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Residence-based disparities of composite index of anthropometric failures in East African under five children; multivariate decomposition analysis

Abstract

Background

Undernutrition remains a global challenge and public health concern, despite the presence of policies, programs and interventions. There is substantial evidence that the majority of the rural children under-5Ìýyears old have composite index of anthropometric failure than the urban counter parts. Hence, identifying the main contributors of these disparities will help health policy makers, program designers and implementers for the reduction of composite index of anthropometric failures in children under-5Ìýyears old in the study areas.

Methods

The most recent and nationally representative samples of demographic and health surveys of five East African countries data were used for the current study. To appreciate the residence-based differences of composite index of anthropometric failure in under-5 children, the Blinder-Oaxaca decomposition analysis and its extensions were employed to determine the effects of covariates and coefficients. The country specific survey data analysis was performed.

Results

The current study revealed that the burden of composite index of anthropometric failure (CIAF) in under-5 children were 40.69%, 22.04%, 34.06%, 31.99%, and 33.27% in Ethiopia, Kenya, Rwanda, Uganda, and Tanzania respectively. The residence-based differences in CIAF were 25.49%, 11.38%, 27%, 22.15%, and 20.55% in Ethiopia, Kenya, Rwanda, Uganda, and Tanzania respectively. Results of the Blinder-Oaxaca decomposition analysis and its extensions revealed that 100% of the rural–urban children under-5 composite index of anthropometric failure disparity was explained by endowment characteristics (covariate effect). Wealth index, mother’s education, age of child, type of birth, sex of child and birth interval inequality between rural and urban households explains most of the composite index of anthropometric failure disparity in children under-5Ìýyears old.

Conclusions

The residence-based CIAF differences were high in all study countries. The rural–urban CIAF gap is ascribed by household, maternal and child characteristics. This result implies that rural children under-5 is disproportionally disadvantaged with respect to characteristics than their consequences. Through identification of the underlying factors behind the rural–urban CIAF disparities, the result of this study is important in planning effective intervention measures aiming at reducing residence-based inequalities and the population health outcomes. Therefore, should be given for rural children to reduce CIAF by improving house hold wealth index, women education and attentions to older children, and female children.

Peer Review reports

Background

In spite of various interventions such as nutrition specific and nutrition sensitive [1,2,3], programs and policies implemented to reduce undernutrition in children under 5, it remains a public health concern. In 2022 there was an estimated 149 million stunted and 45 million wasted children under 5 worldwide. Approximately, half of deaths in children under 5 are linked to undernutrition. These mostly found in low and middle-income countries (LMIC). The global burden of undernutrition has severe and long-lasting consequences, affecting individuals and their families, as well as communities and entire countries, across various aspects of development, economics, society, and healthcare [4].

Sub-Saharan Africa (SSA) has a high burden of undernutrition. For example, stunting is most prevalent in Burundi (57.7%), wasting is highest in Niger (18.0%), and underweight is most common in Burundi (28.8%) [5, 6]. Subsequently, undernutrition is not spread evenly among children under 5 in SSA. East Africa is hit the hardest, with roughly 39% (or 23.1 million) of children under five being stunted. This is followed by West Africa, where around 30% (or 17.8 million) of children under five are stunted. Southern and Northern Africa have the lowest rates of stunting. Within East Africa itself, about 35% (or 4 in 10) children experience stunting, while wasting affects only 3.5% of children under five [7, 8].

Undernutrition has different forms: stunting, wasting, underweight and deficiencies in vitamins and minerals [4]. Stunting, wasting, and underweight are the conventional measures of undernutrition [9, 10]. While the three common measures help assess child undernutrition, they don't give a single overall picture. This makes it harder for policymakers to direct resources and design programs to fight undernutrition effectively in specific areas [11]. Each anthropometric indicator provides valuable information about quite distinct biological processes [12] which in turn require different clinical responses. Since, malnutrition is not due to food deprivation, these anthropometric indicators only serve as proxies for evaluating the prevalence of undernutrition among children. Composite index of anthropometric failure (CIAF) which is first proposed by the Swedish Professor Peter Svedberg in 2000, is means of identifying all undernourished children, be they stunted and/or wasted and/or underweight [13]. The conventional measures are not sufficient to identify the child’s anthropometric failure in a comprehensive approach [10, 11, 13,14,15,16,17,18,19,20,21]. Thus, the recommendation of the use composite index of anthropometric failure (CIAF) is supported by different literatures to assess the children’s under nutrition status [10, 13, 15, 22, 23].

