AIM: The purpose of this study was to determine the relationship between Soil Transmitted Helminth infection on levels of eosinophils among primary school children. In addition, this study also aimed to determine the prevalence of different types of worm infections and the levels of eosinophils in children infected with worms.
MATERIAL AND METHODS: This study was analytic observational using a cross-sectional method. The sampling technique was consecutive and in total 132 samples was obtained. The study involved primary school children in Amplas Medan and Hamparan Perak, Deli Serdang through May to October 2016. Univariate analysis was performed to determine STH infection prevalence and bivariate analysis was used to find the correlation between STH infection and eosinophil levels through a Chi square (χ2) test.
RESULTS: The results showed that the prevalence of Soil Transmitted Helminth was 7.6%. The most common types of STH infection were 3.8% with Trichuris trichiura and 3% with Ascaris lumbricoides. A significant correlation was found between Parasite infection and eosinophil levels (Contingency Coefficient (C) = 0.2, χ2 = 5.3, p = 0.021) and the risk of STH infection that caused eosinophilia or increased eosinophil levels in the children with a Prevalence Ratio (PR) of 1.56 (Confidence Interval (CI) 95%: 1.10-2.22).
CONCLUSION: It is recommended that schools at similar risk improve and maintain hygiene and healthy behaviour in the school environment and that parents and teachers pay greater attention to the cleanliness of their children.
Methods: Cross-sectional data from 62 developing countries were used to run several multivariate linear regressions. R2 was used to compare the powers of MPI with income-poverties (income poverty gaps [IPG] at 1.9 and 3.1 USD) in explaining LE.
Results: Adjusting for controls, both MPI (β =-0.245, P<0.001) and IPG at 3.1 USD (β=-0.135, P=0.044) significantly correlates with LE, but not IPG at 1.9 USD (β=-0.147, P=0.135). MPI explains 12.1% of the variation in LE compared to only 3.2% explained by IPG at 3.1 USD. The effect of MPI on LE is higher on female (β=-0.210, P<0.001) than male (β=-0.177, P<0.001). The relative influence of the deprivation indictors on LE ranks as follows (most to least): Asset ownership, drinking water, cooking fuel, flooring, child school attendance, years of schooling, nutrition, mortality, improved sanitation, and electricity.
Conclusion: Interventions to reduce poverty and improve LE should be guided by MPI, not income poverty indices. Such policies should be female-oriented and prioritized based on the relative influence of the various poverty deprivation indicators on LE.