ABSTRACT
Identifying the prevalence and risk factors of secondary human immunodeficiency virus (HIV) transmission from people living with HIV (PLWH) to other people is crucial for ending the HIV epidemic. However, the data among antiretroviral therapy (ART) patients is limited. This study aims to assess the prevalence and risk factors of secondary HIV transmission among PLWH receiving ART by longitudinal molecular networks in China. In this study, the prevalence of secondary HIV transmission was 10.8%. The R0 was greater than 1 from 2017 to 2021 and peaked in 2019. PLWHs who were male sex, older age, engaged in condomless sex, experienced higher ART follow-up viral load, experienced ART medical omissions, infected with non-CRF01_AE subtype, and self-reported sexually transmitted infections (STIs) at HIV diagnosis increased the risk of secondary HIV transmission. However, those participants with higher education were less likely to be involved in secondary HIV transmission. The diagnostic age of the participants was nonlinearly associated with the risk of secondary HIV transmission, with a cutoff value of 43.13 years indicating a higher risk of secondary HIV transmission for patients diagnosed at or above this age. This study revealed substantial secondary HIV transmission and persistent HIV expansion among local PLWH, highlighting the necessity of enhancing viral load monitor, promoting adherence to ART, and promoting safe sex practices, particularly among older adults with HIV, to mitigate secondary HIV transmission.
KEYWORDS: HIV, PLWH, secondary HIV transmission, condomless sex, HIV molecular network
Introduction
Human-acquired immunodeficiency virus-1 (HIV-1) infections continue to be a significant global public health concern, resulting in approximately 40.1 million deaths to date [1]. Despite significant efforts to control the rapid spread of HIV-1 in China [2,3], the number of people living with HIV (PLWH) remains substantial, with a consistently high number of new diagnoses reported annually [4]. Guangxi Zhuang Autonomous Region, located in the Southwest of China, is experiencing a severe HIV-1 epidemic and has the third-highest number of reported HIV-1 cases, accounting for 9.3% of the total HIV cases in China [5,6]. Therefore, intensive management of HIV care and effective interventions for reducing HIV transmission are urgently needed in Guangxi to mitigate the spread of HIV. The widespread of antiretroviral therapy (ART) has significantly reduced HIV-related morbidity and mortality. Early ART for HIV-infected partners in sero-discordant heterosexual couples could reduce the risk of HIV transmission by 95% [7,8]. Despite this, over 200 million new HIV infections were reported in the year 2022 [9], emphasizing the necessity to address secondary HIV transmission. Secondary HIV transmission refers to the genetic linkage of a newly diagnosed individual to a previously identified individual [10]. A study in Guangxi found the secondary HIV transmission prevalence between 2.9% and 10% [10,11]. Research indicated a potential risk of HIV-1 transmission among sero-discordant couples even with long-term ART [10,12,13]. Therefore, identifying and addressing the risk factors of secondary HIV transmission among the PLWH receiving ART is crucial for preventing secondary HIV transmission and achieving the 95%-95%-95% goal.
Due to sexual activity and reproduction occurring either with unknown or known HIV status, people living with HIV may risk transmitting HIV to their sexual partners [11,14], resulting in secondary transmission. A growing body of research linked HIV-1 transmission risk was factors such as high viral load (VL) [15,16], low adherence to ART [15], male sex [17], frequent substance abuse [14], and older age [18]. In addition, high-risk sexual behaviour, lack of HIV prevention knowledge, and lack of social support are identified as significant high-risk factors for secondary HIV transmission in adolescent PLWH [19]. Furthermore, ART has been shown to reduce the likelihood of secondary HIV transmission among adults infected with HIV [10,11]. However, no quantitative studies have specifically examined the factors associated with secondary transmission among PLWH receiving ART in China.
In resource-limited regions, PLWHs often have limited access to timely VL testing and rarely receive comprehensive sexual health services [20,21]. Additionally, the local expansion of various HIV subtypes among PLWH receiving ART is not well understood. Therefore, identifying the risk of secondary transmission among PLWH receiving ART is critical to interrupting HIV-1 transmission. However, few studies have examined the potential risk factors of secondary HIV transmission in high-prevalence, resource-constrained regions. To address this issue, this study utilized molecular transmission networks to identify secondary HIV transmission and assess the prevalence and potential risk factors of secondary HIV transmission among PLWH during the baseline period (2000-2017) and newly diagnosed HIV-1 patients in the follow-up period (2018-2021) in Guangxi, as well as determine the local expansion of various HIV subtypes and different diagnostic year among PLWH receiving ART.
Methods
Longitudinal cross-sectional study design
This study was designed as a longitudinal cross-sectional study conducted in Guangxi, a province in Southwestern China. Qinzhou City and Chongzuo City were selected as study sites. The two cities are adjacent to each other, and exhibit exceptionally high numbers and incidence of HIV infection, with cumulative HIV cases accounting for one-fifth of the total number of HIV cases in Guangxi. Both of them were early hotspots of the HIV epidemic driven by people who inject drugs.
Participants newly diagnosed with HIV and undergoing ART were recruited for this study from January 1, 2000, to December 31, 2021. When the patients started ART, each participant completed a questionnaire, including socio-demographic characteristics, sex behaviour, and transmission route. Then, blood samples were collected for HIV pol sequencing and laboratory assessments, such as sexually transmitted infections, HCV infections, and HBV infections. ART follow-up was conducted for participants in the first and third months of ART, followed by yearly follow-up. The ART follow-up data were entered into the National Free Antiretroviral Treatment Program (NFATP) database [22].
