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OBJECTIVES: Child malnutrition is a very serious health issue globally, particularly in emerging countries. Among South Asian countries, Pakistan has been observed to have a high prevalence of child malnutrition. In spite of the implementation of many health strategies and preventive measures for vulnerable populations, this issue is still unresolved and needs further investigation. The purpose of this study was to investigate the role of various social-, maternal-, and child-level factors considered to be responsible for nutritional health disparities among children. METHODS: An assessment method of malnutrition, i.e., Composite Index of Anthropometric Failure (CIAF), was used to detect the prevalence of malnutrition among children under 5 years of age in Pakistan in order to present a comprehensive view that was lacking conventional indices of malnutrition. A binary logistic regression model was fitted to assess the link between malnutrition and socioeconomic, maternal, and child attributes based on CIAF data compiled from weight-for-height, weight-for-age, and height-for-age Z-scores using data from the Pakistan Demographic Health Survey (2017-2018). RESULTS: A total of 4224 children under 5 years of age were included in the analysis. Approximately half of the children (45.34%) comprised anthropometric failures for the overall prevalence of undernutrition based on CIAF. The results of this study revealed that the leading determinant associated with CIAF was the child's age in months, small birth size, lack of breastfeeding, lack of maternal education, poor economic status of the household, and poor-quality water sources. The factors associated with stunting comprised the child's age in months, small child birth size, underweight maternal body mass index, and uneducated mothers. Only one factor-low household economic profile-was significantly associated with waste. Sindh, Baluchistan, and Khyber Pakhtunkhwa provinces had a higher risk of having wasted children. On the other hand, children aged 25-36 months had higher, small child birth size, underweight maternal BMI, un-educated mother, un-educated father, low economic profile of household experiencing of being underweight. CONCLUSIONS: The findings of this study reinforce the significance of maternal health, parental education, and household economic profile in the prevention of malnutrition within young children of adequate birth size, as well as better overall health care up to adolescence in Pakistan. Well-nourished individuals are a valuable human resource and a requirement for a nation's progress and prosperity. In emerging nations such evidence-based policies are crucial for fostering children's optimal physical and mental development to ensure a healthier future generation. Therefore, the execution of national health policies aimed at the improvement of maternal and societal factors could result in improved nutrition levels among children below 5 years of age in Pakistan.

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BACKGROUND: This study examines the levels and predictors of malnutrition in Indian children under 5 years of age. METHODS: Composite Index of Anthropometric Failure was applied to data from the India National Family Health Survey 2019-2021. A multivariable logistic regression model was used to assess the predictors. RESULTS: 52.59% of children experienced anthropometric failure. Child predictors of lower malnutrition risk included female gender (adjusted odds ratio (AOR) = 0.881) and average or large size at birth (AOR = 0.729 and 0.715, respectively, compared to small size). Higher birth order increased malnutrition odds (2nd-4th: AOR = 1.211; 5th or higher: AOR = 1.449) compared to firstborn. Maternal predictors of lower malnutrition risk included age 20-34 years (AOR = 0.806), age 35-49 years (AOR = 0.714) compared to 15-19 years, normal BMI (AOR = 0.752), overweight and obese BMI (AOR = 0.504) compared to underweight, and secondary or higher education vs. no education (AOR = 0.865). Maternal predictors of higher malnutrition risk included severe anemia vs. no anemia (AOR = 1.232). Protective socioeconomic factors included middle (AOR = 0.903) and rich wealth index (AOR = 0.717) compared to poor, and toilet access (AOR = 0.803). Children's malnutrition risk also declined with paternal education (primary: AOR = 0.901; secondary or higher: AOR = 0.822) vs. no education. Conversely, malnutrition risk increased with Hindu (AOR = 1.258) or Islam religion (AOR = 1.369) vs. other religions. CONCLUSIONS: Child malnutrition remains a critical issue in India, necessitating concerted efforts from both private and public sectors. A 'Health in All Policies' approach should guide public health leadership in influencing policies that impact children's nutritional status.

