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

doi:10.21203/rs.3.rs-4980853/v1

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

https://doi.org/10.21203/rs.3.rs-4980853/v1

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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

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.

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.

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

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

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.

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.

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No competing interests reported.

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

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Version 1

Abstract

Figures

Introduction

Methods and Materials

Study design and study population

Sample size and Sampling technique

Data Collection

Data analysis

Ethical Considerations

Results

Respondent Profile and Background Characteristics

Developmental Status and Associated Risk Factors of Child Growth Failure

Relationship Between Contextual factors and Stunting. A Bivariate Logistic regression analysis

Structural Equation Modeling

Discussion

Conclusion

Declarations

References

Additional Declarations

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Version 1