Categorical testing of the viral load of people living with HIV to measure the intensity of the epidemic and the effectiveness of the response in the community: a prospective cohort study in Xinjiang China

Categorical testing of the viral load of people living with HIV to measure the intensity of the epidemic and the effectiveness of the response in the community: a prospective cohort study in Xinjiang China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Categorical testing of the viral load of people living with HIV to measure the intensity of the epidemic and the effectiveness of the response in the community: a prospective cohort study in Xinjiang China Qian He, Yongkang Ni, Yuefei Li, Xiaoyuan Hu, Xiaomin Hu, Zhen Ni, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4164996/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Jan, 2025 Read the published version in BMC Public Health → Version 1 posted 4 You are reading this latest preprint version Abstract Background: New indicators of HIV transmission potential are being actively explored. We aim to categorical testing of the viral load of people living with HIV in order to explore new indicators to measure the intensity of the epidemic and the effectiveness of the response in the community. Methods: A dynamic cohort study was conducted in Yining to monitor the viral load (VL) of all individuals with HIV/AIDS from 2017 to 2019. Different PVL surrogate values were calculated and the strength of the associations between different PVL surrogates and HIV new incidence, antiretroviral therapy (ART) coverage, virus unsuppression, and viremia prevalence was assessed. Then we used PVL surrogate markers to describe the current status of HIV transmission potential in different characteristic populations and different communities. Results: All the values of different PVL indicators showed a decreasing trend year by year (P < 0.05). A significant correlation was observed between the decrease in community viral load (CVL) alone and the increase in the incidence of new HIV infections. Mean CVL (r = 1.000, P = 0.006), geometric mean CVL (r = 1.000, P = 0.001) were positively associated with HIV new infection. Both before and after imputation with missing values showed that mean CVL and geometric mean CVL were significantly associated with ART coverage and viral unsuppression (P < 0.05). Relatively high CVLs were found for males, ≤25 years of age, elementary school or less, other place of domicile, other type of health insurance, other source of sample, nonmarital noncommercial heterosexual contact, and nonmarital commercial heterosexual contact in the different characteristics groups. Community-based cross-sectional analyses showed a positive correlation between CVL, Viral unsuppression rate, and Viremia prevalence, and a negative correlation between ART coverage rate and the first three indicators, suggesting that “community 10” is the hotspot for HIV epidemics in the city. Conclusions: CVL can be used as an indicator evaluate the HIV transmission potential. To further reduce the HIV transmission potential, targeted interventions should be developed on key populations and hotspot communities. HIV/AIDS Community viral load (CVL) Rate of HIV new infections Cohort study HIV transmission potential Figures Figure 1 Figure 2 Introduction The epidemiologic situation and modes of transmission of HIV infection have changed dramatically over the past few decades, and interventions to prevent HIV transmission are constantly being explored and updated[ 1 ]. In recent years, AIDS prevention worldwide has been based on the Treatment as Prevention strategy, in which patients receive antiretroviral therapy (ART) at an early stage to control their viral load (VL) levels and achieve viral suppression, thereby reducing the risk of HIV transmission[ 2 – 4 ]. Previous HIV prevention and treatment efforts have focused more on individual VL monitoring, and there is evidence that at the individual level, after ART, HIV VLs are reduced, with a concomitant reduction in the ability to transmit[ 5 ]; theoretically, the higher the ART coverage in that community, the lower the total and average VL in the population, the higher the viral suppression rate, and the lower the ability to transmit HIV in the community. HIV VL, which objectively reflects the replication level of HIV in the body, is a major factor affecting HIV transmission[ 6 – 8 ]. The rate of new HIV infections visualizes the current epidemiological status of the disease and is the best indicator of HIV transmission and control; however, the rate of new infections is difficult and time-consuming to obtain and is somewhat limited by the need for frequent testing of entire populations[ 9 ]. Population viral load (PVL) or community viral load (CVL) has been proposed as a indicator for the rate of new HIV infections[ 10 , 11 ], and PVL is an indicator of HIV transmission potential in a geographic region, reflects the HIV transmission level in that region by the arithmetic mean or geometric mean of the VL of all HIV positive individuals in a particular geographic region or population, and this value represents the level of viremia in that region during a specific period of time[ 12 , 13 ]. However, PVL indicators are difficult to be actually measured because of the partly undiagnosed, confirmed but untreated, undetected VL under treatment, and people who are unaware of their VL status. In the study published by the US CDC guidelines for CVL[ 11 ]、Solomon et al[ 14 ] and Jiang[ 15 ] detailed 4 realistic and feasible surrogate markers of PVL based on different levels of population viral load monitoring, namely, monitored viral load (MVL), in-care viral load (ICVL), awared viral load (AVL), and CVL. CVL has been recognized in several studies as an important indicator for evaluating the potential for HIV transmission in a population or region[ 11 , 14 , 16 ]. Das et al[ 17 ] showed that mean CVL was correlated with HIV incidence in San Francisco. A Colombian study[ 18 ] showed a strong correlation between increases in ART coverage, decreases in VL at the population level, and decreases in the number of new HIV cases. A study of injecting drug users in San Francisco showed that mean CVL was significantly associated with HIV incidence[ 19 ], validating the utility of CVLs as a measure of HIV transmission potential. To date, few studies in China have directly compared the level of VL surveillance among different populations in the same sample and analyzed the strength of associations between PVL surrogate measures and indicators related to HIV transmission potential. Xinjiang is a high HIV incidence area in China with a high HIV burden[ 20 ]. Early efforts to reduce the persistently high transmission potential among populations, targeted implementation of interventions for focused populations, and rational allocation of financial and human resources are particularly important. We have for this time established a PVL dynamic monitoring cohort to obtain the VL-related data of HIV/AIDS by extending the VL monitoring range and monitoring the VL of all reporting HIV / AIDS in 2017–2019 in Yining City, Xinjiang, China. According to the definition of VL surveillance level in different populations, various HIV disease burden indicators were calculated. Longitudinal quantification of the total amount of transmissible HIV and the burden of viremia in the population at different times based on time longitudinal; Based on geographic information, the correlation between indicators related to HIV transmission potential and CVL in different communities was analyzed horizontally, and hotspot communities were found. The dual search can assess the potential of HIV transmission in Yining City, Xinjiang, and identify HIV key population groups based on this, so as to provide scientific information for better formulation of HIV prevention and control plans. Methods Subjects and testing content HIV positive individuals from the outbreak pool of Yining City in Xinjiang from Jan 1, 2017 to Dec 31, 2019 and newly reported infected individuals from the same period of monitoring and testing population were selected. VL testing and questionnaire were performed after they signed informed consent. Inclusion criteria: (a) HIV positive; (b) Present address is Yining City, Xinjiang, China; (c) Age ≥ 13 years; (d) Signed informed consent. Exclusion criteria: those who had died at follow-up. We adopted the methods of prospective dynamic cohort study to establish an expanded VL detection cohort in the city of Yining, Xinjiang and followed up for three years. HIV positive individuals in the outbreak reservoir and those newly reported to be infected in the population undergoing surveillance and testing during the same period were selected for the study. We classified laboratory tests according to baseline (A is an in treatment in the national HIV integrated control information system who attends regular VL testing at set-point hospitals on time and on an annual basis; B are untreatment, with sampling and VL testing by the center for Disease Control and prevention; C is a new reported infection in the contemporaneous surveillance, testing population, with new onset infection testing and VL testing when confirmed). A questionnaire was also administered to study subjects doing VL testing, and the questionnaire was designed with reference to the national sentinel surveillance questionnaire. Based on the HIV-related data of Yining City, Xinjiang, a new index was explored for assessing the potential of HIV transmission in Xinjiang, predicting the development trend of HIV epidemic and evaluating the effect of intervention. Laboratory tests The VL detection reagent was the COBAS TaqMan HIV-1 test v2.0 HIV-1 kit from Roche. VL was measured on blood samples according to the reagent instructions using an automated VL instrument (Roche COBAS AmpliPrep COBAS TaqMan48). HIV primary screening reagents were obtained from the Invitrogen HIV antibody diagnostic kit (enzyme-linked immune assay, ELISA). The retest reagent for those who were positive at the initial screening was used from Beijing kinghao HIV antibody diagnostic kit (ELISA). All samples (including positive and negative samples) should be stored for at least 12 months after the end of investigation. Indicator definitions and calculation methods VL monitoring related indicators In this study, according to the CVL guidelines published by the US Centers for Disease Control and Prevention[11], the cross-sectional study in India[14], and Jiang[15], the VL surveillance indicators of different populations were defined as follows: (1) MVL: VL value in those currently on treatment and monitored for VL; (2) ICVL: VL value currently measured in those receiving therapy (whether VL is monitored or not); (3) AVL: VL value in those who knew their own VL status, regardless of their involvement in therapy; (4) CVL: VL value including all those in treated as well as confirmed but not treated subjects. Different VL metrics The VL limit of detection in this study was 20 copies / mL, and all VL values below the limit of detection consistently obtained half the detection limit (10 copies / mL). VL measures were chosen as total VL, mean VL, and geometric mean VL. In part of the literature[6, 11, 12] the median VL is chosen to measure the VL index value. However, in this study, the median VL was not selected because it was below the limit of detection and the median VL was 10 copies / mL in the population. (1) Total VL: sum of VLs of all HIV / AIDS(copies / mL); (2) Mean VL: the sum of VL of all HIV / AIDS divided by the total number of people living with HIV(copies / mL); (3) Geometric mean VL: VL of all HIV / AIDS individuals were log transformed (base 10 logarithm), then summed and divided by the total number of HIV infected individuals to obtain their mean(log 10 copies / mL). Indicator definitions (1) Viral unsuppression is defined as VL≥1000 copies / mL; Viral suppression is defined as VL＜1000 copies / mL. (2) Antiretroviral treatment coverage (ART coverage) is defined as the number of ART cases / total number of HIV infected individuals × 100%. (3) Virus unsuppressed rate is defined as the number of virus unsuppressed cases / total number of HIV infected individuals × 100% ; Viral ppression rate: number of VL suppressed cases / total number of HIV infected individuals× 100%. (4) Viremia prevalence is defined as the number of cases with VL > 20 copies / mL / total number of HIV infected individuals × 100%. The HIV incidence rate was calculated as: Ir = . N = number of HIV negatives in the study; P = number of HIV positive persons in the study; R = number calibrated to new onset infected persons; Correction parameters ω = Mean time to new infection in several years; ε = FRR (false newly infected rate) for new onset infections. The rate of new infections in this study was estimated from the data provided by the ELISA kit (Jinhao, Beijing), which ω= 130d; ε = 2.3%. Statistical analysis (1)Time-based analysis a. Sensitivity analysis: There were 55 individuals with missing VL values among those tested for VL, since the missing amount is less than the maximum allowable limit (25%). Through SPSS 22.0 software, the Markov Chain Monte Carlo (MCMC)[21] multiple imputation method was used to comprehensively impolate the missing VL, and the analysis indicators involving the missing VL value were analyzed twice before and after VL imputation. At the same time, the sensitivity analysis of the distribution of people with missing VL and people without missing VL was analyzed.