Genomic surveillance uncovers regional variation in HCV transmission networks among people who use drugs in rural U.S. communities

Genomic surveillance uncovers regional variation in HCV transmission networks among people who use drugs in rural U.S. communities | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Genomic surveillance uncovers regional variation in HCV transmission networks among people who use drugs in rural U.S. communities Damien Tully, David Bean, Jacklyn Sarette, Thang Long Ngo, Karen Power, and 19 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6810633/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Dec, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Hepatitis C virus (HCV) remains a significant public health concern in the United States particularly in rural communities where the opioid epidemic has accelerated transmission among people who use drugs (PWUD)/ Despite, this growing burden the genetic features and transmission patterns of HCV in these settings are poorly understood. This study analyzed 692 HCV antibody-positive specimens collected from rural communities in ten U.S. states. Using amplicon-based deep sequencing and the Global Hepatitis Outbreak and Surveillance Technology (GHOST) platform, transmission networks were reconstructed. Among sequenced individuals, 29.5% were linked within clusters. The structure of these clusters varied by region—from sparse networks in Ohio to dense, interconnected clusters in New England. Phylogenetic analysis revealed that some transmission networks persisted for over a decade, highlighting long-term, sustained transmission. Nearly half of all clusters involved individuals connected through social recruitment, suggesting peer-referral strategies can effectively identify transmission chains. Younger age was independently associated with clustering, while recruitment by an intimate partner showed a weaker link. These findings emphasize the importance of ongoing genomic surveillance and social network-informed strategies to detect emerging HCV clusters and guide targeted public health interventions in underserved rural communities. Biological sciences/Microbiology/Virology/Hepatitis C virus Biological sciences/Evolution/Phylogenetics hepatitis c virus transmission cluster persons who use drugs Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Hepatitis C presents a significant global health challenge, affecting an estimated 58 million individuals who are chronically infected with the hepatitis C virus (HCV) 1 . Notably, out of the estimated 11 million people worldwide who engage in injecting drug use each year, nearly 40% have viremic HCV infection 2 . The transmission of HCV is greatly influenced by unsafe injecting practices among persons who use drugs (PWUD) making it a substantial contributor to new infections on a global scale 3 . Within the United States, HCV is the leading cause of liver cancer and death from liver disease. The incidence rate of acute hepatitis C in the US has doubled since 2014 (129% increase) and increased 7% from 2020 to 2021 4 . Rates of acute hepatitis C are highest among males, persons aged 20–39 years and those living in the Eastern and Southeastern states 5 . Central to this increase is the increased frequency of reported injection drug use concurrent with the rise of prescription opioids and increasing availability of heroin/fentanyl and methamphetamine 6 . Rural areas bear a disproportionate burden of HCV, where infection rates are estimated to be twice as high as in urban settings 7 . These outbreaks often occur in communities facing structural barriers such as lower education and income levels, as well as limited access to healthcare. Additionally, rural populations in the US frequently lack essential harm reduction services like sterile syringe services programs and medication for opioid use disorder and often experience housing instability, which may exacerbate vulnerability to drug-related harms 8 . Recent studies reveal a significant prevalence of HCV among young adult PWUD in rural areas, particularly those engaging in polysubstance injection 9 , 10 . Recent outbreaks among persons who inject drugs in Scott County, Indiana 11 , Lowell, Massachusetts 12 and Kanawha County, West Virginia 13 highlight a concerning trend with high rates of HCV infections often preceding HIV outbreaks among persons who inject drugs. This temporal association underscores the urgent need for proactive measures and interventions in rural communities to prevent the emergence of HIV outbreaks. In response to the opioid crisis affecting rural areas of the US, a collaborative effort involving several agencies, including the National Institute on Drug Abuse (NIDA), the Centers for Disease Control and Prevention (CDC), the Substance Abuse and Mental Health Administration (SAMHSA), and the Appalachian Regional Commission (ARC), led to the establishment of the Rural Opioid Initiative (ROI). This initiative aimed to collect and synthesize both quantitative and qualitative data from eight rural regions across ten states, with the goal of deepening our understanding of drug use and the local factors driving opioid consumption, as outlined by Jenkins et al. 14 . As part of this initiative blood specimens were collected for the purpose of conducting rapid HIV, HCV and syphilis testing. HCV positive specimens were subsequently analyzed at a laboratory funded by the ROI for next-generation sequencing and linkage analysis to identify genetically associated transmissions. Drawing a parallel with the pandemic of COVID-19, genomic surveillance has been crucial in tracking the global spread of SARS-CoV-2, with real-time analysis forming a cornerstone for public health decision-making. However, similar genomic surveillance strategies, including genetic-based inferences, have not been routinely employed to investigate HCV transmission, except in specific community-based PWUD cohorts in Baltimore 15 , 16 , San Francisco 17 , and correctional facilities in Wisconsin 18 . Due to the scarcity of both genomic and epidemiological data, our knowledge about the origins of these outbreaks, the dynamics of virus transmission and the genetic diversity of HCV strains in rural US communities remains limited. The primary aim of this study was to characterize HCV strains circulating among PWUD in rural US areas and identify factors associated with HCV transmission clusters. To date, there are no studies surveying the genetic landscape of HCV across rural communities adversely affected by the US opioid crisis and whether the transmission patterns differ across geographic locations. Methods Study participants A cross-sectional survey of people who used drugs in rural counties with high overdose rates from ten U.S. states and 66 U.S counties (Illinois, Kentucky, North Carolina, New England [Massachusetts, New Hampshire, and Vermont], Ohio, Oregon, West Virginia and Wisconsin) was conducted and herein referred to as the Rural Opioid Initiative (ROI). Additional details on the ROI consortium have been previously published 14 . Kentucky samples were largely received from another study termed Social Networks Among Appalachian People (SNAP), but participants were recruited in the same manner and timeframe. Study participants were recruited between January 2018 and December 2021. Individuals were eligible for inclusion if they lived in the study area, reported any past 30-day injection drug use and/or noninjecting opioid use “to get high” (heroin, prescription pain medication). Inclusion criterion for all sites was age ≥ 18 years except two states (Illinois, Wisconsin) where the age criterion was ≥ 15 years. All sites conducted recruitment using respondent-driven sampling to facilitate sampling of hard-to-reach populations. Each study site identified “seed” participants to initiate recruitment chains. Seeds were recruited from syringe service programs, local health departments and community outreach to represent the general demographic characteristics of the local eligible population. Seeds recruited up to six members of their drug use network. Each referred participant recruited their network peers similarly with the goal of maximizing recruitment chains. Participants received $ 10 to $ 20 per successfully enrolled peer and $ 40 to $ 60 for completion of study procedures. A complete evaluation of respondent-driven sampling in this setting has been published elsewhere 19 . Blood specimens were collected for rapid HIV, HCV and syphilis testing at all sites. Specimens that were positive for HCV antibodies were shipped to the GHOST (Global Hepatitis Outbreak Surveillance Technology) Sequencing Center at the Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard for RNA testing and viral genomic analysis. Nucleic acid extraction and PCR Amplification Nucleic acid extraction and PCR Amplification RNA was isolated from 140 µl of plasma using the QIAamp Viral RNA Mini Kit (Qiagen, Hilden, Germany). A one step RT-PCR reaction was performed on all samples to amplify a segment at the E1/E2 junction of the HCV genome, which contains the hypervariable region 1 (HVR1) due to its high variability and its ability to reliably detect transmission events in outbreak settings 20 . The first round RT-PCR consisted of an Illumina adapter specific portion, a sample specific barcode segment, and an HCV HVR specific primer segment, F1- GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT-NNNNNNNNNN-GGA-TAT-GAT-GAT-GAA-CTG-GT and R1-ACA-CTC-TTT-CCC-TAC-ACG-ACG-CTC-TTC-CGA-TCT-NNNNNNNNNN-ATG-TGC-CAG-CTG-CCG-TTG-GTG-T at a final concentration of 4 pM amplified using Superscript III RT/Platinum Taq DNA Polymerase High Fidelity with the following conditions: cDNA synthesis for 30 minutes at 55°C, followed by heat denaturation at 95°C for 2 minutes, the PCR amplification conditions were 40 cycles of denaturation (94°C for 10 seconds), annealing (55°C for 10 seconds) and extension (68°C for 10 seconds) with a final extension at 68°C for 5 minutes. Amplified products were run on a 1% agarose gel and either PCR purified with the QIAquick PCR purification kit (Qiagen), or gel extracted and purified using the PureLink quick gel extraction kit (Invitrogen). A second round limited cycle PCR (94°C for 2 minutes, (94 o C for 15 sec; 55 o C for 30 sec; 68 o C for 30 sec) x 8 cycles, 68 o C for 5 minutes) is performed to add barcode specific indexes and sequencing specific adapters and primers to each sample to allow for multiplexing as well as internal controls for cross-contamination. Negative controls were introduced at each stage of the procedure and all PCR procedures were performed under PCR clean room conditions using established protocols. Indexed samples are 0.7X SPRI purified two times to remove excess primer dimer and short fragments that can interfere with the sequencing process. To avoid contamination all reagents were pre-aliquoted with dedicated equipment ensuring physical separation of sample processing from pre- and post-PCR amplification steps including deep sequencing. Illumina deep sequencing PCR amplicons were quantified using the Picogreen kit (Invitrogen, Carlsbad, CA) on a Fluorometer ST (Promega, Madison, WI) with the integrity of the fragment evaluated using a Bioanalyzer 2100 (Agilent, Santa Clara, CA). Samples were pooled and sequenced on an Illumina MiSeq platform using a 2 x 250 bp V2 Nano reagent kit. In general, a sequence library consisted of between 8–16 specimens including one negative control for every 7 serum specimens. Deep sequencing data analysis Sequencing reads were automatically de-multiplexed and duplicate reads were removed using fastuniq v1.1 21 to limit the influence of PCR artifacts and subsequently quality trimmed using trimmomatic v0.36 22 if sequencing adapters or low-quality bases (Phred scores < 20) were detected. Vicuna v1.1, a de novo consensus assembly algorithm 23 , was used to generate consensus assemblies from genetically heterogeneous populations with automated computational finishing and annotation of de novo viral assemblies performed using V-FAT v1.1 as previously performed 24 . All consensus assemblies were cross checked with the iVar pipeline 25 and any differences were further inspected. Lastly, intra-host single nucleotide variants were identified with V-Phaser 2 26 . Instrain was used to assess the genomic nucleotide diversity (π) based on all reads and calculated as the average number of nucleotide differences per base pair 27 . HCV Genotyping All de novo consensus sequences were classified using the method implemented in the Genome Detective virus tool for phylogenetic genotyping 28 . To detect and quantify the presence of mixed HCV genotypes in Illumina MiSeq sequencing reads paired end reads were mapped using bowtie2 to a set of HCV reference genome sequences (n = 571). The absolute number of reads that mapped to a reference genome of the given HCV genotype was then counted and quantified. The criteria for identifying mixed genotype infections were that it had to be at a frequency of 1% or greater and have at least 200 reads mapped to it. This cutoff was seen as a conservative threshold to exclude cross-contamination between samples. Phylogenetic reconstruction All de novo consensus sequences were aligned using MAFFT v7.470 29 and IQ-TREE v 2.1 30 was used to construct a maximum likelihood phylogenetic tree employing the best-fit model of nucleotide substitution according to the Bayesian Information Criterion (BIC) as indicated by the Model Finder application implemented in IQ-TREE 31 . Statistical robustness of individual nodes was determined using 1000 ultrafast bootstrap replicates 32 . GHOST analysis Global Hepatitis Outbreak and Surveillance Technology (GHOST) detects and visualizes transmission clusters using deep sequencing data from the hypervariable region of the HCV genome. Paired-end reads for each successful sequenced sample were uploaded to the GHOST server where the data were subjected to quality control before being analyzed for the presence of transmission links using hamming distance. Two cases were considered linked by transmission if the distance between them was less than the empirically defined threshold value of 0.037. Further details on GHOST have been published by Longmire et al 33 . Estimation of time of the most recent ancestor To characterize the time of the most recent ancestor (tMRCA) of transmission clusters we used the phylogenetic framework as implemented in the Nextstrain pipeline 34 . As our range of sample dates were not large or wide enough, we did not have sufficient temporal signal as measured by the coefficient of the root-to-tip regression method. In line with prior HCV studies, we employed an independent dataset with significant temporal information to provide the substitution rates of the genomic region of interest 35 . The SRD06 nucleotide partitioned-substitution model, an uncorrelated relaxed lognormal molecular clock and a Bayesian skyline coalescent model with 10 groups was employed using BEAST version 1.10.4 36 from which we obtained rate estimates for the precise subgenomic region sequenced in this analysis. The Markov chain Monte Carlo chains (MCMCs) were run for 500 million generations and sampled regularly to yield a posterior tree distribution based upon 10,000 estimates. The mean rate of evolution and standard deviation of the estimated rate of evolution were then used in the Nextstrain workflow to infer the time scale of HCV clusters. Statistical Analyses Descriptive analyses were performed to examine the factors associated with being in a cluster or not using Chi-Squared, Fisher’s exact and Kruskal-Wallis test as appropriate. Logistic regression analyses were used to identify factors associated with being in a dyad or cluster (yes/no). Univariate logistic analysis was first performed, and variables with p < 0.10 were selected for multivariable logistic regression analysis. All tests were two-tailed and a p-value < 0.05 was considered statistically significant. All analyses were performed using in Python and Stata software (version 18.0; StataCorp, College Station, Texas, USA). Data availability Data are available upon reasonable request, but restrictions apply to the availability of these data. Written permission is required, and a data use agreement will need to be implemented before any sequence data are shared for research. Ethical approval. All study procedures were approved by the Institutional Review Board at each site and study protocols and procedures were reviewed and approved by the Institutional Review Board of Massachusetts General Hospital. Results Study participants A total of 3,084 PWUD completed audio computer-assisted self-interviews (ACASIs) or computer-assisted self-interviews (CASIs). Participants had a mean age of 34 years [IQR: 28-43]) and 42% of respondent’s were female. From each study site, a sub-sample of respondents were selected to give a plasma sample for further HCV sequencing and cluster analysis. A total of 1,201 HCV positive serum specimens were received from eight study sites ( Figure 1 ). Of these 692 (57.7%) successfully completed sequencing and quality control while 293 (24.4%) samples were found to be below our limit of detection and contained little if any viral RNA. The specimens may have represented HCV antibody positive status but viral load negative samples from those who had cleared infection. Two hundred and sixteen samples (18.0%) failed PCR or did not generate adequate sequencing results for inclusion. The PCR failure rate varied depending on the sampling site and ranged from 3.4% for Wisconsin to 46.2% in Illinois ( Supplementary Table 1 ). Participant characteristics among those with available HCV sequencing and completed questionnaires (n = 429) are shown in Table 1 . Overall, the median age was 35 years [IQR: 29-43] and 64% were male and mostly non-Hispanic white with a high school education. Sixty-one percent had experienced homelessness in the past 6-months and most participants were recruited into the study by a friend, associate, or acquaintance. No significant differences were observed between the sequenced samples and those that failed PCR amplification. Therefore, the characteristics of the sequenced subset is representative of the entire ROI cohort ( Table 1 ). Most study participants reported using opioids (64%) followed by stimulants (33%) as their drugs of choice with a substantial proportion simultaneously injecting opioids and stimulants (Table 2). Median age at first injection drug use experience was 21 years [IQR: 18 – 32]. Eighty-two percent of participants reported accessing treatment for addiction, including inpatient/outpatient treatment and medication for opioid use disorder, with 77% reported health insurance or health care coverage. Most participants reported daily or more frequent injection drug use and received their syringes/needles from various sources including pharmacies, syringe services programs, friends/acquaintances, and drug dealers/street. Most participants were tested for HIV (77%) and HCV (75%). Only 3% were diagnosed with HIV, compared to 69% with HCV. Almost one-fifth (19%) cleared