Combining molecular transmission network analysis and spatial epidemiology to reveal HIV-1 transmission pattern among the older people in Nanjing, China

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This study aimed to analyze the pattern and influencing factors of HIV transmission based on a genomic and spatial epidemiological analysis among this population. Methods A total of 432 older individuals who were newly diagnosed with HIV-1 and had not received ART between January 2018 and December 2021 were enrolled. HIV-1 pol gene sequence was obtained by viral RNA extraction and nested PCR. The molecular transmission network was constructed using HIV-TRACE and the spatial distribution analyses were performed in ArcGIS. The multivariate logistic regression analysis was performed to analyze the factors associated with clustering. Results A total of 382 sequences were successfully sequenced, of which CRF07_BC (52.1%), CRF01_AE (32.5%), and CRF08_BC (7.3%) were the main HIV-1 subtypes. A total of 176 sequences entered the molecular network, with a clustering rate of 46.1%. Impressively, the clustering rate among older people infected HIV with commercial heterosexual transmission was as high as 61.7% and three female commercial sex workers were observed in the network. The individuals who were aged ≥ 60 years and transmitted by commercial heterosexual behaviors had a higher risk of clustering, while those who were retirees or engaged other occupations and with higher education degree were less likely to cluster. There was a positive spatial correlation of clustering rate (Global Moran I =0.206, P < 0.001)at the town level and the highly aggregated regions were mainly distributed in rural area. We determined three large clusters and they mainly spread in the intra-region of certain towns in rural areas. Notably, 54.5% of cases in large clusters were transmitted through commercial heterosexual behaviors. Conclusions These findings revealed the spatial aggregation of HIV transmission and highlighted vital role of commercial heterosexual behavior in HIV transmission among older people at the local level. Therefore, health resources should be directed towards highly aggregated rural areas and prevention strategy should take critical regions or persons as entry points. Moreover, continuous monitor and rapid area response to the network should be strengthened to reduce further HIV transmission among older people. HIV/AIDS older people molecular network transmission cluster spatial analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction In the era of antiviral therapy (ART), HIV is no longer a life-limiting infection and AIDS has become a manageable chronic disease. The population of older people with HIV (normally defined as those aged 50 years or older) is increasing[ 1 , 2 ], and the number of people aged 50 years or older with HIV infection globally increased from 5·4 million in 2015 to 8·1 million in 2020 [ 2 ]. In recent years, the number of newly reported older people who were aged ≥ 50 years and infected with HIV in China has also been increasing [ 3 ], and the problem of HIV infection among older people has become a prominent challenge in the HIV prevention and control. The older people still have sex needs to a certain extent, but due to their low level of education, poor knowledge about HIV, and low rate of condoms use, they are prone to HIV infection. Meanwhile, they have poor awareness of active detection, resulting in late diagnosis. A meta-analysis pointed out that the HIV infection rate of older people in China was 2.1% [ 4 ], which was much higher than that of the general population in China (0.09%) [ 5 ]. Hence, controlling HIV infection among this population is of great significance to curb the epidemic of HIV in China. Using the similarity of viral genes, HIV−1 molecular transmission network can be constructed to analyze transmission patterns, determine potential transmission relationship, and identify active transmission clusters, providing an effective scientific basis for precise intervention [ 6 , 7 ]. A study used the molecular transmission network and field epidemiology to quantify the local HIV transmission mode and highlight ongoing epidemics, thus developing precise HIV prevention strategies [ 8 ]. The spatial distribution characteristics of HIV are closely related to geographical factors, affecting the prevalence and transmission of HIV [ 9 ]. However, previous studies have mainly focused on analyses of epidemic trends or infection characteristics only from a temporal perspective, neglecting the spatial information and failing to comprehensively understand the disease and its impact on different regions. Spatial analysis can fully utilize the spatial information in disease data, so it is widely used in describing the spatial distribution characteristics and changing trends of diseases, disease surveillance and so on [ 10 , 11 ]. A previous study, combining the molecular networks and geographic information, effectively quantified the transmission of HIV−1 between key populations and general heterosexual populations, as well as between different geographic regions [ 12 ]. The HIV−1 molecular transmission networks alone can’t capture the spatial characteristics of HIV transmission, so combining the molecular networks and the spatial epidemiology is conducive to understanding the dynamic changes of HIV−1 genotype and clusters from a spatial perspective, and providing evidences for mapping out regional HIV prevention strategies and optimizing allocation of health resources. Currently, domestic and foreign studies among older people mainly focused on HIV high-risk behaviors, attitudes, and risk factors of HIV infection [ 13 , 14 ], but reports of HIV-related spatial analysis or transmission network were relatively few targeting the population. According to the HIV monitoring data in Nanjing, the proportion of HIV-infected persons aged ≥ 50 years in the newly reported cases increased from 10.6% in 2010 to 23.9% in 2023, which suggested it was crucial to analyze HIV transmission characteristics and its impact on different districts of Nanjing among older people. This study utilized the epidemiological data, spatial information, and HIV sequence information of older HIV-infected patients in Nanjing to dissect the spatial distribution characteristics of and detect key persons or critical regions. This could provided reliable information for developing targeted prevention and control strategies, optimal allocation of health resources and precise containment of the local HIV epidemic. Methods Study participants and data collection In our study, 432 ART-naïve individuals who were aged ≥ 50 years old and newly diagnosed with HIV-1 were enrolled between January 1, 2018 and November 31, 2022. Each individual met the following criteria: (1) aged ≥ 50 years old; (2) confirmed diagnosis of HIV-1 infection; (3) no history of antiviral therapy (ART) drugs; (4) agreed to participate in the study. Before the collection of sample and data, each participant signed a written informed consent. This study was approved by the Ethics Committee of the Nanjing Center for Disease Control and Prevention (Approval No. PJ2020-A001-03). For each participant, 5 ml venous blood was collected, and the plasma was separated and stored in a refrigerator at -80°C. Meanwhile, epidemiological information was investigated anonymously, including age, gender, marital status, transmission route, number of non-marital heterosexual partners, number of homosexual partners,screening sources, etc. HIV-1 RNA extraction, amplification and sequencing HIV-1 RNA was extracted from 200μl plasma samples using the QIAamp Viral RNA Mini Kit (Qiagen, Hilden, Germany) according to the instructions. The target gene fragment of 1060bp (HXB2: 2253-3313) was amplified by nested polymerase chain reaction (PCR). The first-round PCR procedure and cDNA synthesis were performed by PrimeScriptTM One Step RT-PCR Ver.2.0 (TakaRa, China). Cycling conditions were 50℃ for 45 min; 94℃ for 2 min; 94℃ for 15 s; 55℃ for 20 s; 72℃ for 2 min, 50 cycles; followed with an extension at 72℃ for 10 min. The nested PCR was conducted using Ex Taq (TaKaRa, China). Cycling conditions were 94°C for 4 min; 94°C for 15 s, 55°C for 20 s, 72°C 2 min, 40 cycles; followed with an extension at 72°C for 10 min. The PCR products were dealt with electrophoresis with 1% agarose gel, and the amplified positive products were purified and sequenced by Sangon Biotechnology Co., Ltd. Subtype analysis Sequencer 4.10.1 (GeneCodes, Ann Arbor, MI) was used for sequence splicing, and BioEdit (version 7.0.9, Informer Technologies Inc.) was used to align with the reference sequences. The reference sequences were downloaded from the LANL HIV database (https://www.hiv.lanl.gov/ content/index) and contained the major international epidemic strains A-D, F-H, and J-K, as well as the major epidemic recombinant strains in China. A phylogenetic tree with the maximum likelihood (ML) method was constructed for subtype identification using the FastTree 2.1 software. The nucleotide substitution model was GTR + G + I, and the support values were calculated by Shimodaira Hasegawa-like test. Clusters with a bootstrap value higher than 0.90 (90%) were defined as the same subtype. The ML phylogenetic tree was imported to FigTree v 1.4.4 for visualization [15]. Construction of HIV molecular transmission network To ensure sequence quality, the WHO HIVDR QC TOOL (Resistance Quality Control Tool provided by the World Health Organization, https://sequenceqc.bccfe.ca/who_qc) was employed. Sequences < 1, 000bp in length or containing ≥5% ambiguities were excluded for analysis. Pairwise genetic distances were calculated using the Tamura-Nei 93 model, and the HIV-TRACE (localized HIV-TRACE, built CentOS 7 platform, wrote program offline) was used to determine the optimal genetic distance threshold and construct molecular network [16]. At a genetic threshold of 1.10%, the transmission network could identify the most clusters in our study. Cytoscape (version 3.6.1) was used to process and generate the molecular network. According to the National Technical Guideline for HIV Transmission Network Monitoring and Intervention in China, a node represents an HIV sequence or a HIV-infected person, an edge represents the potential transmission relationship between two connected nodes, and a link is the number of edges that the node is connected to other nodes, also known as degrees. In our study, large transmission cluster was defined as clusters containing more than 10 nodes. Statistical analysis SPSS (version 18.0, LEAD Technologies Inc.) was used for statistical analysis. Quantitative data was described by (x̄ ± s), while qualitative data was described by frequency (percentage). The Chi-square test was used to compare difference between groups. The multivariate logistic regression model was conducted to analyze the factors associated with clustering. Variables with a P <0.05 in the Chi-square test were included in the multivariate regression analysis. All the results of statistical significance test were reported as p-values. P < 0.05 (two-tailed) was