From the previous literatures, different explanatory variables were associated with composite index of anthropometric failure CIAF in children under 5; sex of the child, birth type of the child, birth order of the child, BMI of the mother, Education of the mother, residence, household sanitation, [9, 24] that were analyzed using the ordinary regression methods like logistic regression and linear regression methods. However, the decomposition analysis method is useful for decomposing the CIAF in children under 5 that were not addressed in the previous literatures [25,26,27,28,29]. The current study is done to identify determinants both effects of residence difference in the proportion of the levels of determinants and effect of the covariates on the outcome. To design effective strategies and generate evidence for health policy makers to reduce the residence-based gap of CIAF in children under 5, an insight of the underlying factors which contribute to the difference in rural and urban is highly relevance in improving the child health and the communities’ welfare at large. Therefore, this study was aimed to identify the source of disparities between the rural and urban areas in East Africa by employing a decomposition data analysis technique.

Methods

Data source, study setting and population

The current study used the most recent demographic and health survey (DHS) data of 5 East African countries (Ethiopia [30], Kenya [31], Rwanda [32], Uganda [33] and Tanzania [34]) conducted in 2019, 2022, 2019, 2022, and 2016 respectively. Demographic and health survey data is a nationally representative survey routinely conducted every five years and collects data on basic health indicators; majorly maternal and child characteristics. For this study, the analysis was done based on the birth history record dataset of 5,530 children in Ethiopia, 17,265 children in Kenya, 4,117 children in Rwanda, 5,128 children in Uganda and 5,489 children in Tanzania comprising of 37,529 under the age of five years old children in East Africa. The source population is children under 5 in in all study countries in the survey time. The study population is children under 5 who were living in the selected enumeration areas in the study countries [30,31,32,33,34]. Inner City Fund (ICF) provided technical support via the DHS program, which is funded by the USAID and offers support and technical assistance for the implementation of population and health surveys in countries worldwide.

Sampling procedures and techniques

Community-based cross-sectional study design was employed among children under 5 in Ethiopia. Stratified by rural and urban two-stage cluster sampling was conducted in all countries. For Ethiopia, stratification was also done by regions. Based on the Probability proportional to Size (PPS) technique, clusters (EAs) were selected first and by applying systematic random sampling technique, equal numbers of households were selected in the second stage [30,31,32,33,34]. About 305, 1,691, and 608 clusters (EAs) were sampled in Ethiopia, Kenya, Rwanda, Uganda and Tanzania in the first stage respectively. About 30, 1,691, and 22 households per cluster (EA) were selected in Ethiopia, Kenya, Rwanda, Uganda and Tanzania in the second stage respectively. The pretest was performed for checking the reliability of the tools. Health professional field staffs were recruited and trained to serve as team supervisors, field editors, interviewers, secondary editors, and reserve interviewers. In addition, individuals were recruited and trained on how to collect biomarker data, including taking height and weight measurements, testing for anemia by measuring hemoglobin levels.

Measurement of the study variables

Dependent variable

Children composite index of anthropometric failure was the outcome of interest that was assessed using anthropometric like weight, height and age; and conventional anthropometric indices such as stunting, wasting and underweight. For the analysis, the dependent variable is dichotomous variable categorized as failure coded as 1 and normal coded as 0.

Equity stratifier variable

Residence which was classified as rural (coded as 1) and urban (coded as 0).

Explanatory variables

Independent variables were extracted from the recent DHS in East African countries. Such as sex of child, age of child in months, birth order of the child, type of birth, birth interval, mother’s age, mother’s education, source of drinking water, toilet facility, family size, number of under 5 children in the household and household wealth index.

Operational definitions

The Composite Index of Anthropometric Failure (CIAF) combines weight-for-age (WAZ), length/height-for-age (HAZ), and weight-for-length (WHZ) to assess the undernutritional status of children under-5. It categorizes children into "anthropometric failure" (coded as 1) or "normal" (coded as 0). The categories include: A) no failure; B) wasting only; C) wasting and underweight; D) wasting, underweight, and stunting; E) stunting and underweight; F) stunting only; and Y) underweight only. Anthropometric failure is the total number of children in categories B, C, D, E, F, and Y. The CIAF can be used to identify specific types of anthropometric failures simultaneously (TableÌý1) [35].

Table 1 Category of Anthropometric Failure in children under-5 using Composite Index of Anthropometric (CIAF)

Data management and analysis

Data management and analysis were conducted using STATA/MP 17.0. Descriptive statistics were calculated for numeric and categorical variables by area of residence, considering the survey design. The Pearson Chi-square test was used for categorical variables to check statistical significance, while the independent t-test was applied to assess mean differences in continuous variables between rural and urban children.

