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The Pattern of Variation between Diarrhea and Malaria Coexistence with Corresponding Risk Factors in, Chikhwawa, Malawi: A Bivariate Multilevel Analysis

Identifieur interne : 003202 ( Ncbi/Checkpoint ); précédent : 003201; suivant : 003203

The Pattern of Variation between Diarrhea and Malaria Coexistence with Corresponding Risk Factors in, Chikhwawa, Malawi: A Bivariate Multilevel Analysis

Auteurs : Salule Masangwi [Malawi] ; Neil Ferguson ; Anthony Grimason [Afrique du Sud] ; Tracy Morse [Afrique du Sud] ; Lawrence Kazembe

Source :

RBID : PMC:4515734

Abstract

Developing countries face a huge burden of infectious diseases, a number of which co-exist. This paper estimates the pattern and variation of malaria and diarrhea coexistence in Chikhwawa, a district in Southern Malawi using bivariate multilevel modelling with Bayesian estimation. A probit link was employed to examine hierarchically built data from a survey of individuals (n = 6,727) nested within households (n = 1,380) nested within communities (n = 33). Results show significant malaria [σu12=0.901(95% CI:0.746,1.056)] and diarrhea [σu22=1.009(95% CI:0.860,1.158)] variations with a strong correlation between them [ru(1,2)=0.565] at household level. There are significant malaria [σv12=0.053(95% CI:0.018,0.088)] and diarrhea [σv22=0.099(95% CI:0.030,0.168)] variations at community level but with a small correlation [rv(1,2)=0.124] between them. There is also significant correlation between malaria and diarrhea at individual level [re(1,2)=0.241]. These results suggest a close association between reported malaria-like illness and diarrheal illness especially at household and individual levels in Southern Malawi.


Url:
DOI: 10.3390/ijerph120708526
PubMed: 26197332
PubMed Central: 4515734


Affiliations:


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PMC:4515734

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<p>Developing countries face a huge burden of infectious diseases, a number of which co-exist. This paper estimates the pattern and variation of malaria and diarrhea coexistence in Chikhwawa, a district in Southern Malawi using bivariate multilevel modelling with Bayesian estimation. A probit link was employed to examine hierarchically built data from a survey of individuals (n = 6,727) nested within households (n = 1,380) nested within communities (n = 33). Results show significant malaria [
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0.241]. These results suggest a close association between reported malaria-like illness and diarrheal illness especially at household and individual levels in Southern Malawi.</p>
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