HCV infection with treatment. HCV genotype distribution By utilizing Genome Detective tools for genotyping, we assigned consensus sequences to various HCV genotypes and subtypes as illustrated in Figure 2 . Out of the 692 sequences analyzed, 65% were classified as genotype 1, 23% as genotype 3, 7% as genotype 2 and one sequence was identified as genotype 4 ( Figure 2A ). Our deep sequencing approach uncovered evidence of mixed infections in approximately 4.8% of cases ( Figure 2A ). Further examination of these mixed infections revealed that the most prevalent combinations were genotype 1a and 3a, comprising 73% followed by genotype 1a/2b at 15% ( Figure 2B ). HCV genotype frequencies remained largely consistent among study sites with subtype 1a being the predominant strain (46% to 71%), followed by 3a (18% to 30%) and 2b (2% to 12%) ( Supplementary Figure 1 ). Although some variations in the prevalence of genotypes and subtypes were observed between sites, Kentucky and New England exhibited the highest variability. In Kentucky, we identified five different circulating genotypes, including a subtype 4a sample, and several low-prevalence mixed genotype infections (genotype 1a/4a, 2b/3a, 1a/2b) ( Supplementary Figure 1A ). Similarly, New England displayed a range of circulating strains, with three subtype 2a samples and a higher frequency of subtype 2b samples compared to other sites. Despite the diversity of subtypes in circulation, only 2% of samples were found to be mixed infections ( Supplementary Figure 1B ). Wisconsin showed relative homogeneity with only genotypes 1a, 3a and 2b detected while mixed infections were exclusively 3a/1a ( Supplementary Figure 1C ). The remaining sites, Ohio, Oregon and North Carolina all exhibited a predominance of genotypes 1a and 3a with a similar frequency of genotype 2b followed by a range of mixed genotype infections ( Supplementary Figure 1D-F ). In Oregon, a rare case of infection was found with a major population of genotype 3a and minor populations of genotypes 1a and 2b. Identification of transmission clusters Phylogenetic analysis of consensus sequences indicates that study site specific sequences are interspersed throughout the tree although some sequences did appear to cluster by geographic location ( Figure 3 ). GHOST analysis of intra-host HCV HVR1 population from all sampled cases identified 85 transmission clusters involving 204 HCV strains (29.5%) ( Figure 4 ). The median cluster size was two members (range 2 - 7) with 63% in dyads. The fraction of clusters HCV strains was statistically different according to study sites ( P < 0.0001 ; Fisher’s exact test) and ranged from sparse clustering in Ohio (9.4%) to almost half of all sequenced strains in New England (43%). Two study sites (Ohio and Oregon) exclusively consisted of dyads while North Carolina had a single cluster of three. More complex networks were observed in Wisconsin, Kentucky and New England although they were limited. The largest cluster of PWUD was found in New England and consisted of a network of 7. Comparison of identified clusters to RDS recruitment chains Across all study sites we found that 49% of transmission clusters were comprised of individuals linked within the RDS social recruitment chains. However, there was notable variability among the sampling sites in their ability to identify transmission networks using RDS recruitment. In Ohio and Oregon, where only dyadic genetic relationships were found, 50% and 67% of individuals, respectively, were not linked in the RDS chains. In Wisconsin, more complex networks were identified with 60% found to be outside the RDS chains. Conversely, in New England and North Carolina, 55% and 77% of transmission clusters, respectively, were linked within the social recruitment chains. Overall, our observations indicated that there is a statistically significant ( P = 0.0001 ) association between transmission cluster size and whether they are found inside or outside of the RDS chain. Specifically, individuals in more complex networks were more likely to be found in clusters that included members of their RDS chain compared to those in genetic dyads (70% vs. 36%, Supplementary Figure 2 ). Relationship between intra-host viral diversity and clustering The level of genomic diversity in HCV varies significantly depending on the infection stage. During the acute and early stages, the viral population tends to be relatively homogeneous, primarily due to serial bottlenecks and the presence of a single founder virus. As the infection progresses, HCV undergoes increased genomic diversification as it adapts to the host's immune response, leading to a positive correlation between the stage of infection and intra-host viral diversity. Analysis of intra-host viral diversity across all sampled individuals indicates that those who clustered exhibited significantly lower diversity compared to non-clustered individuals ( Figure 5A ; P= 0.0038, Mann Whitney test). Furthermore, the study revealed substantial variability in intra-host viral diversity among different study sites. Ohio and Kentucky (KY) showed the highest median diversity, whereas North Carolina displayed less heterogeneity. Despite a similar distribution of intra-host viral diversity between KY and New England, KY had a significantly higher median ( Figure 5B ; P= 0.0002, Mann Whitney test) a trend also observed when comparing KY with Wisconsin (WI) and NC ( Figure 5B ). Further stratification of clustered individuals into dyads and more complex clusters (i.e., clusters with more than two members) demonstrated a significant difference in intra-host viral diversity between individuals not in a cluster and those in dyads ( Figure 5C ; P= 0.0161, Mann Whitney test) and a marginally non-significant difference for those in complex clusters ( P= 0.0506, Mann Whitney test). However, no significant difference was observed between dyads and more complex clusters (P=0.9269, Mann-Whitney test). Taken together, this suggests that those participants not found to be in a transmission cluster may have an infection reminiscent of a longer timeframe (i.e. more chronic like stage of infection) compared to those within clusters who appear to be harboring less diversity which is a known attribute of the early stages of infection. Moreover, the knowledge that different study sites have different levels of intra-host viral diversity from sequenced participants most likely reflects the underlying infection dynamics within that sampled population. Evidence of HCV persistence across rural study sites The rate of evolution for the genomic region used in this study was estimated to be 3.417 x 10 -3 (95% highest posterior density credibility intervals: 2.177 to 4.657 x 10 -3 ) substitutions per site per year. Using this rate, we estimated the tMRCA for each cluster and examined the lag time between inferred introduction date and time of first and last sampling date of the cluster ( Fig. 6 ). The estimated tMRCA varied between clusters and sampling sites and the size of the cluster. Across all states, Oregon had the shortest median lag time of 3.61 years, followed by Kentucky (4.15 years), North Carolina (4.80 years), New England (5.74 years), Wisconsin (6.74 years) while the virus persisted longer in Ohio at 8.75 years. In WI, one cluster had the longest persistence time of approximately 12.91 years while two clusters have the shortest lag times of less than a year ( Fig 6 ). In New England, a cluster comprising a dyad had the longest persistence time of 19.08 years while two dyad clusters had the shortest tMRCA until sampling of less than 6 months ( Fig 6 ). In North Carolina the tMRCA was estimated for four clusters with two having a tMRCA within 3 years while the remaining clusters had a persistence time of over 7 and 12 years respectively ( Fig 6 ). Within KY a cluster comprising 3 participants was found to be persisting for approximately 13 years (tMRCA mid-2005) before this cluster was sampled in 2019. In comparison, a dyad cluster was found to have the shortest lag time within the inferred introduction mirroring the time of sampling ( Fig 6 ). Three transmission clusters were detected in OH where only once was estimated to have a more recent introduction occurring an estimated 1.7 years prior to sampling ( Fig 6 ). The remaining two clusters had long-term persistence with evidence of at least almost two decades of local persistence between the estimated time of introduction and the most recent sampling. In OR, most clusters were estimated to have occurred more recently (within 3 years) but two clusters showed at least a decade of persistence ( Fig 6 ). Complex clusters, although exhibiting a higher median persistence time of 6.2 years compared to dyads (4.2), did not show a statistically significant difference ( P= 0.1359, Mann Whitney test). Factors associated with transmission clusters In unadjusted logistic regression analyses, membership in a cluster was associated with younger age (21% vs. 30%; OR = 2.54 [95% CI: 1.41 – 4.60], P = 0.002; Table 3 ), being recruited by a partner, spouse, boyfriend or girlfriend (10% vs. 19%; OR = 2.07 [95% CI: 1.17 – 3.67], P = 0.013; Table 3 ), and having an illegal source of income (e.g. selling drugs, selling sex and theft) (27% vs. 39%; OR = 1.75 [95% CI: 1.14 – 2.67], P = 0.010; Table 3 ). A borderline significant association was found with those who have shorter incarcerated period and clustering (21 vs. 14 days; OR = 1.01 [95% CI: 1 – 1.01], P = 0.05; Table 3 ) while receiving income assistance (e.g. disability check, military, TANF, AFDC) (29% vs. 16%; OR = 0.477 [95% CI: 0.29 – 0.80], P = 0.005; Table 3 ) decreased the odds of being in a cluster. No significant differences were found between HCV subtype, race and ethnicity, education, drug choice or frequency of injection drug use. The multivariable model included factors that were associated with clustering ( P < 0.10) in the univariable analysis including age, source of recruitment, incarceration time, income source and syringe source. The factors that remained significantly associated with membership in a cluster was being aged 18-29 years (AOR = 2.009 [95% CI: 1.17 – 3.46], P = 0.012; Table 3 ). In contrast, receiving a form of public assistance as a primary income source remained negatively associated with clustering (AOR = 0.544 [95% CI: 0.30 – 0.97], P = 0.040; Table 3 ). Discussion Despite the high incidence of HCV among PWUD in recent years and the ensuing opioid overdose crisis, very little has been understood about the emergence and spread of the virus across the United States. Rural communities have been disproportionately affected by HCV outbreaks, fueled by overlapping epidemics or syndemics of injection drug use, widespread nonmedical use of opioids and stimulants, and compounded by social inequities and social barriers 37–39 . While HCV screening rates among PWUD range from 8-32% 40,41 these rates are markedly lower in rural areas with some estimates as a lows as 6% 41 . In this multi-site cohort study comprising 692 PWUD in the rural United States, we found that nearly one-third of all HCV infections were genetically linked, with genotype 1a predominating across all study sites. Subtle differences in genotype distribution were observed between sites, with Kentucky and New England showing the greatest heterogeneity, including the presence of less common subtypes such as 4a and 2a which may lead to less than optimum treatment outcomes 42 . The prevalence of mixed HCV genotype infections, as determined by sequencing, was 4.8% - this rate is consistent with other studies reporting low frequencies 43–47 . Although a higher proportion (18%) of mixed-strain HCV infections was reported in an outbreak in rural Indiana using the GHOST platform 11 , such findings have not been replicated in other rural settings and are likely due to the rapid transmission dynamics and unique social structure of that specific community. Further, comparisons across studies are challenging due to differences in cohort selection, injection behaviors and the genomic region analyzed 48 . The degree of clustering observed within this study at 29.5% is similar to that observed from other injecting drug cohorts. For example, a study examining HCV transmission across four Indian cities revealed that 28.8% of HCV sequences clustered, 49 while studies conducted in North America have revealed variable levels of clustering from 46% in Baltimore 50 , 33% in Wisconsin 18 , 25% in New York 51 , 31% in Vancouver 52 and 36% in Ottawa 53 . Higher rates of clustering were observed in those studies that enrolled injecting partnerships, with 54% of Australian participants genetically related 54 compared with 52% of injecting partnerships from San Francisco 17 . The elevated clustering rate observed in these known partnerships may be attributed to the study designs, which involves more frequent assessments, thereby increasing the likelihood of capturing transmission events early. Differences in the rate of clustering among participants could reflect regional differences in drug use networks, recruitment approaches, sampling density, clustering analyses methods or behavioral differences. Age has been previously demonstrated to be an important factor in HCV transmission, with higher rates of clustering found in participants of younger age 55 . During the acute and early stages, the viral population tends to be relatively homogeneous, primarily due to serial bottlenecks and the presence of a single founder virus. As the infection progresses, HCV undergoes increased genomic diversification as it adapts to the host's immune response, leading to a positive correlation between the stage of infection and intra-host viral diversity. If a correlation does exist then genetic diversity may be used as a proxy for infection recency as previously suggested, 56–59 then this may well suggest that clusters detected in this study are more driven by individuals who are in the earlier stages of infection, when genomic diversity is relatively homogeneous owing to a genetic bottleneck effect. We found that 49% of transmission clusters stemmed from the same social recruitment chains suggesting that this type of recruitment approach is successful in uncovering transmission networks among PWUD. The time of persistence of these local transmission clusters was estimated from dated phylogenies and revealed prolonged period of persistence over 10 years in some geographical locations, with more complex transmission networks cryptically spreading for longer periods compared to those in a dyadic cluster. Younger age (particularly those aged 18–29) emerged as the strongest independent predictor of transmission cluster membership, consistent with previous findings that highlight elevated transmission risk among younger PWUD 55 . Recruitment by a sexual or intimate partner and having an illegal source of income were also associated with increased odds of clustering in unadjusted analyses, suggesting that close personal networks and high-risk socioeconomic behaviors may be involved in viral spread. Sexual relationships have also been associated with increased sharing of syringes and injecting equipment and having a genetically related infection in young PWUD from San Francisco 17,60,61 . Conversely, receiving public assistance as a primary income source was negatively associated with clustering, potentially reflecting reduced engagement in higher-risk networks. No associations were found between clustering and HCV subtype, race and ethnicity, education level, drug type, or frequency of injection, underscoring the importance of social and structural factors rather than strictly behavioral factors in shaping transmission dynamics. From this study we have shown how genomic sequencing has the potential to provide a high-resolution picture of HCV evolution and transmission, providing public health authorities with actionable information. Molecular epidemiology methods have been used in related fields (e.g. HIV prevention) for many years and have been critical to informing public health interventions. A striking example of this approach was from an implementation case study in Canada that used phylogenetic analysis of routine clinical data and demonstrated that ‘near-real-time’ analysis directly impacted ongoing viral transmission and led to enhanced public health follow-up with linkage to care and treatment initiation 62 . As part of the United States Federal Ending the HIV Epidemic strategic initiative, rapidly detecting and responding to emergent clusters of HIV infection is one of the key pillars that will be used to further reduce new transmissions 63 . Yet, its implementation for HCV remains scarce but there is enormous potential for genomic surveillance to augment traditional surveillance approaches. It is especially valuable for marginalized population groups (i.e. people who use injection drugs), where it could be used for targeted network-based strategies to interrupt transmission, 64 which could aid in microelimination strategies 65 . Moreover, dried blood spots have also been validated for genomic surveillance as a less invasive alternative to venous blood draws 51,66 and could serve as a useful tool for future surveillance efforts. This study has several limitations. Firstly, recruiting marginalized and highly stigmatized populations, such as people who inject drugs, poses significant challenges, especially in rural settings where no universal recruitment tool exists. In this study, a modified form of chain referral sampling, RDS, was utilized to recruit PWUDs. Although RDS has not been widely adopted in rural areas, it has been successfully implemented in multiple rural U.S. regions 67,68 . While there were some differences in recruitment across study sites, little variation was observed when multiple variables were assessed. Although each site was instructed to collect and process specimens in the same manner, there may have been some time delays to centrifugation and freezing of samples, which may have compromised the integrity of the samples leading to different PCR failure rates across sites. Secondly, the study focused on a fragment within the E1/2 region of the HCV genome. While this captures only a small portion of the HCV genome, it was selected for its high variability, making it effective for detecting transmission events in outbreak settings 69 . However, monitoring additional genomic regions, such as NS3 and NS5A/B, would be important for tracking antiviral drug resistance patterns. Thirdly, genetic clustering by similarity does not confirm a transmission event, as there may be un-sampled missing links within transmission chains, leading to an incomplete understanding of the HCV transmission network. Additionally, the direction of HCV transmission cannot be readily inferred from genetic data alone 17 , though phylogenetic analysis can provide insights into transmission direction with varying degrees of reliability 70–72 . Finally, behavioral data were self-reported and may be subject to recall and response biases. In conclusion, this study of PWUD across multiple U.S. rural sites reveals