The Blinder-Oaxaca decomposition analysis was used to explain residence-based disparities in children's composite index of anthropometric failure (CIAF). This technique breaks down the differences in CIAF between two groups into: 1) the endowment or explained component, which is due to differences in the levels or distribution of determinants, and 2) the unexplained component, which is due to differences in the impact of these determinants on the outcome, as well as the interaction of both factors [36, 37].

Yi represents the outcome variable (CIAF), and X is an independent variable. For two groups (rural and urban), the CIAF for children under 5Ìýyears old in each group is described as follows.

$${\text{Yi}}^{\text{rural}} ={\upbeta }^{\text{rural}} {{\text{X}}_{\text{i}+\upvarepsilon } }^{\text{rural}}$$
(1)
$${\text{Yi}}^{\text{urban}} ={\upbeta }^{\text{urban}} {{\text{X}}_{\text{i}+\upvarepsilon } }^{\text{urban}}$$
(2)

Thus, the gap in the average CIAF between rural and urban residences (Yi rural—Yi urban) is presented as [37].

Oaxaca decomposition

$${\text{Y}}^{\text{rural}} - {\text{Y}}^{\text{urban}} = ({\text{X}}^{\text{rural}}-{\text{X}}^{\text{urban}}){\upbeta }^{\text{urban}} + ({\upbeta }^{\text{rural}} - {\upbeta }^{\text{urban}}){\text{X}}^{\text{rural}} + ({\text{X}}^{\text{urban}}- {\text{X}}^{\text{rural}}) ({\upbeta }^{\text{urban}} - {\upbeta }^{\text{rural}})$$
(3)
$${\text{Y}}^{\text{rural}} - {\text{Y}}^{\text{urban}} =\Delta \text{X}{\upbeta }^{\text{rural}} +\Delta{\beta }{\text{X}}^{\text{urban}} +\Delta \text{X}\Delta \beta$$
(4)
$$=\text{E}+\text{C}+\text{CE}$$
(5)

where ΔX is the mean difference explanatory variables (Xrural—Xurban) and the same to Δβ = (βrural—βurban) [37].

Blinder decomposition

$${\text{Y}}^{\text{rural}} - {\text{Y}}^{\text{urban}} = ({\text{X}}^{\text{urban}}-{\text{X}}^{\text{rural}}){\upbeta }^{\text{urban}} + ({\upbeta }^{\text{urban}} - {\upbeta }^{\text{rural}}){\text{X}}^{\text{rural}} + ({\text{X}}^{\text{urban}}- {\text{X}}^{\text{rural}}) ({\upbeta }^{\text{urban}} - {\upbeta }^{\text{rural}})$$
(6)
$${\text{Y}}^{\text{rural}} - {\text{Y}}^{\text{urban}} =\Delta \text{X}{\upbeta }^{\text{urban}} +{ \Delta \beta }{\text{X}}^{\text{rural}} + \Delta \text{X}\Delta \beta$$
(7)
$$=\text{E}+\text{C}+\text{CE}$$
(8)

Therefore, the disparity in average outcomes (CIAF) may arise from differences in endowments (5), differences in coefficients (3), and the interaction between endowments and coefficients (CE) [37].

The Oaxaca [36] decomposition analysis (4)Ìýuses rural children under 5Ìýyears old as the reference group, weighting differences in attributes by their coefficients and differences in coefficients by the covariates of urban children. Conversely, the Blinder decomposition analysis (7) uses urban children under 5Ìýyears old as the reference group, weighting differences in characteristics by the coefficients of urban children and differences in coefficients by the covariates of rural children. Other techniques apply a weighted average of both groups.

Reimers [38] suggests that the weighted mean should be calculated as 0.5 (equal weights for both groups), while Cotton [39] believes it should reflect the sample sizes of the two groups. Because decomposition results are sensitive to the weighting method used, this study applied alternative decompositions for each method (Oaxaca, Blinder, Reimers, and Cotton)) [37]. Regressions were performed separately for rural and urban children, and the resulting covariates and coefficients were used for decompositions. Consistent results across different weights were considered indicative of the study's robustness.

The multivariate decomposition (mvdcmp) technique [40], an extension of the Oaxaca-Blinder method for non-linear dependent variables, was also used. This technique is designed for non-linear decomposition, addresses path dependency [41], and resolves the identification problem related to reference category selection with dummy variables. The mvdcmp method automatically identifies the high-outcome group and uses the low-outcome group as a reference [42], overcoming limitations of other techniques [37].

