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Preplanned Studies: Identifying the Key Nodes of HIV Molecular Transmission Network Among Men Who Have Sex with Men — Guangzhou, Guangdong Province, China, 2015–2017

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  • Summary

    What is already known about this topic?

    Identifying the most influential spreaders in human immunodeficiency virus (HIV) transmission networks is crucial for developing effective prevention strategies.

    What is added by this report?

    This study identified key nodes of the HIV molecular transmission network among men who have sex with men (MSM) by utilizing linkages between sequences to reconstruct the transmission network at the molecular level.

    What are the implications for public health practice?

    This study could act as an important supplement of laboratory results to epidemiological studies and suggests that interdisciplinary research could inspire new ideas for finding breakthroughs on HIV/acquired immunodeficiency syndrome (AIDS) prevention and control.

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  • Funding: Supported by the National Natural Science Foundation of China (NSFC) (No. 71473234 and 71573239)
  • [1] NCAIDS, NCSTD, China CDC. Update on the AIDS/STD epidemic in China in December 2017. Chin J AIDS STD 2018;24(2): 111. http://dx.doi.org/10.13419/j.cnki.aids.2018.02.01 (In Chinese). CrossRef
    [2] Morgan E, Oster AM, Townsell S, Peace D, Benbow N, Schneider JA. HIV-1 infection and transmission networks of younger people in Chicago, Illinois, 2005-2011. Public Health Rep 2017;132(1): 48-55. http://dx.doi.org/10.1177/0033354916679988CrossRef
    [3] Grabowski MK, Redd AD. Molecular tools for studying HIV transmission in sexual networks. Curr Opin HIV AIDS 2014;9(2): 126-33. http://dx.doi.org/10.1097/COH.0000000000000040CrossRef
    [4] Oster AM, Wertheim JO, Hernandez AL, Ocfemia MCB, Saduvala N, Hall HI. Using molecular HIV surveillance data to understand transmission between subpopulations in the united states. J Acquir Immune Defic Syndr 2015;70(4): 444-51. http://dx.doi.org/10.1097/QAI.0000000000000809CrossRef
    [5] Pines HA, Wertheim JO, Liu L, Garfein RS, Little SJ, Karris MY. Concurrency and HIV transmission network characteristics among MSM with recent HIV infection. AIDS 2016;30(18): 2875-83. http://dx.doi.org/10.1097/QAD.0000000000001256CrossRef
    [6] Li XS, Gao R, Zhu KX, Wei FR, Fang K, Li W, et al. Genetic transmission networks reveal the transmission patterns of HIV-1 CRF01_AE in China. Sex Transm Infect 2018;94(2): 111-6. http://dx.doi.org/10.1136/sextrans-2016-053085CrossRef
    [7] Schneider JA, Zhou AN, Laumann EO. A new HIV prevention network approach: sociometric peer change agent selection. Soc Sci Med 2015;125: 192-202. http://dx.doi.org/10.1016/j.socscimed.2013.12.034CrossRef
    [8] Mao X, Wang Z, Hu Q, Huang C, Yan H, Wang Z, et al. HIV incidence is rapidly increasing with age among young men who have sex with men in China: a multicentre cross-sectional survey. HIV Med 2018;19(8): 513-22. http://dx.doi.org/10.1111/hiv.12623CrossRef
    [9] Yang J, Xu HF, Li S, Cheng WB, Gu YZ, Xu P, et al. The characteristics of mixing patterns of sexual dyads and factors correlated with condomless anal intercourse among men who have sex with men in Guangzhou, China. BMC Public Health 2019;19(1): 722. http://dx.doi.org/10.1186/s12889-019-7082-9CrossRef
  • FIGURE 1.  Network diagram of 75 nodes who had at least 1 relationship tie with another node among 184 sequences of men who have sex with men in Guangzhou, Guangdong Province, China, 2015–2017.

