment of the scientific significance of our findings. In response to their suggestions, we have meticulously addressed each point raised. Please find our comprehensive responses to the comments below.

Furthermore, we have attached the revised manuscript file separately for your convenience. We believe that the revisions strengthen the manuscript and align it more closely with the journal’s scope and standards.

Thank you for considering our work for publication. We look forward to your feedback and hope that our revised submission meets the high standards set by PLOS ONE.

Best regards,

Tadesse Tarik Tamir, corresponding author (on behalf of all authors)

University of Gondar, Gondar, Ethiopia

Response to editors and reviewers’ and comments

Response: Dear reviewer, we sincerely appreciate your enthusiasm for our manuscript’s subject and hypotheses. Additionally, we value your detailed perspectives and insightful comments.

1. In the introduction, I will suggest the authors add some policies, strategies and interventions done in Africa to meet the SDG 2.2 by 2030 for stunting and undernutrition.

Response: Dear reviewer, thank you for your insightful suggestion. We incorporated policies, strategies, and interventions done in Africa for meeting SDG 2.2 by 2030. Kindly refer to the introduction section in our revised manuscript.

2. In the variables section, i will suggest the authors clearly state how stunting was operationally defined in the DHS dataset and the responses and not just give how coding was done.

Response: Dear reviewer, Thank you for your invaluable insights regarding the operational definition of the outcome variable. In response to your suggestion, we have clearly stated how the dependent variable was operationally defined. Kindly refer to the relevant section in our revised manuscript.

3. The authors should please provide references for what informed the explanatory variables.

Response: Dear reviewer, I sincerely appreciate your valuable insights regarding variable selection in our study. In response to your suggestion, we have included references that informed our decision-making process regarding explanatory variables. Please find these details in the variable section of our revised manuscript.

Response: Dear reviewer, I extend my gratitude for your insightful suggestion. In light of this, we have elucidated the reasons behind the high prevalence of stunting in low- and lower-middle-income African countries. Kindly find the point in our revised manuscript.

Response: Dear reviewer, we sincerely appreciate your enthusiasm for our manuscript’s subject and hypotheses. Additionally, we value your detailed perspectives and insightful comments.

Reviewer #3: I would like to thank the editor for giving me opportunity to review this article. Here are few points I am concerned about.

Response: Dear reviewer, we sincerely appreciate your enthusiasm for our manuscript’s subject and hypotheses. Additionally, we value your detailed perspectives and insightful comments.

Methodology

1. The wording of second line in section data source, setting and sampling seems need correction.

Response: Dear reviewer, I sincerely appreciate your valuable insights. In response to your feedback, we have diligently made the necessary corrections. I kindly invite you to refer to the relevant section in our revised manuscript for further details.

2. Mentioning how researchers came with those potential predictors in section variables? Referencing again basically. I believe they are 7-11.

Response: Dear reviewer, I sincerely appreciate your valuable insights regarding variable selection in our study. In response to your suggestion, we have included how we selected potential predictors and provided references that informed our decision-making process regarding explanatory variables. Please find these details in the variable section of our revised manuscript.

3. The description provided for the expression of the generic model presented in section The Regression Model need amendments? Basically, correcting subscripts.

Response: Dear reviewer, Thank you for your insightful feedback regarding the model formulation. We have carefully considered your point and made the necessary amendments to the model. Please refer to our revised manuscript for the specific details.

4. In the regression model what assumptions were made? I see three random terms there. What distribution they have? Is r_jk independent of u_ik?

Response: Dear reviewer, Thank you for your valuable scientific inquiry. In our nested model with three levels, we operate under the following assumptions: Variations at different levels are intricately linked to the levels above them. At the foundation, Level 1, individual observations or measurements—variations in stunting among children—serve as the fundamental building blocks for our entire model. As we ascend to Level 2 (communities), we encounter community-level variations influenced by correlations between individuals within the same community. These community-level dynamics, in turn, reverberate upward to impact the highest level (Level 3), which represents countries. Importantly, our model explicitly accounts for the hierarchical structure inherent in the data: individuals nest within communities, and communities nest within countries.

5. Since sample size is large will considering a 0.01 level make sense? So, 99% CI needs to be calculated instead of 95% CI.

Response: Dear reviewer, Thank you for your thoughtful question. While the sample size in our study is indeed large, it’s essential to consider the practical context. However, when compared to the total population of low and lower-middle income African countries, our sample may not be adequate to confidently report a 99% confidence interval (CI). In the literature, similar studies have consistently reported findings using a 95% CI, which is widely accepted as the standard. Therefore, we recommend adhering to the 95% CI for consistency and comparability.