All participants who were at least 18 years old at the time of HIV diagnosis were eligible for inclusion in the study. The inclusion criteria were as follows: (1) laboratory-confirmed HIV-positive individuals; (2) receiving ART; (3) individuals who consented to blood sampling and signed informed consent forms. The exclusion criteria for participants were: (1) lack of baseline and follow-up data; (2) interruption of ART for more than six months; (3) loss to follow-up for more than twelve months; (4) presence of severe mental disorders. In addition, some participants were excluded from data analysis if we failed to obtain their HIV pol sequences. Besides, some pol sequences were excluded if they: (1) were less than 1000 base pairs; (2) had ≥5% mixed bases; (3) had stop codons, hypermutations, or poor insertions/deletions. As the number of newly diagnosed individuals was uncertain each year, the annual enrolled participants were not less than 60% of newly diagnosed HIV patients in the follow-up period (2018-2021), according to the US HIV Network Surveillance Guidelines.
Ethical
This study was reviewed and approved by the Ethics Committee of Guangxi Medical University (NO. 20200087). In this study, when patients first arrived at the Disease Control Center's HIV prevention department and the HIV care clinic, they were informed that their anonymized data and blood samples could be used for research purposes if they signed an informed consent form. The published study results will not contain any personally identifiable information. To ensure patient privacy, data collection and extraction for this study were conducted anonymously. Participants received 50 RMB (approximately 7.14 USD) or free routine blood tests during their initial ART visit and at each subsequent ART follow-up.
HIV-1 DNA extraction, amplification, and subtype analysis
HIV-1 DNA/RNA was extracted from blood samples, using a viral nucleic acid extraction and purification kit according to the manufacturer's protocol (TIANLONG SCIENCE & TECHNOLOGY, XiAn, China). Then, a partial HIV-1 pol region (HXB2 position 2253-3312) was amplified from the viral cDNA via nested PCR. The nucleotide sequences were cleaned and assembled using Sequencher software (version 5.4.6), aligned with the HIV align tool (https://www.hiv.lanl.gov/content/sequence/VIRALIGN/viralign.html). Manual editing was performed using BioEdit software (version 7.2.5), and subtyping was conducted using COMET HIV-1 (https://comet.lih.lu) and HIV BLAST (https://www.hiv.lanl.gov/content/sequence/BASIC_BLAST/basic_blast.html) tools. Detailed methodologies for DNA extraction, PCR amplification, sequencing, and subtyping utilized in our study have been previously described [2].
HIV-1 molecular network construction
The genetic distance (GD) represents the distance of patients with HIV-1 in the molecular network. In this study, the pairwise Tamura-Nei 93 (TN93) GD was calculated for all the sequences by HIV Trace (https://github.com/veg/hivtrace), ranging from 0.001 to 0.02. The HIV-1 molecular network was visualized using Cytoscape software (version 3.8.2). The optimal genetic distance was determined as the point with the maximum number of clusters and nodes.
Maximum likelihood tree construction and R0 estimation
Maximum Likelihood (ML) trees were constructed using IQ-TREE software (version 1.6.12). ML tree was constructed for the participants in the cross-sectional follow-up period based on their HIV-1 subtypes. This included an overall ML tree including the total population, as well as separate ML trees for individuals diagnosed before 2018, 2019, 2020, and 2021. The trees were generated using the General Time Reversible (GTR) substitution model. Parameters for these trees were estimated by the birth–death model for Exposure-Infectious individuals (BDEI). From each ML tree, we estimated the basic reproductive number (R0), the duration of the infectious period (1/γ), and the duration of the latent period (1/ϵ). Python library, phylodeep 0.3 [23] was used to estimate parameters from ML trees.
Definition of secondary HIV transmission
Secondary HIV Transmission is defined as the newly diagnosed PLWH genetic link to one of the individuals diagnosed in the preceding year. Patients diagnosed between 2000 and 2017 were classified as baseline period patients, while patients newly diagnosed with HIV-1 from 2018 to 2021 were defined as follow-up period patients. Secondary HIV transmission prevalence was calculated as follows: secondary HIV transmission prevalence = (number of baseline network individual connecting with newly diagnosed individuals) / (number of total baseline network individual). First, the baseline molecular network of PLWHs diagnosed from 2000 to 2017 was constructed using an optimal GD. Second, the newly diagnosed HIV patients in 2018 were added to the baseline network. Third, we computed the number of genetic links between patients during the baseline period (2000-2017) and newly diagnosed HIV cases during the follow-up period (2018). Following this stepwise, we computed genetic linkages between the baseline period of 2000–2018 and the follow-up period of 2019, between the baseline period of 2000–2019 and the follow-up period of 2020, and between the baseline period of 2000–2020 and the follow-up period of 2021.
Data collection for the risk factors of secondary HIV transmission
The variables used in this study were collected from the first enrolment survey and the NFATP in Guangxi.
The baseline and follow-up data of ART were included in the analysis: (1) Socio-demographic characteristics included sex, ethnicity, age at HIV-1 diagnosis, education, transmission route, and sampled city; (2) Clinical characteristics included time at diagnosis, initial and follow-up CD4+ T cell count and viral load, sexually transmitted infections at baseline; (3) ART characteristics included trimethoprim-sulfamethoxazole (TMP-SMX) use at baseline, ART regimens, and ART medical omissions; and (4) Behaviour characteristics included HIV serostatus of sexual partner and condom use in the past three months during the follow-up.
We collected all the records of VL and CD4+ T cell counts of each participant during their ART follow-up. If the participants did not undergo VL testing for one year, we classified their VL as missing. We classified the follow-up VL into five groups: < 50 copies/mL (virological suppression), 50–199 copies/mL, 200–999 copies/mL, ≥ 1,000 copies/mL, and data missing.