    Note: Genetic distance: the pairwise genetic distance is equal or less than 0.015 substitutions per site within all sequences. Red represents key nodes: The name of the nodes is the laboratory code, and the sample name beginning with “M” came from 2015–2016. The line between any two nodes displayed the propagation relationship; however, the lines do not denote directionality. Cliques and lambda sets were obtained by analysis and cannot be seen directly from the picture. See Supplementary Table S1 and Table S2.

    TABLE 2.  The demographic characteristics of key nodes in the HIV transmission network in Guangzhou, Guangdong Province, China, 2015–2017.

    CharacteristicsKey nodes N (%)Others N (%)Adjusted OR 95% CIP value
    Total9 (4.89)175 (95.11)
    Age (years)
    18–251 (1.45)68 (98.55)0.06 (0.01–0.74)0.03
    26–352 (2.82)69 (97.18)0.12 (0.02–0.84)0.03
    ≥366 (13.64)38 (86.36)1.00
    Educational level
    Primary school3 (6.82)41 (93.19)0.42 (0.06–2.95)0.38
    Junior and senior high school1 (2.27)43 (97.73)0.28 (0.03–3.07)0.29
    College and above5 (5.21)91 (94.79)1.00
    Marital status
    Married3 (10.34)26 (89.66)1.35 (0.11–16.12)0.81
    Unmarried5 (3.50)138 (96.50)1.40 (0.09–22.93)0.81
    Divorced1 (8.33)11 (91.67)1.00
    Sample resource
    NHS4 (4.00)96 (96.00)0.61 (0.14–2.75)0.52
    VCT5 (5.95)79 (94.05)1.00
    Abbreviations: HIV=human immunodeficiency virus; OR=odds ratio; CI=confidence interval; NHS= the National HIV sentinel Surveillance; VCT=HIV voluntary counseling and testing clinics.
    Download: CSV

    TABLE 1.  Characteristics of the study population according to categories of number of connections in Guangzhou, Guangdong Province, China, 2015–2017.

    CharacteristicsNumber of respondents N (%)Number of connectionsP value*
    01≥2
    Total184 (100.00)109 (59.24)36 (19.57)39 (21.20)
    Age (years)0.21
    18–2569 (37.50)47 (68.12)10 (14.49)12 (17.39)
    26–3571 (38.59)37 (52.11)19 (26.76)15 (21.13)
    ≥3644 (23.91)25 (56.82)7 (15.91)12 (27.27)
    Educational level0.53
    Primary school44 (23.91)27 (61.36)6 (13.64)11 (25.00)
    Junior and senior high school44 (23.91)29 (65.91)7 (15.91)8 (18.18)
    College and above96 (52.17)53 (55.21)23 (23.96)20 (20.83)
    Marital status0.26
    Married29 (15.76)13 (44.83)7 (24.14)9 (31.03)
    Unmarried143 (77.72)89 (62.24)28 (19.58)26 (18.18)
    Divorced12 (6.52)7 (58.33)1 (8.33)4 (33.33)
    Time of diagnosis0.01
    2015–201610053 (53.00)28 (28.00)19 (19.00)
    2016–20178456 (66.67)8 (9.52)20 (23.81)
    Note: All percentages are line percentages.
    * P value for chi-square test for categorical variables.
    Download: CSV

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Identifying the Key Nodes of HIV Molecular Transmission Network Among Men Who Have Sex with Men — Guangzhou, Guangdong Province, China, 2015–2017

View author affiliations

Summary

What is already known about this topic?

Identifying the most influential spreaders in human immunodeficiency virus (HIV) transmission networks is crucial for developing effective prevention strategies.

What is added by this report?

This study identified key nodes of the HIV molecular transmission network among men who have sex with men (MSM) by utilizing linkages between sequences to reconstruct the transmission network at the molecular level.

What are the implications for public health practice?

This study could act as an important supplement of laboratory results to epidemiological studies and suggests that interdisciplinary research could inspire new ideas for finding breakthroughs on HIV/acquired immunodeficiency syndrome (AIDS) prevention and control.

