Research Article
Determine Significant Factors Related to Malaria among Pregnant Women in Nigeria by Logistic Regression Analysis
İlker Etikan*, Rukayya Alkassim, Özgür Tosun, Yavuz Sanisoğlu S and Meliz Yuvalı
Department of Biostatistics, Near East University, Cyprus
İlker Etikan, Department of Biostatistics, Near East University, Faculty of Medicine, Nicosia-TRNC, Cyprus.
Received Date: January 28, 2019 Published Date: February 15, 2019
Abstract
Objectives: Malaria during antenatal period was a major health problem that lead to both mother and child death. The aim of this study is to assess the predictors of malaria during pregnancy among six states of Nigeria based on anti-malarial pill prescribed to pregnant women by the health facility.
Methods: The analysis involves chi-square test for independent association between the predictors and risk of malaria diagnosis among the qualitative variables, Mann Whitney u test for quantitative variables and binary logistic regression for the multivariate analysis.
Results: The analysis shows that the risk of malaria during pregnancy was significantly associated with Age, IPTp-up take, ITN use, source of energy for lightening, main material use for room’s rooftop and livestock keeping. This shows that, appropriate use of insecticide treated bed nets, optimal IPTp uptake against malaria and other protective measures, teamed with some elements such as sources of energy for lightening and main material for room’s rooftop decreased the incidence of malaria infectious disease among antenatal women.
Conclusion: The research suggested that the illiterates and poor women are less probable of using these preventive measures in other to reduce the spread of malaria disease among pregnant women and entire population as whole.
Keywords:Malaria; Pregnancy; Chi square test; Mann whitney - U test; Logistic regression
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İlker Etikan, Rukayya Alkassim, Özgür Tosun, Yavuz Sanisoğlu S, Meliz Yuvalı. Determine Significant Factors Related to Malaria among Pregnant Women in Nigeria by Logistic Regression Analysis. Annal Biostat & Biomed Appli. 1(4): 2019. ABBA.MS.ID.000517.
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