（Table S1） b. Through the statistical analysis of 2017~2019 data through SPSS 22.0 software, the Wilcoxon rank sum test was used for the comparison between the measurement data groups. The correlation analysis of normal distribution data was carried out by Pearson correlation. The statistical test level was α=0.05. Use GraphPad Prism 8.3.0 software to plot different temporal trends. (2) Geo-community-based analysis a.Yining City has a total of 9 townships, 8 sub-district offices, and 4 districts (1 economic zone, 3 field districts), for a total of 21 community units (collectively referred to as communities). Considering that 2018 had the fewest VL deficits and a relatively large number of people, this part of the study selected 2018 data for cross-sectional analysis. One of the communities did not have an existing HIV infection, so a total of 20 communities (communities 1 to 20) were included in this study. PVL-related indicators are selected as the best indicators in the longitudinal-based queue. R4.3.0 software was used to collate and analyze the data of 20 study calibration communities and draw lollipop maps; Spearman analysis was used for correlation analysis of nonnormal distribution data; simple linear regression model was used to evaluate the association between unsuppressed rate, viremia prevalence, untreated rate and CVL; the adjusted R2 represented the variance ratio of covariate explanation, which was used to evaluate the advantages and disadvantages of the model, and the Akachi Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used To compare model fitting, the best model minimizes AIC and BIC[22], and the statistical test level is α = 0.05. Results Demographic characteristics of HIV/AIDS patients in Yining City, 2017–2019 This study monitored 3920, 4167, 4080 individuals respectively in 2017, 2018, and 2019. From 2017 to 2019, there was no trend change in the distribution of sex and transmission routes of the study subjects (P > 0.05), and the distribution of HIV-infected patients in three years showed that the proportion of males was higher, and the transmission routes were mainly injecting drug use, non-marital and non-commercial heterosexual sexual contact, and spouse/fixed partner positive. There were trend changes in the three-year distribution of age, education level, household registration place and medical insurance type (P < 0.05). Among them, 36 ~ 45 years old accounted for the largest proportion of infected people, followed by 46 ~ 55 years old, HIV is gradually transitioning to an advanced age; The education level is mainly primary school and below, most of the infected people are registered in their urban areas, and the types of medical insurance are mainly social insurance and new rural cooperation (Table 1 ) . Table 1 Distribution of general demographic characteristics of HIV/AIDS in Yining City, 2017 ~ 2019 Characteristic 2017 (N = 3920) 2018 (N = 4167) 2019 (N = 4080) P Gender 0.183 † Male 2154 (54.95%) 2293 (55.03%) 2302 (56.42%) 0.324 ‡ Female 1766 (45.05%) 1874 (44.97%) 1778 (43.58%) Age(year) <0.001 † ≤ 25 168 (4.29%) 192 (4.61%) 132 (3.24%) 55 217 (5.54%) 272 (6.53%) 348 (8.53%) Education 0.001 † Primary school and below 1791 (45.69%) 1890 (45.36%) 2030 (49.75%) 0.002 ‡ Junior high school 1295 (33.04%) 1419 (34.05%) 1253 (30.71%) High school or technical secondary school 590 (15.05%) 605 (14.52%) 568 (13.92%) College and above 244 (6.22%) 253 (6.07%) 229 (5.61%) Permanent residence address <0.001 † Counties of the city 3569 (91.05%) 3885 (93.23%) 4036 (98.92%) <0.001 ‡ Other 351 (8.95%) 282 (6.77%) 44 (1.08%) Health insurance <0.001 † Social security 1996 (50.92%) 1912 (45.88%) 2583 (63.31%) <0.001 ‡ New agricultural joint 1579 (40.28%) 2137 (51.28%) 1381 (33.85%) Other 345 (8.80%) 118 (2.83%) 116 (2.84%) Route of transmission 0.188 † Injection drug use 1143 (29.16%) 1236 (29.66%) 1212 (29.71%) <0.001 ‡ Non-marital non-commercial heterosexual contact 849 (21.66%) 1000 (24.00%) 1009 (24.73%) Non-marital commercial heterosexual contact 47 (1.20%) 121 (2.90%) 83 (2.03%) Positive for spouse/fixed partner 1252 (31.94%) 1091 (26.18%) 1068 (26.18%) Male–male sexual behaviours 50 (1.28%) 55 (1.32%) 43 (1.05%) Other 579 (14.77%) 664 (15.94%) 665 (16.30%) Screening sources § <0.001 † VCT clinic 2138 (54.54%) 1583 (38.60%) 737 (18.06%) <0.001 ‡ MSM community group 15 (0.38%) 25 (0.61%) 30 (0.74%) PITC 673 (17.17%) 720 (17.56%) 572 (14.02%) Positive sexual companion test 119 (3.04%) 147 (3.58%) 379 (9.29%) Detection and thematic investigation 561 (14.31%) 584 (14.24%) 1529 (37.48%) Other 414 (10.56%) 1042 (25.41%) 833 (20.42%) Treatment conditions <0.001 † Not ART 2903 (74.06%) 3269 (78.45%) 3554 (87.11%) <0.001 ‡ On ART 1017 (25.94%) 898 (21.55%) 526 (12.89%) † Trends Chi-square test; ‡ Chi-square test; § There is a missing value in the screening sources. Comparison of different PVL monitoring indicators for 2017–2019 Total VL, mean VL, and geometric mean VL were calculated separately for MVL, ICVL, AVL, and CVL according to different population VL monitoring definitions. The trend of year-by-year decrease in different VL values (total VL, mean VL, and geometric mean VL) for the different PVL monitoring indicators MVL, ICVL, AVL, and CVL. The difference in VL values between 2017 and 2019 is statistically significant (P < 0.05). Among them, CVL was higher than MVL, ICVL, and AVL (Table 2 ). Two of the indicators, AVL and CVL, were involved in missing VL. Sensitivity analyses were performed to calculate the values of the different VL indicators before and after interpolating the missing values. There was no significant difference between before and after interpolating; the results after interpolation were slightly higher than before interpolation (Table S2). Table 2 Comparison of VL monitoring indicators for different populations from 2017 to 2019 Indicator 2017 2018 2019 N Total VL (copies/mL) Mean VL (copies/mL) Geometric meanVL (log 10 copies/mL) N Total VL (copies/mL) Mean VL (copies/mL) Geometric meanVL (log 10 copies/mL) N Total VL (copies/mL) Mean VL (copies/mL) Geometric meanVL (log 10 copies/mL) MVL 2879 47271786 16420 1.69 3265 37162772 11382 1.63 3533 27542851 7796* 1.59* ICVL 2903 47271786 16284 1.68 3269 37162772 11368 1.63 3554 27542851 7750* 1.58* AVL 3046 63679554 20736 1.83 3468 54866057 15802 1.78 3782 43132816 11342* 1.75* CVL 3890 153702430 39210 2.34 4163 138472837 33231 2.21 4059 76667028 18791** 1.91** There were significant differences in 2019 as compared to 2017 VL ( * P <0.05 and ** P <0.001). Correlation analysis between different PVL monitoring indicators and the rate of new HIV infections The incidence of new HIV infection was 0.002685% in 2017, 0.002252% in 2018, and 0.001246% in 2019, showing a decreasing trend from year to year. The population VL surveillance indicators MVL, ICVL, AVL, and CVL all decreased in 2017–2019, but only the decrease in CVL was correlated with the decrease in the incidence of new HIV infection. The mean CVL (r = 1.000, P = 0.006) and geometric mean CVL (r = 1.000, P = 0.001) were positively correlated with the rate of new HIV infections, The remaining PVL indicators and total CVL were not statistically significantly associated with the incidence of new HIV infection (P > 0.05) (Fig. 1 ). Sensitivity analyses in interpolating missing VL values yielded the same results as above for mean CVL* (r = 1.000, P = 0.003) and geometric mean CVL* (r = 0.999, P = 0.028) (Figure S1 ). Correlation analysis of different PVL surveillance indicators with ART coverage, viral unsuppressed rate, and viremia prevalence During population surveillance from 2017 to 2019, ART coverage increased from 74.01–87.04% (P < 0.001) in the overall population.Viral unsuppression rate decreased from 35.56–23.58% (P < 0.001). Viremia prevalence decreased from 47.35–23.58% (P < 0.001). The results indicated an overall better HIV status in the city.Sensitivity analyses after interpolation of missing VL showed that three indicators changed as before interpolation, with ART coverage increasing from 74.06–87.11% (P < 0.001), viral unsuppression rate decreasing from 35.61–23.63% (P < 0.001), and viremia prevalence decreasing from 47.60–36.89% (P < 0.001). By Pearson's correlation analysis, the rate of HIV new incidence was significantly associated with ART coverage and viral unsuppression rate(P < 0.05). Correlation analysis of different VL index values with ART coverage, viral unsuppression rate and viremia prevalence by VL monitoring indicators in different populations including sensitivity analysis showed that mean and geometric mean CVL were significantly associated with ART coverage, viral unsuppression rate (P < 0.05) (Table 3 ). And the results of the sensitivity analysis after interpolating missing VL values were the same as before interpolating missing values (Table S3).Taken together, mean CVL and geometric mean CVL are better surrogates for PVL for describing the combined HIV transmission potential in the region. Table 3 Correlation analysis of VL monitoring indicators of different population and ART coverage, Viral suppression rate and Viremia prevalence Indicator ART coverage Viral suppression rate Viremia prevalence r P r P r P New HIV infection rate -0.999 0.027 0.998 0.041 0.994 0.071 MVL Total VL - - 0.985 0.112 0.736 0.474 Mean VL - - 0.996 0.059 0.677 0.526 Geometric meanVL - - 0.997 0.048 0.664 0.538 ICVL Total VL - - 0.985 0.112 0.736 0.474 Mean VL - - 0.995 0.065 0.684 0.745 Geometric meanVL - - 0.982 0.121 0.745 0.465 AVL Total VL 0.865 0.335 0.914 0.266 0.736 0.474 Mean VL 0.804 0.406 0.953 0.196 0.656 0.544 Geometric meanVL 0.731 0.478 0.981 0.123 0.566 0.617 CVL Total VL -0.988 0.099 0.985 0.111 1.000 0.001 Mean VL -0.998 0.035 0.997 0.047 0.995 0.066 Geometric meanVL -0.999 0.028 0.998 0.040 0.994 0.072 “-”Full coverage of ART in MVL and ICVL monitoring populations. Comparison of CVL between different treatment statuses, different viral suppression statuses, and different viremia prevalence statuses Comparison of CVL for different treatment statuses, CVL for different suppression statuses, and CVL for different viremia statuses between groups showed significant differences (P 0.05), and the comparison of CVL values of the rest of the indicators for both years had significant differences (P 0.05) in CVL for treatment status, CVL for viral suppression status, and CVL for viremia prevalence status before and after interpolation of missing values (Figure S2); the analysis after interpolation of missing values was the same as before interpolation (Table S4). Table 4 Comparison of CVL between different ART, Viral load suppression and Viremia epidemics before and after interpolation of missing values Indicator 2017 2018 2019 N Mean VL (copies/mL) Geometric meanVL (log 10 copies/mL) N Mean VL (copies/mL) Geometric meanVL (log 10 copies/mL) N Mean VL (copies/mL) Geometric meanVL (log 10 copies/mL) ART Not ART 1011 105272 4.27 898 112817 4.34 526 93392* 4.15* On ART 2879 16420 1.69 3265 11382 1.63 3533 7796* 1.59* Viral load suppression Unsuppressed 1386 110809 4.47 1303 106171 4.44 957 79968* 4.33* Suppressed 2504 48 1.20 2860 46 1.20 3102 44 1.18 Viremia Undetectable 2048 10 1.00 2279 10 1.00 2571 10* 1.00* Detectable 1842 83432 3.87 1884 73487 3.68 1488 51506* 3.51* *There were significant differences in 2019 as compared to 2017 VL ( P <0.05). Comparison of CVL in general characteristics population 2017–2019 Here, we chose data before imputation for this analysis as there was no significant difference in population distribution before and after imputation for missing VL data. A comparison of CVL values between 2017 and 2019 shows significant differences in the sex, age, education level, and Medicare type (P 0.05), and all of the remaining within group CVL comparisons were significant (P < 0.001). Among them, men, ≤ 25 years old, primary school and below, other household registration place, other medicare types, other sample sources, nonmarital noncommercial heterosexual contacts, and nonmarital commercial heterosexual contacts had relatively high CVL (Table 5 ). Table 5 Comparison of the general characteristic population CVL from 2017 to 2019 Characteristic 2017 2018 2019 Mean VL (copies/mL) Geometric meanVL (log 10 copies/mL) Mean VL (copies/mL) Geometric meanVL (log 10 copies/mL) Mean VL (copies/mL) Geometric meanVL (log 10 copies/mL) Gender *** Male 49065 2.55 37159 2.28 23190 * 2.01 ** Female 27891 2.13 28494 2.13 13325 * 1.80 ** Age(year) *** ≤ 25 73443 2.97 29984 2.76 16014 * 2.52 * 26 ~ 35 38323 2.68 44989 2.44 21447 ** 2.22 ** 36 ~ 45 41643 2.33 29711 2.19 14724 ** 1.93 ** 46 ~ 55 30366 2.13 31785 2.04 21737 ** 1.77 ** >55 42959 2.21 34378 2.12 24434 ** 1.79 ** Education *** Primary school and below 45640 2.55 41846 2.28 23975 * 1.96 ** Junior high school 40478 2.36 28545 2.22 16848 ** 1.91 ** High school or technical secondary school 26646 2.05 25007 2.09 10426 1.87 College and above 20551 1.79 15359 2.00 6043 1.75 Permanent residence address *** Counties of the city 41362 2.38 32913 2.19 18346 ** 1.90 ** Other 20687 2.22 38071 2.49 70714 ** 3.57 ** Health insurance Social security 35818 2.16 43396 2.48 21396 ** 1.95 ** New agricultural joint 42482 2.50 21230 1.92 13645 ** 1.87 ** Other † 47281 2.87 86752 3.23 25487 ** 1.96 ** Screening sources ‡*** VCT clinic 29550 2.00 23688 2.01 25325 2.03 MSM Community Group 15579 2.40 15039 2.19 1416 * 1.27 * PITC 32910 2.34 36217 2.32 20482 ** 2.10 * Positive sexual companion test 34061 2.40 24755 1.97 8264 ** 1.63 ** Detection and thematic investigation 44373 2.72 41749 2.31 17417 ** 1.77 ** Other 97288 3.76 44393 2.44 20259 ** 2.13 ** Route of transmission *** Injection drug use 48275 2.68 35416 2.20 24956 ** 1.96 ** Non-marital non-commercial heterosexual contact 59683 2.87 45522 2.63 25961 ** 2.21 ** Non-marital commercial hetero sexual contact 10288 1.33 62810 3.01 23198 ** 2.26 a** Positive for spouse/fixed partner 26876 2.02 27191 1.98 11598 ** 1.71 ** Male–male sexual behaviours 9877 1.54 10862 1.88 6017 1.65 Other 25021 1.88 17200 1.86 9195 1.73 There were significant differences in 2019 as compared to 2017 VL ( * P <0.05 and ** P <0.001); *** There were significant differences in the CVL comparisons within the group ( P <0.001); † Other types of health insurance include: commercial insurance, no health insurance; ‡ There is a missing value in the screening sources. Distribution of CVL, ART coverage, viral unsuppresstion rate, and viremia prevalence by community in Yining City Based on a cross-sectional description of the indicator values for each community in 2018 based on 20 communities in the city of Yining, the CVL ranged from 1.64 log 10 copies/mL to 2.49 log 10 copies/mL, with a median CVL value of 2.18 log 10 copies/mL; ART coverage rate ranged from 68.21–89.47%, with a median value of 78.51%; viral unsuppression rate from 15.79–39.74% with a median value of 30.35%; and viremia prevalencce from 21.05–56.90% with a median value of 44.53%. Community 10 had the highest CVL and viral suppression rate, the lowest ART coverage; and viremia prevalence was second only to community 06 (Fig. 2 ); Community 18 had the lowest CVL, viral unsuppression rate, prevalence of viremia, and the highest ART coverage. There was a correlation between all indicators, with a positive correlation between CVL, viral unsuppression rate and viremia prevalence, and a negative correlation between ART coverage rate and the first three indicators (Figure S3). Linear regression analysis of CVL with viral unsuppression rate, viremia prevalence, and ART coverage Linear correlation analysis and regression model estimation of CVL with ART coverage rate, viral unsuppression rate, and viremia prevalence based on 20 communities showed that the viral unsuppression rate had the strongest correlation with CVL (R 2 adj: 0.982, AIC/BIC: -80.91/-77.92), and for each one-unit increase in viral unsuppression rate, the CVL increased by 0.036 log 10 copies/mL (95% CI: 0.033 ~ 0.038); followed by viremia prevalence, ART coverage rate (Table 6 ). Table 6 Linear regression analysis of CVL with ART coverage rate, Viral unsuppression rate and Viremia prevalence Indicator ρ Linear regression β(95%CI ) t P R 2 adj AIC/BIC ART coverage rate -0.579 * -0.029(-0.044~-0.013) -3.962 <0.001 0.436 -12.55/-9.56 Viral unsuppression rate 0.985 * 0.036(0.033 ~ 0.038) 32.150 <0.001 0.982 -80.91/-77.92 Viremia prevalence 0.909 * 0.026(0.021 ~ 0.032) 10.607 <0.001 0.854 -39.63/-36.64 † There are 21 communities in Yining City, but one community was not