considered statistically significant. Spatial autocorrelation analysis Moran’s I, a widely used spatial autocorrelation metric in spatial epidemiology, was employed to describe the spatial distribution characteristics of the clustering rate and determine whether there was spatial aggregation. If I > 0, it meant there was a positive spatial correlation, showing a clustered distribution; if I<0, it meant that there was a negative spatial correlation, showing a discrete distribution; if I=0, it meant that there was no spatial autocorrelation and the spatial distribution was random. A z-test was performed to determine whether each spatial autocorrelation of clustering rate was significantly different from a random distribution. All the spatial descriptions and analyses were performed in ArcGIS (version 10.3) [17]. Results Characteristics of study population We sequenced and analyzed 382 samples obtained from 432 HIV-1 infected individuals in our research. The median age was 59 years (IQR: 54–66 years), raging from 50 to 89 years. The majority were male (83.2%), 50–59 years old (53.1% ), married (71.7%), rural (60.5%), with primary school degree or below (35.9%), and detected by medical institutions (69.4%). Heterosexual transmission and commercial heterosexual transmission accounted for 36.1% and 28.0%, respectively. The first CD4 + T lymphocyte (CD4) count before ART was mainly less than 200 cells/µl (40.6%). The first viral load (VL) before ART was mainly 10000–99999 copies/ml (49.2%) (Table 1 ). The phylogenetic tree analysis revealed that circulating recombinant form (CRF)07_BC (52.1%), CRF01_AE (32.5%), and CRF08_BC (7.3%) were the main subtypes, accounting for 91.9%, followed by subtype B, CRF55_01B, CRF68_01B, and CRF67_01B. Additionally, 4 HIV-1 strains did not cluster with any present known reference sequences, and were determined as unique recombinant forms (URFs) (Fig. 1 ). Table 1 Analysis of factors associated with clustering in molecular network of older HIV-infected individuals in Nanjing Variables Total(%) Clustering(%) Chi-square Test Multivariate Analysis χ 2 P value aOR(95% CI) P value Gender Male 318(83.2) 140(44.0) 3.205 0.073 Female 64(16.8) 36(56.3) Age group (yrs) 50 ~ 59 203(53.1) 72(35.5) 22.299 < 0.001 1.00 60–69 124(32.5) 67(54.0) 2.029(1.127–3.656) 0.018 ≥ 70 55(14.4) 37(67.3) 3.467(1.607–7.482) 0.002 Marital status Single 26(6.8) 15(57.7) 2.123 0.346 Married 274(71.7) 127(46.4) Divorced/widowed 82(21.5) 34(41.5) Current address Urban area 151(39.5) 49(32.5) 18.652 < 0.001 Rural area 231(60.5) 127(55.0) Occupation Farmers 100(26.2) 68(68.0) 26.267 < 0.001 1.000 Retiree 102(26.7) 40(39.2) 0.361(0.180–0.725) 0.004 Others 180(47.1) 68(37.8) 0.498(0.267–0.928) 0.028 Education degree Primary school degree or below 137(35.9) 83(60.6) 30.099 < 0.001 1.000 Junior 113(29.6) 55(48.7) 1.133(0.628–2.046) 0.678 Senior 94(24.6) 31(33.0) 0.668(0.347–1.283) 0.226 College or above 38(9.9) 7(18.4) 0.356(0.130–0.974) 0.044 Transmission route Homosexual 133(34.8) 40(30.1) 23.495 < 0.001 1 Heterosexual 138(36.1) 70(50.7) 1.502(0.854–2.639) 0.158 Commercial heterosexual 107(28.0) 66(61.7) 2.295(1.260–4.183) 0.007 Others 4(1.0) 0(0) - 0.999 Screening source VCT 92(24.1) 37(40.2) 6.018 0.047 Medical institution 265(69.4) 122(46.0) Others 25(6.5) 17(68.0) Number of non-marital heterosexual partners 0 182(47.6) 71(39.0) 12.744 0.002 1 ~ 2 144(37.7) 68(47.2) ≥ 3 56(14.7) 37(66.1) Number of homosexual partners 0 249(65.2) 136(54.6) 21.034 < 0.001 1 ~ 2 62(16.2) 19(30.6) ≥ 3 71(18.6) 21(29.6) The first CD4 + T cells (cells/µl) < 200 155(40.6) 63(40.6) 3.479 0.176 200–499 134(35.1) 69(51.5) ≥ 500 93(24.3) 44(47.3) The first viral load before ART (copies/ml) < 10000 55(14.4) 18(32.7) 5.110 0.078 10000 ~ 99999 188(49.2) 94(50.0) ≥ 100000 139(36.4) 64(46.0) Subtype CRF01_AE 124(32.5) 47(37.9) 17.339 0.008 a CRF07_BC 199(52.1) 98(49.2) CRF08_BC 28(7.3) 18(64.3) B 12(3.2) 4(33.3) CRF55_01B 6(1.6) 3(50.0) CRF68_01B 5(1.3) 0(0) CRF67_01B 4(1.0) 4(100.0) URFs 4(1.0) 2(50.0) Notes: a Fisher exact probability method. Characteristics analysis of HIV-1 molecular transmission network Additional file 1 showed that a threshold of 1.1% was optimal for constructing a molecular transmission network given sensitivity analysis of GD thresholds ranging from 0.25–1.5%. A total of 176 sequences entered the network, with a clustering rate of 46.1% (176/382). The network consisted of 44 molecular transmission clusters, 176 nodes, and 1140 edges. The size of molecular clusters ranged from 2 to 33 nodes. The CRF07_BC strain exhibited the most clusters, forming a total of 19 clusters, with a clustering rate of 49.2% (98/199), and the median degree value for nodes within these cluster was 4 (IQR: 1,14). Notably, a CRF07_BC molecular cluster comprised 33 nodes, making it the largest in the entire network. CRF01_AE strain formed 15 clusters in total, with a clustering rate of 37.9% (47/124), and the median degree value for nodes within these cluster was 2 (IQR: 1,9). Although only 28 persons were infected with CRF08_BC, 5 clusters was formed, with the highest clustering rate (64.3%, 18/28) (Fig. 2 ). Impressively, the clustering rate of older people infected with commercial heterosexual transmission was as high as 61.7% and a total of three female commercial sex workers were observed in three transmission clusters. The aforementioned three clusters were CRF01_AE, CR08_BC and CRF07_BC, including 14, 9 and 2 nodes, respectively (Fig. 2 ). Factors associated with clustering Univariate analyses showed that there were significant difference between different age groups, different education degree, different current address, different occupation, different transmission route, different screening source, different numbers of non-marital heterosexual partners, different numbers of homosexual partners, and different subtypes ( P < 0.05). Multivariate logistic regression analysis revealed that compared with homosexual transmission, commercial heterosexual sexual transmission (OR = 2.295, 95% CI: 1.260–4.183) was more likely to cluster within the network. Compared with the older people aged between 50–59, those aged 60–69 years (OR = 2.029, 95% CI:1.127–3.656) and those aged ≥ 70 years (OR = 3.467, 95% CI: 1.607–7.482) were more likely to cluster. Moreover, compared with the older people who were with primary school degree or below, those with a college degree or above (OR = 0.356, 95% CI: 0.130–0.974) were less likely to cluster. Additionally, compared with farmers, retirees (OR = 0.361, 95% CI: 0.180–0.725) or other occupations (OR = 0.498, 95% CI: 0.267–0.928) were less likely to cluster (Table 1 ). Further comparative analysis of older people between the rural area and urban area showed that farmer (39.0% vs 6.0%, χ 2 = 54.760, P < 0.001), primary school degree or below (49.8% vs 14.1%, χ 2 = 70.024, P < 0.001), and commercial heterosexual transmission (36.5% vs 14.8%, χ 2 = 52.502, P < 0.001) accounted for a significantly higher proportion in rural area. Analysis of large transmission clusters We identified three large transmission clusters with more than 10 nodes, including two CRF07_BC clusters and one CRF01_AE cluster (Fig. 2 ). These clusters were composed of 33, 14, and 13 cases, respectively, accounting for 34.1% (60/176) of all the clustered cases. The large molecular clusters were predominantly males (80.0%, 48/60), older people aged ≥ 60 years old (78.3%, 47/60) and infected through commercial heterosexual transmission58.3% (35/60). In the three large clusters, the proportion of commercial heterosexual infection were 54.5% (18/33), 71.4%, (10/14) and 53.8% (7/13), respectively and the proportion of nodes with a degree value ≥ 10 accounted for 84.8% (28/33), 78.6(11/14) and 92.3% (12/13), respectively. Spatial analysis of HIV-1 molecular network and large clusters The clustering rate and clustered cases in the HIV transmission network for older people varied in geographical distribution. The rural towns of Yongning in Pukou District, Longpao in Luhe District, and Jiangning in Jiangning District, which had more clustered cases (19, 16, 11), also had higher clustering rates (90.5%, 100%, 68.8%). The standardized clustering rate was calculated according to the age composition of participants in each town, and then the spatial autocorrelation analysis was carried out using the standardized clustering rate. In the geographical space, the global Moran’s I value was 0.206 (P < 0.001), indicating that there was a positive spatial correlation of the clustering rate at the town level in Nanjing. That was, the towns with a higher clustering rate were adjacent to each other, and the towns with a lower clustering were adjacent to each other (Fig. 3 ). Further analysis on the spatial distribution characteristics of three large transmission clusters indicated that they were mainly concentrated in rural regions, such as Pukou District (38.3%, 23/60), Luhe District (21.6%, 13/60), Jiangning District (20.0%, 12/60), and Jiangbei New Area (13.3%, 8/60). Cluster 1 (C1) was primarily distributed in the town of Yongning in Pukou District and the town of Taishan in Jiangbei New Area. A few clustered cases were distributed in the towns of Xindian and Tangquan in Pukou District, and towns of Jiangpu and Yanjiang in Jiangbei New Area. In addition, two cases were spread cross-regionally to Jianye District and Yuhuatai District. Cluster 2 (C2) was predominantly concentrated on the town of Longpao in Luhe District, with one case cross-regional transmitting to the geographically distant town of Moling in Jiangning District as well. Cluster 3 (C3) was mainly concentrated on the town of Jiangning and adjacent town of Guli both in Jiangning District, with one case cross-regional transmitting to Qixia District and the neighbouring Yuhuatai District, respectively (Fig. 4 ). Discussion In this study, we found that the main HIV subtypes among the older people in Nanjing were CRF07_BC, CRF01_AE and CRF08_BC, which was consistent with the previous reports of the surrounding areas such as Shaoxing City in Zhejiang Province [ 18 ] and Pudong New Area in Shanghai [ 19 ]. From 2018 to 2022, seven subtypes and URFs were sequentially identified among older HIV-infected individuals in Nanjing. Of note, CRF67_01B and CRF68_01B were first reported in Anhui Province [ 20 ], while CRF55_01B was predominantly circulated in other provinces [ 21 ]. These results implied that Nanjing, as the capital city of Jiangsu Province and an important city in the Yangtze River Delta region, had increasingly convenient transportation and frequent personnel flow with other provinces and cities, which may led to the gradual complex distribution of HIV−1 genes among the older people, posing a huge challenge for HIV prevention and control. During 2018—2022, the clustering rate of the HIV-infected adults aged ≥ 50 years in Nanjing was 46.1%, which was lower than that of Qinzhou in Guangxi Province (49.8%) [ 22 ], Shaoxing in Zhejiang Province (50.3%) [ 18 ], Pengzhou in Sichuan Province (52.0%) [ 23 ], and Fuyang in Anhui Province(89.6%) [ 24 ]. However, our study identified three large transmission clusters in the network, accounting