Molecular and spatial epidemiology of HCV among people who inject drugs in Boston, Massachusetts.

Integration of genetic, social network, and spatial data has the potential to improve understanding of transmission dynamics in established HCV epidemics. Sequence data were analyzed from 63 viremic people who inject drugs recruited in the Boston area through chain referral or time-location sampling. HCV subtype 1a was most prevalent (57.1%), followed by subtype 3a (33.9%). The phylogenetic distances between sequences were no shorter comparing individuals within versus across networks, nor by location or time of first injection. Social and spatial networks, while interesting, may be too ephemeral to inform transmission dynamics when the date and location of infection are indeterminate.

Introduction

Although now curable, hepatitis C virus (HCV) infections continue to be problematic. In the United States, HCV mortality has increased in recent years, while mortality for 61 other federally reportable infectious diseases, including HIV, has decreased [1]. HCV infections have increased by more than 70% among 15–30 year-olds during the past decade, and new infections are largely attributed to injection drug use (IDU) [1, 2]. In Massachusetts, over 2,000 new cases attributed to IDU have been reported in those <30 years of age in each of the last seven years [3]. More information about transmission networks might improve prevention efforts.

The application of phylogenetic and spatial analysis of HCV has identified a range of HCV subtypes among people who inject drugs (PWID) and provided biological evidence of disease clusters and transmission patterns in outbreaks [4-6]. We aimed to find evidence of HCV sequence alignment or transmission patterns among PWID outside of known outbreaks. As part of a study of PWID in the Boston area, we identified a subsample with active HCV infection and sequenced part of their viral genome. We then integrated behavioral, social network, spatial, and HCV phylogenetic data to explore transmission dynamics in established epidemics. We were especially interested to see if we could find evidence of HCV viral sequence alignment or transmission patterns that could identify potential targets, methods, or venues for HCV prevention and treatment interventions.

Methods

We employed a cross-sectional design focused on PWID in the Boston area. PWID were eligible if they met the following criteria: (1) age 18–45 years; (2) English or Spanish speaking; (3) evidence of track marks; (4) living with, at risk for, or infected with and subsequently cleared HCV; and (5) ability to provide written informed consent. The study protocol was approved by the [Blinded] Institutional Review Board and the [Blinded] Human Investigation Committee. Consenting individuals completed a survey and were administered the Oraquick® HCV Rapid Antibody test (OraSure Technologies).

Between February and October 2016, recruitment relied on time-location sampling and chain referral in Boston and Cambridge. For the approach, trained outreach staff recruited participants through partnership with harm reduction programs, shelters, and social service agencies. Staff members at community-based agencies posted study flyers and identified PWID whom they knew were likely to have acquired HCV and would be interested in participating in the study. For the , enrolled participants (i.e., “seeds”) were asked to refer peers within their networks to the study. In both approaches, research staff described the study to interested recruits, confirmed participant eligibility, provided written materials, and obtained written informed consent. Analysis of recruitment chains was depicted using the StatNet and UserNetR packages in R.

Participants were tested for HCV antibodies and then completed the survey on a tablet, laptop, or desktop computer in our research offices or on tablets or laptops in private spaces provided by community-based partners. Surveys covering demographic, health, and socio-behavioral topics were conducted in English or Spanish and lasted approximately 60 minutes. Participants were informed of HCV test results after completing the survey, and those who tested positive were invited to a separate appointment and were consented to have a blood sample taken. Sample collection continued until 102 individuals were enrolled in the sub-study of HCV-positive participants.

Blood specimens were centrifuged, and the serum was added to RNALater® to preserve the integrity of viral RNA (RNALater; Ambion Inc., Austin, TX). Samples were stored at -80°C. After thawing, viral RNA was purified and a 360-base region in the core gene was amplified and sequenced [7, 8]. Sequences were optimally aligned using the CLUSTAL W program and a phylogenetic tree was constructed by the Neighbor-Joining method based on Kimura’s two-parameter distances (Mega software, version 7.0 [9]. We assessed the reliability of the tree topologies by bootstrapping with 100 replicates. Evolutionary distances were computed using the Maximum Composite Likelihood method [10].