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BACKGROUND: The health of India's children has improved over the past thirty years. Rates of morbidity and anthropometric failure have decreased. What remains unknown, however, is how those patterns have changed when examined by socioeconomic status. We examine changes in 11 indicators of child health by household wealth and maternal education between 1993 and 2021 to fill this critical gap in knowledge. Doing so could lead to policies that better target the most vulnerable children. METHODS: We used data from five rounds of India's National Family Health Survey conducted in 1993, 1999, 2006, 2016, and 2021 for this repeated cross-sectional analysis. We studied mother-reported cases of acute respiratory illness and diarrhea, hemoglobin measurements for anemia, and height and weight measurements for anthropometric failure. We examined how the prevalence rates of each outcome changed between 1993 and 2021 by household wealth and maternal education. We repeated this analysis for urban and rural communities.  RESULTS: The socioeconomic gradient in 11 indicators of child health flattened between 1993 and 2021. This was in large part due to large reductions in the prevalence among children in the lowest socioeconomic groups. For most outcomes, the largest reductions occurred before 2016. Yet as of 2021, except for mild anemia, outcome prevalence remained the highest among children in the lowest socioeconomic groups. Furthermore, we show that increases in the prevalence of stunting and wasting between 2016 and 2021 are largely driven by increases in the severe forms of these outcomes among children in the highest socioeconomic groups. This finding underscores the importance of examining child health outcomes by severity. CONCLUSIONS: Despite substantial reductions in the socioeconomic gradient in 11 indicators of child health between 1993 and 2021, outcome prevalence remained the highest among children in the lowest socioeconomic groups in most cases. Thus, our findings emphasize the need for a continued focus on India's most vulnerable children.

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BACKGROUND: Composite Index of Anthropometric Failure (CIAF) combines all three forms of anthropometric failures to assess undernutrition status of children. There is no study on CIAF to identify the real and severe form of under nutrition among Ethiopian children that addressed community level factors. So, this study determined CIAF and identified important factors which helps to design appropriate intervention strategies by using multi-level advanced statistical model. METHODS: The study included 5,530 under five children and utilized a secondary data (EMDHS 2019) which was collected through community-based and cross-sectionally from March 21 to June 28, 2019. Composite index of anthropometric failure among under five children was assessed and a two-stage sampling technique was used to select the study participants. Descriptive summary statistics was computed. A multi-level binary logistic regression model was employed to identify important predictors of CIAF in under five children. Adjusted odds ratio with its 95% CI was estimated and level of significance 0.05 was used to determine significant predictors of CIAF. RESULTS: The prevalence of composite index of anthropometric failure (CIAF) was 40.69% (95% CI: 39.41, 42.00) in Ethiopia. Both individual and community level predictors were identified for CIAF in under five children. Among individual level predictors being male sex, older age, short birth interval, from mothers who have not formal education, and from poor household wealth quintile were associated with higher odds of CIAF among under five children. Low community women literacy and being from agriculturally based regions were the community level predictors that were associated with higher odds of CIAF in under five children in Ethiopia. CONCLUSIONS: The burden of composite index of anthropometric failure in under five children was high in Ethiopia. Age of child, sex of child, preceding birth interval, mother's education, household wealth index, community women literacy and administrative regions of Ethiopia were identified as significant predictors of CIAF. Therefore, emphasis should be given for those factors to decrease the prevalence of CIAF in under five children in Ethiopia.

