Investigating the contextual drivers and factors impacting child growth failure in Mining Communities; A Structural Equation Modeling approach. 

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HERBERT TATO Nyirenda, David Mulenga, Hildah Nyambe-Silavwe This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4980853/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective : Child growth and nutritional development are significantly impacted by various factors. This paper investigated the contextual drivers influencing child growth failure in local communities dominated by mining activities. Methods: The study employed a cross-sectional study design and comprised a random sample of 781 under-five children and their caregivers. Structured interviews were conducted with caregivers, and anthropometric measurements were taken from the children. Bivariate chi-square, Structural Equation Modeling and multivariate logistic regression analysis were performed. Results: Over half (51%) of the children were female. On average, households consisted of 6.1±2.7 SD persons. Primary caregivers had an average age of 24.2±9.4 while the children's average age was 21.3±15.7 SD months. The average height of children was 80.4±13.7 SD with a height-for-age Z-Score of 0.2±4.9 SD. Further, 35% of children experience child growth failure. Drivers include; age-caregiver [AOR = 1.04, 95% CI = 1.028- 1.056], high-school education [AOR = 0.24, 95% CI = 0.089 - 0.677], unemployment-housewife [AOR = 0.45, 95% CI = 0.226 - 0.901], feeding-strategies [AOR = 0.39, 95% CI = 0.226 - 0.663] and cooking-duration [AOR = 2.16, 95% CI = 1.131 - 4.129]. Conclusion: Child growth failure remains a concern, with individual and contextual-level factors identified as significant contributors and thus crucial to take them into account when designing nutrition interventions in vulnerable communities. Therefore, as mining cooperation’s undertake corporate social investment initiatives, it's crucial to consider contextual factors in the design of community interventions. Child growth failure Contextual factors Mining Communities and Nutrition Figures Figure 1 Introduction Child nutrition represents a significant global public health concern. Ensuring optimal nutrition during the early years of a child's life is crucial, serving as foundational pillars for various aspects of health, including physical development, educational attainment, cognitive growth, and immune system strength. 1 Annually, millions of children succumb to premature death before reaching their fifth birthday due to insufficient Infant and Young Child Nutrition (IYCN). This issue is particularly prevalent in regions such as southern Asia and Sub-Saharan Africa. 23 Poor or malnutrition contributes to 45% of the deaths of children under five. 4 Inadequate food and nutrient intakes coupled with recurrent infections, high prevalence of bacterial and parasitic diseases in developing countries contributes greatly to malnutrition. 2 Stunting or child growth failure is the result of chronic and recurrent malnutrition in children and threatens the survival, growth and development. 5 In 2020, it was estimated that 149.2 million children under the age of 5, accounting for 22.0% of all children in this age group worldwide, were classified as stunted, meaning they were too short for their age. 6 Children residing in low- and middle-income countries (LMICs) have encountered numerous significant hurdles stemming from socioeconomic disparities, including severe poverty, lack of access to healthcare services, food insecurity, and nutritional deficiencies. 7 In the Africa region, the prevalence of stunting is 30.7% which is higher than the global average (22.0%). 8 In Zambia, inadequate nutrition remains a significant burden and continues to pose a pressing public health concern. This issue is intricately linked with multidimensional poverty and systemic challenges affecting food security, water, sanitation, and hygiene (WASH) conditions, as well as health, social, and economic systems. 9 , 10 The 2018 Zambia Demographic Health Survey revealed that 35% of children under the age of 5 were stunted. This prevalence of stunting in Zambia surpasses the regional average for the Africa region, which stands at 30.7%. 8 Child growth and nutritional development are significantly impacted by various factors related to living conditions, encompassing socio-economic, cultural, demographic, and climatic variables, which can vary widely among different nations. 11 Children residing in vulnerable communities encounter disproportionate risks to their well-being, encompassing threats to life, health, and physical safety. Limited evidence exists regarding the nutritional well-being of children in proximity to mining areas. Mining activities present both advantages and potential hazards to individuals residing in communities abundant in mineral resources. 8 , 11 However, there is scant evidence regarding the connections among mining activities, their impact on public health, and the initiatives undertaken by mining companies within affected communities. 12 As a result of the remote nature of mining sites, the majority of mining communities face social marginalization and are often characterized by poverty, which restricts their access to essential healthcare and other health-related amenities including access to nutritious foods. 13 While mining activities are undeniably tied to socioeconomic advancement, they also correlate with negative consequences like poverty, inadequate sanitation, haphazard urbanization, risky behaviors, and population migration, all of which impact health. 14 Therefore, this project investigated the contextual drivers and factors influencing child growth failure in local communities dominated by mining activities. Methods and Materials Study design and study population The study was conducted in Kapijimpanga and Kyafukuma communities of Solwezi district in North-western province of Zambia. Solwezi’s main economic activity is mining and is experiencing rapid growth as a district largely due to the existence and presence of the largest and most productive mine, Kansanshi. The study focused on under-five children and their caregivers who live in mining communities. Sample size and Sampling technique The study utilized a random sampling technique to select participants, specifically a multistage random sampling technique to select health facilities. A comprehensive list of all under-five children and their caregivers within the catchment areas of the selected health facilities was compiled, and participants were then randomly selected from this list. To determine the appropriate sample size, a sample size estimation formula with a dichotomous outcome was used. This formula took into account various factors such as the required sample size in each group (ni), the absolute value of the difference in proportions between the two groups expected under the alternative hypothesis (|p1 - p2|), the overall proportion (p), the selected level of significance (α) set at 5%, and the value from the standard normal distribution (Z1-α/2) that corresponds to a significance level of 1-α/2. Additionally, the selected power of 80% (1- β) and the corresponding value from the standard normal distribution (Z1-β) were also considered. This process resulted in a sample size of 781. Data Collection The primary method of gathering data was through a structured questionnaire. Information was obtained through structured interviews conducted with caregivers, while measurements were directly taken from the children. Various equipment such as measuring boards, measuring tapes, and weight scales were utilized for accurate measurements. A multidisciplinary team consisting of qualified personnel including clinicians, nutritionists, and nurses was responsible for collecting data on nutrition parameters and child health indicators, as well as conducting structured interviews with caregivers. Anthropometric measurements, including age, height/length, and weight, were obtained from the children. To assess anthropometric indices such as stunting, measurements were compared against the WHO Child Growth Standards median. Stunting was defined as length/height-for-age more than 2 standard deviations below the WHO Child Growth Standards median. Data analysis The research employed a comprehensive statistical analysis methodology, incorporating descriptive statistics, such as frequency distributions, were utilized to summarize and explore the characteristics of the variables under investigation. A bivariate analysis using logistic regression was conducted to assess the relationships between individual factors, community level factors, behavioral factors and the outcome variable, allowing for the identification of potential predictors of child growth failure. Subsequently, a multivariate statistical analysis framework was employed, specifically structural equation modeling (SEM) analysis. SEM enables the simultaneous estimation of a system of equations, providing a comprehensive approach