Abstract
Background
The HIV epidemic in key populations such as men who have sex with men (MSM) is a public health issue of worldwide concern. China has seen an increase in newly diagnosed HIV infections through male–male sexual contact in the past decade. In a long-term cohort, how the complex behaviour pattern of MSM changed and the association with the HIV risk are unclear at present.
Methods
This study was conducted from October 2011 to December 2019 in Tianjin. MSM were recruited by snowball sampling through online and offline ways. Demographic and sexual behavioural data were collected for analysis. Three indicators (condom use in last anal sex, frequency of condom use during anal sex and the number of sexual partners) were used to define the behaviour change. Participants with zero, one, and two or three risk indicators were categorised into behaviour types of ‘protective’, ‘moderate’, and ‘fragile’, respectively. Change in behaviour type between baseline and each visit was considered. Time-varying Cox models were performed to evaluate HIV infection risk.
Results
Of 2029 MSM included in the study, 127 were new HIV diagnoses. The overall incidence rate was 3.36 per 100 person-years. The percentage of ‘protective’ and ‘moderate’ behaviour types had a conspicuous growth trend as the follow-up. Furthermore, the HIV incidence rate in each visit among different behaviour transition types showed a general downward trend as the number of total follow-up times increased. Individuals who remained in ‘fragile’ (adjusted HR (aHR): 25.86, 95% CI: 6.92 to 96.57) or changed from ‘protective’ to ‘moderate’ (aHR: 4.79, 95% CI: 1.18 to 19.47), ‘protective’ to ‘fragile’ (aHR: 23.03, 95% CI: 6.02 to 88.13), and ‘moderate’ to ‘fragile’ (aHR: 25.48, 95% CI: 6.79 to 95.40) between baseline and the last follow-up had a higher HIV risk. Gained risk indicators were associated with the increase of HIV risk (gained one indicator, aHR: 2.67, 95% CI: 1.68 to 4.24; gained two or three indicators, aHR: 4.99, 95% CI: 3.00 to 8.31) while losing just one risk indicator could halve the risk (aHR: 0.43, 95% CI: 0.21 to 0.90).
Conclusions
Among MSM in Tianjin, it is necessary to get timely behaviour change for those with high-incidence behaviour patterns while sustaining for those with low-incidence patterns.
Trial registration number
Chinese Clinical Trials Registry (ChiCTR2000039500).
Keywords: HIV & AIDS, EPIDEMIOLOGY, Infectious disease/HIV
STRENGTHS AND LIMITATIONS OF THIS STUDY.
This study was based on an open cohort of men who have sex with men in China with a long time span (2011–2019).
Three indicators related to participants’ sexual behaviours (condom use in last anal sex, frequency of condom use during anal sex and the number of sexual partners) were used to define the behaviour change jointly.
Associations between behaviour change and risk of HIV infection were estimated through Cox models.
Sexual behaviours were self-reported and measured retrospectively, and may have been misremembered or otherwise misreported.
Introduction
The HIV epidemic is a significant public health issue of worldwide concern.1–4 The prevalence of HIV has been low among the general population over the past three decades. However, the burden of HIV is disproportionately concentrated among men who have sex with men (MSM).5–7 China’s HIV epidemic began in the early 1990s among injecting drug users.8 9 The main route of transmission was needle-sharing.10 11 Due to China’s effective legal control and intensive regulations on drugs,12 13 the main spread route of HIV inevitably turned into sexual contact since the 21st century, especially male-to-male sexual contact.7 14 15 Corresponding studies aimed to ascertain the pattern of sexual behaviours among MSM had important practical significance though sexual behaviours between men that were taboo both politically and culturally in China.16 17
Effective interventions and behaviour changes had been responsible for the HIV prevention successes to date.18 19 HIV interventions typically concentrate on unprotected anal intercourse (UAI),20 multiple sex partners,21 alcohol and drug use,13 22 pre-exposure prophylaxis (PrEP)23 24 or post-exposure prophylaxis (PEP)25 26 utilisation and adherence to antiretroviral therapy (ART).27 It was well established from a variety of studies that timely behaviour changes and biomedical interventions can reduce HIV transmission.28–30 However, most studies used a single measurement to describe behaviour changes. In a long-term cohort, how the complex behaviour pattern changed and the association with the HIV risk were largely unknown at present.
