Abstract
The effects of ignoring survey designs during the analysis of complex survey data may lead to biased estimates. This has been a common practice for most researchers. It is more critical for public health data which involve the clinical decisions that decide the fate of people's lives. This analysis compares the estimates of factors of viral load suppression (VLS) with and without including survey designs using the Tanzania HIV Impact Survey (THIS). This survey reveals factors associated with VLS among Tanzanians living with HIV/AIDS. The correlates of VLS were examined using multivariable logistic regression models in both cases with and without including survey design. The study unveils significant correlates such as age, middle wealth quintile, CD4, adherence, and antiretroviral (ARV) detection status of a patient. Furthermore, the study emphasizes the essence of properly accounting for CSD. Failure to do so may result in biased parameter estimates and incorrect variances; hence, incorrect inferences. Thus, the study's findings on VLS determinants have significant practical implications that allow government agencies and stakeholders to establish targeted and successful HIV/AIDS prevention and treatment initiatives. Consequently, this study suggests a complex design as an approach for obtaining unbiased estimates on the national representative surveys.