Abstract
Routinely monitoring viral rebound (VR) is important in a life course of people with HIV (PWH). This study examined risk factors for time to the first VR, number of VR, and their association to VR history in men who have sex with men (MSM). It includes 8,176 adult PWH diagnosed from January 2005 to December 2018, followed until July 2021. We used the Cox model for time to the first VR, the Poisson model for number of VR, and logistic regression for VR history in MSM. Younger individuals (50 – 59 years vs 18 – 29 years, aHR: 0.43, 95% CI: [0.34, 0.55]) were more likely to experience VR. Black individuals (Black vs White, IRR: 1.61, 95% CI [1.38, 1.88]) had more VR, while MSM (MSM vs Heterosexual, IRR: 0.68, 95% CI: [0.57, 0.81]) was negatively associated with number of VR. Furthermore, individuals engaging illicit drug use (IDU) (aOR: 1.50, 95% CI: [1.03, 2.17]) were more likely to experience VR in MSM subgroup. This study highlighted the alarming risk factors related to VR among PWH. Tailored intervention should also be deployed for young, Black MSM patients with substance use for more effective and targeted public health strategies concerning VR.
Keywords: SDG 3: Good health and well-being, SDG 10: Reduced inequalities, viral rebound, longitudinal data, electronic health records, HIV
Introduction
Over the past two decades, significant progress in antiretroviral therapy (ART) has resulted in the successful management of HIV infection, substantially enhancing the quality and duration of life for people living with HIV (PWH) (Cohen et al., 2011; Trickey et al., 2017). Viral load (VL) has often been used to monitor the treatment effectiveness (Organization, 2013). Regularly monitoring the VL of PWH and being aware of their VL status can serve as a motivation for patients to adhere to their treatment which could result in better health conditions, i.e., viral suppression (VS) (Nicholas et al., 2019). With the promotion of U=U (Undetectable = Untransmittable) campaign, maintaining a long term VS is more critical than before as it can benefit both the patients themselves and the public to end the HIV epidemic (Liu et al., 2023).
Nevertheless, some patients may experience viral rebound (VR), which refers to the increase of VL to detectable levels in patients who previously had undetectable VL levels (Gouskova et al., 2014). VR is possibly caused by treatment failure, poor adherence, drug-to-drug interaction or other unknown factors (Bulage et al., 2017; Craw et al., 2020; Wainberg et al., 2011). VR is a concerning and undesired condition which can lead to potential transmission risks, psychological stress and increased HIV morbidity and mortality (Nguyen et al., 2021; Opoku et al., 2022). Thus, preventing VR is essential for long-term health and well-being of PWH.
Several studies have examined VR associated with the different demographic and clinical factors in PWH (Benator et al., 2015; Craw et al., 2020; Min et al., 2020; O’Connor et al., 2017; Palmer et al., 2018). Craw’s study in the US found that Black people aged 18–39 years old and having more frequent VL tests were associated with higher risk of VR (Craw et al., 2020). The relationship between VR and baseline VL and CD4 counts are unknown in Craw’s study. However, baseline VL and CD4 counts are fundamentally used to define the treatment response in practice (Grabar et al., 2000). Additionally, while Craw’s study provides insights, it may not fully support local or state public health initiatives in pinpointing the groups most prone to VR. In a UK study using data from 1996 to 2013, a 7.8% VR rate was identified among 16,101 PWH, with younger Black PWH showing an increased risk (O’Connor et al., 2017). While a Washington, DC study reported a 15% - 26% VR likelihood within the first three years post-HIV diagnosis (Benator et al., 2015). Although those findings are important, those studies are mostly cross-sectional studies and only considered the timepoint that VR occurred following VS instead of the longitudinal VR in the entire follow-up period. Understanding the patterns of VR can provide valuable insights for patient care and treatment strategies (Dessie et al., 2020). Meanwhile, limited research used time-dependent VL/CD4 counts factors to capture the longitudinal information. Moreover, a very recent study in the US reported that a high percentage (58%) of PWH were men who have sex with men (MSM) among those who experienced VR (Liu et al., 2023). It reminds that evidence of the association between VR and potential factors among MSM PWH is limited, and subgroup analyses by focusing on those individuals is crucial since it helps tailoring more effectively interventions to address the unique needs of MSM (O’Connor et al., 2017).