to modeling complex and underlying structural relationships among variables. Factors significantly associated at 90%, 95%and 99% confidence interval with the outcome variable were included in multilevel analysis. A stepwise backwards elimination method was used taking account variables eligible for removal at α criteria 90%, 95% and 99% confidence level. Ethical Considerations Ethical clearance was sought from the Copperbelt University Biomedical Research Committee (CBUBREC) study number CBU/BREC/23/001 to conduct the study. The institutional review board conducted a thorough full review of the protocol upon which approval was granted. Participation was voluntary (freely-given) and participants were informed about facets of the study. We confirm that informed consent /assent to participate and publish were obtained from all participants and parents/or legal guardian of under-five children. Therefore, the study adhered to all ethical guidelines. Results Respondent Profile and Background Characteristics A total of 781 under-five children took part in the survey. Findings show that 51% of the children were female and the average household size was 6.1 ± 2.7 SD persons. Further, the average under-five children per household was 1.7 ± 2.7 SD. The average age of the primary caregivers was 24.2 ± 9.4. Half (50%) of the primary caregivers had a primary school education, 85% were married, 98% were unemployed (housewives), 5% had a disability/chronic illness and 84% of the household heads were in informal employment. Developmental Status and Associated Risk Factors of Child Growth Failure Results in Table 1 show that the average age (months) of children was 21.3 ± 15.7 SD; average height (centimetres) was 80.4 ± 13.7 SD and the average height-for-age Z-Score (SD) was 0.2 ± 4.9. Furthermore, the results reveal that 35% of children under the age of five were classified as stunted (child growth failure), while 15% exhibited severe stunting, indicating significant growth failure. The analysis further reveals a statistically significant association (P < 0.001) between stunting and both age and sex. Specifically, female children and those within the age range of 18–35 months exhibit a higher prevalence of stunting and severe stunting. This highlights the importance of considering demographic factors such as age and sex when assessing nutritional outcomes among children in this population. A chi-square analysis was conducted to explore the contextual factors related to stunting. Results in Table 1 revealed that there is a statistical significant associations between stunting and several factors, including ownership of a garden (P < 0.001), limited access to markets and market products (P < 0.001), financial constraints (P < 0.004), entrepreneurship training (P < 0.039), availability of mainstream grocery stores (P < 0.001), access to farmers' markets (P < 0.001), methods of vegetable preparation (P < 0.023), feeding styles/strategy (P < 0.001), and cultural customs and beliefs regarding food consumption (P < 0.001). Table 1 Contextual factors-child characteristics associated with child growth; Binary logistic regression analysis Child Growth Failure Severe Growth failure Sample None None Percentage below − 2 SD Percentage below − 2 SD None None Percentage below − 3 SD Percentage below − 3 SD Contextual Factor % Conf Inter % Conf Inter % Conf Inter % Conf Inter Childs Sex Male 66.8 [60.3,72.8] 33.2 [27.2,39.7] 88.9 [84.1,92.4] 11.1 [7.6,15.9] 384 Female 64.1 [57.5,70.2] 35.9 [29.8,42.5] 81.9 [76.3,86.4] 18.1 [13.6,23.7] 396 Total 65.4 [60.8,69.8] 34.6 [30.2,39.2] 85.4 [81.8,88.3] 14.6 [11.7,18.2] 781 P-value = 0.550 P-value < 0.001 Childs Age < 6 89.2 [80.6,94.3] 10.8 [5.7,19.4] 96.1 [90.1,98.6] 3.9 [1.4,9.9] 124 6–8 91.9 [78.9,97.1] 8.1 [2.9,21.1] 94.8 [83.4,98.5] 5.2 [1.5,16.6] 92 9–11 80.4 [67.5,89.0] 19.6 [11.0,32.5] 93.8 [83.8,97.8] 6.2 [2.2,16.2] 95 12–17 61.2 [46.2,74.3] 38.8 [25.7,53.8] 90.3 [78.4,96.0] 9.7 [4.0,21.6] 80 18–23 48.7 [34.7,62.9] 51.3 [37.1,65.3] 75.8 [61.2,86.1] 24.2 [13.9,38.8] 79 24–35 35.6 [25.0,47.8] 64.4 [52.2,75.0] 71.2 [59.3,80.7] 28.8 [19.3,40.7] 123 36–47 55.7 [43.1,67.7] 44.3 [32.3,56.9] 75.4 [63.1,84.5] 24.6 [15.5,36.9] 109 48–59 60.1 [45.0,73.4] 39.9 [26.6,55.0] 87.7 [75.0,94.5] 12.3 [5.5,25.0] 79 Total 65.4 [60.8,69.8] 34.6 [30.2,39.2] 85.4 [81.8,88.3] 14.6 [11.7,18.2] 781 P-value < 0.001 P-value < 0.001 Relationship Between Contextual factors and Stunting. A Bivariate Logistic regression analysis The study identified a statistically significant relationship between individual-level factors and child growth indicators. Table 2 shows that children in older age brackets, including those aged 12–17, 18–23, 24–35, 36–47, and 48–59 months, exhibited significantly higher odds [OR = 5.25, 95% CI = 2.103–13.103], [OR = 8.71, 95% CI = 23.548–21.391], [OR = 14.94, 95% CI = 6.377–34.985], [OR = 6.57, 95% CI = 2.799–15.421], and [OR = 5.50, 95% CI = 2.198–13.748], respectively, of experiencing child growth failure compared to children under one-year-old. Table 2 Relationship between individual level factors and child growth failure; Binary logistic regression analysis Child Growth Failure Contextual Factors Odds Ratio (OR) P-value Confidence Interval Comparison 1 - - Age (12–17 months) 5.25*** < 0.001 2.103–13.103 Age (18–23 months) 8.71*** < 0.001 3.548–21.391 Age (24–35 months) 14.94*** < 0.001 6.377–34.985 Age (36–47 months) 6.57*** < 0.001 2.799–15.421 Age (48–59 months) 5.50*** < 0.001 2.198–13.748 Related to household head as spouse 0.62* 0.058 0.384–1.017 Respondent/mother/caregiver aged 40+ 4.77*** 0.004 1.645–13.825 Confidence interval in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 Similarly, the research identified a statistically significant relationship between community-level factors and child growth impairment. Table 3 shows that households that never possessed a garden demonstrated twice the likelihood [OR = 2.01, 95% CI = 1.330–3.047] of having a child with stunted growth. Likewise, households lacking access to mainstream grocery stores, farmers' markets, and outlets offering fresh foods exhibited elevated odds [OR = 2.01, 95% CI = 1.330–3.047], [OR = 2.01, 95% CI = 1.330–3.047], and [OR = 2.01, 95% CI = 1.330–3.047], respectively, of experiencing child growth failure. Moreover, households allocating a significant portion of their income to other commodities or services relative to food expenses had heightened odds [OR = 1.70, 95% CI = 0.927–3.109] of encountering child growth issues. Table 3 Relationship between community-level factors and child growth failure; Binary logistic regression analysis Child Growth Failure Contextual Factors Odds Ratio (OR) P-value Confidence Interval Comparison 1 Does not own a vegetable garden 2.01*** 0.001 1.330–3.047 Income expenditure priorities-Bills, school fees, agro-inputs 1.70* 0.086 0.927–3.109 Participated in an Agriculture training program 1.69* 0.051 0.997–2.855 Participated in an business/Entrepreneurship training program 0.15* 0.073 0.019–1.191 Lack access to a mainstream grocery store 2.51*** 0.000 1.558–4.044 Lack access to a farmers market, organic or local food source outlet 3.53*** 0.000 2.095–5.943 Lack access to a food outlet with fresh and unexpired edible foods 3.89*** 0.000 2.235–6.760 Away from home gone for farming (Magimi)) = 1, Rarely 1.76* 0.050 0.999–3.099 Confidence Interval in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 The study identified a statistically significant association between behavioral factors and child growth impairment. Table 4 shows that households that cooked vegetables for about 30 minutes and 30 minutes to an hour had higher odds [OR = 2.12, 95% CI = 1.063–4.233] and [OR = 3.00, 95% CI = 1.037–8.709], respectively, of having a stunted child compared to those who cooked within 10 minutes. Conversely, households that cooked vegetables just before eating had lower odds [OR = 0.65, 95% CI = 0.395–1.065] of having a stunted child. Additionally, households employing feeding practices such as giving attention during meals, clapping hands, making funny faces/playing/laughing, and drawing the child’s attention had lower odds [OR = 0.58, 95% CI = 0.386–0.880], [OR = 0.28, 95% CI = 0.164–0.479], [OR = 0.16, 95% CI = 0.079–0.333], and [OR = 0.47, 95% CI = 0.257–0.843], respectively, of experiencing child growth failure. Table 4 Relationship between behavioural factors and child growth failure: Binary logistic regression analysis Child Growth Failure Contextual Factors Odds Ratio (OR) P-value Confidence Interval Comparison . . . - . Vegetables cooked less than 30 minutes 2.12** 0.033 1.063–4.233 Vegetables cooked between 30 and 1 hour 3.00** 0.043 1.037–8.709 Vegetables cooked just before eating 0.65* 0.087 0.395–1.065 Feeding strategy-Giving them attention during meals 0.58** 0.010 0.386–0.880 Feeding strategy-Clap hands 0.28*** < 0.001 0.164–0.479 Feeding strategy-Make funny faces/play/laugh 0.16*** < 0.001 0.079–0.333 Feeding strategy-Draw the child's attention 0.47** 0.012 0.257–0.843 Confidence interval in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 