This study was based on an open cohort among MSM in Tianjin, China (2011–2019). The study collected each MSM’s sexual behaviour and HIV infection status at each visit. The aims of the present study were the following: (1) to describe the HIV incidence rate within the cohort; (2) to quantify the behaviour changes over the follow-up and to address the association between behaviour change and the risk of HIV infection; and (3) to explore influencing factors for progression to behaviour changes.
Methods
Study design
The Men who have Sex with Men Health Encouragement Longitudinal Project was a longitudinal cohort study among MSM launched in Tianjin, China. The main objectives of the project include the following: (1) establish community-based HIV prevention service stations aimed at the key population, (2) improve the accessibility and utilisation of HIV testing and prevention services, and (3) provide ART support services for people living with HIV. The organisations involved in the project included the US Centers for Disease Control and Prevention (CDC); National Center for AIDS/STD Control and Prevention, Chinese CDC; Tianjin City CDC and Tianjin Shenlan Community-Based Organization (CBO). This study was registered with the Chinese Clinical Trials Registry (ChiCTR2000039500).
Data collection and participants
Tianjin Shenlan CBO was a formal community-based organisation that provided community-based HIV voluntary counselling and testing (VCT) support services (including HIV testing, counselling and psychological support) for MSM. Participants were mobilised to get tested for HIV in Shenlan’s public health advisory service centre. When participants came to the centre for testing or counselling, experienced MSM investigators would conduct a face-to-face interview with participants in a private room. During this period, a structured questionnaire (including demographics and sexual behaviour information) was completed by the participants under the guidance of the investigators. The investigators of the organisation mostly were also MSM. All investigators had undergone training before entering work, which would be a benefit for smooth unhindered communication with participants and could ensure the reliability of the results. After the interview, every participant would get several condoms and lubricant for free. Information collection adopted a real-name registration system (ID card number, mobile phone number and fingerprint information were collected). Prior to the interviews, all participants signed a written informed consent, including the objective, procedures, confidentiality and participants’ rights. Enrolled participants were encouraged to conduct routine HIV test every 3 months (90 days). The entire research process was supervised and coordinated by Tianjin City CDC.
The snowball sampling method was used to recruit participants. Participants were recruited from gay bars, gay bathhouses, social network sites (WeChat, QQ, gay chat website), gay apps and peer referrals. The initial enrolment took place on 1 October 2011, and follow-up ended on 31 December 2019. Inclusion criteria were the following: (1) had at least two visits during the cohort period and HIV serological test was negative when they first registered, (2) ≥16 years old, (3) biologically male, (4) reported anal intercourse with another man in the past 6 months. If the participants of the cohort changed dynamically, the original participants could drop out continuously, and the new participants could join at any time. Of 6565 MSM captured in this study, 4096 were excluded for having only one visit, 432 for having HIV seropositive result at baseline and 8 for lack of behavioural information. A total of 2029 MSM met the eligibility criteria and were included in the study eventually. The flow diagram of the study was listed in online supplemental figure 1. The comparison of baseline characteristics between included and excluded MSM was listed in online supplemental table 1.
bmjopen-2021-055046supp001.pdf (1.2MB, pdf)
Definitions of behaviour changes
In this study, behaviour changes include the change in the number of risk indicators and change in sexual behaviour type. Sexual behaviour type was used to represent different patterns of sexual behaviour characteristics. It was collectively defined by three indicators related to participants’ sexual behaviour in the past 6 months. The three indicators included variables of condom use in last anal sex,31 frequency of condom use during anal sex21 and the number of sexual partners.32 These three variables were related to the HIV infection (online supplemental table 2). Participants had the information of the three variables in each follow-up. Each indicator was divided into two levels: ‘ideal level’ and ‘risk level’ (online supplemental table 3). Among the three variables mentioned above, if one participant had one variable of ‘risk level’, then it meant the participant had one risk indicator, and so on. The number of risk indicators was in the range of zero to three.
Behaviour type was defined based on the risk indicators. Behaviour type was categorised as ‘protective’ if the participant had no risk indicator; as ‘moderate’ if the participant had one risk indicator; as ‘fragile’ if the participant had more than two risk indicators (two or three risk indicators). The behaviour type and the number of risk indicators could change as time varies (during the follow-up period).
