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Childhood wasting and associated factors in Africa: evidence from standard demographic and health surveys from 35 countries

Abstract

Introduction

Child malnutrition remains a critical public health challenge globally. Childhood wasting, a severe form of malnutrition, indicates acute undernutrition, leading to significant loss of muscle and fat tissue. The World Health Organization’s Global Nutrition Target aims to reduce childhood wasting to less than 5% in over half of low- and middle-income countries by 2025. The enduring hunger crisis in Africa is a complex issue that demands our continuous commitment, innovative solutions, and coordinated efforts. This study aims to assess the prevalence and associated factors of childhood wasting in Africa.

Method

This study conducted a secondary analysis of demographic and health survey datasets from 2010 to 2022 in 35 African countries. A total of 212,715 children under the age of 5 years were included, using a weighted sample. We employed a mixed-effects model to evaluate the factors associated with childhood wasting. The significance of the fixed effects was assessed using the adjusted odds ratio (AOR) and the corresponding 95% confidence interval.

Result

The prevalence of childhood wasting in Africa was estimated to be 7.16% (95% CI: 7.05–7.27). Several factors were significantly associated with childhood wasting, including the child’s age, male gender, birth weight, maternal education level, wealth index, lack of antenatal care (ANC) visits, home delivery, multiple gestational births, and rural residence.

Conclusion

Childhood wasting in Africa exceeds the global target set for 2025, which aims to reduce it to less than 5%. To address this critical issue, educating mothers without formal education and rural residents about antenatal care visits, institutional delivery, and proper child feeding practices is essential. Moreover, a renewed focus on tackling the multifaceted factors of undernutrition, strengthening health systems, and implementing evidence-based interventions tailored to the local context is crucial for achieving meaningful and sustained reductions in wasting prevalence across the region.

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Introduction

Child malnutrition, a serious public health issue, is defined as a pathological condition caused by inadequate nutrition in the first 1000 days post conception [1]. It encompasses deficiency illnesses due to insufficient consumption of specific nutrients, as well as undernutrition (missing protein and energy) and overnutrition (obesity and being overweight) [1]. Acute malnutrition is a condition of low nutritional status brought on by an inadequate intake of calories or proteins [2]. Wasting, an acute form of malnutrition, is a sign that a child has experienced brief episodes of undernutrition, resulting in significant loss of muscle and fat tissue. Among under-five children, wasting is defined as weight for height less than − 2 standard deviation from the median of the World Health Organization (WHO) Child Growth Standards [2].

Undernourishment occurs when the body does not receive enough nutrients, which can impact growth and internal functions [3]. This can weaken the immune system, reducing its ability to defend the body against illnesses and affecting various areas of health [3]. Wasting is a debilitating condition that can have severe long-term consequences for a child’s health, cognitive development, and overall human capital formation [4, 5]. Undernourished children are at higher risk of morbidity and mortality, with wasting being a major contributor to nearly half of all deaths in children under 5 years old globally [6].

Globally, 45.4Ìýmillion children under five were affected by wasting in 2020 [7]. While the prevalence of wasting among children under 5 years of age in Africa decreased from 7.1% in 2010 to 6.9% in 2021 [6], this marginal decline masks considerable heterogeneity across the region. Several African countries, such as Chad, South Sudan, and Niger, have consistently reported wasting prevalence above the World Health Organization’s (WHO) ‘critical’ threshold of 15% [6]. These persistent challenges are often linked to protracted conflict, climate-related shocks, and limited access to essential health and nutrition services [6].

Previous research has identified several factors associated with childhood wasting, including socioeconomic status, maternal education, access to healthcare, sanitation, and food security [8,9,10,11]. However, gaps remain in our understanding of the evolving trends and contextual factors driving the persistence of this public health challenge across the African continent.