  • 1. The National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
  • 2. Department of HIV/AIDS Control and Prevention, Center for Disease Control and Prevention, Guangzhou, Guangdong, China
  • Corresponding author:

    Fan Lyu, fanlv0925@163.com

  • Funding: Supported by the National Natural Science Foundation of China (NSFC) (No. 71473234 and 71573239)
  • Online Date: September 17 2021
    doi: 10.46234/ccdcw2021.198
  • Based on reports of the acquired immunodeficiency syndrome (AIDS) epidemic in China in December 2017, sexual transmission accounted for more than 90% of total infections, and 26.86% of the sexual transmission infections were men who have sex with men (MSM) (1). According to the research conducted by Ethan Morgan and his colleagues, it is necessary to conduct investigations that focus on networks of target populations rather than traditional epidemiological factors such as geographic areas of high incidence (2). Identifying the most influential spreaders of the human immunodeficiency virus (HIV) transmission networks is crucial to develop effective prevention strategies.

    Analyzing the structure of networks provides an optimal way to confirm the location and the role of key nodes that play key roles in accelerating HIV transmission in the network. Given the hidden nature of the MSM population, it is difficult to confirm the relationship ties between any two members of the community, which is the first step to analyze network structure in traditional epidemiologic field investigations. Phylogenetics provides probabilities for network structure analysis in HIV research. Inferring putative transmission is the process of utilizing molecular phylogenetics analyzed by using HIV sequences to identify transmission events in groups of individuals (3). This study identified key nodes of the HIV molecular transmission network among MSM by utilizing linkages between sequences to reconstruct the HIV transmission network at the level of molecular genetics.

    A total of 184 sequences of the HIV-1 pol full-length gene were assessed and stratified over 2 periods based on the year of sample collection (2015–2017). All 184 sequences were aligned with all known sequences in the HIV database (http://hiv-web.lanl.gov/content/index, operated by Triad National Security, LLC for the U.S. Department of Energy’s National Nuclear Security Administration) using the Basic Local Alignment Search Tool (BLAST) before analysis. The length of the HIV-1 pol gene was 3,045 base pairs (bp) and the nucleotide positions of pol were 2,147–5,192 according to HXB2 subtype B reference strain (GenBank accession number K03455). The sequences were edited with the software Sequencher (version 5.0, Gene Codes Corporation, Ann Arbor, MI, USA). The reference sequences that were available on HIV Database) covered the major HIV-1 subtypes/CRFs. Among the 184 successfully amplified pol full-length sequences, 44.02% (81/184) were CRF07_BC, 33.15% (61/184) were CRF01_AE, 13.04% (24/184) were 01_B, 3.80% (7/184) were B, and 5.98% (11/184) were others.

    HIV molecular transmission network was based on genetic distance (4). Putative transmission links in the network were identified with dichotomized data, which was determined by whether the pairwise genetic distance was less than 0.015 substitutions per site within all sequences (5). In our study, the Tamura-Nei 93 pairwise genetic distances were calculated by Mega [Mega 7.0: Molecular Evolutionary Genetics Analysis across computing platforms (Kumar S, Stecher G, and Tamura K 2016)] (6).

    All social network analyses was conducted by UCINET 6.0 (version 6.05; Borgatti, Everett, and Freeman, 2002). The methods were described in the Supplementary Materials. All statistical analyses were performed with SAS (version 9.4, SAS Institute Inc., Cary, NC, USA). Multivariate logistic regression model was used to analyze the demographic characteristics of the key nodes.

    Of the 184 HIV-1 sequences that were of patients diagnosed between 2015–2017, 75 sequences had at least one relationship tie with another patient (Figure 1). The characteristics of the participants are presented in Table 1. Social network analysis demonstrated that there were 14 cliques that included at least 3 nodes (Supplementary Materials, Supplementary Table S1). The biggest clique includes 24 members, and there were some cliques sharing the same members. Cliques 1–8 shared a lot of same members, and clique 9 only included 4 members that did not share any member with others.

