Medication Adherence and Seizure Remission Prediction Model for Patients with Epilepsy at Mbarara Regional Referral Hospital

Abstract

Epilepsy is a global burden and accounts for 50 million people worldwide with an estimated 5 million people diagnosed with epilepsy each year. The estimated proportion of the general population with active epilepsy at a given time is between 4 and 10 per 1000 people worldwide. In high-income countries, there are estimated to be 49 per 100000 people diagnosed with epilepsy each year and an estimate of 139 per 100000 people in low- and middle-income countries like Uganda among other sub-Saharan countries, indicating that close to 80% of people with epilepsy live in low- and middle-income countries. The risk of premature death in people with epilepsy is up to 1.6–9.3 times higher than for the general population and three quarters of people with epilepsy living in low-income countries do not get the treatment they need to live a seizure free life due to a number of factors. It is approximated that up to 70% of people living with epilepsy could live seizure- free if properly diagnosed and treated. This study therefore aimed at identifying the factors associated with epileptic seizure attack, examine patients drug adherence patterns using existing patients‟ records, and formulate a model and its algorithm to predict the likelihood of seizure reoccurrences. The study was retrospective with data from patients‟ records at Mbarara Regional Referral Hospital. Data analysis was done to investigated patients‟ drug adherence and the rate of seizure remissions. The dependent variable was the number of seizure/fits attacks per month (seizure attacks per month) which was a count variable, and the best model for predicting and describing the number of seizure attacks was the Poisson regression model. STATA 13.0 was used to analyze the data. The results indicate that there is a 21% reduction in the incidence rate ratio (IRR) of epileptic seizures as visits (or follow up periods) increase compared with patients who had no visits. According to the confidence interval, the reduction could be as much as 24% or as low as 18% (IRR=0.79; 95%CI [0.76-0.82]; p<0.001). The results further indicate that duration of fits among the patients increases the rate of epileptic seizures by 8% (IRR=1.08; 95%CI [1.07-0.1.09]; p<0.001). These results were used to develop a model which can be used to predict the likelihood of seizure reoccurrences among epileptic patients