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Undernutrition in childhood is a crucial public health issue in Ethiopia. Yet, more than an assessment of undernutrition using conventional index is needed to conclude the overall prevalence of undernutrition among children aged 6-23 months. Therefore, this study aimed to assess the prevalence of undernutrition using composite index of anthropometric failure and its associated factors among children aged 6-23 months in Southwest Ethiopia. A community-based cross-sectional study was employed among 440 mother-child pairs selected using a two-stage cluster sampling method in the rural Kersa district, Jimma Zone, Southwest Ethiopia. A pretested structured questionnaire was used to collect data. Multivariable logistic regression analysis was employed to identify factors associated with undernutrition. Variables with a p-value of <.05 were considered statistically significant. The proportion of undernutrition using composite indexes of anthropometric failure was 57.3% among children aged 6-23 months. Children being male [AOR = 1.55; 95% CI (1.013, 2.373)], not met minimum acceptable diet (MAD) [AOR = 2.104; 95% CI (1.05, 4.214)], larger family size [AOR = 1.699; 95% CI (1.0791, 2.675)], having comorbidity [AOR = 3.31; 95% CI (2.068, 5.327)], and being in food insecurity household [AOR = 3.12; 95% CI (2.0, 4.868)] were more likely to be in anthropometric failure, whereas children from the mother who attended higher and above schooling [AOR = 0.244; 95% CI (0.093, 0.641)] were less likely to be in anthropometric failure. More than half of children aged 6-23 months were experienced anthropometric failure. Male children, those who have not received the MAD, come from larger families, have comorbidities, live in food-insecure households, and have mothers with higher education levels were found to be at higher risk of anthropometric failure.

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Background: Niger, relevant in light of current political coup, is one of the countries with the worst human development indicators, characterized by high fertility rates and extremely high infant mortality rates. Food insecurity in the region is alarming, leading to high malnutrition rates in children. This study aimed to evaluate an integral preventive-curative health program targeting children aged under 2 years in the health area of Tama, district of Bouza, Tahoua. Methodology: Anthropometric follow-up data of 6,962 children aged under 2 years were included in this study. These children received complete vaccination and malaria chemoprevention, and those older than 6 months received nutritional supplementation with a small quantity of lipid-based nutrient supplements. Fundamental growth indicators (height-for-age, weight-for-height, weight-for-age, and middle-upper arm circumference) and the Composite Index of Anthropometric Failure were calculated at the beginning and end of the program (mean time spent in the program: 14.5 ± 6.6 months) The evolution of these indicators was compared with those of a sample from a vertical vaccination program conducted in the neighboring region of Madarounfa on similar dates. Results: The proportion of children without anthropometric failure decreased from 59.5 to 40.2% (p < 0.001), with the categories that included stunting increasing the most. When analyzing the anthropometric indicators according to the months of compliance with the program, there was a slight improvement in the indicators of acute malnutrition, whereas those of chronic malnutrition worsened significantly. However, when compared with the Madarounfa sample, the children in the present study registered a significantly lower worsening in all three indicators: height-age (-0.46 vs. -2.44; p < 0.001), weight-height (+0.31 vs. -0.55; p < 0.001) and weight-age (-0.03 vs. -1.63; p < 0.001) difference. Conclusion: The comprehensive preventive-curative health program slightly slows the worsening of cumulative malnutrition in the early years of life in complex contexts, such as southern Niger.

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Malnutrition among children is pervasive in South Asia and there are also reports of overnutrition. To better understand this phenomenon, we need a composite measure. However, the existing measures such as CIAF (Composite Index of Anthropometric Failure) and its revised version have ignored the overnutrition aspect of the phenomenon. This study proposes an extended version of CIAF which also considers overnutrition. This new measure was compared with the existing measures by using data from 1990 to 2018 for three selected South Asian countries including Pakistan, India and Bangladesh. We also examined the effects of socioeconomic and environmental variables on the outcome variable. The results reveal that the new measure (ECIAF) is better at measuring the phenomena. The burden of overall malnutrition has been decreased in the region. However, an increase in the concomitant prevalence of wasting and underweight is observed in both Pakistan and India and stunting and overweight is observed only in India. Besides, political stability, prevalence of undernourishment, anemia in children, mother's education, household size, dependency ratio, air pollution and unimproved sanitation are significantly correlated with childhood malnutrition. The findings also testified to long-run cointegrating relationship among the variables.