Ethical considerations

Authorization letter for access and utilization of the dataset was obtained from the MEASURE DHS International Program.

Results

Residence-based differences of under-five children’s composite index of anthropometric failure in East Africa

In the current study, weighted samples of 5,530, 17,265, 4,117, 5,128, and 5,489 under-five children in Ethiopia, Kenya, Rwanda, Uganda and Tanzania were analyzed, respectively. The result of this study, the rural and urban, as well as the overall children’s composite index of anthropometric failure (CIAF) in Ethiopia, Kenya, Rwanda, Uganda and Tanzania is illustrated in Fig.Ìý1 below. The burdens of child CIAF were 40.69%, 22.04%, 34.06%, 31.99%, and 33.27% in Ethiopia, Kenya, Rwanda, Uganda and Tanzania respectively. The difference in child CIAF between rural and urban residents was 25.49%, 11.38%, 27%, 22.15%, and 20.55% in Ethiopia, Kenya, Rwanda, Uganda and Tanzania respectively (Fig.Ìý1). In all countries that is, in Ethiopia, Kenya, Rwanda, Uganda and Tanzania, the rural–urban child CIAF disparity was highly statistically significant (chi-square = 100.63, P-value < 0.001), (chi-square = 188.89, P-value < 0.001), (chi-square = 74.64, P-value < 0.001), (chi-square = 22.37, P-value < 0.001), and (chi-square = 43.00, P-value < 0.001) respectively.

Fig.Ìý1
figure 1

Proportion of composite index of anthropometric failure in Ethiopia, Kenya, Rwanda, Uganda, and Tanzania stratified by their residency

Differences in selected characteristics of under-five children’s in the rural and urban residents in East Africa

In East African countries such as Ethiopia, Kenya, Rwanda, Uganda and Tanzania, the household characteristics as presented in the table below showed differences in rural and urban residents. In all countries, majorities are rural residents (74.96%, 64.06%, 83.32%, 79.62% and 73.22) in Ethiopia, Kenya, Rwanda, Uganda and Tanzania respectively. The rural households have lower wealth index, lower women’s education, and more family size.

In Ethiopia, age of the child, birth order of the child, mother’s educational level, age of the mother, source of drinking water, toilet facility, family size of the household, number of U5 children in the household, and household wealth index were significantly associated with place of residence. Whereas in Kenya, birth order, age of the mother, mother’s educational level, source of drinking water, toilet facility, family size of the household, number of U5 children in the household, and household wealth index were significantly associated with place of residence. Likewise, in Rwanda, birth order, type of birth, age of the mother, mother’s educational level, source of drinking water, toilet facility, family size of the household, number of U5 children in the household, and household wealth index were significantly associated with place of residence. Similarly, in Uganda, birth order, mother’s educational level, source of drinking water, toilet facility, family size of the household, number of U5 children in the household, and household wealth index were significantly associated with place of residence. Similar to other countries, in Tanzania, variables such as birth order, type of birth, preceding birth interval, age of the mother, mother’s educational level, source of drinking water, toilet facility, family size of the household, number of U5 children in the household and household wealth index were significantly associated with place of residence (TableÌý2).

Table 2 Characteristics of children under-5 by place of residence in selected East African countries (2016–2022)

Decomposition analysis

Aggregate decomposition estimates

The largest percentage of the gap was contributed by the explained (endowment) component in across the five east African countries. In the mvdcmp analysis 87.35%, 129.41%, 87.60%, 168.25% and 80.62% in Ethiopia, Kenya, Rwanda, Uganda and Tanzania respectively was the percentage made by the endowment (Tables 3, 4 and 5).

Table 3 Detailed decomposition of CIAF by residence for children under 5 in Ethiopia and Kenya
Table 4 Detailed decomposition of CIAF by residence for children under 5 in Rwanda and Uganda
Table 5 Detailed decomposition of CIAF by residence for children under 5 in Tanzania

In the rural–urban decomposition of composite index of anthropometric failure disparity into three components: the gap due to the difference in the level of characteristics, the gap due to the difference in the effect of the coefficients and the gap due to the interaction. The average total difference in predicted composite index of anthropometric failure between the rural and urban categories was high.