The Sustainable Development Goal (SDG) 2.2 aimed to eliminate all forms of malnutrition, including stunting and wasting in children under 5 years of age, as well as address the nutritional needs of adolescent girls, pregnant and lactating women, and older individuals by 2030 [12]. Additionally, the World Health Organization Global Nutrition has a target of reducing the prevalence of childhood wasting to less 5% in over half of low- and middle-income countries by 2025 [13]. Achieving this target in economically disadvantaged countries in Africa will likely require the implementation of a variety of strategies and interventions [14]. Policies and strategies have been put in place in Africa to address the SDGs, including the World Health Organization (WHO) member states’ adoption of a strategic plan to reduce malnutrition in the region [15,16,17]. This plan prioritizes several key interventions, including strengthening legislation and food safety standards, utilizing fiscal measures to promote healthy food choices, and integrating essential nutrition actions into health service delivery platforms.

Wasting is an acute form of undernutrition that demands urgent intervention due to its association with significantly higher mortality risk, particularly in young children. Studies indicate that children with wasting face up to a nine-fold increased risk of death compared to their well-nourished peers, making it a critical public health priority. While stunting and underweight may have higher prevalence rates in the African region, wasting can lead to severe health consequences, including increased susceptibility to infectious diseases and long-term cognitive impairments. Addressing wasting not only improves child survival and health outcomes but also helps mitigate the risk of stunting and underweight. The causes of wasting often differ from those of other forms of undernutrition, making targeted interventions essential for effective management. Thus, the acuity and severity of wasting justify its selection as the primary interest of this study.

The hunger crisis in Africa is a longstanding and multifaceted issue that demands unwavering commitment, innovative approaches, and coordinated action [18]. In this study, we assessed the prevalence and determinants of childhood wasting in Africa using data from standard demographic and health surveys. Understanding the current landscape and drivers of wasting is crucial for informing effective policies and interventions to address this persistent public health challenge across the continent. By identifying key risk factors associated with wasting, our study provides valuable insights that can guide the development and targeting of nutrition-sensitive and nutrition-specific programs, ultimately improving child health outcomes and advancing progress toward the Sustainable Development Goals. Additionally, our findings can facilitate cross-country learning and the sharing of best practices to accelerate reductions in childhood wasting in Africa.

Methods

Study setting, data source and sampling

We conducted a secondary analysis of standard demographic and survey datasets spanning from 2010 to 2022 to assess the prevalence and determinants of wasting among children under 5 years old in Africa. Among the 37 African countries with available datasets during specified survey years. The list of countries included in the study is depicted on the map in Fig.Ìý1. We accessed the data through a formal request from the Monitoring and Evaluation to Assess and Use Results Demographic and Health Survey (MEASURE DHS) program, which is accessible at .

The standard DHS employs a stratified two-stage cluster sampling technique to ensure nationally representative data. Initially, each country is divided into different strata based on relevant characteristics, such as urban/rural location or geographic regions. Within each stratum, enumeration areas (clusters) are randomly selected as the primary sampling units. These clusters typically comprise several households. In the second stage, we draw a sample of households from within each selected enumeration area using either a systematic or random sampling method. Our study population included all children aged less than 5 years residing in the sampled households. We excluded children who were twins, had visible disabilities affecting their growth, or were missing data on key anthropometric indicators necessary for assessing wasting status.

Fig. 1
figure 1

The geographical description of the study setting; Source shape file (URL: )

Variables

The study’s outcome variable was childhood wasting, determined using the World Health Organization’s (WHO) Child Growth Standards [2]. Childhood wasting was defined as weight-for-height below − 2 standard deviations from the median of the WHO Child Growth Standards among children under 5 years of age [2]. Specifically, it included children aged 0–59 months whose weight-for-height z-score was less than − 2.0 SD below the WHO median.

Handling missing values

In this study, as per the guide to DHS statistics, children who were not weighed and measured, as well as those whose weight and height values were not recorded, have been excluded from both the denominators and the numerators. Additionally, children whose month or year of birth has been missing or unknown were flagged and excluded from both the denominators and the numerators for anthropometry indices that rely on age in their calculation. Furthermore, children flagged for out-of-range z-scores or invalid z-scores have also been excluded from both the denominator and the numerators.

In this study, we considered explanatory variables at two levels: individual or household variables (first level) and community (cluster) level variables (second level). Specifically, the first-level variables included the child’s age, sex, birth weight, maternal age, maternal educational status, maternal working status, household size, wealth index, birth order, antenatal care (ANC) visits, place of delivery, postnatal checkup, media exposure, type of gestation, access to electricity, and type of toilet.