for over one-third of all the clustered cases. It implied that the overall epidemic trend of HIV−1 among older people was not sporadic, but with a certain aggregation. Furthermore, our study illustrated that the risk of clustering among the older people infected HIV with heterosexual commercial transmission was more than two times higher than those infected HIV with homosexual transmission. Previous researches have demonstrated that commercial sexual behavior acted as a major risk factor for HIV infection in the elderly [ 14 , 25 ]. Due to lack of companionship and support from spouses or children, old people meet psychological and emotional fulfillment through pursuing commercial sexual services [ 26 ]. Simultaneously, because of poor awareness of active detection, they have a high rate of late detection [ 27 , 28 ], which further increases the risk of HIV infection and transmission. Previous studies in Nanjing have reported that 35.3% of male HIV cases aged ≥ 50 years were infected through commercial heterosexual behavior [ 29 ]. In our current study, more than six tenths of older individuals who were infected through commercial heterosexual behaviors entered the network and three female commercial sex workers were observed in the network, which indicated that commercial sex may play an important role in transmission network. Therefore, the older people could easily become an important bridge population for the HIV transmission from commercial sex workers to their spouses or the general population. Our study also revealed that the older the individuals were, the more likely they were to enter the network, possibly because the older they were, the less inter-regional mobility they were, the more likely they were to cause intra-regional transmission of HIV. As for education, older people with a college degree or above were less likely to cluster. Older people with lower education degree had weak knowledge of HIV prevention, poor self-protection awareness and low rate of condoms use, so they were prone to engage in high-risk sexual behaviors, resulting in a higher risk of HIV infection and transmission [ 26 , 30 ]. In terms of occupation, farmers had a higher risk of clustering, which was consistent with the results reported in Pengzhou City of Sichuan Province [ 23 ]. This may because farmers mainly live in rural areas with relatively scarce health resources, and have limited access to health services, so more health service such as enhancing publicity and education on AIDS prevention and treatment, improving protective skill and enlarging HIV testing should be provided to the older farmers [ 31 , 32 ]. Combining molecular transmission network analysis and spatial epidemiology, our study found for the first time that the HIV−1 clustering rate of the older people in each town showed a significant spatial aggregation, that was, the HIV transmission of older people presented spatial aggregation. Among the newly reported HIV-infected people, the clustering rate and the number of clustered cases were higher, indicating that HIV transmission in this area was more active [ 33 , 34 ]. We found that the areas with a high clustering rate and more clustered cases were mainly concentrated in rural areas, especially in the town of Yongning, Longpao and Jiangning. This finding illustrated that HIV transmission among older adults was more active in rural areas. Further analysis showed that the older people in rural areas had lower education level and more farmers, which also explained the relatively active HIV transmission among older people in rural areas. The AIDS Prevention and Control Commission of The State Council had launched the fifth round of national comprehensive demonstration zones for AIDS prevention and control since 2023 and Nanjing was one of the demonstration zones. Therefore, it necessary to rely on the construction of demonstration zones, allocate government input reasonably in prevention for older people and direct health resources toward rural regions of high aggregation. A "County-Township-Village" HIV prevention and control intervention mode could be applied to carrying out HIV publicity with local cultural characteristics, expanding HIV testing, implementing the whole-process management and treatment mode to improve the effectiveness of ART [ 35 ]. This will gradually interrupt the transmission of HIV and reduce the new infection of older people. We identified three large molecular clusters, in which more than a half were transmitted through commercial heterosexual behavior, suggesting that commercial heterosexual behavior might be an important promoting factor in forming large molecular clusters and rapid transmission of HIV. Fortunately, we found that the large molecular clusters mainly spread in the intra-region of certain rural towns of Pukou District, Luhe District, Jiangning District and Jiangbei New Area. The above four districts were mainly rural area, and three of them except for Jiangning were adjacent and all located in the north of the Yangtze River. Previous studies highlighted that the low-end commercial service places, which mainly served the older males, often appeared in the rural area [ 36 , 37 ], resulting in continuous spread of HIV in the local area. Therefore, it was essential to conduct in-depth interviews among the clustered cases in the above areas, and excavate the key places or persons accelerating the spread of HIV. Future prevention and treatment strategy such as increasing the frequency of condom use or HIV testing, application of post-exposure prophylaxis (PEP) or pre-exposure prophylaxis (PrEP) for high-risk individuals, and rapid initiation of ART for older HIV-infected individuals should take critical regions and key populations as entry points. Meanwhile, dynamic surveillance of transmission network should be strengthened, and intervention priorities should be determined and adjusted given changes in transmission clusters. This will improve prevention and control efficiency under limited resource and contain the further expanding of transmission clusters. Besides, there was a warning sign that a very few cases in large molecular clusters had spread to towns in other districts, which implied that cross-regional transmission had started to occur. Therefore, the above-mentioned health sectors may increase information exchange and jointly launch intervention, so as to reduce cross-region HIV transmission in Nanjing. Understanding transmission pattern and geographic distribution of the HIV−1 subtype is essential to target limited health resources precisely to the region of most needed and to guide decision-making[ 38 ]. This study showed how molecular transmission network analysis and spatial analyses in combination could be used to disentangle epidemiology of HIV at the local level, to address specific populations and produce information that is useful in prevention and control efforts. However, There were several limitations in our study. First, despite our efforts to collect samples, this study couldn't obtained the sequences of all older individuals newly diagnosed with HIV. So our future studies should increase sampling efforts where possible. Second, we didn't to assess transmission dynamics and dynamical change of spatial characteristics in each region over time. Long-term observation studies are needed to gain more information on dynamical HIV transmission patterns. Third, our results only show a representation of the HIV epidemic in Nanjing. Although HIV infections of older people are also increasing in different regions in China, the circumstances may vary from region to region. Therefore, future studies should pay attention to HIV transmission among older people in a larger geographic context. Conclusions This study used cross-diciplinary (epidemiological, genetic, and spatial) approaches to depict that spatial aggregation exited in HIV transmission of older people and towns of high aggregation was mainly located in rural area. Although large transmission clusters mainly spread in the intra-region of certain towns in rural areas, commercial heterosexual behavior played an important role in transmission networks. Future prevention and treatment strategy for older people will consider highly aggregated towns in rural area and take critical regions and key persons as entry points in order to optimize allocation of health resources under limited resource and develop precise intervention in the local HIV context. This will improve prevention and control efficiency and better curb the transmission of HIV. Declarations Supplementary Information Supplementary Material 1: Additional file 1. Additional file 1. Sensitivity analysis graph of gene distance thresholds. Acknowledgments We are highly grateful for Centers for Disease Control and Prevention in 12 districts of Nanjing for all the support to conduct this study. We thank all the participants for their participation in this study and our colleagues for their support. Authors contributions ZZ and YX conceived and designed the study. YX, TJ, and HS analyzed and wrote the manuscript. MQ provideed laboratory supports. XL, SW, XY, RW and JW pefromed the epidemiology survey and data collection. LJ assisted with data cleaning and paper revision. All authors read and approved the final manuscript. Funding This work was supported by Nanjing Medical Science and Technology Development Project (ZKX23059, YKK23192), Jiangsu Province Social Science Application Research Excellent Engineering projects (23SYC-007), Nanjing Medical University Nanjing Institute of Public Health Strong Foundation Project (NQJ2301), the Opening Foundation of Key Laboratory(JSHD202329), Jiangsu Province Capability Improvement Project through Science,Technology and Education(ZDXYS202210) and Nanjing CDC Young Talents Research and Development Team Project (NPY2307). Data Availability The datasets used in this study is not publicly available, but may be available from the corresponding author upon reasonable request, and with permission from Nanjing Municipal Center for Disease Control and Prevention. Ethics approval and consent to participate The study protocol was reviewed and approved by the Ethics Committee of the Nanjing Center for Disease Control and Prevention (Approval No: PJ2020-A001-03). Participants provided written informed consent to participate in this study. Competing interests The authors declare no competing interests. Consent for publication Not applicable References Kiplagat J, Tran DN, Barber T, Njuguna B, Vedanthan R, Triant VA, et al. How health systems can adapt to a population ageing with HIV and comorbid disease. Lancet HIV. 2022;9:e281–92. The Lancet Healthy L. Ageing with HIV. Lancet Healthy Longev. 2022;3:e119. Wang LY, Qin QQ, Ge L, Ding ZW, Cai C, Guo W, et al. Characteristics of HIV infections among over 50-year-olds population in China. Chin J Epidemiol. 2016;37:222–6. Wang YY, Yang Y, Chen C, Zhang L, Ng CH, Ungvari GS, et al. Older adults at high risk of HIV infection in China: a systematic review and meta-analysis of observational studies. PeerJ. 