Detailed decomposition estimates

Difference in characteristics (covariate distribution)

From the output of the mvdcmp decomposition, the contribution of the individual characteristics for the composite index of anthropometric failure disparity by the residence. The results revealed in Ethiopia, Kenya, Rwanda, Uganda and Tanzania, a significant composite index of anthropometric failure disparity in rural and urban residencies (0.13, 0.10, 0.14, 0.06 and 0.12) respectively with all of the P < 0.001. In Ethiopia, Kenya, Rwanda, Uganda, and Tanzania about 87.35%, 129.41%, 87.60%, 168.25% and 80.62% of the composite index of anthropometric failure disparities were explained by the differences in distributions of characteristics (endowments) between rural and urban residencies respectively.

In Ethiopia, the largest disparity of the composite index of anthropometric failure was explained by the household wealth index difference between rural and urban children under 5; household with poorest wealth index (25.36%) contributed for narrowing this disparity and household with middle wealth index (16.85%) contributed for narrowing this disparity and mother’s with primary education (−13.36%), and female sex (−1.56%) contributed for widening this disparity. The distribution of child age 24–59Ìýmonths (2.74%), multiple births (0.47%), and mother’s has no formal education (0.02%) were factors that help to achieve narrowing of the rural–urban composite index of anthropometric failure disparity if these characteristics distribution equalized to the level of rural children for urban children as well.

Difference due to coefficients (the effect of covariate differences)

In this study, the coefficient effect was not significant in all study countries. The rural–urban disparity in composite index of anthropometric failure in children under-5 was totally due to the endowment or the characteristic effect.

Discussion

Literatures reported that the prevalence of composite index of anthropometric failure is higher in rural children under-5 as compared to the urban residents [9, 24]. Different factors are contributing to the higher burden of composite index of anthropometric failure of children under-5 in rural residence. This study thoroughly examined and determined the factors that contribute to the disparity in composite index of anthropometric failure between rural and urban residencies of selected East African Countries: Ethiopia, Kenya, Rwanda, Uganda and Tanzania. We employed a Blinder-Oaxaca and related decomposition techniques which have been underutilized in previous researches. To the best of our literature search, this is the initial study to provide explanations for the observed gap in composite index of anthropometric failure in children under-5 in rural and urban residencies with in East African countries. The findings of this study can inform and guide health policy development and program implementation targeted at reducing health and nutrition inequalities and enhancing overall population health in the study region.

In this study, significant disparities between rural and urban residents were identified in all countries included in the study. However, these disparities were not consistent across the region. The largest gap in composite index of anthropometric failure in children under-5 in rural–urban areas was observed in Rwanda, with a difference of 27 percentage points (30.53% in rural and 3.35% in urban residence area) followed by Ethiopia with a difference of 25.45 percentage points (33.09% in rural and 7.60% in urban residence area); Uganda with a difference of 22.15 percentage points (27.07% in rural and 4.92% in urban residence area) and Tanzania 20.55 percentage points (26.91% in rural and 6.36% in urban residence area). The lowest gap in composite index of anthropometric failure in children under-5 in rural–urban areas was observed in Kenya, with a difference of 11.38 percentage points (16.71% in rural and 5.33% in urban residence area). This variation could be attributed to several factors in Ethiopia including urban children belonging to high socioeconomic status, and high women literacy. The descriptive statistics support these findings, revealing that more than half (53.59%) of the children’s mothers had no formal education and nearly half (45.03%) of children under-5 were belonging to poorer and poorest households. It is obvious that rural women have less access to formal education [43].

Our finding indicated that more than three-fourth of the disparities in composite index of anthropometric failure between rural and urban children under-5 in the study area can be attributed to differences in the distribution of factors related to composite index of anthropometric failure regardless of the type of the decomposition analysis technique used. In other words, the differences in characteristics of rural and urban children under-5 played a larger role than changes in behavior in reducing the gap composite index of anthropometric failure. This is likely because rural children are more likely to get composite index of anthropometric failure than urban children due to factors such as low wealth index of the household, has no formal education of the mothers’ being female sex, child age above 24Ìýmonths and multiple birth type. The descriptive statistics supports this explanation as it shows that a higher proportion of the rural children compared to the urban children have households with low wealth status, and have mothers with no formal education. The findings are also supported by other previous studies done in Ethiopia [26] that wealth quintile and child age are contributors of child undernutrition. Another study done in Sub-Saharan African countries [29] also support our findings that child age, child sex, and mother’s education are contributing factors for the child under nutrition inequalities in rural and urban settings. A similar study done in West Africa [25] demonstrated household wealth index is a contributing factor for the child underweight.