Change in the number of risk indicators was defined as the difference of risk indicators between any two follow-ups. Behaviour transition type was defined as the change of behaviour type between any two visits. Then the behaviour transition type yielded nine possible combinations of behaviour types: consistently protective, protective to moderate, protective to fragile, moderate to protective, consistently moderate, moderate to fragile, fragile to protective, fragile to moderate and consistently fragile.
HIV laboratory test
Before testing, MSM would choose to collect blood or saliva. Patrons’ oral mucosal exudate test (Mano Bio-Pharmaceutical Co, Beijing, China) was used for the former and a blood rapid detection reagent (Wan Fu Biotechnology Co, Guangzhou, China) for the latter. In addition, 5 mL of blood sample was collected from those who get any positive tests above. Then, the blood sample was sent to Tianjin CDC to perform ELISA (Wan Tai Biological Pharmaceutical Co, Beijing, China). HIV infection was confirmed by Western blot assay (MP Biomedical Asia Pacific, Singapore). As the exact infection time was hard to affirm, for the chronic infections (infection time over 6 months), the infection time was defined as the midpoint between the last negative test date and first positive test time, while the acute and early infection (<6 months) was the time diagnosed as HIV infection.33
Statistical methods
Categorical variables were described by frequencies and percentages. For the general demographic characteristics, differences were calculated using Pearson’s Χ2 test or Fisher’s exact test.
HIV incidence rates were estimated within each year and among subgroups of different behaviour transition types in each visit, with person-year (PY) over the follow-up time as the denominator.
Change in behaviour type was calculated in each visit (according to baseline behaviour type). Considering that the total number of each participant was unequal, the proportion of each behaviour type at each visit and the proportion of behaviour transition type from baseline to each visit were calculated.
Univariate and multivariable time-varying Cox models were performed to evaluate (1) risk of HIV infection among different behaviour type subgroups over each follow-up (all records of the participants were included in the model in counting process format); and (2) behaviour changes and association with the risk of HIV infection (two time points were opted to define the behaviour change, baseline and the last follow-up). HRs, adjusted HRs (aHRs) and 95% CI of the variables were estimated. The logistic regression model was applied to investigate the influencing factors for progression to behaviour type of ‘fragile’ or ‘moderate’ in the last follow-up among baseline behaviour type subgroups. ORs, adjusted ORs (aORs) and 95% CI were estimated. In addition, all models were adjusted for baseline covariates. All the data analyses were performed in SAS V.9.4 (SAS Institute).
Patient and public involvement
This study was mainly completed by Tianjin CDC, with Tianjin Shenlan CBO as the specific implementor of the study. Patients or the public were not directly involved in the design and implementation of the study. However, the findings could influence the design of other subsequent studies, such as specific studies targeting drug users or male sex workers in the MSM population.
Results
Characteristics of the participants
A total of 2029 MSM met the eligibility criteria and were included in the study eventually. The total follow-up person time was 3772.03 PY. The median total follow-up time was 1.28 years (IQR: 0.57–2.72; range: 8 days–8.13 years) starting from baseline. Confirmed by the laboratory test, 127 participants were new HIV diagnoses. Sixty-three were classified as acute and early HIV infected and 64 were chronically infected.
The characteristics of the participants when they first registered (baseline) were shown in table 1. Among all the participants, the median age was 34 years old (P25: 25; P75: 45); 53.33% were unmarried; 57.76% had a high school degree; 39.48% had a college degree or above; 71.87% had been living in Tianjin for more than 2 years while 19.96% had been living in Tianjin for less than 3 months; 76.39% used condom in the last anal sex; 38.69% had been consistently using condoms during anal sex over the past 6 months; 84.23% had less than 10 sexual partners over the past 6 months; 3.35% had sexually transmitted infection (STI) over the past 6 months; 46.23% had received HIV health service over the past 1 year; 5.03% had been working as male sex workers.
Table 1.
