Abstract
Background
Antiretroviral therapy (ART) has reduced mortality among people living with HIV (PLWH) in China, but co-infections of hepatitis B virus (HBV) and hepatitis C virus (HCV) may individually or jointly reduce the effect of ART. This study aimed to evaluate the impacts of HBV/HCV coinfections on treatment drop-out and mortality among PLWH on ART.
Methods
A retrospective cohort study analysis of 58 239 people living with HIV (PLWH) who initiated antiretroviral therapy (ART) during 2010–2018 was conducted in Guangxi Province, China. Data were from the observational database of the National Free Antiretroviral Treatment Program. Cox proportional hazard models were fitted to evaluate the effects of baseline infection of HBV or HCV or both on death and treatment attrition among PLWH.
Results
Our study showed high prevalence of HBV (11.5%), HCV (6.6%) and HBV-HCV (1.5%) co-infections. The overall mortality rate and treatment attrition rate was 2.95 [95% confidence interval (CI) 2.88–3.02] and 5.92 (95% CI 5.82–6.01) per 100 person-years, respectively. Compared with HIV-only patients, HBV-co-infected patients had 42% higher mortality [adjusted hazard ratio (aHR) = 1.42; 95% CI 1.32–1.54], HCV-co-infected patients had 65% higher mortality (aHR = 1.65; 95% CI 1.47–1.86), and patients with both HCV and HBV co-infections had 123% higher mortality (aHR = 2.23; 95% CI 1.87–2.66).
Conclusions
HBV and HCV coinfection may have an additive effect on increasing the risk of all-cause death among PLWH who are on ART. It is suggested that there is need for primary prevention and access to effective hepatitis treatment for PLWH.
Graphical Abstract
Keywords: Hepatitis C virus; Hepatitis B virus; HIV; Antiretroviral therapy; Mortality, Retrospective cohort
Background
Highly active antiretroviral therapy (ART) has reduced deaths among people living with HIV (PLWH) in China and globally [1–3]. However, the effectiveness of ART depends on a variety of factors. Studies have shown that comorbid hepatitis B virus (HBV) and hepatitis C virus (HCV) infections could have negative impacts on HIV treatment outcomes, but few studies have assessed their individual and joint effects simultaneously [4–6]. HBV infection may accelerate the development of AIDS with HBV X proteins upregulating HIV replication and transcription by synergizing with kappa B-like enhancers and T-cell activation signals [7, 8]. HBV or HCV coinfection is associated with a higher level of hepatic fibrosis, which may impact the liver’s detoxification function [6, 9–11]. Since some ART drugs have liver toxicity, coinfection of HBV and HCV is a significant risk factor for death in PLWH.
Hepatitis B is endemic in China. Meta analyses showed that the prevalence of HBV infection was around 7% [12] among the adult general population of China and was double (13.7%) among people living with HIV (PLWH) [13]. Additionally, the prevalence of anti-HCV antibodies was lower among the general population (0.9%) [14], but higher among PLWH (24.7%) [13]. Triple infection of HBV, HCV and HIV occurred in about 3.5% of the population [13].
Though coinfections of HBV and HCV are common among PLWH, data on their effects on HIV treatment outcomes in China are sparse. We performed a retrospective cohort study analysis to evaluate the impacts of HBV/HCV coinfections on treatment drop-out and mortality among PLWH on ART in southwestern China.
Methods
Study design and study participants
This study was designed as a retrospective cohort analysis of HIV treatment data in the Guangxi Zhuang Autonomous Region in southwest China. As of October 2020, Guangxi represented 9.3% of the total number of nationally reported HIV/AIDS cases, and this region has accumulated the third highest number of HIV cases reported in China. Sexual transmission accounted for more than 95% of reported cases in Guangxi.
The data were from the observational database of the National Free Antiretroviral Treatment Program (NFATP) of China. The study subjects were HIV patients who received free ART between 2010 and 2018 through NFATP. Physicians administering the ART at the local hospitals managed case report forms at the time of initiating ART and follow-up at 0.5, 1, 2 and 3 months, and every 3 months thereafter. The case report forms were uploaded into a web-based database hosted by Chinese Center for Disease Control (China CDC). Eligibility criteria for the subjects of this study were: (1) HIV patients who initiated free ART between 2010 and 2018; (2) at least 18 years old; (3) tested for HBV or HCV; (4) provided informed consent. The researchers in the Guangxi Province CDC have access to all records in the NFATP for patients who lived in Guangxi Province.
