Phylogenetic analysis and transmission networks highlight the role of older adults in the transmission of HIV-1 in northern Zhejiang, China
The human immunodeficiency virus (HIV) epidemic remains a significant global public health issue, with an estimated 39 million people living with the virus in 2022 and 1.3 million new infections reported in that year according to the data from The Joint United Nations Programme on HIV/AIDS (UNAIDS) . Although UNAIDS has proposed a 95-95-95 target by 2025 , achieving this goal poses considerable challenges. As of the most recent data, 86% of people living with HIV are aware of their status, 89% of those aware are receiving treatment, and 93% of those on treatment have achieved viral suppression . In China (excluding Hong Kong, Macao and Taiwan), there were 1.223 million people living with HIV by the end of 2022, with 107,000 newly reported cases in that year. Notably, 97.6% of transmission occurred via sexual contact , with heterosexual transmission accounting for 72.0% of infections in 2022. This figure is even higher among adults aged 50 and older, where 90.9% of infections were linked to heterosexual transmission , significantly higher than in other countries, such as 39.3% in the United States in 2018 , 37.8% in Australia in 2021 . Consequently, HIV presents a significant challenge to older heterosexual adults in China. Older adults have not traditionally been considered a key population for HIV prevention and control . However, studies have shown that older adults remain sexually active and engaged in a variety of sexual behaviors, including having multiple sexual partners, engaging in commercial sex and having unprotected sex with non-spousal partners [ – ]. Several factors contribute to the increased risk of HIV infection in this age group, such as limited knowledge about HIV, low awareness of self-protection methods, and the presence of chronic diseases . The rising number of HIV infections among older adults is a growing concern in China. The annual number of newly diagnosed HIV infections in this population increased from 32,850 in 2015 to 51,856 in 2022, representing 48.1% of all newly reported cases in 2022 [ , , ]. This trend has also been observed in Zhejiang and other provinces [ – ]. Phylogenetic analysis and transmission networks are increasingly used to monitor HIV transmission dynamics. When combined with epidemiological data, these methods can effectively inform the development of targeted intervention strategies [ – ]. It also allows inference of the effective reproductive number ( Re ) directly from genetic data , instead of the clinical surveillance data . Re represents the average number of secondary infections attributed to an infected individual when only part of the population is susceptible, being used to assess real-time changes in transmission and the effects of interventions [ – ]. These approaches have been applied to various key populations for HIV prevention and control, including men who have sex with men, commercial sex workers, injecting drug users, and, more recently, older adults [ – ]. However, studies focusing on older adults have often been limited by small sample sizes or short observation periods, despite revealing that this group plays a crucial role in local HIV transmission [ – ]. Few studies have explored the changing molecular profile and transmission efficacy among older adults under long time series. This study aimed to better understand the transmission dynamics of HIV infection in northern Zhejiang Province, China, using phylogenetic analysis and transmission networks from 2017 to 2022, and to clarify the role of older adults in HIV transmission. 

