Exploring the association of undernourishment indicators for under-five children in Sub-Saharan Africa: an application of log-linear model for three- way table

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Several studies have considered determinants of childhood undernutrition and haven't focused on the connection of undernourishment indicators, the remains have focused on the link of indicators with their factors and have ignored the connection of the dimensions themselves, therefore this study tested pairwise relationships among the dimensions. Methods: In the current study multiple correspondence analysis (MCA) was applied to look at the visual association of the indicators. Moreover, the log-linear model was wont to see the model of the best suitable examine the nutritional status of youngsters. The interaction terms within the model have represented deviations from independence. Result: The result shows that out of 185,541 weighted sampled children 32.28%, 16.67%, and 6.82% were stunted, underweight, and wasted respectively. The overall prevalence of undernutrition was about 37.85%. The log-linear model showed that being underweight has an association with both stunting (P-value < 0.001), and wasting (P-value < 0.001). There was no association between stunting and wasting (P-value = 0.999). Also, there's no three-way relationship between stunting, wasting, and being underweight (P-value = 1.000). Conclusion: The study shows that there is no three-way interaction between undernourishment indicators. This shows the three undernourishment indicators of under-five children have multi-dimensional characteristics. Accordingly, the alarmed body should assume the three indicators concurrently to assess the actual problem of childhood undernutrition as they are not terminated to each other. Undernutrition Log-linear Association Stunting Underweight Wasting Multiple correspondence analysis Figures Figure 1 Background Childhood undernourishment was a central public health difficulty [ 1 ]. It touches children's mental and physical progress, raises the outlook of infections, and meaningfully contributes to the child’s illness and death [ 2 , 3 ]. A good diet sets children on the trail to persist and succeed. Well-nourished children raise, mature, acquire, play, contribute and participate while undernutrition mugs children of their complete potential, with concerns for teenagers, nations, and thus the world [ 4 ]. Measures of under-five children’s undernourishment are familiarized to touch on development progress and socioeconomic inequalities in several low and middle-income countries. Scarce food within the primary thousand days of duration may a consequence of reduced physical development, which integrates a long-term influence on power thus resulting in reduced educational performance and economic productivity in maturity [ 5 ]. The three commonly known measures of under-five children’s undernourishment indicators are stunting, wasting, and underweight [ 6 ]. Being stunting and wasting indicate chronic and acute malnutrition respectively, underweight may be a pooled indicator, and comprises both acute (wasting) and chronic (stunting) malnutrition [ 6 ]. So, different dimensions of undernourishment may take place concurrently in under-five children [ 6 ]. A baby who is simply very short for his or her age (low height for age) is known as stunting. Children tormented by stunting can suffer severely irreparable physical and cognitive injuries that go together with their stunted growth. The devastating consequences of stunting can last a lifespan and even touch the long run group [ 7 ]. A baby who is simply very thin for his or her height (low weight for height) is called wasting. It is the outcome of current fast weight loss or the failure to achieve weight. A wasted child has an increased risk of death, but management is feasible. Likewise, underweight states to a baby who is simply very small for his or her age (low weight for age) which is the weight for age less than negative 2 standard deviation(SD) of the WHO Child Growth Standards median [ 7 , 8 ]. Height for age, weight for height, and weight for age standard scores were calculated based on the 2006 WHO childhood growth criteria endorsed for worldwide sets [ 4 ]. Children are considered stunted after they have a height for age standard score below minus two compared with the WHO Child Growth criteria median of same age and gender. Wasting is defined by weight for height standard score (WHZ) below negative two and suggests acute undernutrition or rapid weight loss. Underweight is defined by weight for age standard score (WAZ) below negative two [ 9 ]. Globally malnutrition in children is exceptionally prevalent and remains a giant challenge [ 10 ]. Keeping with the worldwide organization Children’s Fund (UNICEF) report in 2020, 22% of youngsters under the age of 5 years are stunted, 12.6% are underweight and 6.7% are wasted [ 4 ]. Two districts in the world such as Asia and Africa bear the two in all the best burdens of undernutrition. Stunting affected around 149.2 million children age under-five in 2020. of those 53% of stunted under-five children lived in Asia and 30.7% lived in Africa [ 4 ]. During this year, wasting contributed to threatening the lives of an estimated 45.4 million under-five children. Of these, over two-thirds of all wasted under-five children are found in Asia and over one quarter was found in Africa [ 4 ]. However, a more detailed have a look at the distribution of undernutrition within the African region shows that Eastern Africa (32.6%) encompasses a better prevalence of stunting compared to Western Africa (30.9%), Central Africa (36.8%), Northern Africa (21.4%) and Southern Africa (23.3%) [ 4 ]. While Western Africa (6.9%) incorporates a better rate of wasting than the remainder of African regions, Southern Africa (3.2%), Central Africa (6.2%, Northern Africa (6.6%), and Eastern Africa (5.2%) [ 4 ]. These estimates reveal regional disparities within the distribution of undernutrition. Numerous studies have assessed determinants of childhood undernutrition [ 11 – 13 ] and haven't focused on the association between undernourishment indicators. The rest studies have focused on the relationship of babyhood undernourishment indicators with their factors [ 1 , 14 ], and have ignored the association of stunting, underweight, and wasting themselves while the other studies consider only single country [ 9 ], therefore the current study intended to investigate the association of under-five children stunting, underweight, and wasting using a log-linear model for the three-way table to evaluate the pairwise and three-dimensional association of indicators for Sub-Saharan region. The significance of showing the three-dimensional relationship is that it helps to judge whether childhood undernourishment indicators are distinct (main effects) or even taken integrated effect. The concurrent (interaction) effect would be reflected to represent possible relationship of the magnitude [ 1 ]. Methods Data source This study was supported the foremost current Demographic and Health Surveys (DHS) conducted within the 33 Sub-Saharan African countries. These datasets were merged to work out the association between three anthropometric measures among under-five children within the region. The DHS may be a nationally representative survey that collects data on basic health indicators like mortality, morbidity, planning service utilization, fertility, maternal and child health. the data for this study were taken from https://www.dhsprogram.com/data/dataset_admin/login_main.cfm . Demographic health (DHS) survey has different datasets (men, women, children, birth, and household datasets). For this study, we used the kid data set (KR file). within the KR file, all children who were born within the previous five years before the survey within the selected enumeration area were included. Sampling procedures The DHS used two stages of stratified sampling technique to select the study participants. We analyzed the DHS surveys conducted in the 33 Sub-Saharan African countries and a total weighted sample of 185,541 under-five children were included in the study. Variables of the study The three undernutrition indicators are measured through z-scores for height for age (stunting), weight for height (wasting), and weight for age (underweight) and are defined as \({Z}_{i}=\frac{{A}_{Ii}-\mu }{\sigma }\) [ 1 ] Where \({A}_{Ii}\) is the single (child) anthropometric indicator, \(\mu\) , and \(\sigma\) denote respectively to the median and standard deviation of the reference population [ 15 , 16 ]. The variables of interest produced were: stunted (0 = No if HAZ ≥ -2 and 1 = Yes if HAZ < − 2), wasted (0 = No if WHZ ≥ − 2 and 1 = Yes if WHZ < − 2), and underweight (0 = No if HAZ ≥ -2 and 1 = Yes if HAZ < − 2). The procedure for calculating the indicators was founded on the 2006 WHO Child Growth Criteria [ 15 , 16 ]. Statistical Analysis Log-linear model for three-way table The log-linear models are wont to see the many relations supported the cross-tabulation. A log-linear model may be a statistical model for the log of the predictable frequency and interpreted as generalized linear models which treat the cells counts as independent observations from the distribution with corresponding means up to