that the HCV epidemic is not uniform across regions, with notable differences in genotype diversity, clustering rates, intra-host viral diversity, and the persistence of transmission clusters. These findings suggest that local HCV epidemics are evolving at different stages and highlight the importance of robust genomic surveillance to guide the development of targeted social and structural public health interventions for high-risk, marginalized rural communities. In memoriam: Dr. Todd M Allen passed away unexpectedly before this manuscript could be published. He was a kind and gentle spirit whose contributions to the field of infectious disease research were immense and far-reaching. Beyond being a brilliant scientist, Dr. Allen was a generous mentor, collaborator, and cherished friend to many in the community. We dedicate this manuscript to his memory, in recognition of his remarkable scientific legacy and his unwavering commitment to support and inspire the next generation of researchers. Declarations Ethical approval. All study procedures were approved by the Institutional Review Board at each site and study protocols and procedures were reviewed and approved by the Institutional Review Board of Massachusetts General Hospital. Competing interests The authors declare no competing interests. Author contributions D.C.T and T.M.A acquired funding, conceived, designed and supervised the study. D.B.J, J.S, T.L.N and K.A.P performed all experiments. D.C.T analyzed all data and prepared the figures. D.B, H.C, J.F, P.F, K.R.H, J.R.H, S.B, C.H, W.J, P.T.K, W.M, M.T.P, G.S, T.S, R.P.W and A.M.Y contributed to the review and editing process and provided funding acquisition. J.I.T and S.M was involved in data curation and contributed to the review and editing process. D.C.T and T.M.A wrote the manuscript. All authors read and approved the final manuscript. Acknowledgements This work was supported by the National Institute of Drug Abuse (NIDA) grant U24DA044801. Data is based upon data collected and/or methods developed as part of the Rural Opioid Initiative (ROI), a multi-site study with a common protocol which was developed collaboratively by investigators at eight research institutions and at the National Institute of Drug Abuse (NIDA), the Appalachian Regional Commission (ARC), the Centers for Disease Control and Prevention (CDC), and the Substance Abuse and Mental Health Services Administration (SAMHSA). Primary data collection was supported by grants UG3DA044798, UG3DA044830, UG3DA044831, UG3DA044826 co-funded by NIDA, ARC, CDC, and SAMHSA. Specimen collection in Kentucky was also supported by R01DA033862 and R01 DA047952. The authors thank the other ROI investigators and their teams, the ROI Executive Steering Committee chair, Dr. Holly Hagan, the NIDA Science Officer, Dr. Richard Jenkins, and, particularly, the participants of the individual ROI studies for their valuable contributions. A full list of participating ROI investigators and institutions can be found on the ROI website at http://ruralopioidinitiative.org/studies.html. References Blach, S. Global change in hepatitis C virus prevalence and cascade of care between 2015 and 2020: a modelling study. Lancet Gastroenterol Hepatol 7 , 396–415 (2022). Grebely, J. et al. Global, regional, and country-level estimates of hepatitis C infection among people who have recently injected drugs. Addiction (Abingdon, England) 114 , 150–166 (2019). Trickey, A. et al. The contribution of injecting drug use as a risk factor for Hepatitis C virus transmission globally, regionally, and at country level: a modelling study. 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Overall ROI cohort ROI participants with samples ROI participants with sequenced samples N (%) N (%) N (%) Total 3,084 737 (100) 429 (58) Age , median (IQR) 34 (28 – 43) 35 (29-43) 35 (29-43) Gender Male 1,737 (57) 428 (58) 273 (64) Female 1,293 (42) 306 (42) 155 (36) Transgender/Gender minority 18 (1) 3 (<1) 1 (<1) Race/Ethnicity Non-Hispanic White 2,527 (83) 621 (84) 364 (85) Non-Hispanic Black 94 (3) 13 (2) 8 (2) Non-Hispanic American Indian 208 (7) 43 (6) 20 (5) Non-Hispanic Other/Unknown 103 (3) 19 (3) 14 (3) Hispanic 1 116 (4) 41 (6) 23 (5) Education Less than high school 688 (23) 180 (24) 94 (22) High school or GED 1,430 (47) 349 (47) 212 (49) Some college or technical school 856 (28) 195 (26) 114 (27) Bachelor’s degree or above 71 (2) 12 (2) 8 (2) Marital Status Single/Not married 1,570 (53) 388 (53) 229 (54) Married 354 (12) 101 (14) 57 (13) Separated/Divorced/Widowed/Don’t Know 1,054 (35) 239 (33) 139 (33) Homelessness , past 6 months 1,612 (53) 434 (59) 260 (61) Geographic region Illinois 173 (6) 12 (2) 0 (0) Kentucky 338 (11) 42 (6) 12 (3) North Carolina 350 (11) 51 (7) 41 (10) New England 589 (19) 228 (31) 128 (30) Ohio 258 (8) 105 (14) 63 (15) Oregon 174 (6) 57 (8) 42 (10) Wisconsin 991 (33) 242 (33) 143 (33) West Virginia 175 (6) 0 (0) 0 (0) Recruited – How Coupon or Code 2315 (76) 571 (77) 342 (80) Told about, but didn’t get a coupon/code 569 (19) 128 (17) 69 (16) Event 32 (1) 3 (<1) 1 (<1) Online 17 (1) 6 (1) 4 (1) Flyer or other advertising 96 (3) 21 (3) 12 (3) Other 117 (4) 27 (4) 13 (3) Recruited – by Whom Partner, spouse, boyfriend, girlfriend 326 (11) 89 (12) 56 (13) Casual sex partner 44 (1) 16 (2) 8 (2) Friend, associate, acquaintance 1706 (56) 414 (56) 242 (56) Family member 255 (8) 53 (7) 30 (7) Neighbor 64 (2) 12 (2) 6 (1) Person I use drugs with 398 (13) 97 (13) 59 (14) Service or program staff 70 (2) 10 (1) 9 (2) Stranger 37 (1) 10 (1) 4 (1) Other 52 (2) 9 (1) 5 (1) Abbreviations: IQR, Inter-quartile range. 1 Race and ethnicity are mutually exclusive categories. Table 2. Substance use practices of sequenced participants. ROI participants with sequenced samples N (%) Total 429 (58) Substance Use Drug of choice Opioids 274 (64) Stimulant 141 (33) Benzos 1 (<1) Other 13 (3) Drug Use Patterns, past 30 days Opioids 1 370 (86) Fentanyl 197 (46) Buprenorphine/methadone 2 225 (52) Methamphetamine 315 (73) Cocaine/crack 208 (48) Benzodiazepines 195 (45) Multiple classes of drugs used 3 352 (82) Number of classes, median (IQR) 3 (2 – 3) Simultaneous injection of an opioid and a stimulant (e.g., speedball) 4 5 182 (45) Personal history of overdose 248 (58) Ever received any treatment for addiction 351 (82) Received any treatment for addiction , past 30 days 152 (35) Attended inpatient/outpatient treatment , past 30 days 109 (25) Received MOUD , past 30 days 99 (23) Injection Drug Use Current injection drug use , past 30 days 408 (95) Injection drug use frequency, past 30 days 5 Daily or more 297 (73) More than weekly, less than daily 40 (10) Weekly 26 (6) Monthly 39 (10) Source of most syringes or needles, past 30 days 5 Pharmacy SSP/NEP, personally 94 (23) SSP/NEP, personally 157 (38) From someone else who got them from SSP/NEP 55 (13) Friend/acquaintance, spouse, or partner 27 (7) Drug dealer/street 67 (16) Other 4 (1) Don’t know/Refused 4 (1) Source of any syringes or needles, past 30 days 5 Pharmacy 126 (31) SSP/NEP, in person 200 (49) SSP/NEP, someone else 140 (34) Farm supply or veterinarian 2 (<1) Drug dealer or street syringe seller 78 (19) Spouse, partner, family member, or relative 65 (16) Friend or acquaintance 143 (35) I found them 14 (3) Person from whom you received most of your syringes was diabetic 5 15 (4) Closest SSP 6 30 min drive 65 (15) Don’t know 37 (9) Number of times got a new syringe from a pharmacy , past 30 days 6 0 (0 – 1) Number of times got a new syringe from a SSP , past 30 days 6 0 (0 – 2) It is easy for me to get new, clean syringes or needles Strongly/Somewhat Disagree 65 (15) Uncertain 42 (10) Strongly/Somewhat Agree 319 (74) Age at first injection , median (IQR) 6 21 (18-30) Age at first opiate pain killer injection , median (IQR) 6 21 (17-28) Age at first heroin injection , median (IQR) 6 24 (19-31) Age at first methamphetamine injection , median (IQR) 6 26 (19-33) Age at first cocaine injection , median (IQR) 6 22 (18-29) Number of days practiced syringe mediated drug sharing , past 30 days 6 ,median (IQR) 1 (0 – 8.5) Number of days practiced multiple injection per injection episode , past 30 days 6 , median (IQR) 4 (1 – 15) Number of times used syringe/needle that was used by somebody else , past 30 days 6 , median (IQR) 1 (0 – 5) Number of times used a cotton, cooker, spoon, or water for rinsing or mixing that was used by somebody else , past 30 days 6 , median (IQR) 2 (0 - 10) Number of times let someone else use a cotton, cooker, spoon, or water for rinsing or mixing after you used it , past 30 days 6 , median (IQR) 2 (0 - 10) Health care and health insurance Main place received medical care, past 6 months Private doctor 107 (25) Community health center 58 (14) Health department 19 (4) Urgent care 54 (13) Emergency room 83 (19) Other 27 (6) Did not receive medical care in the past 6 months 77 (18) Refused/Don’t know 4 (1) Health insurance or health care coverage 330 (77) Health care and health insurance Tested for HIV, ever 331 (77) Diagnosed with HIV, ever 7 11 (3) Tested for HCV, ever 332 (75) Diagnosed with HCV, ever 8 222 (69) Cleared HCV with Treatment, ever 9 43 (19) Rapid HCV Test Positive Result 424 (99) Confirmatory RNA HCV Positive Result 273 (64) Rapid HIV Positive Results 5 (1) Confirmatory HIV Positive Results 3 (1) Abbreviations: SD, standard deviation; 1 Heroin, opiate painkillers, and/or synthetics (e.g., U47700, U4, or “Pink”). 2 Buprenorphine and/or methadone used “to get high.” 3 Use of ≥2 drug categories (opioids, methamphetamine, cocaine/crack, prescription anxiety drugs [not as prescribed], gabapentin, clonidine, and/or other) by any route in past 30 days. 4 Simultaneous injection of an opioid and a stimulant (i.e., speedball, goofball, or screwball). 5 Among participants reporting injection drug use in the past 30 days. 6 Among participants reporting ever injecting drug. Overall ROI Cohort: n = 2,812; ROI participants with a sample: n = 731; ROI participants with a sequenced sample: n= 426 7 Among participants reported ever being tested for HIV 8 Among participants reported ever being tested for HCV 9 Among participants reported ever being diagnosed with HCV Table 3. Crude and multivariable logistic regression analysis of factors associated with being in a cluster for participants in the rural opioid initiative. Characteristic No cluster (n = 286) Cluster (n = 143) Unadjusted OR (95% CI) P value Adjusted OR (95% CI) P value Age groups 18 - 29 69 (20.7%) 43 (30.1%) 2.544 (1.407, 4.599) 0.002 2.009 (1.166, 3.462) 0.012 30 – 35 67 (23.5%) 33 (23.1%) 1.590 (0.852, 2.969) 0.146 36 – 43 71 (24.9%) 39 (27.3%) 1.966 (1.071, 3.610) 0.029 1.630 (0.938, 2.832) 0.083 44 – 65 88 (30.9%) 28 (19.6%) Ref Female (vs. male) 100 (35.0%) 55 (38.5%) 1.162 (0.767, 1.761) 0.477 Recruitment source Friend, associate, acquaintance 163 (57.0%) 79 (55.2%) Ref Partner, spouse, boyfriend, girlfriend 29 (10.1%) 27 (18.9%) 2.069 (1169., 3.663) 0.013 1.736 (0.926, 3.254) 0.085 Casual sex partner 5 (1.8% 3 (2.1%) 1.108 (0.259, 4.745) 0.890 Family member 23 (8.0%) 7 (4.9%) 0.585 (0.244, 1.400) 0.228 Neighbor 4 (1.4%) 2 (1.4%) 1.195 (0.281, 5.078) 0.809 Person I use drugs with 34 (11.9%) 25 (17.5%) 1.014 (0.233, 4.411) 0.125 Service or program staff 5 (1.8%) 4 (2.8%) 1.612 (0.426, 6.104) 0.482 Stranger 3 (1.1%) 1 (0.7%) 0.662 (0.068, 6.422) 0.722 Other 3 (1.1%) 2 (1.4%) 1.333 (0.220, 8.076) 0.754 Race and ethnicity Non-Hispanic White 240 (83.9%) 124 (86.7%) Ref Non-Hispanic Black 7 (2.4%) 1 (0.7%) 0.310 (0.035, 2.707) 0.289 Non-Hispanic American Indian 14 (4.9%) 6 (4.2%) 1.055 (0.333, 3.343) 0.928 Non-Hispanic Other 10 (3.5%) 4 (2.8%) 0.951 (0.260, 3.546) 0.951 Hispanic 15 (5.2%) 8 (5.6%) 1.503 (0.500, 4.514) 0.468 Education High school diploma or GED 143 (50.2%) 69 (48.3%) Ref Some college 73 (25.6%) 41 (28.7%) 1.91 (0.678, 2.093) 0.543 Less than high school 63 (22.1%) 31 (21.7%) 0.915 (0.519, 1.615) 0.760 College graduate or above 6 (2.1%) 2 (1.4%) 0.630 (0.124, 3.200) 0.577 Median duration of recent jail or prison time (days) 20.5 14 1.005 (1.00, 1.011) 0.050 1.002 (0.997, 1.008) 0.437 Homeless, past 6 months 175 (61.2%) 85 (59.4%) 0.928 (0.613, 1.404) 0.724 Drug class of choice Opioids 150 (52.5%) 96 (67.1%) Ref Fentanyl 11 (3.9%) 4 (2.8%) 1.057 (0.320, 3.49) 0.927 Methamphetamine 89 (31.1%) 29 (20.3%) 0.851 (0.428, 1.690) 0.644 Cocaine 15 (5.2%) 8 (5.6%) 1.655 (0.652, 4.198) 0.289 Benzos 1 (0.4%) 0 - - Other 10 (3.5%) 3 (2.1%) 0.859 (0.226, 3.26) 0.824 MOUD 10 (3.5%) 3 (2.1%) 0.859 (0.226, 3.26) 0.824 HIV co-infection 2 (0.7%) 3 (2.1%) 6.110 (0.630, 59.289) 0.119 Health Insurance 219 (76.7%) 111 (77.6%) 1.068 (0.645, 1.769) 0.798 IDU Frequency Daily or more 200 (74.6%) 97 (72.4%) Ref More than weekly but no daily 26 (9.7%) 14 (10.5%) 0.983 (0.431, 2.244) 0.968 Weekly 17 (6.3%) 9 (6.7%) 0.964 (0.380, 2.444 0.939 Monthly 25 (9.3%) 14 (10.5%) 1.047 (0.458, 2.395) 0.913 Income Source Illegal 77 (26.9%) 56 (39.2%) 1.747 (1.142, 2.674) 0.010 1.444 (0.887, 2.351) 0.140 Legal 169 (59.1%) 96 (67.1%) 1.414 (0.928, 2.155) 0.107 Income assistance 82 (28.7%) 23 (16.1%) 0.477 (0.285, 0.798) 0.005 0.544 (0.304, 0.972) 0.040 Source of needles SSP/NEP 103 (38.0%) 54 (40.6%) Ref Pharmacy 60 (22.1%) 34 (25.6%) 1.36 (0.789, 2.348) 0.270 Someone else who got them from an SSP/NEP 33 (12.2%) 22 (16.5%) 1.579 (0.848, 2.940) 0.150 Drug dealer 23 (8.5%) 4 (3.0%) 0.337 (0.112, 1.007) 0.052 0.341 (0.112, 1.038) 0.058 Spouse, partner or relative, friend 49 (18.1%) 18 (13.5%) 0.716 (0.385, 1.334) 0.294 Other 3 (1.1%) 1 (0.8%) 0.705 (0.722, 6.888) 0.764 HCV Subtype 1a 84 (58.7%) 139 (62.6%) Ref 1b 4 (2.8%) 7 (2.5%) 1.143 (0.77, 16.947) 0.923 2a 2 (1.4%) 1 (0.4%) 4.000 (0.134, 119.230) 0.423 2b 7 (4.9%) 23 (8.0%) 0.609 (0.048, 7.758) 0.702 3a 45 (31.5%) 74 (25.9%) 1.216 (0.107, 13.799) 0.874 Additional Declarations There is NO Competing Interest. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6810633","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":467185134,"identity":"ea807e62-e31c-4a07-8275-443dcbddf272","order_by":0,"name":"Damien 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Harvard","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Bean","suffix":""},{"id":467185136,"identity":"ff3e2d9a-e5aa-4142-8c29-801b3e7f86fd","order_by":2,"name":"Jacklyn Sarette","email":"","orcid":"","institution":"Ragon Institute of MGH, MIT and Harvard","correspondingAuthor":false,"prefix":"","firstName":"Jacklyn","middleName":"","lastName":"Sarette","suffix":""},{"id":467185137,"identity":"9bdd4246-ead6-4887-8b54-f5ee8399e41a","order_by":3,"name":"Thang Long Ngo","email":"","orcid":"","institution":"Ragon Institute of MGH, MIT and Harvard","correspondingAuthor":false,"prefix":"","firstName":"Thang","middleName":"Long","lastName":"Ngo","suffix":""},{"id":467185138,"identity":"f7576852-34db-423c-8735-50d11add25ac","order_by":4,"name":"Karen Power","email":"","orcid":"","institution":"Ragon Institute of MGH, MIT and Harvard","correspondingAuthor":false,"prefix":"","firstName":"Karen","middleName":"","lastName":"Power","suffix":""},{"id":467185139,"identity":"044d4a36-3255-488a-aea8-bd9d48764c3c","order_by":5,"name":"Daniel Brook","email":"","orcid":"","institution":"Ohio State University","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Brook","suffix":""},{"id":467185140,"identity":"c836424f-fde1-4ef5-9c85-47a97f96c074","order_by":6,"name":"Hannah Cooper","email":"","orcid":"","institution":"Rollins School of Public Health, Emory University","correspondingAuthor":false,"prefix":"","firstName":"Hannah","middleName":"","lastName":"Cooper","suffix":""},{"id":467185141,"identity":"86323bac-3f22-4565-b5f7-e0de9a3cbcd3","order_by":7,"name":"Judith Feinberg","email":"","orcid":"","institution":"West Virginia 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Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"","lastName":"Havens","suffix":""},{"id":467185145,"identity":"9f94dafa-a227-4e06-8663-881b71aaeebd","order_by":11,"name":"Shanna Babalonis","email":"","orcid":"","institution":"University of Kentucky College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shanna","middleName":"","lastName":"Babalonis","suffix":""},{"id":467185146,"identity":"1ae65dd8-b943-408c-ba49-5b2359b1ac80","order_by":12,"name":"Christopher Hurt","email":"","orcid":"","institution":"University of North Carolina at Chapel Hill","correspondingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"","lastName":"Hurt","suffix":""},{"id":467185147,"identity":"38ebcf89-af78-43bf-b55d-d0e1e967c5bc","order_by":13,"name":"Wiley Jenkins","email":"","orcid":"","institution":"Clemson University","correspondingAuthor":false,"prefix":"","firstName":"Wiley","middleName":"","lastName":"Jenkins","suffix":""},{"id":467185148,"identity":"d47e2a0c-40ea-43e4-83b3-80e22f5bcb65","order_by":14,"name":"Todd Korthuis","email":"","orcid":"","institution":"Oregon Health \u0026 Science University","correspondingAuthor":false,"prefix":"","firstName":"Todd","middleName":"","lastName":"Korthuis","suffix":""},{"id":467185149,"identity":"7cec9dd6-f3ac-462b-9347-2df224222b3c","order_by":15,"name":"William Miller","email":"","orcid":"https://orcid.org/0000-0002-1934-7827","institution":"Ohio State University","correspondingAuthor":false,"prefix":"","firstName":"William","middleName":"","lastName":"Miller","suffix":""},{"id":467185150,"identity":"6a71cff6-a866-4b3e-8d82-597cbb77f42f","order_by":16,"name":"Mai Pho","email":"","orcid":"","institution":"University of Chicago","correspondingAuthor":false,"prefix":"","firstName":"Mai","middleName":"","lastName":"Pho","suffix":""},{"id":467185151,"identity":"bcb53566-1661-4bee-a40a-58800e448f1d","order_by":17,"name":"Gordan Smith","email":"","orcid":"","institution":"West Virginia University","correspondingAuthor":false,"prefix":"","firstName":"Gordan","middleName":"","lastName":"Smith","suffix":""},{"id":467185152,"identity":"e9044438-4284-4917-919b-f29741cc1850","order_by":18,"name":"Thomas Stopka","email":"","orcid":"","institution":"Tufts University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Stopka","suffix":""},{"id":467185153,"identity":"e8f6ba61-bde1-4184-8fb7-9e0d7d9046ed","order_by":19,"name":"Judith Tsui","email":"","orcid":"","institution":"University of Washington","correspondingAuthor":false,"prefix":"","firstName":"Judith","middleName":"","lastName":"Tsui","suffix":""},{"id":467185154,"identity":"19bb9796-12cd-4928-aa4d-755854a1cf37","order_by":20,"name":"Sarah Mixson","email":"","orcid":"","institution":"University of Washington","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Mixson","suffix":""},{"id":467185155,"identity":"934d70fb-b248-4e77-ba3b-e2a417e9d2fa","order_by":21,"name":"Ryan Westergaard","email":"","orcid":"","institution":"University of Wisconsin-Madison","correspondingAuthor":false,"prefix":"","firstName":"Ryan","middleName":"","lastName":"Westergaard","suffix":""},{"id":467185156,"identity":"faf4b04e-e9aa-4137-ab24-6e018ec01414","order_by":22,"name":"April Young","email":"","orcid":"","institution":"University of Kentucky","correspondingAuthor":false,"prefix":"","firstName":"April","middleName":"","lastName":"Young","suffix":""},{"id":467185157,"identity":"54d1fe95-fc48-4c02-af3d-595aac04825c","order_by":23,"name":"Todd Allen","email":"","orcid":"","institution":"Ragon Institute of MGH, MIT and Harvard","correspondingAuthor":false,"prefix":"","firstName":"Todd","middleName":"","lastName":"Allen","suffix":""}],"badges":[],"createdAt":"2025-06-03 11:15:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6810633/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6810633/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-025-66934-y","type":"published","date":"2025-12-02T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84317924,"identity":"97292d02-be30-40f8-b0bb-7b33a0fc0c5b","added_by":"auto","created_at":"2025-06-10 13:47:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":175710,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSampling overview and genotype distribution of HCV from study sites across the United States. (A)\u003c/strong\u003e Geographical representation of study sampling sites \u003cstrong\u003e(B)\u003c/strong\u003eSampling dates of blood specimen collection for different states. X-axis represents the sampling date and the y-axis lists the sampling locations.