We compared sequence similarity by recruitment pattern, location of injection initiation, and number of years injecting drugs. We conducted Fisher’s exact tests to assess whether differences in HCV subtypes by recruitment approach were statistically significant. We subsequently ran simple logistic regression analyses to determine whether place of residence at the time of first injection or number of years injecting was significantly associated with HCV subtypes. Place of residence was divided into three categories: Boston, suburbs of Boston (i.e., outside of the city limits and on a Massachusetts Bay Transit Authority (MBTA) line), or beyond the suburbs (not reachable by MBTA line). The number of years injecting was also divided into three categories: 0–5, 6–10, >10 years.

Results

Among the 102 participants who tested positive for HCV antibodies and provided a subsequent blood sample, 66 were actively infected and sequence data were obtained from 63 of the 66. Out of these 63, 60 completed the survey, and one additional participant completed the survey without providing sequence data. Thus, the sample sizes are 63 for the sequence data and 61 for the survey. The mean age of the 61 respondents was 32±6 years, and males comprised 75% of the sample. Forty participants (66%) described themselves as White non-Latino, nine as Latino, eight as mixed-race or other, and four as African American. Most were single, with 42 (69%) living alone and 9 (15%) living with a partner. About three-quarters (n = 46) reported their sexual orientation as heterosexual, nine as bisexual, and six as gay or lesbian. Three-quarters of the sample reported being homeless and the median monthly income was $500–1000. Ninety percent (n = 53) reported having been incarcerated. The mean duration of IDU was 12 years (SD = 8) (Table 1).

Table 1

Descriptive statistics: People who inject drugs in the Greater Boston Area of Massachusetts, 2016 (n = 61).

	Total
	N = 61
Age (year); mean (standard deviation)	32 (6)
Gender
Male	46 (75%)
Female	15 (25%)
Race/ethnicity
African American	4 (7%)
American Indian/Alaskan Native	1 (2%)
Hispanic, Latino, or Latina	9 (15%)
White, non-Hispanic	40 (66%)
Mixed races & others	7 (11%)
Marital status
Single/living alone	42 (69%)
Single, living w/ partner	9 (15%)
Married/ in domestic partnership	2 (3%)
Divorced	3 (5%)
Separated	3 (5%)
Widowed	2 (3%)
Sexual orientation
Heterosexual/straight	46 (75%)
Gay/lesbian	6 (10%)
Bisexual	9 (15%)
Education level
Grade 6–8	3 (5%)
Grade 9–12, not HS grad	14 (23%)
HS grad/GED	32 (52%)
Some colleage, no degree	9 (15%)
2–4 yrs college degree	1 (2%)
Graduate/professional degree	2 (3%)
Homeless
No	15 (25%)
Yes	46 (75%)
Main residence, last 30 days
Own apartment, house, or room	7 (11%)
Home of parents, relatives, or friends	10 (16%)
Halfway house, group home, or foster home	6 (10%)
Hotel or motel	2 (3%)
Shelter	18 (30%)
Abandoned building	2 (3%)
Public park	4 (7%)
Street, wooded area	7 (11%)
Other	5 (8%)
Income, last 30 days
None	7 (11%)
< $500	17 (28%)
$500 to < $1000	22 (36%)
$1000 to < $2000	8 (13%)
$2000 to < $4000	4 (7%)
$4000 to < $6000	1 (2%)
> = $6000	2 (3%)
Ever in jail, prison, JDC
No	6 (10%)
Yes	55 (90%)
Currenlty on parole
No	53 (87%)
Yes	8 (13%)

HCV subtype by recruitment pattern

Fig 1A shows the participant network. Thirty-one of the 63 participants were identified through chain referral, indicated by being connected with double-headed arrows, and 32 were identified through time-location sampling, indicated by nodes without connection. Genotype 1a was higher for time-location participants (56.3%; 18/32) than for chain referred participants (41.9%; 13/31; p-value: 0.211); genotype 3a was higher for chain referred participants (45.2%; 14/31) than for time-location participants (22.6%; 7/32; p-value: 0.109) [S1 Table]. While there was no exclusive sequence alignment of subtypes within the chain referral clusters, three examples of sequence alignment with n>3 only consisted of two subtypes. In addition, in the largest sequence alignment (n = 13), seven participants with subtype 3a were connected. As indicated in Fig 1A, the genetic differences are nil or very small (0.1%) within the closely aligned sequences and independent of recruitment network.