Characteristics and sexual behaviours at baseline for MSM with HIV infection or negative HIV test
| Variables at baseline | Total N (%) | HIV infection N (%) | HIV negative N (%) |
| Age | |||
| <45 | 1504 (74.13) | 96 (75.59) | 1408 (74.03) |
| 45–60 | 464 (22.87) | 27 (21.16) | 437 (22.98) |
| >60 | 61 (3.01) | 4 (3.15) | 57 (3.00) |
| Marital status | |||
| Married | 947 (46.67) | 70 (55.12) | 877 (46.11) |
| Unmarried | 1082 (53.33) | 57 (44.88) | 1025 (53.89) |
| Education | |||
| Below high school | 56 (2.76) | 5 (3.94) | 51 (2.68) |
| High school | 1172 (57.76) | 90 (70.87) | 1082 (56.89) |
| College or more | 801 (39.48) | 32 (25.20) | 769 (40.43) |
| Residence time in Tianjin | |||
| <3 months | 405 (19.96) | 41 (32.28) | 364 (19.14) |
| 3–7 months | 39 (1.92) | 2 (1.57) | 37 (1.74) |
| 7–12 months | 41 (2.02) | 5 (3.94) | 36 (1.89) |
| 1–2 years | 86 (4.24) | 2 (1.57) | 84 (72.61) |
| >2 years | 1458 (71.86) | 77 (60.63) | 1381 (72.61) |
| Condom use in last anal sex | |||
| Yes | 1550 (76.39) | 78 (61.42) | 1472 (77.39) |
| No | 479 (23.61) | 49 (38.58) | 430 (22.61) |
| Frequency of condom use during anal sex*† | |||
| Consistent use | 785 (38.69) | 43 (33.86) | 742 (39.01) |
| Inconsistent use | 1244 (61.31) | 84 (66.14) | 1160 (60.99) |
| Number of sexual partners† | |||
| <10 | 1709 (84.23) | 116 (91.34) | 1593 (83.75) |
| More than 10 | 320 (15.77) | 11 (8.66) | 309 (16.25) |
| STI† | |||
| Yes | 68 (3.35) | 2 (1.57) | 66 (3.47) |
| No | 1961 (96.65) | 125 (98.43) | 1836 (96.53) |
| Inject drugs† | |||
| Yes | 22 (1.08) | 2 (1.57) | 20 (1.05) |
| No | 2007 (98.92) | 125 (98.43) | 1882 (98.94) |
| Accept health service‡ | |||
| Yes | 938 (46.23) | 53 (41.73) | 885 (46.50) |
| No | 1091 (53.77) | 74 (58.27) | 1017 (53.50) |
| MSW§† | |||
| Yes | 102 (5.03) | 5 (3.94) | 97 (5.10) |
| No | 1927 (94.97) | 122 (96.06) | 1805 (94.90) |
*The frequency of condom use during anal sex in the past 6 months was divided into two categories: consistent use (when engaging in anal intercourse, condoms were used in more than 80% of cases and condoms were used throughout the sex) and inconsistent use (other cases).
†In the past 6 months.
‡Health service represented whether participants had accepted any HIV-related health service (HIV testing, condom distribution, HIV risk-reducing consult, peer education or HIV infection risk assessment) in the past 12 months.
§MSW represented the participants who have been obtaining money or goods through sexual activity.
MSM, men who have sex with men; MSW, male sex worker; STI, sexually transmitted infection.
The total number of follow-up times of each participant was listed in online supplemental table 4. The median follow-up times were 3 times (range from 2 to 34, P25: 2; P75: 5). A total of 41.01% of participants had two follow-up times; 78.46% had follow-up times no more than five times. The percentage of participants with a total number of follow-up times more than 10 times was 7.15%.
HIV incidence rate
Among the whole study population, the overall HIV incidence rate was 3.36 per 100 PY (95% CI: 2.83 to 3.99).
Figure 1 and online supplemental table 5 showed the HIV incidence rates by years. The rates were relatively higher in 2013 (4.57 per 100 PY (95% CI: 3.31 to 6.31)), 2015 (4.65 per 100 PY (95% CI: 3.53 to 6.12)) and 2019 (5.51 per 100 PY (95% CI: 3.97 to 7.66)), though the rates did not show a linear trend significance during 2011–2019. Among age subgroups, incidence rates were as follows: <45 years, 3.83 per 100 PY (95% CI: 3.24 to 4.52); 45–60 years, 2.81 per 100 PY (95% CI: 2.14 to 3.69); >60 years, 2.08 per 100 PY (95% CI: 0.95 to 4.55) (online supplemental table 6).
Figure 1.
HIV incidence rates by year. When calculating the incidence rate, the numerator was the number of HIV infections in each year and the denominator was the sum of the total actual survival time of each participant in this year. The first people newly diagnosed with HIV occurred in 2012, though the study started in 2011.
