This study aims to understand how heavy drinking behaviors among Chinese men have changed in the past and how they might change in the future and assess potential health benefit of intervening on heavy drinking behavior in a target age group. We constructed a simulation model of changes in heavy drinking behavior and calibrated the rates of initiating and ceasing heavy drinking in the model to the birth cohort-specific prevalence of heavy drinking by age. Our study found that the risk of heavy drinking is concentrated in the middle-aged men in age 40s and 50s. We also projected a continuing decline in prevalence of heavy drinking in men in China though with variation by age over time. Lastly, the study highlights that age-targeted interventions on risk have the potential to efficiently improve population health.

This study illustrates how the assumption of no temporal trends in risk behaviors and disease epidemiology in model calibration can lead to drastically different calibrated parameters, future burden projections, and intervention effect estimates when compared to calibration that employs birth cohort-specific parameters. We took the constructed model of heavy drinking among Chinese men as an example. We compared cross-sectional and birth cohort calibration where each approach has different stratification in transition parameters and the level of granularity in calibration target. More importantly, cross-sectional approach took the cross-sectional data as its calibration targets, assuming no birth cohort trends in heavy drinking over time. Our study found that calibrating age-specific parameters in a Markov Cohort model under the assumption that temporal trends do not exist yielded different age patterns in the parameter values compared to calibration that accounted for temporal trends in parameters. In addition, consideration of temporal trends in parameters improves the model’s performance in predicting future heavy drinking prevalence. These findings highlight the impact of temporal trend assumptions should be examined carefully, especially when the model is used to predict future outcomes and the potential effects of health interventions.

Motivated by the potential impact of innovative but expensive technologies on population health in resource-limited settings, this study aims to assess the value of new hepatitis C virus (HCV) treatments in the context of the current healthcare delivery system in China. China has the largest number of people infected with HCV of all countries in the world. It first approved directly acting antivirals to treat HCV in 2017. Efficiently delivering these treatments to this large group of potential patients, many of whom have not yet been identified via screening and not all of whom will benefit equally from treatment, presents important health policy questions. I have constructed a microsimulation model of HCV incidence and progression in China that matches Chinese epidemiological data which is stratified by drinking behavior in terms of risks of mortality and liver fibrosis progression and which tracks individuals’ HCV screening status. I plan to use this model to evaluate combinations of interventions aimed at reducing heavy drinking, increasing the fraction of individuals with chronic HCV who are screened and diagnosed, and increasing treatment. I expect that the value of interventions will depend on: how they are targeted in terms of characteristics like age; in what combinations they are used and at what intensity; and the coverage and accuracy of risk factor targeting and screening interventions.