Statistical analysis
Statistical analysis was performed with R version 4.2.0 software (http://www.R-project.org. The R Foundation, Vienna, Austria) and Python 3.10. Quantitative data are presented as the mean ± standard deviation (SD), and nonparametric variables are presented as the median (interquartile range, IQR). Frequencies and percentages were used to describe categorical data. The chi-squared test was used to determine the statistical significance of group differences. The Cochran–Armitage trend test was used to assess the trend in the prevalence of secondary HIV Transmission over time. The preventive efficacy (PE) in HIV transmission was used to assess the effectiveness of different subtypes in reducing secondary HIV transmission, reported by a previous study [10]. The Generalized Estimating Equations (GEE) model was used to examine the potential risk factors of secondary HIV transmission. The quasi-likelihood under independence model criterion (QIC) was used as the criterion for variable selection, and the model with the smallest QIC was selected by a stepwise approach. Initially, all variables were entered into the GEE model, followed by a stepwise procedure based on the QIC. The model with the lowest QIC value was selected as the final model. This method involved a combination of forward and backward selection using QIC as the selection criterion. We also applied restricted cubic spline (RCS) regression with 3 knots (10th, 50th, and 90th percentiles) to examine the non-linearity of the association between secondary HIV transmission and age at diagnosis. A two-tailed P-value less than 0.05 was considered to be statistically significant. The secondary HIV transmission served as the dependent variable, and the association with independent variables was estimated by calculating adjusted incidence rate ratios (aIRRs) and their 95% confidence intervals (CIs).
Results
The sociodemographic and clinical characteristics of the PLWH with ART
The study included a total of 4,120 individuals, from whom 3,177 HIV pol sequences were successfully obtained. Of these, 2,245 (70.7% of 3,177) were baseline patients spanning the period from 2000 to 2017, while 932 were newly diagnosed with HIV-1 during the follow-up period (2018-2021). Among the follow-up patients who met the inclusion criteria (29.3% of 3,177), the distribution across the years was as follows: 199 (21.4% of 932) in 2018, 216 (23.2% of 932) in 2019, 322 (34.5% of 932) in 2020, and 195 (20.9% of 932) in 2021. Among the 3,177 participants (Table 1), the majority were male (67.8%, 2,154 of 3,177) and had Han ethnicity (60.1%, 1,908 of 3,317). Approximately 52.8% (1,679 of 3,177) reported having an education level of primary school or less. The age distribution showed that 45.0% (1,430 of 3,177) were between 30 and 49 years old, and 41.6% (1,321 of 3,177) were 50 years or older. Heterosexual contact was the primary mode of HIV-1 transmission, accounting for 83.1% (2,639 of 3,177) of cases.
Table 1.
Socio-demographic characteristics and clinical characteristics of participants at baseline and follow-up (N, %).
| Variable | Total | Baseline | Follow-up | ||||
|---|---|---|---|---|---|---|---|
| 2000–2017 | 2018 | 2019 | 2020 | 2021 | Overall | ||
| Total | 3177 (100.0) | 2245 (100.0) | 199 (100.0) | 216 (100.0) | 322 (100.0) | 195 (100.0) | 932 (100.0) |
| Sex | |||||||
| Female | 1023 (32.2) | 730 (32.5) | 63 (31.7) | 72 (33.3) | 97 (30.1) | 61 (31.3) | 293 (31.4) |
| Male | 2154 (67.8) | 1515 (67.5) | 136 (68.3) | 144 (66.7) | 225 (69.9) | 134 (68.7) | 639 (68.6) |
| Ethnicity | |||||||
| Han | 1908 (60.1) | 1364 (60.8) | 137 (68.8) | 139 (64.4) | 202 (62.7) | 66 (33.8) | 544 (58.4) |
| Zhuang | 1246 (39.2) | 871 (38.8) | 61 (30.7) | 71 (32.9) | 117 (36.3) | 126 (64.6) | 375 (40.2) |
| Others | 23 (0.7) | 10 (0.4) | 1 (0.5) | 6 (2.8) | 3 (0.9) | 3 (1.5) | 13 (1.4) |
| Age at HIV-1 diagnosis (years old) | |||||||
| 18–29 | 426 (13.4) | 361 (16.1) | 16 (8.0) | 13 (6.0) | 24 (7.5) | 12 (6.2) | 65 (7.0) |
| 30–49 | 1430 (45.0) | 1118 (49.8) | 76 (38.2) | 80 (37.0) | 101 (31.4) | 55 (28.2) | 312 (33.5) |
| ≥50 | 1321 (41.6) | 766 (34.1) | 107 (53.8) | 123 (56.9) | 197 (61.2) | 128 (65.6) | 555 (59.5) |
| Education | |||||||
| Primary school and below | 1679 (52.8) | 1140 (50.8) | 109 (54.8) | 125 (57.9) | 201 (62.4) | 104 (53.3) | 539 (57.8) |
| Junior high school and above | 1498 (47.2) | 1105 (49.2) | 90 (45.2) | 91 (42.1) | 121 (37.6) | 91 (46.7) | 393 (42.2) |
| Sampling city | |||||||
| Chongzuo | 1399(44.0) | 976(43.5) | 67(33.6) | 92(42.6) | 125(38.9) | 139(71.3) | 423(45.4) |