included in the analysis because it had no HIV/AIDS patients in 2018; * P <0.05. Discussion Our study found that the PVL indicator value in Xinjiang, China, was consistent with the changing trend of the PVL indicator value in cities in other countries, showing a decreasing state from year to year[ 23 , 24 ] in the time-based longitudinal analysis. Although the PVL surrogate indicators MVL, ICVL, AVL, CVL, total VL, mean VL, and geometric mean VL showed the same trend of change in new HIV infection rate, only CVL was positively associated with new HIV infection rate. In correlation analyses with ART coverage, viral unsuppression rate, and viremia prevalence, new HIV incidence was significantly correlated with ART coverage, viral unsuppression rate, and mean CVL, geometric mean CVL, and mean ICVL were also strongly correlated with ART coverage and viral unsuppression rate. Taken together, mean CVL and geometric mean CVL are better as surrogate indicators of PVL to measure the HIV transmission potential and HIV disease burden in Xinjiang population. This is consistent with the findings of India[ 14 ], South Carolina[ 24 ], Rhode Island[ 25 ], and validates the former hypothesis that as ART coverage increases, viral unsuppression rate decrease, CVL decreases, and new HIV infection rate decrease. But the difference is that other indicators of PVL in Solomon's[ 14 ] study also correlated with new HIV infection rate, which is slightly lower than that of CVL. However, this study showed no significant association between MVL, ICVL, and AVL and new HIV infection rate, ART coverage, or viral unsuppression rate. This may be related to the included population size of different PVL indicators in this study. The present study MVL largely coincided with the inclusion population (on treated) of ICVL, who had a predominance of VL suppressed and VL undetectable individuals, a low population VL base, and a relatively slow VL decline trend over the 3-year period. In previous studies[ 14 , 18 , 26 ], MVL and ICVL monitoring in the treated population VL, the easy observation of personnel, and the strong availability of data allowed the study of MVL and ICVL as surrogate indicators to predominate[ 11 ]. However, a limitation is that both are not included in the confirmed but untreated cohort, and the workhorse of HIV transmission[ 27 ]- the high VL population is mostly concentrated in this group, thus prone to low estimated PVL. AVL covers a portion of the untreated population on the basis of MVL and ICVL, but population coverage remains incomplete, and not all diagnosed persons are aware of their VL condition. We found that the CVL values in 2017–2019 in Yining City were higher than those of MVL, ICVL, and AVL, compared with other indicators. CVL is a more comprehensive and representative estimate of PVL, encompassing all populations in whom PVL can be monitored (those who are undiagnosed cannot be monitored), including untreatment individuals. This is in agreement with the US CDC guidelines for CVL[ 11 ], the studies by Rozhnova[ 28 ] and Farahani[ 29 ]. Therefore, we believe that CVL is a better monitor and indicator of the HIV transmission potential and disease burden in the population than other surrogate markers of PVL. Expanding VL surveillance, making full use of VL information, and achieving the cross from individual VL to population VL, transformed PVL from an observational epidemic domain to an indicator for rapid action to improve HIV disease burden[ 27 ]. Based on the above results, we choose mean CVL and geometric mean CVL to quantify the HIV burden of the population in Yining, Xinjiang, China, to find the HIV hotspot populations in different characteristic populations. Among them, CVL was relatively high in male, ≤ 25 years old, Primary school and below, other household registration place, other medicare types, other sample sources, non-married commercial heterosexual contacts and non-married commercial heterosexual contacts. Targeted interventions should be implemented with respect to their characteristics, with the aim of highly effective reduction of the HIV disease burden in the population. Viremia prevalence adjusted for the amount of HIV negative persons in the study observations was also suggested by Solomon[ 14 ] to be a better indicator of HIV transmission potential. However, we only found that there was an association between total CVL and the prevalence of viremia, while there was no significant association between total CVL and the prevalence of new HIV infection, and there was no significant association between total CVL and art coverage, virus uninhibited rate, the results suggest caution when using this indicator to monitor the HIV burden in communities in Xinjiang, China. In addition, it is important to note that limit of VL values for the prevalence of viremia varied among studies, such as 20 copies/mL which we chose, and 150 copies/mL in the study by Patel[ 30 ]. Sensitivity analysis of its values can be considered in future studies to explore whether the results differ at different limit of VL values in the same sample. In the geographical community analysis of the distribution of indicators related to the burden of HIV disease and the effect of HIV prevention and treatment in each community, it is intuitive to find the "hotspot communities" that need to be focused on, accurately locate the high CVL community, and measure the HIV epidemic status in the city. HIV hotspot communities are an important area for large-scale intervention in the future[ 31 ], hotspot communities will shape the environment of high-risk groups for HIV-negative people in the region, increase their risk of infection, and those living in hotspot communities with a longer history of HIV have the potential for sustained HIV transmission[ 32 ], which is easy to form the source of continuous transmission of HIV[ 33 ]. This study supports the combination of geospatial analysis to shift the research objects from individuals to key populations and hotspot communities, combine the regional characteristics of spatial characteristics, use CVL to accurately locate hotspot communities and highly detected populations, and expand HIV testing for communities and populations with high CVL, in order to discover more and more complete new reports and new infections, and achieve efficient epidemic tracking. Integrating geospatial analysis into routine public health planning will help focus interventions on more precise geographical units for maximum epidemiological impact and effective resource allocation, while more nuanced assessment of the effectiveness of HIV responses. Several limitations should be noted. First, the HIV transmission potential was analyzed without taking into account the impact of HIV prevalence and behavior in the population. As in different populations with the same CVL, those with higher HIV prevalence and incidence of high-risk behaviours necessarily have higher HIV transmission potential[ 13 ]. Based on this, we analyzed the CVL of different characteristic populations. Second, in the time-based longitudinal analysis, the study period is shorter, the trend of viremia prevalence is subtle, and the trend of longer follow-up time may be more significant, considering these factors, extending the follow-up time in future CVL studies, and effectively combining HIV prevalence and high-risk behaviors, the results will be more stable and more critical, and more conducive to analyzing the HIV epidemic and prevention and treatment effects from the population level, achieving accurate prevention and control, and matching resources and needs. In conclusion, our results show that mean CVL and geometric mean CVL can be better substitutes for PVL, and affirm the utility of CVL as an indicator of HIV transmission potential through time and geography, and can cooperate with ART coverage, viral unsuppression rate, viremia prevalence and other indicators to dynamically monitor the HIV epidemic and evaluate the effect of HIV prevention and treatment in Yining City. At present, the overall CVL in Yining City has decreased year by year, affirming the current prevention and control efforts, which should expand the scope of VL detection in the future, better apply CVL, and monitor the local HIV transmission potential. Combined with spatial geography to accurately locate hot communities and high-detection populations of HIV, and carry out prevention and treatment work more accurately and rapidly, it provides new, efficient and practical monitoring indicators and indicators for reasonable allocation of health resources to the health department. Conclusions CVL can be used as an indicator evaluate the HIV transmission potential, and it can be used as an indicator for evaluating the effectiveness of HIV interventions in a population or included in a comprehensive social indicator to measure the survival of people living with HIV. To further reduce the CVL and reduce the HIV transmission potential, targeted interventions should be developed for key populations and hotspot communities. Abbreviations HIV/AIDS Human Immunodeficiency Virus /Acquired Immune Deficiency Syndrome ART Antiretroviral therapy VL Viral load CVL Community viral load PVL Population viral load MVL Monitored viral load ICVL In-care viral load AVL Awared viral load Declarations Ethics approval and consent to participate This study was approved by the AIDS research ethics committee of the disease control and prevention center of Xinjiang Uygur Autonomous Region(No. 2018-001). All participants provided informed consent, we confirm that all methods were performed in accordance with the guidelines. All participants received a written informed consent form and an oral explanation of the purpose and content of the research. Consent for publication Not applicable Competing interests All authors declare no competing interests. Data Availability Statement The data that supports the findings of this study is available from the corresponding author upon reasonable request. Acknowledgements The authors thank all the volunteers who participated in the study and all the staff involved in this study from Xinjiang Uighur Autonomous Region Center for Disease Control and Prevention and Yining Center for Disease Control and Prevention. Authors’ contributions QH and YN contributed to the cohort follow-up, data collection, data analysis and manuscript preparation, YL, and XH contributed to the study design and conception, experimental work and data collection. XH and ZN contributed to experimental work and samples collection. CZ, BX, and AA contributed to the cohort follow-up work and data analysis. MN contributed to the study design and conception, supervised the experimental work, data analysis and interpretation. ALL authors reviewed the manuscript. Funding This study was supported by Xinjiang Key Laboratory of HIV/AIDS Prevention and Control Research (No.XJYS1706), National Science and Technology Major Project of the Ministry of Science and Technology of China (No.2018ZX10715-007). References Eshleman SH, Hudelson SE, Redd AD, Swanstrom R, Ou S-S, Zhang XC, et al. Treatment as Prevention: Characterization of Partner Infections in the HIV Prevention Trials Network 052 Trial. JAIDS J Acquir Immune Defic Syndr. 2017;74:112–6. Oldenburg CE, Bärnighausen T, Tanser F, Iwuji CC, De Gruttola V, Seage GR, et al. Antiretroviral Therapy to Prevent HIV Acquisition in Serodiscordant Couples in a Hyperendemic Community in Rural South Africa. Clin Infect Dis. 2016;63:548–54. Riddell J, Amico KR, Mayer KH. HIV Preexposure Prophylaxis: A Review. JAMA. 2018;319:1261. Csete J, Kamarulzaman A, Kazatchkine M, Altice F, Balicki M, Buxton J, et al. Public health and international drug policy. The Lancet. 2016;387:1427–80. Tanser F, Bärnighausen T, Grapsa E, Zaidi J, Newell M-L. High Coverage of ART Associated with Decline in Risk of HIV Acquisition in Rural KwaZulu-Natal, South Africa. Science. 2013;339:966–71. Eisinger RW, Dieffenbach CW, Fauci AS. HIV Viral Load and Transmissibility of HIV Infection: Undetectable Equals Untransmittable. JAMA. 2019;321:451. Shoko C, Chikobvu D. Determinants of viral load rebound on HIV/AIDS patients receiving antiretroviral therapy: results from South Africa. Theor Biol Med Model. 2018;15:10. Hendrickx DM, Delva W, Hens N. Influence of sexual risk behaviour and STI co-infection dynamics on the evolution of HIV set point viral load in MSM. Epidemics. 2021;36:100474. Okano JT, Gerstoft J, Obel N, Blower S. HIV elimination and population viral load. Lancet HIV. 2016;3:e507–9. Miller WC, Powers KA, Smith MK, Cohen MS. Community viral load as a measure for assessment of HIV treatment as prevention. Lancet Infect Dis. 2013;13:459–64. Centers for Disease Control and Prevention. Guidance on community viral load : a family of measures, definitions, and method for calculation. https://stacks.cdc.gov/view/cdc/28147. Accessed 24 Jul 2023. Tanser F, Vandormael A, Cuadros D, Phillips AN, De Oliveira T, Tomita A, et al. Effect of population viral load on prospective HIV incidence in a hyperendemic rural African community. Sci Transl Med. 2017;9:eaam8012. Herbeck J, Tanser F. Community viral load as an index of HIV transmission potential. Lancet HIV. 2016;3:e152–4. Solomon SS, Mehta SH, McFall AM, Srikrishnan AK, Saravanan S, Laeyendecker O, et al. Community viral load, antiretroviral therapy coverage, and HIV incidence in India: a cross-sectional, comparative study. Lancet HIV. 2016;3:e183–90. Jiang Z, Dou Z, Yan Z, Song W. Effect of data missing on population based viral load survey in HIV infected men who have sex with men sampled in 16 large cities, China. Zhonghua Liu Xing Bing Xue Za Zhi. 2017;38:1169–73. Krentz HB, Gill MJ. The Effect of Churn on “Community Viral Load” in a Well-Defined Regional Population. JAIDS J Acquir Immune Defic Syndr. 2013;64:190–6. Das M, Chu PL, Santos G-M, Scheer S, Vittinghoff E, McFarland W, et al. Decreases in Community Viral Load Are Accompanied by Reductions in New HIV Infections in San Francisco. PLoS ONE. 2010;5:e11068. Montaner JS, Lima VD, Barrios R, Yip B, Wood E, Kerr T, et al. Association of highly active antiretroviral therapy coverage, population viral load, and yearly new HIV diagnoses in British Columbia, Canada: a population-based study. The Lancet. 2010;376:532–9. Glasheen C, Johnson EO, Lorvick J, Kral AH. Measures of human immunodeficiency virus (HIV) community viral load and HIV incidence among people who inject drugs. Ann Epidemiol. 2018;28:8–12. Bao Y, Larney S, Peacock A, Colledge S, Grebely J, Hickman M, et al. Prevalence of HIV, HCV and HBV infection and sociodemographic characteristics of people who inject drugs in China: A systematic review and meta-analysis. Int J Drug Policy. 2019;70:87–93. Aßmann C, Gaasch J-C, Stingl D. A Bayesian Approach Towards Missing Covariate Data in Multilevel Latent Regression Models. Psychometrika. 2022. https://doi.org/10.1007/s11336-022-09888-0. Dziak JJ, Coffman DL, Lanza ST, Li R, Jermiin LS. Sensitivity and specificity of information criteria. Brief Bioinform. 