the predictable cell counts. The Log-linear model is employed when all factors will be treated as dependent variables [ 17 ]. Consequently, during this study, the three factors (stunting, wasting, and underweight) were treated as responses, and also the focus is on their structure of relationship by modeling \({2}^{3}+1=9\) log-linear possible models [ 1 ]. Thus, nine log-linear models of the expected cell count from the three factors are presented below. Log-linear models are fitted to check the hypotheses about complex relations, but the parameter estimates are simply interpreted. Estimated parameters are log odds ratios of the associations. Model Expression Description \(\lambda +{\lambda }_{i}^{S}+{\lambda }_{j}^{U}+{\lambda }_{k}^{W}\) Complete independence among the three factors. Concurrent term is not included (pair-wise independent). \(\lambda +{\lambda }_{i}^{S}+{\lambda }_{j}^{U}+{\lambda }_{k}^{W}+{\lambda }_{ik}^{SW}\) This model encompasses the interaction between stunting and wasting. Being underweight is independent of stunting and wasting. \(\lambda +{\lambda }_{i}^{S}+{\lambda }_{j}^{U}+{\lambda }_{k}^{W}+{\lambda }_{ij}^{SU}\) This model holds the interaction between stunting and being underweight. Wasting is independent of stunting and being underweight. \(\lambda +{\lambda }_{i}^{S}+{\lambda }_{j}^{U}+{\lambda }_{k}^{W}+{\lambda }_{jk}^{UW}\) This model encompasses the interaction between underweight and wasting. Stunting is independent of wasting and being underweight. Log of the mean of cell counts = \(\text{l}\text{o}\text{g}\left({\mu }_{ijk}\right)\) \(\lambda +{\lambda }_{i}^{S}+{\lambda }_{j}^{U}+{\lambda }_{k}^{W}+{\lambda }_{ij}^{SU}+{\lambda }_{ik}^{SW}\) This model comprehends the interaction terms between underweight and stunting; stunting and wasting. Wasting & underweight are temporarily independent of stunting. \(\lambda +{\lambda }_{i}^{S}+{\lambda }_{j}^{U}+{\lambda }_{k}^{W}+{\lambda }_{ik}^{SW}+{\lambda }_{jk}^{UW}\) The model encompasses the interaction terms between wasting and underweight; wasting and stunting. Stunting and being underweight are independent of wasting. \(\lambda +{\lambda }_{i}^{S}+{\lambda }_{j}^{U}+{\lambda }_{k}^{W}+{\lambda }_{ij}^{SU}+{\lambda }_{jk}^{UW}\) The model encompasses the interaction terms between wasting and underweight; underweight and stunting. Stunting & wasting are conditionally independent of being underweight. \(\lambda +{\lambda }_{i}^{S}+{\lambda }_{j}^{U}+{\lambda }_{k}^{W}+{\lambda }_{ij}^{SU}+{\lambda }_{jk}^{UW}+{\lambda }_{ik}^{SW}\) Homogenous relation (every factor have relation with each other, but all three factors have no interaction between them) \(\lambda +{\lambda }_{i}^{S}+{\lambda }_{j}^{U}+{\lambda }_{k}^{W}+{\lambda }_{ij}^{SU}+{\lambda }_{jk}^{UW}+{\lambda }_{ik}^{SW}+{\lambda }_{ijk}^{SUW}\) All possible interactions between the factors are included in the model. S, U, W represents stunting, underweight, and wasting respectively; \({\lambda }_{i}^{S}, {\lambda }_{j}^{U}and {\lambda }_{k}^{W}\) are represents stunting, underweight and wasting effects respectively. Reminder that in the last model of the above table the interactions between stunting and underweight; underweight and wasting; stunting and wasting; stunting, underweight, and wasting are included. Unsaturated models can include in the interaction terms for tables with at least three variables. After, log-linear models are used to describe associations through two-factor terms than describing odds through single-factor terms [ 18 ]. Interaction terms resemble to associations among variables. To test whether the saturated (full) model is significantly better than the reduced model, we applied the test called goodness-of-fit tests of a log-linear model, including Chi-squared statistic \(\left({X}^{2}\right)\) and likelihood ratio test \(\left({G}^{2}\right)\) . The two tests can be used to choice the best-fitting model. Likewise, AIC is used to select the best model. The larger the values of \(\left({X}^{2}\right)\) , \(\left({G}^{2}\right),\) and AIC, the more evidence exists against independence [ 18 ]. Saturated model includes all possible main effects and interactions effects. For analyzing the data, the authors were used R version 4.0.5 with a 0.05 significance level. Results Descriptive characteristics of the study participants A weighted total sample size of 185,541 under-five children was included for this study (Table 1 ). Of these, 75,287 (40.6%) from East Africa, 70,346 (37.9%) from West Africa, 3,784 (3.8%) in Southern Africa and 36,122 (19.5%) in Central Africa. Regarding to the nutritional status of under-five children, 55.7% of under-five children in Burundi were stunted, 36.2% and 18.1% of under-five children in Niger were underweight and wasted. This is the highest prevalence of stunted, underweight, and wasted respectively among sampled children in Sub-Saharan African countries. Table 1 Number of under-five children included in the study, and survey years of the countries Region Country Sample size (weighted) Prevalence of stunting Prevalence of underweight Prevalence of wasting DHS year Eastern region Burundi 6213 55.7 29.2 5.1 2016/2017 Comoros 2468 1.3 15.6 11.1 2012 Ethiopia 9494 38.3 23.3 10.1 2016 Kenya 17291 25.8 10.6 4.1 2014 Malawi 5127 36.5 11.0 2.7 2015/2016 Rwanda 3593 37.7 9.1 2.3 2014/2015 Tanzania 8758 34.0 13.4 4.7 2015/2016 Uganda 8630 28.2 9.9 3.7 2016 Zambia 8545 34.4 11.6 4.2 2018 Zimbabwe 5166 25.9 25.9 3.5 2015 Table 1 . (continued) Region Country Sample size (weighted) Prevalence of stunting Prevalence of underweight Prevalence of wasting DHS year Southern region Lesotho 1298 32.4 10.8 3.1 2014 Namibia 1438 22.0 10.0 7.6 2013 South Africa 1049 25.4 5.2 2.5 2016 Western region Burkina Faso 6660 34.5 25.7 15.8 2010 Benin 11682 31.5 16.3 5.0 2017/2018 Cȏte d’Ivoire 3041 29.8 14.7 7.8 2011/2012 Ghana 2636 17.9 10.8 4.7 2014 The Gambia 3503 16.9 11.3 5.3 2019/2020 Guinea 3278 30.7 15.3 8.9 2018 Liberia 2161 28.8 10.0 3.7 2019/2020 Mali 8764 26.6 18.2 8.9 2018 Nigeria 11361 36.4 21.2 6.8 2018 Niger 5113 43.3 36.2 18.10 2012 Sierra leone 4006 28.8 13.1 5.6 2019 Senegal 5082 17.5 14.0 8.0 2019 Togo 3058 26.7 15.7 6.8 2013/2014 Central region Angola 5837 36.8 18.3 5.0 2015/2016 Chad 9805 39.6 28.8 13.2 2014/2015 Congo 3859 23.1 11.1 5.6 2011/2012 Dr. Congo 7900 42.3 22.4 7.9 2013/2014 Cameroon 4646 28.8 10.8 4.4 2018 Gabon 2792 16.0 5.9 3.5 2012 Sao Tome Principe 1285 29.4 13.2 10.4 2008/2009 Table 2 Cross classification of stunting, wasting, and underweight Wasting Underweight Total No Yes No Stunting No 115306 2038 117344 Yes 35115 20425 55540 Yes Stunting No 4215 4219 8434 Yes 0 4223 4223 Total 154636 30905 185541 Table 2 shows cross-tabulation of wasting, stunting, and underweight. There were 32.21% stunted children, 16.67% underweight children and 6.82% wasted children from all sampled children in the region. Table 3 Categories of the Composite Indicator of Anthropometric Failure CIAF category Frequency (%) No failure 115306 (62.15) Stunted only 35115 (18.93) Underweight only 2038 (1.10) Wasted only 4215 (2.27) Stunted and underweight 20425 (11.00) Wasted and underweight 4219 (2.27) Stunted, wasted, and underweight 4223 (2.28) Figure 1 shows that the multiple correspondence analysis (MCA) of the three dimensions of childhood undernutrition. There is no relationship between chronic and acute malnutrition as wasting in the first and third quadrants and stunting in the second and fourth quadrants. Whereas, underweight is located on the boundary line and indicated that it had a relationship with both chronic and acute malnutrition. The whole prevalence of undernourishment for under-five years children can be calculated based on composite index of anthropometric failure (CIAF) [ 19 ]. Rendering to CIAF arrangement, under-five years’ children can be separated to No failure; Stunted only; Underweight only; Wasted only; both Stunted and underweight; both underweight and wasted, and the combination of three indicators (stunted, underweight and wasted. Therefore, the classifications of CIAF are presented in Table 3 and it shows Classifications of the Composite Indicator of Anthropometric Failure along with its frequency calculated from Table 2 . Based on the result about 37.85% = {(18.93 + 1.10 + 2.27 + 11.0 + 2.27 + 2.28) %} of under-five children were suffered with undernourishment whereas 62.15% were not suffered with undernourishment from the sampled children in Sub-Saharan Africa. This suggests that 37.85% of under-five children were malnourished. This implies that the total prevalence of undernourishment in under-five years’ children is about 37.85% in the region. The CIAF result does not indicate any information on the prevalence of undernourishment indicators relative to total undernourishment, but it shows total prevalence of undernourishment. Table 4 The goodness of fits tests for log-linear models relating stunting (S), wasting (W), and underweight (U) Model Log-linear model AIR G 2 (Likelihood ratio) X 2 (Pearson) DF P-value G 2 X 2 1 (S, U, W) 61910 61828.08 63535.70 4 < 0.001 < 0.001 2 (W, SW) 61910 61819.84 62904.10 3 < 0.001 < 0.001 3 (W, US) 25540 25448.58 44893.43 3 < 0.001 < 0.001 4 (S, UW) 44440 44354.49 44112.59 3 < 0.001 < 0.001 5 (US, SW) 25530 25440.33 45284.19 2 < 0.001 < 0.001 6 (UW, SW) 44440 44346.24 44103.22 2 < 0.001 < 