In this population-based cohort study, based on the information that comes from the electronic health records (EHR) of patients in South Carolina (SC) spanning a period of more than 15 years (2005 to 2021), we aim to evaluate the association between risk factors and time to the first VR, as well as number of VR in the whole follow-up period. Subgroup analyses were conducted on MSM group to emphasize the importance of understanding variations within PWH. The results of our study hold the potential to advance our comprehension of HIV treatment response and provide valuable insights for devising more effective strategies to improve the health outcomes of PWH in SC.
Methods
Study Cohort
A population-based cohort was derived from the integrated system of statewide EHR data in SC. Since 1986, the SC Department of Health and Environmental Control (SC DHEC) has implemented an enhanced HIV/AIDS reporting system (eHARS) that collects confidential name-based reports of HIV/AIDS (Olatosi et al., 2019). The de-identified data from SC DHEC’s eHARS, along with claim data from all payers, were linked by the South Carolina Revenue and Fiscal Affairs Office (SC RFA). For further details on the data sources and linkage, refer to previous publications (Zhang et al., 2022).
In this study, we extracted data from all adults PWH (18 years or older at the time of HIV diagnosis) who (1) were diagnosed with HIV in SC between January, 2005 and December, 2018, (2) had at least 24 months of follow-up since their HIV diagnosis, and (3) had at least two VL and/or at least two CD4 tests between the time of HIV diagnosis and the end of study or death, whichever first. Overall, 8,176 PWH were included in this study.
Measures
Outcomes
VR was defined as VL>200 copies/mL after two consecutive VS (i.e., VL≤200 copies/mL) and it should only occur (1) at least one year after first VS and (2) at least 90 days after the two consecutive VS (Craw et al., 2020; Palmer et al., 2018). The primary outcomes of interest including (1) the time to the first VR occurrence, measured as the number of days from the initial HIV diagnosis to the occurrence of the first VR, (2) total number of VR occurrence between the patient’s initial HIV diagnosis and the end of the follow-up period.
HIV related characteristics
The baseline VL and CD4 counts measures were determined based on the first measurements on or after initial HIV diagnosis. The time-dependent CD4 counts were recorded at every laboratory visit of the patients throughout the follow-up period. The VL measurement was divided into four groups: ≤200 copies/mL, >200 and ≤10,000 copies/mL, >10,000 and ≤100,000 copies/mL, and ≥100,000 copies/mL. The CD4 measurements were categorized into four groups: <200 cells/mm3, 200 to 350 cells/mm3, 350 to 500 cells/mm3 and ≥500 cells/mm3. Timely linkage to care was defined as PWH had at least once VL test and/or CD4 count test with 30 days after the time of HIV diagnosis. Age at the time of HIV diagnosis, race and mode of HIV transmission were also included.
Other Predictors
Additional factors that were considered as possible covariates included sex, and residential area (urban or rural). As for substance use, the study referenced ICD-9/10 codes to categorize and consider alcohol consumption, tobacco use, and illicit drug use. The analysis also involved identifying and including the number of baseline comorbidity, such as hypothyroidism, hypertension, arthritis, Chronic obstructive pulmonary disease (COPD), cardiovascular disease, renal disease, diabetes mellitus, obesity, cerebrovascular disease, dyslipidemia, Hepatitis C, cancer, and Hepatitis B, which were referenced by ICD-9/10 codes.
Statistical Analysis
We first described the distribution of HIV related factors and other factors among PWH who had VR history or not during the follow-up period. This analysis was performed using analysis of variance (ANOVA) (Pearson’s Chi-squared test). To assess the association between time to the first VR and the aforementioned factors, we employed a time-dependent Cox proportional hazards (PH) model and the PH assumption was validated by Schoenfeld residuals. The Poisson regression model, specifically designed for count data within a defined period, was utilized to understand the relationship between the number of VR and the risk factors. To understand the combined influence of race and transmission mode on the number of VR, an interaction term between race and mode of HIV transmission was introduced into the model. The interaction term allows us to assess how they might affect each other. Furthermore, logistic regression was used in subgroup analyses to evaluate the association between individual VR history and the same factors since the MSM with more than one VR is limited. The Wald test was used to calculate local p-values in both models. All analyses were conducted using R version 4.1.2, and a two-sided p-value of 0.05 for statistical significance.