Fig 1

A) Network diagram showing the referral network. The different node symbols represent the five major hepatitis C virus (HCV) subtypes. The double-headed arrows indicate connection by referral (n = 31). This provides visual evidence that the social network that was accessed to recruit participants contained people infected with different strains of HCV. B) Phylogenetic tree for HCV positive people who inject drugs in Boston, Massachusetts, 2016 (n = 63). Genetic distance, reported as the proportion of sequence divergence between connections on the tree. Each subgenotype aligns closely, but the relatively short core region of the genome that we chose to sequence and the small number of specimens from people infected with genotype 1b resulted in genotype 1b sequences appearing more closely aligned with genotype 2 and 4 than with genotype 1a sequences.

HCV subtype by location of first injection

Among PWID with Subtype 1a, 32.4% (11/34) first injected in Boston, 20.6% (7/34) first injected in Boston suburbs, and 47.1% (16/34) first injected outside of the Boston suburbs. Among PWID with subtype non-1a, 39.1% (9/23) first injected in Boston, 30.4% (7/23) first injected in Boston suburbs, and 30.4% (7/23) first injected outside of the suburbs (S1 Table). Location of the first injection was not associated with HCV subtype, when comparing subtypes from outside of the Greater Boston Area (odds ratio [OR]: 1.87; 95% confidence interval [CI]: 0.54, 6.53) or in Boston suburbs (OR: 0.82; 95% CI: 0.21, 3.26) to subtypes from Boston.

HCV subtype by number of years injecting

Among PWID with Subtype 1a, 21.9% (7/32) had been injecting for 0–5 years, 31.3% (10/32) had injected for 6–10 years, and 46.9% (15/32) had been injecting for >10 years. Among PWID with Subtype Non-1a, 13.7% (3/22) had been injecting for 0–5 years, 13.7% (3/22) had injected for 6–10 years, and 72.7% (16/22) had been injecting for >10 years. We divided the sample into those infected with genotype 1a versus not 1a, and there was no difference between groups by years of injection experience (OR: 0.97, 95% CI: 0.90, 1.04).

HCV sequencing results

Sequencing results indicated that the distances between sequences in phylogenetic analyses were no shorter comparing sequence data within versus across networks. Four of the oldest injectors in the sample (individuals born in 1977 or earlier) were infected with HCV genotypes 1b and 2b. Only one other individual was infected with either of these two subtypes (Fig 1B). But among closely related sequences with genotypes 1a and 3a, network connections did not predict sequence homology.

Discussion

Integrating social network, spatial, and molecular data from established epidemics poses unique challenges. These stem from the lack of defined dates of infection, the durability of current social network connections, the multilevel nature of risk environments, and missing data about the full network structure that is obtained when chain referral relationships are the sole data source on social connections [11-13]. For this reason, we sought to determine if HCV was more closely related (1) when specimens were obtained from within recruitment chains than those obtained by the less directed recruitment achieved by time-location sampling, and (2) when participants were categorized by time and location that their first exposure to HCV had likely have occurred. For the latter analysis, we made the working hypothesis that HCV infection is an early consequence of initiating injection drug use. This hypothesis is consistent with observations of high HCV incidence in the first year of injection.

Our findings that HCV subtype 1a was the predominant subtype among PWID in the Boston Area (57.1%), followed by subtype 3a (33.9%), differs from studies elsewhere that focused on HCV among PWID. A study from New York, conducted twenty years earlier than ours, found that the predominant genotypes were 1a and 1b, with 3a comprising only 5% of sequences genotyped [14]. A study from Baltimore, concurrent with ours, found no evidence of genotype 3a [15]. Our study population was substantially younger than those in the other two studies, consistent with our aim to recruit a younger sample, but differences in genotype were not associated with age. However, there were two subtypes (1b and 2b) that were restricted to older individuals. It appears, however, that these subtypes have failed to spread among younger members of our study population. Analysis of HCV genotypes from PWID in Vancouver revealed a pattern similar to ours, with nearly half their sample infected with genotype 1a and one-third infected with genotype 3a, but the relevance of this finding is limited by the geographic distance between coasts [6].