| Qinzhou | 1778(66.0) | 1269(56.5) | 132(66.3) | 124(57.4) | 197(61.1) | 56(28.7) | 509(54.6) |
| Transmission routes | |||||||
| Heterosex | 2639 (83.1) | 1802 (80.3) | 173 (86.9) | 195 (90.3) | 285 (88.5) | 184 (94.4) | 837 (89.8) |
| Injecting drug use | 394 (12.4) | 348 (15.5) | 15 (7.5) | 13 (6.0) | 16 (5.0) | 2 (1.0) | 46 (4.9) |
| Others | 144 (4.5) | 95 (4.2) | 11 (5.5) | 8 (3.7) | 21 (6.5) | 9 (4.6) | 49 (5.3) |
| Baseline CD4, cell/µL | |||||||
| <200 | 1215 (38.2) | 844 (37.6) | 74 (37.2) | 81 (37.5) | 132 (41.0) | 84 (43.1) | 371 (39.8) |
| 200–349 | 1058 (33.3) | 768 (34.2) | 60 (30.2) | 70 (32.4) | 102 (31.7) | 58 (29.7) | 290 (31.1) |
| ≥350 | 904 (28.5) | 633 (28.2) | 65 (32.7) | 65 (30.1) | 88 (27.3) | 53 (27.2) | 271 (29.1) |
| First VL after ART, copies/mm3 | |||||||
| <50 | 2158 (67.9) | 1587 (70.7) | 168 (29.4) | 166 (29.1) | 237 (41.5) | 0 (0) | 571 (61.3) |
| 50–199 | 166 (5.23) | 114 (5.08) | 9 (17.3) | 8 (15.4) | 28 (53.8) | 7 (13.5) | 52 (5.58) |
| 200–999 | 71 (2.23) | 48 (2.14) | 2 (8.70) | 6 (26.1) | 9 (39.1) | 6 (26.1) | 23 (2.47) |
| >=1000 | 187 (5.89) | 136 (6.06) | 8 (15.7) | 15 (29.4) | 21 (41.2) | 7 (13.7) | 51 (5.47) |
| Missing | 595 (18.7) | 360 (16.0) | 12 (5.11) | 21 (8.94) | 27 (11.5) | 175 (74.5) | 235 (25.2) |
| HBV-infection at the baseline | |||||||
| Negative | 1888 (59.4) | 137 (21.9) | 137 (21.9) | 145 (23.1) | 220 (35.1) | 125 (19.9) | 627 (67.3) |
| Positive | 308 (9.69) | 22 (23.9) | 22 (23.9) | 22 (23.9) | 29 (31.5) | 19 (20.7) | 92 (9.87) |
| Not detected | 981 (30.9) | 40 (18.8) | 40 (18.8) | 49 (23.0) | 73 (34.3) | 51 (23.9) | 213 (22.9) |
| HCV-infection at the baseline | |||||||
| Negative | 1794 (56.5) | 128 (20.4) | 128 (20.4) | 143 (22.8) | 213 (34.0) | 143 (22.8) | 627 (67.3) |
| Positive | 397 (12.5) | 28 (29.2) | 28 (29.2) | 25 (26.0) | 35 (36.5) | 8 (8.33) | 96 (10.3) |
| Not detected | 986 (31.0) | 43 (20.6) | 43 (20.6) | 48 (23.0) | 74 (35.4) | 44 (21.1) | 209 (22.4) |
| Subtype | |||||||
| CRF01_AE | 1598 (50.3) | 1222 (54.4) | 79 (39.7) | 96 (44.4) | 127 (39.4) | 74 (37.9) | 376 (40.3) |
| CRF07_BC | 311 (9.8) | 158 (7.0) | 25 (12.6) | 36 (16.7) | 49 (15.2) | 43 (22.1) | 153 (16.4) |
| CRF08_BC | 1137 (35.8) | 792 (35.3) | 87 (43.7) | 71 (32.9) | 125 (38.8) | 62 (31.8) | 345 (37.0) |
| Others | 131 (4.1) | 73 (3.3) | 8 (4.0) | 13 (6.0) | 21 (6.5) | 16 (8.2) | 58 (6.2) |
| ART regimen | |||||||
| AZT-based | 1210 (38.1) | 948 (42.2) | 74 (37.2) | 60 (27.8) | 91 (28.3) | 37 (19.0) | 262 (28.1) |
| D4T-based | 4 (0.1) | 4 (0.2) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) |
| LPV/r-based | 135 (4.2) | 86 (3.8) | 6 (3.0) | 10 (4.6) | 21 (6.5) | 12 (6.2) | 49 (5.3) |
| TDF-based | 1572 (49.5) | 959 (42.7) | 118 (59.3) | 146 (67.6) | 208 (64.6) | 141 (72.3) | 613 (65.8) |
| Others | 256 (8.1) | 248 (11.0) | 1 (0.5) | 0 (0.0) | 2 (0.6) | 5 (2.6) | 8 (0.9) |
| TMP-SMX usea at baseline | - | ||||||
| No | 2018 (63.5) | 1497 (66.7) | 108 (54.3) | 121 (56.0) | 179 (55.6) | 113 (57.9) | 521 (55.9) |
| Yes | 1159 (36.5) | 748 (33.3) | 91 (45.7) | 95 (44.0) | 143 (44.4) | 82 (42.1) | 411 (44.1) |
| ART medication omissions per month | |||||||
| No | 2715 (85.5) | 1882 (83.8) | 180 (21.6) | 183 (22.0) | 279 (33.5) | 191 (22.9) | 833 (89.4) |
| 1–9 times | 194 (6.11) | 163 (7.26) | 8 (25.8) | 11 (35.5) | 12 (38.7) | 0 (0) | 31 (3.33) |
| 10–20 times | 72 (2.27) | 56 (2.49) | 2 (12.5) | 5 (31.3) | 7 (43.8) | 2 (12.5) | 16 (1.72) |
| >20 times | 196 (6.17) | 144 (6.41) | 9 (17.3) | 17 (32.7) | 24 (46.2) | 2 (3.85) | 52 (5.58) |
| Condom use at the first visit | |||||||
| Condom used or no sex | 2392(75.3) | 1586(70.6) | 161(80.9) | 177(81.9) | 290(90.1) | 178(91.3) | 157(37.8) |
| No condom used | 785 (24.7) | 659 (29.4) | 38 (19.1) | 39 (18.1) | 32 (9.9) | 17 (8.7) | 580 (62.2) |
| HIV serostatus of the sex partner at the baseline | - | ||||||
| Sero-concordant | 478 (15.0) | 342 (15.2) | 18 (9.0) | 32 (14.8) | 42 (13.0) | 44 (22.6) | 136 (14.6) |
| Sero-discordant | 1078 (33.9) | 820 (36.5) | 59 (29.6) | 64 (29.6) | 84 (26.1) | 51 (26.2) | 258(27.7) |
| No sexual partner | 1621 (51.0) | 1083 (48.2) | 122 (61.3) | 120 (55.6) | 196 (60.9) | 100 (51.3) | 538 (57.7) |
| Self-reported STIs at the baseline | |||||||
| No | 2611 (82.2) | 1827 (81.4) | 165 (21.0) | 176 (22.4) | 259 (33.0) | 184 (23.5) | 784 (84.1) |
| Yes | 279 (8.78) | 197 (8.78) | 16 (19.5) | 20 (24.4) | 37 (45.1) | 9 (11.0) | 82 (8.80) |
| Unknown | 287 (9.03) | 221 (9.84) | 18 (27.3) | 20 (30.3) | 26 (39.4) | 2 (3.03) | 66 (7.08) |
TMP-SMX, trimethoprim-sulfamethoxazole; STIs, Sex Transmission infections.