Structural Equation Modeling Table 5 provides an overview of the measurement model, detailing each latent construct alongside its respective measurement items. The analysis involved fitting three Generalized Structural Equation Models (GSEM), with each model incorporating the latent constructs and their associated measurement items. The final model, as depicted in Fig. 1 , integrated the causal pathways between the constructs. The path analysis and model depicts the impact of a latent factors such as feeding strategies and food access as well as observed factors influencing child growth failure through several causal routes. Notably, this model was adjusted for various variables, including the child’s age, access to food markets, feeding strategies, and household factors. Stepwise logistic regression was employed to identify significant predictor variables for constructing the most effective logistic regression model. The results indicate that each additional month in a child's age increased the odds [AOR = 1.04, 95% CI = 1.028–1.056] of child growth failure. Moreover, having a high school education and being unemployed-housewife reduced the odds [AOR = 0.24, 95% CI = 0.089–0.677] and [AOR = 0.45, 95% CI = 0.226–0.901], respectively, of child growth failure. Implementing specific feeding strategies decreased the odds [AOR = 0.39, 95% CI = 0.226–0.663] of child growth failure. Conversely, households that spent longer durations cooking vegetables faced higher odds [AOR = 2.16, 95% CI = 1.131–4.129] of child growth failure. Table 5 Multivariate logistic regression; drivers of child growth failure Child Growth Failure Contextual Factor Adjusted odds ratio (AOR) P-value Conf Inter (CI) Comparison 1 - - Child’s Age 1.04*** 0.001 1.028–1.056 Education Level-high school 0.24*** 0.007 0.089–0.677 Respondent/mother/caregiver aged 40+ 2.61* 0.092 0.856–7.982 Employment- None (Doesn't work) i.e housewife 0.45** 0.024 0.226–0.901 Disable/Chronically ill 3.13* 0.053 0.988–9.946 No access to a food market, grocery store or farmers market 2.06** 0.013 1.167–3.649 Feeding strategy-Make funny faces/play/laugh 0.39*** 0.001 0.226–0.663 Vegetable cooking- Over cooked 2.16** 0.020 1.131–4.129 Confidence Interval (CI) in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 Discussion The prevalence observed in this study was higher than the global findings yet mirrored and was consistent with the national findings, indicating that 35% of children were stunted. 15 , 6 The findings emphasize the crucial role of demographic factors, including the child's age and sex in evaluating nutritional outcomes among children. These results align with findings not only at the national level but also regionally and globally that found statistical significant associations. 16 , 17 , 18 , 19 Additionally, education remains pivotal across various domains, as evidenced by findings in this study indicating that mothers with an education had decreased odds of encountering child growth failure. The results of this study substantiate those of similar studies that found that educational the mother plays a critical role in childhood stunting. 19 , 20 The employment status and commitments of mothers/caregivers serve as crucial determinants directly associated with the growth and development of children under the age of five. The findings from this study, which suggest that women or caregivers not involved in economic activities had lower chances of experiencing child growth failure, align with research conducted in India. 20 That study revealed that children of unemployed mothers exhibited significantly greater height compared to children of employed mothers. Socioeconomic disparities contribute to disparities in nutrition access and quality. The household burden of disease can have significant negative impacts on child nutrition and growth. When members of a household are affected by illness or disease, it can disrupt the family's ability to provide adequate care, resources, and support for children's nutritional needs, leading to various adverse outcomes. The findings reveal that vulnerable households, particularly those burdened with a higher prevalence of disease, were more prone to stunting. This observation is consistent with findings from other research papers. 21 , 22 Families affected by illness may prioritize spending on healthcare expenses over purchasing nutritious foods, leading to food insecurity and inadequate nutrition for children. Illness-related fatigue or incapacitation can hinder caregivers' ability to prepare and serve nutritious meals, leading to irregular feeding patterns or reliance on convenient but less nutritious food options. Systemic inequalities have profound and far-reaching implications for child development and growth. These inequalities, rooted in various social, economic, and political structures, create disparities in access to resources, opportunities, and essential services, which directly impact children's well-being. Poverty is widely recognized as a primary cause of malnutrition. However, the absence of access to nutritious foods also substantially contributes to child growth failure and food insecurity. These findings align with the key pillars of food security, namely availability, access, utilization, and stability. It is imperative for children under the age of five to have reliable and uninterrupted access to an adequate supply of food with the appropriate nutritional composition. The insufficiency of food availability, accessibility, utilization, and stability increases the vulnerability of children under the age of five to growth failure. The results of this study are consistent with those of other research, which underscores the detrimental impact of insufficient food markets or stores on child growth failure. 20 , 23 Food insecurity is highest in deprived communities including mining communities contributing substantially to child growth failure. 24 However, these results contrast with findings from Ethiopia, which revealed that food-secure households were 1.96 times more likely to experience the double burden of malnutrition compared to food-insecure households (AOR = 1.96, 95% CI 1.13, 3.39). 22 Feeding strategies play a crucial role in under-five nutrition and can significantly impact children's growth, development, and overall health. Findings show that responsive feeding practices involve attentively responding to a child's hunger and satiety cues, allowing them to self-regulate their food intake. Encouraging responsive feeding promotes healthy eating behaviors as well as the development of healthy appetite regulation in children. 25 , 26 , 27 Conclusion The results suggest that child growth failure remains a substantial concern, with individual and contextual-level factors identified as significant contributors. Mining communities face susceptibility to diverse shocks, which can contribute to stunting in children. Therefore, as mining companies undertake corporate social investment initiatives, it's crucial to consider both individual and contextual factors in the design of interventions. This ensures effectiveness and efficiency in addressing child growth failure within these communities. Overall, implementing appropriate strategies that prioritize responsive feeding, access to nutritious foods and emphasizing the importance of nutrition education is vital in improving the nutritional status and well-being of children. Declarations Author Contribution All authors participated in all facets of the article. Authors also contributed to the development, conceptualization, and editing of the article. All authors provided their final approval for the version intended for publication. Competing Interests The authors declare that they have no competing interests. Funding No funding arrangements. Data availability Data is available upon request from the corresponding Author. References Alwabr, G. M. A. & Alwabr, N. M. A. Nutritional status of children under five years of age and factors associated in rural areas of Sana’a Governorate, Yemen. CHRISMED J. Heal. Res. 8 , 102 (2021). Müller, O. & Krawinkel, M. Malnutrition and health in developing countries. C. Can. Med. Assoc. J. 173 , 279 (2005). Tekin, M. Under-Five Mortality Causes and Prevention. Mortal. Rates Middle Low-Income Ctries. [Working Title] (2021) doi:10.5772/INTECHOPEN.100526. Nutrition International. 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J. Equity Health 6 , 1–12 (2007). Christian, A. K. & Dake, F. A. A. Profiling household double and triple burden of malnutrition in sub-Saharan Africa: prevalence and influencing household factors. Public Health Nutr. 25 , 1563 (2022). Fisher, M. R. 8.1 Food Security. (2017). Santa-Ramírez, H. A. et al. Small area vulnerability, household food insecurity and child malnutrition in Medellin, Colombia: results from a repeated cross-sectional study. Lancet Reg. Heal. - Am. 23 , 100521 (2023). Black, M. M. & Aboud, F. E. Responsive Feeding Is Embedded in a Theoretical Framework of Responsive Parenting. J. Nutr. 141 , 490 (2011). Engle, P. L. & Pelto, G. H. Responsive Feeding: Implications for Policy and Program Implementation,. J. Nutr. 141 , 508–511 (2011). Bentley, M. E., Wasser, H. M. & Creed-Kanashiro, H. M. Responsive Feeding and Child Undernutrition in Low- and Middle-Income Countries. J. Nutr. 141 , 502 (2011). Additional Declarations No competing interests reported. 