0.001 7 (US, UW) 8065 7974.99 7631.01 2 < 0.001 < 0.001 8 (US, UW, SW) 329.60 237.98 127.19 1 < 0.001 < 0.001 9 (USW) 93.50 0 0 0 1.0 1.0 The likelihood ratios chi-square ( \({G}^{2}\) ), Pearson chi-square ( \({x}^{2}\) ), and Akaike Information Criteria (AIC) are reported in Table 4 . The observed and fitted cell counts are associated with the fit statistics. The null hypothesis is stated the observed and also fitted cell counts are identical (the data is well fitted by the model). The choice hypothesis states that the observed and the fitted cell counts are different. A P-value less than 0.05 level of significance for the fit statistics indicates stronger suggestion in contradiction of the model’s goodness of fit and a P-value superior than 0.05 level of significance for the fit statistics displays strong suggestion that the model fits the data well. The p-value for models one to eight are less than the significance level, therefore model 1 to eight don't fit the data well, while the p-value for the fit statistics is greater than the 0.05 level of significance (P-value = 1.000) and has the tiniest AIC value for model 9. Accordingly, the results of the saturated model (model 9) are presented and interpreted further. Table 5 shows the results of the saturated model (model 9). The null hypothesis of the individual test of coefficient of the interaction term is stated as there is no interaction term between indicators of undernourishment. Therefore, there is a highly significant interaction between underweight and stunting since the P-value of the estimates of the interaction term Stunting*underweight is smaller than the 0.05 significance level (P-value < 0.001). there's also a highly significant interaction between underweight and wasting (P-value < 0.001), but no interaction between stunting and wasting because the P-value is way greater than 0.05 significance level (P-value = 0.999). Also, the complete log-linear model displays a scarcity of a three-factor relationship among undernourishment indicators (P-value = 1.000). Table 5 Estimates for the saturated log-linear model for stunting, wasting and underweight Coefficient Estimate Standard error Z-value P-value Intercept 11.66 0.003 3957.78 < 0.001 Underweight -4.04 0.022 -180.60 < 0.001 Stunting -1.19 0.006 -195.07 < 0.001 Wasting -3.31 0.016 -211.00 < 0.001 Underweight and stunting 3.49 0.024 145.47 < 0.001 Underweight and wasting 4.04 0.031 129.37 < 0.001 Stunting and wasting -29.46 42250 -0.001 0.999 Underweight, stunting, and, wasting 27.16 42250 0.001 0.999 Discussion In the current study, a log-linear model was fitted for the three-way table to investigate the relationship among undernutrition indicators. Within a log-linear model, the relationship of the undernutrition indicators was shown by the concurrent terms. The log-linear models have the benefit of determining the three-way concurrent terms besides exploring pairwise relationships, [ 9 , 17 , 19 ]. Hence the p-values for the saturated model (model 9) are bigger than the 0.05 significance level (P-value = 1.000) as compared to the rest unsaturated models; data were well fitted by the saturated log-linear model (model 9). The saturated model indicates that being underweight is a statistically significant association with stunting (P-value < 0.001). Saturated model also displays that being underweight and wasting are statistically significant relationships (P value < 0.001). These findings coincide with earlier studies conducted in Malawi, Ethiopia and India respectively [ 1 , 9 , 14 ] respectively. Besides, this result's agreed with fact that underweight can be a composite measure of stunting and wasting [ 16 , 20 ]. The current study indicates stunting and wasting aren't interrelated (P-value = 0.999). This finding goes with studies done in Malawi, Ethiopia, and India [ 1 , 9 , 14 ] respectively. Furthermore, the saturated model shows the three-way interaction among undernourishment indicators was insignificant (P-value = 1.000). This finding also coincides with the findings in Malawi, Ethiopia, and India [ 1 , 9 , 14 ]. The dearth of three-way interaction indicates that undernourishment indicators have a statistically significant valid multidimensional nature. Conclusion The current study concludes that both stunting and wasting have significantly correlated with being underweight. The observed relationship of both stunting and wasting with underweight doesn't imply one undernutrition indicator causes the other indicator because the analysis was based on cross-sectional data. furthermore, the study suggested that stunting and wasting were not related. Keeping the above result, the present study concludes that there is no three-way association among undernourishment indicators. From the dearth of three-way association, the authors conclude that the undernourishment indicators of under-five children have multidimensional characteristics. Accordingly, the concerned body should consider the three dimensions simultaneously to estimate the particular load of under-five children undernourishment as they do not seem to be ended to every other. Lastly, the authors recommended that supplementary studies can undertake whether a causal relationship occurs between the indications or not through the data from prospective or retrospective cohort studies. Abbreviations AIC: Akaike’s information criterion; CIAF: Composite Indicator of Anthropometric Failure; EDHS: Demographic and Health Survey; HAZ: Height-for-age z-score; UNICEF: United Nations International Children's Emergency Fund; WAZ: Weight for age z-score; WHO: World health organization; WHZ: Weight for height z-score Declarations Acknowledgments The authors are grateful to the measure of DHS program for giving us permission to use the data for this study. Authors’ contributions AA wrote the proposal, analyzed the data and manuscript writing. YA accredited the proposal with revisions, analysis the data and manuscript writing. Both YA and AA read and approved the very last manuscript. Funding The authors have no support or funding to report. Availability of data and materials The data used in this study are available from http://dhsprogram.com. Ethics approval and consent to participate This study was based on an analysis of secondary data with all identifier information removed. The Institutional Review Board (IRB) of Inner City Fund (ICF) International Macro at Fairfax, Virginia in the USA reviewed and approved the MEASURE Demographic and Health Surveys Project Phase III. The 2010–2018 DHS’s were categorized under that approval. The Institutional Review Board (IRB) of Inner City Fund (ICF) International Macro complied with the United States Department of Health and Human Services guidelines and requirements for the “Protection of Human Subjects” (45 CFR 46). All protocols were carried out in accordance with relevant guidelines and regulations on confidentiality, benevolence, non-maleficence, and informed consent. All study participants gave written informed consent before participation and all information was collected confidentially. DHS Program has remained consistent with confidentiality and informed consent over the years. We obtained express approval to use the data from ICF Macro with Accession number 140625. No further approval was required for this study. The data owners can be contacted at https://dhsprogram.com/Data/terms-of-use.cfm and data can be found at https://www.dhsprogram.com/data/dataset_admin/login_main.cfm. Further documentations on ethical issues relating to the surveys are available at http://dhsprogram.com. Consent for publication Not applicable Competing interests The authors declare that they have no competing interests. Author details AAA: Department of Statistics, Mekdela Amba University, P.O. Box: 32, Tuluawlyia, Ethiopia. YAA: Department of Rural Development and Agricultural Extension, Mekdela Amba University, P.O. Box: 32, Tuluawlyia, Ethiopia References Ngwira, A., E.C. Munthali, and K.D. Vwalika, Analysis on the association among stunting, wasting and underweight in Malawi: an application of a log-linear model for the three-way table . Journal of public health in Africa, 2017. 8 (1). 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Agresti, A., Categorical data analysis . 2003: John Wiley & Sons. Al-Sadeeq, A.H., A.Z. Bukair, and A.-W.M. Al-Saqladi, Assessment of undernutrition using Composite Index of Anthropometric Failure among children aged < 5 years in rural Yemen . Eastern Mediterranean Health Journal, 2018. 24 (12). Sellen, D., Physical Status: The Use and Interpretation of Anthropometry. Report of a WHO Expert Committee. WHO Technical Report Series No. 854. Pp. 452. (WHO, Geneva, 1995.) Swiss Fr 71.00. Journal of Biosocial Science, 1998. 30 (1): p. 135–144. