Shu Liang

Introduction: The increasing utilization of antiretroviral (ART) medications by people living with HIV(PLWH) poses a potential risk to the efficacy of standardized ART management protocols in resource-limited settings, particularly as it may accelerate the emergence of transmitted drug resistance (TDR). Our research aimed to elucidate the prevalence, risk factors, geographical variations, and transmission patterns of TDR to inform and enhance ART management strategies. Methods: The study covered newly diagnosed PLWH in Sichuan from July 2022 to June 2023. We conducted sequencing - based resistance profiling and then analyzed TDR via the Stanford HIV Drug Resistance Database and logistic regression to identify associated factors via SPSS. ArcGIS and spatial statistics were used to create LISA maps showing the TDR distribution. HIV-TRACE’s Tamura Nei 93 model was used to assess genetic clustering, and QGIS was used to analyze TDR intensity within and between cities. Results: This study found that the prevalence of TDR in Sichuan had reached a moderate level, with a resistance rate of 10.91%. Resistance varied by drug class: 8.08% for NNRTIs, 1.34% for NRTIs, and 2.22% for PIs. Regions like LS, DY, SN, and LZ had higher TDR rates. Within NNRTIs, efavirenz (EFV) and nevirapine (NVP) resistance rates were 6.26% and 6.50%, respectively. Furthermore, risk factors for TDR included age under 15, HIV-1 subtypes, and residing in areas with high ART failure rates. In addition, spatial analysis showed significant clustering of TDR, especially for PIs and NNRTIs, with hotspots identified in certain regions. Among 1,486 drug-resistant PLWH, 52.76% formed resistance-linked networks, comprising 181 molecular clusters. Key resistance mutations included K103N, E138A, V179D, Y181C, and Q58E. The transmission intensity of TDR varied across regions, highlighting complex spatial dynamics. Discussion: The prevalence of TDR in Sichuan had reached moderate epidemic levels, with certain cities already experiencing high epidemic rates. It is urgent that we urgently expand targeted genotypic drug resistance detection and implement interventions to improve medication adherence.

BACKGROUND Incident HIV infection is a critical indicator of an ongoing epidemic, particularly in high-burden regions such as Liangshan Yi Autonomous Prefecture in China, where HIV prevalence exceeds 1% in 4 key counties (Butuo, Zhaojue, Meigu, and Yuexi). Identifying spatial clusters and drivers of recent infections is essential for implementing targeted interventions. Despite advancements in geospatial analyses of HIV prevalence, studies identifying drivers of incident HIV clustering remain limited, especially in low-resource settings. OBJECTIVE This study aims to identify spatial clusters of recent HIV infections and investigate potential driving factors in 4 key counties of the Liangshan Yi Autonomous Prefecture to inform targeted intervention strategies. METHODS From November 2017 to June 2018, we identified 246 (4.42%) recent HIV infection cases from 5555 newly diagnosed cases through expanded testing of the whole population in 4 key counties of Liangshan Yi Autonomous Prefecture. Recent infection cases were confirmed using limiting antigen avidity enzyme immunoassays or documented seroconversion within 6 months. The spatial distribution of incident HIV infection cases was analyzed using kernel density. Potential drivers, including population density, HIV prevalence, elevation, nighttime light index, urban proximity, and antiretroviral therapy (ART) coverage, were analyzed. The spatial lag regression model was used to identify factors associated with clustering of recent infection cases. The Geodetector q-statistic was used to quantify nonlinear interactive effects among these factors. RESULTS Significant spatial autocorrelation was observed in the distribution of recent HIV cases (Moran I=0.11; P<.01). Six spatial clusters were identified, and all were located near urban centers or major roads. Furthermore, 5 factors were identified by the spatial lag regression model as being significantly correlated with the clustering of recent HIV infection cases, including population density (β=0.59; P<.001), HIV prevalence (β=0.02; P<.001), distance to local urban area (β=–3.10; P=.01), SD of elevation (β=–0.15; P=.02), and ART coverage rate (β=183.80; P<.01). Geodetector analysis revealed strong interactive effects among these 5 factors, with population density and HIV prevalence exhibiting the largest interactive effect (q=0.69). CONCLUSIONS This study reveals that besides HIV prevalence, urbanization-related factors (population density and proximity to urban area) and transportation accessibility drive incident HIV clustering in Liangshan Yi Autonomous Prefecture. Paradoxically, higher ART coverage was associated with increased transmission, suggesting the need for integrated prevention strategies beyond ART expansion. Furthermore, the township-level geospatial approach provides a valuable model for pinpointing transmission hot spots and tailoring interventions in high-burden regions globally.