Regarding immunological and virological markers, approximately 80.8% (2,567 of 3,177) had a CD4+ T cell count of less than 350 cells/µL, while 67.9% (2,158 of 3,177) exhibited a VL of less than 50 copies/ml and 5.89% (187 of 3,177) exhibited a VL ≥ 1,000 copies/ml. In addition, more than half of the participants were negative for both HBV (59.4%) and HCV infection (56.5%). At the ART baseline, the majority reported the absence of any STIs (82.2%). In terms of viral subtypes, CRF01_AE was the most prevalent (50.8%, 1,598 of 3,177), followed by CRF08_BC (35.8%, 1,347 of 3,177) and CRF07_BC (9.8%, 311 of 3,177). Nearly half of the patients (49.5%, 1,572 of 3,177) began ART with a TDF-based regimen, while one-third received AZT-based regimens (38.1%, 1,210 of 3,177). Furthermore, the majority of PLWH reported no medical omission (85.5%, 2,715 of 3,177) in the first ART year.
In terms of sexual behaviour in the three months before follow-up, 75.3% (2,392 of 3,177) of the participants reported condom use or no sexual activities, and 24.7% (785 of 3,177) of them reported condomless sex. Additionally, 63.5% (2,018 of 3,177) of the patients did not use TMP-SMX when they initiated ART. At the time of diagnosis, 51.0% (1,621 of 3,177) of them reported not having a sexual partner, and 33.9% (1,078 of 3,177) of them reported having a sero-discordant sexual partner for HIV-1. Further details regarding participant characteristics are provided in Table 1.
The prevalence of secondary HIV transmission and R0
The optimal GD threshold identified by the sensitivity analysis was 0.9% (Figure S1), representing the value at which the maximum number of clusters was formed. Among the 3,177 HIV-1 patients, 166 clusters comprising 1,480 patients were identified based on a 0.9% GD threshold (Figure S2).
Phylogenetic trees were constructed according to year, and most newly diagnosed individuals were of the Paraphyletic-Monophyletic (PM) class (Figure S4).
Figure 1 illustrates the genetic linkages in the molecular networks between PLWH at the baseline period and newly diagnosed HIV patients in the follow-up period. By analyzing the genetic relationships among HIV-1 patients at baseline from 2000 to 2017, from 2000 to 2018, from 2000 to 2019, and from 2000 to 2020, there were 262 (11.7%), 204 (8.3%), 391 (14.7%), and 260 (8.7%) had genetic links to newly diagnosed HIV patients in 2018, 2019, 2020, and 2021, respectively (Figure 2). There was no significant trend in the prevalence of secondary HIV transmission over time (Z = 0.784, p = 0.432). In the GEE analysis, including 10,331 baseline participants with repeated measures, the overall prevalence of secondary HIV transmission was 10.8% (1,117 out of 10,331).
Figure 1.
Genetic links in the molecular networks among PLWH between years of baseline and follow-up in Guangxi, China.
Note: (A) Genetic linkage among PLWH diagnosed between years of 2000–2017 and those diagnosed in 2018. (B) Genetic linkage among PLWH diagnosed between years of 2000–2018 and those diagnosed in 2019. (C) Genetic linkage among PLWH diagnosed between years of 2000–2019 and those diagnosed in 2020. (D) Genetic linkage among PLWH diagnosed between years of 2000–2020 and those diagnosed in 2021. Note. IDU, Injecting Drug Use; HET, Heterosexual.
Figure 2.
Trend and Prevalence of Secondary HIV Transmission from 2018 to 2021 in Guangxi. The blue line is the trend of the fitted time and prevalence of secondary HIV-1 transmission, indicating that there is no trend in the incidence of secondary HIV transmission. The gray shaded area is the fitted 95% CI obtained by fitting a linear trend model.
The estimated R₀ for the CRF01_AE subtype exceeded 1.0 throughout the observation period, peaking at 1.44 (95% CI: 1.24–1.69) in 2019 (Table 2, Figure S4). Similarly, the CRF08_BC subtype maintained a relatively high R₀ over the observation period, reaching its highest value of 1.96 (95% CI: 1.65–2.38) in 2019. In contrast, the R₀ for the CRF07_BC subtype and other subtypes remained relatively low, consistently hovering around 1.0 with only minor fluctuations.
Table 2.