HCV phylogenetic sequence alignment has been found in those recently initiating drug injection. Sequence alignment was common in the large HIV-HCV outbreak in Scott County, Indiana. But even there, multiple introductions of HCV were identified. Genotypes 1a and 3b accounted for 72% and 20% of the patient samples, respectively, but additional genotypes had entered the mix, and some people were infected with multiple genotypes [16]. One study from Melbourne found within-network correlation with HCV genotype, but only in individuals infected with genotype 3 and reporting recent drug injection initiation [17]. In our study, we did not find any significant associations between HCV subtypes by recruitment types, network characteristics, number of years injecting, nor location of first injection.

Several limitations should be considered in interpreting our findings. First, our sample is small and needs to be expanded. Second, while we aimed to use respondent driven sampling (RDS), we ultimately used a modified RDS approach combined with a time-location sampling approach to recruit our sample during a relatively tight timeframe based on funding requirements. Third, we used self-reported measures from our survey, and questions referring to events from the distant past (place where first injected, date of first injection), could have introduced recall bias. Fourth, our results are not generalizable to the entire Boston Area, nor to other cities. Finally, although we sought to recruit a relatively young sample of PWID, many people had long injecting experiences, so their social networks at the time they were infected with HCV may have been different from their social networks at the time of the survey. In lieu of trying to design studies that collect detailed individual network and phylogenetics to reconstruct patterns of disease transmission, it may be more reasonable to collect information sufficient to populate random mixing and other forms of stochastic models [18]. Sequencing of infectious disease transmission among PWID may be most effective in the case of disease outbreaks [19].

Conclusion

Social networks, while interesting, are too ephemeral to inform transmission dynamics if the date and location of infection are indeterminate. Expanding research efforts to obtain extensive social network data from PWID populations in established epidemics may not be worthwhile or cost-effective given the expense of obtaining full social network data. Alternatives, such as quasi network data obtained through RDS and random mixing models, may be sufficient when modeling transmission networks.

HCV subtype by town of first injection and number of years injecting, Boston and Cambridge, Massachusetts, 2016.

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22 Apr 2022

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Reviewer #1: In their manuscript, “Molecular and special epidemiology of HCV among who inject drugs in Boston, Massachusetts”, the authors use survey data from 61 individuals and sequence data from 63 individuals in attempt to find evidence of viral clustering and factors associated with that clustering for potential targets for intervention. Unfortunately, their survey size was entirely too small for the goals of the research presented, namely “We aimed to find evidence of disease clusters or transmission patterns outside of known outbreaks” lines 66-67. Otherwise this is a very good study and the paper is well written. The limitations section was particularly well written and enlightening.

There are a couple issues to address to clarify the study.

1) The conclusion in the abstract, “Social and special networks, while interesting, may be too ephemeral to inform transmission dynamics when the date and location of infection are indeterminant”, is not supported by the data presented. The data presented simply does not have the depth of sampling to explore transmission dynamics of HCV in Boston. The authors estimate 14,000 new HCV infections over the last seven years (line 60) and the average duration of infection of the 102 individuals recruited was 12 years. The authors, were able to survey about <1% of HCV infected individuals in their population. It would seem incredibly unlikely that any clusters or evidence of transmission patterns would be identified in such a small survey size.

2) The authors spent 9 months (February to October 2016, line 82) recruiting subjects. Why was there such a low yield in study subjects?

3) How many individuals were surveyed to identify the 102 HCV antibody positive subjects? How many individuals were approached? How many individuals had their blood tested?

4) The authors state in their method that a phylogenetic tree was generated (lines 105 to 110) and provide a figure of the tree (Figure 1B), however, there is no presentation of the results It is unclear if there was any viral clustering. Were there any viral clusters? The figure legend needs more information. It is not clear what the values of on the phylogenetic tree mean.

Reviewer #2: In this study, the authors aim to see if phylogenetic analysis, when combined with behavioural, network and spatial data, can add to the understanding of transmission patterns among PWID outside of outbreak settings. I think the topic is interesting, and the manuscript is well written but having said that, I do think the sample size used for the study was small. I wonder if re-framing the study slightly, to be clearer that the authors were trying to see if this recruitment and analysis approach can be informative at uncovering disease clusters and transmission patterns might help a bit more – currently this is only mentioned at the end of the article in the conclusions.