Study population and sample collection In total, 1196 HIV-positive cases were newly diagnosed between 2017 and 2022 in Huzhou, Zhejiang Province. Of which, 393 (32.9%) older cases were reported, showing an increasing trend during the period ( z = 3.723). Whole blood samples, collected using ethylenediaminetetraacetic acid as an anticoagulant, were obtained at the first follow-up from January 2017 to December 2022. All patients were newly confirmed as HIV-1-positive and without any antiretroviral therapy at the time of sampling. Sociodemographic characteristics, transmission routes, and other HIV-related information were collected by local Center for Disease Control and Prevention staff using standardized forms and face-to-face interviews conducted in a private room. All data used in this study were anonymized to protect participants’ privacy. Sequence data Viral RNA was extracted from plasma isolated from blood samples using the QIAamp Viral RNA Mini Kit (Qiagen, Valencia, CA, USA) following the manufacturer’s instructions. Reverse transcription polymerase chain reaction (RT-PCR) and nested PCR were used to amplify the partial HIV-1 pol coding region, which encodes the protease and the first 300 codons of the reverse transcriptase gene. Amplification and sequencing were performed as described previously . The PCR products were sent to Hangzhou TsingKe Bio-Tech Co. for purification and sequencing using five overlapping primers. Subtypes identification Sequencher version 5.0 (Gene Codes, Ann Arbor, MI, USA) was used to trim, assemble sequences and interpret ambiguous bases. All sequences were submitted to the HIV-1 Sequence Quality Control Tool ( https://www.hiv.lanl.gov/content/sequence/QC/index.html ) to confirm sequence quality. Assembled sequences were aligned using MAFFT v7.520  and trimmed to identical lengths (≥ 1,000 bp, HXB2: 2253–3257 nt). Reference sequences were obtained from the Los Alamos National Laboratory HIV Sequence Database ( https://www.hiv.lanl.gov ), covering the major HIV-1 subtypes and Circulating Recombinant Forms (CRFs). Context-based Modeling for Expeditious Typing (COMET) v2.4.0 , HIV Basic Local Alignment Search Tool (BLAST) analysis, and phylogenetic analyses were used to identify HIV-1 subtypes. Unclassified sequences were designated as unique recombinant forms (URFs), and the recombination pattern was confirmed using a jumping profile hidden Markov model (jpHMM) and Simplot v3.5.1 . Phylogenetic analyses were conducted using MEGA v6.0 software to generate a phylogenetic tree by the neighbor-joining method, based on the Kimura two-parameter model with 1000 bootstrap replicates. Construction of the transmission network Pairwise genetic distances between sequences were calculated using HIV Trace, based on the Tamura-Nei 93 (TN93) method. The transmission network was constructed in Cytoscape v3.9.1. Genetic distances ranging from 0.001 to 0.020 substitutions per site were evaluated to determine the optimal genetic distance threshold for forming the maximum number of clusters. In the network, each node represents a pol sequence, and the edges (links) between nodes indicate potential transmission. Clusters were defined as groups consisting of at least two nodes within the molecular network. Large clusters were defined as those containing 10 or more nodes, and small/ medium clusters were defined as having between two and nine nodes. Estimating the effective reproductive number ( Re ) of large clusters For each large cluster, the Re was estimated with the birth-death skyline model in BEAST v2.4.2 . The best-fitting model was confirmed via jModelTest v2.0.1 based on the Akaike information criterion and Bayesian information criterion . The selected best-fitting model incorporated gamma distribution rate changes, invariant site proportions, and uncorrelated log-normal relaxation molecular clocks while considering the date of diagnosis for each individual. The become uninfectious rate (δ) was calibrated based on the average duration of infection for HIV-1, assuming a constant δ across all large clusters. We used a greater number of dimensions to estimate the mean effective reproductive number for each large cluster. Conducting Markov chain Monte Carlo (MCMC) simulations for 100 million generations ensured the attainment of stationarity, with the initial 10% discarded as burn-in factors. Convergence was assessed using Tracer v1.7, ensuring an effective sample size (ESS) greater than 200 for all the parameters . The analyses were implemented with literature informed set of parameters . This value is used to describe the temporal dynamics of an epidemic, with Re > 1 indicating epidemic growth, and Re < 1 signifying a decline. Statistical analyses The Cochran-Armitage trend test was used to assess the trend characteristics over time. The demographics of subjects in clusters were described as frequencies. Uni- and multivariate logistic regression analyses were performed to explore associations with clustering. Crude and adjusted odds ratios (aORs) and their 95% confidence intervals (CIs) were calculated. All factors in the univariate analysis were included in the multivariate regression model. The final multivariate logistic model was constructed using backward elimination. All analyses were conducted using R v4.4.3 software. 

In total, 1196 HIV-positive cases were newly diagnosed between 2017 and 2022 in Huzhou, Zhejiang Province. Of which, 393 (32.9%) older cases were reported, showing an increasing trend during the period ( z = 3.723). Whole blood samples, collected using ethylenediaminetetraacetic acid as an anticoagulant, were obtained at the first follow-up from January 2017 to December 2022. All patients were newly confirmed as HIV-1-positive and without any antiretroviral therapy at the time of sampling. Sociodemographic characteristics, transmission routes, and other HIV-related information were collected by local Center for Disease Control and Prevention staff using standardized forms and face-to-face interviews conducted in a private room. All data used in this study were anonymized to protect participants’ privacy. 