Background: Sichuan Province was severely affected by the HIV, and there was a scarcity of data regarding the survival time and influencing factors for People Living with HIV/AIDS (PLWH) in Sichuan Province who have received Antiretroviral Therapy (ART). Therefore, it is necessary to conduct a survival analysis for PLWH receiving ART. Methods: A retrospective cohort study was conducted on PLWH who had received ART≥ 6 months in Sichuan Province from January 1, 2003, to December 31, 2022. The Kaplan-Meier method was used to calculate median survival time and plot survival curves, while a Cox proportional hazards regression model was applied to analyze factors affecting survival time. Bilateral tests were performed, with P≤ 0.05 considered statistically significant. Results: The cumulative survival rates at 1, 3, 5, and 10 years for the 223,386 subjects were 94.54%, 89.07%, 84.82%, and 76.44%, respectively. Multivariate analysis using the Cox regression model indicated lower mortality risks for females (HR=0.59, 95% CI: 0.54– 0.65), homosexual transmission (HR=0.43, 95% CI: 0.33– 0.55), and baseline BMI≥ 24 (HR=0.81, 95% CI: 0.72– 0.90). Higher mortality risks were associated with age≥ 50 years at diagnosis (HR=3.21, 95% CI: 2.94– 3.50), being unmarried or divorced (HR=1.23, 95% CI: 1.11– 1.37), living separately (HR=1.32, 95% CI: 1.22– 1.43), baseline BMI < 18.5 (HR=1.27, 95% CI: 1.13– 1.41), presence of single-drug resistance (HR=1.25, 95% CI: 1.15– 1.36), baseline WHO stage IV (HR=1.27, 95% CI: 1.09– 1.47), and a diagnosis-to-treatment interval > 12 months (HR=1.27, 95% CI: 1.15– 1.41). Compared to those with CD4(+) T cell count of 200– 350cells/μL, 350– 500cells/μL, and > 500cells/μL at baseline, individuals with < 200cells/μL had higher mortality risks (HR=0.73, 95% CI: 0.67– 0.79; HR=0.57, 95% CI: 0.51– 0.64; and HR=0.58, 95% CI: 0.51– 0.66, respectively). Conclusion: The survival rate for PLWH receiving ART in Sichuan Province was relatively high. Male gender, age over 50 at diagnosis, being unmarried, divorced, or living separately, presence of single-drug resistance, low baseline BMI, baseline CD4+ T cell < 200cells/μL, baseline WHO stage IV, and a diagnosis-to-treatment interval > 12 months were risk factors for the survival of PLWH.

Background: Men who have sex with men (MSM) is one main type of high-risk activities facilitating HIV-1 transmission in Sichuan province. Previous works on HIV-1 incidence and prevalence among MSM only concentrated before 2018, the situation after that is unknown. In addition, the distribution of hot-spots related to current HIV-1 epidemic is also rarely known among MSM in Sichuan. Objective: To update trends of HIV-1 prevalence and incidence and to visualize hot-spots of ongoing transmission in Sichuan province during surveillance period among MSM between 2018 and 2022. Methods: Limiting Antigen Avidity assay was performed to detect recent infection within new HIV-1 diagnoses founded during surveillance period among MSM. The HIV-1 prevalence and incidence were calculated according to an extrapolation method proposed by publications and guidelines. Trend tests were performed using χ² tests with linear-by-linear association. The spatial analysis was conducted with ArcGIS 10.7 to figure hot-spots of HIV-1 recent infections among MSM. Results: Between 2018 and 2022, 16,697 individuals participated in HIV-1 MSM sentinel surveillance program, of which 449 samples (98.25%) were tested with LAg-Avidity EIA, and 230 samples were classified as recent infection. Respectively, the overall prevalence and incidence were 2.74% and 3.69% (95% CI: 3.21, 4.16) and both had significant declining trends (p < 0.001). Luzhou city had a highest HIV-1 incidence (10.74%, 95% CI: 8.39, 13.10) over the study period and was recognized as a hot-spot for recent HIV-1 infection among MSM. Conclusion: During the surveillance period, both HIV-1 prevalence and incidence were declining. However, Luzhou city had an unusually high HIV-1 incidence and became an emerging hot-spot of recent HIV-1 infection among MSM. This finding suggested focused attention, cross-regional intervention strategies, and prevention programs are urgently required to curb the spread of ongoing transmission.