Results
Among a total of 8,176 PWH, a majority of them were male (75.1%), Black Americans (70.4%), living in urban areas (83.5%) and had a timely linkage to care (73.0%). Additionally, 52.6% were MSM (Table 1). At the time of HIV diagnosis, 41.8% individuals fell within the age range of 18 to 30 years. Before or at the time of HIV diagnosis, 28.9% of PWH had at least one comorbidity. 1,175 (14.4%) PWH experienced at least one VR during their follow-up period and 182 (2.2%) PWH experienced at least two VRs.
Table 1:
Social demographics, baseline viral load/CD4 count, substance use, and comorbidities among eligible PWH in South Carolina.
| Characteristic | Overall | #VR1=0 | #VR 1≥1 | P value2 |
|---|---|---|---|---|
|
| ||||
| N (%) | 8,176 (100.0%) | 7,001 (85.6%) | 1175 (14.4%) | |
| Age (Years) | <0.001 | |||
| 18 – 29 | 3,416 (41.8%) | 2,871 (41.0%) | 545 (46.4%) | |
| 30 – 39 | 1,805 (22.1%) | 1,538 (22.0%) | 267 (22.7%) | |
| 40 – 49 | 1,724 (21.1%) | 1,475 (21.1%) | 249 (21.2%) | |
| 50 – 59 | 946 (11.6%) | 857 (12.2%) | 89 (7.6%) | |
| ≥60 | 285 (3.5%) | 260 (3.7%) | 25 (2.1%) | |
| Sex | 0.01 | |||
| Male | 6,138 (75.1%) | 5,291 (75.6%) | 847 (72.1%) | |
| Female | 2,038 (24.9%) | 1,710 (24.4%) | 328 (27.9%) | |
| Race/Ethnicity | <0.001 | |||
| White | 1,835 (22.4%) | 1,652 (23.6%) | 183 (15.6%) | |
| Black | 5,756 (70.4%) | 4,815 (68.8%) | 941 (80.1%) | |
| Hispanic | 394 (4.8%) | 363 (5.2%) | 31 (2.6%) | |
| Others | 191 (2.3%) | 171 (2.4%) | 20 (1.7%) | |
| Transmission mode | <0.001 | |||
| Heterosexual | 1,671 (20.4%) | 1,376 (19.7%) | 295 (25.1%) | |
| MSM/IDU | 423 (5.2%) | 336 (4.8%) | 87 (7.4%) | |
| MSM | 4,304 (52.6%) | 3,739 (53.4%) | 565 (48.1%) | |
| Others | 1,778 (21.7%) | 1,550 (22.1%) | 228 (19.4%) | |
| Residence area | 0.235 | |||
| Urban | 6,826 (83.5%) | 5,859 (83.7%) | 967 (82.3%) | |
| Rural | 1,350 (16.5%) | 1,142 (16.3%) | 208 (17.7%) | |
| Timely linkage to care | <0.001 | |||
| No | 2,209 (27.0%) | 1,844 (26.3%) | 365 (31.1%) | |
| Yes | 5,967 (73.0%) | 5,157 (73.7%) | 810 (68.9%) | |
| Alcohol use | 0.01 | |||
| No | 5,574 (68.2%) | 4,811 (68.7%) | 763 (64.9%) | |
| Yes | 2,602 (31.8%) | 2,190 (31.3%) | 412 (35.1%) | |
| Tobacco use | 0.025 | |||
| No | 5,899 (72.2%) | 5,083 (72.6%) | 816 (69.4%) | |
| Yes | 2,277 (27.8%) | 1,918 (27.4%) | 359 (30.6%) | |
| Illicit drug use | 0.005 | |||
| No | 7,456 (91.2%) | 6,410 (91.6%) | 1,046 (89.0%) | |
| Yes | 720 (8.8%) | 591 (8.4%) | 129 (11.0%) | |
| Number of comorbidity | 0.992 | |||
| =0 | 5,816 (71.1%) | 4,982 (71.2%) | 834 (71.0%) | |
| =1 | 1,607 (19.7%) | 1,375 (19.6%) | 232 (19.7%) | |
| ≥2 | 753 (9.2%) | 644 (9.2%) | 109 (9.3%) | |
| Baseline viral load (copies/mL) | 0.192 | |||
| ≤200 | 777 (9.5%) | 680 (9.7%) | 97 (8.3%) | |
| >200 and ≤10,000 | 1,931 (23.6%) | 1,668 (23.8%) | 263 (22.4%) | |
| >10,000 and ≤100,000 | 3,130 (38.3%) | 2,656 (37.9%) | 474 (40.3%) | |
| >100,000 | 2,338 (28.6%) | 1,997 (28.5%) | 341 (29.0%) | |
| Baseline CD4 count (cells/mm3) | <0.001 | |||
| <200 | 2,441 (29.9%) | 2,032 (29.0%) | 409 (34.8%) | |
| ≥200 and <350 | 1,786 (21.8%) | 1,528 (21.8%) | 258 (22.0%) | |
| ≥350 and <500 | 1,659 (20.3%) | 1,448 (20.7%) | 211 (18.0%) | |
| ≥500 | 2,290 (28.0%) | 1,993 (28.5%) | 297 (25.3%) | |
#VR = Number of viral rebound.