Viral RNA was extracted from plasma isolated from blood samples using the QIAamp Viral RNA Mini Kit (Qiagen, Valencia, CA, USA) following the manufacturer’s instructions. Reverse transcription polymerase chain reaction (RT-PCR) and nested PCR were used to amplify the partial HIV-1 pol coding region, which encodes the protease and the first 300 codons of the reverse transcriptase gene. Amplification and sequencing were performed as described previously . The PCR products were sent to Hangzhou TsingKe Bio-Tech Co. for purification and sequencing using five overlapping primers. 

Sequencher version 5.0 (Gene Codes, Ann Arbor, MI, USA) was used to trim, assemble sequences and interpret ambiguous bases. All sequences were submitted to the HIV-1 Sequence Quality Control Tool ( https://www.hiv.lanl.gov/content/sequence/QC/index.html ) to confirm sequence quality. Assembled sequences were aligned using MAFFT v7.520  and trimmed to identical lengths (≥ 1,000 bp, HXB2: 2253–3257 nt). Reference sequences were obtained from the Los Alamos National Laboratory HIV Sequence Database ( https://www.hiv.lanl.gov ), covering the major HIV-1 subtypes and Circulating Recombinant Forms (CRFs). Context-based Modeling for Expeditious Typing (COMET) v2.4.0 , HIV Basic Local Alignment Search Tool (BLAST) analysis, and phylogenetic analyses were used to identify HIV-1 subtypes. Unclassified sequences were designated as unique recombinant forms (URFs), and the recombination pattern was confirmed using a jumping profile hidden Markov model (jpHMM) and Simplot v3.5.1 . Phylogenetic analyses were conducted using MEGA v6.0 software to generate a phylogenetic tree by the neighbor-joining method, based on the Kimura two-parameter model with 1000 bootstrap replicates. 

Pairwise genetic distances between sequences were calculated using HIV Trace, based on the Tamura-Nei 93 (TN93) method. The transmission network was constructed in Cytoscape v3.9.1. Genetic distances ranging from 0.001 to 0.020 substitutions per site were evaluated to determine the optimal genetic distance threshold for forming the maximum number of clusters. In the network, each node represents a pol sequence, and the edges (links) between nodes indicate potential transmission. Clusters were defined as groups consisting of at least two nodes within the molecular network. Large clusters were defined as those containing 10 or more nodes, and small/ medium clusters were defined as having between two and nine nodes. 

Re ) of large clusters For each large cluster, the Re was estimated with the birth-death skyline model in BEAST v2.4.2 . The best-fitting model was confirmed via jModelTest v2.0.1 based on the Akaike information criterion and Bayesian information criterion . The selected best-fitting model incorporated gamma distribution rate changes, invariant site proportions, and uncorrelated log-normal relaxation molecular clocks while considering the date of diagnosis for each individual. The become uninfectious rate (δ) was calibrated based on the average duration of infection for HIV-1, assuming a constant δ across all large clusters. We used a greater number of dimensions to estimate the mean effective reproductive number for each large cluster. Conducting Markov chain Monte Carlo (MCMC) simulations for 100 million generations ensured the attainment of stationarity, with the initial 10% discarded as burn-in factors. Convergence was assessed using Tracer v1.7, ensuring an effective sample size (ESS) greater than 200 for all the parameters . The analyses were implemented with literature informed set of parameters . This value is used to describe the temporal dynamics of an epidemic, with Re > 1 indicating epidemic growth, and Re < 1 signifying a decline. 

The Cochran-Armitage trend test was used to assess the trend characteristics over time. The demographics of subjects in clusters were described as frequencies. Uni- and multivariate logistic regression analyses were performed to explore associations with clustering. Crude and adjusted odds ratios (aORs) and their 95% confidence intervals (CIs) were calculated. All factors in the univariate analysis were included in the multivariate regression model. The final multivariate logistic model was constructed using backward elimination. All analyses were conducted using R v4.4.3 software. 

