Modeling the Number of Attacks in Multiple Sclerosis Patients Using Zero-Inflated Negative Binomial Model

AbstractBackground and aims: Multiple sclerosis (MS) is an inflammatory disease of the central nervous system.The impact of the number of attacks on the disease is undeniable. The aim of this study was to analyze thenumber of attacks in these patients.Methods: In this descriptive-analytical study, the registered data of ۱۸۴۰ MS patients referred to the MS clinicof Ayatollah Kashani hospital in Isfahan were used. The number of attacks during the treatment period wasdefined as the response variable, age at diagnosis, sex, employment, level of education, marital status, familyhistory, course of disease, and expanded disability as the explanatory variables. The analysis was performedusing zero-inflated negative binomial model via Bayesian framework in OpenBUGS software.Results: Age at diagnosis (CI: -۰.۰۴, -۰.۲۰), marital status (CI: -۰.۵۶, ۰.۰۰۲), level of education (CI: -۰.۸۱,-۰.۲۶), Job (CIHousewives vs Employee=[۰.۰۴, ۰.۶۴], CIUnemployee vs Employee=[-۱.۱۰,۰.۰۰۸])), and course of disease (CI:-۰.۵۱, -۰.۰۸) had a significant effect on the number of attacks. In relapsing-remitting patients, the number ofattacks was partial significantly affected by expanded disability status scale (EDSS) (CI: -۰.۰۱۹, ۰.۱۶).Conclusion: Aging, being single (never married), high education, and not having a job decrease the numberof attacks; therefore, lower age, being married, primary education, and being a housewife increase thenumber of attacks. An interventional or educational program is suggested in order to prevent the occurrenceof further attacks in high-risk groups of patients and to increase their chances of recovery.

Keywords:

multiple sclerosis , Attack , Negative binomial , Zero-inflated , Markov Chain Monte Carlo