The authors have declared that no competing interests exist.
Conceived and designed the experiments: RSD CA PH SB PP GR AB MH. Analyzed the data: RSD GD. Wrote the paper: RSD. Managed the data: RSD RJ CA LT. Supervised the data analysis: CA PP GR JG AB MH. Read and contributed to the manuscript: GD CA PH LT SB RJ DR PP GR JG AB MH. Coordinated the field work: PH. Conducted HCV antibody and qualitative PCR tests and genetic sequencing: LT. Supervised the HCV antibody and qualitative PCR tests and genetic sequencing: SB. Coordinated the social network analyses: DR.
It is hypothesized that social networks facilitate transmission of the
Globally, it is estimated that 170 million people are infected with the
Social network epidemiology is a novel method that facilitates investigation of factors relating to the patterns connecting individuals socially
Molecular phylogenetics is the study of evolutionary relatedness among genetic sequences and can be used to reconstruct the shared history of sampled viral strains
The Networks 2 Study
The study conformed to the ethical guidelines of the 1975 Declaration of Helsinki; ethical approval was obtained from the Victorian Department of Health Human Research Ethics Committee (project 02/05). Participation was voluntary, written informed consent was obtained from each participant, and all were offered pre- and post-test counselling for HCV, HBV and HIV.
Networks 2 is a cohort study of PWID recruited from three major illicit drug markets located across metropolitan Melbourne, Australia. Recruitment utilized a social networks approach: at specified interviews, participants were asked to describe their relationships with up to five injecting partners and to introduce them to our field researchers
Blood samples were screened for antibodies to HCV (anti-HCV) by a third-generation enzyme immunoassay (Abbott Laboratories, Chicago, IL, USA) and anti-HCV positive specimens were tested again by Murex anti-HCV version 4.0 (Murex Biotech, Kyalami, South Africa) for confirmation. Irrespective of anti-HCV status, all samples were tested for HCV RNA by the COBAS AMPLICOR HCV test version 2.0 (Roche Molecular Systems, Branchburg, NJ, USA). HIV and hepatitis B virus (HBV) status were determined by measuring serological markers as described previously
HCV RNA positive blood samples were genotyped by a reverse-phase hybridisation line probe assay (LiPA, Versant HCV Genotype Assay, Siemens Healthcare Diagnostics, Tarrytown, NY, USA)
HCV core sequences were aligned using Clustal W through MEGA version 4.0 and MUSCLE
For minor genotypes (<20 infections), clusters were verified by phlyogenetic analysis with reference sequences from the Los Alamos HCV Sequence Database. Reference sequences were identified from the database as follows: all HCV sequences of the relevant HCV genotypes that contained the core region were downloaded and checked for sequencing ambiguities. Sequences with ambiguities were removed. If more than 50 sequences were available, random samples of 50 sequences were chosen using the MS Excel random number generator. Only 11 sequences were available for genotype
In order to estimate the false discovery rate for identifying possible transmission clusters using the viral region analysed, 300 previously published sequences from the same region of the virus were randomly selected from the Los Alamos HCV Sequence Database and analysed as a control experiment. The random sample was stratified by genotype and included 70 genotype 1a and 3a, 50 genotype 1b, 6a, and 6e, and ten 6l sequences. Among those sequences for which the participant identification code was specified in the sequence database, duplicate sequences from the same participant were removed prior to selecting the sample. Sequence alignment and phylogenetic analysis were undertaken using the methods described above. Sequences that clustered in the phylogenetic analyses were investigated using PubMed. Those that were epidemiologically related (from studies of multiple sequences isolated from the same individual) were discarded.
In general, social networks encompass a set of nodes (points) and edges (connections). In this case (similar to our previous publication
Participants with
Among participants without
Participants with
A positive HCV RNA test and negative anti-HCV test at the first study visit (indicating very early infection); or
A negative HCV RNA test and negative anti-HCV test at their first study visit and a subsequent positive anti-HCV/HCV RNA test during follow-up (HCV seroconversion).