Minor comments

• Line 103/4 – it would be helpful if the authors referenced where they got the sequencing protocol they used here. More detail on genotyping methods would be useful – I note the authors mention mixed genotype infections in the discussion but didn’t say if they found any themselves. Did the genotyping method mean they were unable to identify these?

• Is there a particular reason the core region was chosen for this analysis – is it the best region for this type of analysis?

• HCV sequencing results and comparison – is gt1a vs non gt1a a reasonable comparison? From my understanding, the spread of gt3a among PWID has been more recent than gt1a and seems consistent with the observation that older individuals in the cohort were infected with gt1b/2b. Whilst numbers are low, I wonder if the authors would see more trends associated with age/location/IDU duration if they further split the non-1a group?

• Perhaps consider adding a table with some of the data from lines 133-164 comparing people with different HCV genotypes (1a, 3a, other genotypes?) – might make all the information easier to digest for readers

• Line 200 - given the authors were aiming to look at transmission clusters between PWID outside of known outbreaks, I agree the sample size is small particularly outside of an outbreak context. The authors say it needs to be expanded – I would be interested to know what they think a reasonable size would be?

• I think some discussion around the issue that phylogenetic linkage will only be possible for samples with the same genotype is missing. Did the authors consider if there was a better way to sample the population to get a larger cohort?

• Table 1, is the age mean or median?

• Could the authors please confirm they will upload the HCV sequences into a sequence repository and cite these in the manuscript if accepted.

Typos

Line 64/5; analysis of HCV has identified [a range of] HCV subtypes among

Line 126; Forty [participants] (66%) described themselves as

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20 Jun 2022

Reviewer 1 Comments:

Reviewer #1: In their manuscript, “Molecular and special epidemiology of HCV among who inject drugs in Boston, Massachusetts”, the authors use survey data from 61 individuals and sequence data from 63 individuals in attempt to find evidence of viral clustering and factors associated with that clustering for potential targets for intervention. Unfortunately, their survey size was entirely too small for the goals of the research presented, namely “We aimed to find evidence of disease clusters or transmission patterns outside of known outbreaks” lines 66-67. Otherwise this is a very good study and the paper is well written. The limitations section was particularly well written and enlightening.

Response: Thank you for the positive feedback on the writing and enlightening limitations sections of our manuscript. While a typical survey with a sample size such as ours (n=61) would be considered small for typical analyses, phylogenetic analyses introduce an additional level of complexity, and there are few studies in the US that have large samples sizes that facilitate assessment of phylogenetic clusters. Admittedly, future studies, with larger funding levels should aim to recruit, enroll, and analyze larger samples, but our study is one of only a few in the Northeastern US that have begun to employ combined spatial and phylogenetic analyses. Please also see our related response to the first comment from Reviewer #2 below.

There are a couple issues to address to clarify the study.

1) The conclusion in the abstract, “Social and special networks, while interesting, may be too ephemeral to inform transmission dynamics when the date and location of infection are indeterminant”, is not supported by the data presented. The data presented simply does not have the depth of sampling to explore transmission dynamics of HCV in Boston. The authors estimate 14,000 new HCV infections over the last seven years (line 60) and the average duration of infection of the 102 individuals recruited was 12 years. The authors, were able to survey about <1% of HCV infected individuals in their population. It would seem incredibly unlikely that any clusters or evidence of transmission patterns would be identified in such a small survey size.

Response: Our study was relatively small, by design, as an exploratory mixed methods study, and we met our recruitment goals. The comments from Reviewer 1 regarding the external validity of our study may have some merit. It is also important to bear in mind that we don’t have a definition for a cluster. We were looking for relative sequence similarity, acknowledging that people in a linked social network would have more similarity in their sequences than people in a chain who were not connected through a social network. We have removed the term “cluster” from our manuscript and replaced it with “sequence alignment” throughout the manuscript. We have also defined the genetic distance terminology. There is no concordance between the network structure of recruitment and the genetic distance.

2) The authors spent 9 months (February to October 2016, line 82) recruiting subjects. Why was there such a low yield in study subjects?