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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-4980853","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":358913836,"identity":"da53217c-1ef7-4c60-8d1a-4803dfc9acbe","order_by":0,"name":"HERBERT TATO Nyirenda","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYPACCRBhwPABSLKxE1DLw8CM0MI4A6SFmTgtYGDAzAOiCGmxZz9/8HFhm0U+v3Tzxs82v7bJ8zEzMH74mIPHFp5kZuOZbRKWM+ccK5bO7btt2MbMwCw5cxs+hyWzSfO2SRgY3MgxkM7tuc0I1MLGzItPC/9jiBb7GznGvy17btsT1iIBs0Uix0ya4cftRMJabjw2NuY5J2EgcSOtzLK34XZyGzNjM16/sPcnPnzMU1ZnwD8jefONH39u285vbz744SMeLWDAyAZjtIHJBgLqQeAPBmMUjIJRMApGAQIAAPIWRjOBlL2YAAAAAElFTkSuQmCC","orcid":"","institution":"Copperbelt University","correspondingAuthor":true,"prefix":"","firstName":"HERBERT","middleName":"TATO","lastName":"Nyirenda","suffix":""},{"id":358913838,"identity":"056a06f2-f501-4366-8a5d-369d9049df6e","order_by":1,"name":"David Mulenga","email":"","orcid":"","institution":"Copperbelt University","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Mulenga","suffix":""},{"id":358913839,"identity":"3972a3b0-be9e-4f0c-b96f-222fcd89439a","order_by":2,"name":"Hildah Nyambe-Silavwe","email":"","orcid":"","institution":"Copperbelt University","correspondingAuthor":false,"prefix":"","firstName":"Hildah","middleName":"","lastName":"Nyambe-Silavwe","suffix":""}],"badges":[],"createdAt":"2024-08-27 02:25:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4980853/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4980853/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67119689,"identity":"cd1bf765-e746-42fd-bccc-fc24f8ccdc06","added_by":"auto","created_at":"2024-10-21 11:05:12","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":282413,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate regression path diagram (Structural Equation Modeling approach)\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4980853/v1/d1ef220e6f161cd8504858f7.jpeg"},{"id":69367806,"identity":"474210d8-b07c-4c41-80f6-ddc190df230d","added_by":"auto","created_at":"2024-11-19 15:32:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":977451,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4980853/v1/754b49cb-8cc4-4a7c-9815-addab01a3263.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigating the contextual drivers and factors impacting child growth failure in Mining Communities; A Structural Equation Modeling approach. ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChild nutrition represents a significant global public health concern. Ensuring optimal nutrition during the early years of a child's life is crucial, serving as foundational pillars for various aspects of health, including physical development, educational attainment, cognitive growth, and immune system strength.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Annually, millions of children succumb to premature death before reaching their fifth birthday due to insufficient Infant and Young Child Nutrition (IYCN). This issue is particularly prevalent in regions such as southern Asia and Sub-Saharan Africa.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Poor or malnutrition contributes to 45% of the deaths of children under five.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Inadequate food and nutrient intakes coupled with recurrent infections, high prevalence of bacterial and parasitic diseases in developing countries contributes greatly to malnutrition.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eStunting or child growth failure is the result of chronic and recurrent malnutrition in children and threatens the survival, growth and development.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e In 2020, it was estimated that 149.2\u0026nbsp;million children under the age of 5, accounting for 22.0% of all children in this age group worldwide, were classified as stunted, meaning they were too short for their age.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Children residing in low- and middle-income countries (LMICs) have encountered numerous significant hurdles stemming from socioeconomic disparities, including severe poverty, lack of access to healthcare services, food insecurity, and nutritional deficiencies.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e In the Africa region, the prevalence of stunting is 30.7% which is higher than the global average (22.0%).\u003csup\u003e8\u003c/sup\u003e In Zambia, inadequate nutrition remains a significant burden and continues to pose a pressing public health concern. This issue is intricately linked with multidimensional poverty and systemic challenges affecting food security, water, sanitation, and hygiene (WASH) conditions, as well as health, social, and economic systems.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e The 2018 Zambia Demographic Health Survey revealed that 35% of children under the age of 5 were stunted. This prevalence of stunting in Zambia surpasses the regional average for the Africa region, which stands at 30.7%. \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eChild growth and nutritional development are significantly impacted by various factors related to living conditions, encompassing socio-economic, cultural, demographic, and climatic variables, which can vary widely among different nations.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Children residing in vulnerable communities encounter disproportionate risks to their well-being, encompassing threats to life, health, and physical safety. Limited evidence exists regarding the nutritional well-being of children in proximity to mining areas. Mining activities present both advantages and potential hazards to individuals residing in communities abundant in mineral resources.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e However, there is scant evidence regarding the connections among mining activities, their impact on public health, and the initiatives undertaken by mining companies within affected communities.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e As a result of the remote nature of mining sites, the majority of mining communities face social marginalization and are often characterized by poverty, which restricts their access to essential healthcare and other health-related amenities including access to nutritious foods.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e While mining activities are undeniably tied to socioeconomic advancement, they also correlate with negative consequences like poverty, inadequate sanitation, haphazard urbanization, risky behaviors, and population migration, all of which impact health.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Therefore, this project investigated the contextual drivers and factors influencing child growth failure in local communities dominated by mining activities.\u003c/p\u003e"},{"header":"Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and study population\u003c/h2\u003e \u003cp\u003eThe study was conducted in Kapijimpanga and Kyafukuma communities of Solwezi district in North-western province of Zambia. Solwezi\u0026rsquo;s main economic activity is mining and is experiencing rapid growth as a district largely due to the existence and presence of the largest and most productive mine, Kansanshi. The study focused on under-five children and their caregivers who live in mining communities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSample size and Sampling technique\u003c/h2\u003e \u003cp\u003eThe study utilized a random sampling technique to select participants, specifically a multistage random sampling technique to select health facilities. A comprehensive list of all under-five children and their caregivers within the catchment areas of the selected health facilities was compiled, and participants were then randomly selected from this list. To determine the appropriate sample size, a sample size estimation formula with a dichotomous outcome was used. This formula took into account various factors such as the required sample size in each group (ni), the absolute value of the difference in proportions between the two groups expected under the alternative hypothesis (|p1 - p2|), the overall proportion (p), the selected level of significance (α) set at 5%, and the value from the standard normal distribution (Z1-α/2) that corresponds to a significance level of 1-α/2. Additionally, the selected power of 80% (1- β) and the corresponding value from the standard normal distribution (Z1-β) were also considered. This process resulted in a sample size of 781.