Cargando…

Using the hierarchical ordinal regression model to analyse the intensity of urinary schistosomiasis infection in school children in Lusaka Province, Zambia

BACKGROUND: Urinary schistosomiasis has been a major public health problem in Zambia for many years. However, the disease profile may vary in different locale due to the changing ecosystem that contributes to the risk of acquiring the disease. The objective of this study was to quantify risk factors...

Descripción completa

Detalles Bibliográficos
Autores principales: Simoonga, Christopher, Kazembe, Lawrence N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5319044/
https://www.ncbi.nlm.nih.gov/pubmed/28219411
http://dx.doi.org/10.1186/s40249-017-0262-x
_version_ 1782509304597708800
author Simoonga, Christopher
Kazembe, Lawrence N.
author_facet Simoonga, Christopher
Kazembe, Lawrence N.
author_sort Simoonga, Christopher
collection PubMed
description BACKGROUND: Urinary schistosomiasis has been a major public health problem in Zambia for many years. However, the disease profile may vary in different locale due to the changing ecosystem that contributes to the risk of acquiring the disease. The objective of this study was to quantify risk factors associated with the intensity of urinary schistosomiasis infection in school children in Lusaka Province, Zambia, in order to better understand local transmission. METHODS: Data were obtained from 1 912 school children, in 20 communities, in the districts of Luangwa and Kafue in Lusaka Province. Both individual- and community-level covariates were incorporated into an ordinal logistic regression model to predict the probability of an infection being a certain intensity in a three-category outcome response: 0 = no infection, 1 = light infection, and 2 = moderate/heavy infection. Random effects were introduced to capture unobserved heterogeneity. RESULTS: Overall, the risk of urinary schistosomiasis was strongly associated with age, altitude at which the child lived, and sex. Weak associations were observed with the normalized difference vegetation index, maximum temperature, and snail abundance. Detailed analysis indicated that the association between infection intensities and age and altitude were category-specific. Particularly, infection intensity was lower in children aged between 5 and 9 years compared to those aged 10 to 15 years (OR = 0.72, 95% CI = 0.51–0.99). However, the age-specific risk changed at different levels of infection, such that when comparing children with light infection to those who were not infected, age was associated with a lower odds (category 1 vs category 0: OR = 0.71, 95% CI: 0.50–0.99), yet such a relation was not significant when considering children who were moderately or heavily infected compared to those with a light or no infection (category 2 vs category 0: OR = 0.96, 95% CI: 0.45–1.64). Overall, we observed that children living in the valley were less likely to acquire urinary schistosomiasis compared to those living in plateau areas (OR = 0.48, 95% CI: 0.16–0.71). However, category-specific effects showed no significant association in category 1 (light infection), whereas in category 2 (moderate/high infection), the risk was still significantly lower for those living in the valley compared to those living in plateau areas (OR = 0.18, 95% CI: 0.04–0.75). CONCLUSIONS: This study demonstrates the importance of understanding the dynamics and heterogeneity of infection in control efforts, and further suggests that apart from the well-researched factors of Schistosoma intensity, various other factors influence transmission. Control programmes need to take into consideration the varying infection intensities of the disease so that effective interventions can be designed. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40249-017-0262-x) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-5319044
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-53190442017-02-24 Using the hierarchical ordinal regression model to analyse the intensity of urinary schistosomiasis infection in school children in Lusaka Province, Zambia Simoonga, Christopher Kazembe, Lawrence N. Infect Dis Poverty Research Article BACKGROUND: Urinary schistosomiasis has been a major public health problem in Zambia for many years. However, the disease profile may vary in different locale due to the changing ecosystem that contributes to the risk of acquiring the disease. The objective of this study was to quantify risk factors associated with the intensity of urinary schistosomiasis infection in school children in Lusaka Province, Zambia, in order to better understand local transmission. METHODS: Data were obtained from 1 912 school children, in 20 communities, in the districts of Luangwa and Kafue in Lusaka Province. Both individual- and community-level covariates were incorporated into an ordinal logistic regression model to predict the probability of an infection being a certain intensity in a three-category outcome response: 0 = no infection, 1 = light infection, and 2 = moderate/heavy infection. Random effects were introduced to capture unobserved heterogeneity. RESULTS: Overall, the risk of urinary schistosomiasis was strongly associated with age, altitude at which the child lived, and sex. Weak associations were observed with the normalized difference vegetation index, maximum temperature, and snail abundance. Detailed analysis indicated that the association between infection intensities and age and altitude were category-specific. Particularly, infection intensity was lower in children aged between 5 and 9 years compared to those aged 10 to 15 years (OR = 0.72, 95% CI = 0.51–0.99). However, the age-specific risk changed at different levels of infection, such that when comparing children with light infection to those who were not infected, age was associated with a lower odds (category 1 vs category 0: OR = 0.71, 95% CI: 0.50–0.99), yet such a relation was not significant when considering children who were moderately or heavily infected compared to those with a light or no infection (category 2 vs category 0: OR = 0.96, 95% CI: 0.45–1.64). Overall, we observed that children living in the valley were less likely to acquire urinary schistosomiasis compared to those living in plateau areas (OR = 0.48, 95% CI: 0.16–0.71). However, category-specific effects showed no significant association in category 1 (light infection), whereas in category 2 (moderate/high infection), the risk was still significantly lower for those living in the valley compared to those living in plateau areas (OR = 0.18, 95% CI: 0.04–0.75). CONCLUSIONS: This study demonstrates the importance of understanding the dynamics and heterogeneity of infection in control efforts, and further suggests that apart from the well-researched factors of Schistosoma intensity, various other factors influence transmission. Control programmes need to take into consideration the varying infection intensities of the disease so that effective interventions can be designed. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40249-017-0262-x) contains supplementary material, which is available to authorized users. BioMed Central 2017-02-21 /pmc/articles/PMC5319044/ /pubmed/28219411 http://dx.doi.org/10.1186/s40249-017-0262-x Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Simoonga, Christopher
Kazembe, Lawrence N.
Using the hierarchical ordinal regression model to analyse the intensity of urinary schistosomiasis infection in school children in Lusaka Province, Zambia
title Using the hierarchical ordinal regression model to analyse the intensity of urinary schistosomiasis infection in school children in Lusaka Province, Zambia
title_full Using the hierarchical ordinal regression model to analyse the intensity of urinary schistosomiasis infection in school children in Lusaka Province, Zambia
title_fullStr Using the hierarchical ordinal regression model to analyse the intensity of urinary schistosomiasis infection in school children in Lusaka Province, Zambia
title_full_unstemmed Using the hierarchical ordinal regression model to analyse the intensity of urinary schistosomiasis infection in school children in Lusaka Province, Zambia
title_short Using the hierarchical ordinal regression model to analyse the intensity of urinary schistosomiasis infection in school children in Lusaka Province, Zambia
title_sort using the hierarchical ordinal regression model to analyse the intensity of urinary schistosomiasis infection in school children in lusaka province, zambia
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5319044/
https://www.ncbi.nlm.nih.gov/pubmed/28219411
http://dx.doi.org/10.1186/s40249-017-0262-x
work_keys_str_mv AT simoongachristopher usingthehierarchicalordinalregressionmodeltoanalysetheintensityofurinaryschistosomiasisinfectioninschoolchildreninlusakaprovincezambia
AT kazembelawrencen usingthehierarchicalordinalregressionmodeltoanalysetheintensityofurinaryschistosomiasisinfectioninschoolchildreninlusakaprovincezambia