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6810633/v1/f573831e533bff8174a6e050.png"},{"id":84317923,"identity":"fba85ec8-d2bb-4198-864a-42161073a74a","added_by":"auto","created_at":"2025-06-10 13:47:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":127094,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of HCV genotypes across all study sites\u003c/strong\u003e. \u003cstrong\u003e(A)\u003c/strong\u003e Overall composition of genotypes (n = 692) \u003cstrong\u003e(B)\u003c/strong\u003eBreakdown of the 4.77% mixed infections by genotype (n = 33).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6810633/v1/8b9d155db870f6aaccdd7850.png"},{"id":84319620,"identity":"97acd0b5-4ffc-4d36-a4c6-dc957acc493f","added_by":"auto","created_at":"2025-06-10 14:03:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":105534,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhylogenetic relationships between samples from individuals with HCV collected from the ROI, 2018 – 2021. \u003c/strong\u003eMaximum likelihood phylogenetic tree of consensus HCV sequences collected from the ROI cohort. The tree was rooted on a genotype 7 reference sequence. Genotypes are labelled and sampling sites are shown by different colored tips.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6810633/v1/10c2dbd2d7a207fe1eddea6d.png"},{"id":84319179,"identity":"f525711a-c09d-40d8-bec6-a8b49228d795","added_by":"auto","created_at":"2025-06-10 13:55:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":198921,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHCV transmission networks as identified by GHOST, 2018 - 2021. \u003c/strong\u003eEach node represents an HCV strain sampled from an individual study participant. A connecting line between nodes is drawn if the genetic distance between the strains is less than 0.037. Study sites are labelled, and color coded as illustrated.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6810633/v1/ea9aff4f456bf63f32a8cf3c.png"},{"id":84319622,"identity":"0de669fa-1bdc-4851-a933-007412482e43","added_by":"auto","created_at":"2025-06-10 14:03:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":87979,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between intra-host viral diversity and clustering illustrates that more recent infections may be driving transmission clusters. (A)\u003c/strong\u003e Comparison of intra-host viral diversity between individuals who are part of a cluster and those who are not. \u003cstrong\u003e(B) \u003c/strong\u003eIntra-host viral diversity across different study sites\u003cstrong\u003e (C) \u003c/strong\u003eClustered individuals segregated into cluster size either dyads or more complex clusters (\u0026gt;2 individuals).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6810633/v1/8ab6ae133e2369f59a7a4ee6.png"},{"id":84319177,"identity":"116ae032-e722-4a90-8bb8-39135c760bb4","added_by":"auto","created_at":"2025-06-10 13:55:26","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":103982,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTimescale of persistence of each detected transmission cluster by study site. \u003c/strong\u003eEach horizontal line represents a genomic cluster of HCV sequences showing the timespan from the inferred introduction time of the cluster to the latest sampled sequence. Clusters are grouped by study sites (Wisconsin, Oregon, Ohio, North Carolina, New England and Kentucky) and separated by dotted horizontal lines and labeled along the vertical axis. The x-axis indicated calendar year, allowing for a comparison of persistence times across states and over time. Note that some clusters may not be present as this only represents those clusters found using a timescaled phylogenetic tree of the consensus sequence data. Other clusters such as minor variant clusters detected from the analyses of deep sequencing data from GHOST are not depicted here.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6810633/v1/3003646c7e9193d67997f3b2.png"},{"id":99866216,"identity":"3a64a96a-5d45-4577-8d52-80745d85dbee","added_by":"auto","created_at":"2026-01-09 08:10:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3535076,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6810633/v1/65a8a6b4-30de-46ca-925b-144b4ebe851e.pdf"},{"id":84317929,"identity":"4d0f6be5-6f40-4be7-97ed-6a7a9d218721","added_by":"auto","created_at":"2025-06-10 13:47:26","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":463501,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-6810633/v1/e36d61ac744c59ea3d6cbdce.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Genomic surveillance uncovers regional variation in HCV transmission networks among people who use drugs in rural U.S. communities","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatitis C presents a significant global health challenge, affecting an estimated 58\u0026nbsp;million individuals who are chronically infected with the hepatitis C virus (HCV) \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Notably, out of the estimated 11\u0026nbsp;million people worldwide who engage in injecting drug use each year, nearly 40% have viremic HCV infection \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The transmission of HCV is greatly influenced by unsafe injecting practices among persons who use drugs (PWUD) making it a substantial contributor to new infections on a global scale \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Within the United States, HCV is the leading cause of liver cancer and death from liver disease. The incidence rate of acute hepatitis C in the US has doubled since 2014 (129% increase) and increased 7% from 2020 to 2021 \u003csup\u003e4\u003c/sup\u003e. Rates of acute hepatitis C are highest among males, persons aged 20\u0026ndash;39 years and those living in the Eastern and Southeastern states \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Central to this increase is the increased frequency of reported injection drug use concurrent with the rise of prescription opioids and increasing availability of heroin/fentanyl and methamphetamine \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRural areas bear a disproportionate burden of HCV, where infection rates are estimated to be twice as high as in urban settings \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. These outbreaks often occur in communities facing structural barriers such as lower education and income levels, as well as limited access to healthcare. Additionally, rural populations in the US frequently lack essential harm reduction services like sterile syringe services programs and medication for opioid use disorder and often experience housing instability, which may exacerbate vulnerability to drug-related harms \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Recent studies reveal a significant prevalence of HCV among young adult PWUD in rural areas, particularly those engaging in polysubstance injection \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Recent outbreaks among persons who inject drugs in Scott County, Indiana \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, Lowell, Massachusetts \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e and Kanawha County, West Virginia \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e highlight a concerning trend with high rates of HCV infections often preceding HIV outbreaks among persons who inject drugs. This temporal association underscores the urgent need for proactive measures and interventions in rural communities to prevent the emergence of HIV outbreaks.\u003c/p\u003e \u003cp\u003eIn response to the opioid crisis affecting rural areas of the US, a collaborative effort involving several agencies, including the National Institute on Drug Abuse (NIDA), the Centers for Disease Control and Prevention (CDC), the Substance Abuse and Mental Health Administration (SAMHSA), and the Appalachian Regional Commission (ARC), led to the establishment of the Rural Opioid Initiative (ROI). This initiative aimed to collect and synthesize both quantitative and qualitative data from eight rural regions across ten states, with the goal of deepening our understanding of drug use and the local factors driving opioid consumption, as outlined by Jenkins et al. \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAs part of this initiative blood specimens were collected for the purpose of conducting rapid HIV, HCV and syphilis testing. HCV positive specimens were subsequently analyzed at a laboratory funded by the ROI for next-generation sequencing and linkage analysis to identify genetically associated transmissions. Drawing a parallel with the pandemic of COVID-19, genomic surveillance has been crucial in tracking the global spread of SARS-CoV-2, with real-time analysis forming a cornerstone for public health decision-making. However, similar genomic surveillance strategies, including genetic-based inferences, have not been routinely employed to investigate HCV transmission, except in specific community-based PWUD cohorts in Baltimore \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, San Francisco \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, and correctional facilities in Wisconsin \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Due to the scarcity of both genomic and epidemiological data, our knowledge about the origins of these outbreaks, the dynamics of virus transmission and the genetic diversity of HCV strains in rural US communities remains limited. The primary aim of this study was to characterize HCV strains circulating among PWUD in rural US areas and identify factors associated with HCV transmission clusters. To date, there are no studies surveying the genetic landscape of HCV across rural communities adversely affected by the US opioid crisis and whether the transmission patterns differ across geographic locations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants\u003c/h2\u003e \u003cp\u003e A cross-sectional survey of people who used drugs in rural counties with high overdose rates from ten U.S. states and 66 U.S counties (Illinois, Kentucky, North Carolina, New England [Massachusetts, New Hampshire, and Vermont], Ohio, Oregon, West Virginia and Wisconsin) was conducted and herein referred to as the Rural Opioid Initiative (ROI). Additional details on the ROI consortium have been previously published \u003csup\u003e \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e \u003c/sup\u003e. Kentucky samples were largely received from another study termed Social Networks Among Appalachian People (SNAP), but participants were recruited in the same manner and timeframe. Study participants were recruited between January 2018 and December 2021. Individuals were eligible for inclusion if they lived in the study area, reported any past 30-day injection drug use and/or noninjecting opioid use \u0026ldquo;to get high\u0026rdquo; (heroin, prescription pain medication). Inclusion criterion for all sites was age\u0026thinsp;\u0026ge;\u0026thinsp;18 years except two states (Illinois, Wisconsin) where the age criterion was \u0026ge;\u0026thinsp;15 years. All sites conducted recruitment using respondent-driven sampling to facilitate sampling of hard-to-reach populations. Each study site identified \u0026ldquo;seed\u0026rdquo; participants to initiate recruitment chains. Seeds were recruited from syringe service programs, local health departments and community outreach to represent the general demographic characteristics of the local eligible population. Seeds recruited up to six members of their drug use network. Each referred participant recruited their network peers similarly with the goal of maximizing recruitment chains. Participants received \u003cspan\u003e$\u003c/span\u003e10 to \u003cspan\u003e$\u003c/span\u003e20 per successfully enrolled peer and \u003cspan\u003e$\u003c/span\u003e40 to \u003cspan\u003e$\u003c/span\u003e60 for completion of study procedures. A complete evaluation of respondent-driven sampling in this setting has been published elsewhere \u003csup\u003e \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e \u003c/sup\u003e. Blood specimens were collected for rapid HIV, HCV and syphilis testing at all sites. Specimens that were positive for HCV antibodies were shipped to the GHOST (Global Hepatitis Outbreak Surveillance Technology) Sequencing Center at the Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard for RNA testing and viral genomic analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNucleic acid extraction and PCR Amplification\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eNucleic acid extraction and PCR Amplification\u003c/div\u003e \u003cp\u003eRNA was isolated from 140 \u0026micro;l of plasma using the QIAamp Viral RNA Mini Kit (Qiagen, Hilden, Germany). A one step RT-PCR reaction was performed on all samples to amplify a segment at the E1/E2 junction of the HCV genome, which contains the hypervariable region 1 (HVR1) due to its high variability and its ability to reliably detect transmission events in outbreak settings \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The first round RT-PCR consisted of an Illumina adapter specific portion, a sample specific barcode segment, and an HCV HVR specific primer segment, F1- GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT-NNNNNNNNNN-GGA-TAT-GAT-GAT-GAA-CTG-GT and R1-ACA-CTC-TTT-CCC-TAC-ACG-ACG-CTC-TTC-CGA-TCT-NNNNNNNNNN-ATG-TGC-CAG-CTG-CCG-TTG-GTG-T at a final concentration of 4 pM amplified using Superscript III RT/Platinum Taq DNA Polymerase High Fidelity with the following conditions: cDNA synthesis for 30 minutes at 55\u0026deg;C, followed by heat denaturation at 95\u0026deg;C for 2 minutes, the PCR amplification conditions were 40 cycles of denaturation (94\u0026deg;C for 10 seconds), annealing (55\u0026deg;C for 10 seconds) and extension (68\u0026deg;C for 10 seconds) with a final extension at 68\u0026deg;C for 5 minutes. Amplified products were run on a 1% agarose gel and either PCR purified with the QIAquick PCR purification kit (Qiagen), or gel extracted and purified using the PureLink quick gel extraction kit (Invitrogen). A second round limited cycle PCR (94\u0026deg;C for 2 minutes, (94\u003csup\u003eo\u003c/sup\u003eC for 15 sec; 55\u003csup\u003eo\u003c/sup\u003eC for 30 sec; 68\u003csup\u003eo\u003c/sup\u003eC for 30 sec) x 8 cycles, 68\u003csup\u003eo\u003c/sup\u003eC for 5 minutes) is performed to add barcode specific indexes and sequencing specific adapters and primers to each sample to allow for multiplexing as well as internal controls for cross-contamination. Negative controls were introduced at each stage of the procedure and all PCR procedures were performed under PCR clean room conditions using established protocols. Indexed samples are 0.7X SPRI purified two times to remove excess primer dimer and short fragments that can interfere with the sequencing process. To avoid contamination all reagents were pre-aliquoted with dedicated equipment ensuring physical separation of sample processing from pre- and post-PCR amplification steps including deep sequencing.\u003c/p\u003e\n\u003ch3\u003eIllumina deep sequencing\u003c/h3\u003e\n\u003cp\u003ePCR amplicons were quantified using the Picogreen kit (Invitrogen, Carlsbad, CA) on a Fluorometer ST (Promega, Madison, WI) with the integrity of the fragment evaluated using a Bioanalyzer 2100 (Agilent, Santa Clara, CA). Samples were pooled and sequenced on an Illumina MiSeq platform using a 2 x 250 bp V2 Nano reagent kit. In general, a sequence library consisted of between 8\u0026ndash;16 specimens including one negative control for every 7 serum specimens.\u003c/p\u003e\n\u003ch3\u003eDeep sequencing data analysis\u003c/h3\u003e\n\u003cp\u003eSequencing reads were automatically de-multiplexed and duplicate reads were removed using fastuniq v1.1\u003csup\u003e21\u003c/sup\u003e to limit the influence of PCR artifacts and subsequently quality trimmed using trimmomatic v0.36\u003csup\u003e22\u003c/sup\u003e if sequencing adapters or low-quality bases (Phred scores\u0026thinsp;\u0026lt;\u0026thinsp;20) were detected. Vicuna v1.1, a \u003cem\u003ede novo\u003c/em\u003e consensus assembly algorithm \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, was used to generate consensus assemblies from genetically heterogeneous populations with automated computational finishing and annotation of \u003cem\u003ede novo\u003c/em\u003e viral assemblies performed using V-FAT v1.1 as previously performed \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. All consensus assemblies were cross checked with the iVar pipeline\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e and any differences were further inspected. Lastly, intra-host single nucleotide variants were identified with V-Phaser 2 \u003csup\u003e26\u003c/sup\u003e. Instrain was used to assess the genomic nucleotide diversity (π) based on all reads and calculated as the average number of nucleotide differences per base pair \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eHCV Genotyping\u003c/h3\u003e\n\u003cp\u003eAll \u003cem\u003ede novo\u003c/em\u003e consensus sequences were classified using the method implemented in the Genome Detective virus tool for phylogenetic genotyping \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. To detect and quantify the presence of mixed HCV genotypes in Illumina MiSeq sequencing reads paired end reads were mapped using bowtie2 to a set of HCV reference genome sequences (n\u0026thinsp;=\u0026thinsp;571). The absolute number of reads that mapped to a reference genome of the given HCV genotype was then counted and quantified. The criteria for identifying mixed genotype infections were that it had to be at a frequency of 1% or greater and have at least 200 reads mapped to it. This cutoff was seen as a conservative threshold to exclude cross-contamination between samples.