P values were calculated by ANOVA test (Pearson’s Chi-squared test).
Table 2 presents a summary the time to first VR. We observed that PWH with CD4 counts ≥500 cells/mm3 experienced reduced risk of VR compared to those with CD4 counts <200 cells/mm3 (adjusted hazard ratio [aHR]: 0.66, 95% CI: [0.54, 0.81]). Compared to the PWH diagnosis between the age 18–29, those who diagnosed between the ages of 50–59 years has less risk to the VR (aHR: 0.43, 95% CI: [0.34, 0.55]). PWH who are both MSM and people with illicit drug (PWID) had a significantly higher risk VR compared to PWH who are heterosexual (aHR: 1.44, 95% CI: [1.11, 1.85]). Moreover, comparing to White, Black individuals has higher risk of VR (aHR: 1.59, 95% CI: [1.35, 1.88].
Table 2:
Hazard ratio from the Cox proportional hazards model for the time to first VR.
| Characteristic | aHR1 | 95% CI2 | P value3 |
|---|---|---|---|
|
| |||
| Age (Years) | |||
| 18 – 29 | 1 | 1 | |
| 30 – 39 | 0.73 | 0.62, 0.85 | <0.001 |
| 40 – 49 | 0.67 | 0.57, 0.79 | <0.001 |
| 50 – 59 | 0.43 | 0.34, 0.55 | <0.001 |
| ≥60 | 0.45 | 0.30, 0.68 | <0.001 |
| Sex | |||
| Male | 1 | 1 | |
| Female | 1.04 | 0.88, 1.23 | 0.624 |
| Race/Ethnicity | |||
| White | 1 | 1 | |
| Black | 1.59 | 1.35, 1.88 | <0.001 |
| Hispanic | 0.85 | 0.58, 1.25 | 0.399 |
| Others | 1.33 | 0.84, 2.11 | 0.23 |
| Transmission mode | |||
| Heterosexual | 1 | 1 | |
| MSM/IDU | 1.44 | 1.11, 1.85 | 0.006 |
| MSM | 0.87 | 0.72, 1.05 | 0.143 |
| Others | 0.96 | 0.81, 1.15 | 0.688 |
| Residence area | |||
| Urban | 1 | 1 | |
| Rural | 1.02 | 0.87, 1.18 | 0.828 |
| Linkage to care | |||
| No | 1 | 1 | |
| Yes | 0.96 | 0.85, 1.09 | 0.53 |
| Alcohol use | |||
| No | 1 | 1 | |
| Yes | 1.17 | 0.86, 1.58 | 0.324 |
| Tobacco use | |||
| No | 1 | 1 | |
| Yes | 1.25 | 0.93, 1.68 | 0.137 |
| Illicit drug use | |||
| No | 1 | 1 | |
| Yes | 1.19 | 0.95, 1.48 | 0.122 |
| Number of comorbidity | |||
| =0 | 1 | 1 | |
| =1 | 1.01 | 0.87, 1.18 | 0.886 |
| ≥2 | 1.12 | 0.90, 1.39 | 0.305 |
| Baseline viral load (copies/mL) | |||
| ≤200 | 1 | 1 | |
| >200 and ≤10,000 | 0.82 | 0.65, 1.04 | 0.1 |
| >10,000 and ≤100,000 | 0.96 | 0.77, 1.20 | 0.724 |
| >100,000 | 1.09 | 0.87, 1.38 | 0.454 |
| Time-dependent CD4 counts (cells/mm3) | |||
| <200 | 1 | 1 | |
| ≥200 and <350 | 1.09 | 0.88, 1.37 | 0.426 |
| ≥350 and <500 | 0.92 | 0.74, 1.15 | 0.463 |
| ≥500 | 0.66 | 0.54, 0.81 | <0.001 |
aHR= Adjusted hazards ratio, which was calculated by PH model.
