Policy and Strategy for Hepatitis C Virus Elimination at the National Level: Experience in Taiwan.

The estimated prevalence of anti-HCV was 3.1% in Taiwan. Studies have shown iatrogenic behavior was the major transmission route. It is highest in specific populations including patients with end stage renal disease (ESRD), human immunodeficiency virus infection, who inject drug (PWID), and under opioid substitution treatment. Approximately 405,160 patients were seropositive for HCV RNA and in need of treatment. Taiwan government claims to reach WHO's 2030 goal of HCV elimination by 2025 and works hard to resolve several barriers of HCV elimination including political commitment, sustainable financing, minimize reimbursement restrictions, instituted monitoring, and perform micro-elimination of specific populations. The last stage of HCV elimination is to accelerate the universal HCV screening program of populations aged 45-79 years and resolve the unawareness issue of HCV infection. Hopefully, we can achieve the targets of HCV elimination set by WHO and reach the goal earlier in 2025.

A bayesian random-effects markov model for tumor progression in women with a family history of breast cancer.

SUMMARY: It makes intuitive sense to model the natural history of breast cancer, tumor progression from preclinical screen-detectable phase (PCDP) to clinical disease, as a multistate process, but the multilevel structure of the available data, which generally comes from cluster (family)-based service screening programs, makes the required parameter estimation intractable because there is a correlation between screening rounds in the same individual, and between subjects within clusters (families). There is also residual heterogeneity after adjusting for covariates. We therefore proposed a Bayesian hierarchical multistate Markov model with fixed and random effects and applied it to data from a high-risk group (women with a family history of breast cancer) participating in a family-based screening program for breast cancer. A total of 4867 women attended (representing 4464 families) and by the end of 2002, a total of 130 breast cancer cases were identified. Parameter estimation was carried out using Markov chain Monte Carlo (MCMC) simulation and the Bayesian software package WinBUGS. Models with and without random effects were considered. Our preferred model included a random-effect term for the transition rate from preclinical to clinical disease (sigma(2)(2f)), which was estimated to be 0.50 (95% credible interval = 0.22-1.49). Failure to account for this random effect was shown to lead to bias. The incorporation of covariates into multistate models with random effect not only reduced residual heterogeneity but also improved the convergence of stationary distribution. Our proposed Bayesian hierarchical multistate model is a valuable tool for estimating the rate of transitions between disease states in the natural history of breast cancer (and possibly other conditions). Unlike existing models, it can cope with the correlation structure of multilevel screening data, covariates, and residual (unexplained) variation.