Response: Our overall study incorporated a mixed methods approach, including qualitative in-depth interviews, development and fielding of an Audio Computer Assisted Self Interview (ACASI), piloting of a text messaging (chatbot) system, HCV rapid testing, phlebotomy, specimen preparation, PCR testing, phylogenetic and bioinformatics analyses and additional study components. Thus, as part of a mixed methods exploratory study, we completed all of our target recruitment goals for our qualitative (n=24) [https://pubmed.ncbi.nlm.nih.gov/30071457/], ACASI survey (n=252) [https://pubmed.ncbi.nlm.nih.gov/29482054/], HCV testing (n=102), and phylogenetic analyses (n=62). This was the first such mixed methods study conducted with PWID in the Boston Area, and as an exploratory study, we were interested in assessing the relationship between social network structure and phylogenetic similarity, and we achieved this goal.

3) How many individuals were surveyed to identify the 102 HCV antibody positive subjects? How many individuals were approached? How many individuals had their blood tested?

Response: We aimed to conduct HCV rapid testing until we achieved identification of 100 study participants who tested HCV antibody positive. While we surveyed 252 individuals using our ACASI instrument, we limited the number of participants who would undergo confirmatory blood draws and phylogenetic analyses given the costs and the limited budget of our pilot project. We ultimately obtained blood specimens from 102 participants. Among these 102 participants, 40% had cleared the virus or did not have sufficient virus to be sequenced. We could only confirm chronic infection in 63 participants and 61 yielded readable sequences. We don’t know how many individuals were approached as the protocol did not require that we keep information on potential recruits who were not ultimately consented.

4) The authors state in their method that a phylogenetic tree was generated (lines 105 to 110) and provide a figure of the tree (Figure 1B), however, there is no presentation of the results. It is unclear if there was any viral clustering. Were there any viral clusters? The figure legend needs more information. It is not clear what the values of on the phylogenetic tree mean.

Response: Given word count limitations, we were succinct in our results section, but we acknowledge that additional details are merited. We have now added additional text in our results section and in the legend for Figure 1B to denote that genetic distances are reported.

Reviewer #2: In this study, the authors aim to see if phylogenetic analysis, when combined with behavioural, network and spatial data, can add to the understanding of transmission patterns among PWID outside of outbreak settings. I think the topic is interesting, and the manuscript is well written but having said that, I do think the sample size used for the study was small. I wonder if re-framing the study slightly, to be clearer that the authors were trying to see if this recruitment and analysis approach can be informative at uncovering disease clusters and transmission patterns might help a bit more – currently this is only mentioned at the end of the article in the conclusions.

Response: Thank you for the positive feedback. Considering the first comments from both Reviewers 1 and 2, we have reframed the manuscript slightly, per recommendations, clarifying that our study aimed to determine whether recruitment and analysis approaches can be informative at uncovering disease sequences and transmission patterns, better aligning our intro, methods, results and discussion. We were looking to determine if there was an alignment of sequences among individuals who appeared to be linked to each other/know each other based on recruitment patterns compared to others for whom we did not have linkage data. Although we were mildly disappointed to discover that this was not the case, our discussion helps to contextualize our findings in the larger literature and how our approach can help to inform future studies.

Minor comments

• Line 103/4 – it would be helpful if the authors referenced where they got the sequencing protocol they used here. More detail on genotyping methods would be useful.

Response: We have added references for sequencing of HCV core region and sequence alignment as suggested by the reviewer.

– I note the authors mention mixed genotype infections in the discussion but didn’t say if they found any themselves. Did the genotyping method mean they were unable to identify these?

Response: We don’t think so, but we only performed consensus and not deep sequencing.

• Is there a particular reason the core region was chosen for this analysis – is it the best region for this type of analysis?

Response: The core region is a very conservative region and quite popular in these types of analyses. Like others, we used this approach in a previous study in St. Petersburg, Russia (citation #8).

• HCV sequencing results and comparison – is gt1a vs non gt1a a reasonable comparison? From my understanding, the spread of gt3a among PWID has been more recent than gt1a and seems consistent with the observation that older individuals in the cohort were infected with gt1b/2b. Whilst numbers are low, I wonder if the authors would see more trends associated with age/location/IDU duration if they further split the non-1a group?