Basic reproduction numbers of subtypes in the baseline years.a
| Year of diagnosed | Subtypes | R0(95% CI) | 1/γ (95% CI) | 1/ϵ (95% CI) |
|---|---|---|---|---|
| 2017 and before | CRF08_BC | 1.48(1.28,1.73) | 0.0093(0.0079,0.011) | 0.0059(0.0041,0.0082) |
| 2017 and before | CRF07_BC and others | 1.10(1.00,1.41) | 0.0062(0.0050,0.0080) | 0.0091(0.0056,0.014) |
| 2017 and before | CRF01_AE | 1.15(1.0004,1.34) | 0.0028(0.0025,0.0034) | 0.0028(0.0020,0.0036) |
| 2018 and before | CRF08_BC | 1.37(1.16,1.64) | 0.010(0.0082,0.012) | 0.0061(0.0038,0.0089) |
| 2018 and before | CRF07_BC and others | 1.09(1.00,1.40) | 0.0056(0.0044,0.0071) | 0.0095(0.0060,0.014) |
| 2018 and before | CRF01_AE | 1.36(1.18,1.60) | 0.0027(0.0023,0.0032) | 0.0025(0.0018,0.0035) |
| 2019 and before | CRF08_BC | 1.96(1.65,2.38) | 0.0098(0.0080,0.012) | 0.0084(0.0063,0.011) |
| 2019 and before | CRF07_BC and others | 1.003(1.00,1.24) | 0.0062(0.0050,0.0077) | 0.0087(0.0057,0.012) |
| 2019 and before | CRF01_AE | 1.44(1.24,1.69) | 0.0027(0.0023,0.0032) | 0.0022(0.0014,0.0031) |
| 2020 and before | CRF08_BC | 1.46(1.25,1.71) | 0.0027(0.0021,0.0032) | 0.0017(−0.00031,0.0040) |
| 2020 and before | CRF07_BC and others | 1.003(1.00,1.21) | 0.0047(0.0039,0.0057) | 0.010(0.0076,0.013) |
| 2020 and before | CRF01_AE | 1.35(1.17,1.58) | 0.0034(0.0029,0.0041) | 0.0010(0.00031,0.0018) |
Analyzed based on the birth-death model with exposed and infectious classes (BDEI) model.
R0, basic reproduction number; 1/γ, infectious period; 1/ϵ, latency period.
The PE for all non-CRF01_AE subtypes was less than 0 (Table 3), with the CRF08_BC subtype showing the lowest PE at −125.40%
Table 3.
Genetic linkages between newly diagnosed HIV patients and PLWHs at baseline in Guangxi of China, stratified by HIV-1 subtype, respectively.
| Subtype | Total number of HIV patients at baseline from 2017 to 2020 | Total number of genetic linkages between newly diagnosed HIV infections from 2018 to 2021 and HIV patients at baseline | PE (95%CI) (%) |
|---|---|---|---|
| CRF01_AE | 1524 | 378 | Ref |
| Non-CRF01_AE | 1458 | 379 | −208.88(−318.1, −99.66) |
| CRF07_BC | 268 | 99 | −48.93(−106.36, 8.5) |
| CRF08_BC | 1075 | 601 | −125.40(−205.74, −45.06) |
| Other subtypes | 115 | 39 | −36.73(−96.22, 22.76) |
PE: Prevention Efficacy.
Potential risk factors of secondary HIV transmission
The multivariate Poisson regression model analysis identified several risk factors of secondary HIV transmission(Figure 3): male sex (aIRR = 1.32, 95% CI: 1.13-1.54), diagnosis at age of 30–49 (aIRR = 1.36, 95% CI: 1.07-1.73) and age ≥ 50 years (aIRR = 1.76, 95% CI: 1.38- 2.2), ART follow-up CD4+ T-cell count ≥ 350 cell/ µL (aIRR = 1.43, 95% CI: 1.21-1.7), condomless sex (aIRR = 1.23, 95% CI: 1.03-1.48), ART follow-up VL ≥ 1,000 copies/ml (aIRR = 2.30, 95% CI: 1.99-2.65) and missing record of VL (aIRR = 1.21, 95% CI: 1.02-1.43), ART medical omissions(1-9 times/m: aIRR = 1.79, 95% CI: 1.43-2.24; 10–20 times/m: aIRR = 2.49, 95% CI: 1.96-3.15; > 20 times/m: aIRR = 2.48, 95% CI: 2.16-2.86), and STIs at diagnosis (Yes: aIRR = 3.40, 95% CI: 3.00-3.85; Unknown: aIRR = 1.34, 95% CI: 1.12-1.59). In contrast, higher education level (aIRR = 0.84, 95% CI: 0.73-0.97) decreased the risk of secondary HIV transmission. Stratified analysis by HIV subtypes showed that participants with an ART follow-up VL ≥1,000 copies/ml, ART medication omissions ≥10 times, and self-reported STIs at baseline were more likely to increase the risk of secondary HIV transmission regardless of the HIV subtypes. However, higher education(aIRR = 0.58, 95% CI: 0.40-0.86) significantly reduced the risk of secondary HIV transmission only in the CRF07_BC and other subtypes. Additionally, male sex (aIRR = 1.70, 95% CI: 1.29-2.23) and ART follow-up CD4+ T-cell count ≥350 cells/μL (aIRR = 2.73, 95% CI: 1.56-4.76) were associated with an increased risk of secondary HIV transmission in the CRF01_AE subtype. In the CRF08_BC subtype, condomless sex (aIRR = 1.31, 95% CI: 1.04-1.67) was significantly associated with increased transmission risk.
Figure 3.
Potential risk factors associated with secondary HIV transmission among PLWH diagnosed at years of baseline and follow-up in the final Poisson model.
Sensitivity analysis for age at diagnosis as a predictor of increasing secondary HIV transmission
In the univariate analysis, we observed a nonlinear association between age at diagnosis and secondary HIV transmission across all subtypes (P for nonlinear = 0.038), including CRF01_AE (P for nonlinear = 0.041) and CRF08_BC (P for nonlinear < 0.001) (Figure S3). After adjusting for the same variables as in the multivariable Poisson regression model, significant differences remained (P < 0.001) (Figure 4). The analysis indicated a nonlinear relationship between age and HIV transmission risk (P for Nonlinear = 0.0489), with an approximately S-shaped curve. Both CRF01_AE and CRF08_BC subtypes demonstrated significant correlations between age and secondary HIV transmission risk (P < 0.001). Specifically, CRF01_AE exhibited an S-shaped nonlinear pattern (P = 0.0135), while CRF08_BC showed an inverted U-shaped nonlinear pattern (P = 0.0066). Other subtypes, such as CRF07_BC, also showed significant correlations with age (P < 0.001), although the nonlinear relationship was not significant (P = 0.695).
