Published online Feb 12, 2016. doi: 10.5501/wjv.v5.i1.14
Peer-review started: May 18, 2015
First decision: July 26, 2015
Revised: August 24, 2015
Accepted: September 16, 2015
Article in press: September 18, 2015
Published online: February 12, 2016
AIM: To determine hepatitis C virus (HCV) seroprevalence among the Libyan population using blood donors and applying the autoregressive integrated moving average (ARIMA) model to predict future trends and formulate plans to minimize the burden of HCV infection.
METHODS: HCV positive cases were collected from 1008214 healthy blood donors over a 6-year period from 2008 to 2013. Data were used to construct the ARIMA model to forecast HCV seroprevalence among blood donors. The validity of the model was assessed using the mean absolute percentage error between the observed and fitted seroprevalence. The fitted ARIMA model was used to forecast the incidence of HCV beyond the observed period for the year 2014 and further to 2055.
RESULTS: The overall prevalence of HCV among blood donors was 1.8%, varying over the study period from 1.7% to 2.5%, though no significant variation was found within each calendar year. The ARIMA model showed a non-significant auto-correlation of the residuals, and the prevalence was steady within the last 3 years as expressed by the goodness-of-fit test. The forecast incidence showed an increase in HCV seropositivity in 2014, ranging from 500 to 700 per 10000 population, with an overall prevalence of 2.3%-2.7%. This may be extended to 2055 with minimal periodical variation within each 6-year period.
CONCLUSION: The applied model was found to be valuable in evaluating the seroprevalence of HCV among blood donors, and highlighted the growing burden of such infection on the Libyan health care system. The model may help in formulating national policies to prevent increases in HCV infection and plan future strategies that target the consequences of the infection.
Core tip: Hepatitis C virus (HCV) infection has major consequences and an overwhelming impact particularly among developing countries, hence prediction of its prevalence is important for future planning to mitigate its impact. This is an innovative study highlighting the importance of using a modified mathematical model to forecast and predict the future prevalence and consequence of HCV infection using data collected from blood donors. The results will allow strategists in health care services to plan immediate and long-term policies.