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1649758","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":109166169,"identity":"3040f1ee-cf78-41fc-8c85-0b832277c7cc","order_by":0,"name":"Abebew Aklog Asmare","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYDACHuYGBoYKGzkQ+8AD4rQwArWcSTMGa0kgWgtj2+HEBhCHKC38PQcbH35hS0ufH3b4IdAWOzndBgJaJM42NhvL8NjkbrydZgDUkmxsdoCQNecZ26QlJNJyN85OAGk5kLiNkBZ5sBaDw+mGs9M/EKfF4Gxjm+SHhMMJ8tI5RNpieOZgszHDgTTDDdI5BQcSDIjwi9yZ5IMPf/6zkZefnb75w4cKOznC3gcCZh6QC8EqDYhQDgKMP4CEfAORqkfBKBgFo2DkAQAd6EoCZIStmAAAAABJRU5ErkJggg==","orcid":"","institution":"Mekdela Amba University","correspondingAuthor":true,"prefix":"","firstName":"Abebew","middleName":"Aklog","lastName":"Asmare","suffix":""},{"id":109166170,"identity":"6aa44631-be66-45cc-8134-16b44fa51b98","order_by":1,"name":"Yitateku Adugna Agmas","email":"","orcid":"","institution":"Mekdela Amba University","correspondingAuthor":false,"prefix":"","firstName":"Yitateku","middleName":"Adugna","lastName":"Agmas","suffix":""}],"badges":[],"createdAt":"2022-05-12 13:29:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1649758/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1649758/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":22100069,"identity":"f153552d-8f85-4185-9d70-c1ce97b868e2","added_by":"auto","created_at":"2022-05-31 21:34:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":31207,"visible":true,"origin":"","legend":"\u003cp\u003eMultiple correspondence analyses of three indicators of child undernutrition\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1649758/v1/46714029485750107a053b59.png"},{"id":26228958,"identity":"0a603d15-7686-43cc-8bcf-9b9b8b5dd8e8","added_by":"auto","created_at":"2022-09-08 18:44:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":498883,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1649758/v1/6890d55c-3ed9-4fa4-b935-7d8c4dedab1c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the association of undernourishment indicators for under-five children in Sub-Saharan Africa: an application of log-linear model for three- way table","fulltext":[{"header":"Background","content":"\u003cp\u003eChildhood undernourishment was a central public health difficulty [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It touches children's mental and physical progress, raises the outlook of infections, and meaningfully contributes to the child\u0026rsquo;s illness and death [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. A good diet sets children on the trail to persist and succeed. Well-nourished children raise, mature, acquire, play, contribute and participate while undernutrition mugs children of their complete potential, with concerns for teenagers, nations, and thus the world [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Measures of under-five children\u0026rsquo;s undernourishment are familiarized to touch on development progress and socioeconomic inequalities in several low and middle-income countries. Scarce food within the primary thousand days of duration may a consequence of reduced physical development, which integrates a long-term influence on power thus resulting in reduced educational performance and economic productivity in maturity [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe three commonly known measures of under-five children\u0026rsquo;s undernourishment indicators are stunting, wasting, and underweight [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Being stunting and wasting indicate chronic and acute malnutrition respectively, underweight may be a pooled indicator, and comprises both acute (wasting) and chronic (stunting) malnutrition [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. So, different dimensions of undernourishment may take place concurrently in under-five children [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA baby who is simply very short for his or her age (low height for age) is known as stunting. Children tormented by stunting can suffer severely irreparable physical and cognitive injuries that go together with their stunted growth. The devastating consequences of stunting can last a lifespan and even touch the long run group [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A baby who is simply very thin for his or her height (low weight for height) is called wasting. It is the outcome of current fast weight loss or the failure to achieve weight. A wasted child has an increased risk of death, but management is feasible. Likewise, underweight states to a baby who is simply very small for his or her age (low weight for age) which is the weight for age less than negative 2 standard deviation(SD) of the WHO Child Growth Standards median [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHeight for age, weight for height, and weight for age standard scores were calculated based on the 2006 WHO childhood growth criteria endorsed for worldwide sets [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Children are considered stunted after they have a height for age standard score below minus two compared with the WHO Child Growth criteria median of same age and gender. Wasting is defined by weight for height standard score (WHZ) below negative two and suggests acute undernutrition or rapid weight loss. Underweight is defined by weight for age standard score (WAZ) below negative two [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGlobally malnutrition in children is exceptionally prevalent and remains a giant challenge [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Keeping with the worldwide organization Children\u0026rsquo;s Fund (UNICEF) report in 2020, 22% of youngsters under the age of 5 years are stunted, 12.6% are underweight and 6.7% are wasted [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Two districts in the world such as Asia and Africa bear the two in all the best burdens of undernutrition. Stunting affected around 149.2\u0026nbsp;million children age under-five in 2020. of those 53% of stunted under-five children lived in Asia and 30.7% lived in Africa [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. During this year, wasting contributed to threatening the lives of an estimated 45.4\u0026nbsp;million under-five children. Of these, over two-thirds of all wasted under-five children are found in Asia and over one quarter was found in Africa [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, a more detailed have a look at the distribution of undernutrition within the African region shows that Eastern Africa (32.6%) encompasses a better prevalence of stunting compared to Western Africa (30.9%), Central Africa (36.8%), Northern Africa (21.4%) and Southern Africa (23.3%) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. While Western Africa (6.9%) incorporates a better rate of wasting than the remainder of African regions, Southern Africa (3.2%), Central Africa (6.2%, Northern Africa (6.6%), and Eastern Africa (5.2%) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These estimates reveal regional disparities within the distribution of undernutrition.\u003c/p\u003e \u003cp\u003eNumerous studies have assessed determinants of childhood undernutrition [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and haven't focused on the association between undernourishment indicators. The rest studies have focused on the relationship of babyhood undernourishment indicators with their factors [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and have ignored the association of stunting, underweight, and wasting themselves while the other studies consider only single country [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], therefore the current study intended to investigate the association of under-five children stunting, underweight, and wasting using a log-linear model for the three-way table to evaluate the pairwise and three-dimensional association of indicators for Sub-Saharan region. The significance of showing the three-dimensional relationship is that it helps to judge whether childhood undernourishment indicators are distinct (main effects) or even taken integrated effect. The concurrent (interaction) effect would be reflected to represent possible relationship of the magnitude [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003eData source\u003c/h2\u003e\n \u003cp\u003eThis study was supported the foremost current Demographic and Health Surveys (DHS) conducted within the 33 Sub-Saharan African countries. These datasets were merged to work out the association between three anthropometric measures among under-five children within the region. The DHS may be a nationally representative survey that collects data on basic health indicators like mortality, morbidity, planning service utilization, fertility, maternal and child health. the data for this study were taken from\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u0026nbsp;\u003cspan class=\"RefSource\"\u003ehttps://www.dhsprogram.com/data/dataset_admin/login_main.cfm\u003c/span\u003e \u0026nbsp;\u003c/span\u003e. Demographic health (DHS) survey has different datasets (men, women, children, birth, and household datasets). For this study, we used the kid data set (KR file). within the KR file, all children who were born within the previous five years before the survey within the selected enumeration area were included.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec4\"\u003e\n \u003ch2\u003eSampling procedures\u003c/h2\u003e\n \u003cp\u003eThe DHS used two stages of stratified sampling technique to select the study participants. We analyzed the DHS surveys conducted in the 33 Sub-Saharan African countries and a total weighted sample of 185,541 under-five children were included in the study.