Chinese free ART eligibility criteria have gone through several phases: From 2008, PLWH with CD4 cell counts lower than 350 cells/mm3 were eligible for treatment; since 2014, the treatment threshold was CD4 counts below 500; and since 2016, China has provided free ART for all PLWH regardless of CD4 count. Currently, first-line regimens for free ART in China are tenofovir (TDF) or azidothymidine (AZT) + lamivudine (3TC) + efavirenz (EFV) or nevirapine (NVP). Second-line regimens are TDF + 3TC + EFV or lopinavir/ritonavir (LPV/r).
Data collection
Information about HIV patients in the electronic database NFATP includes two parts: baseline data and follow-up data. Baseline data included demographics such as age, sex, marital status and clinical characteristics such as route of HIV transmission, CD4 count (cells/mm3) before ART, WHO clinic stage before ART, initial first-line ART regimen, current ART regimen and calendar year of ART initiation. Follow-up data included transferal to another clinic, cessation of ART, loss to follow-up, duration of ART, and survival status. HBV infection was tested by finding Hepatitis B surface antigens (HBsAg) and HCV infection was tested by finding antibodies of HCV.
Statistical analysis
We conducted a prospective follow-up study analysis. Time zero was defined as the date of ART initiation, and data was censored on December 31, 2019. Outcome variables included death and ART attrition. Survival status was recorded as censored if patients were still alive or transferred to another clinic. Attrition was defined as cessation of ART and loss to follow-up. Loss to follow-up or withdrawal of ART was defined as missing visits more than 90 days after the last record in a clinic. Incidence rates of mortality and attrition were calculated based on Poisson distribution and reported as the number of deaths and attritions per 100 person-years, respectively.
Cox proportional hazard models were used to evaluate the effects of baseline infection of HBV or HCV or both on death and attrition among PLWH. Competing risks for cause-specific hazard models were censored accordingly [20, 21]. Potential confounders were controlled by adjusting the model with the following baseline covariates: age, sex, marital status, route of HIV transmission, baseline CD4 count, WHO clinical stage before ART, initial first-line regimen, current ART regimen, duration of tenofovir disoproxil fumarate (TDF)-containing ART regimens, and calendar year of ART initiation.
Statistical significance was determined to have a two-sided P ≤ 0.05. All the statistical analyses were performed using SAS V9.1 (SAS Institute Inc., Cary, NC, USA).
Results
Baseline characteristics of study patients
As of December 31, 2019, 79,245 PLWH initiated free ART between 2010 and 2018 in Guangxi, China. Excluding 291 patients under 18 years old, two without follow-up data and 20,713 without HBV and HCV testing results, a total of 58,239 individuals were eligible and included in the analysis (Fig. 1). Of those included participants, 12% died, 16% were lost to follow-up, 8% dropped out of treatment and 64% were active on treatment by the end of follow-up.
Fig. 1.
Flow chart of study sample selection. ART Antiretroviral therapy
The baseline characteristics of the study patients are shown in Table 1; 6,707 (11.5%) participants had HIV-HBV co-infection, 3,828 (6.6%) had HIV-HCV co-infection, 857 (1.5%) and had triple infection. Two fifths (40.9%) of patients were over 50 years old; 68.3% were male and 63.7% were married. The majority (87.3%) of patients were infected through heterosexual intercourse, followed by homosexual intercourse (5.9%), intravenous drug use (4.9%) and other causes (1.9%). Prior to ART initiation, 59.8% of the patients had CD4 counts ≤ 350 cells/mm3, and 5.9% of the patients were classified as WHO clinical stage III or IV. Patients with initial ART regimens of stavudine (D4T)-based, azidothymidine (AZT)-based, tenofovir disoproxil fumarate (TDF)-based and lopinavir-ritonavir (LPV/r)-based accounted for 8.5%, 33.7%, 46.8% and 10.1% of all patients, respectively. Most patients (78.7%) used first-line ART regimens, 21.3% used second-line regimens, and 43.1% used TDF-based ART regimens for more than 2 years.
