Impact of COVID-19 on epidemic trend of hepatitis C in Henan Province assessed by interrupted time series analysis

Abstract

Abstract Objective Hepatitis C presents a profound global health challenge. The impact of COVID-19 on hepatitis C, however, remain uncertain. This study aimed to ascertain the influence of COVID-19 on the hepatitis C epidemic trend in Henan Province. Methods We collated the number of monthly diagnosed cases in Henan Province from January 2013 to September 2022. Upon detailing the overarching epidemiological characteristics, the interrupted time series (ITS) analysis using autoregressive integrated moving average (ARIMA) models was employed to estimate the hepatitis C diagnosis rate pre and post the COVID-19 emergence. In addition, we also discussed the model selection process, test model fitting, and result interpretation. Results Between January 2013 and September 2022, a total of 267,968 hepatitis C cases were diagnosed. The yearly average diagnosis rate stood at 2.42/100,000 persons. While 2013 witnessed the peak diagnosis rate at 2.97/100,000 persons, 2020 reported the least at 1.7/100,000 persons. The monthly mean hepatitis C diagnosed numbers culminated in 2291 cases. The optimal ARIMA model chosen was ARIMA (0,1,1) (0,1,1)12 with AIC = 1459.58, AICc = 1460.19, and BIC = 1472.8; having coefficients MA1=-0.62 (t=-8.06, P < 0.001) and SMA1=-0.79 (t=-6.76, P < 0.001). The final model’s projected step change was − 800.0 (95% confidence interval [CI] -1179.9 ~ -420.1, P < 0.05) and pulse change was 463.40 (95% CI 191.7 ~ 735.1, P < 0.05) per month. Conclusion The measures undertaken to curtail COVID-19 led to a diminishing trend in the diagnosis rate of hepatitis C. The ARIMA model is a useful tool for evaluating the impact of large-scale interventions, because it can explain potential trends, autocorrelation, and seasonality, and allow for flexible modeling of different types of impacts.