2020;21:553–65. Castel AD, Befus M, Willis S, Griffin A, West T, Hader S, et al. Use of the community viral load as a population-based biomarker of HIV burden. AIDS. 2012;26:345–53. Chakraborty H, Weissman S, Duffus WA, Hossain A, Varma Samantapudi A, Iyer M, et al. HIV community viral load trends in South Carolina. Int J STD AIDS. 2017;28:265–76. Touzard Romo F, Gillani FS, Ackerman P, Rana A, Kojic EM, Beckwith CG. Monitored viral load: a measure of HIV treatment outcomes in an outpatient setting in Rhode Island. R I Med J 2013. 2014;98:26–30. Monno L, Saracino A, Scudeller L, Santoro C, Brindicci G, Punzi G, et al. Reduced community viral load does not coincide with a reduction in the rate of new HIV diagnoses and recent infections: data from a region of southern Italy. HIV Med. 2017;18:711–23. Jain V, Petersen M, Havlir DV. Population HIV viral load metrics for community health. Lancet HIV. 2021;8:e523–4. Rozhnova G, Anastasaki M, Kretzschmar M. Modelling the dynamics of population viral load measures under HIV treatment as prevention. Infect Dis Model. 2018;3:160–70. Farahani M, Radin E, Saito S, Sachathep KK, Hladik W, Voetsch AC, et al. Population Viral Load, Viremia, and Recent HIV-1 Infections: Findings From Population-Based HIV Impact Assessments (PHIAs) in Zimbabwe, Malawi, and Zambia. JAIDS J Acquir Immune Defic Syndr. 2021;87:S81–8. Patel EU, Solomon SS, Lucas GM, McFall AM, Srikrishnan AK, Kumar MS, et al. Temporal change in population-level prevalence of detectable HIV viraemia and its association with HIV incidence in key populations in India: a serial cross-sectional study. Lancet HIV. 2021;8:e544–53. Tanser F, Bärnighausen T, Dobra A, Sartorius B. Identifying ‘corridors of HIV transmission’ in a severely affected rural South African population: a case for a shift toward targeted prevention strategies. Int J Epidemiol. 2018;47:537–49. Tomita A, Vandormael A, Bärnighausen T, Phillips A, Pillay D, De Oliveira T, et al. Sociobehavioral and community predictors of unsuppressed HIV viral load: multilevel results from a hyperendemic rural South African population. AIDS. 2019;33:559–69. Philip NM. Population-level HIV risk and combination implementation of HIV services. Additional Declarations No competing interests reported. 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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-4164996","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":284480106,"identity":"7713f8cd-7151-4fe2-9846-e6c970566c0f","order_by":0,"name":"Qian He","email":"","orcid":"","institution":"Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"He","suffix":""},{"id":284480107,"identity":"de63bf96-9ff9-46ee-aeb0-9d410f6b0bd1","order_by":1,"name":"Yongkang Ni","email":"","orcid":"","institution":"Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yongkang","middleName":"","lastName":"Ni","suffix":""},{"id":284480108,"identity":"b1a44dbf-fad4-4281-8de2-5c6860a2e992","order_by":2,"name":"Yuefei Li","email":"","orcid":"","institution":"Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuefei","middleName":"","lastName":"Li","suffix":""},{"id":284480109,"identity":"365d3c06-cf00-4ffe-a7c5-9a782ba4addd","order_by":3,"name":"Xiaoyuan Hu","email":"","orcid":"","institution":"Xinjiang Uighur Autonomous Region Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyuan","middleName":"","lastName":"Hu","suffix":""},{"id":284480111,"identity":"274638f1-3456-4ec1-8bc2-03b029471bbf","order_by":4,"name":"Xiaomin Hu","email":"","orcid":"","institution":"Xinjiang Uighur Autonomous Region Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Xiaomin","middleName":"","lastName":"Hu","suffix":""},{"id":284480113,"identity":"bfa48f4d-fe5c-4f2a-9f31-d84ba2ee87bb","order_by":5,"name":"Zhen Ni","email":"","orcid":"","institution":"Xinjiang Uighur Autonomous Region Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Ni","suffix":""},{"id":284480115,"identity":"77613479-8a7d-4d81-aa2e-eb514d1f7fb6","order_by":6,"name":"Changyu Zeng","email":"","orcid":"","institution":"Xinjiang Uighur Autonomous Region Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Changyu","middleName":"","lastName":"Zeng","suffix":""},{"id":284480117,"identity":"fe23866d-80bd-430f-a30e-c0cd96a035ad","order_by":7,"name":"Azmat Akbar","email":"","orcid":"","institution":"Xinjiang medical University Affiliated Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Azmat","middleName":"","lastName":"Akbar","suffix":""},{"id":284480119,"identity":"8d19818d-a687-49cb-bb2c-d21d9bad6a05","order_by":8,"name":"Bixin Xu","email":"","orcid":"","institution":"Yining Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Bixin","middleName":"","lastName":"Xu","suffix":""},{"id":284480120,"identity":"ee471d44-bbc7-4978-b572-d72ab17b5d12","order_by":9,"name":"Mingjian Ni","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqElEQVRIiWNgGAWjYFCCAwwMEhUScvwkajljYSzZQJJFjG0ViRuI1mJw8OzRDZbzJBg3MDA/fHSDGC2SDefSbkhuk2A2Z2AzNs4hRgs/wxkzkBY2ywYeNmmitLCBtcyR4DE4QKwWiC0NEhLEa5FsAGqROCZhINlMrF8Mbpwxuy1RU1ffz9788DFRWhgkDjAwS4AYzEQpBwH+BgbGD0SrHgWjYBSMghEJAFBkLsT0D8jJAAAAAElFTkSuQmCC","orcid":"","institution":"Xinjiang Uighur Autonomous Region Center for Disease Control and Prevention","correspondingAuthor":true,"prefix":"","firstName":"Mingjian","middleName":"","lastName":"Ni","suffix":""}],"badges":[],"createdAt":"2024-03-25 17:50:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4164996/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4164996/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-21278-6","type":"published","date":"2025-01-11T15:58:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53881192,"identity":"4382be37-0ee5-41b6-a7da-06cd5faa6dd5","added_by":"auto","created_at":"2024-04-01 17:52:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":133986,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe trend chart of the correlation between different VL indicators and the rate of new HIV infections. A: Total VL；B: Mean VL；C: Geogetric mean VL.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4164996/v1/48dc97dd93372a15cd1ac2ca.png"},{"id":53881193,"identity":"2b47b5bf-de1a-4153-9642-45087deaca67","added_by":"auto","created_at":"2024-04-01 17:52:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":180086,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of CVL, ART coverage, uninhibition, and prevalence of viremia in various communities in Yining City (A: CVL；B: ART coverage rate(%)；C: Viral unsuppression rate(%); D: Viremia prevalence(%))\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4164996/v1/4f732d2d922f0864cf677724.png"},{"id":73694781,"identity":"f8d7e688-9120-4ac1-ad56-8ad82bebc5b1","added_by":"auto","created_at":"2025-01-13 16:14:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1991682,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4164996/v1/ce0f971f-9df8-4636-8974-0c47873cc177.pdf"},{"id":53881194,"identity":"972945a8-e58f-43ba-8089-9f85ed9aff52","added_by":"auto","created_at":"2024-04-01 17:52:10","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6544896,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.doc","url":"https://assets-eu.researchsquare.com/files/rs-4164996/v1/06e9b98f7e8417d7a6db7bdc.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eCategorical testing of the viral load of people living with HIV to measure the intensity of the epidemic and the effectiveness of the response in the community: a prospective cohort study in Xinjiang China\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe epidemiologic situation and modes of transmission of HIV infection have changed dramatically over the past few decades, and interventions to prevent HIV transmission are constantly being explored and updated[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In recent years, AIDS prevention worldwide has been based on the Treatment as Prevention strategy, in which patients receive antiretroviral therapy (ART) at an early stage to control their viral load (VL) levels and achieve viral suppression, thereby reducing the risk of HIV transmission[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Previous HIV prevention and treatment efforts have focused more on individual VL monitoring, and there is evidence that at the individual level, after ART, HIV VLs are reduced, with a concomitant reduction in the ability to transmit[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]; theoretically, the higher the ART coverage in that community, the lower the total and average VL in the population, the higher the viral suppression rate, and the lower the ability to transmit HIV in the community. HIV VL, which objectively reflects the replication level of HIV in the body, is a major factor affecting HIV transmission[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The rate of new HIV infections visualizes the current epidemiological status of the disease and is the best indicator of HIV transmission and control; however, the rate of new infections is difficult and time-consuming to obtain and is somewhat limited by the need for frequent testing of entire populations[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePopulation viral load (PVL) or community viral load (CVL) has been proposed as a indicator for the rate of new HIV infections[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and PVL is an indicator of HIV transmission potential in a geographic region, reflects the HIV transmission level in that region by the arithmetic mean or geometric mean of the VL of all HIV positive individuals in a particular geographic region or population, and this value represents the level of viremia in that region during a specific period of time[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, PVL indicators are difficult to be actually measured because of the partly undiagnosed, confirmed but untreated, undetected VL under treatment, and people who are unaware of their VL status. In the study published by the US CDC guidelines for CVL[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]、Solomon et al[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and Jiang[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] detailed 4 realistic and feasible surrogate markers of PVL based on different levels of population viral load monitoring, namely, monitored viral load (MVL), in-care viral load (ICVL), awared viral load (AVL), and CVL. CVL has been recognized in several studies as an important indicator for evaluating the potential for HIV transmission in a population or region[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Das et al[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] showed that mean CVL was correlated with HIV incidence in San Francisco. A Colombian study[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] showed a strong correlation between increases in ART coverage, decreases in VL at the population level, and decreases in the number of new HIV cases. A study of injecting drug users in San Francisco showed that mean CVL was significantly associated with HIV incidence[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], validating the utility of CVLs as a measure of HIV transmission potential. To date, few studies in China have directly compared the level of VL surveillance among different populations in the same sample and analyzed the strength of associations between PVL surrogate measures and indicators related to HIV transmission potential. Xinjiang is a high HIV incidence area in China with a high HIV burden[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Early efforts to reduce the persistently high transmission potential among populations, targeted implementation of interventions for focused populations, and rational allocation of financial and human resources are particularly important.\u003c/p\u003e \u003cp\u003eWe have for this time established a PVL dynamic monitoring cohort to obtain the VL-related data of HIV/AIDS by extending the VL monitoring range and monitoring the VL of all reporting HIV / AIDS in 2017\u0026ndash;2019 in Yining City, Xinjiang, China. According to the definition of VL surveillance level in different populations, various HIV disease burden indicators were calculated. Longitudinal quantification of the total amount of transmissible HIV and the burden of viremia in the population at different times based on time longitudinal; Based on geographic information, the correlation between indicators related to HIV transmission potential and CVL in different communities was analyzed horizontally, and hotspot communities were found. The dual search can assess the potential of HIV transmission in Yining City, Xinjiang, and identify HIV key population groups based on this, so as to provide scientific information for better formulation of HIV prevention and control plans.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eSubjects and testing content\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHIV positive individuals from the outbreak pool of Yining City in Xinjiang from Jan 1, 2017 to Dec 31, 2019 and newly reported infected individuals from the same period of monitoring and testing population were selected. VL testing and questionnaire were performed after they signed informed consent. Inclusion criteria: (a) HIV positive; (b) Present address is Yining City, Xinjiang, China; (c) Age\u0026nbsp;\u0026ge;\u0026nbsp;13 years; (d) Signed informed consent. Exclusion criteria: those who had died at follow-up.\u003c/p\u003e\n\u003cp\u003eWe adopted the methods of prospective dynamic cohort study to establish an expanded VL detection cohort in the city of Yining, Xinjiang and followed up for three years. HIV positive individuals in the outbreak reservoir and those newly reported to be infected in the population undergoing surveillance and testing during the same period were selected for the study.