Hepatitis A virus (HAV) genotype IA was most common among strains tested in US outbreak investigations and surveillance during 1996-2015. However, HAV genotype IB gained prominence during 2016-2019 person-to-person multistate outbreaks. Detection of previously uncommon strains highlights the changing molecular epidemiology of HAV infection in the United States.

The Gulf of Mexico (GoM) region is prone to disasters, including recurrent oil spills, hurricanes, floods, industrial accidents, harmful algal blooms, and the current COVID-19 pandemic. The GoM and other regions of the U.S. lack sufficient baseline health information to identify, attribute, mitigate, and facilitate prevention of major health effects of disasters. Developing capacity to assess adverse human health consequences of future disasters requires establishment of a comprehensive, sustained community health observing system, similar to the extensive and well-established environmental observing systems. We propose a system that combines six levels of health data domains, beginning with three existing, national surveys and studies plus three new nested, longitudinal cohort studies. The latter are the unique and most important parts of the system and are focused on the coastal regions of the five GoM States. A statistically representative sample of participants is proposed for the new cohort studies, stratified to ensure proportional inclusion of urban and rural populations and with additional recruitment as necessary to enroll participants from particularly vulnerable or under-represented groups. Secondary data sources such as syndromic surveillance systems, electronic health records, national community surveys, environmental exposure databases, social media, and remote sensing will inform and augment the collection of primary data. Primary data sources will include participant-provided information via questionnaires, clinical measures of mental and physical health, acquisition of biological specimens, and wearable health monitoring devices. A suite of biomarkers may be derived from biological specimens for use in health assessments, including calculation of allostatic load, a measure of cumulative stress. The framework also addresses data management and sharing, participant retention, and system governance. The observing system is designed to continue indefinitely to ensure that essential pre-, during-, and post-disaster health data are collected and maintained. It could also provide a model/vehicle for effective health observation related to infectious disease pandemics such as COVID-19. To our knowledge, there is no comprehensive, disaster-focused health observing system such as the one proposed here currently in existence or planned elsewhere. Significant strengths of the GoM Community Health Observing System (CHOS) are its longitudinal cohorts and ability to adapt rapidly as needs arise and new technologies develop.

In this study, the prevalence rate, associated risk factors and genetic diversity of hepatitis C virus (HCV) infection were determined among people who use crack from an international drug trafficking route in Central-West, Brazil. Blood samples were collected from 700 users of crack from Campo Grande and two border cities of Mato Grosso do Sul State and tested for HCV infection using serological and molecular testing methodologies. Anti-HCV was detected in 31/700 (4.5%, 95% CI: 2.9-6.0%) and HCV RNA in 26/31 (83.9%) of anti-HCV positive samples. Phylogenetic analysis of three HCV sub-genomic regions (5'UTR, NS5B and HVR-1) revealed the circulation of 1a (73.9%), 1b (8.7%) and 3a (17.4%) genotypes. Next-generation sequencing and phylogenetic analysis of intra-host viral populations of HCV HVR-1 showed a significant variation in intra-host genetic diversity among infected individuals, with 58.8% composed of more than one sub-population. Bayesian analysis estimated that the most recent common HCV ancestor for strains identified here was introduced to this region after 1975 following expansion of intravenous drug use in Brazil. Multivariate analyses showed that only 'ever having injected drugs' was independently associated with HCV infection. These results indicate an increasing spread of multiple HCV strains requiring public health intervention, such as harm reduction, testing services and treatment among crack users in this important border region of Central Brazil.