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePhylogenetic reconstruction\u003c/h2\u003e \u003cp\u003eAll \u003cem\u003ede novo\u003c/em\u003e consensus sequences were aligned using MAFFT v7.470 \u003csup\u003e29\u003c/sup\u003e and IQ-TREE v 2.1\u003csup\u003e30\u003c/sup\u003e was used to construct a maximum likelihood phylogenetic tree employing the best-fit model of nucleotide substitution according to the Bayesian Information Criterion (BIC) as indicated by the Model Finder application implemented in IQ-TREE \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Statistical robustness of individual nodes was determined using 1000 ultrafast bootstrap replicates \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGHOST analysis\u003c/h3\u003e\n\u003cp\u003eGlobal Hepatitis Outbreak and Surveillance Technology (GHOST) detects and visualizes transmission clusters using deep sequencing data from the hypervariable region of the HCV genome. Paired-end reads for each successful sequenced sample were uploaded to the GHOST server where the data were subjected to quality control before being analyzed for the presence of transmission links using hamming distance. Two cases were considered linked by transmission if the distance between them was less than the empirically defined threshold value of 0.037. Further details on GHOST have been published by Longmire et al \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eEstimation of time of the most recent ancestor\u003c/h3\u003e\n\u003cp\u003eTo characterize the time of the most recent ancestor (tMRCA) of transmission clusters we used the phylogenetic framework as implemented in the Nextstrain pipeline \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. As our range of sample dates were not large or wide enough, we did not have sufficient temporal signal as measured by the coefficient of the root-to-tip regression method. In line with prior HCV studies, we employed an independent dataset with significant temporal information to provide the substitution rates of the genomic region of interest \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The SRD06 nucleotide partitioned-substitution model, an uncorrelated relaxed lognormal molecular clock and a Bayesian skyline coalescent model with 10 groups was employed using BEAST version 1.10.4\u003csup\u003e36\u003c/sup\u003e from which we obtained rate estimates for the precise subgenomic region sequenced in this analysis. The Markov chain Monte Carlo chains (MCMCs) were run for 500\u0026nbsp;million generations and sampled regularly to yield a posterior tree distribution based upon 10,000 estimates. The mean rate of evolution and standard deviation of the estimated rate of evolution were then used in the Nextstrain workflow to infer the time scale of HCV clusters.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analyses\u003c/h2\u003e \u003cp\u003eDescriptive analyses were performed to examine the factors associated with being in a cluster or not using Chi-Squared, Fisher\u0026rsquo;s exact and Kruskal-Wallis test as appropriate. Logistic regression analyses were used to identify factors associated with being in a dyad or cluster (yes/no). Univariate logistic analysis was first performed, and variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.10 were selected for multivariable logistic regression analysis. All tests were two-tailed and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All analyses were performed using in Python and Stata software (version 18.0; StataCorp, College Station, Texas, USA).\u003c/p\u003e \u003c/div\u003e \u003cp\u003e\u003cstrong\u003eData availability \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available upon reasonable request, but restrictions apply to the availability of these data. Written permission is required, and a data use agreement will need to be implemented before any sequence data are shared for research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval.\u0026nbsp;\u003c/strong\u003eAll study procedures were approved by the Institutional Review Board at each site and study protocols and procedures were reviewed and approved by the Institutional Review Board of Massachusetts General Hospital.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy participants\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 3,084 PWUD completed audio computer-assisted self-interviews (ACASIs) or computer-assisted self-interviews (CASIs). Participants had a mean age of 34 years [IQR: 28-43]) and 42% of respondent’s were female. From each study site, a sub-sample of respondents were selected to give a plasma sample for further HCV sequencing and cluster analysis. A total of 1,201 HCV positive serum specimens were received from eight study sites (\u003cstrong\u003eFigure 1\u003c/strong\u003e). Of these 692 (57.7%) successfully completed sequencing and quality control while 293 (24.4%) samples were found to be below our limit of detection and contained little if any viral RNA. The specimens may have represented HCV antibody positive status but viral load negative samples from those who had cleared infection. Two hundred and sixteen samples (18.0%) failed PCR or did not generate adequate sequencing results for inclusion. The PCR failure rate varied depending on the sampling site and ranged from 3.4% for Wisconsin to 46.2% in Illinois (\u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eParticipant characteristics among those with available HCV sequencing and completed questionnaires (n = 429) are shown in \u003cstrong\u003eTable 1\u003c/strong\u003e. Overall, the median age was 35 years [IQR: 29-43] and 64% were male and mostly non-Hispanic white with a high school education. Sixty-one percent had experienced homelessness in the past 6-months and most participants were recruited into the study by a friend, associate, or acquaintance. No significant differences were observed between the sequenced samples and those that failed PCR amplification. Therefore, the characteristics of the sequenced subset is representative of the entire ROI cohort (\u003cstrong\u003eTable 1\u003c/strong\u003e). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMost study participants reported using opioids (64%) followed by stimulants (33%) as their drugs of choice with a substantial proportion simultaneously injecting opioids and stimulants (Table 2). Median age at first injection drug use experience was 21 years [IQR: 18 – 32]. \u0026nbsp;Eighty-two percent of participants reported accessing treatment for addiction, including inpatient/outpatient treatment and medication for opioid use disorder, with 77% reported health insurance or health care coverage. Most participants reported daily or more frequent injection drug use and received their syringes/needles from various sources including pharmacies, syringe services programs, friends/acquaintances, and drug dealers/street. Most participants were tested for HIV (77%) and HCV (75%). \u0026nbsp;Only 3% were diagnosed with HIV, compared to 69% with HCV. Almost one-fifth (19%) cleared HCV infection with treatment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHCV genotype distribution\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy utilizing Genome Detective tools for genotyping, we assigned consensus sequences to various HCV genotypes and subtypes as illustrated in \u003cstrong\u003eFigure 2\u003c/strong\u003e. Out of the 692 sequences analyzed, 65% were classified as genotype 1, 23% as genotype 3, 7% as genotype 2 and one sequence was identified as genotype 4 (\u003cstrong\u003eFigure 2A\u003c/strong\u003e). Our deep sequencing approach uncovered evidence of mixed infections in approximately 4.8% of cases (\u003cstrong\u003eFigure 2A\u003c/strong\u003e). Further examination of these mixed infections revealed that the most prevalent combinations were genotype 1a and 3a, comprising 73% followed by genotype 1a/2b at 15% (\u003cstrong\u003eFigure 2B\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eHCV genotype frequencies remained largely consistent among study sites with subtype 1a being the predominant strain (46% to 71%), followed by 3a (18% to 30%) and 2b (2% to 12%) (\u003cstrong\u003eSupplementary Figure 1\u003c/strong\u003e). Although some variations in the prevalence of genotypes and subtypes were observed between sites, Kentucky and New England exhibited the highest variability. In Kentucky, we identified five different circulating genotypes, including a subtype 4a sample, and several low-prevalence mixed genotype infections (genotype 1a/4a, 2b/3a, 1a/2b) (\u003cstrong\u003eSupplementary Figure 1A\u003c/strong\u003e). Similarly, New England displayed a range of circulating strains, with three subtype 2a samples and a higher frequency of subtype 2b samples compared to other sites. Despite the diversity of subtypes in circulation, only 2% of samples were found to be mixed infections (\u003cstrong\u003eSupplementary Figure 1B\u003c/strong\u003e). Wisconsin showed relative homogeneity with only genotypes 1a, 3a and 2b detected while mixed infections were exclusively 3a/1a (\u003cstrong\u003eSupplementary Figure 1C\u003c/strong\u003e). The remaining sites, Ohio, Oregon and North Carolina all exhibited a predominance of genotypes 1a and 3a with a similar frequency of genotype 2b followed by a range of mixed genotype infections (\u003cstrong\u003eSupplementary Figure 1D-F\u003c/strong\u003e). \u0026nbsp;In Oregon, a rare case of infection was found with a major population of genotype 3a and minor populations of genotypes 1a and 2b.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIdentification of transmission clusters\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePhylogenetic analysis of consensus sequences indicates that study site specific sequences are interspersed throughout the tree although some sequences did appear to cluster by geographic location (\u003cstrong\u003eFigure 3\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;GHOST analysis of intra-host HCV HVR1 population from all sampled cases identified 85 transmission clusters involving 204 HCV strains (29.5%) (\u003cstrong\u003eFigure 4\u003c/strong\u003e). The median cluster size was two members (range 2 - 7) with 63% in dyads. The fraction of clusters HCV strains was statistically different according to study sites (\u003cem\u003eP \u0026lt; 0.0001\u003c/em\u003e; Fisher’s exact test) and ranged from sparse clustering in Ohio (9.4%) to almost half of all sequenced strains in New England (43%). \u0026nbsp;Two study sites (Ohio and Oregon) exclusively consisted of dyads while North Carolina had a single cluster of three. More complex networks were observed in Wisconsin, Kentucky and New England although they were limited. The largest cluster of PWUD was found in New England and consisted of a network of 7.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eComparison of identified clusters to RDS recruitment chains\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcross all study sites we found that 49% of transmission clusters were comprised of individuals linked within the RDS social recruitment chains. However, there was notable variability among the sampling sites in their ability to identify transmission networks using RDS recruitment. In Ohio and Oregon, where only dyadic genetic relationships were found, 50% and 67% of individuals, respectively, were not linked in the RDS chains. In Wisconsin, more complex networks were identified with 60% found to be outside the RDS chains. Conversely, in New England and North Carolina, 55% and 77% of transmission clusters, respectively, were linked within the social recruitment chains. Overall, our observations indicated that there is a statistically significant (\u003cem\u003eP = 0.0001\u003c/em\u003e) association between transmission cluster size and whether they are found inside or outside of the RDS chain. Specifically, individuals in more complex networks were more likely to be found in clusters that included members of their RDS chain compared to those in genetic dyads (70% vs. 36%, \u003cstrong\u003eSupplementary Figure 2\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRelationship between intra-host viral diversity and clustering\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe level of genomic diversity in HCV varies significantly depending on the infection stage. During the acute and early stages, the viral population tends to be relatively homogeneous, primarily due to serial bottlenecks and the presence of a single founder virus. As the infection progresses, HCV undergoes increased genomic diversification as it adapts to the host's immune response, leading to a positive correlation between the stage of infection and intra-host viral diversity. Analysis of intra-host viral diversity across all sampled individuals indicates that those who clustered exhibited significantly lower diversity compared to non-clustered individuals (\u003cstrong\u003eFigure 5A\u003c/strong\u003e; \u003cem\u003eP= 0.0038,\u0026nbsp;\u003c/em\u003eMann Whitney test). Furthermore, the study revealed substantial variability in intra-host viral diversity among different study sites. Ohio and Kentucky (KY) showed the highest median diversity, whereas North Carolina displayed less heterogeneity. Despite a similar distribution of intra-host viral diversity between KY and New England, KY had a significantly higher median (\u003cstrong\u003eFigure 5B\u003c/strong\u003e; \u003cem\u003eP= 0.0002,\u0026nbsp;\u003c/em\u003eMann Whitney test) a trend also observed when comparing KY with Wisconsin (WI) and NC (\u003cstrong\u003eFigure 5B\u003c/strong\u003e). Further stratification of clustered individuals into dyads and more complex clusters (i.e., clusters with more than two members) demonstrated a significant difference in intra-host viral diversity between individuals not in a cluster and those in dyads (\u003cstrong\u003eFigure 5C\u003c/strong\u003e; \u003cem\u003eP= 0.0161,\u0026nbsp;\u003c/em\u003eMann Whitney test) and a marginally non-significant difference for those in complex clusters (\u003cem\u003eP= 0.0506,\u0026nbsp;\u003c/em\u003eMann Whitney test). However, no significant difference was observed between dyads and more complex clusters (P=0.9269, Mann-Whitney test). Taken together, this suggests that those participants not found to be in a transmission cluster may have an infection reminiscent of a longer timeframe (i.e. more chronic like stage of infection) compared to those within clusters who appear to be harboring less diversity which is a known attribute of the early stages of infection. Moreover, the knowledge that different study sites have different levels of intra-host viral diversity from sequenced participants most likely reflects the underlying infection dynamics within that sampled population.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEvidence of HCV persistence across rural study sites\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe rate of evolution for the genomic region used in this study was estimated to be 3.417 x 10\u003csup\u003e-3\u0026nbsp;\u003c/sup\u003e(95% highest posterior density credibility intervals: 2.177 to 4.657 x 10\u003csup\u003e-3\u003c/sup\u003e) substitutions per site per year. \u0026nbsp;Using this rate, we estimated the tMRCA for each cluster and examined the lag time between inferred introduction date and time of first and last sampling date of the cluster (\u003cstrong\u003eFig. 6\u003c/strong\u003e). The estimated tMRCA varied between clusters and sampling sites and the size of the cluster. Across all states, Oregon had the shortest median lag time of 3.61 years, followed by Kentucky (4.15 years), North Carolina (4.80 years), New England (5.74 years), Wisconsin (6.74 years) while the virus persisted longer in Ohio at 8.75 years. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn WI, one cluster had the longest persistence time of approximately 12.91 years while two clusters have the shortest lag times of less than a year (\u003cstrong\u003eFig 6\u003c/strong\u003e). In New England, a cluster comprising a dyad had the longest persistence time of 19.08 years while two dyad clusters had the shortest tMRCA until sampling of less than 6 months (\u003cstrong\u003eFig 6\u003c/strong\u003e). In North Carolina the tMRCA was estimated for four clusters with two having a tMRCA within 3 years while the remaining clusters had a persistence time of over 7 and 12 years respectively (\u003cstrong\u003eFig 6\u003c/strong\u003e). Within KY a cluster comprising 3 participants was found to be persisting for approximately 13 years (tMRCA mid-2005) before this cluster was sampled in 2019. In comparison, a dyad cluster was found to have the shortest lag time within the inferred introduction mirroring the time of sampling (\u003cstrong\u003eFig 6\u003c/strong\u003e). Three transmission clusters were detected in OH where only once was estimated to have a more recent introduction occurring an estimated 1.7 years prior to sampling (\u003cstrong\u003eFig 6\u003c/strong\u003e). The remaining two clusters had long-term persistence with evidence of at least almost two decades of local persistence between the estimated time of introduction and the most recent sampling. In OR, most clusters were estimated to have occurred more recently (within 3 years) but two clusters showed at least a decade of persistence (\u003cstrong\u003eFig 6\u003c/strong\u003e). Complex clusters, although exhibiting a higher median persistence time of 6.2 years compared to dyads (4.2), did not show a statistically significant difference (\u003cem\u003eP= 0.1359,\u0026nbsp;\u003c/em\u003eMann Whitney test).