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec5\"\u003e\n \u003ch2\u003eVariables of the study\u003c/h2\u003e\n \u003cp\u003eThe three undernutrition indicators are measured through z-scores for height for age (stunting), weight for height (wasting), and weight for age (underweight) and are defined as\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\({Z}_{i}=\\frac{{A}_{Ii}-\\mu }{\\sigma }\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({A}_{Ii}\\)\u003c/span\u003e\u003c/span\u003e is the single (child) anthropometric indicator, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\mu\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sigma\\)\u003c/span\u003e\u003c/span\u003e denote respectively to the median and standard deviation of the reference population [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. The variables of interest produced were: stunted (0\u0026thinsp;=\u0026thinsp;No if HAZ \u0026ge; -2 and 1\u0026thinsp;=\u0026thinsp;Yes if HAZ\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;2), wasted (0\u0026thinsp;=\u0026thinsp;No if WHZ\u0026thinsp;\u0026ge;\u0026thinsp;\u0026minus;\u0026thinsp;2 and 1\u0026thinsp;=\u0026thinsp;Yes if WHZ\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;2), and underweight (0\u0026thinsp;=\u0026thinsp;No if HAZ \u0026ge; -2 and 1\u0026thinsp;=\u0026thinsp;Yes if HAZ\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;2). The procedure for calculating the indicators was founded on the 2006 WHO Child Growth Criteria [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv class=\"Section2\" id=\"Sec6\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003c/div\u003e\u003cdiv class=\"Section2\" id=\"Sec7\"\u003e\u003ch2\u003eLog-linear model for three-way table\u003c/h2\u003e\u003cp\u003eThe log-linear models are wont to see the many relations supported the cross-tabulation. A log-linear model may be a statistical model for the log of the predictable frequency and interpreted as generalized linear models which treat the cells counts as independent observations from the distribution with corresponding means up to the predictable cell counts. The Log-linear model is employed when all factors will be treated as dependent variables [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. Consequently, during this study, the three factors (stunting, wasting, and underweight) were treated as responses, and also the focus is on their structure of relationship by modeling \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({2}^{3}+1=9\\)\u003c/span\u003e\u003c/span\u003e log-linear possible models [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. Thus, nine log-linear models of the expected cell count from the three factors are presented below. Log-linear models are fitted to check the hypotheses about complex relations, but the parameter estimates are simply interpreted. Estimated parameters are log odds ratios of the associations.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Taba\"\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003eExpression\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\lambda +{\\lambda }_{i}^{S}+{\\lambda }_{j}^{U}+{\\lambda }_{k}^{W}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eComplete independence among the three factors. Concurrent term is not included (pair-wise independent).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\lambda +{\\lambda }_{i}^{S}+{\\lambda }_{j}^{U}+{\\lambda }_{k}^{W}+{\\lambda }_{ik}^{SW}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eThis model encompasses the interaction between stunting and wasting. Being underweight is independent of stunting and wasting.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\lambda +{\\lambda }_{i}^{S}+{\\lambda }_{j}^{U}+{\\lambda }_{k}^{W}+{\\lambda }_{ij}^{SU}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eThis model holds the interaction between stunting and being underweight. Wasting is independent of stunting and being underweight.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\lambda +{\\lambda }_{i}^{S}+{\\lambda }_{j}^{U}+{\\lambda }_{k}^{W}+{\\lambda }_{jk}^{UW}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eThis model encompasses the interaction between underweight and wasting. Stunting is independent of wasting and being underweight.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eLog of the mean of cell counts =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{l}\\text{o}\\text{g}\\left({\\mu }_{ijk}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\lambda +{\\lambda }_{i}^{S}+{\\lambda }_{j}^{U}+{\\lambda }_{k}^{W}+{\\lambda }_{ij}^{SU}+{\\lambda }_{ik}^{SW}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eThis model comprehends the interaction terms between underweight and stunting; stunting and wasting. Wasting \u0026amp; underweight are temporarily independent of stunting.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\lambda +{\\lambda }_{i}^{S}+{\\lambda }_{j}^{U}+{\\lambda }_{k}^{W}+{\\lambda }_{ik}^{SW}+{\\lambda }_{jk}^{UW}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eThe model encompasses the interaction terms between wasting and underweight; wasting and stunting. Stunting and being underweight are independent of wasting.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\lambda +{\\lambda }_{i}^{S}+{\\lambda }_{j}^{U}+{\\lambda }_{k}^{W}+{\\lambda }_{ij}^{SU}+{\\lambda }_{jk}^{UW}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eThe model encompasses the interaction terms between wasting and underweight; underweight and stunting. Stunting \u0026amp; wasting are conditionally independent of being underweight.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\lambda +{\\lambda }_{i}^{S}+{\\lambda }_{j}^{U}+{\\lambda }_{k}^{W}+{\\lambda }_{ij}^{SU}+{\\lambda }_{jk}^{UW}+{\\lambda }_{ik}^{SW}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eHomogenous relation (every factor have relation with each other, but all three factors have no interaction between them)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\lambda +{\\lambda }_{i}^{S}+{\\lambda }_{j}^{U}+{\\lambda }_{k}^{W}+{\\lambda }_{ij}^{SU}+{\\lambda }_{jk}^{UW}+{\\lambda }_{ik}^{SW}+{\\lambda }_{ijk}^{SUW}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eAll possible interactions between the factors are included in the model.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eS, U, W represents stunting, underweight, and wasting respectively; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\lambda }_{i}^{S}, {\\lambda }_{j}^{U}and {\\lambda }_{k}^{W}\\)\u003c/span\u003e\u003c/span\u003e are represents stunting, underweight and wasting effects respectively.\u003c/p\u003e\u003cp\u003eReminder that in the last model of the above table the interactions between stunting and underweight; underweight and wasting; stunting and wasting; stunting, underweight, and wasting are included. Unsaturated models can include in the interaction terms for tables with at least three variables. After, log-linear models are used to describe associations through two-factor terms than describing odds through single-factor terms [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. Interaction terms resemble to associations among variables.\u003c/p\u003e\u003cp\u003eTo test whether the saturated (full) model is significantly better than the reduced model, we applied the test called goodness-of-fit tests of a log-linear model, including Chi-squared statistic \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left({X}^{2}\\right)\\)\u003c/span\u003e\u003c/span\u003e and likelihood ratio test \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left({G}^{2}\\right)\\)\u003c/span\u003e\u003c/span\u003e. The two tests can be used to choice the best-fitting model. Likewise, AIC is used to select the best model. The larger the values of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left({X}^{2}\\right)\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left({G}^{2}\\right),\\)\u003c/span\u003e\u003c/span\u003e and AIC, the more evidence exists against independence [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. Saturated model includes all possible main effects and interactions effects. For analyzing the data, the authors were used R version 4.0.5 with a 0.05 significance level.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv class=\"Section2\" id=\"Sec9\"\u003e\n \u003ch2\u003eDescriptive characteristics of the study participants\u003c/h2\u003e\n \u003cp\u003eA weighted total sample size of 185,541 under-five children was included for this study (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Of these, 75,287 (40.6%) from East Africa, 70,346 (37.9%) from West Africa, 3,784 (3.8%) in Southern Africa and 36,122 (19.5%) in Central Africa. Regarding to the nutritional status of under-five children, 55.7% of under-five children in Burundi were stunted, 36.2% and 18.1% of under-five children in Niger were underweight and wasted. This is the highest prevalence of stunted, underweight, and wasted respectively among sampled children in Sub-Saharan African countries.