6. Did authors do data cleaning? If so, what was their procedure?

Response: Dear reviewer, Thank you for your insightful feedback on our manuscript. We sincerely appreciate your thorough review and valuable suggestions. We have incorporated a detailed explanation of our data cleaning procedures into the revised methodology section of the manuscript. This update ensures transparency and allows readers to understand the steps taken to prepare the dataset for analysis. We believe that these enhancements significantly strengthen the scientific rigor of our study. Kindly refer to the data cleaning and management section of our revised manuscript.

Results

7. With table 2 researchers selected model IV. Is there a need for mentioning results from other models in table 3?

Response: Dear reviewer, Thank you for your thoughtful question. Including results from other models in Table 3 is valuable for contextual relevance, transparency, and guidance for readers. By presenting a comprehensive view of different modeling choices, we enhance the robustness of our findings and facilitate meaningful discussions. This approach aligns with standard reporting practices in the scientific literature.

(Refer: https://doi.org/10.1371/journal.pone.0295289, https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-019-0876-8)

8. After considering 0.01 level, I believe there will more variables that will be insignificant. In that case will keeping only the significant variables in the model make sense?

Response: Dear reviewer, Thank you for your insightful question. When considering a stricter significance level (0.01), it’s essential to evaluate the practical context. While some variables may become insignificant, we recommend assessing their theoretical relevance. If a non-significant variable has practical importance or contributes to interpretability, it may still be valuable to retain it. The principle of parsimony suggests balancing statistical rigor with meaningful predictors. Additionally, retaining significant variables at the standard 0.05 level is reasonable.

9. If authors do not agree with 0.01 level, will keeping only significant variable in the model make sense?

Response: Thank you for your inquiry. While retaining significant variables is standard practice, we must balance statistical rigor with practical relevance. If a non-significant variable contributes to understanding or aligns with existing literature, it may be valuable to retain it. Authors’ agreement on the significance level is crucial, considering that some fields tolerate a 0.05 level due to practical constraints. Transparent reasoning remains essential.

Response: Dear reviewer, Thank you for your scientifically sound and valuable corrections regarding the interpretation of our results. We have diligently addressed the points you raised and made the necessary revisions in our manuscript. Kindly refer to the same section in our revised version.

We sincerely thank the anonymous reviewers and the editor for their constructive comments and suggestions!

Attachment

Submitted filename: Rebutal letter.docx

pone.0302212.s002.docx^{(16.9KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0302212.r003

Decision Letter 1

Satyajit Kundu

1 Apr 2024

Prevalence of Childhood Stunting and Determinants in Low and Lower-Middle Income African Countries: Evidence from Standard Demographic and Health Survey

PONE-D-24-04373R1

Dear Dr. Tadesse Tarik Tamir,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Satyajit Kundu

Academic Editor

PLOS ONE

Reviewers' comments:

Reviewer #1: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

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6. Review Comments to the Author

Reviewer #1: Thank you to the authors for addressing all my comments and the changes made to the manuscript. I have no additional comments.

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Reviewer #1: No

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Checklist. Human participants research checklist.

(DOCX)

pone.0302212.s001.docx^{(52.6KB, docx)}

Attachment

Submitted filename: Rebutal letter.docx

pone.0302212.s002.docx^{(16.9KB, docx)}

Data Availability Statement

The data used for analysis of the current study are available publicly online at the MEASURE DHS program [https://www.dhsprogram.com/data/available-datasets.cfm].

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PERMALINK

Prevalence of childhood stunting and determinants in low and lower-middle income African countries: Evidence from standard demographic and health survey

Tadesse Tarik Tamir

Soliyana Adisu Gezhegn

Dejen Tegegne Dagnew

Abebe Tilahun Mekonenne

Genetu Tadese Aweke

Ayenew Molla Lakew

Roles

Abstract

Introduction

Method

Result

Conclusion

Introduction

Method

Data source, setting and sampling

Variables

Fig 1. Level of explanatory variables of the study.

Data cleaning and management

The regression model

Ethical consideration and approval

Results

Table 1. Description of childhood stunting by level of variables in low and lower-middle income African countries (N = 204,214).

Prevalence of childhood stunting in low and lower-middle income African countries

Fig 2. The prevalence of childhood stunting in low and lower-middle income African countries.

Random effect and model comparison

Table 2. Random effect and model comparison of childhood stunting in low and lower-middle income African countries.

Fixed effect of childhood stunting in low and lower-middle income African countries

Table 3. Fixed effect of childhood stunting in low and lower-middle income African countries.

Discussion

Conclusion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Satyajit Kundu

Roles

Author response to Decision Letter 0

Decision Letter 1

Satyajit Kundu

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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