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFactors associated with transmission clusters\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn unadjusted logistic regression analyses, membership in a cluster was associated with younger age (21% vs. 30%; OR = 2.54 [95% CI: 1.41 – 4.60], \u003cem\u003eP =\u0026nbsp;\u003c/em\u003e0.002; \u003cstrong\u003eTable 3\u003c/strong\u003e), being recruited by a partner, spouse, boyfriend or girlfriend (10% vs. 19%; OR = 2.07 [95% CI: 1.17 – 3.67], \u003cem\u003eP =\u0026nbsp;\u003c/em\u003e0.013; \u003cstrong\u003eTable 3\u003c/strong\u003e), and having an illegal source of income (e.g. selling drugs, selling sex and theft) (27% vs. 39%; OR = 1.75 [95% CI: 1.14 – 2.67], \u003cem\u003eP =\u0026nbsp;\u003c/em\u003e0.010; \u003cstrong\u003eTable 3\u003c/strong\u003e). A borderline significant association was found with those who have shorter incarcerated period and clustering (21 vs. 14 days; OR = 1.01 [95% CI: 1 – 1.01], \u003cem\u003e\u0026nbsp;P =\u0026nbsp;\u003c/em\u003e0.05; \u003cstrong\u003eTable 3\u003c/strong\u003e) \u0026nbsp;while receiving income assistance (e.g. disability check, military, TANF, AFDC) (29% vs. 16%; OR = 0.477 [95% CI: 0.29 – 0.80], \u003cem\u003eP =\u0026nbsp;\u003c/em\u003e0.005; \u003cstrong\u003eTable 3\u003c/strong\u003e) decreased the odds of being in a cluster. No significant differences were found between HCV subtype, race and ethnicity, education, drug choice or frequency of injection drug use. The multivariable model included factors that were associated with clustering (\u003cem\u003eP \u0026lt;\u003c/em\u003e 0.10) in the univariable analysis including age, source of recruitment, incarceration time, income source and syringe source. The factors that remained significantly associated with membership in a cluster was being aged 18-29 years (AOR = 2.009 [95% CI: 1.17 – 3.46], \u003cem\u003eP =\u0026nbsp;\u003c/em\u003e0.012; \u003cstrong\u003eTable 3\u003c/strong\u003e). In contrast, receiving a form of public assistance as a primary income source remained negatively associated with clustering (AOR = 0.544 [95% CI: 0.30 – 0.97], \u003cem\u003eP =\u0026nbsp;\u003c/em\u003e0.040; \u003cstrong\u003eTable 3\u003c/strong\u003e). \u0026nbsp; \u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDespite the high incidence of HCV among PWUD in recent years and the ensuing opioid overdose crisis, very little has been understood about the emergence and spread of the virus across the United States. Rural communities have been disproportionately affected by HCV outbreaks, fueled by overlapping epidemics or syndemics of injection drug use, widespread nonmedical use of opioids and stimulants, and compounded by social inequities and social barriers \u003csup\u003e37–39\u003c/sup\u003e. While HCV screening rates among PWUD range from 8-32% \u003csup\u003e40,41\u003c/sup\u003e these rates are markedly lower in rural areas with some estimates as a lows as 6% \u003csup\u003e41\u003c/sup\u003e. In this multi-site cohort study comprising 692 PWUD in the rural United States, we found that nearly one-third of all HCV infections were genetically linked, with genotype 1a predominating across all study sites. Subtle differences in genotype distribution were observed between sites, with Kentucky and New England showing the greatest heterogeneity, including the presence of less common subtypes such as 4a and 2a which may lead to less than optimum treatment outcomes \u003csup\u003e42\u003c/sup\u003e. The prevalence of mixed HCV genotype infections, as determined by sequencing, was 4.8% - this rate is consistent with other studies reporting low frequencies \u003csup\u003e43–47\u003c/sup\u003e. Although a higher proportion (18%) of mixed-strain HCV infections was reported in an outbreak in rural Indiana using the GHOST platform\u0026nbsp;\u003csup\u003e11\u003c/sup\u003e, such findings have not been replicated in other rural settings and are likely due to the rapid transmission dynamics and unique social structure of that specific community. Further, comparisons across studies are challenging due to differences in cohort selection, injection behaviors and the genomic region analyzed \u003csup\u003e48\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe degree of clustering observed within this study at 29.5% is similar to that observed from other injecting drug cohorts. For example, a study examining HCV transmission across four Indian cities revealed that 28.8% of HCV sequences clustered, \u003csup\u003e49\u003c/sup\u003e while studies conducted in North America have revealed variable levels of clustering from 46% in Baltimore\u003csup\u003e50\u003c/sup\u003e , 33% in Wisconsin \u003csup\u003e18\u003c/sup\u003e, 25% in New York \u003csup\u003e51\u003c/sup\u003e, 31% in Vancouver \u003csup\u003e52\u003c/sup\u003eand 36% in Ottawa \u003csup\u003e53\u003c/sup\u003e. Higher rates of clustering were observed in those studies that enrolled injecting partnerships, with 54% of Australian participants genetically related \u003csup\u003e54\u003c/sup\u003e compared with 52% of injecting partnerships from San Francisco \u003csup\u003e17\u003c/sup\u003e. The elevated clustering rate observed in these known partnerships may be attributed to the study designs, which involves more frequent assessments, thereby increasing the likelihood of capturing transmission events early. Differences in the rate of clustering among participants could reflect regional differences in drug use networks, recruitment approaches, sampling density, clustering analyses methods or behavioral differences. Age has been previously demonstrated to be an important factor in HCV transmission, with higher rates of clustering found in participants of younger age \u003csup\u003e55\u003c/sup\u003e. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDuring the acute and early stages, the viral population tends to be relatively homogeneous, primarily due to serial bottlenecks and the presence of a single founder virus. As the infection progresses, HCV undergoes increased genomic diversification as it adapts to the host's immune response, leading to a positive correlation between the stage of infection and intra-host viral diversity. If a correlation does exist then genetic diversity may be used as a proxy for infection recency as previously suggested, \u003csup\u003e56–59\u003c/sup\u003e then this may well suggest that clusters detected in this study are more driven by individuals who are in the earlier stages of infection, when genomic diversity is relatively homogeneous owing to a genetic bottleneck effect.\u0026nbsp;We found that 49% of transmission clusters stemmed from the same social recruitment chains suggesting that this type of recruitment approach is successful in uncovering transmission networks among PWUD. The time of persistence of these local transmission clusters was estimated from dated phylogenies and revealed prolonged period of persistence over 10 years in some geographical locations, with more complex transmission networks cryptically spreading for longer periods compared to those in a dyadic cluster.\u003c/p\u003e\n\u003cp\u003eYounger age (particularly those aged 18–29) emerged as the strongest independent predictor of transmission cluster membership, consistent with previous findings that highlight elevated transmission risk among younger PWUD \u003csup\u003e55\u003c/sup\u003e. Recruitment by a sexual or intimate partner and having an illegal source of income were also associated with increased odds of clustering in unadjusted analyses, suggesting that close personal networks and high-risk socioeconomic behaviors may be involved in viral spread. Sexual relationships have also been associated with increased sharing of syringes and injecting equipment and having a genetically related infection in young PWUD from San Francisco \u003csup\u003e17,60,61\u003c/sup\u003e. \u0026nbsp;Conversely, receiving public assistance as a primary income source was negatively associated with clustering, potentially reflecting reduced engagement in higher-risk networks. No associations were found between clustering and HCV subtype, race and ethnicity, education level, drug type, or frequency of injection, underscoring the importance of social and structural factors rather than strictly behavioral factors in shaping transmission dynamics.\u003c/p\u003e\n\u003cp\u003eFrom this study we have shown how genomic sequencing has the potential to provide a high-resolution picture of HCV evolution and transmission, providing public health authorities with actionable information. Molecular epidemiology methods have been used in related fields (e.g. HIV prevention) for many years and have been critical to informing public health interventions. A striking example of this approach was from an implementation case study in Canada that used phylogenetic analysis of routine clinical data and demonstrated that ‘near-real-time’ analysis directly impacted ongoing viral transmission and led to enhanced public health follow-up with linkage to care and treatment initiation \u003csup\u003e62\u003c/sup\u003e. \u0026nbsp; As part of the United States Federal Ending the HIV Epidemic strategic initiative, rapidly detecting and responding to emergent clusters of HIV infection is one of the key pillars that will be used to further reduce new transmissions\u003csup\u003e63\u003c/sup\u003e. Yet, its implementation for HCV remains scarce but there is enormous potential for genomic surveillance to augment traditional surveillance approaches. It is especially valuable for marginalized population groups (i.e. people who use injection drugs), where it could be used for targeted network-based strategies to interrupt transmission, \u003csup\u003e64\u003c/sup\u003e which could aid in microelimination strategies \u003csup\u003e65\u003c/sup\u003e. Moreover, dried blood spots have also been validated for genomic surveillance as a less invasive alternative to venous blood draws \u003csup\u003e51,66\u003c/sup\u003e and could serve as a useful tool for future surveillance efforts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. Firstly, recruiting marginalized and highly stigmatized populations, such as people who inject drugs, poses significant challenges, especially in rural settings where no universal recruitment tool exists. In this study, a modified form of chain referral sampling, RDS, was utilized to recruit PWUDs. Although RDS has not been widely adopted in rural areas, it has been successfully implemented in multiple rural U.S. regions \u003csup\u003e67,68\u003c/sup\u003e. While there were some differences in recruitment across study sites, little variation was observed when multiple variables were assessed. Although each site was instructed to collect and process specimens in the same manner, there may have been some time delays to centrifugation and freezing of samples, which may have compromised the integrity of the samples leading to different PCR failure rates across sites. Secondly, the study focused on a fragment within the E1/2 region of the HCV genome. While this captures only a small portion of the HCV genome, it was selected for its high variability, making it effective for detecting transmission events in outbreak settings \u003csup\u003e69\u003c/sup\u003e. However, monitoring additional genomic regions, such as NS3 and NS5A/B, would be important for tracking antiviral drug resistance patterns. Thirdly, genetic clustering by similarity does not confirm a transmission event, as there may be un-sampled missing links within transmission chains, leading to an incomplete understanding of the HCV transmission network. Additionally, the direction of HCV transmission cannot be readily inferred from genetic data alone \u003csup\u003e17\u003c/sup\u003e, though phylogenetic analysis can provide insights into transmission direction with varying degrees of reliability \u003csup\u003e70–72\u003c/sup\u003e. Finally, behavioral data were self-reported and may be subject to recall and response biases.\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study of PWUD across multiple U.S. rural sites reveals that the HCV epidemic is not uniform across regions, with notable differences in genotype diversity, clustering rates, intra-host viral diversity, and the persistence of transmission clusters. These findings suggest that local HCV epidemics are evolving at different stages and highlight the importance of robust genomic surveillance to guide the development of targeted social and structural public health interventions for high-risk, marginalized rural communities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIn memoriam:\u003c/em\u003e\u003c/strong\u003eDr. Todd M Allen passed away unexpectedly before this manuscript could be published. He was a kind and gentle spirit whose contributions to the field of infectious disease research were immense and far-reaching. Beyond being a brilliant scientist, Dr. Allen was a generous mentor, collaborator, and cherished friend to many in the community. We dedicate this manuscript to his memory, in recognition of his remarkable scientific legacy and his unwavering commitment to support and inspire the next generation of researchers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthical approval.\u003c/h2\u003e\n\u003cp\u003e All study procedures were approved by the Institutional Review Board at each site and study protocols and procedures were reviewed and approved by the Institutional Review Board of Massachusetts General Hospital.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cp\u003eD.C.T and T.M.A acquired funding, conceived, designed and supervised the study. D.B.J, J.S, T.L.N and K.A.P performed all experiments. D.C.T analyzed all data and prepared the figures. D.B, H.C, J.F, P.F, K.R.H, J.R.H, S.B, C.H, W.J, P.T.K, W.M, M.T.P, G.S, T.S, R.P.W and A.M.Y contributed to the review and editing process and provided funding acquisition. J.I.T and S.M was involved in data curation and contributed to the review and editing process. D.C.T and T.M.A wrote the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the National Institute of Drug Abuse (NIDA) grant U24DA044801. Data is based upon data collected and/or methods developed as part of the Rural Opioid Initiative (ROI), a multi-site study with a common protocol which was developed collaboratively by investigators at eight research institutions and at the National Institute of Drug Abuse (NIDA), the Appalachian Regional Commission (ARC), the Centers for Disease Control and Prevention (CDC), and the Substance Abuse and Mental Health Services Administration (SAMHSA). Primary data collection was supported by grants UG3DA044798, UG3DA044830, UG3DA044831, UG3DA044826 co-funded by NIDA, ARC, CDC, and SAMHSA. Specimen collection in Kentucky was also supported by R01DA033862 and R01 DA047952. The authors thank the other ROI investigators and their teams, the ROI Executive Steering Committee chair, Dr. Holly Hagan, the NIDA Science Officer, Dr. Richard Jenkins, and, particularly, the participants of the individual ROI studies for their valuable contributions. A full list of participating ROI investigators and institutions can be found on the ROI website at http://ruralopioidinitiative.org/studies.html.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBlach, S. 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Demographic characteristics of participants from the Rural Opioid Initiative. Shown are the overall characteristics from the entire ROI cohort compared to those that were sent to the GHOST laboratory for sequencing analysis (n = 1,196).\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"696\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall ROI cohort\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eROI participants with samples\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eROI participants with sequenced samples\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e3,084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e737 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e429 (58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e34 (28 \u0026ndash; 43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e35 (29-43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e35 (29-43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e1,737 (57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e428 (58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e273 (64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e1,293 (42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e306 (42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e155 (36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Transgender/Gender minority\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e18 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e3 (\u0026lt;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e1 (\u0026lt;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace/Ethnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Non-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e2,527 (83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e621 (84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e364 (85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Non-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e94 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e13 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e8 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Non-Hispanic American Indian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e208 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e43 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e20 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Non-Hispanic Other/Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e103 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e19 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e14 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Hispanic\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e116 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e41 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e23 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Less than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e688 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e180 (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e94 (22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;High school or GED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e1,430 (47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e349 (47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e212 (49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Some college or technical school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e856 (28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e195 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e114 (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Bachelor\u0026rsquo;s degree or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e71 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e12 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e8 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Single/Not married\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e1,570 (53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e388 (53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e229 (54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Married\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e354 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e101 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e57 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Separated/Divorced/Widowed/Don\u0026rsquo;t Know\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e1,054 (35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e239 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e139 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHomelessness\u003c/strong\u003e, past 6 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e1,612 (53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e434 (59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e260 (61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeographic region\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Illinois\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e173 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e12 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Kentucky\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e338 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e42 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e12 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;North