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eNumber of under-five children included in the study, and survey years of the countries\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample size (weighted)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePrevalence of stunting\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePrevalence of underweight\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePrevalence of wasting\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDHS year\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"10\"\u003e\n \u003cp\u003eEastern region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBurundi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2016/2017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComoros\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKenya\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalawi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2015/2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRwanda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2014/2015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTanzania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2015/2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUganda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZambia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZimbabwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. (continued)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample size (weighted)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevalence of stunting\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevalence of underweight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevalence of wasting\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDHS year\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eSouthern region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLesotho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNamibia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"13\"\u003e\n \u003cp\u003eWestern region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBurkina Faso\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBenin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2017/2018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCȏte d\u0026rsquo;Ivoire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2011/2012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGhana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe Gambia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2019/2020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGuinea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiberia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2019/2020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMali\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNiger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSierra leone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSenegal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTogo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2013/2014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"7\"\u003e\n \u003cp\u003eCentral region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAngola\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2015/2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2014/2015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCongo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2011/2012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDr. Congo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2013/2014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCameroon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGabon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSao Tome Principe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2008/2009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCross classification of stunting, wasting, and underweight\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eWasting\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eStunting\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e115306\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2038\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e117344\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e35115\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e20425\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e55540\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eStunting\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4215\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4219\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e8434\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4223\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4223\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e154636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e185541\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows cross-tabulation of wasting, stunting, and underweight. There were 32.21% stunted children, 16.67% underweight children and 6.82% wasted children from all sampled children in the region.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCategories of the Composite Indicator of Anthropometric Failure\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCIAF category\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrequency (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115306 (62.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStunted only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35115 (18.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderweight only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2038 (1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWasted only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4215 (2.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStunted and underweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20425 (11.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWasted and underweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4219 (2.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStunted, wasted, and underweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4223 (2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows that the multiple correspondence analysis (MCA) of the three dimensions of childhood undernutrition. There is no relationship between chronic and acute malnutrition as wasting in the first and third quadrants and stunting in the second and fourth quadrants. Whereas, underweight is located on the boundary line and indicated that it had a relationship with both chronic and acute malnutrition.\u003c/p\u003e\n \u003cp\u003eThe whole prevalence of undernourishment for under-five years children can be calculated based on composite index of anthropometric failure (CIAF) [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. Rendering to CIAF arrangement, under-five years\u0026rsquo; children can be separated to No failure; Stunted only; Underweight only; Wasted only; both Stunted and underweight; both underweight and wasted, and the combination of three indicators (stunted, underweight and wasted. Therefore, the classifications of CIAF are presented in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and it shows Classifications of the Composite Indicator of Anthropometric Failure along with its frequency calculated from Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Based on the result about 37.85% = {(18.93\u0026thinsp;+\u0026thinsp;1.10\u0026thinsp;+\u0026thinsp;2.27\u0026thinsp;+\u0026thinsp;11.0\u0026thinsp;+\u0026thinsp;2.27\u0026thinsp;+\u0026thinsp;2.28) %} of under-five children were suffered with undernourishment whereas 62.15% were not suffered with undernourishment from the sampled children in Sub-Saharan Africa. This suggests that 37.85% of under-five children were malnourished. This implies that the total prevalence of undernourishment in under-five years\u0026rsquo; children is about 37.85% in the region. The CIAF result does not indicate any information on the prevalence of undernourishment indicators relative to total undernourishment, but it shows total prevalence of undernourishment.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe goodness of fits tests for log-linear models relating stunting (S), wasting (W), and underweight (U)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLog-linear model\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAIR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eG\u003csup\u003e2\u003c/sup\u003e (Likelihood ratio)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e (Pearson)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eX\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(S, U, W)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61828.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63535.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(W, SW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61819.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62904.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(W, US)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25448.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44893.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(S, UW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44354.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44112.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(US, SW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25440.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45284.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(UW, SW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44346.