Background: Sichuan Province has gradually become a hot-spot for HIV/AIDS. Little is known about the HIV-1 incidence and prevalence among four sub-groups since 2015. Meanwhile, the distributions of hot-spot areas related to recent infection cases that indicate current transmission among the four subgroups are also rare. Objective: The main purpose of this study was to assess the HIV-1 prevalence and incidence and to visualize the distributions of hot-spot areas of current transmission among four subgroups (people who inject drugs, male STD clinic attendees, female sex workers and men who had sex with men) during the surveillance period in Sichuan province between 2016 and 2022. Results: Of the 267,617 individuals, 2158 HIV-positive samples were tested with Lag-Avidity EIA, among which 493 samples were identified as recent infections. Among people who inject drugs (PWID), both HIV-1 prevalence (from 1.41% to 0.34%) and incidence (from 0.03% to 0.140%) showed a significant decreasing trend. Among men who had sex with men (MSM), female sex workers (FSWs), and male STD clinic attendees, HIV-1 prevalence indicated significant decreasing trends, whereas HIV-1 incidence showed no significant changes. Spatial analysis demonstrated the formation of hot-spots and clusters of current transmissions sharing regional differences, mainly concentrated in the southeast, and most of these were consecutive hot-spots. Conclusion: The prevention and control were efficacious and persistent. However, among the other three subgroups, there is a need for a regional cooperative in prevention and control approaches and collaborative research in many aspects.

Background: Sichuan Province is severely affected by the HIV epidemic in China. Little is known about the characteristics of recent infections among new HIV diagnoses, which is critical to prevention strategies, evaluation of the HIV epidemic and health resource allocation. Meanwhile, individuals at primary stages of infection are related to the hot-spot areas of ongoing transmission in new HIV diagnoses, which is also rarely known. Objective: This article aimed to report the proportion of recent infections among new HIV diagnoses, and to reveal demographic characteristics associated with HIV recent infections, and finally, to indicate the hot-spot areas of ongoing transmission in Sichuan province between 2018 and 2020. Methods: Limiting Antigen (LAg)-Avidity assay was performed to detect recent infection within new HIV diagnoses reported in odd months between 2018 and 2020. Results were reclassified according to the data on CD4 cell count, antiretroviral treatment and the existence of an AIDS-defining illness. Logistic regression was used to determine characteristics associated with HIV recent infections. The spatial analysis was conducted with ArcGIS 10.7 to figure hot-spot areas of HIV recent infections. Results: 42,089 newly diagnosed HIV-1 cases were tested using the LAg-Avidity EIA. In total, 5848 (13.89%) of those were classified as HIV recent infections. Female, age between 18– 25 years and men who had sex with men were related to higher proportion of HIV recent infections. Logistic regression revealed that MSM aged between 18– 25 years were more likely to be classified as recent infection. Spatial analysis demonstrated significant clustering in Chengdu, Yibin, Luzhou city between 2018 and 2020. Hot spots were mainly clustered in the center of Sichuan in 2018, but gradually spread to southwest and northwest between 2019 and 2020. Conclusion: Enhanced preventive measures among relevant risk groups and areas where the potential HIV-1 transmission is ongoing is urgently needed to curb further spread.

Objective: The objective of this study was to understand the molecular transmission characteristics of newly reported HIV infections in the city of Pengzhou, Sichuan Province, to analyze the risk factors of transmission network and spatial clustering and the transmission characteristics, and to provide a scientific basis for precision prevention and intervention. Methods: Anticoagulated whole blood was collected from newly reported HIV infections in Pengzhou from March 2019 to August 2021. After the plasma was isolated, the HIV-1 pol gene was amplified and sequenced by reverse transcriptase polymerase chain reaction (PCR). The obtained gene sequences were used to construct a maximum likelihood phylogenetic tree for the analysis of virus subtypes, and a molecular transmission network was constructed using the genetic distance method to evaluate the transmission pattern of people living with HIV/AIDS in Pengzhou. A logistic regression model was used to find out the potential risk factors for entering the molecular transmission network with the number of nodes ≥ 2. Spatial analysis is used to show the geographical pattern of the proportion of newly reported HIV infections entering the molecular transmission network, and a flow map is used to show the intensity of transmission within and between townships. Results: A total of 463 newly reported HIV-infection sequences were obtained in this study, including 237 cases (51.19%) of CRF01_ AE, 159 cases (34.34%) of CRF07_BC, 45 cases (9.72%) of B, 15 cases (3.24%) of CRF08_BC and 7 cases (1.5%) of others. The number of clusters was the highest when the gene distance was 0.009, with a total of 246 sequences entering the network, forming 54 clusters, and the network entry rate was 55.36%. There were 170 sequences with more than two nodes in the network sequence. The logistic regression showed that compared with age < 50 years old, age ≥ 50 years old has a higher risk of transmission (OR = 3.43, 95% CI = 2.06–5.71); compared with farmers, the risk of transmission within industry is lower (OR = 0.046, 95% CI = 0.25–0.87); and compared with CRF07_BC, CRF01_AE (OR = 6.09, 95% CI = 3.60–10.30) and B (OR = 20.31, 95% CI = 8.94–46.13) had a higher risk of transmission. Men aged ≥ 50 years are mainly clustered with women between 50 and 70 years of age. In addition to being clustered with gay men, there are nine (50%) and three (16.7%) chains of transmission between gay men and heterosexual men and women, respectively. In the geographical space, there is no hot spot clustering of the molecular propagation network. The subtype B was mainly distributed in the town of Tianpeng and formed transmission networks in eastern Pengzhou;0020CRF01_AE is mainly distributed in the town of Lichun and formed transmission networks in the west and north of Pengzhou. Conclusion: This study reveals the characteristics and influencing factors of molecular network transmission in the region, as well as the spatial transmission characteristics of newly reported HIV infections in recent years, and reveals the geographical differences in HIV-1 transmission. The results provide a scientific basis for the development of local AIDS-specific intervention measures.

