Understanding the joint effect of the behavioural and biological risk reduction factors on HIV
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Authors
Gundidza, Patricia Tapiwa
Issue Date
2021-10-01
Type
Dissertation
Language
en
Keywords
Logistic regression , Multicollinearity , Confounding , Interaction , Principal Component Analysis (PCA)
Alternative Title
Abstract
The conventional approach in determining HIV risk factors fails to consider the influence behavioural and biological factors may have when modelled jointly. This study investigates the extent of influence behavioural and biological factors jointly have on HIV prevalence. The approach adapted in the modelling includes assessment of multicollinearity among the variables, principal component regression analysis to deal with multicollinearity problem, checking for the presence of confounding factors and significant interaction effects. In determining the joint effect, logistic regression model was fitted to the HIV data obtained from the Zimbabwe Demographic and Health Survey of 2011 (ZDHS, 2010). Besides age, marital status, total number of lifetime sex partners and condom use being significant for both gender, genital discharge and paid for sex for males and place of residence, age at sexual debut, genital sore, relationship with recent partner, educational attainment and STI for females were significant. Significant interaction terms between biological and behavioural factors were revealed and thus demonstrated the importance of being cautious when interpreting the results of joint modelling. Generalised Variance Inflation Factors (GVIF) detected multicollinearity for some variables in both models and Principal Component analysis obtained four factors (sexual relation, residential status, STI status and sexual partnership) for females and three (STI occurrence, sexual relationship and residential status) for males thus addressing the problem. Significant interaction between behavioural and biological factors were noted. The findings suggest meticulous consideration in assessing interrelationships among independent variables and give an appreciation and understanding of how statistical methods can be applied in the public health sector.