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData Collection\u003c/h2\u003e \u003cp\u003eThe primary method of gathering data was through a structured questionnaire. Information was obtained through structured interviews conducted with caregivers, while measurements were directly taken from the children. Various equipment such as measuring boards, measuring tapes, and weight scales were utilized for accurate measurements. A multidisciplinary team consisting of qualified personnel including clinicians, nutritionists, and nurses was responsible for collecting data on nutrition parameters and child health indicators, as well as conducting structured interviews with caregivers. Anthropometric measurements, including age, height/length, and weight, were obtained from the children. To assess anthropometric indices such as stunting, measurements were compared against the WHO Child Growth Standards median. Stunting was defined as length/height-for-age more than 2 standard deviations below the WHO Child Growth Standards median.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eThe research employed a comprehensive statistical analysis methodology, incorporating descriptive statistics, such as frequency distributions, were utilized to summarize and explore the characteristics of the variables under investigation. A bivariate analysis using logistic regression was conducted to assess the relationships between individual factors, community level factors, behavioral factors and the outcome variable, allowing for the identification of potential predictors of child growth failure. Subsequently, a multivariate statistical analysis framework was employed, specifically structural equation modeling (SEM) analysis. SEM enables the simultaneous estimation of a system of equations, providing a comprehensive approach to modeling complex and underlying structural relationships among variables. Factors significantly associated at 90%, 95%and 99% confidence interval with the outcome variable were included in multilevel analysis. A stepwise backwards elimination method was used taking account variables eligible for removal at α\u003csub\u003ecriteria\u003c/sub\u003e 90%, 95% and 99% confidence level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eEthical Considerations\u003c/h2\u003e \u003cp\u003e Ethical clearance was sought from the Copperbelt University Biomedical Research Committee (CBUBREC) study number CBU/BREC/23/001 to conduct the study. The institutional review board conducted a thorough full review of the protocol upon which approval was granted. Participation was voluntary (freely-given) and participants were informed about facets of the study. We confirm that informed consent /assent to participate and publish were obtained from all participants and parents/or legal guardian of under-five children. Therefore, the study adhered to all ethical guidelines.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eRespondent Profile and Background Characteristics\u003c/h2\u003e \u003cp\u003eA total of 781 under-five children took part in the survey. Findings show that 51% of the children were female and the average household size was 6.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7 SD persons. Further, the average under-five children per household was 1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7 SD. The average age of the primary caregivers was 24.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4. Half (50%) of the primary caregivers had a primary school education, 85% were married, 98% were unemployed (housewives), 5% had a disability/chronic illness and 84% of the household heads were in informal employment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDevelopmental Status and Associated Risk Factors of Child Growth Failure\u003c/h2\u003e \u003cp\u003eResults in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e show that the average age (months) of children was 21.3\u0026thinsp;\u0026plusmn;\u0026thinsp;15.7 SD; average height (centimetres) was 80.4\u0026thinsp;\u0026plusmn;\u0026thinsp;13.7 SD and the average height-for-age Z-Score (SD) was 0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9. Furthermore, the results reveal that 35% of children under the age of five were classified as stunted (child growth failure), while 15% exhibited severe stunting, indicating significant growth failure. The analysis further reveals a statistically significant association (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between stunting and both age and sex. Specifically, female children and those within the age range of 18\u0026ndash;35 months exhibit a higher prevalence of stunting and severe stunting. This highlights the importance of considering demographic factors such as age and sex when assessing nutritional outcomes among children in this population.\u003c/p\u003e \u003cp\u003eA chi-square analysis was conducted to explore the contextual factors related to stunting. Results in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e revealed that there is a statistical significant associations between stunting and several factors, including ownership of a garden (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), limited access to markets and market products (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), financial constraints (P\u0026thinsp;\u0026lt;\u0026thinsp;0.004), entrepreneurship training (P\u0026thinsp;\u0026lt;\u0026thinsp;0.039), availability of mainstream grocery stores (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), access to farmers' markets (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), methods of vegetable preparation (P\u0026thinsp;\u0026lt;\u0026thinsp;0.023), feeding styles/strategy (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and cultural customs and beliefs regarding food consumption (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eContextual factors-child characteristics associated with child growth; Binary logistic regression analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eChild Growth Failure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eSevere Growth failure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage below \u0026minus;\u0026thinsp;2 SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePercentage below \u0026minus;\u0026thinsp;2 SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePercentage below \u0026minus;\u0026thinsp;3 SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003ePercentage below \u0026minus;\u0026thinsp;3 SD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContextual Factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConf Inter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eConf Inter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eConf Inter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eConf Inter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eChilds Sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[60.3,72.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[27.2,39.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e88.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[84.1,92.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[7.6,15.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e384\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[57.5,70.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[29.8,42.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[76.3,86.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[13.6,23.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e396\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[60.8,69.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[30.2,39.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[81.8,88.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[11.7,18.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e781\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eP-value\u0026thinsp;=\u0026thinsp;0.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c10\" namest=\"c6\"\u003e \u003cp\u003eP-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eChilds Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[80.6,94.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[5.7,19.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[90.1,98.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[1.4,9.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[78.9,97.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[2.9,21.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[83.4,98.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[1.5,16.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u0026ndash;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[67.5,89.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[11.0,32.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[83.8,97.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[2.2,16.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u0026ndash;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[46.2,74.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[25.7,53.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[78.4,96.