    The clique co-membership method yields a large subgroup consisting of cliques 1–8 with a median subgroup of cliques 10 and 12, 4 smaller groups including cliques 9, 11, 13, and 14, and the outsiders. We denoted the 6 subgroups as A, B, C, D, E, and F. M026 acted as a broker between Subgroup B and F, E and F, D and F, as well as D and E. Subgroup B and D shared 2 actors {30, M026} acting as brokers between them. There were 3 shared members between groups B and E, respectively: {R12, M026, M056}.

    From the result of lambda analysis (Supplementary Table S2), there were 17 lambda sets with λ 1 that have a minimum of 1 independent path linking for any two actors. The largest λ was 19; it include 2 actors {27、M057}. A little bit smaller λ were 15 and 10, the actors in the lambda sets were {4, 27, M057} and {26, M050, 4, 27, M057} respectively. All of the above 5 nodes were nested hierarchically in the set with λ 1, which has the largest number of members. These five nodes have the most relationship ties in the set and were in the most active central position.

    Finally, we identified 9 key nodes by using cohesive subgroup analysis in the HIV molecular transmission network; {30, M026, R12, M056} acted as brokers between subgroups, and {26, M050, 4, 27, M057} were confirmed as the most active nodes in one subgroup. We analyzed the demographic characteristics of these key nodes. From the results of multivariate logistic regression model, young MSM born in the 1990s (aged 18–25) and 1980s (aged 26–35) was 0.06 and 0.12 times, respectively, likely to be a key node than older MSM born in the 1970s (aged 36 and older) or before (Table 2).

    CharacteristicsKey nodes N (%)Others N (%)Adjusted OR 95% CIP value
    Total9 (4.89)175 (95.11)
    Age (years)
    18–251 (1.45)68 (98.55)0.06 (0.01–0.74)0.03
    26–352 (2.82)69 (97.18)0.12 (0.02–0.84)0.03
    ≥366 (13.64)38 (86.36)1.00
    Educational level
    Primary school3 (6.82)41 (93.19)0.42 (0.06–2.95)0.38
    Junior and senior high school1 (2.27)43 (97.73)0.28 (0.03–3.07)0.29
    College and above5 (5.21)91 (94.79)1.00
    Marital status
    Married3 (10.34)26 (89.66)1.35 (0.11–16.12)0.81
    Unmarried5 (3.50)138 (96.50)1.40 (0.09–22.93)0.81
    Divorced1 (8.33)11 (91.67)1.00
    Sample resource
    NHS4 (4.00)96 (96.00)0.61 (0.14–2.75)0.52
    VCT5 (5.95)79 (94.05)1.00
    Abbreviations: HIV=human immunodeficiency virus; OR=odds ratio; CI=confidence interval; NHS= the National HIV sentinel Surveillance; VCT=HIV voluntary counseling and testing clinics.

    Table 2.  The demographic characteristics of key nodes in the HIV transmission network in Guangzhou, Guangdong Province, China, 2015–2017.

    • Of 184 newly-HIV diagnosed MSM, 40.76% were linked to other MSM. Social network analysis demonstrated that 9 key nodes were detected.

      By using the clique co-membership method, there were four key nodes acting as brokers between subgroups. It could be inferred that there were a lot of subgroups connected by sharing co-members in the HIV molecular transmission network among MSM in Guangzhou City, Guangdong Province, China.

      The four nodes that occupied important bridge locations were critical in controlling and understanding the spread processes as well as for developing effective prevention strategies.

      Selecting candidates who connect across groups of otherwise disconnected individuals (such individuals are known as “bridging actors”) based on their network positions was shown to be more likely to enhance the diffusion of innovative HIV prevention interventions when compared to other centrally located popular opinion leaders (7). Some HIV-infected MSM called as key nodes mediated the transmission of HIV among different subpopulations. Young MSM were less likely to promote HIV transmission than older MSM.