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OBJECTIVES: To assess nutritional status of apparently-healthy under-five Indian children using Composite Index of Anthropometric Failure (CIAF) and to compare anthropometric failure prevalence using conventional indices and CIAF on World Health Organization (WHO) vs. synthetic Indian growth charts. METHODS: This observational study was conducted over 2 y. The inclusion criteria was apparently-healthy children (0-60 mo) and the exclusion criteria were acute/chronic illness and small for gestational age. RESULTS: A total of 1557 children (762 girls) were included in the study. The mean age of the subjects was 21 mo. The Z-scores for height, weight, body mass index (BMI) for age and weight for height in children were lower on WHO vs. synthetic charts (p = 0.0001). Significantly higher proportion of children were moderately and severely underweight, stunted and wasted on WHO charts. Synthetic charts identified significantly higher proportion as normal for weight, height, BMI for age, weight for height, overweight (overall), and a higher prevalence of severe stunting, and severe acute malnutrition (SAM) was noted among girls compared to boys. Using CIAF, 54.1% children were normal on WHO charts vs. 78.0% on synthetic (p = 0.0001). Larger proportion of girls (8.8%) were stunted+underweight (category-E) vs. boys (4.3%) on synthetic charts (p = 0.0003). Significantly higher proportion of children demonstrated failure (single/dual/multiple) on WHO charts except category-Y (higher proportion of underweight on synthetic charts). Maximum difference in CIAF (WHO vs. synthetic) was observed between 0-24 mo age. Of 1215 children normal on synthetic charts, 837 (68.9%) were normal on WHO charts. Of 116 underweight children (category-Y) on synthetic charts, 20 (17.2%) were underweight on WHO charts; remaining had compound failure (wasting+underweight = 49.1%, wasting+stunting+underweight = 14.7%, stunting+underweight = 12.1%) on WHO charts. Among those stunted+underweight (category-E) on synthetic charts, WHO charts classified 1/4th as wasted+stunted+underweight (category-D). CONCLUSIONS: Synthetic references are more representative of Indian growth patterns, and seem more appropriate for monitoring growth of Indian children to avoid mislabelling as malnourished.

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Background: Undernutrition in children seems to be one of the major health issues in developing nations including India. Stunting, underweight, and wasting are the three most often used anthropometric indicators to evaluate childhood undernutrition. Children who exhibit one or more indicators of undernutrition are considered as anthropometric failure (AF). The present study aims to determine the distribution and determinants of anthropometric failure in children under the age of five in different regions of India. Methods: NFHS-5 data, collected between 2019 and 2021, were utilized for the study. Pearson's chi-square (χ2) test was used to look into the association between categorical variables. Binary logistic regression was used to find the explanatory factors that influence anthropometric failure. Results: More than half of the under-five children (52.18%) in India are suffering from anthropometric failure, out of these West (57.88%), East (56.58%), and Central (53.94%) regions have covered half of the total occurrence. State-wise, Bihar (61.66%), followed by Gujarat (60.26%), and Jharkhand (58.05%) have recorded the highest rates of anthropometric failure. Anthropometric failure is higher among anemic children, boys, parent not alives, the higher number of birth order, lower educated mothers, rural dwellers, belonging to scheduled tribes and scheduled castes communities, living in nuclear families, and having lower household wealth indexes than their other counterparts. Conclusion: These aspects imply that regional determinants should be taken into consideration when implementing child nutrition development programs.

