Estimating the Prevalence of Injection Drug Use Among Acute Hepatitis C Cases From a National Surveillance System: Application of Random Forest-Based Multiple Imputation.

BACKGROUND: Injection drug use (IDU) is a major contributor to the syndemic of viral hepatitis, human immunodeficiency virus, and drug overdose. However, information on IDU is frequently missing in national viral hepatitis surveillance data, which limits our understanding of the full extent of IDU-associated infections. Multiple imputation by chained equations (MICE) has become a popular approach to address missing data, but its application for IDU imputation is less studied. METHODS: Using the 2019-2021 National Notifiable Diseases Surveillance System acute hepatitis C case data and publicly available county-level measures, we evaluated listwise deletion (LD) and 3 models imputing missing IDU data through MICE: parametric logistic regression, semi-parametric predictive mean matching (PMM), and nonparametric random forest (RF) (both standard RF [sRF] and fast implementation of RF [fRF]). RESULTS: The estimated IDU prevalence among acute hepatitis C cases increased from 63.5% by LD to 65.1% by logistic regression, 66.9% by PMM, 76.0% by sRF, and 85.1% by fRF. Evaluation studies showed that RF-based MICE imputation, especially fRF, has the highest accuracy (as measured by smallest raw bias, percent bias, and root mean square error) and highest efficiency (as measured by smallest 95% confidence interval width) compared to LD and other models. Sensitivity analyses indicated that fRF remained robust when data were missing not at random. CONCLUSION: Our analysis suggested that RF-based MICE imputation, especially fRF, could be a valuable approach for addressing missing IDU data in the context of population-based surveillance systems like National Notifiable Diseases Surveillance System. The inclusion of imputed IDU data may enhance the effectiveness of future surveillance and prevention efforts for the IDU-driven syndemic.

Hepatitis C Virus Testing, Infection, and Cases Reported Through Public Health Surveillance During Expanded Screening Recommendations, United States, 2013-2021.

OBJECTIVES: Hepatitis C virus (HCV) infection is the most common bloodborne infection in the United States. We assessed trends in HCV testing, infection, and surveillance cases among US adults. METHODS: We used Quest Diagnostics data from 2013-2021 to assess trends in the numbers tested for HCV antibody and proportion of positivity for HCV antibody and HCV RNA. We also assessed National Notifiable Diseases Surveillance System 2013-2020 data for trends in the number and proportion of hepatitis C cases. We applied joinpoint regression for trends testing. RESULTS: Annual HCV antibody testing increased from 1.7 million to 4.8 million from 2013 to 2021, and the positivity proportion declined (average, 0.2% per year) from 5.5% to 3.7%. The greatest percentage-point increase in HCV antibody testing occurred in hospitals and substance use disorder treatment facilities and among addiction medicine providers. HCV RNA positivity was stable at about 60% in 2013-2015 and declined to 41.0% in 2021 (2015-2021 average, -3.2% per year). Age-specific HCV RNA positivity was highest among people aged 40-59 years during 2013-2015 and among people aged 18-39 years during 2016-2021. The number of reported hepatitis C cases (acute and chronic) declined from 179 341 in 2015 to 105 504 in 2020 (average decline, -13 177 per year). The proportion of hepatitis C cases among those aged 18-39 years increased by an average of 1.4% per year during 2013-2020; among individuals aged 40-59 years, it decreased by an average of 2.3% per year during 2013-2018. CONCLUSIONS: HCV testing increased, suggesting improved universal screening. Various data sources are valuable for monitoring elimination progress.

Hepatitis B Care Continuum Models-Data to Inform Public Health Action.

The application of a care continuum model (CCM) can identify gaps in diagnosis, care, and treatment of populations with a common condition, but challenges are inherent in developing a CCM for chronic hepatitis B. In contrast with treatment for HIV or hepatitis C, treatment is not indicated for all people with chronic hepatitis B, clinical endpoints are not clear for those receiving treatment, and those for whom treatment is not indicated remain at risk for complications. This topical review examines the data elements necessary to develop and apply chronic hepatitis B CCMs at the jurisdictional health department level. We conducted a nonsystematic review of US-based publications in Ovid MEDLINE (1946-present), Ovid Embase (1974-present), and Scopus (not date limited) databases, which yielded 724 publications for review. Jurisdictional health departments, if properly supported, could develop locale-specific focused CCMs using person-level chronic hepatitis B registries, updated longitudinally using electronic laboratory reporting data and case reporting data. These CCMs could be applied to identify disparities and improve rates in testing and access to care and treatment, which are necessary to reduce liver disease and chronic hepatitis B mortality. Investments in public health surveillance infrastructure, including substantial enhancements in electronic laboratory reporting and case reporting and the use of supplementary data sources, could enable jurisdictional health departments to develop modified CCMs for chronic hepatitis B that focus, at least initially, on "early" CCM steps, which emphasize optimization of hepatitis B diagnosis, linkage to care, and ongoing clinical follow-up of diagnosed people, all of which can lead to improved outcomes.