Representativeness of Tuberculosis Genotyping Surveillance in the United States, 2009-2010

Infectious Diseases Among People Experiencing Homelessness: A Systematic Review of the Literature in the United States and Canada, 2003-2022

Homelessness increases the risk of acquiring an infectious disease. We conducted a systematic review of the literature to identify quantitative data related to infectious diseases and homelessness. We searched Google Scholar, PubMed, and SCOPUS for quantitative literature published from January 2003 through December 2022 in English from the United States and Canada. We excluded literature on vaccine-preventable diseases and HIV because these diseases were recently reviewed. Of the 250 articles that met inclusion criteria, more than half were on hepatitis C virus or Mycobacterium tuberculosis. Other articles were on COVID-19, respiratory syncytial virus, Staphylococcus aureus, group A Streptococcus, mpox (formerly monkeypox), 5 sexually transmitted infections, and gastrointestinal or vectorborne pathogens. Most studies showed higher prevalence, incidence, or measures of risk for infectious diseases among people experiencing homelessness as compared with people who are housed or the general population. Although having increased published data that quantify the infectious disease risks of homelessness is encouraging, many pathogens that are known to affect people globally who are not housed have not been evaluated in the United States or Canada. Future studies should focus on additional pathogens and factors leading to a disproportionately high incidence and prevalence of infectious diseases among people experiencing homelessness.

Design and Implementation of Data Exchange Formats for Molecular Detection of Drug-Resistant Tuberculosis

Drug-resistant tuberculosis (TB) remains a public health threat to the United States and worldwide control of TB. Rapid and reliable drug susceptibility testing (DST) is essential for aiding clinicians in selecting an optimal treatment regimen for TB patients and to prevent ongoing transmission. Growth-based DST results for culture-confirmed cases are routinely reported to the U.S. Centers for Disease Control and Prevention through the National TB Surveillance System (NTSS). However, the NTSS currently lacks the capacity and functionality to accept laboratory results from advanced molecular methods that detect mutations associated with drug resistance. The objective of this study is to design and implement novel comprehensive data exchange formats that utilize the Health Level Seven (HL7) version 2.5.1 messaging hierarchy to capture, store, and monitor molecular DST data, thereby, improving the quality of data, specifications and exchange formats within the NTSS as well as ensuring full reporting of drug-resistant TB.

Integrating standardized whole genome sequence analysis with a global Mycobacterium tuberculosis antibiotic resistance knowledgebase

Drug-resistant tuberculosis poses a persistent public health threat. The ReSeqTB platform is a collaborative, curated knowledgebase, designed to standardize and aggregate global Mycobacterium tuberculosis complex (MTBC) variant data from whole genome sequencing (WGS) with phenotypic drug susceptibility testing (DST) and clinical data. We developed a unified analysis variant pipeline (UVP) ( https://github.com/CPTR-ReSeqTB/UVP ) to identify variants and assign lineage from MTBC sequence data. Stringent thresholds and quality control measures were incorporated in this open source tool. The pipeline was validated using a well-characterized dataset of 90 diverse MTBC isolates with conventional DST and DNA Sanger sequencing data. The UVP exhibited 98.9% agreement with the variants identified using Sanger sequencing and was 100% concordant with conventional methods of assigning lineage. We analyzed 4636 publicly available MTBC isolates in the ReSeqTB platform representing all seven major MTBC lineages. The variants detected have an above 94% accuracy of predicting drug based on the accompanying DST results in the platform. The aggregation of variants over time in the platform will establish confidence-graded mutations statistically associated with phenotypic drug resistance. These tools serve as critical reference standards for future molecular diagnostic assay developers, researchers, public health agencies and clinicians working towards the control of drug-resistant tuberculosis.