\u003c/p\u003e\n\u003cp\u003eWe classified laboratory tests according to baseline (A is an in treatment in the national HIV integrated control information system who attends regular VL testing at set-point hospitals on time and on an annual basis; B are untreatment, with sampling and VL testing by the center for Disease Control and prevention; C is a new reported infection in the contemporaneous surveillance, testing population, with new onset infection testing and VL testing when confirmed). A questionnaire was also administered to study subjects doing VL testing, and the questionnaire was designed with reference to the national sentinel surveillance questionnaire. Based on the HIV-related data of Yining City, Xinjiang, a new index was explored for assessing the potential of HIV transmission in Xinjiang, predicting the development trend of HIV epidemic and evaluating the effect of intervention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLaboratory tests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;VL\u0026nbsp;detection reagent was the COBAS TaqMan HIV-1 test v2.0 HIV-1 kit from Roche. VL was measured on blood samples according to the reagent instructions using an automated\u0026nbsp;VL\u0026nbsp;instrument (Roche COBAS AmpliPrep COBAS TaqMan48). HIV primary screening reagents were obtained from the Invitrogen HIV antibody diagnostic kit (enzyme-linked immune assay, ELISA). The retest reagent for those who were positive at the initial screening was used from Beijing kinghao HIV antibody diagnostic kit (ELISA). All samples (including positive and negative samples) should be stored for at least 12 months after the end of investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndicator definitions and calculation methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVL monitoring related indicators\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, according to the CVL guidelines published by the US Centers for Disease Control and Prevention[11], the cross-sectional study in India[14], and Jiang[15], the\u0026nbsp;VL\u0026nbsp;surveillance indicators of different populations were defined as follows: (1) MVL:\u0026nbsp;VL\u0026nbsp;value in those currently on treatment and monitored for\u0026nbsp;VL;\u0026nbsp;(2) ICVL:\u0026nbsp;VL\u0026nbsp;value currently measured in those receiving therapy (whether\u0026nbsp;VL\u0026nbsp;is monitored or not);\u0026nbsp;(3) AVL:\u0026nbsp;VL\u0026nbsp;value in those who knew their own\u0026nbsp;VL\u0026nbsp;status, regardless of their involvement in therapy;\u0026nbsp;(4) CVL:\u0026nbsp;VL\u0026nbsp;value including all those in treated as well as confirmed but not treated subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Different VL metrics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;VL\u0026nbsp;limit of detection in this study was 20 copies / mL, and all\u0026nbsp;VL\u0026nbsp;values below the limit of detection consistently obtained half the detection limit (10 copies / mL).\u0026nbsp;VL\u0026nbsp;measures were chosen as total\u0026nbsp;VL, mean\u0026nbsp;VL, and geometric mean\u0026nbsp;VL. In part of the literature[6, 11, 12]\u0026nbsp;the median\u0026nbsp;VL\u0026nbsp;is chosen to measure the\u0026nbsp;VL\u0026nbsp;index value. However, in this study, the median\u0026nbsp;VL\u0026nbsp;was not selected because it was below the limit of detection and the median\u0026nbsp;VL\u0026nbsp;was 10 copies / mL\u0026nbsp;in the population.\u0026nbsp;(1) Total\u0026nbsp;VL: sum of\u0026nbsp;VLs\u0026nbsp;of all HIV / AIDS(copies / mL);\u0026nbsp;(2) Mean\u0026nbsp;VL: the sum of\u0026nbsp;VL\u0026nbsp;of all HIV / AIDS divided by the total number of people living with HIV(copies / mL);\u0026nbsp;(3) Geometric mean\u0026nbsp;VL:\u0026nbsp;VL\u0026nbsp;of all HIV / AIDS individuals were log transformed (base 10 logarithm), then summed and divided by the total number of HIV infected individuals to obtain their mean(log\u003csub\u003e10\u003c/sub\u003e copies / mL).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndicator definitions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;(1) Viral unsuppression is defined as\u0026nbsp;VL\u0026ge;1000 copies / mL;\u0026nbsp;Viral suppression is defined as\u0026nbsp;VL＜1000 copies / mL. \u0026nbsp;(2) Antiretroviral treatment coverage (ART coverage) is defined as the number of ART cases / total number of HIV infected individuals \u0026times; 100%. \u0026nbsp;(3) Virus\u0026nbsp;unsuppressed\u0026nbsp;rate is defined as the number of virus unsuppressed\u0026nbsp;cases / total number of HIV infected individuals \u0026times; 100%\u0026nbsp;; Viral ppression rate: number of VL suppressed cases / total number of\u0026nbsp;HIV infected individuals\u0026times;\u0026nbsp;100%. \u0026nbsp;(4) Viremia prevalence is defined as the number of cases with\u0026nbsp;VL\u0026nbsp;\u0026gt; 20 copies / mL\u0026nbsp;/ total number of HIV infected individuals \u0026times; 100%.\u003c/p\u003e\n\u003cp\u003eThe HIV incidence rate was calculated as: Ir = \u003cimg src=\"data:image/png;base64,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\"\u003e. N = number of HIV negatives in the study; P = number of HIV positive persons in the study; R = number calibrated to new onset infected persons; Correction parameters \u0026omega; = Mean time to new infection in several years; \u0026epsilon; = FRR (false newly infected rate) for new onset infections. The rate of new infections in this study was estimated from the data provided by the ELISA kit (Jinhao, Beijing), which \u0026omega;= 130d; \u0026epsilon; = 2.3%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(1)Time-based analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea. Sensitivity analysis: There were 55 individuals with missing VL values among those tested for VL, since the missing amount is less than the maximum allowable limit (25%). Through SPSS 22.0 software, the Markov Chain Monte Carlo (MCMC)[21]\u0026nbsp;multiple imputation method was used to comprehensively impolate the missing VL, and the analysis indicators involving the missing VL value were analyzed twice before and after VL imputation. At the same time, the sensitivity analysis of the distribution of people with missing VL and people without missing VL was analyzed.（Table S1）\u003c/p\u003e\n\u003cp\u003eb. Through the statistical analysis of 2017~2019 data through SPSS 22.0 software, the Wilcoxon rank sum test was used for the comparison between the measurement data groups. The correlation analysis of normal distribution data was carried out by Pearson correlation. The statistical test level was\u0026nbsp;\u0026alpha;=0.05. Use GraphPad Prism 8.3.0 software to plot different temporal trends.\u003c/p\u003e\n\u003cp\u003e(2) Geo-community-based analysis\u003c/p\u003e\n\u003cp\u003ea.Yining City has a total of 9 townships, 8 sub-district offices, and 4 districts (1 economic zone, 3 field districts), for a total of 21 community units (collectively referred to as communities). Considering that 2018 had the fewest VL deficits and a relatively large number of people, this part of the study selected 2018 data for cross-sectional analysis. One of the communities did not have an existing HIV infection, so a total of 20 communities (communities 1 to 20) were included in this study. PVL-related indicators are selected as the best indicators in the longitudinal-based queue.\u003c/p\u003e\n\u003cp\u003eR4.3.0 software was used to collate and analyze the data of 20 study calibration communities and draw lollipop maps; Spearman analysis was used for correlation analysis of nonnormal distribution data; simple linear regression model was used to evaluate the association between unsuppressed rate, viremia prevalence, untreated rate and CVL; the adjusted R2 represented the variance ratio of covariate explanation, which was used to evaluate the advantages and disadvantages of the model, and the Akachi Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used To compare model fitting, the best model minimizes AIC and BIC[22], and the statistical test level is \u0026alpha; = 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eDemographic characteristics of HIV/AIDS patients in Yining City, 2017\u0026ndash;2019\u003c/h2\u003e\n \u003cp\u003eThis study monitored 3920, 4167, 4080 individuals respectively in 2017, 2018, and 2019. From 2017 to 2019, there was no trend change in the distribution of sex and transmission routes of the study subjects (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and the distribution of HIV-infected patients in three years showed that the proportion of males was higher, and the transmission routes were mainly injecting drug use, non-marital and non-commercial heterosexual sexual contact, and spouse/fixed partner positive. There were trend changes in the three-year distribution of age, education level, household registration place and medical insurance type (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among them, 36\u0026thinsp;~\u0026thinsp;45 years old accounted for the largest proportion of infected people, followed by 46\u0026thinsp;~\u0026thinsp;55 years old, HIV is gradually transitioning to an advanced age; The education level is mainly primary school and below, most of the infected people are registered in their urban areas, and the types of medical insurance are mainly social insurance and new rural cooperation (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) .\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDistribution of general demographic characteristics of HIV/AIDS in Yining City, 2017\u0026thinsp;~\u0026thinsp;2019\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2017 (N\u0026thinsp;=\u0026thinsp;3920)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2018 (N\u0026thinsp;=\u0026thinsp;4167)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2019 (N\u0026thinsp;=\u0026thinsp;4080)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.183\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2154 (54.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2293 (55.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2302 (56.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.324\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1766 (45.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1874 (44.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1778 (43.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge(year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e168 (4.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e192 (4.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e132 (3.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u0026thinsp;~\u0026thinsp;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e710 (18.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e724 (17.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e569 (13.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u0026thinsp;~\u0026thinsp;45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1801 (45.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1812 (43.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1664 (40.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46\u0026thinsp;~\u0026thinsp;55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1024 (26.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1167 (28.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1367 (33.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e217 (5.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e272 (6.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e348 (8.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary school and below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1791 (45.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1890 (45.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2030 (49.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJunior high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1295 (33.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1419 (34.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1253 (30.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school or technical secondary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e590 (15.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e605 (14.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e568 (13.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollege and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e244 (6.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e253 (6.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e229 (5.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePermanent residence address\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCounties of the city\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3569 (91.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3885 (93.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4036 (98.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e351 (8.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e282 (6.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44 (1.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial security\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1996 (50.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1912 (45.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2583 (63.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNew agricultural joint\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1579 (40.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2137 (51.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1381 (33.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e345 (8.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e118 (2.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116 (2.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoute of transmission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.188\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInjection drug use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1143 (29.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1236 (29.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1212 (29.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-marital non-commercial heterosexual contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e849 (21.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1000 (24.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1009 (24.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-marital commercial heterosexual contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47 (1.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e121 (2.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83 (2.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive for spouse/fixed partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1252 (31.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1091 (26.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1068 (26.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u0026ndash;male sexual behaviours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50 (1.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55 (1.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43 (1.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e579 (14.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e664 (15.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e665 (16.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScreening sources\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVCT clinic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2138 (54.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1583 (38.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e737 (18.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSM community group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15 (0.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25 (0.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30 (0.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePITC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e673 (17.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e720 (17.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e572 (14.