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44103.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(US, UW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7974.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7631.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(US, UW, SW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e329.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e237.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(USW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe likelihood ratios chi-square (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({G}^{2}\\)\u003c/span\u003e\u003c/span\u003e), Pearson chi-square (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}^{2}\\)\u003c/span\u003e\u003c/span\u003e), and Akaike Information Criteria (AIC) are reported in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The observed and fitted cell counts are associated with the fit statistics. The null hypothesis is stated the observed and also fitted cell counts are identical (the data is well fitted by the model). The choice hypothesis states that the observed and the fitted cell counts are different. A P-value less than 0.05 level of significance for the fit statistics indicates stronger suggestion in contradiction of the model\u0026rsquo;s goodness of fit and a P-value superior than 0.05 level of significance for the fit statistics displays strong suggestion that the model fits the data well. The p-value for models one to eight are less than the significance level, therefore model 1 to eight don\u0026apos;t fit the data well, while the p-value for the fit statistics is greater than the 0.05 level of significance (P-value\u0026thinsp;=\u0026thinsp;1.000) and has the tiniest AIC value for model 9. Accordingly, the results of the saturated model (model 9) are presented and interpreted further. Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows the results of the saturated model (model 9). The null hypothesis of the individual test of coefficient of the interaction term is stated as there is no interaction term between indicators of undernourishment. Therefore, there is a highly significant interaction between underweight and stunting since the P-value of the estimates of the interaction term Stunting*underweight is smaller than the 0.05 significance level (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). there\u0026apos;s also a highly significant interaction between underweight and wasting (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but no interaction between stunting and wasting because the P-value is way greater than 0.05 significance level (P-value\u0026thinsp;=\u0026thinsp;0.999). Also, the complete log-linear model displays a scarcity of a three-factor relationship among undernourishment indicators (P-value\u0026thinsp;=\u0026thinsp;1.000).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab5\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEstimates for the saturated log-linear model for stunting, wasting and underweight\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandard error\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZ-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3957.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-180.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStunting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-195.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWasting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-211.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderweight and stunting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderweight and wasting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStunting and wasting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-29.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderweight, stunting, and, wasting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the current study, a log-linear model was fitted for the three-way table to investigate the relationship among undernutrition indicators. Within a log-linear model, the relationship of the undernutrition indicators was shown by the concurrent terms. The log-linear models have the benefit of determining the three-way concurrent terms besides exploring pairwise relationships, [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Hence the p-values for the saturated model (model 9) are bigger than the 0.05 significance level (P-value\u0026thinsp;=\u0026thinsp;1.000) as compared to the rest unsaturated models; data were well fitted by the saturated log-linear model (model 9). The saturated model indicates that being underweight is a statistically significant association with stunting (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Saturated model also displays that being underweight and wasting are statistically significant relationships (P value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings coincide with earlier studies conducted in Malawi, Ethiopia and India respectively [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] respectively. Besides, this result's agreed with fact that underweight can be a composite measure of stunting and wasting [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The current study indicates stunting and wasting aren't interrelated (P-value\u0026thinsp;=\u0026thinsp;0.999). This finding goes with studies done in Malawi, Ethiopia, and India [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] respectively. Furthermore, the saturated model shows the three-way interaction among undernourishment indicators was insignificant (P-value\u0026thinsp;=\u0026thinsp;1.000). This finding also coincides with the findings in Malawi, Ethiopia, and India [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The dearth of three-way interaction indicates that undernourishment indicators have a statistically significant valid multidimensional nature.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe current study concludes that both stunting and wasting have significantly correlated with being underweight. The observed relationship of both stunting and wasting with underweight doesn't imply one undernutrition indicator causes the other indicator because the analysis was based on cross-sectional data. furthermore, the study suggested that stunting and wasting were not related. Keeping the above result, the present study concludes that there is no three-way association among undernourishment indicators. From the dearth of three-way association, the authors conclude that the undernourishment indicators of under-five children have multidimensional characteristics. Accordingly, the concerned body should consider the three dimensions simultaneously to estimate the particular load of under-five children undernourishment as they do not seem to be ended to every other. Lastly, the authors recommended that supplementary studies can undertake whether a causal relationship occurs between the indications or not through the data from prospective or retrospective cohort studies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAIC: Akaike\u0026rsquo;s information criterion; CIAF: Composite Indicator of Anthropometric Failure; EDHS: Demographic and Health Survey; HAZ: Height-for-age z-score; UNICEF: United Nations International Children\u0026apos;s Emergency Fund; WAZ: Weight for age z-score; WHO: World health organization; WHZ: Weight for height z-score \u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to the measure of DHS program for giving us permission to use the data for this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAA wrote the proposal, analyzed the data and manuscript writing. YA accredited the proposal with revisions, analysis the data and manuscript writing. Both YA and AA read and approved the very last manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no support or funding to report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are available from http://dhsprogram.com.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was based on an analysis of secondary data with all identifier information removed. The Institutional Review Board (IRB) of Inner City Fund (ICF) International Macro at Fairfax, Virginia in the USA reviewed and approved the MEASURE Demographic and Health Surveys Project Phase III. The 2010\u0026ndash;2018 DHS\u0026rsquo;s were categorized under that approval. The Institutional Review Board (IRB) of Inner City Fund (ICF) International Macro complied with the United States Department of Health and Human Services guidelines and requirements for the \u0026ldquo;Protection of Human Subjects\u0026rdquo; (45 CFR 46). All protocols were carried out in accordance with relevant guidelines and regulations on confidentiality, benevolence, non-maleficence, and informed consent. All study participants gave written informed consent before participation and all information was collected confidentially. DHS Program has remained consistent with confidentiality and informed consent over the years. We obtained express approval to use the data from ICF Macro with Accession number 140625. No further approval was required for this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe data owners can be contacted at https://dhsprogram.com/Data/terms-of-use.cfm and data can be found at https://www.dhsprogram.com/data/dataset_admin/login_main.cfm. Further documentations on ethical issues relating to the surveys are available at http://dhsprogram.com.