Study population characteristics In total, 931 HIV pol sequences (77.8%, 931/1196) were successfully obtained from newly diagnosed HIV-positive patients in Huzhou between 2017 and 2022. The demographic characteristics of the study subjects are presented in Table . The median age was 41.0 years (IQR: 29–54 years), with the majority being men (85.1%). Nearly half of all individuals were divorced/with the spouse passed away (49.4%), and most were of Han ethnicity (94.2%). A significant portion had an education level of junior high school or below (65.8%). Heterosexual transmission (58.8%) was the primary route of infection, and 57.3% of individuals had registered households in Huzhou. Additionally, 57.8% had a first cluster of differentiation 4 (CD4) count of 200–499 after HIV confirmation, and 17.3% had a history of sexually transmitted diseases (Table ). 

In total, 931 HIV pol sequences (77.8%, 931/1196) were successfully obtained from newly diagnosed HIV-positive patients in Huzhou between 2017 and 2022. The demographic characteristics of the study subjects are presented in Table . The median age was 41.0 years (IQR: 29–54 years), with the majority being men (85.1%). Nearly half of all individuals were divorced/with the spouse passed away (49.4%), and most were of Han ethnicity (94.2%). A significant portion had an education level of junior high school or below (65.8%). Heterosexual transmission (58.8%) was the primary route of infection, and 57.3% of individuals had registered households in Huzhou. Additionally, 57.8% had a first cluster of differentiation 4 (CD4) count of 200–499 after HIV confirmation, and 17.3% had a history of sexually transmitted diseases (Table ). 