Table 1.
Baseline characteristics of HIV patients who initiated ART between 2010 and 2018 in Guangxi, China
| Variable | Total | % | HIV only | % | HIV-HBV co-infection | % | HIV-HCV co-infection | % | HIV-HBV-HCV Triple infection | % |
|---|---|---|---|---|---|---|---|---|---|---|
| Total | 58,239 | 100.0 | 46,847 | 100 | 6,707 | 100.0 | 3,828 | 100.0 | 857 | 100.0 |
| Age, years | ||||||||||
| 18–50 | 34,424 | 59.1 | 25,731 | 54.9 | 4,504 | 67.2 | 3,404 | 88.9 | 785 | 91.6 |
| ≥ 50 | 23,815 | 40.9 | 21,116 | 45.1 | 2,203 | 32.8 | 424 | 11.1 | 72 | 8.4 |
| Sex | ||||||||||
| Male | 39,754 | 68.3 | 31,097 | 66.4 | 4,798 | 71.5 | 3,110 | 81.2 | 749 | 87.4 |
| Female | 18,485 | 31.7 | 15,750 | 33.6 | 1,909 | 28.5 | 718 | 18.8 | 108 | 12.6 |
| Marital status | ||||||||||
| Married | 37,104 | 63.7 | 30,197 | 64.5 | 4,306 | 64.2 | 2,136 | 55.8 | 465 | 54.3 |
| Other | 21,135 | 36.3 | 16,650 | 35.5 | 2,401 | 35.8 | 1,692 | 44.2 | 392 | 45.7 |
| Route of HIV transmission | ||||||||||
| Heterosexual intercourse | 50,836 | 87.3 | 42,911 | 91.6 | 6,118 | 91.2 | 1,480 | 38.7 | 327 | 38.2 |
| Homosexual intercourse | 3,455 | 5.9 | 534 | 1.1 | 152 | 2.3 | 2,257 | 59.0 | 512 | 59.7 |
| Intravenous drug use | 2,827 | 4.9 | 2,488 | 5.3 | 297 | 4.4 | 36 | 0.9 | 6 | 0.7 |
| Other | 1,121 | 1.9 | 914 | 2.0 | 140 | 2.1 | 55 | 1.4 | 12 | 1.4 |
| CD4 count before ART, cells/mm3 | ||||||||||
| ≤ 350 | 34,837 | 59.8 | 28,133 | 60.1 | 3,853 | 57.4 | 2,345 | 61.3 | 506 | 59.0 |
| > 350 | 23,402 | 40.2 | 18,714 | 39.9 | 2,854 | 42.6 | 1,483 | 38.7 | 351 | 41.0 |
| WHO clinical stage before ART | 47,524 | 81.6 | 38,079 | 81.3 | 5,513 | 82.2 | 3,187 | 83.3 | 745 | 86.9 |
| I/II | 7,268 | 12.5 | 5,937 | 12.7 | 807 | 12.0 | 443 | 11.6 | 81 | 9.5 |
| III/IV | 3,447 | 5.9 | 2,831 | 6.0 | 387 | 5.8 | 198 | 5.2 | 31 | 3.6 |
| Initial first-line ART regimen | ||||||||||
| ART containing D4T | 4,966 | 8.5 | 4,011 | 8.6 | 412 | 6.1 | 430 | 11.2 | 113 | 13.2 |
| ART containing AZT | 19,621 | 33.7 | 17,208 | 36.7 | 1,027 | 15.3 | 1,176 | 30.7 | 210 | 24.5 |
| ART containing TDF | 27,246 | 46.8 | 20,270 | 43.3 | 4,634 | 69.1 | 1,881 | 49.1 | 461 | 53.8 |
| ART containing LPV/r | 5,863 | 10.1 | 4,862 | 10.4 | 610 | 9.1 | 319 | 8.3 | 72 | 8.4 |
| Other | 543 | 0.9 | 496 | 1.1 | 24 | 0.4 | 22 | 0.6 | 1 | 0.1 |
| Current ART regimen | ||||||||||
| First-line ART | 45,854 | 78.7 | 36,743 | 78.4 | 5,311 | 79.2 | 3,107 | 81.2 | 693 | 80.9 |