At the second level, we examined community-related factors, including the source of drinking water, distance to a health facility, place of residence, community literacy rate, community media exposure, and community poverty. To categorize the source of drinking water, we followed the classification used by the WHO and the United Nations Children’s Fund (UNICEF) Joint Monitoring Program for Water Supply, Sanitation, and Hygiene. Improved water sources encompass safe and clean options such as piped water, protected wells, and public taps. In contrast, unimproved water sources are more likely to be contaminated and include unprotected wells, surface water (rivers, lakes, ponds), and water from tanker trucks or carts.

Control variable: The DHS data used for this study were from different years. To control the differential time effect, we used the year of the surveys as a control variable.

Data cleaning and management

The children’s recode (KR) datasets in Stata form were separately extracted from the archive of the MEASURE DHS for all countries and appended together into a single data file for analysis. The Stata 17 statistical software was used for formal analysis. Prior to analysis, we conducted thorough data cleaning procedures. This included checking for duplicate records, identifying and removing any implausible or invalid values (like out-of-range anthropometric measurements).

The weighted prevalence and frequencies of the analysis results were reported using tables and bar charts. The analysis in this study accounted for the complex survey design of the DHS data used. Specifically, the estimates were weighted using the sampling weights provided in the DHS dataset, and the standard errors of the regression coefficients were adjusted to account for the probability of sample selection and the multistage cluster sampling design. This ensures the estimates are representative of the target population and the statistical inferences are valid.

Regression analysis

The study utilized DHS data, which is structured at both the household and cluster levels. To address the violation of the assumption of independent observations required for ordinary logistic regression, we employed mixed-effects models with a binary outcome. We fit four different model specifications: a null model to assess the random effect and suitability of mixed effect regression, Model I including outcome variables and first-level control variables, Model II with outcome variables and second-level control variables, and a final comprehensive Model III incorporating all variables - outcome, first-level controls, and second-level controls. By using this mixed effect modeling approach, the researchers were able to account for the hierarchical structure of the DHS data and more effectively explore the determinants of childhood wasting. The following is the equation for mixed effect model [19].

$$\begin{gathered}{\text{log}}\left( {\frac{{{\pi _{ij}}}}{{1 - {\pi _{ij}}}}} \right) = \,\beta 0\, + \beta 1{\text{x}}{1_{ij}}\, + \, \ldots \, + \,\beta nx{n_{ij}}\, + \gamma 0 \\ + \gamma 1z{1_{ij}}\, + \, \ldots \, + \,\gamma mz{m_{ij}} + u{o_{ij}} \\ \end{gathered} $$

Where πij represents the probability of wasting for the ith child in the jth cluster, while (1 − πij) denotes the probability of the ith child in the jth cluster not experiencing wasting. The intercept term β0 characterizes the baseline of the regression equation. The coefficients β1 to βn are linked to the level 1 variables x1ij to xnij, which exert an influence on the response variable at the individual level. The intercept γ0j captures the random effect at level 2, reflecting variations in the baseline probability of wasting across different clusters. The coefficients γ1 to γm are associated with the level 2 variables z1j to zmj, reflecting cluster-level effects. Finally, the term u0ij represents the random effects that capture unobserved heterogeneity and account for variations in the baseline probability of wasting across different clusters.

Both the fixed and random components of the mixed effect were evaluated. The random effect was assessed using the following parameters: intra-class correlation coefficient (ICC), median odds ratio (MOR), and proportional change in variation (PCV). The fixed effect was evaluated by calculating the adjusted odds ratio (AOR) and the corresponding 95% confidence interval (CI). We declared the significance of the association between explanatory variables and outcomes when the p-value was less than the predetermined level of significance (0.05).

For model comparison, we utilized the log likelihood and deviance. To address multicollinearity, which occurs when two or more independent variables in a regression model are highly correlated, we calculated the variance inflation factor (VIF) for each variable. The VIF values were below five, falling within an acceptable range, indicating that multicollinearity was not a significant concern. Additionally, we employed multivariable regression techniques to control for potential confounding factors—variables that might be associated with both the independent and dependent variables and could influence the observed relationship. This approach allowed us to isolate the effect of each independent variable on the dependent variable while accounting for other relevant factors.