Overall, CRF07_BC (51.7%, 481/931) and CRF01_AE (27.5%, 256/931) were the predominant subtypes, followed by CRF08_BC (8.6%, 80/931), B (2.7%, 25/931), CRF55_01B (1.8%, 17/931), CRF67_01B (1.2%, 11/931), CRF85_BC (1.3%, 12/931), CRF59_01B (0.4%, 4/931), A1 (0.4%, 4/931), CRF02_AG (0.3%, 3/931), C (0.3%, 3/931), CRF68_01B (0.2%, 2/931), BC (0.1%, 1/931), CRF52_01B (0.1%, 1/931), CRF65_cpx (0.1%, 1/931), CRF79_0107 (0.1%, 1/931) (Fig. ). Additionally, 29 sequences were classified as unique recombinant forms (URFs) (3.1%, 29/931), with 28 sequences displaying a recombinant genomic structure of CRF01_AE and CRF07_BC, and the remaining sequence consisting of CRF01_AE and B. The proportion of CRF07_BC increased across years from 43.5% in 2017 to 59.8% in 2022 ( z = 3.266, P = 0.001), and that of CRF01_AE decreased from 33.3% in 2017 to 19.5% in 2022 ( z = 3.761, P < 0.001) (Fig. ). In contrast, the proportion of individuals infected through homosexual transmission did not show a significant change over time ( z = 0.351, p = 0.726). However, the proportion of older individuals increased over time ( z = 3.251, P = 0.001), with the highest proportion observed among those with the subtype of CRF07_BC (Fig. ). Molecular transmission network analysis Molecular transmission networks were analyzed based on pol sequences using an optimal genetic distance threshold of 0.01 substitutions per site. In total, 448 individuals formed 110 putative transmission networks, with sizes ranging from 2 to 83 sequences, encompassing eight subtypes. The subtype of CRF07 BC formed 50 transmission clusters, including the largest cluster of 83 cases (range: 2–83, median: 2). The subtype of CRF01 AE formed 34 transmission clusters, with the largest containing 14 cases (range: 2–14, median: 2). Other subtypes accounted for 26 transmission clusters, with the largest comprising 6 cases (range: 2–6, median: 2) (Fig. ). Overall, 448 of the 931 individuals belonged to transmission networks, yielding a clustering rate of 48.1%. Among different age groups, the clustering rate for individuals younger than 30 years showed a significant downward trend, decreasing from 46.8% in 2017 to 17.5% in 2022 ( z = 3.183, p = 0.001). However, no significant changes were observed in the clustering rates for the 30–39, 40–49, and ≥ 50 age groups ( P > 0.05). The contribution of individuals aged ≥ 50 years to transmission networks across clusters of varying sizes was also evaluated. Individuals aged ≥ 50 years constituted the highest proportion in large clusters (49.0%), compared to those in small/ medium clusters (35.5%) and non-clustered cases (26.1%) (χ 2 = 29.336, P < 0.001, Fig. ). We performed multivariate logistic regression to assess the associations between clustering in a putative transmission network and various demographic or behavioral characteristics. The odds of clustering were 2.125 (95% CI: 1.251–3.632) times higher in individuals aged ≥ 50 years compared to those under 30 years of age. Additionally, individuals with registered households in Huzhou had significantly higher odds of clustering (aOR = 1.677, 95% CI: 1.252–2.249) compared to those from other areas. Furthermore, individuals with the subtype of CRF07_BC were more likely to cluster than those with the subtype of CRF01_AE (aOR = 2.119, 95% CI: 1.542–2.924) (Table ). Age groups according to HIV-1 subtypes in the network Sankey diagrams were created to illustrate the relationships among the four age groups and different HIV-1 subtypes within the transmission networks, based on individual linkages (Fig. ). The strongest correlations were observed within the same age group for individuals aged ≥ 50 years in the CRF07_BC, CRF01_AE, and other subtypes, accounting for 24.1% (163/676), 32.2% (39/121), and 34.1% (15/44), respectively. Among cross-age group interactions, the highest correlations were between individuals aged ≥ 50 years and those aged 40–49 years, accounting for 15.5% (105/676) in CRF07_BC, (14.0%, 17/121) in CRF01_AE and 15.9% (7/44) in other subtypes. Characteristics and Re of large clusters Four clusters comprised 10 or more nodes, with three belonging to the CRF07_BC, and one to CRF01_AE. The largest cluster was predominantly linked to homosexual contact (73.5%, 61/83), whereas the remaining three clusters were primarily driven by heterosexual contact among males, accounting for 76.1% (35/46), 85.7% (12/14), and 100.0% (12/12), respectively. In terms of age distribution across the large clusters, the largest cluster was dominated by individuals under 30 years of age (32.5%, 27/83), whereas the other three clusters were dominated by individuals aged ≥ 50 years, representing 87.0% (40/46), 78.6% (111/14) and 58.3% (7/12), respectively. The peak Re for CRF07_BC-Cluster 1 occurred between 2013 and 2017, after which it declined and remained below 1 from 2017 onward (Fig. A). For CRF07_BC-Cluster 3, the Re peaked during 2019–2020, then fluctuated and fell below 1 by 2022 (Fig. C). CRF01_AE-Cluster 4 reached its peak Re between 2017 and 2018, then declined and increased gradually below 1 by 2021 (Fig. D). In contrast, the Re of CRF07_BC-Cluster 2 peaked in 2017, but declined and fluctuated, reaching a Re of 2.2 after 2022 (Fig. B). In CRF07_BC-Cluster 2, where Re > 1, 73.9% (34/46) of individuals reported a history of commercial sexual contact (comprising 32 males [29 aged ≥ 50, 3 aged < 50] and 2 females). Additionally, 13.0% (6/46) indicated having extramarital non-commercial sexual contact, and another 13.0% (6/46) stated that their spouse was their only sexual partners. One female, identified as HIV positive by public health authorities while working as a female sex worker, had the highest degree of 37 in the cluster. In total, five sero-concordant couples were reported within this cluster. 