2020;8:e9731. National Health Commission of the People's Republic of China. The overall infection rate of AIDS in China is about 9/10000. https://www.gov.cn/xinwen/2018-11/23/content_5342852 . htm. 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The prevalence of CRF55_01B among HIV-1 strain and its connection with traffic development in China. Emerg Microbes Infect. 2021;10:256–65. Zhang F, Yang Y, Liang N, Liang H, Chen Y, Lin Z et al. Transmission network and phylogenetic analysis reveal older male-centered transmission of CRF01_AE and CRF07_BC in Guangxi, China. 2023; 12:2147023. Zhong X, Yuan D, Liu Y, Wu X, Li N, Li YP, et al. Analysis of the characteristics of HIV-1 molecular transmission network among people aged 50 years and above in Pengzhou city of Sichuan province. Chin J Epidemiol. 2022;43:1107–11. Wu J, Zhang Y, Shen Y, Wang X, Xing H, Yang X, et al. Phylogenetic analysis highlights the role of older people in the transmission of HIV-1 in Fuyang, Anhui Province, China. BMC Infect Dis. 2019;19:562. Chen X, Qin C, Chen R, Huang Y, Xu Y, Tang Q, et al. Epidemiological profile and molecular genetic characterization of HIV-1 among female sex workers and elderly male clients in Guangxi. China. 2021;10:384–95. Sun X, Feng C, Feng L, Xiao T, Zhao X, Liu H, et al. An exploratory transmission mode of HIV/AIDS among older people based on data from multiple sources in China. Sci Rep. 2022;12:16077. Justice AC, Goetz MB, Stewart CN, Hogan BC, Humes E, Luz PM, et al. Delayed presentation of HIV among older individuals: a growing problem. Lancet HIV. 2022;9:e269–80. Chen J, Xu J, Zhou Y, Luo Y. HIV Detection and Delayed Diagnosis. A Time Series Analysis in China. Int J Environ Res Public Health. 2022; 19. Xu YY, Zhu ZP, Wu SS. Analysis on characteristics of high-risk sexual behaviors before confirmation of HIV infections/AIDS patients aged ≥ 50 years old in Nanjing City. Occup Health. 2022;38:1202–06. Jiang GJ, Guo W, Pei YX, Cai C, Wu GH, Zhou C, et al. Survey on extramarital sexual behaviors and HIV infection in middle-aged and elderly people aged 50 and above in selected areas of Chongqing. Chin J Epidemiol. 2018;39:1438–42. Xu J, Sullivan SG, Dou Z, Wu Z. Economic stress and HIV-associated health care utilization in a rural region of China: a qualitative study. AIDS Patient Care STDS. 2007;21:787–98. Qin QQ, Jin YC, Cai C, Chen FF, Tang HL, et al. Epidemiological characteristics of HIV-infected farmers aged 60 years and older reported in China, 2018–2022. Chin J Epidemiol. 2023;44:1686–91. Yuan D, Zhong X, Li Y, He Q, Li N, Li H et al. Molecular Transmission Network of Newly Reported HIV Infections in Pengzhou, Sichuan Province: A Study Based on Genomics and Spatial Epidemiology. Int J Environ Res Public Health. 2023; 20. Yuan D, Liu S, Ouyang F, Ai W, Shi L, Liu X et al. Prevention and Control Are Not a Regional Matter: A Spatial Correlation and Molecular Linkage Analysis Based on Newly Reported HIV/AIDS Patients in 2021 in Jiangsu, China. Viruses. 2023; 15. Yu J, Zhang Y, Jiang J, Lu Q, Liang B, Liu D, Fang K, et al. Implementation of a County-Township-Village Allied HIV Prevention and Control Intervention in Rural China. AIDS Patient Care STDS. 2017;31:384–93. Deng YQ, Li JJ, Fang NY, Wang B, Wang JW, Liang J, et al. Study on HIV-1 subtype among elderly male clients and female sex workers of low-cost venues in Guangxi Zhuang Autonomous Region China. Chin J Epidemiol. 2017;38:326–30. Wu YQ, Zhou XB, Qin R, He JM, Zhang PF, Jiang Y, et al. Correlativity of subtype B viral transmission among elderly HIV-1 infected individuals in Yongding district, Zhangjiajie city, Hunan province. Chin J Epidemiol. 2016;37:1639–43. Yuan D, Yu B, Liang S, Fei T, Tang H, Kang R, et al. HIV-1 genetic transmission networks among people living with HIV/AIDS in Sichuan, China: a genomic and spatial epidemiological analysis. Lancet Reg Health West Pac. 2022;18:100318. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.jpg Additional file 1. Sensitivity analysis graph of gene distance thresholds Cite Share Download PDF Status: Published Journal Publication published 15 Sep, 2024 Read the published version in Virology Journal → Version 1 posted Editorial decision: Revision requested 13 Jul, 2024 Reviews received at journal 13 Jul, 2024 Reviews received at journal 12 Jul, 2024 Reviews received at journal 10 Jul, 2024 Reviews received at journal 02 Jul, 2024 Reviewers agreed at journal 23 Jun, 2024 Reviewers agreed at journal 23 Jun, 2024 Reviewers agreed at journal 22 Jun, 2024 Reviewers agreed at journal 21 Jun, 2024 Reviewers invited by journal 20 Jun, 2024 Editor assigned by journal 13 Jun, 2024 Submission checks completed at journal 13 Jun, 2024 First submitted to journal 10 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4556295","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":317897561,"identity":"7ef147ee-a205-4e03-b129-d4ad0fb4c177","order_by":0,"name":"Yuanyuan Xu","email":"","orcid":"","institution":"Nanjing Municipal Central for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Xu","suffix":""},{"id":317897562,"identity":"8167a9c6-4340-417e-806c-ea02b42865bb","order_by":1,"name":"Tingyi Jiang","email":"","orcid":"","institution":"Nanjing Medical 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Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAm0lEQVRIiWNgGAWjYHACxgcMPAyMDaRoYTYgWQubBMgq4rXI9589Vl0gYyfb38D87AFRWhgbzqXdnsGTbDzjAJu5AVFamBl7zG7z8BxI3MDAA3YhYcDGzGNWTJoWHjYeM2bStEjw8CVL84D8cpjNjDgtwBA7+Jm3Bxhi7c3PiNMCdBow2HqANDOR6iFaGH4Qr3wUjIJRMApGIAAAtygjLt2xSysAAAAASUVORK5CYII=","orcid":"","institution":"Nanjing Municipal Central for Disease Control and Prevention","correspondingAuthor":true,"prefix":"","firstName":"Zhengping","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2024-06-10 07:00:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4556295/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4556295/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12985-024-02493-w","type":"published","date":"2024-09-15T15:58:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":59965797,"identity":"4f60511c-2da6-4ca4-b87b-935dd790cae0","added_by":"auto","created_at":"2024-07-10 02:01:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":36025,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic tree analysis of nucleotide sequences from older HIV-infected individuals in Nanjing.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4556295/v1/e755f15229261a6e700b637b.png"},{"id":59965799,"identity":"18328bf2-05e3-4e01-8d88-7cf42d39856d","added_by":"auto","created_at":"2024-07-10 02:01:07","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1404129,"visible":true,"origin":"","legend":"\u003cp\u003eThe HIV-1 molecular transmission network of older HIV-infected individuals in Nanjing.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4556295/v1/44c4bf64dc88a5ae1ce1fdbb.jpg"},{"id":59965802,"identity":"cf48a832-1426-4ea9-a914-2071829fede2","added_by":"auto","created_at":"2024-07-10 02:01:07","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4315908,"visible":true,"origin":"","legend":"\u003cp\u003eThe spatial distribution graph of molecular transmission network of older HIV-infected individuals in Nanjing.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4556295/v1/4beb303b700bdd4d6d13893d.jpg"},{"id":59965801,"identity":"e63a0ab9-4bef-4b6c-a3cd-94652ff4636b","added_by":"auto","created_at":"2024-07-10 02:01:07","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":15879619,"visible":true,"origin":"","legend":"\u003cp\u003eThe spatial distribution graph of three large transmission clusters of older HIV-infected individuals in Nanjing. (a) Spatial distribution of cluster 1, (b) Spatial distribution of cluster 2, (b) Spatial distribution of cluster 3.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4556295/v1/ac17312f396c1da81f4e3d94.jpg"},{"id":64619557,"identity":"8ffaa5ed-4b7f-489a-9112-c41b8c12f6d9","added_by":"auto","created_at":"2024-09-16 16:16:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":22436736,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4556295/v1/79e60e3f-c25d-4917-8670-a3b7084c6d41.pdf"},{"id":59966463,"identity":"d532a31e-4469-43c3-81b1-09483a8c9cac","added_by":"auto","created_at":"2024-07-10 02:09:07","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":31545,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 1. \u003c/strong\u003eSensitivity analysis graph of gene distance thresholds\u003c/p\u003e","description":"","filename":"Additionalfile1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4556295/v1/4875eff72c803dd37b34fb93.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Combining molecular transmission network analysis and spatial epidemiology to reveal HIV-1 transmission pattern among the older people in Nanjing, China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn the era of antiviral therapy (ART), HIV is no longer a life-limiting infection and AIDS has become a manageable chronic disease. The population of older people with HIV (normally defined as those aged 50 years or older) is increasing[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and the number of people aged 50 years or older with HIV infection globally increased from 5\u0026middot;4\u0026nbsp;million in 2015 to 8\u0026middot;1\u0026nbsp;million in 2020 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In recent years, the number of newly reported older people who were aged\u0026thinsp;\u0026ge;\u0026thinsp;50 years and infected with HIV in China has also been increasing [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and the problem of HIV infection among older people has become a prominent challenge in the HIV prevention and control. The older people still have sex needs to a certain extent, but due to their low level of education, poor knowledge about HIV, and low rate of condoms use, they are prone to HIV infection. Meanwhile, they have poor awareness of active detection, resulting in late diagnosis. A meta-analysis pointed out that the HIV infection rate of older people in China was 2.1% [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], which was much higher than that of the general population in China (0.09%) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Hence, controlling HIV infection among this population is of great significance to curb the epidemic of HIV in China.