To compare the HIV incidence rate among different behaviour transition types, participants were divided into subgroups according to the number of their total follow-up times. The calculation of the HIV incidence rate was done within each subgroup. Stratified by the behaviour type categories at baseline, the overall HIV incidence rates were as follows: ‘protective’, 3.14 per 100 PY (95% CI: 2.30 to 4.29); ‘moderate’, 2.92 per 100 PY (95% CI: 2.13 to 3.99); ‘fragile’, 4.02 per 100 PY (95% CI: 3.07 to 5.26) (online supplementary table 7). Overall, the HIV incidence rate has shown a general downward trend as the number of total follow-up times increased (two times: 4.88 (95% CI: 3.65 to 6.53); three times: 4.49 (95% CI: 3.06 to 6.59); four times: 2.89 (95% CI: 1.72 to 4.84); five times: 3.21 (95% CI: 1.74 to 5.91); six times and more: 2.27 (95% CI: 1.64 to 3.15)). MSM who had more visits showed a low risk of infection in comparison with those who had fewer visits. Within each subgroup, the HIV incidence rate tended to concentrate towards the lower right corner of the heatmap (ie, concentrated towards the behaviour type of ‘fragile’) (figure 2).
Figure 2.
Heatmap of HIV incidence rates among MSM with different follow-up times. MSM with two follow-up times represented the participants whose number of total follow-up times was 2, and so on. Participants were divided into subgroups according to the number of their total follow-up times. The calculation of HIV incidence rate was done within each subgroup. Values and colours indicated HIV incidence rate per 100 PY (for example, in (A), 0.37 that represented the HIV incidence rate in ‘protective’ to ‘protective’ group was 0.37 per 100 PY). The lighter colour indicates that the HIV incidence rate was lower for MSM in that subgroup, whereas the darker colour indicates a higher incidence rate in that subgroup. MSM, men who have sex with men; PY, person-year.
Time-varying behaviour type and risk of HIV infection
The risk of HIV infection was evaluated through Cox models with time-varying behaviour types. Compared with ‘protective’ behaviour type, ‘moderate’ behaviour type (aHR: 2.22, 95% CI: 1.05 to 4.71) and ‘fragile’ behaviour type (aHR: 16.53, 95% CI: 8.57 to 31.88) were associated with a greater risk of HIV infection. Likewise, there was a significant and linear increase in the risk of HIV infection for each additional time-varying risk indicator (aHR: 3.37, 95% CI: 2.76 to 4.11) (table 2).
Table 2.
Univariate and multivariable Cox proportional hazards model with time-varying covariates for HIV infection
| HIV infection risk models* | Per 1-risk indicator increase† | Sexual behaviour type | ||
| Protective | Moderate | Fragile | ||
| HR (95% CI) | 3.68 (3.03 to 4.46) | Reference | 2.38 (1.12 to 5.02) | 19.74 (10.28 to 37.91) |
| Adjusted HR (95% CI)‡ | 3.37 (2.76 to 4.11) | Reference | 2.22 (1.05 to 4.71) | 16.53 (8.57 to 31.88) |
*All follow-up records of each participant were included into the model in counting process format.
†The number of risk indicators was in the range of 0–3. Per 1-risk indicator increase meant that the number of indicators was included into the model as a continuous variable.
‡Multivariable Cox regression analysis was adjusted for age, education, marital status, residence time in Tianjin and MSW.
MSW, male sex worker.
Behaviour types in each visit
The percentage of each behaviour type (‘protective’, ‘moderate’ and ‘fragile’) at baseline was: 31.69% (643 of 2029), 37.60% (763 of 2029) and 30.70% (623 of 2029), respectively. As the number of follow-up visits increased, the percentage of ‘protective’ and ‘moderate’ behaviour types had a growth trend (35.06% and 50.18% at six and more visits, respectively), while the percentage of ‘fragile’ behaviour type declined (14.77% at six and more visits) (figure 3).
Figure 3.
Percentage of behaviour type in each visit. Values and area of the rectangles indicated percentage of behaviour type in each visit (for example, 31.69, which represented the percentage of ‘protective’ behaviour type, was 31.69% at baseline).