Molecular transmission networks were analyzed based on pol sequences using an optimal genetic distance threshold of 0.01 substitutions per site. In total, 448 individuals formed 110 putative transmission networks, with sizes ranging from 2 to 83 sequences, encompassing eight subtypes. The subtype of CRF07 BC formed 50 transmission clusters, including the largest cluster of 83 cases (range: 2–83, median: 2). The subtype of CRF01 AE formed 34 transmission clusters, with the largest containing 14 cases (range: 2–14, median: 2). Other subtypes accounted for 26 transmission clusters, with the largest comprising 6 cases (range: 2–6, median: 2) (Fig. ). Overall, 448 of the 931 individuals belonged to transmission networks, yielding a clustering rate of 48.1%. Among different age groups, the clustering rate for individuals younger than 30 years showed a significant downward trend, decreasing from 46.8% in 2017 to 17.5% in 2022 ( z = 3.183, p = 0.001). However, no significant changes were observed in the clustering rates for the 30–39, 40–49, and ≥ 50 age groups ( P > 0.05). The contribution of individuals aged ≥ 50 years to transmission networks across clusters of varying sizes was also evaluated. Individuals aged ≥ 50 years constituted the highest proportion in large clusters (49.0%), compared to those in small/ medium clusters (35.5%) and non-clustered cases (26.1%) (χ 2 = 29.336, P < 0.001, Fig. ). We performed multivariate logistic regression to assess the associations between clustering in a putative transmission network and various demographic or behavioral characteristics. The odds of clustering were 2.125 (95% CI: 1.251–3.632) times higher in individuals aged ≥ 50 years compared to those under 30 years of age. Additionally, individuals with registered households in Huzhou had significantly higher odds of clustering (aOR = 1.677, 95% CI: 1.252–2.249) compared to those from other areas. Furthermore, individuals with the subtype of CRF07_BC were more likely to cluster than those with the subtype of CRF01_AE (aOR = 2.119, 95% CI: 1.542–2.924) (Table ). 

Sankey diagrams were created to illustrate the relationships among the four age groups and different HIV-1 subtypes within the transmission networks, based on individual linkages (Fig. ). The strongest correlations were observed within the same age group for individuals aged ≥ 50 years in the CRF07_BC, CRF01_AE, and other subtypes, accounting for 24.1% (163/676), 32.2% (39/121), and 34.1% (15/44), respectively. Among cross-age group interactions, the highest correlations were between individuals aged ≥ 50 years and those aged 40–49 years, accounting for 15.5% (105/676) in CRF07_BC, (14.0%, 17/121) in CRF01_AE and 15.9% (7/44) in other subtypes. 

Re of large clusters Four clusters comprised 10 or more nodes, with three belonging to the CRF07_BC, and one to CRF01_AE. The largest cluster was predominantly linked to homosexual contact (73.5%, 61/83), whereas the remaining three clusters were primarily driven by heterosexual contact among males, accounting for 76.1% (35/46), 85.7% (12/14), and 100.0% (12/12), respectively. In terms of age distribution across the large clusters, the largest cluster was dominated by individuals under 30 years of age (32.5%, 27/83), whereas the other three clusters were dominated by individuals aged ≥ 50 years, representing 87.0% (40/46), 78.6% (111/14) and 58.3% (7/12), respectively. The peak Re for CRF07_BC-Cluster 1 occurred between 2013 and 2017, after which it declined and remained below 1 from 2017 onward (Fig. A). For CRF07_BC-Cluster 3, the Re peaked during 2019–2020, then fluctuated and fell below 1 by 2022 (Fig. C). CRF01_AE-Cluster 4 reached its peak Re between 2017 and 2018, then declined and increased gradually below 1 by 2021 (Fig. D). In contrast, the Re of CRF07_BC-Cluster 2 peaked in 2017, but declined and fluctuated, reaching a Re of 2.2 after 2022 (Fig. B). In CRF07_BC-Cluster 2, where Re > 1, 73.9% (34/46) of individuals reported a history of commercial sexual contact (comprising 32 males [29 aged ≥ 50, 3 aged < 50] and 2 females). Additionally, 13.0% (6/46) indicated having extramarital non-commercial sexual contact, and another 13.0% (6/46) stated that their spouse was their only sexual partners. One female, identified as HIV positive by public health authorities while working as a female sex worker, had the highest degree of 37 in the cluster. In total, five sero-concordant couples were reported within this cluster. 