| Second-line ART | 12,385 | 21.3 | 10,104 | 21.6 | 1,396 | 20.8 | 721 | 18.8 | 164 | 19.1 |
| Duration of TDF -containing regimens | ||||||||||
| ≤ 2 years | 33,115 | 56.9 | 27,865 | 59.5 | 2,740 | 40.9 | 2,088 | 54.5 | 422 | 49.2 |
| > 2 years | 25,124 | 43.1 | 18,982 | 40.5 | 3,967 | 59.1 | 1,740 | 45.4 | 435 | 50.8 |
| Calendar year of ART initiation | ||||||||||
| 2010 | 3,515 | 6.0 | 2,518 | 5.4 | 437 | 6.5 | 425 | 11.1 | 135 | 15.8 |
| 2011 | 4,982 | 8.6 | 3,839 | 8.2 | 604 | 9.0 | 414 | 10.8 | 125 | 14.6 |
| 2012 | 6,226 | 10.7 | 4,876 | 10.4 | 700 | 10.4 | 537 | 14.0 | 113 | 13.2 |
| 2013 | 6,384 | 11.0 | 5,057 | 10.8 | 738 | 11.0 | 487 | 12.7 | 102 | 11.9 |
| 2014 | 7,290 | 12.5 | 5,755 | 12.3 | 883 | 13.2 | 558 | 14.6 | 94 | 11.0 |
| 2015 | 8,016 | 13.8 | 6,524 | 13.9 | 954 | 14.2 | 450 | 11.8 | 88 | 10.3 |
| 2016 | 7,421 | 12.7 | 6,170 | 13.2 | 823 | 12.3 | 349 | 9.1 | 79 | 9.2 |
| 2017 | 7,164 | 12.3 | 5,954 | 12.7 | 830 | 12.4 | 315 | 8.2 | 65 | 7.6 |
| 2018 | 7,241 | 12.4 | 6,154 | 13.1 | 738 | 11.0 | 293 | 7.7 | 56 | 6.4 |
ART, Antiretroviral therapy; AZT, Zidovudine; D4T, Stavudine; LPV/r, Lopinavir-ritonavir; TDF, Tenofovir; HBV, Hepatitis B virus; HCV, Hepatitis C virus; HIV, Human immunodeficiency virus
Impact of HBV and HCV co-infections on death among PLWH who initiated ART
The unadjusted and adjusted effects of HBV and HCV co-infections on death are shown in Table 2. Among 58,239 patients who initiated ART between 2010 and 2018, 6,916 deaths were observed, and the overall mortality rate was 2.95 per 100 person-years [95% confidence interval (CI) 2.88–3.02]. The crude mortality rate was 2.86% in HIV-only patients, 2.84% in HBV-coinfected, 3.89% in HCV-coinfected and 4.66% in HBV/HCV-coinfected HIV patients. Multivariate cox models showed that compared with patients with HIV infection only, HBV co-infected patients had a 42% higher risk of death [adjusted hazard ratio (aHR) = 1.42; 95% CI 1.32–1.54; P < 0.001); HCV co-infected patients had a 65% higher risk (aHR = 1.65; 95% CI 1.47–1.86; P < 0.001); and patients with both HBV and HCV coinfections had a 123% higher risk (aHR = 2.23; 95% CI 1.87–2.66; P < 0.001). The increase of death risk among patients with triple infection (123%) approximately equals to the sum of increases in death among PLWH with co-HBV (42%) and those with co-HCV (65%) infection. There is an additive interaction between HBV- and HCV-co-infection on mortality among PLWH.
Table 2.