Behaviour changes and association with the risk of HIV infection from baseline to the last follow-up
The behaviour types at baseline and the last follow-up were selected to evaluate the behaviour changes (change in risk indicators: difference in risk indicators between baseline and the last follow-up; change in behaviour type: ‘behaviour types at baseline’ to ‘behaviour type in the last follow-up’) and the association with the risk of HIV infection. The choice of two time points was based on a main argument: the pattern of behaviour changes from baseline to each follow-up was relatively stable (details in online supplemental table 8 and online supplemental figure 2). The first and the last visits were good proxies for behaviour changes during follow-up.
In multivariable analysis, as ‘no change in indicators’ as the reference level, losing one risk indicator could decline the risk at 0.43 (95% CI: 0.21 to 0.90), while gained indicators were related to the increasing risk of HIV infection (gained one risk indicator: aHR: 2.67, 95% CI: 1.68 to 4.24); gained two or three risk indicators: aHR: 4.99, 95% CI: 3.00 to 8.31).
As for the different behaviour transition types, the risk of HIV infection was not statistically different from that of the consistently protective group (reference level) in the moderate to protective group (aHR: 0.65, 95% CI: 0.08 to 5.16), the consistently moderate group (aHR: 1.52, 95% CI: 0.31 to 7.41), the fragile to protective group (aHR: 2.87, 95% CI: 0.66 to 12.46) and the fragile to moderate group (aHR: 2.66, 95% CI: 0.61 to 11.53). Instead, the risk of HIV infection was distinctly greater in the protective to moderate group (aHR: 4.79, 95% CI: 1.18 to 19.47), the protective to fragile group (aHR: 23.03, 95% CI: 6.02 to 88.13), the moderate to fragile group (aHR: 25.48, 95% CI: 6.79 to 95.40) and the consistently fragile group (aHR: 25.86, 95% CI: 6.92 to 96.57) (table 3 and online supplemental figure3).
Table 3.
Behaviour changes and association with the risk of HIV infection between baseline and the last follow-up
| Behaviour changes* | HIV infection status (N, %) | Univariate Cox regression analysis | Multivariable Cox regression analysis† | |||||
| Negative | HIV | HR | 95% CI | P value | aHR | 95% CI | P value | |
| Change in risk indicators | ||||||||
| Lost 2–3 indicators | 236 (12.4) | 7 (5.5) | 0.56 | 0.25 to 1.27 | 0.1680 | 0.57 | 0.25 to 1.29 | 0.1822 |
| Lost 1 indicator | 525 (27.6) | 9 (7.1) | 0.41 | 0.19 to 0.86 | 0.0182 | 0.43 | 0.21 to 0.90 | 0.0251 |
| No change in indicators | 725 (38.1) | 30 (23.6) | Reference | Reference | ||||
| Gained 1 indicator | 325 (17.1) | 48 (37.8) | 2.82 | 1.78 to 4.46 | 0.0001 | 2.67 | 1.68 to 4.24 | 0.0001 |
| Gained 2–3 indicators | 91 (4.8) | 33 (26.0) | 6.13 | 3.73 to 10.08 | 0.0001 | 4.99 | 3.00 to 8.31 | 0.0001 |
| Behaviour transition type | ||||||||
| Consistently protective | 318 (16.7) | 2 (1.6) | Reference | Reference | ||||
| Protective to moderate | 210 (11.0) | 11 (8.7) | 4.78 | 1.18 to 19.26 | 0.0275 | 4.79 | 1.18 to 19.47 | 0.0283 |
| Protective to fragile | 77 (4.0) | 25 (19.7) | 28.76 | 7.66 to 108.05 | 0.0001 | 23.03 | 6.02 to 88.13 | 0.0001 |
| Moderate to protective | 301 (16.0) | 1 (0.8) | 0.64 | 0.08 to 5.03 | 0.6703 | 0.65 | 0.08 to 5.16 | 0.6912 |
| Consistently moderate | 315 (16.6) | 4 (3.1) | 1.62 | 0.33 to 7.84 | 0.5449 | 1.52 | 0.31 to 7.41 | 0.6019 |
| Moderate to fragile | 109 (5.7) | 33 (26.0) | 34.32 | 9.27 to 127.06 | 0.0001 | 25.48 | 6.79 to 95.40 | 0.0001 |
| Fragile to protective | 219 (11.5) | 7 (5.5) | 2.95 | 0.68 to 12.67 | 0.1457 | 2.87 | 0.66 to 12.46 | 0.1590 |
| Fragile to moderate | 237 (12.5) | 7 (5.5) | 3.06 | 0.71 to 13.17 | 0.1316 | 2.66 | 0.61 to 11.53 | 0.1906 |
| Consistently fragile | 116 (6.1) | 37 (29.1) | 31.43 | 8.53 to 115.75 | 0.0001 | 25.86 | 6.92 to 96.57 | 0.0001 |
| Total | 1902 | 127 | ||||||
*Two time points (baseline and the last follow-up) were opted to evaluate behaviour changes.