CI = Confidence Interval.
P values were calculated by Wald test.
Table 3 provides a summary of the number of VR during the follow-up period. We found that PWH with baseline CD4 count ≥500 cells/mm3 tended to have fewer VR episodes compared to those with baseline CD4 counts <200 cells/mm3 (incidence rate ratio [IRR]: 0.72, 95% CI: [0.62, 0.85]). We also found that PWH diagnosis at ≥60 years tended to have fewer VR compared to whose were diagnosis between the age of 18–29 years old( IRR: 0.46, 95% CI: [0.31, 0.67]). Compared to heterosexual group PWH and PWH without timely linkage to care, individuals who were MSM (IRR: 0.68, 95% CI: [0.57,0.81]) and had timely linkage to care (IRR:0.80, 95% CI: [0.71, 0.90]) tended to have fewer VR. On the other hand, compared to White, Black individuals (IRR: 1.61, 95% CI: [1.38, 1.88]) tended to have more VR episodes.
Table 3:
Incidence rate ratio from the Poisson regression model for the number of VR.
| Characteristic | IRR1 | 95% CI2 | P value3 |
|---|---|---|---|
|
| |||
| Age | |||
| 18 – 29 | 1 | 1 | |
| 30 – 39 | 0.87 | 0.75, 1.00 | 0.049 |
| 40 – 49 | 0.79 | 0.68, 0.92 | 0.003 |
| 50 – 59 | 0.48 | 0.39, 0.60 | <0.001 |
| ≥60 | 0.46 | 0.31, 0.67 | <0.001 |
| Sex | |||
| Male | 1 | 1 | |
| Female | 1.02 | 0.87, 1.19 | 0.84 |
| Race/Ethnicity | |||
| White | 1 | 1 | |
| Black | 1.61 | 1.38, 1.88 | <0.001 |
| Hispanic | 0.7 | 0.48, 0.99 | 0.055 |
| Others | 1.01 | 0.63, 1.52 | 0.982 |
| Transmission mode | |||
| Heterosexual | 1 | 1 | |
| MSM/IDU | 1.24 | 0.97, 1.56 | 0.082 |
| MSM | 0.68 | 0.57, 0.81 | <0.001 |
| Others | 0.73 | 0.62, 0.85 | <0.001 |
| Residence area | |||
| Urban | 1 | 1 | |
| Rural | 1.06 | 0.92, 1.22 | 0.388 |
| Linkage to care | |||
| No | 1 | 1 | |
| Yes | 0.8 | 0.71, 0.90 | <0.001 |
| Alcohol use | |||
| No | 1 | 1 | |
| Yes | 1.2 | 0.91, 1.56 | 0.188 |
| Tobacco use | |||
| No | 1 | 1 | |
| Yes | 0.94 | 0.73, 1.23 | 0.665 |
| Illicit drug use | |||
| No | 1 | 1 | |
| Yes | 1.13 | 0.92, 1.38 | 0.236 |
| Number of comorbidity | |||
| =0 | 1 | 1 | |
| =1 | 0.93 | 0.80, 1.07 | 0.294 |
| ≥2 | 0.98 | 0.80, 1.19 | 0.816 |
| Baseline viral load (copies/mL) | |||
| ≤200 | 1 | 1 | |
| >200 and ≤10,000 | 1 | 0.80, 1.25 | 0.975 |
| >10,000 and ≤100,000 | 1.21 | 0.98, 1.51 | 0.08 |
| >100,000 | 1.17 | 0.93, 1.48 | 0.178 |
| Baseline CD4 counts (cells/mm3) | |||
| <200 | 1 | 1 | |
| ≥200 and <350 | 0.79 | 0.68, 0.92 | 0.003 |
| ≥350 and <500 | 0.72 | 0.61, 0.84 | <0.001 |
| ≥500 | 0.72 | 0.62, 0.85 | <0.001 |
IRR= Incidence Rate Ratio.