Results

Socio-demographic characteristics of the study subjects

In this study, we analyzed a weighted sample of 212,715 children under the age of 5 years (107,625 male and 105,090 female). The birth weight of 83,052 children (42.30%) fell within the normal range (2.5Ìýkg to 3.9Ìýkg). Most children (71.02%) were born to mothers aged 20–34 years. Additionally, over half (58.18%) of the children came from households with six or more members.

Regarding the wealth index, 47,563 children (22.36%) belonged to households with the poorest index. Approximately two-thirds (66.97%) of the children were born at health facilities. The primary source of water for 65.04% of households with children was unimproved. Furthermore, 67.75% of the children resided in rural areas. Lastly, slightly more than half (52.04%) of the children came from communities with high poverty levels (as shown in TableÌý1).

Table 1 Socio-demographic characteristics of study subjects

Prevalence of wasting among under five children in Africa

The prevalence of childhood wasting in Africa was found to be 7.16% at a 95% CI (7.05, 7.27). It was lower (1.16%) in Rwanda and higher (18.07%) in Niger (Fig.Ìý2).

Fig. 2
figure 2

The pooled and national prevalence of childhood wasting among African countries with the respective year of DHS

Measures of variation (random effect) and model comparison

The random effect from the null model indicates that there was significant variation in the incidence of childhood wasting between the clusters (geographic sampling units) included in the analysis (variance = 0.36012, p < 0.0001). This suggests that the risk of childhood wasting was not homogeneous across the different clusters, but rather there were meaningful differences in wasting prevalence between the clusters sampled. In other words, cluster-level factors appear to play an important role in explaining the observed variation in childhood wasting, beyond just individual-level factors. The value of ICC in the null model indicates that 9.87% of the total variation in childhood wasting was due to variation between clusters.

The median odds ratio (MOR) statistic provides a measure of the extent of unexplained variation or heterogeneity in the risk of childhood wasting between the clusters (geographic sampling units) included in the analysis. An MOR of 1.76 in the null model indicates that if a child was to move to a cluster with a higher risk, their odds of wasting would (on average) increase by a factor of 1.76. This suggests there were substantial unobserved cluster-level factors contributing to the variation in wasting prevalence across the study areas. After including the first and second level factors in the full model, the MOR decreased to 1.19, meaning the heterogeneity in wasting risk between clusters was reduced but still statistically significant. This implies that while the measured covariates explained some of the between-cluster variation, there remained meaningful unexplained differences in wasting prevalence across the geographic areas sampled.

The value of the PCV in models I, II, and III implies that 90.08%, 26.08%, and 90.53% of the total variation in wasting was attributed to the first level, second level, and both levels, respectively (TableÌý2).

Table 2 Random effect and model comparison

Measures of association (fixed effect)

The mixed effects analysis of this study revealed that the age of the child, sex, birth weight, maternal education, wealth index, antenatal care (ANC) visits, place of delivery, type of gestation, and place of residence were significantly associated with childhood wasting.

Compared to children aged 36–59 months, the odds of childhood wasting were 2.22 times higher among children aged 0–11 months (AOR = 2.22, 95% CI: 2.03, 2.43), 2.19 times higher among children aged 12–23 months (AOR = 2.19, 95% CI: 2.00, 2.39), and 1.33 times higher among children aged 24–35 months (AOR = 1.33, 95% CI: 1.20, 1.47).

For males, the odds of wasting were 34% higher (AOR = 1.34, 95% CI: 1.28, 1.41) compared to females. The odds of wasting were 29% higher among children with low birth weight (AOR = 1.29, 95% CI: 1.21, 1.38) and 28% lower among children with high birth weight (AOR = 0.72, 95% CI: 0.64, 0.82), compared to children with normal birth weight.

When compared to children born to mothers with higher educational status, the odds of wasting were 53% higher for children born to mothers with no formal education (AOR = 1.53, 95% CI: 1.29, 1.81). Taking children of households with the richest wealth index as a reference, the odds of wasting were 18% higher for children of households with the poorest wealth index (AOR = 1.18, 95% CI: 1.05, 1.34). Additionally, the odds of wasting were 12% higher among children born to mothers who had no ANC visits (AOR = 1.22, 95% CI: 1.12, 1.32) and 6% higher among children born to mothers who had one to three visits (AOR = 1.06, 95% CI: 1.01, 1.13), compared to children born to mothers who had four or more ANC visits.