Some participants had multiple infections during the study. Participants were defined as having a
tested HCV RNA negative on two occasions (at least 28 days apart) and subsequently tested HCV RNA positive; or
tested HCV RNA positive and subsequently tested HCV RNA negative on one occasion, then tested HCV RNA positive; and
at least 28 days had elapsed between the HCV RNA negative test and the subsequent positive test; and
the sequence distance between the two HCV RNA positive tests was at least 4% in the core region (331 nucleotides). The methodology used to determine the 4% cut-off is described below (definition 6).
Participants were defined as having a
All blood tests positive for HCV RNA underwent viral sequencing (HCV core region, 331 nucleotides). Viral sequences were compared pairwise, and the maximum composite likelihood distances were calculated. The mean (SD) distance between viral sequences taken from different participants with the same genotype and subtype was 3.5% (1.3%). The cut-off for defining a new viral strain was set at 4%, equal to approximately three standard deviations (3×1.3%) of the distribution of pairwise differences from viral sequences from different participants with the same genotype and subtype. This method for defining a cut-off was based on the method used by Pham and colleagues
To address the study aim (to determine the relationship between social networks and molecular phylogenetics in the context of incident HCV infection), the association between reported injecting partnerships (injecting partner - yes/no) and relationships defined by clustering in the phylogenetic analysis (in the same phylogenetic cluster – yes/no), and the association between social geodesic distance (the smallest number of injecting partnerships connecting two nodes) and HCV core sequence distance (maximum composite likelihood) were measured. An adjusted Jaccard similarity coefficient was used to measure the association between the reported injecting partnerships and relationships defined by clustering in the phylogenetic analysis in the baseline and flattened injecting networks. Tau c rank correlation was used to measure the association between social distance and HCV core sequence distance in the baseline and flattened social networks. To control for confounding between HCV genotype and socio-behavioural characteristics and mixing between participants infected with different HCV genotypes at baseline, this analysis was stratified by genotype and undertaken only amongst participants infected with the major genotypes in the study population,
In order to assess whether the relationship between the phylogenetic clusters and the social networks was dependent on the methodology used to define the phylogenetic clusters or the social network, the following variations were implemented:
Defining ties by reporting ever having used a needle/syringe before or after the other participant without sterilisation, rather than having used in the same room as the other participant in the past three months;
Defining phylogenetic clusters using cut-off for branch support of 80% rather than 70%; and
Defining phylogenetic clusters based on the maximum likelihood phylogeny rather than the neighbour-joining phylogeny.
For each of these variations, an adjusted Jaccard similarity coefficient was used to measure the association between the reported injecting partnerships and relationships defined by clustering in the phylogenetic analysis in the baseline and flattened injecting networks. The statistical significance of observed Jaccard similarity coefficients was assessed using the quadratic assignment procedure (QAP, 12500 permutations). Adjusted Jaccard similarity coefficients were similar across these variations; results from these sensitivity analyses are reported in
To explore the effect of conducting this investigation in the context of a longitudinal rather than cross-sectional study, using logistic regression in Stata 11 we investigated whether incident infections and new viral strains increased the likelihood of being in a phylogenetic cluster compared to other infections. Age, gender, ethnicity, neighbourhood of recruitment, and HCV genotype (
Finally, we examined whether
Between July 2005 and August 2008, 398 participants were recruited into the study. Four more participants were included in this analysis to maximize the completeness of the social network information because they were nominated as injecting partners before August 2008 but recruited at a later date. Of the 402 participants included in the flattened injecting network, 307 were recruited prior to February 2006. Another 19 participants recruited later were nominated as injecting partners before February 2006. Therefore, the baseline injecting network consisted of 326 participants. Participant characteristics were very similar in the baseline and flattened injecting networks (
Baseline Networkn (%)N = 326 |
Flattened Networkn (%)N = 402 |
||
|
Median (IQR) |
25.1 (22.2–29.4) | 25.6 (22.7–30.4) |
|
Female | 107 (33) | 133 (33) |
Male | 219 (67) | 269 (67) | |
|
Australian | 226 (70) | 279 (70) |
Vietnamese | 45 (14) | 54 (14) | |
Other | 50 (16) | 63 (16) | |
|
Unemployed | 236 (74) | 294 (74) |
Paid work |
70 (22) | 85 (22) | |
Student |
8 (2) | 8 (2) | |
Other |
6 (2) | 9 (2) | |
|
Stable |
225 (69) | 281 (70) |
Unstable |
99 (31) | 119 (30) | |
|
Median (IQR) | 8 (4–11) | 8 (5–12) |
|
Yes | 216 (66) | 269 (67) |
No | 110 (34) | 133 (33) | |
|
Heroin | 226 (70) | 278 (70) |
Speed | 50 (15) | 62 (16) | |
Buprenorphine | 37 (11) | 40 (10) | |
Other | 12 (4) | 20 (5) | |
|
Median (IQR) | 4 (2–7) | 3 (2–6) |
|
Median (IQR) | 24 (10–56) | 23 (10–56) |
Totals may not sum to
IQR: interquartile range.