Figure 4.
Restricted cubic spline for association between secondary HIV-1 transmission and age at diagnosis. Adjusted for sex, education, follow-up CD4+ T cell count and viral load, condom used in the past three months, ART medication omissions times per month subtype, and STIs self-reported at baseline.
Note: The left y-axis is the adjusted IRR and the right y-axis is the distribution of age at diagnosis. Data were fitted by a Poisson regression model, and the model was conducted with 3 knots at the 10th, 50th, and 90th percentiles of Age at diagnosis (reference is the 10th percentile).
Discussion
This was the first study to determine the trend, prevalence, and risk factors of secondary HIV transmission among ART-experience PLWH, using large longitudinal data. We found that the secondary HIV transmission prevalence was at a moderate level (10.8%) with no discernible trend of increase. The R0 for all HIV subtypes during the observation period was more than 1, suggesting that the HIV strain in this area exhibited a sustained ability to propagate within the population. The primary risk factors of secondary HIV transmission included male sex, advanced age at diagnosis, condomless sex in the last three months, higher viral loads during the ART follow-up period, frequent ART medical omissions, and the presence of STIs at diagnosis. These findings highlight the necessity of emphasizing ART adherence, promoting safe sexual practice, and targeting high-risk groups with tailored interventions to curtail secondary HIV transmission.
The prevalence of secondary HIV transmission in this study was modestly elevated compared to that in the central city of Guangxi from 2015 to 2018 (9.8%) [11]. The prevalence of secondary HIV transmission ranged from 8.3% to 14.7% from 2018 to 2021, showing a stable trend, which indicated that the risk factors of HIV-1 transmission dynamics remain persistent in the area. A significant reduction in secondary HIV transmission was observed in 2021. We hypothesized that could be attributed to the impact of the COVID-19 pandemic on HIV testing and diagnosis accessibility. China’s COVID-19 lockdown measures might curtail high-risk sexual behaviours [20,24], contributing to the decline in secondary HIV transmission. Additionally, the HIV epidemic has been effectively controlled since the implementation of the Treat-all policy in 2016 and with the extension of pre-exposure prophylaxis (PrEP) [25]. Therefore, enhancing the accessibility of HIV testing, advancing rapid initiation of ART, and expanding the use of PrEP is crucial for curtailing secondary HIV transmission.
R0 is the average number of secondary infections produced by one infected individual (over their lifetime) in a community in which all individuals are susceptible. In this study, R0 of all subtypes were greater than 1.0, indicating an HIV-infected individual transmitted to more than one new individual on average. The peak of R0 of all subtypes was at the baseline of 2019, which aligned with the highest prevalence of secondary HIV transmission observed in 2020. The main reason for this persistent transmission may be that male older adults had become the center of HIV transmission in Guangxi [26], supported by our other finding that the male sex and older age of diagnosis were the risk factors of secondary HIV transmission. The increasing prevalence of commercial sex [27] and the high proportion of condomless [28] have elevated the risk of HIV transmission among older adults in recent years.
In this study, the R0 of CRF08_BC was the highest in the observation period. One of the possible reasons was that the CRF08_BC subtype predominantly spread among people who inject drugs, with transmission occurring more rapidly through injection drug use. Additionally, the CRF08_BC subtype exhibited a relatively high R0 and a significantly high aIRR, indicating a higher risk of secondary transmission. Furthermore, the PE of CRF08_BC was the lowest. These findings indicated that CRF08_BC might be the crucial subtype driving local HIV-1 spreading. CRF08_BC was primarily transmitted among IDU, but in recent years there was a rapid outward expansion [29,30]. CRF07_BC and CRF08_BC first originated among IDUs and then spread to the men who have sex with men (MSM) [31]. In the last decade, these strains have transitioned from being predominantly MSM-associated to becoming more widespread within the general population [32,33]. The high transmission potential of CRF08_BC noted in the study may be linked to its spread pathway, transitioning from IDUs to MSM and finally into the general population. Besides, we observed that the condomless sex was a significant risk factor of CRF08_BC secondary transmission, which laterally indicated that the transmission pattern of CRF08_BC was switching and spreading rapidly. These findings suggested that health policymakers need to monitor the dynamic transmission of different HIV subtypes and the key factors driving the increasing transmission. Incorporating longitudinal monitoring of R0 at a programmatic level could help identify opportunities for optimizing PrEP programs [34], which lead to more targeted and effective interventions for preventing secondary HIV transmission among PLWH receiving ART.
We found that male HIV-infected individuals diagnosed at the age of 50 or older had a higher risk of secondary HIV transmission, and this increased risk was more pronounced among individuals infected with the CRF01_AE subtype, suggesting that the older HIV-infected males were the main population for the secondary HIV transmission in the local area. Similar results were observed in two other long-term observational studies [35,36]. This increased risk may be attributed to the historical neglect of sexual needs among older adults [37], coupled with a lack of targeted HIV prevention interventions [13,38]. Additionally, older adults may engage in high-risk sexual behaviours [39] and have limited knowledge of safe sex practices, such as condom use, PrEP, and PEP. A substantial body of research has demonstrated that the CRF01_AE subtype was the predominant strain circulating among the local elderly male population [26,40], which also presented the primary group affected by HIV in this area. To address the increased risk of secondary HIV transmission, clinical practices should focus on targeted prevention and education, routine HIV screening, and rapid initiation of ART. Additionally, integrating tailored behavioural interventions and offering PrEP for high-risk individuals can further mitigate transmission risks.