BACKGROUND: A high prevalence (92.3%) of hepatitis C virus (HCV) co-infection among HIV patients identified during a large HIV outbreak associated with injection of oxymorphone in Indiana prompted genetic analysis of HCV strains. METHODS: Molecular epidemiological analysis of HCV-positive samples included genotyping, sampling intra-host HVR1 variants by next-generation sequencing (NGS) and constructing transmission networks using Global Hepatitis Outbreak and Surveillance Technology (GHOST). FINDINGS: Results from the 492 samples indicate predominance of HCV genotypes 1a (72.2%) and 3a (20.4%), and existence of 2 major endemic NS5B clusters involving 49.8% of the sequenced strains. Among 76 HIV co-infected patients, 60.5% segregated into 2 endemic clusters. NGS analyses of 281 cases identified 826,917 unique HVR1 sequences and 51 cases of mixed subtype/genotype infections. GHOST mapped 23 transmission clusters. One large cluster (n=130) included 50 cases infected with >/=2 subtypes/genotypes and 43 cases co-infected with HIV. Rapid strain replacement and superinfection with different strains were found among 7 of 12 cases who were followed up. INTERPRETATION: GHOST enabled mapping of HCV transmission networks among persons who inject drugs (PWID). Findings of numerous transmission clusters, mixed-genotype infections and rapid succession of infections with different HCV strains indicate a high rate of HCV spread. Co-localization of HIV co-infected patients in the major HCV clusters suggests that HIV dissemination was enabled by existing HCV transmission networks that likely perpetuated HCV in the community for years. Identification of transmission networks is an important step to guiding efficient public health interventions for preventing and interrupting HCV and HIV transmission among PWID. FUND: US Centers for Disease Control and Prevention, and US state and local public health departments.

BACKGROUND: Molecular surveillance and outbreak investigation are important for elimination of hepatitis C virus (HCV) infection in the United States. A web-based system, Global Hepatitis Outbreak and Surveillance Technology (GHOST), has been developed using Illumina MiSeq-based amplicon sequence data derived from the HCV E1/E2-junction genomic region to enable public health institutions to conduct cost-effective and accurate molecular surveillance, outbreak detection and strain characterization. However, as there are many factors that could impact input data quality to which the GHOST system is not completely immune, accuracy of epidemiological inferences generated by GHOST may be affected. Here, we analyze the data submitted to the GHOST system during its pilot phase to assess the nature of the data and to identify common quality concerns that can be detected and corrected automatically. RESULTS: The GHOST quality control filters were individually examined, and quality failure rates were measured for all samples, including negative controls. New filters were developed and introduced to detect primer dimers, loss of specimen-specific product, or short products. The genotyping tool was adjusted to improve the accuracy of subtype calls. The identification of "chordless" cycles in a transmission network from data generated with known laboratory-based quality concerns allowed for further improvement of transmission detection by GHOST in surveillance settings. Parameters derived to detect actionable common quality control anomalies were incorporated into the automatic quality control module that rejects data depending on the magnitude of a quality problem, and warns and guides users in performing correctional actions. The guiding responses generated by the system are tailored to the GHOST laboratory protocol. CONCLUSIONS: Several new quality control problems were identified in MiSeq data submitted to GHOST and used to improve protection of the system from erroneous data and users from erroneous inferences. The GHOST system was upgraded to include identification of causes of erroneous data and recommendation of corrective actions to laboratory users.

BACKGROUND: Hepatitis C is a major public health problem in the United States and worldwide. Outbreaks of hepatitis C virus (HCV) infections associated with unsafe injection practices, drug diversion, and other exposures to blood are difficult to detect and investigate. Effective HCV outbreak investigation requires comprehensive surveillance and robust case investigation. We previously developed and validated a methodology for the rapid and cost-effective identification of HCV transmission clusters. Global Hepatitis Outbreak and Surveillance Technology (GHOST) is a cloud-based system enabling users, regardless of computational expertise, to analyze and visualize transmission clusters in an independent, accurate and reproducible way. RESULTS: We present and explore performance of several GHOST implemented algorithms using next-generation sequencing data experimentally obtained from hypervariable region 1 of genetically related and unrelated HCV strains. GHOST processes data from an entire MiSeq run in approximately 3 h. A panel of seven specimens was used for preparation of six repeats of MiSeq libraries. Testing sequence data from these libraries by GHOST showed a consistent transmission linkage detection, testifying to high reproducibility of the system. Lack of linkage among genetically unrelated HCV strains and constant detection of genetic linkage between HCV strains from known transmission pairs and from follow-up specimens at different levels of MiSeq-read sampling indicate high specificity and sensitivity of GHOST in accurate detection of HCV transmission. CONCLUSIONS: GHOST enables automatic extraction of timely and relevant public health information suitable for guiding effective intervention measures. It is designed as a virtual diagnostic system intended for use in molecular surveillance and outbreak investigations rather than in research. The system produces accurate and reproducible information on HCV transmission clusters for all users, irrespective of their level of bioinformatics expertise. Improvement in molecular detection capacity will contribute to increasing the rate of transmission detection, thus providing opportunity for rapid, accurate and effective response to outbreaks of hepatitis C. Although GHOST was originally developed for hepatitis C surveillance, its modular structure is readily applicable to other infectious diseases. Worldwide availability of GHOST for the detection of HCV transmissions will foster deeper involvement of public health researchers and practitioners in hepatitis C outbreak investigation.