†Multivariable Cox regression analysis was adjusted for age, education, marital status, residence time in Tianjin and MSW.
aHR, adjusted HR; MSW, male sex worker.
Influencing factors for progression to behaviour type of ‘fragile’ or ‘moderate’ in the last follow-up
We also investigated which influencing factors were related to the progression of behaviour transition type of ‘fragile’ or ‘moderate’ by the end of follow-up among baseline behaviour type subgroups.
Within age subgroups, the younger participants had a higher likelihood of progression to behaviour type of ‘fragile’ or ‘moderate’ by the end of follow-up.
Notably, the likelihood of progression to behaviour type of ‘fragile’ or ‘moderate’ was weakened as the number of total follow-up times increased (per 1-time increase in the total follow-up times: aOR=0.95, 95% CI: 0.91 to 0.99) among participants ‘fragile’ at baseline.
Compared with participants who lived in Tianjin for less than 3 months, participants who lived in Tianjin for more than 2 years had a lower likelihood of progression to behaviour type of ‘fragile’ or ‘moderate’ by the end of follow-up (aOR: 0.65, 95% CI: 0.37 to 0.87) (online supplemental table 9).
Discussion
The characteristics of MSM are mainly high mobility and concealment. It was a challenge to ensure long-term cohort retention and sustain routine HIV test programmes among this population. Therefore, we conducted an open cohort study among MSM in Tianjin, China. A total of 2029 MSM were included in the study eventually from 2011 to 2019. Among the whole study population, the overall HIV incidence rate was 3.36 per 100 PY (95% CI: 2.83 to 3.99). The incidence rate was at a lower level. Previous studies conducted in other cities in China had shown a pretty high HIV incidence rate among MSM: 6.6 per 100 PY in Hangzhou,34 6.78 per 100 PY in Yangzhou, 5.77 per 100 PY in Guangzhou,35 5.3 per 100 PY in Shenyang,36 5.12 per 100 PY in Nanjing,37 3.5 per 100 PY in Yunnan38 and 7.1 per 100 PY in Beijing.39 The incidence is also at a low level compared with related studies in other regions (sub-Saharan Africa,40 Latin America41 and the USA42–44). In this study, the percentage of ‘protective’ and ‘moderate’ behaviour types had a conspicuous growth trend as the follow-up. Furthermore, the HIV incidence rate in each visit among different behaviour transition types showed a general downward trend as the number of total follow-up times increased. The evidence mentioned above indicated the effectiveness of the community-based VCT project in reducing HIV incidence. However, challenges still exist and need to be addressed. The incidence rate in 2019 was much higher than in other years (5.51 per 100 PY (95% CI: 3.97 to 7.66)). We speculated that the reason might be the publicity and promotion of PrEP/PEP started in 2018 among the MSM population in Tianjin. PrEP/PEP were effective biomedical strategies to prevent the further transmission of HIV.23 24 Although the utilisation of PrEP/PEP was not widespread among MSM in Tianjin, this might promote the cognitive concepts of ‘treatment optimism’ about HIV.45 MSM might rationalise their risk behaviour before (or after) engaging in sexual behaviours,46 which promoted the generation of risk behaviours.47 48
Previous studies concentrate on using one standalone indicator to describe the behaviour change. For example, one study in China49 used the percentage of UAI occurrences to describe the behaviour change. However, this study evaluated behaviour change with greater precision (behaviour changes were collectively defined by three indicators and divided into two patterns: change in the number of risk indicators and behaviour transition type) among a great sample size (N=2029) in a long-term follow-up (2011–2019). What is more, this study quantified the behaviour changes and association with the risk of HIV infection from baseline to the last follow-up. The results of this study showed that individuals who remained ‘fragile’ or changed from ‘protective’ to ‘moderate’, ‘protective’ to ‘fragile’ and ‘moderate’ to ‘fragile’ between baseline and the last follow-up had a higher HIV risk as compared with individuals with persistently ‘protective’ behaviour type. When this analysis was conducted in risk indicators, similar results were found. Gained risk indicators were associated with the increase of HIV risk while losing just one risk indicator could halve the risk (aHR: 0.43). This study demonstrated the importance of maintaining protective sexual practices and timely behaviour changes in high-incidence population that could help reduce the risk of HIV infection.