Including full-time, part-time and casual employment.
Including full-time and part-time students.
Pensioners, home-duties.
Including own home, renting, and living with parents.
Including homeless, squat and boarding.
Anti-HCV and HCV RNA results were available for 376 participants at study entry. The remaining 26 participants could not be bled or had indeterminate anti-HCV or HCV RNA results at study entry. Initial HCV status and HCV infection events during the study are summarized in
HCV test results: anti-HCV and qualitative HCV RNA results. Those with missing results could not be bled or had indeterminate results; HCV infection at enrolment: anti-HCV positive and positive HCV RNA test (HCV RNA limit of detection: 50 IU/mL); past HCV infection at enrolment: positive anti-HCV test and negative HCV RNA test at the first study visit; seroconverting at enrolment: anti-HCV negative and positive HCV RNA test at the first study visit (indicating very early infection); never HCV infected at enrolment: negative anti-HCV test and negative HCV RNA test at first study visit. Participants with
Note: The infection status of each participant in the network is denoted by the shape of the node. Amongst participants who were infected at enrolment – that is, infection status is “current infection” or “seroconverting” – genotype is indicated by colour. Nodes that are not coloured represent participants who were not infected at baseline (white upward triangle or diamond) or participants who were infected at baseline but for whom the genotype could not be determined due to insufficient serum and/or low viral load (white square or downward triangle).
HIV and HBV infection were rare in this cohort. Of the 376 participants with anti-HCV and HCV RNA results at study entry, two (0.5%) were HIV infected – both of these were HCV coinfected – and no additional HIV seroconversions were observed. At study entry, 135 (36%) participants had evidence of prior but not current HBV infection. A further 13 (3%) participants were infected with HBV at study entry (hepatitis B surface antigen positive); eight of these were HCV coinfected, including one who was seroconverting to HCV. Two participants became HBV infected during the study but did not have evidence of HCV coinfection. One of these participants spontaneously cleared their HBV infection; the other became infected with HBV within the last three months of follow-up so it was not possible to determine whether they cleared their infection.
The baseline injecting network consisted of 326 participants and 259 injecting relationships (
A total of 526 HCV core DNA sequences from 227 study participants (sampled over the course of the study) were obtained from HCV infected participants. Phylogenetic analyses identified 26 clusters containing 69 distinct infections (
Phylogenetic clusters defined by bootstrap analysis (cut-off 70%) with infections from multiple individuals in the study are highlighted in blue and bootstrap values for these clusters are indicated. Newly acquired infections and changes in viral sequence are denoted using black-filled circles.
Comparison sequences were genotype 1b sequences randomly selected from the LANL HCV database. Study participants are denoted by diamonds. The first four letters of the name of the country of origin of LANL sequences is included in the ID. Phylogenetic clusters defined by bootstrap analysis (cut-off 70%) with infections from multiple individuals in the study are highlighted in blue and bootstrap values for these clusters are indicated.
Phylogenetic clusters defined by bootstrap analysis (cut-off 70%) with infections from multiple individuals in the study are highlighted in blue and bootstrap values for these clusters are indicated. Newly acquired infections and changes in viral sequence are denoted using black-filled circles.