Despite the significant public health problems associated with hepatitis B virus (HBV) in sub-Saharan Africa, many countries in this region do not have systematic HBV surveillance or genetic information on HBV circulating locally. Here, we report on the genetic characterization of 772 HBV strains from Tanzania. Phylogenetic analysis of the S-gene sequences showed prevalence of HBV genotype A (HBV/A, n=671, 86.9 %), followed by genotypes D (HBV/D, n=95, 12.3 %) and E (HBV/E, n=6, 0.8 %). All HBV/A sequences were further classified into subtype A1, while the HBV/D sequences were assigned to a new cluster. Among the Tanzanian sequences, 84 % of HBV/A1 and 94 % of HBV/D were unique. The Tanzanian and global HBV/A1 sequences were compared and were completely intermixed in the phylogenetic tree, with the Tanzanian sequences frequently generating long terminal branches, indicating a long history of HBV/A1 infections in the country. The time to the most recent common ancestor was estimated to be 188 years ago [95 % highest posterior density (HPD): 132 to 265 years] for HBV/A1 and 127 years ago (95 % HPD: 79 to 192 years) for HBV/D. The Bayesian skyline plot showed that the number of transmissions 'exploded' exponentially between 1960-1970 for HBV/A1 and 1970-1990 for HBV/D, with the effective population of HBV/A1 having expanded twice as much as that of HBV/D. The data suggest that Tanzania is at least a part of the geographic origin of the HBV/A1 subtype. A recent increase in the transmission rate and significant HBV genetic diversity should be taken into consideration when devising public health interventions to control HBV infections in Tanzania.

BACKGROUND: Drug diversion by health care personnel poses a risk for serious patient harm. Public health identified 2 patients diagnosed with acute hepatitis C virus (HCV) infection who shared a common link with a hospital. Further investigation implicated a drug-diverting, HCV-infected surgical technician who was subsequently employed at an ambulatory surgical center. METHODS: Patients at the 2 facilities were offered testing for HCV infection if they were potentially exposed. Serum from the surgical technician and patients testing positive for HCV but without evidence of infection before their surgical procedure was further tested to determine HCV genotype and quasi-species sequences. Parenteral medication handling practices at the 2 facilities were evaluated. RESULTS: The 2 facilities notified 5970 patients of their possible exposure to HCV, 88% of whom were tested and had results reported to the state public health departments. Eighteen patients had HCV highly related to the surgical technician's virus. The surgical technician gained unauthorized access to fentanyl owing to limitations in procedures for securing controlled substances. CONCLUSIONS: Public health surveillance identified an outbreak of HCV infection due to an infected health care provider engaged in diversion of injectable narcotics. The investigation highlights the value of public health surveillance in identifying HCV outbreaks and uncovering a method of drug diversion and its impacts on patients.

The molecular detection of transmission of rapidly mutating pathogens such as hepatitis C virus (HCV) is commonly achieved by assessing the genetic relatedness of strains among infected patients. We describe the development of a novel mass spectrometry (MS)-based approach to identification of HCV transmissions. MS was used to detect products of base-specific cleavage of RNA molecules obtained from HCV PCR fragments. The MS-peak profiles (MSPs) were found to reflect variation in the HCV genomic sequence and the intra-host composition of the HCV population. Serum specimens (n=60) originating from case-patients of 14 epidemiologically confirmed outbreaks and unrelated controls (n=25) were tested. Neighbor-joining trees constructed using MSP-based Hamming distances showed 100% accuracy, and linkage networks constructed using a threshold established from the Hamming distances between epidemiologically unrelated cases showed 100% sensitivity and 99.93% specificity in transmission detection. The MS approach is rapid, robust, reproducible and cost-effective, and applicable to investigating transmissions of other pathogens.