FACULTY OF PUBLIC HEALTH

Permanent URI for this communityhttps://repository.ui.edu.ng/handle/123456789/273

Browse

Author: search.filters.author.Fagbamigbe, O. S.Subject: search.filters.subject.Fairlie decomposition

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Exploring the socio-economic determinants of educational inequalities in diarrhoea among under-five children in low- and middle-income countries: a Fairlie decomposition analysis
    (BioMed Central, 2021) Fagbamigbe, A. F.; Adebola, O. G.; Dukhi, N.; Fagbamigbe, O. S.; Uthman, O. A.
    Background: What explains the underlying causes of educational inequalities in diarrhoea among under-five children in low- and middle-income countries (LMIC) is poorly exploited, operationalized, studied and understood. This paper aims to assess the magnitude of educational-related inequalities in the development of diarrhoea and decompose risk factors that contribute to these inequalities among under-five children (U5C) in LMIC. Methods: Secondary data of 796,150 U5C from 63,378 neighbourhoods in 57 LMIC was pooled from the Demographic and Health Surveys (DHS) conducted between 2010 and 2019. The main determinate variable in this decomposition study was mothers’ literacy levels. Descriptive and inferential statistics comprising of bivariable analysis and binary logistic multivariable Fairlie decomposition techniques were employed at p = 0.05. Results: Of the 57 countries, we found a statistically significant pro-illiterate odds ratio in 6 countries, 14 showed pro-literate inequality while the remaining 37 countries had no statistically significant educational-related inequality. The countries with pro-illiterate inequalities are Burundi (OR = 1.11; 95% CI: 1.01–1.21), Cameroon (OR = 1.84; 95% CI: 1.66–2.05), Egypt (OR = 1.26; 95% CI: 1.12–1.43), Ghana (OR = 1.24; 95% CI: 1.06–1.47), Nigeria (OR = 1.80; 95% CI: 1.68–1.93), and Togo (OR = 1.21; 95% CI: 1.06–1.38). Although there are variations in factors that contribute to pro illiterate inequality across the 6 countries, the overall largest contributors to the inequality are household wealth status, maternal age, neighbourhood SES, birth order, toilet type, birth interval and place of residence. The widest pro-illiterate risk difference (RD) was in Cameroon (118.44/1000) while the pro-literate risk difference was widest in Albania (− 61.90/1000). Conclusions: The study identified educational inequalities in the prevalence of diarrhoea in children with wide variations in magnitude and contributions of the risk factors to pro-illiterate inequalities. This suggests that diarrhoea prevention strategies is a must in the pro-illiterate inequality countries and should be extended to educated mothers as well, especially in the pro-educated countries. There is a need for further studies to examine the contributions of structural and compositional factors associated with pro-educated inequalities in the prevalence of diarrhoea among U5C in LMIC.
  • Thumbnail Image
    Item
    Decomposition analysis of the compositional and contextual factors associated with poor-non-poor inequality in diarrhoea among under-five children in low- and middle-income countries
    (Elsevier Ltd, 2021) Fagbamigbe, A. F.; Ologunwa, O. P.; Afolabi, E. K.; Fagbamigbe, O. S.; Uthman, A. O.
    Objectives: The aim of the study was to assess the magnitude of wealth inequalities in the development of diarrhoea among under-five children in low- and middle-income countries (LMICs) and to identify and quantify contextual and compositional factors' contribution to the inequalities. Design: This is a cross-sectional study. Methods: We used cross-sectional data from 57 Demographic and Health Surveys conducted between 2010 and 2018 in LMICs. Descriptive statistics were used to understand the gap in having diarrhoea between the children from poor and non-poor households and across the selected covariates using Fairlie decomposition techniques with multivariable binary logistic regressions at P ¼ 0.05. Results: Of the 57 countries, we found a statistically significant pro-poor odds ratio in only 29 countries, 7 countries showed pro-non-poor inequality and others showed no statistically significant inequality. Among the countries with statistically significant pro-poor inequality, the risk difference was largest in Cameroon (94.61/1000), whereas the largest pro-non-poor risk difference in diarrhoea was widest in Timor-Leste ( 41.80/1000). Important factors responsible for pro-poor inequality varied across countries. The largest contributors to the pro-poor inequalities in having diarrhoea are maternal education, access to media, neighbourhood socio-economic status, place of residence, birth order and maternal age. Conclusion: Diarrhoea remains a major challenge in most LMICs, with a wide range of pro-poor in equalities. These disparities were explained by both compositional and contextual factors, which varied widely across the countries. Thus, multifaceted geographically specific economic alleviation intervention may prove to be a potent approach for addressing the poor and non-poor differentials in the risk of diarrhoea with policies tailored to country-specific risk factors. There is a need for further investigation of factors that drive pro-non-poor inequalities found in 9 of the LMICs.