The odds of wasting were 9% higher among children born at home (AOR = 1.09, 95% CI: 1.02, 1.17) compared to children born at a health facility. Children born in multiple gestation had 40% higher odds of being wasted (AOR = 1.40, 95% CI: 1.18, 1.67) compared to children born in single gestation. Moreover, children residing in rural areas of Africa had 3% higher odds of being wasted (AOR = 1.03, 95% CI: 0.96, 1.10) than children residing in urban areas (TableÌý3).

Table 3 The fixed effect of childhood wasting in Africa

Discussion

In this study, we assessed the prevalence and determinants of childhood wasting in Africa using data from standard demographic and health surveys. Understanding the current landscape and drivers of wasting is crucial for informing effective policies and interventions to address this persistent public health challenge across the continent. By identifying key risk factors associated with wasting, our study provides valuable insights that can guide the development and targeting of nutrition-sensitive and nutrition-specific programs, ultimately improving child health outcomes and advancing progress toward the Sustainable Development Goals.

The prevalence of childhood wasting in Africa was found to be 7.16% (95% CI: 7.05–7.27). This rate aligns with the prevalence of wasted children in northern Africa, which stands at 7.20% [20]. Notably, even though most excluded countries in our study were from the northern region (as shown in Fig.Ìý1), the prevalence of wasted children in those areas remains similar to that observed in our study.

Compared to low- and middle-income countries, the prevalence of childhood wasting in our study was higher: 6.4% [21] and 6.3% [22], respectively. Additionally, our findings exceeded the World Health Organization’s (WHO) Global Nutrition Target, which aims to reduce childhood wasting to less than 5% in over half of low- and middle-income countries by 2025 [13]. It is crucial to contextualize our findings within the framework of the WHO’s Global Nutrition Target, particularly since our data covers a 12-year period. This extended timeframe allows for the possibility that the prevalence of childhood wasting may have fluctuated due to various factors, such as changes in health policies, economic conditions, and nutrition programs. These changes could influence our results in comparison to the WHO’s goal of reducing childhood wasting to less than 5% by 2025.

The high proportion of wasted children in Africa, similar to trends observed in many low and middle-income nations, reflects a complex interplay of factors. These include poor nutritional status among pregnant women, inadequate breastfeeding and feeding practices, limited sanitation and hygiene, restricted access to quality health services, and food insecurity [23]. Addressing childhood wasting requires concerted efforts, such as ensuring that children are born to healthy, well-nourished mothers who receive appropriate antenatal care. Additionally, households must have access to adequate food, proper care practices, functional primary health services, reliable water supply, safe sanitation, and good hygiene [24]. These multifaceted interventions are crucial for tackling childhood wasting in Africa.

In this study, significant differences in childhood wasting rates were observed between African countries, attributed to various factors such as economic conditions, government policies, and health infrastructure. Notably, Niger exhibited higher rates of wasting compared to countries like Rwanda and South Africa. For example, South Africa’s robust child grant system plays a critical role in mitigating child malnutrition, as government assistance programs provide essential support to vulnerable families [25]. The availability of government assistance and a country’s overall GDP are crucial determinants of childhood wasting. Countries with higher GDP and more effective social support systems tend to report lower rates of wasting. This dose-response relationship between poverty and wasting underscores the importance of addressing economic disparities and improving social safety nets to effectively reduce malnutrition [26]. While we observed some differences in prevalence depending on the survey clusters, a country-level comparison provides valuable insights into the broader socio-economic determinants of wasting. Future research should further explore these differences to develop targeted interventions that address the unique challenges faced by each country.

The age of child was significantly associated with childhood wasting. Compared to children aged 36 months or older, the odds of wasting were high for children aged 35 or younger. This is in agreement with previous findings [27, 28]. The high rate of wasting in infants is caused by a complex interplay of many diverse factors. According to the World Health Organization [29], infants’ high nutritional needs and rapid growth make them particularly vulnerable to undernutrition. Additionally, pneumonia, measles, and diarrhea diseases, which are more common in younger children than in older ones, are frequently associated with the development of wasting [29].