Effect of HBV and HCV co-infections on death among HIV patients who initiated ART between 2010 and 2018 in Guangxi, China
| Coinfection | Number of HIV patients | Deaths | Person-years (PY) | Mortality rate per 100 person-years (95% CI) | HR (95% CI) | P value | aHRa (95% CI) | P value |
|---|---|---|---|---|---|---|---|---|
| Total | 58,239 | 6,916 | 234,421.19 | 2.95 (2.88–3.02) | ||||
| HIV only | 46,847 | 5,366 | 187,680.8 | 2.86 (2.78–2.93) | Reference | Reference | ||
| HIV + HBV | 6,707 | 797 | 28,092.09 | 2.84 (2.65–3.03) | 0.99 (0.92–1.07) | 0.784 | 1.42 (1.32–1.54) | < 0.001 |
| HIV + HCV | 3,828 | 590 | 15,148.30 | 3.89 (3.59–4.20) | 1.35 (1.24–1.47) | < 0.001 | 1.65 (1.47–1.86) | < 0.001 |
| HIV + HBV + HCV | 857 | 163 | 3,500.00 | 4.66 (3.96–5.35) | 1.60 (1.37–1.87) | < 0.001 | 2.23 (1.87–2.66) | < 0.001 |
CI, Confidence interval; HR, Hazard ratio; aHR, Adjusted hazard ratio; HBV, Hepatitis B virus; HCV, Hepatitis C virus; HIV, Human immunodeficiency virus
aAdjusted for Age, gender, marital status, route of HIV transmission, CD4 count before ART, WHO clinical stage before ART, initial first-line ART regimen, current ART regimen, duration of using TDF-containing regimens, calendar year of ART initiation
Impact of HBV and HCV co-infections on treatment attrition among PLWH who initiated ART
The unadjusted and adjusted effects of HBV and HCV co-infections on treatment attrition are presented in Table 3. Among 58,329 patients, 13,872 patients dropped out from the treatment including 9,107 patients lost to follow-up and 4,765 stopping ART. The overall drop-out rate was 5.92 (95% CI 5.82–6.01) per 100 person-years. The crude drop-out rate was 5.42% in HIV-only patients, 5.13% in HBV-coinfected, 12.03% in HCV-coinfected and 12.51% in HBV/HCV-coinfected HIV patients. Multivariate cox models showed that compared to HIV-only patients, HBV co-infected patients were 34% more likely to drop out of treatment (aHR = 1.34; 95% CI 1.27–1.42; P < 0.001); HCV co-infected patients had a 73% increased risk (aHR = 1.73; 95% CI 1.61–1.87; P < 0.001); patients with both HBV and HCV co-infections had a 107% increased risk (aHR = 2.07; 95% CI 1.85–2.31; P < 0.001). The increase of attrition risk among patients with triple infection (107%) equals to the sum of increases in treatment attrition among PLWH with co-HBV (34%) and those with co-HCV (73%) infection. There is an additive interaction between HBV- and HCV-co-infection on treatment attrition among PLWH.
Table 3.
Effect of HBV and HCV co-infections on ART attrition among HIV patients who initiated ART between 2010 and 2018 in Guangxi, China
| Variables | Number of HIV patients | Attritions | Person-years (PY) | Attrition rate per 100 person-years (95% CI) | HR (95% CI) | P value | aHRa (95% CI) | P value |
|---|---|---|---|---|---|---|---|---|
| Total | 58,239 | 13,872 | 234,421.19 | 5.92 (5.82–6.01) | ||||
| HIV | 46,847 | 10,169 | 187,680.8 | 5.42 (5.32–5.52) | Reference | Reference | ||
| HIV + HBV | 6,707 | 1,442 | 28,092.09 | 5.13 (4.87–5.39) | 0.95 (0.90–1.01) | 0.074 | 1.34 (1.27–1.42) | < 0.001 |
| HIV + HCV | 3,828 | 1,823 | 15,148.30 | 12.03 (11.50–12.57) | 2.22 (2.11–2.33) | < 0.001 | 1.73 (1.61–1.87) | < 0.001 |
| HIV + HBV + HCV | 857 | 438 | 3500.00 | 12.51 (11.37–13.66) | 2.31 (2.10–2.54) | < 0.001 | 2.07 (1.85–2.31) | < 0.001 |
CI, Confidence interval; HR, Hazard ratio; aHR, Adjusted hazard ratio; HBV, Hepatitis B virus; HCV, Hepatitis C virus; HIV, Human immunodeficiency virus
aAdjusted for Age, gender, marital status, route of HIV transmission, CD4 count before ART, WHO clinical stage before ART, initial first-line ART regimen, current ART regimen, duration of using TDF-containing regimens, calendar year of ART initiation
Discussion
Our study confirmed the previous study finding that the Chinese national free ART program has significantly reduced HIV related mortality in China [15, 16]. The overall mortality rate in our study sample who started ART between 2010 and 2018 in Guangxi, China, was as low as 2.95 per 100 person-years. HBV and HCV co-infection could independently increase mortality. This is consistent with findings among the Asia–Pacific PLWH population [17]. In addition, co-infection with both HCV and HBV had an additive effect on the risk of death among PLWH.
