Statistical models for longitudinal zero-inflated count data: application to seizure attacks

Background: Chronic non-communicable diseases:- such as epilepsy, are increasingly recognized as public health problems in developing and African countries. This study aimed at finding determinants of the number of epileptic seizure attacks using different count data modeling techniques.
Methods: Four common fixed-effects Poisson family models were reviewed to analyze the count data with a high proportion of zeros in longitudinal outcome, i.e., the number of seizure attacks in epilepsy patients. This is because, in addition to the problem of extra zeros, the correlation between measurements upon the same patient at different occasions needs to be taken into consideration.
Results: The investigation remarkably identified some important factors associated with epileptic seizure attacks. As people grow old , the number of seizure attacks increased and male patients had more seizures than their female counterparts. In general, a patient’s age, sex, monthly income, family history of epilepsy andservice satisfaction were some of the significant factors responsible for the frequency of seizure attacks (P value<0.05).
Conclusion: This study suggests that zero-inflated negative binomial is the best model for predicting and describing the number of seizure attacks as well as identifying the potential risk factors. Addressing these risk factors will definitely contain the progression of seizure attack.

Keywords
linear mixed model; hurdle model; seizure attacks; zero-inflated models.