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[4.0,21.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[34.7,62.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[37.1,65.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[61.2,86.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[13.9,38.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[25.0,47.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[52.2,75.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[59.3,80.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[19.3,40.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u0026ndash;47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[43.1,67.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[32.3,56.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[63.1,84.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[15.5,36.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[45.0,73.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[26.6,55.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[75.0,94.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[5.5,25.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[60.8,69.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[30.2,39.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[81.8,88.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[11.7,18.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e781\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eP-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c10\" namest=\"c6\"\u003e \u003cp\u003eP-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRelationship Between Contextual factors and Stunting. A Bivariate Logistic regression analysis\u003c/h2\u003e \u003cp\u003eThe study identified a statistically significant relationship between individual-level factors and child growth indicators. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that children in older age brackets, including those aged 12\u0026ndash;17, 18\u0026ndash;23, 24\u0026ndash;35, 36\u0026ndash;47, and 48\u0026ndash;59 months, exhibited significantly higher odds [OR\u0026thinsp;=\u0026thinsp;5.25, 95% CI\u0026thinsp;=\u0026thinsp;2.103\u0026ndash;13.103], [OR\u0026thinsp;=\u0026thinsp;8.71, 95% CI\u0026thinsp;=\u0026thinsp;23.548\u0026ndash;21.391], [OR\u0026thinsp;=\u0026thinsp;14.94, 95% CI\u0026thinsp;=\u0026thinsp;6.377\u0026ndash;34.985], [OR\u0026thinsp;=\u0026thinsp;6.57, 95% CI\u0026thinsp;=\u0026thinsp;2.799\u0026ndash;15.421], and [OR\u0026thinsp;=\u0026thinsp;5.50, 95% CI\u0026thinsp;=\u0026thinsp;2.198\u0026ndash;13.748], respectively, of experiencing child growth failure compared to children under one-year-old.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship between individual level factors and child growth failure; Binary logistic regression analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eChild Growth Failure\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContextual Factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (12\u0026ndash;17 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.25***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.103\u0026ndash;13.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (18\u0026ndash;23 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.71***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.548\u0026ndash;21.391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (24\u0026ndash;35 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.94***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.377\u0026ndash;34.985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (36\u0026ndash;47 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.57***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.799\u0026ndash;15.421\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (48\u0026ndash;59 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.50***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.198\u0026ndash;13.748\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelated to household head as spouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.62*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.384\u0026ndash;1.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespondent/mother/caregiver aged 40+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.77***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.645\u0026ndash;13.825\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfidence interval in parentheses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSimilarly, the research identified a statistically significant relationship between community-level factors and child growth impairment. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that households that never possessed a garden demonstrated twice the likelihood [OR\u0026thinsp;=\u0026thinsp;2.01, 95% CI\u0026thinsp;=\u0026thinsp;1.330\u0026ndash;3.047] of having a child with stunted growth. Likewise, households lacking access to mainstream grocery stores, farmers' markets, and outlets offering fresh foods exhibited elevated odds [OR\u0026thinsp;=\u0026thinsp;2.01, 95% CI\u0026thinsp;=\u0026thinsp;1.330\u0026ndash;3.047], [OR\u0026thinsp;=\u0026thinsp;2.01, 95% CI\u0026thinsp;=\u0026thinsp;1.330\u0026ndash;3.047], and [OR\u0026thinsp;=\u0026thinsp;2.01, 95% CI\u0026thinsp;=\u0026thinsp;1.330\u0026ndash;3.047], respectively, of experiencing child growth failure. Moreover, households allocating a significant portion of their income to other commodities or services relative to food expenses had heightened odds [OR\u0026thinsp;=\u0026thinsp;1.70, 95% CI\u0026thinsp;=\u0026thinsp;0.927\u0026ndash;3.109] of encountering child growth issues.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship between community-level factors and child growth failure; Binary logistic regression analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eChild Growth Failure\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContextual Factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoes not own a vegetable garden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.01***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.330\u0026ndash;3.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome expenditure priorities-Bills, school fees, agro-inputs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.70*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.927\u0026ndash;3.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipated in an Agriculture training program\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.69*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.997\u0026ndash;2.855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipated in an business/Entrepreneurship training program\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.019\u0026ndash;1.191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack access to a mainstream grocery store\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.51***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.558\u0026ndash;4.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack access to a farmers market, organic or local food source outlet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.53***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.095\u0026ndash;5.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack access to a food outlet with fresh and unexpired edible foods\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.89***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.235\u0026ndash;6.760\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAway from home gone for farming (Magimi))\u0026thinsp;=\u0026thinsp;1, Rarely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.76*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.999\u0026ndash;3.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfidence Interval in parentheses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe study identified a statistically significant association between behavioral factors and child growth impairment. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that households that cooked vegetables for about 30 minutes and 30 minutes to an hour had higher odds [OR\u0026thinsp;=\u0026thinsp;2.12, 95% CI\u0026thinsp;=\u0026thinsp;1.063\u0026ndash;4.233] and [OR\u0026thinsp;=\u0026thinsp;3.00, 95% CI\u0026thinsp;=\u0026thinsp;1.037\u0026ndash;8.709], respectively, of having a stunted child compared to those who cooked within 10 minutes. Conversely, households that cooked vegetables just before eating had lower odds [OR\u0026thinsp;=\u0026thinsp;0.65, 95% CI\u0026thinsp;=\u0026thinsp;0.395\u0026ndash;1.065] of having a stunted child. Additionally, households employing feeding practices such as giving attention during meals, clapping hands, making funny faces/playing/laughing, and drawing the child\u0026rsquo;s attention had lower odds [OR\u0026thinsp;=\u0026thinsp;0.58, 95% CI\u0026thinsp;=\u0026thinsp;0.386\u0026ndash;0.880], [OR\u0026thinsp;=\u0026thinsp;0.28, 95% CI\u0026thinsp;=\u0026thinsp;0.164\u0026ndash;0.479], [OR\u0026thinsp;=\u0026thinsp;0.16, 95% CI\u0026thinsp;=\u0026thinsp;0.079\u0026ndash;0.333], and [OR\u0026thinsp;=\u0026thinsp;0.47, 95% CI\u0026thinsp;=\u0026thinsp;0.257\u0026ndash;0.843], respectively, of experiencing child growth failure.