The current presentation will discuss a study examining the effects of several socioeconomic characteristics on the frequency Nigerian children consumed vitamin A capsule (VAC) supplements at the individual, household, and community levels.
The study aims to investigate the effects of neighbourhood socioeconomic development on the likelihood of receiving VAC supplements using a nationally representative population-based survey dataset from 2008 (Aremu, Lawoko, & Dalai, 2010).
The study highlights the need to address socioeconomic and geographic disparities in access to preventive child health interventions, specifically VAC supplementation, and the factors affecting coverage of these programmes.
The research methodology used in this paper is sound for examining the variables affecting young Nigerians' usage of supplementary Vitamin A Capsules (VAC).
According to Aremu, Lawoko, and Dalai (2010), adopting a dataset from a population-based survey nationally representative in 2008 improved the generalizability of results. It made it possible to analyse the factors influencing VAC supplement coverage in great detail.
Multilevel modelling techniques, which consider the layered structure of the data and enable the evaluation of clustering effects, are necessary to comprehend the impact of neighborhood-level socioeconomic determinants (Huang, 2018).
Table 3 presents the findings of multilevel logistic regression models examining factors associated with Vitamin A Capsule (VAC) supplement use among Nigerian children using data from the NDHS 2008 (Aremu, Lawoko, & Dalai, 2010).
Aremu, Lawoko, and Dalai (2010) investigated the relationship between various demographic and environmental variables and the use of Vitamin A Capsules (VAC) by children in Nigeria.
To investigate the impact of these factors on the usage of VAC supplements, the researchers added child characteristics, including sex and birth month, as covariates.
The researchers examined demographic data, such as mothers' ages, levels of education, and occupation, to examine the impact of maternal characteristics on the use of VAC supplements.
The researchers combined the fathers' employment and educational data to investigate the relationship between dads' traits and their childs' usage of VAC supplements.
The researchers looked at the relationship between the usage of VAC supplements and the wealth index, a proxy for household financial health.
The researchers included community-level variables like geography (rural vs. urban) and the socioeconomic disadvantage index of the area in order to capture the contextual elements that may influence the acceptance of VAC supplements.
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The researchers utilised multilevel logistic regression modelling as our analytical method to determine the characteristics influencing why children in Nigeria consume VAC supplements.
By employing multilevel modeling, researchers could account for the data's hierarchical structure and examine it from both an individual and a societal standpoint (Rights and Sterba, 2019).
In order to look into the correlation between these independent variables and the usage of VAC supplements, the researchers developed three models, each of which corrects for a different set of covariates.
Model 1 served as an exposure-free null model to evaluate the baseline variance in using VAC supplements among communities.
Model 2 adjusted for child characteristics, parent characteristics, and the household wealth index to account for individual-level predictors of VAC supplement use.
Model 3 incorporates community-level elements such as geography, place of residence, and socioeconomic disadvantage in the neighbourhood to explore further the consequences of context on adopting VAC supplements.
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Aremu, Lawoko, and Dalai (2010) found a link between more significant levels of financial literacy and better retirement planning.
According to Aremu, Lawoko, and Dalai's (2010) research, those with higher levels of education were more likely to have planned for retirement.
The results show that retirement planning is more often when people are younger but less likely as people get older.
In order to evaluate the social and personal factors that affect people's inclination to purchase VAC supplements, this study used multilevel logistic regression modelling (Huang, 2018).
Using a nationally representative sample and a consistent survey technique increased the study's external validity.
Despite its limitations, using a wealth index as a stand-in for a household's wealth status without direct income data assures the validity and comparability of the results.
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Using an asset-based index as a proxy for family wealth may not accurately reflect the complexity of socioeconomic situation since it does not account for changes in personal income or social norms and attitudes that influence the adoption of health interventions (Ledermann and Kenny, 2017).
Using "community" for "administrative boundaries" might lead to prejudice and hide regional variations.
The study did not consider several contextual-level factors, such as societal norms and attitudes, which are known to affect the use of preventive child health therapies.
In research on the Vitamin A Capsule (VAC) supplementation programme in Nigeria, Aremu,Lawoko, and Dalai (2010) identified socioeconomic and regional disparities in the coverage of preventive child health therapies.
Children from higher-income households are more likely to get VAC supplements than children from lower-income households, indicating a persistent access gap (Aremu, Lawoko, & Dalai, 2010).
It is crucial to enhance VAC supplementation uptake across all socioeconomic strata to guarantee that all children benefit from this intervention.
Aremu, O., Lawoko, S. and Dalal, K., 2010. Childhood vitamin A capsule supplementation coverage in Nigeria: a multilevel analysis of geographic and socioeconomic inequities. TheScientificWorldJOURNAL, 10, pp.1901-1914.
Huang, F.L., 2018. Multilevel modeling myths. School Psychology Quarterly, 33(3), p.492.
Ledermann, T. and Kenny, D.A., 2017. Analyzing dyadic data with multilevel modeling versus structural equation modeling: A tale of two methods. Journal of Family Psychology, 31(4), p.442.
Rights, J.D. and Sterba, S.K., 2019.Quantifying explained variance in multilevel models: An integrative framework for defining R-squared measures—Psychological Methods, 24(3), p.309.
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