Limitations and strengths

This study relies on data from the DHS, which is a survey. Surveys can't definitively prove cause and effect, so the findings about what factors contribute to composite index of anthropometric failure (CIAF) need to be interpreted with caution. Additionally, social desirability bias, where people might answer questions in a way they think is socially desirable rather than accurately reflecting their reality, could also influence the results. In addition, the current study didn’t conduct the pooled analysis which may limit comparison across countries considered in this study.

Despite, the above limitations, this study has certain strengths which include: the data analyzed here is recent data for each country; hence, findings reflect exactly the current condition in the study countries in respective to CIAF. In addition, the data were quality assured and representative which can be generalized to similar countries. Moreover, by studying these issues, we can gain a clear understanding of what drivers contribute to the gap in CIAF between rural and urban settings, and how big that gap actually is.

Conclusions

This study reveals a troubling gap in child composite index of anthropometric failure (CIAF) between rural and urban in the study countries especially in Rwanda. Children under-5 in rural areas experience CIAF at a much higher rate than their urban counterparts. Factors like Mother’s education, household wealth index, child birth order, child’s age and family size, seem to play a role in this disparity. The results highlight the need for multi-pronged solutions. Nutrition policies should address the underlying issues like education, income, and healthcare access, especially in rural areas. Additionally, providing tailored age focused care programs for rural children could be a crucial step in closing the gap in CIAF. To better understand the complex web of factors influencing CIAF rates, researchers suggest conducting studies that follow children over time. This would provide stronger evidence about how these factors directly contribute to the problem.

Data availability

The source dataset used for this study is publicly found from the MEASURE DHS program web site (www.measuredhs.com). The specific dataset utilized during the current study is available from the corresponding author upon reasonable request.

Abbreviations

CIAF:

Composite Index of Anthropometric Failure

EDHS:

Ethiopian Demographic and Health Surveys

SDG:

Sustainable Development Goal

SSA:

Sub-Saharan Africa

UNICEF's:

United Nations Children's Fund

WHO:

World Health Organization

References

  1. Kraemer K, Cordaro J, Fanzo J, Gibney M, Kennedy E, Labrique A, et al. Nutrition-specific and nutrition-sensitive interventions. Good nutrition: perspectives for the 21st century: Karger Publishers; 2016. p. 276–88.

  2. Abdullahi LH, Rithaa GK, Muthomi B, Kyallo F, Ngina C, Hassan MA, et al. Best practices and opportunities for integrating nutrition specific into nutrition sensitive interventions in fragile contexts: a systematic review. Ó£»¨ÊÓƵ Nutrition. 2021;7(1):46.

    Ìý Ìý Ìý Ìý

  3. Escher NA, Andrade GC, Ghosh-Jerath S, Millett C, Seferidi P. The effect of nutrition-specific and nutrition-sensitive interventions on the double burden of malnutrition in low-income and middle-income countries: a systematic review. Lancet Glob Health. 2024;12(3):e419–32.

    Ìý CASÌý Ìý Ìý Ìý

  4. Organization Wh. World Health Organization fact sheets on malnutrition, March 1, 2024. 2024.

  5. Aboagye RG, Ahinkorah BO, Seidu A-A, Frimpong JB, Archer AG, Adu C, et al. Birth weight and nutritional status of children under five in sub-Saharan Africa. PLoS ONE. 2022;17(6): e0269279.

    Ìý CASÌý Ìý Ìý Ìý

  6. Akombi BJ, Agho KE, Hall JJ, Wali N, Renzaho AM, Merom D. Stunting, wasting and underweight in sub-Saharan Africa: a systematic review. Int J Environ Res Public Health. 2017;14(8):863.

    Ìý Ìý Ìý Ìý

  7. Maniragaba VN, Atuhaire LK, Rutayisire PC. Undernutrition among the children below five years of age in Uganda: a spatial analysis approach. Ó£»¨ÊÓƵ. 2023;23(1):390.

    Ìý Ìý Ìý Ìý

  8. Organization WH. Levels and trends in child malnutrition: UNICEF. 2021.

  9. Bidira K, Tamiru D, Belachew T. Anthropometric failures and its associated factors among preschool-aged children in a rural community in southwest Ethiopia. PLoS ONE. 2021;16(11): e0260368.

    Ìý CASÌý Ìý Ìý Ìý

  10. Nandy S, Svedberg P. The Composite Index of Anthropometric Failure (CIAF): An alternative indicator for malnutrition in young children. Handbook of anthropometry: Physical measures of human form in health and disease: Springer; 2012. p. 127–37.