\u003c/p\u003e \u003cp\u003eUsing the similarity of viral genes, HIV\u0026minus;1 molecular transmission network can be constructed to analyze transmission patterns, determine potential transmission relationship, and identify active transmission clusters, providing an effective scientific basis for precise intervention [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A study used the molecular transmission network and field epidemiology to quantify the local HIV transmission mode and highlight ongoing epidemics, thus developing precise HIV prevention strategies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The spatial distribution characteristics of HIV are closely related to geographical factors, affecting the prevalence and transmission of HIV [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, previous studies have mainly focused on analyses of epidemic trends or infection characteristics only from a temporal perspective, neglecting the spatial information and failing to comprehensively understand the disease and its impact on different regions. Spatial analysis can fully utilize the spatial information in disease data, so it is widely used in describing the spatial distribution characteristics and changing trends of diseases, disease surveillance and so on [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A previous study, combining the molecular networks and geographic information, effectively quantified the transmission of HIV\u0026minus;1 between key populations and general heterosexual populations, as well as between different geographic regions [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The HIV\u0026minus;1 molecular transmission networks alone can\u0026rsquo;t capture the spatial characteristics of HIV transmission, so combining the molecular networks and the spatial epidemiology is conducive to understanding the dynamic changes of HIV\u0026minus;1 genotype and clusters from a spatial perspective, and providing evidences for mapping out regional HIV prevention strategies and optimizing allocation of health resources.\u003c/p\u003e \u003cp\u003eCurrently, domestic and foreign studies among older people mainly focused on HIV high-risk behaviors, attitudes, and risk factors of HIV infection [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], but reports of HIV-related spatial analysis or transmission network were relatively few targeting the population. According to the HIV monitoring data in Nanjing, the proportion of HIV-infected persons aged\u0026thinsp;\u0026ge;\u0026thinsp;50 years in the newly reported cases increased from 10.6% in 2010 to 23.9% in 2023, which suggested it was crucial to analyze HIV transmission characteristics and its impact on different districts of Nanjing among older people. This study utilized the epidemiological data, spatial information, and HIV sequence information of older HIV-infected patients in Nanjing to dissect the spatial distribution characteristics of and detect key persons or critical regions. This could provided reliable information for developing targeted prevention and control strategies, optimal allocation of health resources and precise containment of the local HIV epidemic.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy participants and data collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our study, 432 ART-na\u0026iuml;ve individuals who were aged\u0026nbsp;\u0026ge; 50 years old and\u0026nbsp;newly diagnosed with HIV-1 were enrolled between January 1, 2018 and November 31, 2022.\u0026nbsp;Each individual met the following criteria: (1) aged \u0026ge; 50 years old;\u0026nbsp;(2) confirmed diagnosis of HIV-1 infection; (3) no history of\u0026nbsp;antiviral therapy (ART) drugs;\u0026nbsp;(4) agreed to participate in the study. Before the collection of sample and data, each participant signed a written informed consent. This study was approved by the Ethics Committee of the Nanjing Center for Disease Control and Prevention (Approval No. PJ2020-A001-03). For each participant, 5 ml venous blood was collected, and the plasma was separated and stored in a refrigerator at -80\u0026deg;C. Meanwhile, epidemiological information was investigated anonymously, including age, gender, marital status, transmission route,\u0026nbsp;number of non-marital heterosexual partners, number of homosexual partners,screening sources, etc.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHIV-1 RNA extraction, amplification and sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHIV-1 RNA was extracted from 200\u0026mu;l plasma samples using the QIAamp Viral RNA Mini Kit (Qiagen, Hilden, Germany) according to the instructions. The target gene fragment of 1060bp (HXB2: 2253-3313) was amplified by nested polymerase chain reaction (PCR). The first-round PCR procedure and cDNA synthesis were performed by PrimeScriptTM One Step RT-PCR Ver.2.0 (TakaRa, China). Cycling conditions were 50℃ for 45 min; 94℃ for 2 min; 94℃ for 15 s; 55℃ for 20 s; 72℃ for 2 min, 50 cycles; followed with an extension at 72℃ for 10 min. The nested PCR was conducted using Ex Taq (TaKaRa, China). Cycling conditions were 94\u0026deg;C for 4 min; 94\u0026deg;C for 15 s, 55\u0026deg;C for 20 s, 72\u0026deg;C 2 min, 40 cycles; followed with an extension at 72\u0026deg;C for 10 min. The PCR products were dealt with electrophoresis with 1% agarose gel, and the amplified positive products were purified and sequenced by Sangon Biotechnology Co., Ltd.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubtype analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSequencer 4.10.1 (GeneCodes, Ann Arbor, MI) was used for sequence splicing, and BioEdit (version 7.0.9, Informer Technologies Inc.) was used to align with the reference sequences. The reference sequences were downloaded from the LANL HIV database (https://www.hiv.lanl.gov/\u003c/p\u003e\n\u003cp\u003econtent/index) and contained the major international epidemic strains A-D, F-H, and J-K, as well as the major epidemic recombinant strains in China. A phylogenetic tree with the maximum likelihood (ML) method was constructed for subtype identification using the FastTree 2.1 software. The nucleotide substitution model was GTR + G + I, and the support values were calculated by Shimodaira Hasegawa-like test. Clusters with a bootstrap value higher than 0.90 (90%) were defined as the same subtype. The ML phylogenetic tree was imported to FigTree v 1.4.4 for visualization [15].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of HIV molecular transmission network\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ensure sequence quality, the WHO HIVDR QC TOOL (Resistance Quality Control Tool provided by the World Health Organization, https://sequenceqc.bccfe.ca/who_qc) was employed. Sequences \u0026lt; 1, 000bp in length or containing \u0026ge;5% ambiguities were excluded for analysis. Pairwise genetic distances were calculated using the Tamura-Nei 93 model, and the HIV-TRACE (localized HIV-TRACE, built CentOS 7 platform, wrote program offline) was used to determine the optimal genetic distance threshold and construct molecular network [16]. At a genetic threshold of 1.10%, the transmission network could identify the most clusters in our study. Cytoscape (version 3.6.1) was used to process and generate the molecular network. According to the National Technical Guideline for HIV Transmission Network Monitoring and Intervention in China, a node represents an HIV sequence or a HIV-infected person, an edge represents the potential transmission relationship between two connected nodes, and a link is the number of edges that the node is connected to other nodes, also known as degrees. In our study, large transmission cluster was defined as clusters containing more than 10 nodes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSPSS (version 18.0, LEAD Technologies Inc.) was used for statistical analysis. Quantitative data was described by (x̄\n\u0026plusmn; s), while qualitative data was described by frequency (percentage). The Chi-square test was used to compare difference between groups. The multivariate logistic regression model was conducted to analyze the factors associated with clustering. Variables with a \u003cem\u003eP\u003c/em\u003e \u0026lt;0.05 in the Chi-square test were included in the multivariate regression analysis. All the results of statistical significance test were reported as p-values. \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 (two-tailed) was considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial autocorrelation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMoran\u0026rsquo;s I, a widely used spatial autocorrelation metric in spatial epidemiology, was employed to describe the spatial distribution characteristics of the clustering rate and determine whether there was spatial aggregation. If I \u0026gt; 0, it meant there was a positive spatial correlation, showing a clustered distribution; if I\u0026lt;0, it meant that there was a negative spatial correlation, showing a discrete distribution; if I=0, it meant that there was no spatial autocorrelation and the spatial distribution was random. A z-test was performed to determine whether each spatial autocorrelation of clustering rate was significantly different from a random distribution. All the spatial descriptions and analyses were performed in ArcGIS (version 10.3) [17].\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of study population\u003c/h2\u003e \u003cp\u003e We sequenced and analyzed 382 samples obtained from 432 HIV-1 infected individuals in our research. The median age was 59 years (IQR: 54\u0026ndash;66 years), raging from 50 to 89 years. The majority were male (83.2%), 50\u0026ndash;59 years old (53.1% ), married (71.7%), rural (60.5%), with primary school degree or below (35.9%), and detected by medical institutions (69.4%). Heterosexual transmission and commercial heterosexual transmission accounted for 36.1% and 28.0%, respectively. The first CD4\u0026thinsp;+\u0026thinsp;T lymphocyte (CD4) count before ART was mainly less than 200 cells/\u0026micro;l (40.6%). The first viral load (VL) before ART was mainly 10000\u0026ndash;99999 copies/ml (49.2%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The phylogenetic tree analysis revealed that circulating recombinant form (CRF)07_BC (52.1%), CRF01_AE (32.5%), and CRF08_BC (7.3%) were the main subtypes, accounting for 91.9%, followed by subtype B, CRF55_01B, CRF68_01B, and CRF67_01B. Additionally, 4 HIV-1 strains did not cluster with any present known reference sequences, and were determined as unique recombinant forms (URFs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of factors associated with clustering in molecular network of older HIV-infected individuals in Nanjing\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClustering(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eChi-square Test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eMultivariate Analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eaOR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e318(83.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140(44.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64(16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36(56.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group (yrs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026thinsp;~\u0026thinsp;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e203(53.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72(35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124(32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67(54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.029(1.127\u0026ndash;3.656)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55(14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37(67.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.467(1.607\u0026ndash;7.482)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(57.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e274(71.