Background: Choosing patients with HIV/AIDS (PLWH) with virologic failure observed in the course of treatment, and analyzing the CD4(+) T cell count of PLWH with different subtypes of virus and the risk factors of CD4 (+) T cell count recovery Methods: Analyze and evaluate the change of median CD4(+) T cell count of PLWH infected with different HIV-1 subtypes in Sichuan Province after treatment by rank sum test. Univariate and multivariate logistic regression models were used to analyze the risk factors on CD4(+) T cell count growth after PLWH treatment. Results: A total of 4977 cases were analysed, including CRF07_BC 2358 cases (47.38%), CRF01_AE 1507 cases (30.28%), CRF08_BC 577 cases (11.59%), CRF85_BC 303 cases (6.09%) and other subtypes 232 cases (4.66%). PLWH infected with CRF85_BC had lower median CD4(+) T cell count after treatment (p < 0.05). It was found that protective factors to increase CD4(+) T cell count (p < 0.05) including females, Yi people, married or cohabiting, educational level at primary school or above, initial and recent treatment with Tenofovir + Lamivudine + Nevirapine Wellen, the second-line regimen with lopinavir/ritonavir (LPV/r) and the treatment time is more than 24 months. However, over 50 years old, the disease course is in AIDS stage, the infection of CRF01_AE and CRF85_BC virus was the risk factor on CD4(+) T cell count increase (p < 0.05). PLWH with higher initial CD4 level had a lower ratio of CD4(+) T cell count increase (≥ 50 cells/μL) (p < 0.05). Conclusion: Age, sex, ethnicity, marriage, education, disease status, treatment protocols, treatment time, initial CD4(+) T cell count, and virus subtype are the influencing factors of PLWH CD4(+) T cell count increase. The poor recovery of CD4(+) T cell count after treatment was observed in PLWH infected with CRF85_BC virus.

Background: Evidence on the efficacy of antiretroviral therapy (ART) regimen switches on the mortality of patients with HIV drug resistance (HIVDR) is limited. Objective: We aim to provide policy guidance for ART regimen selection and evaluate the effectiveness of ART regime switches for people living with HIV and HIV-1 drug resistance. Methods: This retrospective observational cohort study included 179 people living with HIV and HIV-1 drug resistance from 2011 to 2020. The time that participants switched treatment regimens either to protease inhibitor (PI)–based ART regimens (PIs) or nonnucleoside reverse transcriptase inhibitor (NNRTI)–based ART regimens (NNRTIs) was taken as an observation starting point and followed up every 12 months. The parametric g-formula was used to estimate the 5-year risk of mortality under the situations of (1) natural course, (2) immediate switch to NNRTIs, (3) immediate switch to PIs, and (4) if CD4(+) T cells<200 switched to PIs. Results: The follow-up time of the 179 patients ranged from 30 to 119 months. The median follow-up time was 90 months. During a follow-up of 15,606 person-months, 27 individuals died in the cohort. The estimated 5-year risk of mortality under natural course, immediate switch to NNRTIs, immediate switch to PIs, and if CD4(+), and switch to PIs if T cells<200 were 11.62% (95% CI 7.82-17.11), 31.88% (95% CI 20.79-44.94), 2.87% (95% CI 0.32-7.07), and 5.30% (95% CI 2.07-10.21), respectively. The risk ratios (RRs) of immediate switch to NNRTIs, immediate switch to PIs, and switch to PIs if CD4(+) T cells<200, compared with natural course mortality rate, were 2.74 (95% CI 2.01-3.47), 0.25 (95% CI: 0.04-0.54), and 0.46 (95% CI 0.22-0.71), respectively. The risk differences were 20.26% (95% CI 10.96-28.61), –8.76% (95% CI –13.34 to –5.09) and –6.32% (95% CI –9.75 to –3.11), respectively. Conclusions: Our study found that a PI-based ART regimen was beneficial for reducing mortality in people living with HIV and HIV-1 drug resistance. More effort should be given to find HIV-1 drug resistance earlier to ensure a timely adjustment to PI-based ART, thereby maximizing the benefit of early switch treatment for people living with HIV and HIV-1 drug resistance.