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Wealth inequality in anthropometric failure is a persistent concern for policymakers in India. This necessitates a comprehensive analysis and identification of various risk factors that can explain the poor-rich gap in anthropometric failure among children in India. We analyze the fifth and fourth rounds of the Indian National Family Health Survey collected from June 2019 to April 2021 and January 2015 to December 2016, respectively. Two samples of children aged 0-59 and 6-23 months old with singleton birth, alive at the time of the survey with non-pregnant mothers, and with valid data on stunting, severe stunting, underweight, severely underweight, wasting, and severe wasting are included in the analytical samples from both rounds. We estimate the wealth gradients and distribution of wealth among children with anthropometric failure. Wealth gap in anthropometric failure is identified using logistic regression analysis. The contribution of risk factors in explaining the poor-rich gap in AF is estimated by the multivariate decomposition analysis. We observe a negative wealth gradient for each measure of anthropometric failure. Wealth distributions indicate that at least 60% of the population burden of anthropometric failure is among the poor and poorest wealth groups. Even among children with similar modifiable risk factors, children from poor and poorest backgrounds have a higher prevalence of anthropometric failure compared to children from the richest backgrounds. Maternal BMI, exposure to mass media, and access to sanitary facility are the most significant risk factors that explain the poor-rich gap in anthropometric failure. This evidence suggests that the burden of anthropometric failure and its risk factors are unevenly distributed in India. The policy interventions focusing on maternal and child health, implemented with a targeted approach prioritizing the vulnerable groups, can only partially bridge the poor-rich gap in anthropometric failure. The role of anti-poverty programs and growth is essential to narrow this gap in anthropometric failure.

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Increasing body of health planning and policy research focused upon unravelling the fundamental drivers of population health and nutrition inequities, such as wealth status, educational status, caste/ethnicity, gender, place of residence, and geographical context, that often interact to produce health inequalities. However, very few studies have employed intersectional framework to explicitly demonstrate how intersecting dimensions of privilege, power, and resources form the burden of anthropometric failures of children among low-and-middle income countries including India. Data on 2,15,554 sampled children below 5 years of age from the National Family Health Survey 2015-2016 were analysed. This study employed intersectional approach to examine caste group inequalities in the anthropometric failure (i.e. moderate stunting, severe stunting, moderate underweight, severe underweight, moderate wasting, severe wasting) among children in India. Descriptive statistics and multinomial logistic regression models were fitted to investigate the heterogeneities in the burden of anthropometric failure across demographic, socioeconomic and contextual factors. Interaction effects were estimated to model the joint effects of socioeconomic position (household wealth, maternal education, urban/rural residence and geographical region) and caste groups with the likelihood of anthropometric failure among children.More than half of under-5 children suffered from anthropometric failure in India. Net of the demographic and socioeconomic characteristics, children from the disadvantageous caste groups whose mother were illiterate, belonged to economically poor households, resided in the rural areas, and coming from the central and eastern regions experienced disproportionately higher risk of anthropometric failure than their counterparts in India. Concerted policy processes must recognize the existing heterogeneities between and within population groups to improve the precision targeting of the beneficiary and enhance the efficiency of the nutritional program among under-5 children, particularly for the historically marginalized caste groups in India.