The cohort had a high rate of loss to follow-up (for 4096 MSM only one visit record), which may have biased the results. Indeed, the complexity of the MSM population made maintaining a long-term follow-up cohort difficult. This study was based on an open cohort whose main purpose was to cover as many MSM as possible. Whether or not a participant will proceed to the next visit is entirely driven by their personal endogenous motivation, although messages were sent to MSM 90 days after their testing to remind them to perform another HIV testing. In the future, plans of following research including recruiting more staff are under consideration. Special staff will conduct regular return visits and urge MSM to carry out routine HIV testing.
Limitations
This research has several limitations. First, in our study, the snowball sampling method was used to recruit participants, which may have selection bias. However, we recruited participants in a variety of ways (gay bars, gay bathhouses, social network sites, gay apps and peer referrals) to increase the representativeness. Second, our data lacked information on the use of alcohol, recreational drugs and PrEP usage. The presence of these substances might affect the sexual behaviour of participants, which should be addressed in future research. Further, our data collection adopted a real-name registration system (ID card number, mobile phone number and fingerprint information were involved). Due to the privacy of some questions (such as whether you had ever had STI), the participants might have concealed the actual situation, which may result in social expectation bias. We conducted an interview-style questionnaire collection to avoid this. Besides, all MSM investigators had received professional training to ensure the authenticity of the data.
Conclusion
In this study, sexual behaviour changes are collectively defined by three indicators (condom use in last anal sex, frequency of condom use during anal sex and the number of sexual partners) related to participants’ sexual behaviour in the past 6 months. Behaviour changes from 2011 to 2019 and ensuing HIV infection risk are calculated. Long-term sexual behaviour patterns of MSM keep changing and gradually have a tendency to be less risky during the study period. The proportion of high-risk behaviours is 30.70% at baseline. This number declined to 14.77% at six and more visits. This result may provide clues for the effectiveness of community-based HIV/AIDS interventions.
Transition from low-incidence to high-incidence population is associated with a similar risk of HIV infection than continuing high incidence. Further strategies are needed to promote the low incidence change.
MSM should sustain low-incidence behaviour patterns otherwise would undergo higher HIV infection risk. Future HIV interventions should be prioritised not only to those MSM who were part of the high-incidence population, but also to those MSM who have been in the temporarily low-incidence population but are at risk of being in the high-incidence population.
Supplementary Material
Footnotes
Contributors: ZY, CL, ZC, YL and JM contributed to the conception of the study. HH, T-TZ and DS contributed to the framework of the study. MY and JY collected the data. QC and XW cleaned the raw data. HH and HZ helped perform the analysis with constructive discussions. T-TZ and ZY analysed the data and wrote the manuscript. ZC revised the manuscript and was responsible for the overall content as guarantor.
Funding: This work was funded by the Humanities and Social Sciences Fund of the Ministry of Education, China (20YJAZH021) and Tianjin Health Science and Technology Project, Chian (No.ZC20022). It was also supported by the President’s Emergency Plan For Aids Relief (PEPFAR), USA (NU2GGH001153) through Tianjin Centers for Disease Control and Prevention (CDC).
Competing interests: None declared.
Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review: Not commissioned; externally peer reviewed.
Supplemental material: This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.
Data availability statement
No data are available.
Ethics statements
Patient consent for publication
Not required.
Ethics approval
This study involves human participants and was approved by the Institutional Review Board (IRB) of the National Center for AIDS/STD Control and Prevention, China CDC (IRB approval number: X130205267). Participants gave informed consent to participate in the study before taking part.
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Associated Data
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Supplementary Materials
bmjopen-2021-055046supp001.pdf (1.2MB, pdf)
Data Availability Statement
No data are available.