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive sexual companion test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119 (3.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e147 (3.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e379 (9.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDetection and thematic investigation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e561 (14.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e584 (14.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1529 (37.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e414 (10.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1042 (25.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e833 (20.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTreatment conditions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot ART\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2903 (74.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3269 (78.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3554 (87.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOn ART\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1017 (25.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e898 (21.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e526 (12.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e Trends Chi-square test; \u003csup\u003e\u0026Dagger;\u003c/sup\u003e Chi-square test; \u003csup\u003e\u0026sect;\u003c/sup\u003eThere is a missing value in the screening sources.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eComparison of different PVL monitoring indicators for 2017\u0026ndash;2019\u003c/h2\u003e\n \u003cp\u003eTotal VL, mean VL, and geometric mean VL were calculated separately for MVL, ICVL, AVL, and CVL according to different population VL monitoring definitions. The trend of year-by-year decrease in different VL values (total VL, mean VL, and geometric mean VL) for the different PVL monitoring indicators MVL, ICVL, AVL, and CVL. The difference in VL values between 2017 and 2019 is statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among them, CVL was higher than MVL, ICVL, and AVL (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eTwo of the indicators, AVL and CVL, were involved in missing VL. Sensitivity analyses were performed to calculate the values of the different VL indicators before and after interpolating the missing values. There was no significant difference between before and after interpolating; the results after interpolation were slightly higher than before interpolation (Table S2).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of VL monitoring indicators for different populations from 2017 to 2019\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"13\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eIndicator\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal VL\u003c/p\u003e\n \u003cp\u003e(copies/mL)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean VL\u003c/p\u003e\n \u003cp\u003e(copies/mL)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGeometric meanVL\u003c/p\u003e\n \u003cp\u003e(log\u003csub\u003e10\u003c/sub\u003e copies/mL)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal VL\u003c/p\u003e\n \u003cp\u003e(copies/mL)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean VL\u003c/p\u003e\n \u003cp\u003e(copies/mL)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGeometric meanVL\u003c/p\u003e\n \u003cp\u003e(log\u003csub\u003e10\u003c/sub\u003e copies/mL)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal VL\u003c/p\u003e\n \u003cp\u003e(copies/mL)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean VL\u003c/p\u003e\n \u003cp\u003e(copies/mL)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGeometric meanVL\u003c/p\u003e\n \u003cp\u003e(log\u003csub\u003e10\u003c/sub\u003e copies/mL)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMVL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47271786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37162772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27542851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7796*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.59*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICVL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47271786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37162772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27542851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7750*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.58*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAVL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63679554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54866057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43132816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11342*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.75*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCVL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e153702430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e138472837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76667028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18791**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.91**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThere were significant differences in 2019 as compared to 2017 VL (\u003csup\u003e*\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 and \u003csup\u003e**\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eCorrelation analysis between different PVL monitoring indicators and the rate of new HIV infections\u003c/h2\u003e\n \u003cp\u003eThe incidence of new HIV infection was 0.002685% in 2017, 0.002252% in 2018, and 0.001246% in 2019, showing a decreasing trend from year to year. The population VL surveillance indicators MVL, ICVL, AVL, and CVL all decreased in 2017\u0026ndash;2019, but only the decrease in CVL was correlated with the decrease in the incidence of new HIV infection. The mean CVL (r\u0026thinsp;=\u0026thinsp;1.000, P\u0026thinsp;=\u0026thinsp;0.006) and geometric mean CVL (r\u0026thinsp;=\u0026thinsp;1.000, P\u0026thinsp;=\u0026thinsp;0.001) were positively correlated with the rate of new HIV infections, The remaining PVL indicators and total CVL were not statistically significantly associated with the incidence of new HIV infection (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Sensitivity analyses in interpolating missing VL values yielded the same results as above for mean CVL* (r\u0026thinsp;=\u0026thinsp;1.000, P\u0026thinsp;=\u0026thinsp;0.003) and geometric mean CVL* (r\u0026thinsp;=\u0026thinsp;0.999, P\u0026thinsp;=\u0026thinsp;0.028) (Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelation analysis of different PVL surveillance indicators with ART coverage, viral unsuppressed rate, and viremia prevalence\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eDuring population surveillance from 2017 to 2019, ART coverage increased from 74.01\u0026ndash;87.04% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the overall population.Viral unsuppression rate decreased from 35.56\u0026ndash;23.58% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Viremia prevalence decreased from 47.35\u0026ndash;23.58% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The results indicated an overall better HIV status in the city.Sensitivity analyses after interpolation of missing VL showed that three indicators changed as before interpolation, with ART coverage increasing from 74.06\u0026ndash;87.11% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), viral unsuppression rate decreasing from 35.61\u0026ndash;23.63% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and viremia prevalence decreasing from 47.60\u0026ndash;36.89% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003cp\u003eBy Pearson\u0026apos;s correlation analysis, the rate of HIV new incidence was significantly associated with ART coverage and viral unsuppression rate(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Correlation analysis of different VL index values with ART coverage, viral unsuppression rate and viremia prevalence by VL monitoring indicators in different populations including sensitivity analysis showed that mean and geometric mean CVL were significantly associated with ART coverage, viral unsuppression rate (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). And the results of the sensitivity analysis after interpolating missing VL values were the same as before interpolating missing values (Table S3).Taken together, mean CVL and geometric mean CVL are better surrogates for PVL for describing the combined HIV transmission potential in the region.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation analysis of VL monitoring indicators of different population and ART coverage, Viral suppression rate and Viremia prevalence\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eIndicator\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eART coverage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eViral suppression rate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eViremia prevalence\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNew HIV infection rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eMVL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal VL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean VL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.526\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeometric meanVL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eICVL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal VL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean VL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeometric meanVL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eAVL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal VL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean VL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeometric meanVL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eCVL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal VL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean VL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeometric meanVL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026ldquo;-\u0026rdquo;Full coverage of ART in MVL and ICVL monitoring populations.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eComparison of CVL between different treatment statuses, different viral suppression statuses, and different viremia prevalence statuses\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eComparison of CVL for different treatment statuses, CVL for different suppression statuses, and CVL for different viremia statuses between groups showed significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); In the analysis of the comparison of CVL values between 2017 and 2019 at different follow-up times, there was no significant difference in the inhibition group (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and the comparison of CVL values of the rest of the indicators for both years had significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). In the sensitivity analysis after interpolation of missing VL values, there were no significant differences (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in CVL for treatment status, CVL for viral suppression status, and CVL for viremia prevalence status before and after interpolation of missing values (Figure S2); the analysis after interpolation of missing values was the same as before interpolation (Table S4).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of CVL between different ART, Viral load suppression and Viremia epidemics before and after interpolation of missing values\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"11\"\u003e\u003c/colgroup\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eIndicator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean VL\u003c/p\u003e\n \u003cp\u003e(copies/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeometric meanVL\u003c/p\u003e\n \u003cp\u003e(log\u003csub\u003e10\u003c/sub\u003e copies/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean VL\u003c/p\u003e\n \u003cp\u003e(copies/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeometric meanVL\u003c/p\u003e\n \u003cp\u003e(log\u003csub\u003e10\u003c/sub\u003e copies/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean VL\u003c/p\u003e\n \u003cp\u003e(copies/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeometric meanVL\u003c/p\u003e\n \u003cp\u003e(log\u003csub\u003e10\u003c/sub\u003e copies/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eART\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot ART\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93392*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.15*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOn ART\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7796*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.59*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eViral load suppression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnsuppressed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79968*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.33*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSuppressed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eViremia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUndetectable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDetectable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51506*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.51*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e*There were significant differences in 2019 as compared to 2017 VL (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eComparison of CVL in general characteristics population 2017\u0026ndash;2019\u003c/h2\u003e\n \u003cp\u003eHere, we chose data before imputation for this analysis as there was no significant difference in population distribution before and after imputation for missing VL data. A comparison of CVL values between 2017 and 2019 shows significant differences in the sex, age, education level, and Medicare type (P\u0026lt;0.05), and with a decreasing trend from year to year. There was no significant difference in CVL among the within group CVL comparisons for only the different Medicare types (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and all of the remaining within group CVL comparisons were significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among them, men, \u0026le; 25 years old, primary school and below, other household registration place, other medicare types, other sample sources, nonmarital noncommercial heterosexual contacts, and nonmarital commercial heterosexual contacts had relatively high CVL (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of the general characteristic population CVL from 2017 to 