• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2016, Volume: 9, Issue: 42, Pages: 1-7

Original Article

Logistic Regression Approach for Prediction of Gender Bias in the Prevalence of Tuberculosis in India


Objectives: India is one of major contributors to global burden of tuberculosis which alone accounted for an estimated one quarter (26%) of all tuberculosis cases worldwide. The estimation of disease burden of tuberculosis is a challenge, considering its varied epidemiology and dynamics of transmission. As true disease burden cannot be estimated with count data therefore, statistical modelling techniques have been employed to analyze the disease burden in terms of prevalence of tuberculosis among males and females. Methods: An attempt has been made to predict gender bias in the prevalence of tuberculosis. The statistical models for both male and female tuberculosis positivity are developed on the information taken from NHFS (2015-16). The gender wise prevalence of tuberculosis has been compared by considering different indicators. In our analysis, the binary logistic regression model has been used by considering some factors to know their impact on prevalence of the tuberculosis. Results: Some of the variables under socioeconomic factors, Demographic Factors, Cultural factors and Health factors have shown decline their impact on prevalence of tuberculosis in males as compared females. However rests of the variables have the impact on the prevalence of tuberculosis without any variation. Conclusion: The study reveals that there are some factors which are responsible for prevalence of tuberculosis in India among male as well as in females and these factors are continuously contributing in increasing the prevalence of tuberculosis. Hence it is suggested that there is a need to redesign the policies to minimize the risk factors generated on the part of the factors having same impact on the prevalence of tuberculosis to avoid gender bias.

Keywords: Body Mass Index, Explanatory Variables and Logistic Regression, Risk Factors, Tuberculosis


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