Studies have shown that in China there is a high prevalence of HCV infection among people who inject drugs [18, 19], and injection drug use (IDU) is associated with faster HIV disease progression and increased risk of death [20, 21]. IDU is unlikely to explain the association between HCV infection and risk of death in our study, as only 4.8% of our study sample were PWID and IDU was adjusted for in assessing the association. HCV may cause hepatic fibrosis and reduce liver detoxification function [9, 10], which reduces patients’ tolerance to side effects of ART drugs and therefore increases HIV treatment drop-out and increases mortality. Our study also showed that patients with HCV co-infection were more likely to have treatment attrition than those without any co-infection, and this might be one factor explaining for the increased risk of death among PLWHI with HCV-co-infection.
Our study has limitations. First, HCV status was assessed by antibody testing in this study, and a positive HCV antibody test might indicate past or current infection. However, most HCV infections could become chronic as there was virtually no treatment for HCV patients in China during the study period, a positive HCV antibody test is a good indicator of HCV infection status. The misclassification of HCV infection to non-infection might be possible among a small proportion of infected individuals might experience spontaneous clearance after acute infection, but it could lead to bias toward a reduced effect size of HCV infection on ART attrition or death. Second, we assessed all-cause mortality rather than HIV-related mortality. Other potential confounders such as alcohol use and tuberculosis coinfection were not assessed and adjusted in the analysis, and they may have impact on the mortality. In addition, accounting for only HIV-related mortality would probably result in more accurate data about the effectiveness of HIV treatment and the adverse impact of HBV/HCV co-infection. Third, our study sample did not include PWID who had not started ART. About two-thirds of diagnosed PLWH in China have used ART [22]. Our study findings among those on ART may not be extrapolated to the one-third of PLWH who were not on ART. In addition, the transmission mode of the participants was dominated by heterosexual intercourse in Guangxi (87.3%, Table 1), while about two thirds of cases were from heterosexual transmission up to the end of 2015 across China. The mode of HIV transmission may affect the relationship between HBV/HCV co-infection and HIV treatment outcome. However, our analyses were adjusted for HIV transmission route. Therefore, HIV transmission mode may not affect the generalizability of our study finding.
Studies have assessed the individual effects of HCV or HBV co-infection on HIV treatment outcomes, but few studies have ever assessed the joint effect. Our study provided important evidence on both individual and joint impacts of HCV and HBV co-infections on mortality among PLWH. Antiviral drugs such as Mavyret® (glecaprevir and pibrentasvir) have revolutionized the treatment of hepatitis C, and it can cure the disease. However, these drugs are not included in the Chinese free ART program, and most patients with HCV infection have no access to these new medications. Considering high prevalence of HCV and HBV co-infections and their adverse effect on HIV treatment, the national HIV free ART program should incorporate screening and treatment for HCV and HBV infections.
Conclusions
This cohort study showed that both HBV and HCV coinfection was associated with higher mortality and treatment attrition among PLWH who were on ART. There is need for primary prevention and effective hepatitis treatment among PLWH.
Acknowledgements
None.
Abbreviations
- HBV
Hepatitis B virus
- HCV
Hepatitis C virus
- HIV
Human immunodeficiency virus
- ART
Antiretroviral therapy
- PLWH
People living with HIV
- IDU
Injection drug use
- WHO
World Health Organization
- NFATP
The National Free Antiretroviral Treatment Program
- aHR
Adjusted hazard ratio
- CI
Confidence interval
Authors' contributions
YR, JZ and HZQ designed the study. QZ, GL, HC, ZS and JL collected the data. JJ, QZ, LD, YR, JZ and HZQ analysed the data. JJ, QZ, LD, AJ and HZQ draft the manuscript. All authors interpreted the results and revised the manuscript. All authors read and approved the final manuscript.