2019\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean VL\u003c/p\u003e\n \u003cp\u003e(copies/mL)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGeometric meanVL\u003c/p\u003e\n \u003cp\u003e(log\u003csub\u003e10\u003c/sub\u003e copies/mL)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean VL\u003c/p\u003e\n \u003cp\u003e(copies/mL)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGeometric meanVL\u003c/p\u003e\n \u003cp\u003e(log\u003csub\u003e10\u003c/sub\u003e copies/mL)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean VL\u003c/p\u003e\n \u003cp\u003e(copies/mL)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGeometric meanVL\u003c/p\u003e\n \u003cp\u003e(log\u003csub\u003e10\u003c/sub\u003e copies/mL)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23190\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.01\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13325\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.80\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge(year)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16014\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.52\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u0026thinsp;~\u0026thinsp;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21447\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.22\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u0026thinsp;~\u0026thinsp;45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14724\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.93\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46\u0026thinsp;~\u0026thinsp;55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21737\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.77\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24434\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.79\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary school and below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23975\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.96\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJunior high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16848\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.91\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school or technical secondary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollege and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePermanent residence address\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCounties of the city\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18346\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.90\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70714\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.57\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial security\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21396\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.95\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNew agricultural joint\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13645\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.87\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25487\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.96\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScreening sources\u003csup\u003e\u0026Dagger;***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVCT clinic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSM Community Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1416\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePITC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20482\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.10\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive sexual companion test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8264\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.63\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDetection and thematic investigation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17417\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.77\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20259\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.13\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoute of transmission\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInjection drug use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24956\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.96\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-marital non-commercial heterosexual contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25961\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.21\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-marital commercial hetero sexual contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23198\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.26\u003csup\u003ea**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive for spouse/fixed partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11598\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.71\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u0026ndash;male sexual behaviours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThere were significant differences in 2019 as compared to 2017 VL (\u003csup\u003e*\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 and \u003csup\u003e**\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001); \u003csup\u003e***\u003c/sup\u003e There were significant differences in the CVL comparisons within the group (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001); \u003csup\u003e\u0026dagger;\u003c/sup\u003eOther types of health insurance include: commercial insurance, no health insurance; \u003csup\u003e\u0026Dagger;\u003c/sup\u003eThere is a missing value in the screening sources.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDistribution of CVL, ART coverage, viral unsuppresstion rate, and viremia prevalence by community in Yining City\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eBased on a cross-sectional description of the indicator values for each community in 2018 based on 20 communities in the city of Yining, the CVL ranged from 1.64 log\u003csub\u003e10\u003c/sub\u003e copies/mL to 2.49 log\u003csub\u003e10\u003c/sub\u003e copies/mL, with a median CVL value of 2.18 log\u003csub\u003e10\u003c/sub\u003e copies/mL; ART coverage rate ranged from 68.21\u0026ndash;89.47%, with a median value of 78.51%; viral unsuppression rate from 15.79\u0026ndash;39.74% with a median value of 30.35%; and viremia prevalencce from 21.05\u0026ndash;56.90% with a median value of 44.53%. Community 10 had the highest CVL and viral suppression rate, the lowest ART coverage; and viremia prevalence was second only to community 06 (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e); Community 18 had the lowest CVL, viral unsuppression rate, prevalence of viremia, and the highest ART coverage. There was a correlation between all indicators, with a positive correlation between CVL, viral unsuppression rate and viremia prevalence, and a negative correlation between ART coverage rate and the first three indicators (Figure S3).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003eLinear regression analysis of CVL with viral unsuppression rate, viremia prevalence, and ART coverage\u003c/h2\u003e\n \u003cp\u003eLinear correlation analysis and regression model estimation of CVL with ART coverage rate, viral unsuppression rate, and viremia prevalence based on 20 communities showed that the viral unsuppression rate had the strongest correlation with CVL (R\u003csup\u003e2\u003c/sup\u003eadj: 0.982, AIC/BIC: -80.91/-77.92), and for each one-unit increase in viral unsuppression rate, the CVL increased by 0.036 log\u003csub\u003e10\u003c/sub\u003e copies/mL (95% CI: 0.033\u0026thinsp;~\u0026thinsp;0.038); followed by viremia prevalence, ART coverage rate (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLinear regression analysis of CVL with ART coverage rate, Viral unsuppression rate and Viremia prevalence\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eIndicator\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026rho;\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eLinear regression\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;(95%CI\u003c/em\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eadj\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAIC/BIC\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eART coverage rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.579\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.029(-0.044~-0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-12.55/-9.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eViral unsuppression rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.985\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.036(0.033\u0026thinsp;~\u0026thinsp;0.038)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-80.91/-77.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eViremia prevalence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.909\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026(0.021\u0026thinsp;~\u0026thinsp;0.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-39.63/-36.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003eThere are 21 communities in Yining City, but one community was not included in the analysis because it had no HIV/AIDS patients in 2018; \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study found that the PVL indicator value in Xinjiang, China, was consistent with the changing trend of the PVL indicator value in cities in other countries, showing a decreasing state from year to year[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] in the time-based longitudinal analysis. Although the PVL surrogate indicators MVL, ICVL, AVL, CVL, total VL, mean VL, and geometric mean VL showed the same trend of change in new HIV infection rate, only CVL was positively associated with new HIV infection rate. In correlation analyses with ART coverage, viral unsuppression rate, and viremia prevalence, new HIV incidence was significantly correlated with ART coverage, viral unsuppression rate, and mean CVL, geometric mean CVL, and mean ICVL were also strongly correlated with ART coverage and viral unsuppression rate.\u003c/p\u003e \u003cp\u003eTaken together, mean CVL and geometric mean CVL are better as surrogate indicators of PVL to measure the HIV transmission potential and HIV disease burden in Xinjiang population. This is consistent with the findings of India[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], South Carolina[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], Rhode Island[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and validates the former hypothesis that as ART coverage increases, viral unsuppression rate decrease, CVL decreases, and new HIV infection rate decrease. But the difference is that other indicators of PVL in Solomon's[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] study also correlated with new HIV infection rate, which is slightly lower than that of CVL. However, this study showed no significant association between MVL, ICVL, and AVL and new HIV infection rate, ART coverage, or viral unsuppression rate. This may be related to the included population size of different PVL indicators in this study. The present study MVL largely coincided with the inclusion population (on treated) of ICVL, who had a predominance of VL suppressed and VL undetectable individuals, a low population VL base, and a relatively slow VL decline trend over the 3-year period. In previous studies[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], MVL and ICVL monitoring in the treated population VL, the easy observation of personnel, and the strong availability of data allowed the study of MVL and ICVL as surrogate indicators to predominate[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, a limitation is that both are not included in the confirmed but untreated cohort, and the workhorse of HIV transmission[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]- the high VL population is mostly concentrated in this group, thus prone to low estimated PVL. AVL covers a portion of the untreated population on the basis of MVL and ICVL, but population coverage remains incomplete, and not all diagnosed persons are aware of their VL condition. We found that the CVL values in 2017\u0026ndash;2019 in Yining City were higher than those of MVL, ICVL, and AVL, compared with other indicators. CVL is a more comprehensive and representative estimate of PVL, encompassing all populations in whom PVL can be monitored (those who are undiagnosed cannot be monitored), including untreatment individuals. This is in agreement with the US CDC guidelines for CVL[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], the studies by Rozhnova[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and Farahani[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Therefore, we believe that CVL is a better monitor and indicator of the HIV transmission potential and disease burden in the population than other surrogate markers of PVL.\u003c/p\u003e \u003cp\u003eExpanding VL surveillance, making full use of VL information, and achieving the cross from individual VL to population VL, transformed PVL from an observational epidemic domain to an indicator for rapid action to improve HIV disease burden[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Based on the above results, we choose mean CVL and geometric mean CVL to quantify the HIV burden of the population in Yining, Xinjiang, China, to find the HIV hotspot populations in different characteristic populations. Among them, CVL was relatively high in male, \u0026le; 25 years old, Primary school and below, other household registration place, other medicare types, other sample sources, non-married commercial heterosexual contacts and non-married commercial heterosexual contacts. Targeted interventions should be implemented with respect to their characteristics, with the aim of highly effective reduction of the HIV disease burden in the population.\u003c/p\u003e \u003cp\u003eViremia prevalence adjusted for the amount of HIV negative persons in the study observations was also suggested by Solomon[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] to be a better indicator of HIV transmission potential. However, we only found that there was an association between total CVL and the prevalence of viremia, while there was no significant association between total CVL and the prevalence of new HIV infection, and there was no significant association between total CVL and art coverage, virus uninhibited rate, the results suggest caution when using this indicator to monitor the HIV burden in communities in Xinjiang, China. In addition, it is important to note that limit of VL values for the prevalence of viremia varied among studies, such as 20 copies/mL which we chose, and 150 copies/mL in the study by Patel[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Sensitivity analysis of its values can be considered in future studies to explore whether the results differ at different limit of VL values in the same sample.