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127(46.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced/widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82(21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34(41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent address\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e151(39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49(32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e231(60.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127(55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100(26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68(68.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetiree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102(26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40(39.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.361(0.180\u0026ndash;0.725)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180(47.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68(37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.498(0.267\u0026ndash;0.928)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school degree or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137(35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83(60.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113(29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55(48.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.133(0.628\u0026ndash;2.046)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94(24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31(33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.668(0.347\u0026ndash;1.283)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38(9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.356(0.130\u0026ndash;0.974)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransmission route\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHomosexual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133(34.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40(30.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeterosexual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138(36.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70(50.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.502(0.854\u0026ndash;2.639)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommercial heterosexual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107(28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66(61.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.295(1.260\u0026ndash;4.183)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScreening source\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92(24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37(40.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical institution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e265(69.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122(46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(68.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNumber of non-marital heterosexual partners\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e182(47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71(39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026thinsp;~\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e144(37.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68(47.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56(14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37(66.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNumber of homosexual partners\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e249(65.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136(54.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026thinsp;~\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62(16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(30.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71(18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eThe first CD4\u0026thinsp;+\u0026thinsp;T cells (cells/\u0026micro;l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155(40.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63(40.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e200\u0026ndash;499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134(35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69(51.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93(24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44(47.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eThe first viral load before ART (copies/ml)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55(14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10000\u0026thinsp;~\u0026thinsp;99999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e188(49.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;100000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139(36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64(46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRF01_AE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124(32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47(37.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRF07_BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e199(52.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98(49.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRF08_BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28(7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(64.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRF55_01B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRF68_01B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRF67_01B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eURFs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNotes:\u003csup\u003ea\u003c/sup\u003e Fisher exact probability method.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics analysis of HIV-1 molecular transmission network\u003c/h2\u003e \u003cp\u003eAdditional file 1 showed that a threshold of 1.1% was optimal for constructing a molecular transmission network given sensitivity analysis of GD thresholds ranging from 0.25\u0026ndash;1.5%. A total of 176 sequences entered the network, with a clustering rate of 46.1% (176/382). The network consisted of 44 molecular transmission clusters, 176 nodes, and 1140 edges. The size of molecular clusters ranged from 2 to 33 nodes.\u003c/p\u003e \u003cp\u003eThe CRF07_BC strain exhibited the most clusters, forming a total of 19 clusters, with a clustering rate of 49.2% (98/199), and the median degree value for nodes within these cluster was 4 (IQR: 1,14). Notably, a CRF07_BC molecular cluster comprised 33 nodes, making it the largest in the entire network. CRF01_AE strain formed 15 clusters in total, with a clustering rate of 37.9% (47/124), and the median degree value for nodes within these cluster was 2 (IQR: 1,9). Although only 28 persons were infected with CRF08_BC, 5 clusters was formed, with the highest clustering rate (64.3%, 18/28) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Impressively, the clustering rate of older people infected with commercial heterosexual transmission was as high as 61.7% and a total of three female commercial sex workers were observed in three transmission clusters. The aforementioned three clusters were CRF01_AE, CR08_BC and CRF07_BC, including 14, 9 and 2 nodes, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eFactors associated with clustering\u003c/h2\u003e \u003cp\u003eUnivariate analyses showed that there were significant difference between different age groups, different education degree, different current address, different occupation, different transmission route, different screening source, different numbers of non-marital heterosexual partners, different numbers of homosexual partners, and different subtypes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Multivariate logistic regression analysis revealed that compared with homosexual transmission, commercial heterosexual sexual transmission (OR\u0026thinsp;=\u0026thinsp;2.295, 95% CI: 1.260\u0026ndash;4.183) was more likely to cluster within the network. Compared with the older people aged between 50\u0026ndash;59, those aged 60\u0026ndash;69 years (OR\u0026thinsp;=\u0026thinsp;2.029, 95% CI:1.127\u0026ndash;3.656) and those aged\u0026thinsp;\u0026ge;\u0026thinsp;70 years (OR\u0026thinsp;=\u0026thinsp;3.467, 95% CI: 1.607\u0026ndash;7.482) were more likely to cluster. Moreover, compared with the older people who were with primary school degree or below, those with a college degree or above (OR\u0026thinsp;=\u0026thinsp;0.356, 95% CI: 0.130\u0026ndash;0.974) were less likely to cluster. Additionally, compared with farmers, retirees (OR\u0026thinsp;=\u0026thinsp;0.361, 95% CI: 0.180\u0026ndash;0.725) or other occupations (OR\u0026thinsp;=\u0026thinsp;0.498, 95% CI: 0.267\u0026ndash;0.928) were less likely to cluster (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Further comparative analysis of older people between the rural area and urban area showed that farmer (39.0% vs 6.0%, χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;54.760, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), primary school degree or below (49.8% vs 14.1%, χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;70.024, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and commercial heterosexual transmission (36.5% vs 14.8%, χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;52.502, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) accounted for a significantly higher proportion in rural area.