Coherent with previous studies [30,31,32], sex of child was significantly associated with wasting. This may be related to the biological differences in morbidity that exist between young males and females [33]. Male children are also more likely to require more energy due to their higher birth weight than female children, which raises the possibility that they will suffer from wasting [34]. Furthermore, breastfeeding alone may not be sufficient or adequate for male children [33] because it is believed that they are hungrier than female youngsters, enhancing their vulnerability to the three dietary disorders.

In this study, birth weight was significantly associated with childhood wasting. Accordingly, the odds of wasting were high for children with low birth weight and low for children with high birth weight compared to children with normal birth weight. This is consistent with findings from previous studies [35, 36]. This could be due to the reason that low-birth-weight babies are more susceptible to infections and are at greater risk of malnutrition, which can result in wasting [37]. This also underscores the importance of the fetal and gestational environment, where inadequate maternal nutrition and poor antenatal care are pivotal contributors [38, 39]. Enhancing maternal health through adequate nutrition and comprehensive antenatal care is essential. Ensuring that mothers are healthy and well-nourished throughout pregnancy can significantly reduce the incidence of low birthweight and, consequently, childhood wasting [40]. Likewise, it was implicated that high birth weight is progressive throughout child hood and possible cause of obesity, diabetes mellitus and other cardiovascular diseases in the adulthood [37].

In agreement with findings from previous studies [41,42,43], this study maternal educational attainment was significantly associated with childhood wasting. It is clear that maternal education is an important factor in the health and well-being of children. This could be because of the following reasons: First, an educated woman is most likely aware of how to nurture her child. Second, an educated woman has knowledge and practice of infection prevention techniques for herself and her child, which reduces the risk of childhood wasting. In addition, an educated mother is likely to have the employment and income necessary to adequately feed her child [44]. Moreover, higher maternal education leads to greater female autonomy, which in turn influences decisions about health and how household resources are distributed for food [45]. Teaching mothers about the nutritional value of food and the optimal preparation techniques is essential to improving the nutritional status of their children [43].

Wealth index was significantly associated with childhood wasting [46,47,48,49]. The odds of childhood wasting were high among children from households with the poorest wealth index compared to children from households with richest wealth quintile. It is not surprising that poorest wealth index was associated with wasting. Childhood malnutrition is directly influenced by the house wealth index [46]. The association between wealth index and childhood wasting is mediated by environmental, maternal, biological, and behavioral factors, such as poor feeding and care practices, limited access to safe drinking water, infection exacerbated by food insecurity, and poverty, which can lead to acute malnutrition, including childhood wasting [48].

The ANC follow up status during pregnancy was significantly association with childhood stunting in this status. This was also reported by previous studies [50, 51]. ANC can frequently serve as a springboard for further interventions that have been shown to improve the health of mothers and their children, such as nutrition plans and breastfeeding counseling, or to inform women about the benefits of family planning and spacing out childbirth [52, 53]. Additionally, ANC initiatives are used to address non-pregnancy-related risk factors for mothers, such as encouraging healthy lifestyles, addressing hunger, or educating people about gender-based violence [50].

As it was observed from previous studies [43, 48], this study also found that the odds of wasting were high for children born at home than those born in health facilities. Many of these mothers face structural barriers, such as limited access to education and healthcare facilities, which can impact their ability to make informed health decisions for their children. These barriers often include living in rural areas far from medical facilities and economic constraints. Moreover, mothers who give birth at home miss out on the opportunity to get the helpful postnatal counseling provided by medical facilities, which may improve the nutritional health of the mother and child [43].

Multiple birth was significantly associated with the childhood wasting. The odds of wasting were higher for children born multiple compared to children single. This also agrees with studies on undernutrition [28, 54]. This could be due to the fact that raising twins is more demanding than raising a single child; it calls for twice as many resources and hours of work [28].

Furthermore, this study found a significant association of place of residence with childhood wasting. This is consistent with findings of previous studies [30, 55]. This could be due to the fact that rural areas often lack access to sanitary facilities, a safe water supply, and suitable housing—all of which have a negative influence on health and are primary requirements for obtaining enough nourishment [43]. This disparity hinders growth since it makes a person more prone to illness and slows their recovery from illnesses [43].