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Introduction: Composite Index of Anthropometric Failure (CIAF) and its further modifications have not incorporated all the combinations of malnutrition. We propose a new model incorporating all the forms of malnutrition among children under five years of age. However, the current models might misclassify a growing child as malnourished. Our objective is to develop a comprehensive scoring system using the three anthropometric Z-scores [height-for-age (HAZ), weight-for-age (WAZ), and weight-for-height (WHZ) Z-scores] and demonstrate the proposed CIAF model using the National Family Health Survey-5 (NFHS-5) data from India. Methods: A new scoring system was developed using the WAZ, HAZ, and WHZ scores to determine the child's nutritional status. We also proposed a new CIAF model by including all possible categories of malnutrition and practically demonstrated it using the NFHS-5 dataset after applying the new scoring system. Under-five children with heights, weights, and ages available were included in the analysis. The groups of malnutrition are presented as weighted proportions before and after applying the new score to the proposed model. Results: Our final analysis included individual-level data of 198,802 children under five years of age (weighted N = 195,197). After applying the new scoring system to the proposed model, the prevalence of stunting has reduced to 11.8% (95% CI 11.66-11.94) from 13.2% (95% CI 13.09-13.39) and wasting prevalence has reduced to 4.9% (95% CI 4.85-5.04) from 6.4% (95% CI 6.29-6.51). The most common forms of anthropometric failures among Indian children by using the newly developed CIAF model are: "Stunting and underweight" (30,127; 15.4%), Stunting only (23,035; 11.8%), and "wasting and underweight" (14,698; 7.5%). We found a new category called "Stunting, underweight, and overweight" (stunting = HAZ < -2SD, underweight = WAZ < -2SD, overweight = WHZ > +2SD). It constituted 0.1% (220 children) of the total sample. Conclusion: When the new scoring system is applied to the proposed CIAF model, it captures all forms and combinations of malnutrition among under-five children without overlap and prevents misclassifying a growing child as malnourished. The newly identified category shows that stunting (HAZ < -2SD), overweight (WHZ > +2SD) and underweight (WAZ < -2SD) can co-exist in the same child.

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BACKGROUND: The Composite Index of Anthropometric Failure (CIAF) can comprehensively identify undernutrition by combining several indicators of nutritional status - namely, weight-for-age, length/height-for-age and weight-for-length/height - to determine the nutritional status of children under five years of age. This study aims to assess undernutrition using the CIAF and its determinants on children under five years of age in the Bogor District, Indonesia. METHODS: A cross-sectional study was conducted during February-May 2019 among 330 mother-children pairs (with children under five), selected by systematic random sampling from four villages as undernutrition pockets in the rural area of Bogor District, Indonesia. The nutritional status of the children was assessed by measuring weight and length/height. Z-score was calculated using WHO Anthro software and was categorized based on conventional indices, including weight-for-age (WAZ), length/height-for-age (HAZ) and weight-for-length/height (WHZ). The CIAF is measured based on a combination of conventional index measurements. In addition, mothers' and childrens' characteristics and clean living behaviour are assessed via structured questionnaires. Environmental sanitation is assessed using the environment meter. Binary logistic regression analysis with SPSS version 22.0 is used to analyse the dominant factors associated with undernutrition. RESULTS: Among children under five, 42.1% experienced anthropometric failure (overall prevalence of undernutrition based on the CIAF), 2.4% experienced wasting only, 5.8% were classified as both wasting and underweight, 2.1% as wasting, underweight and stunting, 16.4% as underweight and stunting, 11.5% as stunting only, and 3.9% as underweight only. Assessment of nutritional status using a conventional anthropometric index shows that respective prevalences of underweight, stunted and wasted were 27.8, 29.7, and 10.6%. The mother's height is the most dominant factor associated with anthropometric failure [p = 0.008; AOR = 1.95; 95% CI: 2.19-3.19]. The most dominant factors associated with the conventional undernutrition indices of underweight, stunted and wasted are, respectively, family income [p = 0.018; AOR = 5.44; 95% CI: 1.34-22.11], mother's height [p = < 0.001; AOR = 3.29; 95% CI:1.83-5.91] and child's age [p = 0.013; AOR = 2.59; 95% CI: 1.22-5.47]. CONCLUSION: Nearly half of children under five experience anthropometric failure. Specific nutrition improvement interventions and specific nutrition interventions during pregnancy and lactation are needed, especially for malnourished mothers, to prevent malnutrition in infant.