  11. Nandy S, Miranda JJ. Overlooking undernutrition? Using a composite index of anthropometric failure to assess how underweight misses and misleads the assessment of undernutrition in young children. Soc Sci Med. 2008;66(9):1963–6.

    Ìý Ìý Ìý Ìý

  12. Organization WH. The world health report: 1996: fighting disease, fostering development: World Health Organization; 1996.

  13. Svedberg P. Poverty and undernutrition: theory, measurement, and policy: Clarendon press; 2000.

  14. Nandy S, Irving M, Gordon D, Subramanian S, Smith GD. Poverty, child undernutrition and morbidity: new evidence from India. Bull World Health Organ. 2005;83:210–6.

    Ìý Ìý Ìý

  15. Rasheed W, Jeyakumar A. Magnitude and severity of anthropometric failure among children under two years using Composite Index of Anthropometric Failure (CIAF) and WHO standards. International Journal of Pediatrics and Adolescent Medicine. 2018;5(1):24–7.

    Ìý Ìý Ìý Ìý

  16. Fenta HM, Workie DL, Zike DT, Taye BW, Swain PK. Determinants of stunting among under-five years children in Ethiopia from the 2016 Ethiopia demographic and Health Survey: Application of ordinal logistic regression model using complex sampling designs. Clinical Epidemiology and Global Health. 2020;8(2):404–13.

    Ìý Ìý

  17. Endris N, Asefa H, Dube L. Prevalence of malnutrition and associated factors among children in rural Ethiopia. BioMed Res Int. 2017;2017;(1):1-6.

  18. Khamis AG, Mwanri AW, Kreppel K, Kwesigabo G. The burden and correlates of childhood undernutrition in Tanzania according to composite index of anthropometric failure. Ó£»¨ÊÓƵ Nutrition. 2020;6:1–13.

    Ìý Ìý

  19. Shit S, Taraphdar P, Mukhopadhyay DK, Sinhababu A, Biswas AB. Assessment of nutritional status by composite index for anthropometric failure: a study among slum children in Bankura, West Bengal. Indian J Public Health. 2012;56(4):305–7.

    Ìý Ìý Ìý

  20. Mandal GC, Kaushik Bose KB. Assessment of overall prevalence of undernutrition using composite index of anthropometric failure (CIAF) among preschool children of West Bengal, India. 2009.

  21. Sen J, Mondal N. Socio-economic and demographic factors affecting the Composite Index of Anthropometric Failure (CIAF). Ann Hum Biol. 2012;39(2):129–36.

    Ìý Ìý Ìý

  22. Al-Sadeeq AH, Bukair AZ, Al-Saqladi A-WM. Assessment of undernutrition using Composite Index of Anthropometric Failure among children aged years in rural Yemen. East Mediterr Health J. 2018;24(12):1–8.

    Ìý Ìý

  23. Biswas S, Giri SP, Bose K. Assessment of nutritional status by composite index of anthropometric failure (CIAF): A study among preschool children of Sagar Block, South 24 Parganas District, West Bengal. India AnthropologicAl review. 2018;81(3):269–77.

    Ìý Ìý

  24. Fenta HM, Zewotir T, Muluneh EK. Disparities in childhood composite index of anthropometric failure prevalence and determinants across Ethiopian administrative zones. PLoS ONE. 2021;16(9): e0256726.

    Ìý CASÌý Ìý Ìý Ìý

  25. Adamou H, Naba G, Koné H. Socioeconomic inequalities in underweight children: a cross-sectional analysis of trends in West Africa over two decades. BMJ Open. 2024;14(2): e074522.

    Ìý Ìý Ìý Ìý

  26. Bekele T, Rawstorne P, Rahman B. Socioeconomic inequalities in child growth failure in Ethiopia: findings from the 2000 and 2016 Demographic and Health Surveys. BMJ Open. 2021;11(12): e051304.

    Ìý Ìý Ìý Ìý

  27. Osgood-Zimmerman A, Millear AI, Stubbs RW, Shields C, Pickering BV, Earl L, et al. Mapping child growth failure in Africa between 2000 and 2015. Nature. 2018;555(7694):41–7.

    Ìý CASÌý Ìý Ìý Ìý

  28. Bredenkamp C, Buisman LR, Van de Poel E. Persistent inequalities in child undernutrition: evidence from 80 countries, from 1990 to today. Int J Epidemiol. 2014;43(4):1328–35.

