Co-infections of tuberculosis, hepatitis B or C viruses in a cohort of people living with HIV/AIDS in China: predictors and sequelae

Abstract

The co-infection of viral hepatitis and tuberculosis (TB) among people living with HIV/AIDS (PLWHA) makes the syndemic of HIV even worse as there is higher mortality and morbidity among PLWHA with co-infections compared to people with HIV mono-infection. In the current study, we explored predictors and sequelae among a group of Chinese PLWHA to guide future program strategies and enhance the repertoire of action for both preventative and clinical purposes. Between October 2012 and August 2013, we conducted a cross-sectional study in Guangxi Autonomous Region (Guangxi) of China. With an overall participation rate of 90%, we finally recruited 3002 patients with 2987 (99.5%) completed the survey and were included in the data analysis. We employed both predictive and explanatory modeling strategies to explore predictors and sequelae of co-infections among PLWHA. The overall prevalence of co-infection was 15.6% with 4.4% of HBV, 5.4% of HCV and 4.8% of TB, respectively. Predictors of co-infections included history of injecting drugs or drinking alcohol, sharing needles, having sex with sex workers or casual partners, higher viral loads and lower CD4 counts. Meanwhile, co-infections were associated with various physical and psychological problems among PLWHA. As an entangled phenomenon, co-infections among PLWHA produce continuous and shifting scenarios, which add complexity to clinic, epidemiological and political ways of dealing with health risks among PLWHA in China. Exploring predictors and sequelae can help to prevent and manage co-infection comorbidities among PLWHA.

Introduction

The introduction and advancement of antiretroviral therapy (ART) has significantly reduced the mortality among people living with HIV/AIDS (PLWHA) worldwide and shifted HIV/AIDS from a lethal to a chronic-manageable condition (Simon, Ho, & Abdool Karim, 2006). Nonetheless, the emerging epidemics of tuberculosis (TB), hepatitis B virus (HBV) and hepatitis C virus (HCV) co-infections have become a critical challenge to the well-beings of PLWHA. Based upon a report of the World Health Organization (WHO), TB has become the leading cause of death among PLWHA including those on ART (Furin et al., 2015). Evidence suggests that co-infections of viral hepatitis and TB might worsen the quality of life or treatment/survival outcome among PLWHA compared to people with HIV mono-infection (Antiretroviral Therapy Cohort Collaboration, 2010; WHO, 2016). By the year of 2007, approximately 40% of PLWHA in Eastern Europe were co-infected with HCV, and the prevalence of HCV co-infection is even higher among areas with heavy injected drug use (Lazarus, Shete, Eramova, Merkinaite, & Matic, 2007). About 5–10% of PLWHA were co-infected with HBV worldwide (Alter, 2006). It is estimated that one third of PLWHA are infected with latent TB, among which 10% of them will develop active TB disease each year worldwide (Furin et al., 2015; WHO, 2013).

Prior studies have explored potential mechanisms of co-infection risk among PLWHA (Odone et al., 2014; Pawlowski, Jansson, Skold, Rottenberg, & Kallenius, 2012). First, the suppressed immune system of PLWHA are less likely to response to infections like HCV or TB compared to people without HIV (Montales, Chaudhury, Beebe, Patil, & Patil, 2015; Sereno et al., 2012). Second, the hepatotoxicity among PLWHA who take antiretroviral drugs or other substances (e.g., drugs and alcohol) may aggravate liver diseases (Sereno et al., 2012). From a behavioral standpoint, acquisition of HIV and some infections (e.g., HBV and HCV) share similar transmission routes (e.g., unprotected sex for HIV/HBV, injected drug use for HIV/HCV and blood-borne for HIV/HBV/HCV), which might facilitate dual or triple infections through a high-risk taking behavior (Alter, 2006).

In the current study, we sought to explore predictors and sequelae of TB, HBV and HCV co-infections among a large PLWHA cohort in order to guide future program strategies and enhance the repertoire of actions for both preventative and clinical purposes.

Methods

Study design

From 2012 to 2013, we collaborated with Guangxi Center for Disease Control and Prevention (Guangxi CDC) to selected HIV/AIDS cases from the top 12 sites in Guangxi, including 2 cities and 10 counties with the highest number of cumulative HIV incidence. A total of 2987 PLWHA provided written informed consent, completed the cross-sectional survey and were included in the data analysis. The study protocol was approved by the Institutional Review Boards at Wayne State University in the United States and Guangxi CDC in China.

Measurements

Individual characteristics: we asked participants to provide information regarding their gender, age, years of schooling, ethnicity, religion, marital status, place or original residence, types of job, as well as their routes of HIV infection. Clinical outcomes: The most recent CD4 counts (<250 cells/mm³, 251–500 cells/mm³ and 251–500 cells/mm³) and viral load (<50 copies/ml, 50–1000 copies/ml and > 1000 copies/ml), if available, were taken from participants’ medical records with appropriate consent (Healthline, 2015; Zhang et al., 2016a). Concurrent infections: Participants were asked if they had any of the concurrent infections (e.g., HBV, HCV and TB) in addition to their HIV infection at the time of the survey. HIV stigma: The validated Berger HIV stigma scale (Berger, Ferrans, & Lashley, 2001) was used to measure three types of HIV stigma (e.g., perceived, internalized and enacted). Psychological status: Psychological status was assessed by depression (e.g., short version of Center for Epidemiological Studies Depression Scale with a Cronbach’s α of 0.76) and anxiety (Zung Self-Rating Anxiety Scale with Cronbach’s α of 0.91). Physical function and quality of life: We employed the modified Medical Outcomes Study HIV Health Survey to assess PLWHA’s both physical functioning (Cronbach’s α = 0.92) and quality of life (Cronbach’s α = 0.93).