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(1) Background: Guatemala is the Latin American country with the highest prevalence of childhood stunting. Short height can bias the diagnosis of wasting when using the weight-for-height indicator. The aim of this study was to evaluate the diagnostic concordance of the anthropometric indicators of wasting and the relationship between wasting and stunting in children from highly vulnerable communities in Guatemala. (2) Methods: The sample consisted of 13,031 anthropometric records of children under five years of age (49.5% girls, average age of 27.9 months), including weight, height, and mid-upper arm circumference (MUAC), collected in March-August 2019. The proportions of stunting, underweight, and wasting, assessed by three different indicators, as well as their concurrence through the Composite Index of Anthropometric Failure were calculated. (3) Results: Stunting affected 73% of the sample, and 74.2% showed anthropometric failure. Wasting varied by indicator (weight-for-height: 2.8%; MUAC: 4.4%; MUAC-for-age: 10.6%). Concordance between MUAC and weight-for-height was very low (Kappa: 0.310; sensitivity: 40.9%). MUAC identified more wasted children in the stunted group (53.6% vs. 26.5%), while the opposite occurred in the non-stunted group (34.8% vs. 46.7%). (4) Conclusion: The presence of stunting affected the diagnosis of wasting, and both indicators should be included as diagnostic criteria for screening campaigns and in the treatment of moderate to acute wasting in vulnerable populations affected by multiple forms of undernutrition.

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OBJECTIVES: This research measures the occurrence of malnutrition amongst under-five children in the Rahimyar Khan district of Southern Punjab in Pakistan. Employing different anthropometric measurement approaches such as (1) conventional indices (HAZ, WAZ, and WHZ), (2) CIAF, (3) BMI-for-age, and (4) MUAC, we compare their estimated results and examine the relationship between socioeconomic determinants and different anthropometric indicators. METHODS: The study employs a proportional purposive random sampling method to collect data from 384 rural households in the community-based study using a self-administered survey and following the Lady Health Workers (LHWs) registered records. The nutritional status of 517 under-five children is measured with references to WHO (2009) child growth standards. Furthermore, the investigation used the model of binary logistic regression to measure the impact of socioeconomic factors on child malnutrition. RESULTS: Compared with other approaches, the CIAF identifies more malnourished children (63%). The results of binary logistic regression illustrate that all the explanatory variables indicate a more significant empirical association with CIAF than conventional indices, BMI-for-age, and MUAC. CONCLUSION: CIAF is a more reliable tool for assessing child nutrition because it not only demonstrates more accurate estimates of malnutrition but also recognizes children with multiple anthropometric failures.

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Asymptomatic or subclinical infection by diarrheal enteropathogens during childhood has been linked to poor health and nutritional outcomes. In this study, we aimed to assess the impact of asymptomatic Shigella infection on different forms of childhood malnutrition including the composite index of anthropometric failure (CIAF). We used data from 1715 children enrolled in the multi-country birth cohort study, MAL-ED, from November 2009 to February 2012. Monthly non-diarrheal stools were collected and assessed using TaqMan Array Cards (TAC). Poisson regression was used to calculate incidence rates of asymptomatic Shigella infection. Generalized estimating equations (GEE) were used to assess the association between asymptomatic Shigella infection and nutritional indicators after adjusting for relevant covariates. Incidence rates per 100 child-months were higher in Tanzania, Bangladesh and Peru. Overall, after adjusting for relevant covariates, asymptomatic Shigella infection was significantly associated with stunting (aOR 1.60; 95% CI: 1.50, 1.70), wasting (aOR 1.26; 95% CI: 1.09, 1.46), underweight (aOR 1.45; 95% CI: 1.35, 1.56), and CIAF (aOR 1.55; 95% CI: 1.46, 1.65) in all the study sites except for Brazil. The high incidence rates of asymptomatic Shigella infection underscore the immediate need for Shigella vaccines to avert the long-term sequelae involving childhood growth.

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In 2018, a cross-sectional study was conducted in six communities of Tecoluca, Bajo Lempa (El Salvador). Weight, height, sitting-height, skinfolds thickness and head, arm, and waist circumferences were measured in a sample of 334 schoolchildren. Nutritional status, body composition, and Extended Composite Index for Anthropometric Failure (ECIAF) were estimated. The Food Security Perception Survey (Spanish acronym: EPSA) was applied to 143 households. Anthropometric failure was observed in 37.5% of the schoolchildren. Association between stunting and underweight in boys and stunting and weight excess in girls was observed. About 58.7% of the households suffered from food insecurity.