CI = Confidence Interval.
P values were calculated by Wald test.
Table 4 presents the results for the same outcome number of VR, incorporating an interaction term between race and HIV transmission mode. We identified the significant interaction between race (Black) and transmission mode (MSM) (IRR: 1.76, 95% CI: [1.19, 2.57]), which means Black MSM individuals had a notably increased risk of VR than what would be expected from just being Black or MSM alone.
Table 4:
Incidence rate ratio from the Poisson regression model with interaction term for the number of VR.
| Characteristic | IRR1 | 95% CI2 | P value3 |
|---|---|---|---|
|
| |||
| Age | |||
| 18 – 29 | 1 | 1 | |
| 30 – 39 | 0.88 | 0.77, 1.02 | 0.089 |
| 40 – 49 | 0.82 | 0.71, 0.95 | 0.011 |
| 50 – 59 | 0.5 | 0.40, 0.62 | <0.001 |
| ≥60 | 0.48 | 0.32, 0.69 | <0.001 |
| Sex | |||
| Male | 1 | 1 | |
| Female | 1.01 | 0.87, 1.18 | 0.858 |
| Race/Ethnicity | |||
| White | 1 | 1 | |
| Black | 1.14 | 0.84, 1.58 | 0.405 |
| Hispanic | 0.73 | 0.38, 1.32 | 0.323 |
| Others | 1.71 | 0.75, 3.43 | 0.162 |
| Transmission mode | |||
| Heterosexual | 1 | 1 | |
| MSM/IDU | 0.87 | 0.56, 1.35 | 0.545 |
| MSM | 0.44 | 0.30, 0.64 | <0.001 |
| Others | 0.7 | 0.45, 1.09 | 0.116 |
| Residence area | |||
| Urban | 1 | 1 | |
| Rural | 1.07 | 0.93, 1.22 | 0.359 |
| Linkage to care | |||
| No | 1 | 1 | |
| Yes | 0.81 | 0.72, 0.91 | <0.001 |
| Alcohol use | |||
| No | 1 | — | |
| Yes | 1.18 | 0.90, 1.54 | 0.216 |
| Tobacco use | |||
| No | 1 | 1 | |
| Yes | 0.94 | 0.73, 1.23 | 0.661 |
| Illicit drug use | |||
| No | 1 | 1 | |
| Yes | 1.14 | 0.93, 1.40 | 0.187 |
| Number of comorbidity | |||
| =0 | 1 | 1 | |
| =1 | 0.92 | 0.80, 1.06 | 0.263 |
| ≥2 | 0.97 | 0.79, 1.18 | 0.743 |
| Baseline viral load (copies/mL) | |||
| ≤200 | 1 | 1 | |
| >200 and ≤10,000 | 1 | 0.80, 1.26 | 0.995 |
| >10,000 and ≤100,000 | 1.23 | 0.99, 1.53 | 0.065 |
| >100,000 | 1.18 | 0.94, 1.50 | 0.151 |
| Baseline CD4 counts (cells/mm3) | |||
| <200 | 1 | 1 | |
| ≥200 and <350 | 0.8 | 0.68, 0.92 | 0.003 |
| ≥350 and <500 | 0.72 | 0.61, 0.85 | <0.001 |
| ≥500 | 0.73 | 0.62, 0.85 | <0.001 |
| Race/Ethnicity*Transmission mode | |||
| Black * MSM/IDU | 1.54 | 0.92, 2.60 | 0.102 |
| Hispanic * MSM/IDU | 1.55 | 0.34, 5.25 | 0.521 |
| Others * MSM/IDU | 0.67 | 0.14, 2.50 | 0.572 |
| Black * MSM | 1.76 | 1.19, 2.57 | 0.004 |
| Hispanic * MSM | 0.58 | 0.20, 1.50 | 0.271 |
| Others * MSM | 0.5 | 0.17, 1.45 | 0.198 |
| Black * Others | 1.05 | 0.66, 1.69 | 0.825 |
| Hispanic * Others | 0.93 | 0.37, 2.31 | 0.871 |
| Others * Others | 0.3 | 0.06, 1.13 | 0.092 |
IRR= Incidence Rate Ratio.