Data analytical plan

We used Chi-square tests (for categorical variables) and independent t-tests (for continuous variables) to compare sociodemographic characteristics between participants with and without each of the co-infections (TB, HBV and HCV). Second, we employed a stepwise predictive model to explore predictors for each specific type of co-infection (e.g., HBV, HCV and TB). Third, we used multivariable regression models as an explanatory model to assess association of the potential sequelae (e.g., physical condition, psychosocial status, quality of life and stigmatized experience) with each specific co-infection (e.g., HBV, HCV and TB). In addition to examining each single co-infection, we also examined the sequelae of multiple co-infections (e.g., HBV + HCV, HBV + TB, HCV + TB and HBV + HCV + TB) using a three-way interaction strategy in the explanatory model. All analyses were conducted using the STATA^® package (Version 12, College Station, TX).

Results

A total of 2987 PLWHA were included in the data analysis. The overall prevalence of having any co-infection was 15.6%, including 4.4% co-infected with HBV, 5.4% co-infected with HCV and 4.8% co-infected with TB, respectively.

Predictors of having HBV co-infection included younger age (aOR (adjusted odds ratio) = 0.97, 95%CI = 0.95, 0.99) and alcohol use (aOR = 1.66, 95%CI = 1.14, 2.42). For HCV co-infection, the predictors included being male (aOR = 0.48, 95%CI = 0.26, 0.91), being unemployed (aOR = 1.97; 95%CI = 1.25, 3.10), having a history of injecting drugs (aOR = 3.90, 95%CI = 2.21, 6.88), ever-smoking cigarettes (aOR = 2.93, 95% CI = 1.47, 5.83) and being infected via sharing needles (aOR = 5.16, 95%CI = 2.59, 10.28). For TB co-infection, predictors included being infected via sex with causal partners (aOR = 3.18, 95%CI = 1.23, 8.24) or sex workers (aOR = 3.14, 95%CI = 1.22, 8.07) or via other routes (aOR = 14.56, 95%CI = 2.58, 82.27); having higher viral load (e.g., > 1000 copies/ml) (aOR = 2.71, 95%CI = 1.44, 5.10) and low CD4 counts.

In the multivariable regression analyses of the sequelae among co-infected PLWHA, we found having TB co-infection (β = −0.82, 95%CI = −1.26, −0.38), HBV + HCV co-infection (β = −1.45; 95%CI = −2.80, −0.11) and TB + HCV co-infection (β = −2.55, 95%CI = −4.33, −0.77) might be associated with deteriorated physical fitness capacity. We also found participants with any co-infection (β = 1.69, 95%CI = 0.85, 2.53), having HBV (β = 2.10, 95%CI = 0.67, 3.54) or HCV (β = 1.93, 95%CI = 0.50, 3.35) co-infection might experience anxiety. Meanwhile, PLWHA with co-infections usually reported less encountered HIV stigma (p < .005). No interaction effects of multiple co-infections on physical and psychological comorbidity have been identified among this group of PLWHA.

Discussion

Our study is the first study to comprehensively examine predictors and consequences of co-infections among PLWHA in China. Predictors of HBV, HCV and TB co-infections included history of injecting drugs or drinking alcohol, sharing needles, having sex with sex workers or causal partners, higher viral loads and lower CD4 counts. Meanwhile, co-infections may lead to various physical and psychological problems among PLWHA.

In the current study, we found the prevalence of HIV–HCV co-infection was only 5.4%, which was inconsistent with findings from studies conducted in other settings (Bao et al., 2012; Segurado, Braga, Etzel, & Cardoso, 2004). We speculated that since our co-infection data were self-reported, the misclassification of key variables may lead to biased estimates. We also found the predictors of TB co-infection included having sex with causal partners or sex workers. As TB was usually acquired by the activation of latent TB infection as the result of the suppressed immune system among PLWHA (WHO, 2008), the observed effects of sex-facilitated predictors of TB may be mediated by some unobserved factors that we need to further examine in future research. In addition, a significant reduction in the magnitude of encountering perceived and internalized stigma has been identified among PLWHA with co-infections. Perhaps PLWHA with co-infections can use these concurrent comorbidities as excuses for their illness, as HIV is one of the most stigmatized and blamable disease in China (Zhang et al., 2016a, 2016b). On the other hand, our study did not find much interaction effects of multiple co-infections on physical and psychological comorbidity among the group of PLWHA. One possible reason may be the low rate of PLWHA who reported multiple co-infections in the current study. A future study should be specifically designed to explore effects of multiple co-infections among PLWHA.

Several limitations should be acknowledged when interpreting findings from the current study. First, as participants’ co-infection status was self-reported, it may be subject to misclassification and lead to biased estimates. Second, the majority of participants were recruited from rural areas in China. Findings in the current study may have limited generalizability to PLWHA in other regions or countries. Third, the nature of a cross-sectional study limited the capacity of causal inference between the exposure and outcome variables. Fourth, as the current study was based upon a secondary data analysis rather than being hypothesis-driven, findings may result in residual confounding due to insufficient or incomplete measurements.

Despite these caveats, this study provides empirical evidence of predictors and sequelae of HBV, HCV and TB co-infections among PLWHA in China. As an entangled phenomenon, co-infections among PLWHA produce continuous and shifting scenarios, which add complexity to clinic, epidemiological and political ways of dealing with health risks among PLWHA in China. Findings in the current study can be used to inform future research and intervention efforts to curb the emerging epidemic for PLWHA with co-infections.