Introduction Although hepatitis C virus (HCV) infection remains a major clinical, economic, and societal burden, the development of curative antiviral therapy may accelerate the path toward elimination. This analysis assessed the progress of United States (US) states towards achieving the World Health Organization’s (WHO) 2030 HCV elimination targets for incidence, mortality, diagnosis, and treatment. Methods A previously published Markov model was used to simulate HCV progression over time to estimate the path to HCV elimination in each state based on prevalence, annual treatment, and diagnosis inputs from two large US laboratory datasets from January 2013 to December 2017. State-specific fibrosis stage restrictions on treatment in 2017 were included. The model estimated the year individual states would meet the WHO targets for diagnosing 90% of the HCV-infected population, treating 80% of the eligible population, reducing new HCV infections by 80%, and reducing HCV-related deaths by 65%. The minimum number of annual treatments needed between 2020 and 2030 to achieve the WHO treatment target was also calculated. Results Overall, the USA is projected to achieve HCV elimination by 2037, with individual targets related to mortality, diagnosis, treatment, and incidence being achieved by 2020, 2027, 2033, and 2037, respectively. Three states (Connecticut, South Carolina, and Washington) are on track to meet all four elimination targets by 2030, and 18 states are not expected to meet these targets before 2040. The estimated annual number of treatments required during 2020–2030 nationally to reach the WHO treatment target is 173,514. Conclusion With the exception of three states, the USA is not on target to meet the WHO 2030 elimination targets and 35% are off track by 10 years or more. Strategies must be implemented to reduce overall prevalence by preventing new infections, increasing rates of screening, improving linkage to care, and implementing unfettered access to curative therapy. Electronic supplementary material The online version of this article (10.1007/s12325-020-01535-3) contains supplementary material, which is available to authorized users.
Background: People who use drugs (PWUD) are among those with the highest risk for hepatitis C virus (HCV) infection. Highly effective direct-acting antiviral agents offer an opportunity to eliminate HCV. A simple tool for the prediction of HCV infection risk in PWUD is urgently needed. This study aimed to develop and validate a risk prediction tool to identify people at greater risk of having hepatitis C among PWUD that is applicable in resource-limited settings. Methods: We extracted data from national HIV/AIDS sentinel surveillance in PWUD (Zhejiang Province, 2016–2021) and developed and validated a risk score to improve HCV testing in PWUD. This risk score consists of seven risk factors identified using multivariable logistic regression modeling (2016–2020, exploratory group). We validated this score using surveillance data for 2021 (validation group). The accuracy of the model was determined using C-statistics. Results: We identified seven risk factors, including sex, age, marital status, educational attainment, and the use of heroin, morphine, and methamphetamine. In the exploratory group, the positive rates of detecting the HCV antibody in the low-risk (0–9 points), intermediate-risk (10–16 points), and high-risk (≥17 points) groups were 6.72%, 17.24%, and 38.02%, respectively (Ptrend < 0.001). In the validation group, the positive rates in the low-, medium-, and high-risk groups were 4.46%, 12.23%, and 38.99%, respectively (Ptrend < 0.001). Conclusions: We developed and validated a drug-specific risk prediction tool for identifying PWUD at increased risk of HCV infection. This tool can complement and integrate the screening strategy for the purpose of early diagnosis and treatment.
BACKGROUND The first case of acquired immunodeficiency syndrome (AIDS) was reported in 1981; since then, over 84 million individuals have been infected with the human immunodeficiency virus (HIV) with over 40 million deaths attributed to AIDS-related illnesses. OBJECTIVE This study analyzed the spatial and temporal distribution of HIV/AIDS among older adults in Eastern China, from 2004 to 2021 to improve prevention and intervention for HIV/AIDS. METHODS We extracted data on newly reported HIV/AIDS cases between 2004 and 2021 from a case-reporting system and used a joinpoint regression model and an age-period-cohort model to analyze the temporal trends in HIV/AIDS prevalence. Spatial autocorrelation and geographically weighted regression (GWR) models were used for spatial aggregation and influence factor analysis. RESULTS A total of 12,376 HIV/AIDS cases were included in the study. The average annual percent change (AAPC) in reported HIV/AIDS incidences was 28.0% (95% CI: 21.6–34.8%). The results of the age-period-cohort model showed that age, period, and cohort factors affected the incidence of HIV/AIDS among older adults. The newly reported HIV/AIDS cases in men who had sex with men (MSM) had spatial clustering, and the hotspots were mainly concentrated in Hangzhou. The disposable income of urban residents, illiteration rate, and number of hospital beds per 1,000 residents were associated with the risk of HIV/AIDS among older MSM in the Zhejiang province. CONCLUSIONS HIV/AIDS among older adults showed an increasing trend and was influenced by age, period, and cohort effects. Older MSM showed regional clustering and was associated with factors such as the disposable income of urban residents, illiteracy rate, and the number of hospital beds per 1,000 people, which require strengthening targeted prevention and control.
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