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship between behavioural factors and child growth failure: Binary logistic regression analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eChild Growth Failure\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContextual Factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e. - .\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetables cooked less than 30 minutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.12**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.063\u0026ndash;4.233\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetables cooked between 30 and 1 hour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.00**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.037\u0026ndash;8.709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetables cooked just before eating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.395\u0026ndash;1.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeeding strategy-Giving them attention during meals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.58**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.386\u0026ndash;0.880\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeeding strategy-Clap hands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.28***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.164\u0026ndash;0.479\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeeding strategy-Make funny faces/play/laugh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.16***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.079\u0026ndash;0.333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeeding strategy-Draw the child's attention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.47**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.257\u0026ndash;0.843\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfidence interval in parentheses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStructural Equation Modeling\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e provides an overview of the measurement model, detailing each latent construct alongside its respective measurement items. The analysis involved fitting three Generalized Structural Equation Models (GSEM), with each model incorporating the latent constructs and their associated measurement items. The final model, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, integrated the causal pathways between the constructs. The path analysis and model depicts the impact of a latent factors such as feeding strategies and food access as well as observed factors influencing child growth failure through several causal routes. Notably, this model was adjusted for various variables, including the child\u0026rsquo;s age, access to food markets, feeding strategies, and household factors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStepwise logistic regression was employed to identify significant predictor variables for constructing the most effective logistic regression model. The results indicate that each additional month in a child's age increased the odds [AOR\u0026thinsp;=\u0026thinsp;1.04, 95% CI\u0026thinsp;=\u0026thinsp;1.028\u0026ndash;1.056] of child growth failure. Moreover, having a high school education and being unemployed-housewife reduced the odds [AOR\u0026thinsp;=\u0026thinsp;0.24, 95% CI\u0026thinsp;=\u0026thinsp;0.089\u0026ndash;0.677] and [AOR\u0026thinsp;=\u0026thinsp;0.45, 95% CI\u0026thinsp;=\u0026thinsp;0.226\u0026ndash;0.901], respectively, of child growth failure. Implementing specific feeding strategies decreased the odds [AOR\u0026thinsp;=\u0026thinsp;0.39, 95% CI\u0026thinsp;=\u0026thinsp;0.226\u0026ndash;0.663] of child growth failure. Conversely, households that spent longer durations cooking vegetables faced higher odds [AOR\u0026thinsp;=\u0026thinsp;2.16, 95% CI\u0026thinsp;=\u0026thinsp;1.131\u0026ndash;4.129] of child growth failure.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate logistic regression; drivers of child growth failure\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eChild Growth Failure\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContextual Factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003cp\u003eodds ratio (AOR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConf Inter\u003c/p\u003e \u003cp\u003e(CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild\u0026rsquo;s Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.028\u0026ndash;1.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation Level-high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.24***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.089\u0026ndash;0.677\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespondent/mother/caregiver aged 40+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.61*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.856\u0026ndash;7.982\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment- None (Doesn't work) i.e housewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.45**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.226\u0026ndash;0.901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisable/Chronically ill\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.13*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.988\u0026ndash;9.946\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo access to a food market, grocery store or farmers market\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.06**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.167\u0026ndash;3.649\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeeding strategy-Make funny faces/play/laugh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.39***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.226\u0026ndash;0.663\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetable cooking- Over cooked\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.16**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.131\u0026ndash;4.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfidence Interval (CI) in parentheses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe prevalence observed in this study was higher than the global findings yet mirrored and was consistent with the national findings, indicating that 35% of children were stunted.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e The findings emphasize the crucial role of demographic factors, including the child's age and sex in evaluating nutritional outcomes among children. These results align with findings not only at the national level but also regionally and globally that found statistical significant associations.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Additionally, education remains pivotal across various domains, as evidenced by findings in this study indicating that mothers with an education had decreased odds of encountering child growth failure. The results of this study substantiate those of similar studies that found that educational the mother plays a critical role in childhood stunting.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe employment status and commitments of mothers/caregivers serve as crucial determinants directly associated with the growth and development of children under the age of five. The findings from this study, which suggest that women or caregivers not involved in economic activities had lower chances of experiencing child growth failure, align with research conducted in India.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e That study revealed that children of unemployed mothers exhibited significantly greater height compared to children of employed mothers. Socioeconomic disparities contribute to disparities in nutrition access and quality.\u003c/p\u003e \u003cp\u003eThe household burden of disease can have significant negative impacts on child nutrition and growth. When members of a household are affected by illness or disease, it can disrupt the family's ability to provide adequate care, resources, and support for children's nutritional needs, leading to various adverse outcomes. The findings reveal that vulnerable households, particularly those burdened with a higher prevalence of disease, were more prone to stunting. This observation is consistent with findings from other research papers.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Families affected by illness may prioritize spending on healthcare expenses over purchasing nutritious foods, leading to food insecurity and inadequate nutrition for children. Illness-related fatigue or incapacitation can hinder caregivers' ability to prepare and serve nutritious meals, leading to irregular feeding patterns or reliance on convenient but less nutritious food options.