Comparison sequences were randomly selected genotype
Number of infections (%)N = 247 | OR (95% CI) | p-value | AOR (95% CI) | p-value | |
|
|||||
No | 195 (79) | 1.00 | |||
Yes | 52 (21) | 2.07 (1.09–3.94) | 0.026 | 2.03 (1.04–3.96) | 0.037 |
|
|||||
16–22 | 60 (24) | 0.80 (0.39–1.65) | 0.543 | ||
23–25 | 71 (29) | 1.26 (0.66–2.39) | 0.484 | ||
26+ | 116 (47) | 1.00 | |||
|
|||||
Male | 170 (69) | 1.00 | |||
Female | 77 (31) | 0.87 (0.47–1.59) | 0.644 | ||
|
|||||
Footscray | 149 (62) | 1.00 | |||
Frankston | 46 (19) | 0.52 (0.22–1.20) | 0.126 | ||
Collingwood/Richmond | 46 (19) | 1.58 (0.79–3.16) | 0.191 | ||
|
|||||
|
114 (46) | 1.00 | |||
|
116 (47) | 2.81 (1.54–5.13) | 0.001 | 2.72 (1.48–4.99) | 0.001 |
|
16 (7) | 1.02 (0.27–3.91) | 0.975 | 0.87 (0.22–3.40) | 0.840 |
|
|||||
Australian | 161 (66) | 1.00 | |||
Vietnamese | 40 (16) | 1.0 (0.5–2.2) | 0.920 | ||
Other | 42 (17) | 0.8 (0.3–1.7) | 0.490 |
Note: Goodness of fit for the multivariable model was assessed using the Hosmer-Lemeshow test: p = 0.408.
Among 300 previously published HCV core DNA sequences that were randomly selected from the Los Alamos sequence database, three phylogenetic clusters were supported at the 70% bootstrap level and one was supported at the 80% bootstrap level. These results suggest an approximate false discovery rate of one per 100 infections.
Panel A. Baseline injecting network including all injecting ties reported in baseline interviews in main recruitment wave (July 2005–Feb 2006). Phylogenetic clusters identified using data from throughout the study period (July 2005–August 2008). Panel B. Flattened injecting network including all injecting ties reported throughout the study (July 2005–August 2008). Phylogenetic clusters identified using data from throughout the study period (July 2005–August 2008).
Being in the same phylogenetic cluster was correlated with reported injecting relationships in both the baseline and flattened injecting networks (
Network |
Participants |
Network measure | Phylogeny measure | Correlation coefficient |
p-value |
Mean |
SD |
|
|
|
|
|
|
|
|
Genotype |
geodesic distance | MCL distance | 0.018 | 0.096 | −0.000 | 0.014 | |
Genotype |
geodesic distance | MCL distance | −0.002 | 0.419 | 0.001 | 0.013 | |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Genotype |
geodesic distance | MCL distance | −0.007 | 0.422 | 0.001 | 0.038 |
Notes:
HCV: hepatitis C virus; MCL: maximum composite likelihood.
Geodesic distances calculated using complete baseline and flattened networks. Correlations between geodesic distances and MCL distances calculated for the subgroup of participants indicated.
Adjusted Jaccard coefficients provided for association between binary injecting networks and phylogenetic clusters. QAP analysis for adjusted Jaccard coefficients conducted in UCINET, 12500 permutations. Tau c coefficients provided for correlation between geodesic injecting network distances and MCL genetic distances. QAP analysis for tau c coefficients conducted in STATA, 5000 permutations. Statistical significance defined as p<0.001. An explanation of the QAP is provided in the
The p-value is based on the percentile of the empirical sampling distribution generated by the QAP in which the observed test statistic falls.
The mean and standard deviation of the test statistic in the empirical sampling distribution.
Statistically significant results are presented in
In contrast, genetic distance (by maximum composite likelihood) was not well correlated with social distance (geodesic), neither in the baseline social network nor in the flattened social network (
This study found that participants who had closely related HCV infections (defined as being in a phylogenetically related cluster) were also likely to report having injected together. This is a valuable result because it is an unequivocal empirical demonstration and measurement of the importance of injecting networks in HCV transmission. This has important public health implications due to the considerable opportunities for developing targeted interventions within the injecting social network to prevent HCV transmission (and potentially other infectious diseases). Interestingly, the genetic distance between all studied HCV infections was not well correlated with social distance more broadly, highlighting the complexity of HCV transmission. In addition, the construction of two injecting networks – the baseline injecting network representing a snapshot of the injecting network in the first six months of recruitment, and the flattened injecting network representing injecting relationships reported from 2005–2008 – provides insight into the network epidemiology of HCV transmission in PWID over time.