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of large transmission clusters\u003c/h2\u003e \u003cp\u003eWe identified three large transmission clusters with more than 10 nodes, including two CRF07_BC clusters and one CRF01_AE cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These clusters were composed of 33, 14, and 13 cases, respectively, accounting for 34.1% (60/176) of all the clustered cases. The large molecular clusters were predominantly males (80.0%, 48/60), older people aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years old (78.3%, 47/60) and infected through commercial heterosexual transmission58.3% (35/60). In the three large clusters, the proportion of commercial heterosexual infection were 54.5% (18/33), 71.4%, (10/14) and 53.8% (7/13), respectively and the proportion of nodes with a degree value\u0026thinsp;\u0026ge;\u0026thinsp;10 accounted for 84.8% (28/33), 78.6(11/14) and 92.3% (12/13), respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSpatial analysis of HIV-1 molecular network and large clusters\u003c/h2\u003e \u003cp\u003eThe clustering rate and clustered cases in the HIV transmission network for older people varied in geographical distribution. The rural towns of Yongning in Pukou District, Longpao in Luhe District, and Jiangning in Jiangning District, which had more clustered cases (19, 16, 11), also had higher clustering rates (90.5%, 100%, 68.8%). The standardized clustering rate was calculated according to the age composition of participants in each town, and then the spatial autocorrelation analysis was carried out using the standardized clustering rate. In the geographical space, the global Moran\u0026rsquo;s I value was 0.206 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that there was a positive spatial correlation of the clustering rate at the town level in Nanjing. That was, the towns with a higher clustering rate were adjacent to each other, and the towns with a lower clustering were adjacent to each other (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurther analysis on the spatial distribution characteristics of three large transmission clusters indicated that they were mainly concentrated in rural regions, such as Pukou District (38.3%, 23/60), Luhe District (21.6%, 13/60), Jiangning District (20.0%, 12/60), and Jiangbei New Area (13.3%, 8/60). Cluster 1 (C1) was primarily distributed in the town of Yongning in Pukou District and the town of Taishan in Jiangbei New Area. A few clustered cases were distributed in the towns of Xindian and Tangquan in Pukou District, and towns of Jiangpu and Yanjiang in Jiangbei New Area. In addition, two cases were spread cross-regionally to Jianye District and Yuhuatai District. Cluster 2 (C2) was predominantly concentrated on the town of Longpao in Luhe District, with one case cross-regional transmitting to the geographically distant town of Moling in Jiangning District as well. Cluster 3 (C3) was mainly concentrated on the town of Jiangning and adjacent town of Guli both in Jiangning District, with one case cross-regional transmitting to Qixia District and the neighbouring Yuhuatai District, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we found that the main HIV subtypes among the older people in Nanjing were CRF07_BC, CRF01_AE and CRF08_BC, which was consistent with the previous reports of the surrounding areas such as Shaoxing City in Zhejiang Province [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and Pudong New Area in Shanghai [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. From 2018 to 2022, seven subtypes and URFs were sequentially identified among older HIV-infected individuals in Nanjing. Of note, CRF67_01B and CRF68_01B were first reported in Anhui Province [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], while CRF55_01B was predominantly circulated in other provinces [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These results implied that Nanjing, as the capital city of Jiangsu Province and an important city in the Yangtze River Delta region, had increasingly convenient transportation and frequent personnel flow with other provinces and cities, which may led to the gradual complex distribution of HIV−1 genes among the older people, posing a huge challenge for HIV prevention and control.\u003c/p\u003e \u003cp\u003eDuring 2018—2022, the clustering rate of the HIV-infected adults aged ≥ 50 years in Nanjing was 46.1%, which was lower than that of Qinzhou in Guangxi Province (49.8%) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], Shaoxing in Zhejiang Province (50.3%) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], Pengzhou in Sichuan Province (52.0%) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and Fuyang in Anhui Province(89.6%) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, our study identified three large transmission clusters in the network, accounting for over one-third of all the clustered cases. It implied that the overall epidemic trend of HIV−1 among older people was not sporadic, but with a certain aggregation. Furthermore, our study illustrated that the risk of clustering among the older people infected HIV with heterosexual commercial transmission was more than two times higher than those infected HIV with homosexual transmission. Previous researches have demonstrated that commercial sexual behavior acted as a major risk factor for HIV infection in the elderly [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Due to lack of companionship and support from spouses or children, old people meet psychological and emotional fulfillment through pursuing commercial sexual services [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Simultaneously, because of poor awareness of active detection, they have a high rate of late detection [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], which further increases the risk of HIV infection and transmission. Previous studies in Nanjing have reported that 35.3% of male HIV cases aged ≥ 50 years were infected through commercial heterosexual behavior [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In our current study, more than six tenths of older individuals who were infected through commercial heterosexual behaviors entered the network and three female commercial sex workers were observed in the network, which indicated that commercial sex may play an important role in transmission network. Therefore, the older people could easily become an important bridge population for the HIV transmission from commercial sex workers to their spouses or the general population.\u003c/p\u003e \u003cp\u003eOur study also revealed that the older the individuals were, the more likely they were to enter the network, possibly because the older they were, the less inter-regional mobility they were, the more likely they were to cause intra-regional transmission of HIV. As for education, older people with a college degree or above were less likely to cluster. Older people with lower education degree had weak knowledge of HIV prevention, poor self-protection awareness and low rate of condoms use, so they were prone to engage in high-risk sexual behaviors, resulting in a higher risk of HIV infection and transmission [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In terms of occupation, farmers had a higher risk of clustering, which was consistent with the results reported in Pengzhou City of Sichuan Province [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This may because farmers mainly live in rural areas with relatively scarce health resources, and have limited access to health services, so more health service such as enhancing publicity and education on AIDS prevention and treatment, improving protective skill and enlarging HIV testing should be provided to the older farmers [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCombining molecular transmission network analysis and spatial epidemiology, our study found for the first time that the HIV−1 clustering rate of the older people in each town showed a significant spatial aggregation, that was, the HIV transmission of older people presented spatial aggregation. Among the newly reported HIV-infected people, the clustering rate and the number of clustered cases were higher, indicating that HIV transmission in this area was more active [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. We found that the areas with a high clustering rate and more clustered cases were mainly concentrated in rural areas, especially in the town of Yongning, Longpao and Jiangning. This finding illustrated that HIV transmission among older adults was more active in rural areas. Further analysis showed that the older people in rural areas had lower education level and more farmers, which also explained the relatively active HIV transmission among older people in rural areas. The AIDS Prevention and Control Commission of The State Council had launched the fifth round of national comprehensive demonstration zones for AIDS prevention and control since 2023 and Nanjing was one of the demonstration zones. Therefore, it necessary to rely on the construction of demonstration zones, allocate government input reasonably in prevention for older people and direct health resources toward rural regions of high aggregation. A \"County-Township-Village\" HIV prevention and control intervention mode could be applied to carrying out HIV publicity with local cultural characteristics, expanding HIV testing, implementing the whole-process management and treatment mode to improve the effectiveness of ART [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This will gradually interrupt the transmission of HIV and reduce the new infection of older people.\u003c/p\u003e \u003cp\u003eWe identified three large molecular clusters, in which more than a half were transmitted through commercial heterosexual behavior, suggesting that commercial heterosexual behavior might be an important promoting factor in forming large molecular clusters and rapid transmission of HIV. Fortunately, we found that the large molecular clusters mainly spread in the intra-region of certain rural towns of Pukou District, Luhe District, Jiangning District and Jiangbei New Area. The above four districts were mainly rural area, and three of them except for Jiangning were adjacent and all located in the north of the Yangtze River. Previous studies highlighted that the low-end commercial service places, which mainly served the older males, often appeared in the rural area [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], resulting in continuous spread of HIV in the local area. Therefore, it was essential to conduct in-depth interviews among the clustered cases in the above areas, and excavate the key places or persons accelerating the spread of HIV. Future prevention and treatment strategy such as increasing the frequency of condom use or HIV testing, application of post-exposure prophylaxis (PEP) or pre-exposure prophylaxis (PrEP) for high-risk individuals, and rapid initiation of ART for older HIV-infected individuals should take critical regions and key populations as entry points. Meanwhile, dynamic surveillance of transmission network should be strengthened, and intervention priorities should be determined and adjusted given changes in transmission clusters. This will improve prevention and control efficiency under limited resource and contain the further expanding of transmission clusters. Besides, there was a warning sign that a very few cases in large molecular clusters had spread to towns in other districts, which implied that cross-regional transmission had started to occur. Therefore, the above-mentioned health sectors may increase information exchange and jointly launch intervention, so as to reduce cross-region HIV transmission in Nanjing.\u003c/p\u003e \u003cp\u003eUnderstanding transmission pattern and geographic distribution of the HIV−1 subtype is essential to target limited health resources precisely to the region of most needed and to guide decision-making[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. This study showed how molecular transmission network analysis and spatial analyses in combination could be used to disentangle epidemiology of HIV at the local level, to address specific populations and produce information that is useful in prevention and control efforts. However, There were several limitations in our study. First, despite our efforts to collect samples, this study couldn't obtained the sequences of all older individuals newly diagnosed with HIV. So our future studies should increase sampling efforts where possible. Second, we didn't to assess transmission dynamics and dynamical change of spatial characteristics in each region over time. Long-term observation studies are needed to gain more information on dynamical HIV transmission patterns. Third, our results only show a representation of the HIV epidemic in Nanjing. Although HIV infections of older people are also increasing in different regions in China, the circumstances may vary from region to region. Therefore, future studies should pay attention to HIV transmission among older people in a larger geographic context.