Funding
This work was supported by National Natural Science Foundation of China [82160636, 11971479, 31871329], Guangxi Natural Science Foundation Project (Grants 2020GXNSFAA159020), Guangxi Key Laboratory of AIDS Prevention Control and Translation [ZZH2020010], Guangxi Bagui Honor Scholarship, and Chinese State Key Laboratory of Infectious Disease Prevention and Control.
Availability of data and materials
The datasets are available from the corresponding authors on reasonable request.
Declarations
Ethics approval and consent to participate
The institutional review board of Guangxi Province CDC had reviewed and approved use of deidentified data from the NFATP observational database before conducting the study.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Footnotes
Jingya Jia, Qiuying Zhu and Luojia Deng contributed equally to this work
Contributor Information
Jinhui Zhu, Email: gxzhujinhui@qq.com.
Han-Zhu Qian, Email: han-zhu.qian@yale.edu.
References
- 1.Zhang F, Dou Z, Ma Y, Zhang Y, Zhao Y, Zhao D, et al. Effect of earlier initiation of antiretroviral treatment and increased treatment coverage on HIV-related mortality in China: a national observational cohort study. Lancet Infect Dis. 2011;11(7):516–524. doi: 10.1016/S1473-3099(11)70097-4. [DOI] [PubMed] [Google Scholar]
- 2.Zhang F, Dou Z, Ma Y, Zhao Y, Liu Z, Bulterys M, et al. Five-year outcomes of the China national free antiretroviral treatment program. Ann Intern Med. 2009;151(4):241–251. doi: 10.7326/0003-4819-151-4-200908180-00006. [DOI] [PubMed] [Google Scholar]
- 3.Crum NF, Riffenburgh RH, Wegner S, Agan BK, Tasker SA, Spooner KM, et al. Comparisons of causes of death and mortality rates among HIV-infected persons: analysis of the pre-, early, and late HAART (highly active antiretroviral therapy) eras. J Acquir Immune Defic Syndr. 2006;41(2):194–200. doi: 10.1097/01.qai.0000179459.31562.16. [DOI] [PubMed] [Google Scholar]
- 4.Zhu J, Yang W, Feng Y, Lo C, Chen H, Zhu Q, et al. Treatment effects of the differential first-line antiretroviral regimens among HIV/HBV co-infected patients in southwest China: an observational study. Sci Rep. 2019;9(1):1–7. doi: 10.1038/s41598-018-37148-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Murray MCM, Barrios R, Zhang W, Hull M, Montessori V, Hogg RS, et al. Hepatitis C virus treatment rates and outcomes in HIV/hepatitis C virus co-infected individuals at an urban HIV clinic. Eur J Gastroenterol Hepatol. 2011;23(1):45–50. doi: 10.1097/MEG.0b013e328341ef54. [DOI] [PubMed] [Google Scholar]
- 6.Rana U, Driedger M, Sereda P, Pan S, Ding E, Wong A, et al. Characteristics and outcomes of antiretroviral-treated HIV-HBV co-infected patients in Canada? BMC Infect Dis. 2019;19(1):982. doi: 10.1186/s12879-019-4617-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Twu JS, Chu K, Robinson WS. Hepatitis B virus X gene activates kappa B-like enhancer sequences in the long terminal repeat of human immunodeficiency virus 1. Proc Natl Acad Sci. 1989;86(13):5168–5172. doi: 10.1073/pnas.86.13.5168. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Gómez-Gonzalo M, Carretero M, Rullas J, Lara-Pezzi E, Aramburu J, Berkhout B, et al. The hepatitis B virus X protein induces HIV-1 replication and transcription in synergy with T-cell activation signals functional roles of nf-κb/nf-at and sp1-binding sites in the hiv-1 long terminal repeat promoter. J Biol Chem. 2001;276(38):35435–35443. doi: 10.1074/jbc.M103020200. [DOI] [PubMed] [Google Scholar]
- 9.De Ledinghen V, Barreiro P, Foucher J, Labarga P, Castera L, Vispo ME, et al. Liver fibrosis on account of chronic hepatitis C is more severe in HIV-positive than HIV-negative patients despite antiretroviral therapy. J Viral Hepat. 2008;15(6):427–433. doi: 10.1111/j.1365-2893.2007.00962.x. [DOI] [PubMed] [Google Scholar]