Injecting network factors have previously been identified as determinants of injecting risk behaviours
Although this study found that those participants with closely related HCV infections were likely to report injecting together, it did not find a strong association between genetic distance and social distance within individual (major) genotypes (
Given that HCV transmission is fairly uncommon, it is unsurprising that years of injecting network data are required to explain HCV transmission patterns. In this study, the proportion of participants who had sequences in phylogenetic clusters that had direct network connections with at least one of the other participants within that cluster was slightly higher in the flattened social network (53.7%), which represents injecting relationships over the three year study period, than in the baseline injecting network (47.8%), which represents injecting relationships in the first six months of the study. Although numbers were small (n = 20 participants), the difference in proportions was slightly greater amongst participants with newly acquired infection or change in viral sequence, with 50.0% connected to one of the other participants in their phylogenetic cluster in the baseline injecting network and 65.0% in the flattened injecting network. Nonetheless, the level of correlation between HCV phylogeny and injecting network relationships was similar in the baseline and flattened injecting networks. This is because – although more pairs of closely related infections were concordant with reported injecting relationships in the flattened than the baseline injecting network – there were also more reported injecting relationships in the flattened injecting network. In part, the observed association between HCV phylogenetic clusters and injecting partnerships in the baseline injecting network may be attributed to the relatively high number of participants with evidence of newly-acquired primary HCV infection at baseline (anti-HCV negative, HCV RNA positive at study entry). However, as described in the methods section, infections acquired later in the study were also included in the HCV phylogeny that correlated with the baseline injecting network. Therefore it is also possible that this result indicates the presence of relatively long-term injecting relationships that are important to HCV transmission. This is consistent with the self-report data collected on duration of injecting relationships (median: 3 years; IQR: 2–6 years). In this sense, the correlation between HCV phylogeny and the baseline injecting network can be interpreted as indicating that the baseline injecting network is predictive of future HCV transmission throughout the study.
Note that the results of the phylogenetic analysis do not necessarily imply direct transmission pathways. A potential shortcoming of our analyses was the reliance on sequencing of the HCV core gene. This region is relatively conserved so it is possible that some of the phylogenetic clusters are not closely genetically related, but rather distantly related or even unrelated isolates. We found a small amount of phylogenetic clustering in the HCV core gene in sequences randomly selected from the Los Alamos HCV Sequence database. This demonstrates that some of the clustering identified amongst study participant samples might be explained by homoplasy rather than genetic relationships between HCV infections but it is unlikely to explain the larger quantity of phylogenetic clustering identified among study participants (26 clusters identified among study participants compared to a potential three clusters identified among randomly selected previously published sequences). Indeed, in our earlier study of PWID in Melbourne, phylogenetic analyses of the HCV core region were confirmed by analysis of the NS5a region
This study has limitations. Data on injecting relationships are based on self-reporting and may be subject to information bias, and relationships could only be included in the analysis if both injecting partners were introduced to study personnel and recruited into the study. This means that in general, the injecting partnerships that were included are likely to represent closer relationships (such as sexual and kinship ties) than those that were not. In the context of HCV transmission, this type of selection bias is likely to result in the inclusion of ties that involve more frequent injection over a longer period of time than other ties and may therefore represent higher than average transmission risk pathways
In summary, the finding that participants who had closely related HCV infection (defined by being in a phylogenetic cluster) were likely to also report having injected together is valuable because it demonstrates the importance of the injecting network in HCV transmission. Not only does this highlight the necessity of investigating network factors in studies of HCV transmission, but it raises the possibility of using social network methods in public health interventions aimed at reducing HCV transmission risk. Injecting relationships of the kind reported in this study may be effective pathways for communicating information about safe-injecting practices, and the importance of strong and reasonably lengthy relationships in HCV transmission also raises the idea of developing interventions targeted at injecting partners or groups rather than individuals.
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We would like to acknowledge the field work team for their efforts recruiting, interviewing and taking blood from participants and the study participants for giving up their time to participate.