Carolina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e350 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e51 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e41 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;New England\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e589 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e228 (31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e128 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Ohio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e258 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e105 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e63 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Oregon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e174 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e57 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e42 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Wisconsin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e991 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e242 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e143 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;West Virginia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e175 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecruited \u0026ndash; How\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Coupon or Code\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e2315 (76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e571 (77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e342 (80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Told about, but didn\u0026rsquo;t get a coupon/code\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e569 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e128 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e69 (16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e32 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e3 (\u0026lt;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e1 (\u0026lt;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Online\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e17 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e6 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e4 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Flyer or other advertising\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e96 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e21 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e12 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Other\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e117 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e27 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e13 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecruited \u0026ndash; by Whom\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Partner, spouse, boyfriend, girlfriend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e326 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e89 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e56 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Casual sex partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e44 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e16 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e8 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Friend, associate, acquaintance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e1706 (56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e414 (56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e242 (56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Family member\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e255 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e53 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e30 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Neighbor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e64 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e12 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e6 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Person I use drugs with\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e398 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e97 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e59 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Service or program staff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e70 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e10 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e9 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Stranger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e37 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e10 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e4 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e52 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e9 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e5 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: IQR, Inter-quartile range.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u0026nbsp;\u003c/sup\u003eRace and ethnicity are mutually exclusive categories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Substance use practices of sequenced participants.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"468\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eROI participants with sequenced samples\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e429 (58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubstance Use\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrug of choice\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Opioids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e274 (64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Stimulant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e141 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Benzos\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e1 (\u0026lt;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Other\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e13 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrug Use Patterns, past 30 days\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Opioids\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e370 (86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Fentanyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e197 (46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Buprenorphine/methadone\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e225 (52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Methamphetamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e315 (73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Cocaine/crack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e208 (48)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Benzodiazepines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e195 (45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Multiple classes of drugs used\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e352 (82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Number of classes, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e3 (2 \u0026ndash; 3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Simultaneous injection of an opioid and a\u0026nbsp;\u003c/p\u003e\n \u003cp\u003estimulant (e.g., speedball)\u003csup\u003e4 5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e182 (45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePersonal history of overdose\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e248 (58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEver received any treatment for addiction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e351 (82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReceived any treatment for addiction\u003c/strong\u003e, past 30 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e152 (35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAttended inpatient/outpatient treatment\u003c/strong\u003e, past 30 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e109 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReceived MOUD\u003c/strong\u003e, past 30 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e99 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInjection Drug Use\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrent injection drug use\u003c/strong\u003e, past 30 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e408 (95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInjection drug use frequency, past 30 days\u003csup\u003e5\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Daily or more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e297 (73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; More than weekly, less than daily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e40 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Weekly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e26 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Monthly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e39 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource of \u003cu\u003emost\u003c/u\u003e syringes or needles, past 30 days\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e5\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Pharmacy SSP/NEP, personally\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e94 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; SSP/NEP, personally\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e157 (38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; From someone else who got them from SSP/NEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e55 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Friend/acquaintance, spouse, or partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e27 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Drug dealer/street\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e67 (16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e4 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Don\u0026rsquo;t know/Refused\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e4 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource of \u003cu\u003eany\u003c/u\u003e syringes or needles, past 30 days\u003csup\u003e5\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Pharmacy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e126 (31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;SSP/NEP, in person\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e200 (49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;SSP/NEP, someone else\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e140 (34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Farm supply or veterinarian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e2 (\u0026lt;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Drug dealer or street syringe seller\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e78 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Spouse, partner, family member, or relative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e65 (16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Friend or acquaintance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e143 (35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;I found them\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e14 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerson from whom you received most of your syringes was diabetic\u003csup\u003e5\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e15 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClosest SSP\u003c/strong\u003e\u003csup\u003e6\u003c/sup\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;30 min drive/Mobile SSP comes to town\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e313 (73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026gt;30 min drive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e65 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Don\u0026rsquo;t know\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e37 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of times got a new syringe from a pharmacy\u003c/strong\u003e, past 30 days\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0 (0 \u0026ndash; 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of times got a new syringe from a SSP\u003c/strong\u003e, past 30 days\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0 (0 \u0026ndash; 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIt is easy for me to get new, clean syringes or needles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Strongly/Somewhat Disagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e65 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Uncertain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e42 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Strongly/Somewhat Agree \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e319 (74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge at first injection\u003c/strong\u003e, median (IQR)\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e21 (18-30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge at first opiate pain killer injection\u003c/strong\u003e, median (IQR)\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e21 (17-28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge at first heroin injection\u003c/strong\u003e, median (IQR)\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e24 (19-31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge at first methamphetamine injection\u003c/strong\u003e, median (IQR)\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e26 (19-33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge at first cocaine injection\u003c/strong\u003e, median (IQR)\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e22 (18-29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of days practiced syringe mediated drug sharing\u003c/strong\u003e, past 30 days\u003csup\u003e6\u0026nbsp;\u003c/sup\u003e,median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e1 (0 \u0026ndash; 8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of days practiced multiple injection per injection episode\u003c/strong\u003e, past 30 days\u003csup\u003e6\u003c/sup\u003e, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e4 (1 \u0026ndash; 15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of times used syringe/needle that was used by somebody else\u003c/strong\u003e, past 30 days\u003csup\u003e6\u003c/sup\u003e, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e1 (0 \u0026ndash; 5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of times used a cotton, cooker, spoon, or water for rinsing or mixing that was used by somebody else\u003c/strong\u003e, past 30 days\u003csup\u003e6\u003c/sup\u003e, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e2 (0 - 10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of times let someone else use a cotton, cooker, spoon, or water for rinsing or mixing after you used it\u003c/strong\u003e, past 30 days\u003csup\u003e6\u003c/sup\u003e, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e2 (0 - 10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth care and health insurance\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Main place received medical care, past 6 months\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Private doctor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e107 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Community health center\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e58 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Health department\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e19 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Urgent care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e54 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Emergency room\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e83 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e27 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Did not receive medical care in the past 6 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e77 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Refused/Don\u0026rsquo;t know\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e4 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth insurance or health care coverage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e330 (77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth care and health insurance\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Tested for HIV, ever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e331 (77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Diagnosed with HIV, ever\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e11 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Tested for HCV, ever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e332 (75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Diagnosed with HCV, ever\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e222 (69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Cleared HCV with Treatment, ever\u003csup\u003e9\u0026nbsp;\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e43 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Rapid HCV Test Positive Result\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e424 (99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Confirmatory RNA HCV Positive Result\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e273 (64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Rapid HIV Positive Results\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e5 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Confirmatory HIV Positive Results\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e3 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: SD, standard deviation;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u0026nbsp;\u003c/sup\u003eHeroin, opiate painkillers, and/or synthetics (e.g., U47700, U4, or \u0026ldquo;Pink\u0026rdquo;).\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eBuprenorphine and/or methadone used \u0026ldquo;to get high.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u0026nbsp;\u003c/sup\u003eUse of \u0026ge;2 drug categories (opioids, methamphetamine, cocaine/crack, prescription anxiety drugs [not as prescribed], gabapentin, clonidine, and/or other) by any route in past 30 days.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u0026nbsp;\u003c/sup\u003eSimultaneous injection of an opioid and a stimulant (i.e., speedball, goofball, or screwball).\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e5\u0026nbsp;\u003c/sup\u003eAmong participants reporting injection drug use in the past 30 days.