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Cook, R., Ciampa, P., Sidat, M., Blevin, M., Burlison, J., Davidson, M.A., Arroz, J.A., Vergara, A.E., Vermund, S.H. and Moon, T.D. (2011) Predictors of Successful Early Infant Diagnosis of HIV in Rural District Hospital in Zambezia Mozambique. Journal Acquired Immune Deficiency Syndromes, 56, e104-e109.
https://doi.org/10.1097/QAI.0b013e318207a535

has been cited by the following article:

• TITLE: Comparative Assessment of Zero-Inflated Models with Application to HIV Exposed Infants Data

  AUTHORS: Faith Nekesa, Collins Odhiambo, Linda Chaba

  KEYWORDS: Zero-Inflated Models, HIV Exposed Infants, Structured Zeroes, Mother-to-Child Transmission, Count Data

  JOURNAL NAME: Open Journal of Statistics, Vol.9 No.6, December 13, 2019

  ABSTRACT: 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.