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAAA: Department of Statistics, Mekdela Amba University, P.O. Box: 32, Tuluawlyia, Ethiopia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYAA: Department of Rural Development and Agricultural Extension, Mekdela Amba University, P.O. Box: 32, Tuluawlyia, Ethiopia \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eNgwira, A., E.C. Munthali, and K.D. Vwalika, \u003cem\u003eAnalysis on the association among stunting, wasting and underweight in Malawi: an application of a log-linear model for the three-way table\u003c/em\u003e. Journal of public health in Africa, 2017. \u003cstrong\u003e8\u003c/strong\u003e(1).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003ePelletier, D.L., et al., \u003cem\u003eThe effects of malnutrition on child mortality in developing countries\u003c/em\u003e. Bulletin of the World Health Organization, 1995. \u003cstrong\u003e73\u003c/strong\u003e(4): p.\u0026nbsp;443.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003ePelletier, D.L. and E.A. Frongillo, \u003cem\u003eChanges in child survival are strongly associated with changes in malnutrition in developing countries\u003c/em\u003e. The Journal of nutrition, 2003. \u003cstrong\u003e133\u003c/strong\u003e(1): p.\u0026nbsp;107\u0026ndash;119.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eOrganization, W.H., \u003cem\u003eLevels and trends in child malnutrition: UNICEF.\u003c/em\u003e 2021.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eAgho, K.E., et al., \u003cem\u003eChildhood undernutrition in three disadvantaged East African Districts: a multinomial analysis\u003c/em\u003e. BMC pediatrics, 2019. \u003cstrong\u003e19\u003c/strong\u003e(1): p.\u0026nbsp;1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eDe Onis, M. and M. Bl\u0026ouml;ssner, \u003cem\u003eThe World Health Organization global database on child growth and malnutrition: methodology and applications\u003c/em\u003e. International journal of epidemiology, 2003. \u003cstrong\u003e32\u003c/strong\u003e(4): p.\u0026nbsp;518\u0026ndash;526.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eTurck, D., et al., \u003cem\u003eWorld health organization 2006 child growth standards and 2007 growth reference charts: a discussion paper by the committee on nutrition of the European society for pediatric gastroenterology, hepatology, and nutrition.\u003c/em\u003e Journal of pediatric gastroenterology and nutrition, 2013. \u003cstrong\u003e57\u003c/strong\u003e(2): p.\u0026nbsp;258\u0026ndash;264.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eOrganization, W.H., \u003cem\u003eWorking together for health: the World health report 2006: policy briefs\u003c/em\u003e. 2006: World Health Organization.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKassie, G.W. and D.L. Workie, \u003cem\u003eExploring the association of anthropometric indicators for under-five children in Ethiopia\u003c/em\u003e. BMC Public Health, 2019. \u003cstrong\u003e19\u003c/strong\u003e(1): p.\u0026nbsp;1\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKandala, N.-B., et al., \u003cem\u003eMalnutrition among children under the age of five in the Democratic Republic of Congo (DRC): does geographic location matter?\u003c/em\u003e BMC public health, 2011. \u003cstrong\u003e11\u003c/strong\u003e(1): p.\u0026nbsp;1\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eTesema, G.A., et al., \u003cem\u003ePooled prevalence and associated factors of chronic undernutrition among under-five children in East Africa: a multilevel analysis\u003c/em\u003e. PloS one, 2021. \u003cstrong\u003e16\u003c/strong\u003e(3): p.\u0026nbsp;e0248637.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBoah, M., et al., \u003cem\u003eThe epidemiology of undernutrition and its determinants in children under five years in Ghana\u003c/em\u003e. Plos one, 2019. \u003cstrong\u003e14\u003c/strong\u003e(7): p.\u0026nbsp;e0219665.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eRicci, C., et al., \u003cem\u003eDeterminants of undernutrition prevalence in children aged 0\u0026ndash;59 months in sub-Saharan Africa between 2000 and\u003c/em\u003e 2015. \u003cem\u003eA report from the World Bank database.\u003c/em\u003e Public health nutrition, 2019. \u003cstrong\u003e22\u003c/strong\u003e(9): p.\u0026nbsp;1597\u0026ndash;1605.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eGupta, A.K. and K. Borkotoky, \u003cem\u003eExploring the multidimensional nature of anthropometric indicators for under-five children in India\u003c/em\u003e. Indian journal of public health, 2016. \u003cstrong\u003e60\u003c/strong\u003e(1): p.\u0026nbsp;68.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eButte, N.F., C. Garza, and M. de Onis, \u003cem\u003eEvaluation of the feasibility of international growth standards for school-aged children and adolescents\u003c/em\u003e. The Journal of nutrition, 2007. \u003cstrong\u003e137\u003c/strong\u003e(1): p.\u0026nbsp;153\u0026ndash;157.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eRutstein, S.O. and G. Rojas, \u003cem\u003eGuide to DHS statistics\u003c/em\u003e. Calverton, MD: ORC Macro, 2006. \u003cstrong\u003e38\u003c/strong\u003e.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eAgresti, A., \u003cem\u003eAn introduction to categorical data analysis\u003c/em\u003e. 2018: John Wiley \u0026amp; Sons.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eAgresti, A., \u003cem\u003eCategorical data analysis\u003c/em\u003e. 2003: John Wiley \u0026amp; Sons.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eAl-Sadeeq, A.H., A.Z. Bukair, and A.-W.M. Al-Saqladi, \u003cem\u003eAssessment of undernutrition using Composite Index of Anthropometric Failure among children aged \u0026lt; 5 years in rural Yemen\u003c/em\u003e. Eastern Mediterranean Health Journal, 2018. \u003cstrong\u003e24\u003c/strong\u003e(12).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSellen, D., \u003cem\u003ePhysical Status: The Use and Interpretation of Anthropometry. Report of a WHO Expert Committee. WHO Technical Report Series No. 854. Pp. 452. (WHO, Geneva, 1995.) Swiss Fr 71.00.\u003c/em\u003e Journal of Biosocial Science, 1998. \u003cstrong\u003e30\u003c/strong\u003e(1): p. 135\u0026ndash;144.\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Undernutrition, Log-linear, Association, Stunting, Underweight, Wasting, Multiple correspondence analysis","lastPublishedDoi":"10.21203/rs.3.rs-1649758/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1649758/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Childhood undernutrition was a central public health difficulty. Several studies have considered determinants of childhood undernutrition and haven't focused on the connection of undernourishment indicators, the remains have focused on the link of indicators with their factors and have ignored the connection of the dimensions themselves, therefore this study tested pairwise relationships among the dimensions.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eIn the current study multiple correspondence analysis (MCA) was applied to look at the visual association of the indicators. Moreover, the log-linear model was wont to see the model of the best suitable examine the nutritional status of youngsters. The interaction terms within the model have represented deviations from independence.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResult:\u003c/strong\u003e The result shows that out of 185,541 weighted sampled children 32.28%, 16.67%, and 6.82% were stunted, underweight, and wasted respectively. The overall prevalence of undernutrition was about 37.85%. The log-linear model showed that being underweight has an association with both stunting (P-value \u0026lt; 0.001), and wasting (P-value \u0026lt; 0.001). There was no association between stunting and wasting (P-value = 0.999). Also, there's no three-way relationship between stunting, wasting, and being underweight (P-value = 1.000).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The study shows that there is no three-way interaction between undernourishment indicators. This shows the three undernourishment indicators of under-five children have multi-dimensional characteristics. Accordingly, the alarmed body should assume the three indicators concurrently to assess the actual problem of childhood undernutrition as they are not terminated to each other.\u003c/p\u003e","manuscriptTitle":"Exploring the association of undernourishment indicators for under-five children in Sub-Saharan Africa: an application of log-linear model for three- way table","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-05-31 21:34:52","doi":"10.21203/rs.3.rs-1649758/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c2cff527-c786-45e3-b739-75d8ef051dc3","owner":[],"postedDate":"May 31st, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-09-08T18:44:21+00:00","versionOfRecord":[],"versionCreatedAt":"2022-05-31 21:34:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1649758","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1649758","identity":"rs-1649758","version":["v1"]},"buildId":"J0_U0BvcaRcwD8yVFaRlm","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}