We found that patients with a VL of more than 1,000 copies/mL during the ART follow-up were more likely to secondarily transmitted HIV, which was consistent with the previous studies [41,42]. It was reported that the most important factor for HIV direct transmission is high VL [43]. Our results highlight the crucial role of prevention efforts, showing that the risk of HIV transmission is effectively eliminated when the VL is under 1,000 copies per mL. In this study, ART medication omissions increased the risk of secondary HIV transmission by 1.79 to 2.48 folds. Previous studies have demonstrated that higher adherence to ART was strongly associated with a reduction in viral load, following a linear dose–response relationship [44]. It was well established that effective combination therapy and adherence to ART efficiently block viral replication [45], reducing circulating virus to undetectable levels by standard clinical assays [46]. Furthermore, greater perceived psychological and behavioural barriers to ART adherence were associated with lower self-efficacy [47], which in turn was linked to reduced adherence. Self-efficacy in ART adherence was influenced by education level, knowledge, and commitment to ART and HIV management [48]. This study demonstrates that the association between ART adherence and secondary HIV transmission suggests that improving adherence to ART may lead to a significant reduction in viral load, subsequently decreasing the risk of secondary HIV transmission. Therefore, given the considerable challenges in controlling HIV/AIDS control in China, expanding knowledge about ART is crucial for improving self-efficacy in ART adherence, decreasing omissions of ART medication, and ensuring effective virological suppression through ART.
Self-reported STIs at diagnosis were one of the important risk factors for secondary HIV transmission. One possible explanation for this finding was that HIV-infected individuals with STIs may have a large number of sexual partners and/or casual relationships [49], leading to increased secondary HIV transmission. Furthermore, the immunosuppressive effects of HIV lead to greater vulnerability to STIs [50], which not only decrease CD4+ T cell counts but also increase viral load [51], thereby amplifying the risk of HIV transmission [52].
We also found that inconsistent condom use was associated with a high risk of secondary HIV transmission among PLWH receiving ART. This implicated the possible existence of risk compensation behaviours among PLWH receiving ART [53,54], with inconsistent condom use and other risk behaviours. The effectiveness of ART for preventing HIV transmission depends on maintaining virological suppression in the plasma, and consistent condom use becomes the last line of defense against HIV among PLWH with non-suppressed VL. Therefore, it is important to stress the necessity of using condoms correctly, particularly among older individuals with lower levels of education and a high risk of transmission. The findings emphasized the importance of condom use, regular VL and drug resistance testing, and ART adherence.
In this study, the use of dynamically changing behavioural and immunological data at the follow-up helped us to more accurately reflect the relationship between these dynamic variables and the risk of secondary HIV transmission. Additionally, a series of diagnostic age data also more accurately revealed the dynamic relationship between age and the risk of secondary HIV transmission across HIV-1 subtypes.
However, this study had several limitations. First, due to privacy concerns in HIV/AIDS research, random sampling was not possible, and we relied on convenience sampling, which may introduce some bias. However, we sampled more than 60% of newly diagnosed individuals each year, and our data was sufficiently representative. Second, we did not use an epidemiological survey to verify the transmission relationships among individuals in the molecular network. However, molecular network analysis has been proven to be effective for identifying HIV transmission chains and informing intervention strategies [55]. In addition, through the phylogenetic tree, newly diagnosed individuals entering the molecular network were all PM class, with more than 90% certainty that these individuals were in a transmission relationship [56]. Third, due to the limited geographic scope of the study, the results were not representative of the entire region. Finally, because some of the variables were from the PLWH self-report, there was some recall bias. Thus, we affirmed that provirus sequences were a valid representation of transmission dynamics.
Conclusion
This study revealed a substantial prevalence of secondary HIV transmission and an expansion R0 among PLWH receiving ART, indicating ongoing transmission dynamics and persistent risk of HIV expansion within this population that warrant urgent attention. Key risk factors of secondary HIV transmission included male sex, older age, condomless sex, elevated ART follow-up viral loads, ART medical omissions, infection with non-CRF01_AE subtypes, and the presence of STIs at diagnosis. These findings underscore the necessity for targeted interventions addressing these high-risk behaviours and characteristics to reduce secondary HIV transmission effectively. Conversely, higher education was associated with a decreased risk of secondary transmission, suggesting that educational initiatives could play a vital role in promoting safer practices and adherence to ART. To effectively mitigate secondary HIV transmission, it is crucial to enhance viral load monitoring, improve adherence to ART, and promote safe sex practices, especially among older adults living with HIV. This study emphasizes the urgent need for comprehensive public health strategies aimed at reducing secondary transmission and ultimately contributing to the goal of ending the HIV epidemic.
Supplementary Material
Acknowledgments
Jinfeng He, Shanmei Zhong, and Cai Qin contributed equally to this study. Jinfeng He drafted the first version of the manuscript, which was reviewed and edited by Shanmei Zhong and Cai Qin. Jiaxiao Jiang, Peijiang Pan, Deping Liu, and Aidan Nong were responsible for the sample collection and data collection. Jie Liu, Huayue Liang, and Fei Zhang were responsible for concept, bioinformatical and statistical analyses. Li Ye, Hao Liang, and Bingyu Liang reviewed and edited the manuscript. Funding acquisition: Bingyu Liang and Hao Liang. All authors had full access to all the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis. All authors reviewed, revised, and approved the final submission.
Funding Statement
This study was supported by the National Natural Science Foundation of China (Grant No. 82060610 and 82103899), National Key R&D Program of China (No. 2022YFC2305001), the Guangxi Natural Science Foundation (grant number 2022JJA141110), the China Scholarship Council (To Bingyu Liang), the Thousands of Young and Middleaged Key Teachers Training Program in Guangxi Colleges and Universities (To Bingyu Liang), and the Guangxi Bagui Young Top Scholar (To Bingyu Liang).
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The datasets generated and/or analyzed in this study are not publicly available because of ethical and legal reasons but are available from the corresponding author Bingyu Liang on reasonable request. The tree files were stored in GitHub (https://github.com/Hejinfeng1/phylogenetic-trees).
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets generated and/or analyzed in this study are not publicly available because of ethical and legal reasons but are available from the corresponding author Bingyu Liang on reasonable request. The tree files were stored in GitHub (https://github.com/Hejinfeng1/phylogenetic-trees).