      Based on connectivity cohesive subgroup analysis, known as the lambda sets method, we detected 5 key nodes. They were possibly taking on some kind of leadership role. In fact, they were active only in several subgroups of the transmission network in this study, rather than participating in the whole network of HIV transmission. In our study, there were at least three independent subgroups with members closely connected to each other within them. Therefore, it is immensely vital for HIV prevention and control to determine subgroups with different characteristics in HIV transmission network among MSM.

      In recent years, HIV incidence in young Chinese MSM was significantly higher than that of older MSM (8). However, based on our results, MSM who were younger than 25 years old were less likely to promote the wide spread of HIV than older MSM. The results of our survey on the social interaction patterns of this group also confirmed this point: MSM aged about 30 and above were more likely to have condomless anal intercourse (CAI) with those of different ages (9). The point of intervention activities should be to improve awareness of self-protective measures in young MSM and to promote HIV testing and antiretroviral therapy in older MSM.

      This study was subject to some limitations. Without a universally accepted standard, we used genetic distance less than 0.015 as the criterion when inferring putative transmission ties of the sequences. Some sequences with propagative relationships may be misclassified as false negatives. Furthermore, the network used to analyze structure characteristics in this paper was a partial network, so the number and scale of the subgroups may be underestimated, and some key nodes were not successfully identified. Large sample size research is needed to explore the demographic and behavioral characteristics of key nodes. Moreover, sequences were obtained from newly-HIV-diagnosed MSM during 2015–2017. We did not include the cases of patients who were infected through heterosexual and drug injection, and our conclusions did not apply to other populations.

      There were a lot of subgroups connected by sharing co-members in HIV molecular transmission network among MSM in Guangzhou. Some HIV-infected MSM, known as key nodes, mediated the transmission of HIV among different subpopulations. Young MSM under 25 were less likely to promote HIV transmission than older MSM. This study reflected the important supplement of laboratory results to epidemiological studies and provided new ideas for finding breakthroughs in HIV prevention and control.

      Figure 1.  Network diagram of 75 nodes who had at least 1 relationship tie with another node among 184 sequences of men who have sex with men in Guangzhou, Guangdong Province, China, 2015–2017.

      Note: Genetic distance: the pairwise genetic distance is equal or less than 0.015 substitutions per site within all sequences. Red represents key nodes: The name of the nodes is the laboratory code, and the sample name beginning with “M” came from 2015–2016. The line between any two nodes displayed the propagation relationship; however, the lines do not denote directionality. Cliques and lambda sets were obtained by analysis and cannot be seen directly from the picture. See Supplementary Table S1 and Table S2.

      CharacteristicsNumber of respondents N (%)Number of connectionsP value*
      01≥2
      Total184 (100.00)109 (59.24)36 (19.57)39 (21.20)
      Age (years)0.21
      18–2569 (37.50)47 (68.12)10 (14.49)12 (17.39)
      26–3571 (38.59)37 (52.11)19 (26.76)15 (21.13)
      ≥3644 (23.91)25 (56.82)7 (15.91)12 (27.27)
      Educational level0.53
      Primary school44 (23.91)27 (61.36)6 (13.64)11 (25.00)
      Junior and senior high school44 (23.91)29 (65.91)7 (15.91)8 (18.18)
      College and above96 (52.17)53 (55.21)23 (23.96)20 (20.83)
      Marital status0.26
      Married29 (15.76)13 (44.83)7 (24.14)9 (31.03)
      Unmarried143 (77.72)89 (62.24)28 (19.58)26 (18.18)
      Divorced12 (6.52)7 (58.33)1 (8.33)4 (33.33)
      Time of diagnosis0.01
      2015–201610053 (53.00)28 (28.00)19 (19.00)
      2016–20178456 (66.67)8 (9.52)20 (23.81)
      Note: All percentages are line percentages.
      * P value for chi-square test for categorical variables.

      Table 1.  Characteristics of the study population according to categories of number of connections in Guangzhou, Guangdong Province, China, 2015–2017.

    • No conflicts of interest declared.

    • Yali Zhang and Qiuyan Yu.

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