\u003c/p\u003e \u003cp\u003eIn the geographical community analysis of the distribution of indicators related to the burden of HIV disease and the effect of HIV prevention and treatment in each community, it is intuitive to find the \"hotspot communities\" that need to be focused on, accurately locate the high CVL community, and measure the HIV epidemic status in the city. HIV hotspot communities are an important area for large-scale intervention in the future[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], hotspot communities will shape the environment of high-risk groups for HIV-negative people in the region, increase their risk of infection, and those living in hotspot communities with a longer history of HIV have the potential for sustained HIV transmission[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], which is easy to form the source of continuous transmission of HIV[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This study supports the combination of geospatial analysis to shift the research objects from individuals to key populations and hotspot communities, combine the regional characteristics of spatial characteristics, use CVL to accurately locate hotspot communities and highly detected populations, and expand HIV testing for communities and populations with high CVL, in order to discover more and more complete new reports and new infections, and achieve efficient epidemic tracking. Integrating geospatial analysis into routine public health planning will help focus interventions on more precise geographical units for maximum epidemiological impact and effective resource allocation, while more nuanced assessment of the effectiveness of HIV responses.\u003c/p\u003e \u003cp\u003eSeveral limitations should be noted. First, the HIV transmission potential was analyzed without taking into account the impact of HIV prevalence and behavior in the population. As in different populations with the same CVL, those with higher HIV prevalence and incidence of high-risk behaviours necessarily have higher HIV transmission potential[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Based on this, we analyzed the CVL of different characteristic populations. Second, in the time-based longitudinal analysis, the study period is shorter, the trend of viremia prevalence is subtle, and the trend of longer follow-up time may be more significant, considering these factors, extending the follow-up time in future CVL studies, and effectively combining HIV prevalence and high-risk behaviors, the results will be more stable and more critical, and more conducive to analyzing the HIV epidemic and prevention and treatment effects from the population level, achieving accurate prevention and control, and matching resources and needs.\u003c/p\u003e \u003cp\u003eIn conclusion, our results show that mean CVL and geometric mean CVL can be better substitutes for PVL, and affirm the utility of CVL as an indicator of HIV transmission potential through time and geography, and can cooperate with ART coverage, viral unsuppression rate, viremia prevalence and other indicators to dynamically monitor the HIV epidemic and evaluate the effect of HIV prevention and treatment in Yining City. At present, the overall CVL in Yining City has decreased year by year, affirming the current prevention and control efforts, which should expand the scope of VL detection in the future, better apply CVL, and monitor the local HIV transmission potential. Combined with spatial geography to accurately locate hot communities and high-detection populations of HIV, and carry out prevention and treatment work more accurately and rapidly, it provides new, efficient and practical monitoring indicators and indicators for reasonable allocation of health resources to the health department.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eCVL can be used as an indicator evaluate the HIV transmission potential, and it can be used as an indicator for evaluating the effectiveness of HIV interventions in a population or included in a comprehensive social indicator to measure the survival of people living with HIV. To further reduce the CVL and reduce the HIV transmission potential, targeted interventions should be developed for key populations and hotspot communities.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHIV/AIDS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Human Immunodeficiency Virus /Acquired Immune Deficiency Syndrome\u003c/p\u003e\n\u003cp\u003eART \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Antiretroviral therapy\u003c/p\u003e\n\u003cp\u003eVL \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Viral load\u003c/p\u003e\n\u003cp\u003eCVL \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Community viral load\u003c/p\u003e\n\u003cp\u003ePVL \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Population viral load\u003c/p\u003e\n\u003cp\u003eMVL \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Monitored\u0026nbsp;viral load\u003c/p\u003e\n\u003cp\u003eICVL \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;In-care\u0026nbsp;viral load\u003c/p\u003e\n\u003cp\u003eAVL \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Awared\u0026nbsp;viral\u0026nbsp;load\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the AIDS research ethics committee of the disease control and prevention center of Xinjiang Uygur Autonomous Region(No. 2018-001).\u0026nbsp;All participants provided informed consent, we confirm that all methods were performed in accordance with the guidelines. All participants received a written informed consent form and an oral explanation of the purpose and content of the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that supports\u0026nbsp;the findings of this study\u0026nbsp;is\u0026nbsp;available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all the volunteers who participated in the study and all the staff involved in this study from Xinjiang Uighur Autonomous Region Center for Disease Control and Prevention and Yining Center for Disease Control\u0026nbsp;and Prevention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQH and\u0026nbsp;\u0026nbsp;YN\u0026nbsp;contributed to the cohort follow-up, data collection, data analysis and manuscript preparation, YL, and XH contributed to the study design and conception, experimental work and data collection. XH and ZN contributed to experimental work and samples collection. CZ, BX, and AA contributed to the cohort follow-up work and data analysis. MN contributed to the study design and conception, supervised the experimental work, data analysis and interpretation. ALL authors reviewed the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Xinjiang Key Laboratory of HIV/AIDS Prevention and Control Research (No.XJYS1706), National Science and Technology Major Project of the Ministry of Science and Technology of China (No.2018ZX10715-007).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEshleman SH, Hudelson SE, Redd AD, Swanstrom R, Ou S-S, Zhang XC, et al. Treatment as Prevention: Characterization of Partner Infections in the HIV Prevention Trials Network 052 Trial. 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Effect of data missing on population based viral load survey in HIV infected men who have sex with men sampled in 16 large cities, China. Zhonghua Liu Xing Bing Xue Za Zhi. 2017;38:1169\u0026ndash;73.\u003c/li\u003e\n\u003cli\u003eKrentz HB, Gill MJ. The Effect of Churn on \u0026ldquo;Community Viral Load\u0026rdquo; in a Well-Defined Regional Population. JAIDS J Acquir Immune Defic Syndr. 2013;64:190\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eDas M, Chu PL, Santos G-M, Scheer S, Vittinghoff E, McFarland W, et al. Decreases in Community Viral Load Are Accompanied by Reductions in New HIV Infections in San Francisco. PLoS ONE. 2010;5:e11068.\u003c/li\u003e\n\u003cli\u003eMontaner JS, Lima VD, Barrios R, Yip B, Wood E, Kerr T, et al. Association of highly active antiretroviral therapy coverage, population viral load, and yearly new HIV diagnoses in British Columbia, Canada: a population-based study. The Lancet. 2010;376:532\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eGlasheen C, Johnson EO, Lorvick J, Kral AH. Measures of human immunodeficiency virus (HIV) community viral load and HIV incidence among people who inject drugs. Ann Epidemiol. 2018;28:8\u0026ndash;12.\u003c/li\u003e\n\u003cli\u003eBao Y, Larney S, Peacock A, Colledge S, Grebely J, Hickman M, et al. Prevalence of HIV, HCV and HBV infection and sociodemographic characteristics of people who inject drugs in China: A systematic review and meta-analysis. Int J Drug Policy. 2019;70:87\u0026ndash;93.\u003c/li\u003e\n\u003cli\u003eA\u0026szlig;mann C, Gaasch J-C, Stingl D. A Bayesian Approach Towards Missing Covariate Data in Multilevel Latent Regression Models. Psychometrika. 2022. https://doi.org/10.1007/s11336-022-09888-0.\u003c/li\u003e\n\u003cli\u003eDziak JJ, Coffman DL, Lanza ST, Li R, Jermiin LS. Sensitivity and specificity of information criteria. Brief Bioinform. 2020;21:553\u0026ndash;65.\u003c/li\u003e\n\u003cli\u003eCastel AD, Befus M, Willis S, Griffin A, West T, Hader S, et al. Use of the community viral load as a population-based biomarker of HIV burden. AIDS. 2012;26:345\u0026ndash;53.\u003c/li\u003e\n\u003cli\u003eChakraborty H, Weissman S, Duffus WA, Hossain A, Varma Samantapudi A, Iyer M, et al. HIV community viral load trends in South Carolina. Int J STD AIDS. 2017;28:265\u0026ndash;76.\u003c/li\u003e\n\u003cli\u003eTouzard Romo F, Gillani FS, Ackerman P, Rana A, Kojic EM, Beckwith CG. Monitored viral load: a measure of HIV treatment outcomes in an outpatient setting in Rhode Island. R I Med J 2013. 2014;98:26\u0026ndash;30.\u003c/li\u003e\n\u003cli\u003eMonno L, Saracino A, Scudeller L, Santoro C, Brindicci G, Punzi G, et al. Reduced community viral load does not coincide with a reduction in the rate of new HIV diagnoses and recent infections: data from a region of southern Italy. 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Lancet HIV. 2021;8:e544\u0026ndash;53.\u003c/li\u003e\n\u003cli\u003eTanser F, B\u0026auml;rnighausen T, Dobra A, Sartorius B. Identifying \u0026lsquo;corridors of HIV transmission\u0026rsquo; in a severely affected rural South African population: a case for a shift toward targeted prevention strategies. Int J Epidemiol. 2018;47:537\u0026ndash;49.\u003c/li\u003e\n\u003cli\u003eTomita A, Vandormael A, B\u0026auml;rnighausen T, Phillips A, Pillay D, De Oliveira T, et al. Sociobehavioral and community predictors of unsuppressed HIV viral load: multilevel results from a hyperendemic rural South African population. AIDS. 2019;33:559\u0026ndash;69.\u003c/li\u003e\n\u003cli\u003ePhilip NM. Population-level HIV risk and combination implementation of HIV services.\u003c/li\u003e\n\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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"HIV/AIDS, Community viral load (CVL), Rate of HIV new infections, Cohort study, HIV transmission potential","lastPublishedDoi":"10.21203/rs.3.rs-4164996/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4164996/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: New indicators of HIV transmission potential are being actively explored. We aim to categorical testing of the viral load of people living with HIV in order to explore new indicators to measure the intensity of the epidemic and the effectiveness of the response in the community.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: A dynamic cohort study was conducted in Yining to monitor the viral load (VL) of all individuals with HIV/AIDS from 2017 to 2019. Different PVL surrogate values were calculated and the strength of the associations between different PVL surrogates and HIV new incidence, antiretroviral therapy (ART) coverage, virus unsuppression, and viremia prevalence was assessed. Then we used PVL surrogate markers to describe the current status of HIV transmission potential in different characteristic populations and different communities.\u003c/p\u003e\n\u003cp\u003eResults: All the values of different PVL indicators showed a decreasing trend year by year (P \u0026lt; 0.05). A significant correlation was observed between the decrease in community viral load (CVL) alone and the increase in the incidence of new HIV infections. Mean CVL (r = 1.000, P = 0.006), geometric mean CVL (r = 1.000, P = 0.001) were positively associated with HIV new infection. Both before and after imputation with missing values showed that mean CVL and geometric mean CVL were significantly associated with ART coverage and viral unsuppression (P \u0026lt; 0.05). Relatively high CVLs were found for males, ≤25 years of age, elementary school or less, other place of domicile, other type of health insurance, other source of sample, nonmarital noncommercial heterosexual contact, and nonmarital commercial heterosexual contact in the different characteristics groups. Community-based cross-sectional analyses showed a positive correlation between CVL, Viral unsuppression rate, and Viremia prevalence, and a negative correlation between ART coverage rate and the first three indicators, suggesting that “community 10” is the hotspot for HIV epidemics in the city.\u003c/p\u003e\n\u003cp\u003eConclusions: CVL can be used as an indicator evaluate the HIV transmission potential. To further reduce the HIV transmission potential, targeted interventions should be developed on key populations and hotspot communities.\u003c/p\u003e","manuscriptTitle":"Categorical testing of the viral load of people living with HIV to measure the intensity of the epidemic and the effectiveness of the response in the community: a prospective cohort study in Xinjiang China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-01 17:52:04","doi":"10.21203/rs.3.rs-4164996/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-27T10:41:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-26T14:08:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-26T14:08:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-03-25T17:48:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d6512c72-8668-4c9a-aded-a9fa0c02f939","owner":[],"postedDate":"April 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-13T16:11:26+00:00","versionOfRecord":{"articleIdentity":"rs-4164996","link":"https://doi.org/10.1186/s12889-025-21278-6","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-01-11 15:58:01","publishedOnDateReadable":"January 11th, 2025"},"versionCreatedAt":"2024-04-01 17:52:04","video":"","vorDoi":"10.1186/s12889-025-21278-6","vorDoiUrl":"https://doi.org/10.1186/s12889-025-21278-6","workflowStages":[]},"version":"v1","identity":"rs-4164996","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4164996","identity":"rs-4164996","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}