\u003c/p\u003e "},{"header":"Conclusions","content":"\u003cp\u003eThis study used cross-diciplinary (epidemiological, genetic, and spatial) approaches to depict that spatial aggregation exited in HIV transmission of older people and towns of high aggregation was mainly located in rural area. Although large transmission clusters mainly spread in the intra-region of certain towns in rural areas, commercial heterosexual behavior played an important role in transmission networks. Future prevention and treatment strategy for older people will consider highly aggregated towns in rural area and take critical regions and key persons as entry points in order to optimize allocation of health resources under limited resource and develop precise intervention in the local HIV context. This will improve prevention and control efficiency and better curb the transmission of HIV.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary Material 1: Additional file 1. Additional file 1. Sensitivity analysis graph of gene distance thresholds.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are highly grateful for Centers for Disease Control and Prevention in 12 districts of Nanjing for all the support to conduct this study. We thank all the participants for their participation in this study and our colleagues for their support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZZ and YX conceived and designed the study. YX, TJ, and HS analyzed and wrote the manuscript. MQ provideed laboratory supports. XL, SW, XY, RW and JW pefromed the epidemiology survey and data collection. LJ assisted with data cleaning and paper revision. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Nanjing Medical Science and Technology Development Project (ZKX23059, YKK23192), Jiangsu Province Social Science Application Research Excellent Engineering projects (23SYC-007), Nanjing Medical University Nanjing Institute of Public Health Strong Foundation Project (NQJ2301), the Opening Foundation of Key Laboratory(JSHD202329), Jiangsu Province Capability Improvement Project through Science,Technology and Education(ZDXYS202210) and Nanjing CDC Young Talents Research and Development Team Project (NPY2307).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used in this study is not publicly available, but may be available from the corresponding author upon reasonable request, and with permission from Nanjing Municipal Center for Disease Control and Prevention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was reviewed and approved by\u0026nbsp;the Ethics Committee of the Nanjing Center for Disease Control and Prevention (Approval No: PJ2020-A001-03).\u0026nbsp;Participants provided written informed consent to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKiplagat J, Tran DN, Barber T, Njuguna B, Vedanthan R, Triant VA, et al. 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Int J Environ Res Public Health. 2022; 19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu YY, Zhu ZP, Wu SS. Analysis on characteristics of high-risk sexual behaviors before confirmation of HIV infections/AIDS patients aged\u0026thinsp;\u0026ge;\u0026thinsp;50 years old in Nanjing City. Occup Health. 2022;38:1202\u0026ndash;06.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang GJ, Guo W, Pei YX, Cai C, Wu GH, Zhou C, et al. Survey on extramarital sexual behaviors and HIV infection in middle-aged and elderly people aged 50 and above in selected areas of Chongqing. Chin J Epidemiol. 2018;39:1438\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu J, Sullivan SG, Dou Z, Wu Z. Economic stress and HIV-associated health care utilization in a rural region of China: a qualitative study. 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Viruses. 2023; 15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu J, Zhang Y, Jiang J, Lu Q, Liang B, Liu D, Fang K, et al. Implementation of a County-Township-Village Allied HIV Prevention and Control Intervention in Rural China. AIDS Patient Care STDS. 2017;31:384\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng YQ, Li JJ, Fang NY, Wang B, Wang JW, Liang J, et al. Study on HIV-1 subtype among elderly male clients and female sex workers of low-cost venues in Guangxi Zhuang Autonomous Region China. Chin J Epidemiol. 2017;38:326\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu YQ, Zhou XB, Qin R, He JM, Zhang PF, Jiang Y, et al. Correlativity of subtype B viral transmission among elderly HIV-1 infected individuals in Yongding district, Zhangjiajie city, Hunan province. Chin J Epidemiol. 2016;37:1639\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan D, Yu B, Liang S, Fei T, Tang H, Kang R, et al. HIV-1 genetic transmission networks among people living with HIV/AIDS in Sichuan, China: a genomic and spatial epidemiological analysis. Lancet Reg Health West Pac. 2022;18:100318.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"virology-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"virj","sideBox":"Learn more about [Virology Journal](http://virologyj.biomedcentral.com/)","snPcode":"12985","submissionUrl":"https://submission.nature.com/new-submission/12985/3","title":"Virology Journal","twitterHandle":"@VirologyJ","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"HIV/AIDS, older people, molecular network, transmission cluster, spatial analysis","lastPublishedDoi":"10.21203/rs.3.rs-4556295/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4556295/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e In China, the problem of HIV infection among the older people has become increasingly prominent. This study aimed to analyze the pattern and influencing factors of HIV transmission based on a genomic and spatial epidemiological analysis among this population.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eA total of 432 older individuals who were newly diagnosed with HIV-1 and had not received ART between January 2018 and December 2021 were enrolled. HIV-1 \u003cem\u003epol\u003c/em\u003egene sequence was obtained by viral RNA extraction and nested PCR. The molecular transmission network was constructed using HIV-TRACE and the spatial distribution analyses were performed in ArcGIS.\u003cstrong\u003e \u003c/strong\u003eThe\u003cstrong\u003e \u003c/strong\u003emultivariate logistic regression analysis was performed to analyze the factors associated with clustering.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eA total of 382 sequences were successfully sequenced, of which CRF07_BC (52.1%), CRF01_AE (32.5%), and CRF08_BC (7.3%) were the main HIV-1 subtypes. A total of 176 sequences entered the molecular network, with a clustering rate of 46.1%. Impressively, the clustering rate among older people infected HIV with commercial heterosexual transmission was as high as 61.7% and three female commercial sex workers were observed in the network. The individuals who were aged ≥ 60 years and transmitted by commercial heterosexual behaviors had a higher risk of clustering, while those who were retirees or engaged other occupations and with higher education degree were less likely to cluster. There was a positive spatial correlation of clustering rate (Global Moran I =0.206, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001)at the town level and the highly aggregated regions were mainly distributed in rural area. We determined three large clusters and they mainly spread in the intra-region of certain towns in rural areas.\u003cstrong\u003e \u003c/strong\u003eNotably, 54.5% of cases in large clusters were transmitted through commercial heterosexual behaviors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003eThese findings revealed the spatial aggregation of HIV transmission and highlighted vital role of commercial heterosexual behavior in HIV transmission among older people at the local level. Therefore, health resources should be directed towards highly aggregated rural areas and prevention strategy should take critical regions or persons as entry points. Moreover, continuous monitor and rapid area response to the network should be strengthened to reduce further HIV transmission among older people.\u003c/p\u003e","manuscriptTitle":"Combining molecular transmission network analysis and spatial epidemiology to reveal HIV-1 transmission pattern among the older people in Nanjing, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-10 02:01:02","doi":"10.21203/rs.3.rs-4556295/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-14T03:03:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-13T12:46:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-12T18:38:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-10T09:08:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-02T13:24:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"169383857399819252125967764837759377323","date":"2024-06-23T09:34:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"40818717340273882409793846682082257756","date":"2024-06-23T09:14:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"158595351377867805651234461881039455845","date":"2024-06-23T03:39:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"104145279934201684202928234141142148089","date":"2024-06-21T12:23:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-21T03:39:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-13T08:54:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-13T06:18:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Virology Journal","date":"2024-06-10T06:58:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"virology-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"virj","sideBox":"Learn more about [Virology Journal](http://virologyj.biomedcentral.com/)","snPcode":"12985","submissionUrl":"https://submission.nature.com/new-submission/12985/3","title":"Virology Journal","twitterHandle":"@VirologyJ","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"86d25347-57a7-46f3-8975-1e64d3055368","owner":[],"postedDate":"July 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-16T16:10:03+00:00","versionOfRecord":{"articleIdentity":"rs-4556295","link":"https://doi.org/10.1186/s12985-024-02493-w","journal":{"identity":"virology-journal","isVorOnly":false,"title":"Virology Journal"},"publishedOn":"2024-09-15 15:58:19","publishedOnDateReadable":"September 15th, 2024"},"versionCreatedAt":"2024-07-10 02:01:02","video":"","vorDoi":"10.1186/s12985-024-02493-w","vorDoiUrl":"https://doi.org/10.1186/s12985-024-02493-w","workflowStages":[]},"version":"v1","identity":"rs-4556295","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4556295","identity":"rs-4556295","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}