- 10.Sierra CM, Arizcorreta A, Díaz F, Roldá R, Herrera ML, Pérez-Guzmán E, et al. Progression of chronic hepatitis C to liver fibrosis and cirrhosis in patients co-infected with hepatitis C virus and human immunodeficiency virus. Clin Infect Dis. 2003;36(4):491–498. doi: 10.1086/367643. [DOI] [PubMed] [Google Scholar]
- 11.Markowitz JS, Gutterman EM, Hodes D, Klaskala W. Factors associated with the initiation of alpha-interferon treatment in Medicaid patients diagnosed with hepatitis C. J Viral Hepat. 2005;12(2):176–185. doi: 10.1111/j.1365-2893.2005.00607.x. [DOI] [PubMed] [Google Scholar]
- 12.Wang H, Men P, Xiao Y, Gao P, Lv M, Yuan Q, et al. Hepatitis B infection in the general population of China: a systematic review and meta-analysis. BMC Infect Dis. 2019;19(1):1–10. doi: 10.1186/s12879-018-3567-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Yu S, Yu C, Li J, Liu S, Wang H, Deng M. Hepatitis B and hepatitis C prevalence among people living with HIV/AIDS in China: a systematic review and Meta-analysis. Virol J. 2020;17(1):1–10. doi: 10.1186/s12985-019-1274-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Gao Y, Yang J, Sun F, Zhan S, Fang Z, Liu X, et al. Prevalence of anti-HCV antibody among the general population in mainland china between 1991 and 2015: a systematic review and meta-analysis. Open Forum Infect Dis. 2019;6(3):ofz040. doi: 10.1093/ofid/ofz040. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Zhu H, Napravnik S, Eron JJ, Cole SR, Ma Y, Wohl DA, et al. Decreasing excess mortality of HIV-infected patients initiating antiretroviral therapy: comparison with mortality in general population in China, 2003–2009. J Acquir Immune Defic Syndr. 2013;63(5):e150–e157. doi: 10.1097/QAI.0b013e3182948d82. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Zhang X, Wang N, Vermund SH, Zou H, Li X, Zhang F, et al. Interventions to improve the HIV continuum of care in China. Curr HIV/AIDS Rep. 2019;16(6):448–457. doi: 10.1007/s11904-019-00469-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Chen M, Wong WW, Law MG, Keirtiburanakul S, Yunihastuti E, Merati TP, et al. Hepatitis B and C co-infection in HIV patients from the TREAT Asia HIV observational database: analysis of risk factors and survival. PLoS One. 2016;11(3):e0150512. doi: 10.1371/journal.pone.0150512. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Ruan Y, Qin G, Yin L, Chen K, Qian HZ, Hao C, et al. Incidence of HIV, hepatitis C and hepatitis B viruses among injection drug users in southwestern China: a 3-year follow-up study. AIDS. 2007;21:S39–46. doi: 10.1097/01.aids.0000304695.54884.4f. [DOI] [PubMed] [Google Scholar]
- 19.Jiang H, Zhang X, Zhang C, Lu R, Zhou C, Oyyang L, et al. Trends of HIV, hepatitis C virus and syphilis seroprevalence among injection and non-injection drug users in southwestern China, 2010–2017. AIDS Care. 2020; 1–6. [DOI] [PMC free article] [PubMed]
- 20.Qian HZ, Stinnette SE, Rebeiro PF, Kepp AM, Shepherd BE, Samenow CP, et al. The relationship between injection and noninjection drug use and HIV disease progression. J Subst Abuse Treat. 2011;41(1):14–20. doi: 10.1016/j.jsat.2011.01.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Mathers BM, Degenhardt L, Bucello C, Lemon J, Wiessing L, Hickman M. Mortality among people who inject drugs: a systematic review and meta-analysis. Bull World Health Organ. 2013;91(2):102–123. doi: 10.2471/BLT.12.108282. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Ma Y, Dou Z, Guo W, Mao Y, Zhang F, McGoogan JM, et al. The human immunodeficiency virus care continuum in China: 1985–2015. Clin Infect Dis. 2018;66(6):833–839. doi: 10.1093/cid/cix911. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets are available from the corresponding authors on reasonable request.