    Ìý Ìý Ìý Ìý

  29. Amadu I, Seidu A-A, Duku E, Frimpong JB, Jnr JEH, Aboagye RG, et al. Risk factors associated with the coexistence of stunting, underweight, and wasting in children under 5 from 31 sub-Saharan African countries. BMJ Open. 2021;11(12): e052267.

    Ìý Ìý Ìý Ìý

  30. Ethiopian Public Health Institute - EPHI FMoH-F, ICF. Ethiopia Mini Demographic and Health Survey 2019. Addis Ababa, Ethiopia: EPHI/FMoH/ICF; 2021.

    Ìý

  31. Kenya, Survey DaH, 2022. Kenya National Bureau of Statistics Nairobi, Kenya Ministry of Health Nairobi, Kenya The DHS Program ICF Rockville, Maryland, USA. 2023.

  32. and RD, Survey H, 2019–20. National Institute of Statistics of Rwanda Kigali, Rwanda Ministry of Health Kigali, Rwanda The DHS Program ICF Rockville, Maryland, USA. 2021.

  33. Uganda, 2022 DaHS. Uganda Bureau of Statistics Kampala, Uganda The DHS Program ICF Rockville, Maryland, USA. 2024.

  34. Tanzania, Survey DaH, and, Survey MI, 2015–2016, Report F. Ministry of Health, Community Development, Gender, Elderly and Children Dar es Salaam Ministry of Health Zanzibar National Bureau of Statistics Dar es Salaam Office of Chief Government Statistician Zanzibar ICF Rockville, Maryland USA. 2016.

  35. Bose K. The Concept of Composite Index of Anthropometric Failure (CIAF): Revisited and Revised. 2018.

  36. Oaxaca R. Male-female wage differentials in urban labor markets. Int Econ Rev. 1973;14(3):693–709.

    Ìý Ìý

  37. Dewau R, Angaw DA, Kassa GM, Dagnew B, Yeshaw Y, Muche A, et al. Urban-rural disparities in institutional delivery among women in East Africa: A decomposition analysis. PLoS ONE. 2021;16(7): e0255094.

    Ìý CASÌý Ìý Ìý Ìý

  38. Reimers CW. Labor market discrimination against Hispanic and black men. The review of economics and statistics. 1983:570–9.

  39. Cotton J. On the decomposition of wage differentials. The review of economics and statistics. 1988:236–43.

  40. Powers DA, Yoshioka H, Yun M-S. mvdcmp: Multivariate decomposition for nonlinear response models. Stand Genomic Sci. 2011;11(4):556–76.

    Ìý

  41. Yun M-S. Decomposing differences in the first moment. Econ Lett. 2004;82(2):275–80.

    Ìý Ìý

  42. Yun M-S. Identification problem and detailed Oaxaca decomposition: a general solution and inference. J Econ Soc Meas. 2008;33(1):27–38.

    Ìý Ìý

  43. Ayres A, Dawed YA, Wedajo S, Alene TD, Gedefie A, Getahun FB, et al. Anthropometric failures and its predictors among under five children in Ethiopia: multilevel logistic regression model using 2019 Ethiopian demographic and health survey data. Ó£»¨ÊÓƵ. 2024;24(1):1149.

    Ìý Ìý Ìý Ìý

Acknowledgements

The authors would like to thank the MEASURE DHS program for giving permission to use the data for further analysis.

Funding

The authors did not receive funding from any organization.

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All authors made a substantial contribution to this study. AA, AM, YT, AE and AMM: contribute to the conception or design of the work; AA, ETA, SDK, EB, CD, CA and AM: contribute to the acquisition, analysis and interpretation of data; AA, FDB, MA, AAT, AK, KMA, NA, EM and AM: have drafted the work or substantively revised it. All authors read, revised it critically for important intellectual content, and gave the final approval of the submitted manuscript version. All authors agreed to each contributions and integrity of any part of the work.

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Correspondence to Aznamariam Ayres.

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Authorization letter was obtained from the MEASURE DHS program to download and use the data for our research purposes. Ethical clearance was obtained from Wollo University. The dataset is publicly available in the MEASURE DHS () program's official database, with no personal identifiers that can be linked to study participants.

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Not applicable as there is no image or other confidentiality related issues.

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The authors declare no competing interests.

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Ayres, A., Tsega, Y., Endawkie, A. et al. Residence-based disparities of composite index of anthropometric failures in East African under five children; multivariate decomposition analysis. Ó£»¨ÊÓƵ 25, 430 (2025). https://doi.org/10.1186/s12889-025-21634-6

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  • DOI: https://doi.org/10.1186/s12889-025-21634-6

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