The findings of this study should be interpreted with consideration of both the strengths and limitations. The large, multi-country sample size of 35 African nations provides substantial statistical power to identify general patterns and associated factors related to childhood wasting across the region. However, we recognize that aggregating data at this broad regional level may obscure important within-region heterogeneity. Therefore, we caution that the results should be viewed as indicative of overarching trends, rather than definitive conclusions applicable to all of Africa. Moving forward, we recommend that future research explore subnational-level dynamics within individual countries. By delving deeper into the nuances of wasting patterns at the regional, district, or community level, these complementary country-specific and local-level analyses can provide crucial insights into the context-specific drivers of this public health issue. This targeted, geographically-disaggregated approach will ultimately enable the development of more effective, evidence-based policy responses tailored to the diverse needs across the African continent.

Another limitation is the lack of accounting for potential spatial heterogeneity in the relationships between predictors and childhood wasting. Our modeling approach assumed that the strength and direction of these associations were constant throughout the 35 countries. However, it is likely that the determinants of wasting vary considerably by country or geographic area due to unobserved contextual factors. Future research should incorporate spatial modeling techniques to capture this geographic heterogeneity and explore how risk factors for wasting differ across regions of Africa. Additionally, the cross-sectional nature of the data limits our ability to establish causal relationships. Longitudinal studies would strengthen the evidence base and shed light on the dynamic factors influencing wasting patterns.

Despite these limitations, the findings provide valuable insights into the multilevel determinants of childhood wasting across a large, representative sample of African countries. Addressing this critical public health challenge will require a nuanced understanding of how risk factors vary geographically. Incorporating spatial perspectives into future research on childhood nutrition is an important direction for advancing knowledge in this area and informing more effective, context-appropriate policy responses. While our study indicates that childhood wasting in Africa exceeds the global target set for 2025, it is essential to recognize that some of the data analyzed are over a decade old. This time gap may affect the relevance of our conclusions regarding current realities. Although our findings provide valuable insights into the trajectories of childhood wasting across the continent, they should be interpreted with caution. Future research should consider more recent data to assess the current status and trends in childhood wasting in Africa.

Conclusion

Childhood wasting in Africa exceeds the global target set for 2025, which aims to reduce it to less than 5%. To address this critical issue, educating mothers without formal education and rural residents about antenatal care visits, institutional delivery, and proper child feeding practices is essential. Moreover, a renewed focus on tackling the multifaceted determinants of undernutrition, strengthening health systems, and implementing evidence-based interventions tailored to the local context is crucial for achieving meaningful and sustained reductions in wasting prevalence across the region.

Data availability

The datasets generated and/or analyzed during the current study are available publicly online at (https://www.dhsprogram.com).

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Contributions

T.T.T.: involved in designing the study, data extraction, data analysis, interpretation, report writing, and manuscript writing. A. F. Z.: involved in designing the study, interpretation, analysis, report, and manuscript writing. B.S.W.: involved in review and editing, validation, and visualization. E.G.M.: involved in interpretation and manuscript writing. B.T.: involved in data curation and manuscript writing. Y.A.W.: involved in result interpretation and manuscript review. M.S.A.: involved in data curation and methodology. A.T.G.: involved in software, supervision, and data validation. M.A.A: involved in formal analysis and methodology S.S.T.: involved in conceptualization and validation. A.T.K.: involved in conceptualization, validation, and writing the original draft. M.A.T.: involved in conceptualization and methodology. M.W.: involved in software and validation.

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Correspondence to Tadesse Tarik Tamir.

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This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving research study participants were approved by the ICF Institutional Review Board (IRB). Written informed consent was obtained from all participants. 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.

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Tamir, T.T., Zegeye, A.F., Workneh, B.S. et al. Childhood wasting and associated factors in Africa: evidence from standard demographic and health surveys from 35 countries. Ó£»¨ÊÓƵ 25, 454 (2025). https://doi.org/10.1186/s12889-025-21673-z

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  • DOI: https://doi.org/10.1186/s12889-025-21673-z

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