Analysis of Zero Altered & Inflated Data
Application to Helminths
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Zero altered models were fitted and applied to two datasets (one from Malawi and another from Zambia). Malawi data were collected in a cluster randomized study, in Chikhwawa district in 2004, with 18 villages randomized to intervention and control arms with a total of 1642 participants. Zambia data were collected from school children in a cross-sectional study in Lusaka province in 2004 with a total of 2040 participants. Results from the study showed that Negative Binomial Logit Hurdle (NBLH) model offered best-fit to data inflated with zeros; with capability to handle over-dispersion, excess ...
Zero altered models were fitted and applied to two datasets (one from Malawi and another from Zambia). Malawi data were collected in a cluster randomized study, in Chikhwawa district in 2004, with 18 villages randomized to intervention and control arms with a total of 1642 participants. Zambia data were collected from school children in a cross-sectional study in Lusaka province in 2004 with a total of 2040 participants. Results from the study showed that Negative Binomial Logit Hurdle (NBLH) model offered best-fit to data inflated with zeros; with capability to handle over-dispersion, excess zeros and capture true zeros in the data. Its implementation and interpretation, ease of components, and its direct link with observed data make it a valuable alternative for analyzing zero inflated count data. Conclusions drawn from the study indicated that Helminths were highly localized, with small section of people harboring parasites; showing heterogeneous infection risk for both Malawiand Zambia settings. Joint modeling approach allowed identification of risk factors for infection presence and severity hence provide a platform to design combative control efforts.
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