We conducted a high-density sampling of 931 newly diagnosed patients in a developed city in northern Zhejiang from 2017 to 2022 to perform a phylogenetic analysis of HIV-1 epidemic strains. The subtype of CRF07_BC was predominant locally and exhibited an upward trend over time. Specifically, we generated molecular transmission networks and conducted multiple logistic analyses to illustrate the role of older adults in transmission. Our findings clearly indicate that older adults played a crucial role in the transmission dynamics observed in this study. The study revealed that HIV-1 subtypes were diverse, identifying 16 distinct subtypes along with some URFs, with CRF07_BC being the predominant subtype, followed by CRF01_AE. CRF07_BC showed an increasing trend from 2017 to 2022, whereas CRF01_AE decreased during the same period. Previous studies indicated that CRF07_BC and CRF01_AE were the predominant subtypes in China in 2015 and 2018 , as well as in Huzhou in 2015  and Shenzhen from September 2019 to December 2020 . The upward trend of CRF07_BC and the downward trend of CRF01_AE were also observed in other studies conducted in China and Shenzhen . Some research has suggested that the upward trend of CRF07_BC is attributable to the population of men who have sex with men ; however, our study emphasizes the significant influence of the older population instead. Alongside the increasing trend of CRF07_BC, the diversity of HIV-1 subtypes and URFs across years, highlights the complexity of HIV epidemiology and underscores the importance of ongoing genetic surveillance of the virus. In our study, multivariate logistic regression indicated that individuals aged ≥ 50 years and those with registered households in Huzhou were more likely to be clustered within the transmission networks. Notably, the proportion of individuals aged ≥ 50 years was highest in large clusters, suggesting that older individuals from local registered residences contribute significantly to local HIV transmission and should be prioritized for intervention. This finding contrasts with many other studies that identify floating populations as a contributing factor to the local HIV epidemic . Currently, HIV infections among older adults aged ≥ 50 years represent a considerable burden in China . The neglect of the sexual needs of the older population by both partners and society has led to high-risk sexual behaviors. Epidemiological surveys and molecular network analyses have shown that HIV infections among the elderly primarily result from commercial heterosexual behavior, highlighting the transmission links between this demographic and female sex workers . Furthermore, the substantial reliance on lower-tier female sex workers has facilitated an increase in the prevalence of HIV among older clients . There was a tendency towards increased popularity of transmission network analyses based on HIV-1 sequence data, of which distance-based methodologies were often used to define . The threshold selection is the foundation for network construction. Some studies selected 1.5% as the optimal threshold based on subtype B threshold [ – ]. Here, we used optimal thresholds for subtypes according to the principle outlined in previous studies with the maximum number of clusters [ – ]. In our study, four large clusters were identified, with the largest consisting of 83 individuals, who tended to be younger and exhibited a higher proportion of homosexual contact. The Re remained high from 2013 to 2017 but then declined rapidly to below one, indicating decline in HIV transmission during the study period. This decline may be linked to public health interventions, particularly the expansion of highly active antiretroviral therapy in China since the end of 2016 . Notably, only one of the remaining three large transmission clusters had a Re greater than 1; this cluster tended to include older individuals and had a higher proportion of commercial heterosexual contact. Although a key individual identified as a female sex worker was present in this cluster, the estimated Re remained above 1, suggesting epidemic growth in HIV transmission during this period. To effectively control transmission, active HIV testing targeting local older individuals, along with additional specific interventions (e.g., partner services), should be implemented. Our results should be interpreted with the following limitations in mind. First, the data do not allow for an assessment of the directionality of transmission, such that we cannot distinguish between transmitters and recipients within our clusters. However, we found that older individuals are more likely to be involved in HIV transmission clusters, underscoring the importance of directing public health services toward this key population. Second, the incomplete coverage of sequences may subject our results to sampling bias. We anticipate that improving the completeness of molecular surveillance data in future will enable us to draw even stronger inferences about the characteristics of the transmission network. Third, the threshold genetic distance of 0.01 substitutions/site applied to construct the transmission network, which was selected based on experience and calculation, may not have been the optimal threshold for our analysis. 

Our study applied phylogenetic analysis and transmission networks to reveal the epidemic patterns of HIV-1 epidemic strains and the role of older adults played in transmission in northern Zhejiang, China. The subtype of CRF07_BC was predominant locally and exhibited an increasing trend over time. Our findings highlight the crucial role of older adults played in local HIV-1 transmission, with results of older adults being more likely to be clustered, being the highest proportion in large clusters and having a transmission cluster acquired Re greater than 1. Therefore, public health services should target this key population to curb the spread of HIV-1. 

Below is the link to the electronic supplementary material. Supplementary Material 1 