\u003c/p\u003e \u003cp\u003eSystemic inequalities have profound and far-reaching implications for child development and growth. These inequalities, rooted in various social, economic, and political structures, create disparities in access to resources, opportunities, and essential services, which directly impact children's well-being. Poverty is widely recognized as a primary cause of malnutrition. However, the absence of access to nutritious foods also substantially contributes to child growth failure and food insecurity. These findings align with the key pillars of food security, namely availability, access, utilization, and stability. It is imperative for children under the age of five to have reliable and uninterrupted access to an adequate supply of food with the appropriate nutritional composition. The insufficiency of food availability, accessibility, utilization, and stability increases the vulnerability of children under the age of five to growth failure. The results of this study are consistent with those of other research, which underscores the detrimental impact of insufficient food markets or stores on child growth failure.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Food insecurity is highest in deprived communities including mining communities contributing substantially to child growth failure.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e However, these results contrast with findings from Ethiopia, which revealed that food-secure households were 1.96 times more likely to experience the double burden of malnutrition compared to food-insecure households (AOR\u0026thinsp;=\u0026thinsp;1.96, 95% CI 1.13, 3.39).\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eFeeding strategies play a crucial role in under-five nutrition and can significantly impact children's growth, development, and overall health. Findings show that responsive feeding practices involve attentively responding to a child's hunger and satiety cues, allowing them to self-regulate their food intake. Encouraging responsive feeding promotes healthy eating behaviors as well as the development of healthy appetite regulation in children. \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe results suggest that child growth failure remains a substantial concern, with individual and contextual-level factors identified as significant contributors. Mining communities face susceptibility to diverse shocks, which can contribute to stunting in children. Therefore, as mining companies undertake corporate social investment initiatives, it's crucial to consider both individual and contextual factors in the design of interventions. This ensures effectiveness and efficiency in addressing child growth failure within these communities. Overall, implementing appropriate strategies that prioritize responsive feeding, access to nutritious foods and emphasizing the importance of nutrition education is vital in improving the nutritional status and well-being of children.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors participated in all facets of the article. Authors also contributed to the development, conceptualization, and editing of the article. All authors provided their final approval for the version intended for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding arrangements.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is available upon request from the corresponding Author.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlwabr, G. M. A. \u0026amp; Alwabr, N. M. A. Nutritional status of children under five years of age and factors associated in rural areas of Sana\u0026rsquo;a Governorate, Yemen. \u003cem\u003eCHRISMED J. Heal. 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Factors associated with stunting among children aged 6\u0026ndash;59 months in Bensa District, Sidama Region, South Ethiopia: unmatched case-control study. \u003cem\u003eBMC Pediatr.\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 1\u0026ndash;11 (2021).\u003c/li\u003e\n\u003cli\u003eRugema, J. \u003cem\u003eet al.\u003c/em\u003e Predictors and factors associated with stunting among under- five-year children: a cross-sectional population-based study in Rwanda of the 2014\u0026ndash;2015 demographic and Health Survey. \u003cem\u003eAfr. Health Sci.\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 671 (2022).\u003c/li\u003e\n\u003cli\u003eMzumara, B., Bwembya, P., Halwiindi, H., Mugode, R. \u0026amp; Banda, J. Factors associated with stunting among children below five years of age in Zambia: Evidence from the 2014 Zambia demographic and health survey. \u003cem\u003eBMC Nutr.\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 1\u0026ndash;8 (2018).\u003c/li\u003e\n\u003cli\u003eRoy, T. B., Das, T., Das, P. \u0026amp; Das, P. Analyzing determinants from both compositional and contextual level impeding desired linear growth of children in Indian context. \u003cem\u003eBMC Nutr.\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 1\u0026ndash;13 (2023).\u003c/li\u003e\n\u003cli\u003eVan De Poel, E., Hosseinpoor, A. R., Jehu-Appiah, C., Vega, J. \u0026amp; Speybroeck, N. Malnutrition and the disproportional burden on the poor: The case of Ghana. \u003cem\u003eInt. J. Equity Health\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 1\u0026ndash;12 (2007).\u003c/li\u003e\n\u003cli\u003eChristian, A. K. \u0026amp; Dake, F. A. A. Profiling household double and triple burden of malnutrition in sub-Saharan Africa: prevalence and influencing household factors. \u003cem\u003ePublic Health Nutr.\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 1563 (2022).\u003c/li\u003e\n\u003cli\u003eFisher, M. R. 8.1 Food Security. (2017).\u003c/li\u003e\n\u003cli\u003eSanta-Ram\u0026iacute;rez, H. A. \u003cem\u003eet al.\u003c/em\u003e Small area vulnerability, household food insecurity and child malnutrition in Medellin, Colombia: results from a repeated cross-sectional study. \u003cem\u003eLancet Reg. Heal. - Am.\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 100521 (2023).\u003c/li\u003e\n\u003cli\u003eBlack, M. M. \u0026amp; Aboud, F. E. Responsive Feeding Is Embedded in a Theoretical Framework of Responsive Parenting. \u003cem\u003eJ. Nutr.\u003c/em\u003e \u003cstrong\u003e141\u003c/strong\u003e, 490 (2011).\u003c/li\u003e\n\u003cli\u003eEngle, P. L. \u0026amp; Pelto, G. H. Responsive Feeding: Implications for Policy and Program Implementation,. \u003cem\u003eJ. Nutr.\u003c/em\u003e \u003cstrong\u003e141\u003c/strong\u003e, 508\u0026ndash;511 (2011).\u003c/li\u003e\n\u003cli\u003eBentley, M. E., Wasser, H. M. \u0026amp; Creed-Kanashiro, H. M. Responsive Feeding and Child Undernutrition in Low- and Middle-Income Countries. \u003cem\u003eJ. Nutr.\u003c/em\u003e \u003cstrong\u003e141\u003c/strong\u003e, 502 (2011).\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":"Child growth failure, Contextual factors, Mining Communities and Nutrition","lastPublishedDoi":"10.21203/rs.3.rs-4980853/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4980853/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: Child growth and nutritional development are significantly impacted by various factors. This paper investigated the contextual drivers influencing child growth failure in local communities dominated by mining activities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe study employed a cross-sectional study design and comprised a random sample of 781 under-five children and their caregivers. Structured interviews were conducted with caregivers, and anthropometric measurements were taken from the children. Bivariate chi-square, Structural Equation Modeling and multivariate logistic regression analysis were performed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eOver half (51%) of the children were female. On average, households consisted of 6.1±2.7 SD persons. Primary caregivers had an average age of 24.2±9.4 while the children's average age was 21.3±15.7 SD months. The average height of children was 80.4±13.7 SD with a height-for-age Z-Score of 0.2±4.9 SD. Further, 35% of children experience child growth failure. Drivers include; age-caregiver [AOR = 1.04, 95% CI = 1.028- 1.056], high-school education [AOR = 0.24, 95% CI = 0.089 - 0.677], unemployment-housewife [AOR = 0.45, 95% CI = 0.226 - 0.901], feeding-strategies [AOR = 0.39, 95% CI = 0.226 - 0.663] and cooking-duration [AOR = 2.16, 95% CI = 1.131 - 4.129].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eChild growth failure remains a concern, with individual and contextual-level factors identified as significant contributors and thus crucial to take them into account when designing nutrition interventions in vulnerable communities. Therefore, as mining cooperation’s undertake corporate social investment initiatives, it's crucial to consider contextual factors in the design of community interventions.\u003c/p\u003e","manuscriptTitle":"Investigating the contextual drivers and factors impacting child growth failure in Mining Communities; A Structural Equation Modeling approach. 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