Response: We conducted these analyses employing a number of approaches. We re-ran the analyses of the 3a genotypes, but we found no differences. We have edited a sentence to the manuscript to clarify that we looked but did not find any network characteristics associated with genotypes. (p. 7, lines: 216-217).

• Perhaps consider adding a table with some of the data from lines 133-164 comparing people with different HCV genotypes (1a, 3a, other genotypes?) – might make all the information easier to digest for readers

Response: Since brief reports only allow 2 tables and figures, we refrained from creating additional tables and figures. However, we now include a supplemental table, per the reviewer’s recommendation, while retaining the existing table and figure.

• Line 200 - given the authors were aiming to look at transmission clusters between PWID outside of known outbreaks, I agree the sample size is small particularly outside of an outbreak context. The authors say it needs to be expanded – I would be interested to know what they think a reasonable size would be?

Response: We have removed mentions of clusters throughout the manuscript. We do not have an explicit definition of the closeness of a sequence to be considered a cluster, so we can’t really answer this question. As the reviewer noted, we obtained sequences from a very small subset of people in Boston infected with HCV, so we have no information on how the 61 sequences we have align with we vast array of virus in circulation among PWID in the Boston area. Thus, we do not have the necessary data to make such an estimate.

• I think some discussion around the issue that phylogenetic linkage will only be possible for samples with the same genotype is missing. Did the authors consider if there was a better way to sample the population to get a larger cohort?

Response: The purpose of this exploratory study was to determine what resources would be necessary to obtain a fuller picture of HCV transmission dynamics. Unless we have a way to link rapid deep sequencing to recruitment patterns, the effort is unlikely to identify recent outbreaks in a timely fashion and to an extent that is superior to simply enhancing widespread HCV testing in the focal/key populations who inject drugs. This would require collaboration with other sites and pooling data.

• Table 1, is the age mean or median?

Response: Mean age and standard deviation. We have updated wording in the table to make this clear.

• Could the authors please confirm they will upload the HCV sequences into a sequence repository and cite these in the manuscript if accepted.

Response: Yes, we have submitted the sequences to GenBank and they are under review. We will provide the accession number can confirm that Dr. Paintsil has the sequences and will upload them if the manuscript is accepted for publication.

Typos

Line 64/5; analysis of HCV has identified [a range of] HCV subtypes among

Line 126; Forty [participants] (66%) described themselves as

Response: Thank you for catching these. We have corrected the typos.

Submitted filename: ResponseToReviewers_FINAL copy.docx

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4 Jul 2022

27 Jul 2022

Responses to Reviewer #2

Reviewer #2: I am happy with the authors responses to my comments. After reviewing the updates I have a couple more minor queries relating to Figure 1B –

I note that genotype 1b sequences cluster with gt2/4 sequences, rather than with gt1a sequences – I assume this is because core is being used rather than NS5B or whole genome? It might be worth commenting on this in the figure legend for those less familiar with HCV phylogenies if so.

We have added this point to the figure legend, which now reads: “Genetic distance, reported as the proportion of sequence divergence between connections on the tree. Each sub-genotypes aligns closely, but the relatively short core region of the genome that we chose to sequence and the small number of specimens from people infected with genotype 1b resulted in genotype 1b sequences appearing more closely aligned with genotype 2 and 4 than with genotype 1a sequences.”

Could the authors add more information on what exactly the labels on the tree mean into the figure legend? I initially assumed they were labelling HCV subtypes, but I don’t think this is correct. If they are highlighting more closely related sequences, could they please list the criteria (ie distance threshold) for doing this?

We address this point in the revision by amending the text to read: “The double-headed arrows indicate connection by referral (n=31). This provides visual evidence that the social network that was accessed to recruit participants contained people infected with different strains of HCV.”

Submitted filename: Responses to Reviewer_July revision.docx

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1 Aug 2022

Molecular and spatial epidemiology of HCV among people who inject drugs in Boston, Massachusetts

PONE-D-22-07761R2

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9 Aug 2022

PONE-D-22-07761R2

Molecular and spatial epidemiology of HCV among people who inject drugs in Boston, Massachusetts

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