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BACKGROUND/OBJECTIVES: Stunting, underweight, and wasting are used to monitor nutritional status in children, but they do not identify children with concurrent anthropometric failures (AF). Our study estimates the association between AF and mortality in children with single versus multiple failures, then calculates the percentage of child deaths attributable to AF. SUBJECTS/METHODS: Using data from a prospective, longitudinal study of 3605 children from age 1 to age 5 years in Ethiopia and India, we estimate the association between AF and mortality using conventional definitions (stunting, underweight, and wasting) and the mutually exclusive categories of stunted only underweight only, wasted only, stunted and underweight (SU), underweight and wasted, and stunted, underweight, and wasted (SUW), adjusting for socioeconomic status and other demographic variables. Last, we calculate the population attributable fraction. RESULTS: Children who were SU and SUW had 3.20 (95% CI: 1.69, 6.06; p < 0.001) and 5.52 (95% CI: 2.25, 13.56; p < 0.001) times the odds of death in fully adjusted models by Round 2 compared to children with no failure, while no increased mortality risk was found among children with other categories of failure. We estimate that 42.69% of child deaths can be attributed to children who are SUW (17.02%) or SU (25.67%), accounting for nearly 80% of child deaths from AF. CONCLUSIONS: This study provides new insight to programs and policy to better identify children most at risk of malnutrition-related mortality.

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BACKGROUND: Childhood malnutrition is a major cause of child mortality under the age of 5 in the sub-Saharan Africa region. This study sought to identify the risk factors and spatial distribution of the composite index of anthropometric failure (CIAF). METHODS: Secondary data from 2000, 2005, 2011, and 2016 Ethiopian Health and Demographic Survey (EDHS) were used. The generalized geo-additive mixed model was adopted via the Integrated Nested Laplace Approximation (INLA) with a binomial family and logit link function. RESULTS: The CIAF status of children was found to be positively associated with the male gender, the potency of contracting a disease, and multiple births. However, it was negatively associated with family wealth quartiles, parental level of education, place of residence, unemployment status of mothers, improved sanitation, media exposure, and survey years. Moreover, the study revealed significant spatial variations on the level of CIAF among administrative zones. CONCLUSIONS: The generalized geo-additive mixed-effects model results identified gender of the child, presence of comorbidity, size of child at birth, dietary diversity, birth type, place of residence, age of the child, parental level of education, wealth index, sanitation facilities, and media exposure as main drivers of CIAF. The results would help decision-makers to develop and carry out target-oriented programs.

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BACKGROUND: Undernutrition is the main cause of child death in developing countries. This paper aimed to explore the efficacy of machine learning (ML) approaches in predicting under-five undernutrition in Ethiopian administrative zones and to identify the most important predictors. METHOD: The study employed ML techniques using retrospective cross-sectional survey data from Ethiopia, a national-representative data collected in the year (2000, 2005, 2011, and 2016). We explored six commonly used ML algorithms; Logistic regression, Least Absolute Shrinkage and Selection Operator (L-1 regularization logistic regression), L-2 regularization (Ridge), Elastic net, neural network, and random forest (RF). Sensitivity, specificity, accuracy, and area under the curve were used to evaluate the performance of those models. RESULTS: Based on different performance evaluations, the RF algorithm was selected as the best ML model. In the order of importance; urban-rural settlement, literacy rate of parents, and place of residence were the major determinants of disparities of nutritional status for under-five children among Ethiopian administrative zones. CONCLUSION: Our results showed that the considered machine learning classification algorithms can effectively predict the under-five undernutrition status in Ethiopian administrative zones. Persistent under-five undernutrition status was found in the northern part of Ethiopia. The identification of such high-risk zones could provide useful information to decision-makers trying to reduce child undernutrition.

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