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e6\u0026nbsp;\u003c/sup\u003eAmong participants reporting ever injecting drug. Overall ROI Cohort: n = 2,812; ROI participants with a sample: n = 731; ROI participants with a sequenced sample: n= \u0026nbsp;426\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e7\u0026nbsp;\u003c/sup\u003eAmong participants reported ever being tested for HIV\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e8\u003c/sup\u003e Among participants reported ever being tested for HCV\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e9\u003c/sup\u003e Among participants reported ever being diagnosed with HCV\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Crude and multivariable logistic regression analysis of factors associated with being in a cluster for participants in the rural opioid initiative.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"910\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 39.2491%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo cluster\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 286)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 143)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnadjusted\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 11.149%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"102\" style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge groups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003e18 - 29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e69 (20.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e43 (30.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e2.544 (1.407, 4.599)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\n \u003cp\u003e2.009\u003c/p\u003e\n \u003cp\u003e(1.166, 3.462)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003e30 \u0026ndash; 35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e67 (23.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e33 (23.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e1.590 (0.852, 2.969)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003e36 \u0026ndash; 43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e71 (24.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e39 (27.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e1.966 (1.071, 3.610)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.029\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\n \u003cp\u003e1.630\u003c/p\u003e\n \u003cp\u003e(0.938, 2.832)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003e44 \u0026ndash; 65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e88 (30.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e28 (19.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eFemale (vs. male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e100 (35.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e55 (38.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e1.162 (0.767, 1.761)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecruitment source\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eFriend, associate, acquaintance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e163 (57.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e79 (55.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003ePartner, spouse, boyfriend, girlfriend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e29 (10.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e27 (18.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e2.069 (1169., 3.663)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\n \u003cp\u003e1.736\u003c/p\u003e\n \u003cp\u003e(0.926, 3.254)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eCasual sex partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e5 (1.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e3 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e1.108 (0.259, 4.745)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eFamily member\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e23 (8.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e7 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e0.585 (0.244, 1.400)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eNeighbor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e4 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e2 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e1.195 (0.281, 5.078)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003ePerson I use drugs with\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e34 (11.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e25 (17.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e1.014 (0.233, 4.411)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eService or program staff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e5 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e4 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e1.612 (0.426, 6.104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eStranger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e3 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e1 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e0.662 (0.068, 6.422)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e3 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e2 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e1.333 (0.220, 8.076)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace and ethnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e240 (83.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e124 (86.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e7 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e1 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e0.310 (0.035, 2.707)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eNon-Hispanic American Indian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e14 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e6 (4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e1.055 (0.333, 3.343)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eNon-Hispanic Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e10 (3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e4 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e0.951 (0.260, 3.546)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e15 (5.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e8 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e1.503 (0.500, 4.514)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eHigh school diploma or GED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e143 (50.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e69 (48.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eSome college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e73 (25.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e41 (28.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e1.91 (0.678, 2.093)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eLess than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e63 (22.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e31 (21.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e0.915 (0.519, 1.615)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eCollege graduate or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e6 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e2 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e0.630 (0.124, 3.200)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eMedian duration of recent jail or prison time (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e20.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e1.005 (1.00, 1.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.050\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\n \u003cp\u003e1.002\u003c/p\u003e\n \u003cp\u003e(0.997, 1.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eHomeless, past 6 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e175 (61.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e85 (59.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e0.928 (0.613, 1.404)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrug class of choice\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eOpioids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e150 (52.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e96 (67.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eFentanyl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e11 (3.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e4 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e1.057 (0.320, 3.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eMethamphetamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e89 (31.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e29 (20.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e0.851 (0.428, 1.690)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eCocaine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e15 (5.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e8 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e1.655 (0.652, 4.198)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eBenzos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e1 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e10 (3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e3 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e0.859 (0.226, 3.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eMOUD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e10 (3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e3 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e0.859 (0.226, 3.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eHIV co-infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e2 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e3 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e6.110 (0.630, 59.289)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eHealth Insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e219 (76.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e111 (77.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e1.068 (0.645, 1.769)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.2491%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIDU Frequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eDaily or more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e200 (74.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e97 (72.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eMore than weekly but no daily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e26 (9.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e14 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e0.983 (0.431, 2.244)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eWeekly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e17 (6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e9 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e0.964 (0.380, 2.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eMonthly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e25 (9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e14 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e1.047 (0.458, 2.395)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.2491%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome Source\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eIllegal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e77 (26.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e56 (39.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e1.747 (1.142, 2.674)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.149%;\"\u003e\n \u003cp\u003e1.444\u003c/p\u003e\n \u003cp\u003e(0.887, 2.351)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eLegal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e169 (59.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e96 (67.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e1.414 (0.928, 2.155)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eIncome assistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e82 (28.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e23 (16.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e0.477 (0.285, 0.798)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.149%;\"\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003cp\u003e(0.304, 0.972)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.040\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.2491%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource of needles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eSSP/NEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e103 (38.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e54 (40.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.2491%;\"\u003e\n \u003cp\u003ePharmacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e60 (22.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e34 (25.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e1.36 (0.789, 2.348)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.149%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eSomeone else who got them from an SSP/NEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e33 (12.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e22 (16.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e1.579 (0.848, 2.940)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.149%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eDrug dealer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e23 (8.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e4 (3.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e0.337 (0.112, 1.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.052\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.149%;\"\u003e\n \u003cp\u003e0.341\u003c/p\u003e\n \u003cp\u003e(0.112, 1.038)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eSpouse, partner or relative, friend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e49 (18.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e18 (13.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e0.716 (0.385, 1.334)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.149%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.2491%;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e3 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e1 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e0.705 (0.722, 6.888)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.149%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.2491%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHCV Subtype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.149%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.2491%;\"\u003e\n \u003cp\u003e1a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e84 (58.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e139 (62.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.149%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.2491%;\"\u003e\n \u003cp\u003e1b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e4 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e7 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e1.143 (0.77, 16.947)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.149%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.2491%;\"\u003e\n \u003cp\u003e2a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e2 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e1 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e4.000 (0.134, 119.230)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.149%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.2491%;\"\u003e\n \u003cp\u003e2b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e7 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e23 (8.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e0.609 (0.048, 7.758)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.149%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39.2491%;\"\u003e\n \u003cp\u003e3a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e45 (31.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.6701%;\"\u003e\n \u003cp\u003e74 (25.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.5199%;\"\u003e\n \u003cp\u003e1.216 (0.107, 13.799)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e0.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.149%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.0296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.6826%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"hepatitis c virus, transmission, cluster, persons who use drugs","lastPublishedDoi":"10.21203/rs.3.rs-6810633/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6810633/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHepatitis C virus (HCV) remains a significant public health concern in the United States particularly in rural communities where the opioid epidemic has accelerated transmission among people who use drugs (PWUD)/ Despite, this growing burden the genetic features and transmission patterns of HCV in these settings are poorly understood. This study analyzed 692 HCV antibody-positive specimens collected from rural communities in ten U.S. states. Using amplicon-based deep sequencing and the Global Hepatitis Outbreak and Surveillance Technology (GHOST) platform, transmission networks were reconstructed. Among sequenced individuals, 29.5% were linked within clusters. The structure of these clusters varied by region\u0026mdash;from sparse networks in Ohio to dense, interconnected clusters in New England. Phylogenetic analysis revealed that some transmission networks persisted for over a decade, highlighting long-term, sustained transmission. Nearly half of all clusters involved individuals connected through social recruitment, suggesting peer-referral strategies can effectively identify transmission chains. Younger age was independently associated with clustering, while recruitment by an intimate partner showed a weaker link. These findings emphasize the importance of ongoing genomic surveillance and social network-informed strategies to detect emerging HCV clusters and guide targeted public health interventions in underserved rural communities.\u003c/p\u003e","manuscriptTitle":"Genomic surveillance uncovers regional variation in HCV transmission networks among people who use drugs in rural U.S. communities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-10 13:47:21","doi":"10.21203/rs.3.rs-6810633/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"46bdba10-f92c-4078-a673-76d57ab9e09b","owner":[],"postedDate":"June 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":49605267,"name":"Biological sciences/Microbiology/Virology/Hepatitis C virus"},{"id":49605268,"name":"Biological sciences/Evolution/Phylogenetics"}],"tags":[],"updatedAt":"2026-01-09T08:09:52+00:00","versionOfRecord":{"articleIdentity":"rs-6810633","link":"https://doi.org/10.1038/s41467-025-66934-y","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2025-12-02 05:00:00","publishedOnDateReadable":"December 2nd, 2025"},"versionCreatedAt":"2025-06-10 13:47:21","video":"","vorDoi":"10.1038/s41467-025-66934-y","vorDoiUrl":"https://doi.org/10.1038/s41467-025-66934-y","workflowStages":[]},"version":"v1","identity":"rs-6810633","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6810633","identity":"rs-6810633","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}