In a typical Kenyan HIV clinical setting, there is a
likelihood of registering many zeros during the routine monthly data collection
of new HIV infections among HIV exposed infants (HEI). This is attributed to
the implementation of the prevention of mother to child transmission (PMTCT)
policies. However, even though the PMTCT policy is implemented uniformly across
all public health facilities, implementation naturally differs from every facility due to differential
health systems and infrastructure. This leads to structured zero among reported
positive HEI (where PMTCT implementation is optimum) and non-structured zero
among reported positive HEI (where PMTCT implementation is not optimum). Hence
the classical zero-inflated and hurdle models that do not account for the
abundance of structured and non-structured zeros in the data can give
misleading results. The purpose of this study is to systematically compare
performance of the various zero-inflated models with an application to HIV
Exposed Infants (HEI) in the context of structured and unstructured zeros. We
revisit zero-inflated, hurdle models, Poisson and negative binomial count
models and conduct the simulations by varying sample size and levels of
abundance zeros. Results from simulation study and real data analysis of
exposed infant diagnosis show the negative binomial emerging as the best
performing model when fitting data with both structured and non-structured
zeros under various settings.
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