Logistic regression model deals with the relationship that exists between a dependent variable and one or more independent variables. It provides a method for modeling a binary response variable, which takes values 1 and 0. Further, Logistic regression is useful for situations in which you want to be able to predict the presence or absence of a characteristic or outcome based on values of a set of predictor variables. It is similar to a linear regression model but is suited to models where the dependent variable is dichotomous[34].Logistic regression model have been applied in a number of contexts. Some examples include applications to adjust for “bias” in comparing two groups in observational studies( Rosenbaun & Rubin,1998)[35]. Effron[36], compared logistic regression to discriminant analysis (which assumes the explanatory variables are multivariate normal at each level of the response variable); it has also been applied to a study investigating the risk factors for low birth weight babies (Hosmer&Lemeshow,1998)[37]. Other applications include using logistic regression analysis to determine the factors that affect ”green card” usage for health services(Sansli & Gozde,2006) [38]. Application of logistic regression have also been extended to cases where the dependent variable is more than two cases, known as multinomial or polychotomous (Tabachanick & Fidell 1996)[39].  


The aim of the study is to build a model using the six symptoms variables to predict the presence or absence of Tuberculosis and malaria in a sample of patients. The model can then be used to derive estimates of the odds ratios for each factor. Estimate the effects of malaria and tuberculosis on CD4 count on the recovery rate of HIV/AIDS patients. Estimate the effect of malaria and tuberculosis on CD4 count on their rate of response to drugs. Estimate the percentage of HIV/AIDS patients with malaria and tuberculosis. Since the CD4 cell counts has been an important surrogate marker for HIV prognosis, attempt were made in this study to examine any possible impact of opportunistic or concurrent infections with HIV/AIDS on the CD4 cell counts. This research focused on tuberculosis(TB), as the most prevalent opportunistic infection and malaria, as the most prevalent endemic infection in Nigeria. Episodes of malaria fever in HIV patients  elicit varying immune responses with  attendant effect on  the patients. It is therefore important to further investigate the various  components of the immune system to malaria and the parts  they  play in the presence of malaria parasites.Also,due to the fact that a significantly higher number of the patients eventually showed a decrease in CD4 count post-fever , it is imperative to continue the ongoing efforts to protect HIV patients ,as well as everybody from malaria attacks.[40]. The data used for the study is a secondary and primary data collected from Isolo General Hospital Lagos; to critically investigate how logistic regression model performs in fitting a disease related data and its effectiveness in non-linear modeling.

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ONUKWUBE, O. (2018). LOGISTIC REGRESSION ON EFFECT OF TUBERCULOSIS AND MALARIA ON HIV. Afribary. Retrieved from https://afribary.com/works/logistic-regression-on-effect-of-tuberculosis-and-malaria-on-hiv

MLA 8th

ONUKWUBE, Obioma "LOGISTIC REGRESSION ON EFFECT OF TUBERCULOSIS AND MALARIA ON HIV" Afribary. Afribary, 30 Nov. 2018, https://afribary.com/works/logistic-regression-on-effect-of-tuberculosis-and-malaria-on-hiv. Accessed 21 Jun. 2024.


ONUKWUBE, Obioma . "LOGISTIC REGRESSION ON EFFECT OF TUBERCULOSIS AND MALARIA ON HIV". Afribary, Afribary, 30 Nov. 2018. Web. 21 Jun. 2024. < https://afribary.com/works/logistic-regression-on-effect-of-tuberculosis-and-malaria-on-hiv >.


ONUKWUBE, Obioma . "LOGISTIC REGRESSION ON EFFECT OF TUBERCULOSIS AND MALARIA ON HIV" Afribary (2018). Accessed June 21, 2024. https://afribary.com/works/logistic-regression-on-effect-of-tuberculosis-and-malaria-on-hiv