CI = Confidence Interval.
P values were calculated by Wald test.
Among a total 4,470 PWH in the MSM subgroup (Table 5), similar to the whole cohort group, a majority of them were Black Americans (66.2%), living in urban areas (84.9%), and had a timely linkage to care (74.9%). 56.9% individuals fell within the age range of 18 to 30 years at the time of HIV diagnosis. Before or at the time of HIV diagnosis, 22.9% of PWH had at least one comorbidity. 14.4% MSM PWH experienced at least one VR during their follow-up period. The forest plot (Figure 1) summarized the results of the logistic regression in the MSM subgroup analyses. Compared to those with baseline CD4 counts <200 cells/mm3, MSM with baseline CD4 counts ≥500 cells/mm3 also had higher risk of VR (adjusted odds ratio [aOR]: 0.63, 95% CI: [0.48, 0.82]). Compared to those with diagnosis age of 18–29 years old, MSM who diagnosed at the age of 50–59 years had less risk to experience VR (aOR: 0.51, 95% CI: [0.30, 0.82]). Compared to MSM who were White and not having illicit drug use, Black individuals (aOR: 2.12, 95% CI: [1.66, 2.72]) with illicit drug use (aOR: 1.50, 95% CI: [1.03, 2.17]) had more risk to have VR. MSM PWH with one comorbidity before or at the HIV diagnosis had lower risk of having VR (aOR: 0.75, 95% CI: [0.58, 0.96]) compared to those without any comorbidity.
Table 5:
Social demographics, baseline viral load/CD4 count, substance use, and comorbidities among eligible MSM subgroup PWH in South Carolina.
| Characteristic | Overall | #VR1=0 | #VR 1≥1 | P value2 |
|---|---|---|---|---|
|
| ||||
| N (%) | 4,470 (100.0%) | 3,874 (85.6%) | 596 (14.4%) | |
| Age | 0.002 | |||
| 18 – 29 | 2,544 (56.9%) | 2,170 (56.0%) | 374 (62.8%) | |
| 30 – 39 | 923 (20.6%) | 800 (20.7%) | 123 (20.6%) | |
| 40 – 49 | 685 (15.3%) | 609 (15.7%) | 76 (12.8%) | |
| 50 – 59 | 256 (5.7%) | -3 | -3 | |
| ≥60 | 62 (1.4%) | -3 | -3 | |
| Race/Ethnicity | <0.001 | |||
| White | 1,211 (27.1%) | 1,117 (28.8%) | 94 (15.8%) | |
| Black | 2,961 (66.2%) | 2,475 (63.9%) | 486 (81.5%) | |
| Hispanic | 190 (4.3%) | -3 | -3 | |
| Others | 108 (2.4%) | -3 | -3 | |
| Residence area | 0.065 | |||
| Urban | 3,795 (84.9%) | 3,304 (85.3%) | 491 (82.4%) | |
| Rural | 675 (15.1%) | 570 (14.7%) | 105 (17.6%) | |
| Timely linkage to care | 0.018 | |||
| No | 1,123 (25.1%) | 950 (24.5%) | 173 (29.0%) | |
| Yes | 3,347 (74.9%) | 2,924 (75.5%) | 423 (71.0%) | |
| Alcohol use | 0.073 | |||
| No | 3,256 (72.8%) | 2,840 (73.3%) | 416 (69.8%) | |
| Yes | 1,214 (27.2%) | 1,034 (26.7%) | 180 (30.2%) | |
| Tobacco use | 0.021 | |||
| No | 3,365 (75.3%) | 2,939 (75.9%) | 426 (71.5%) | |
| Yes | 1,105 (24.7%) | 935 (24.1%) | 170 (28.5%) | |
| Illicit drug use | 0.025 | |||
| No | 4,188 (93.7%) | 3,642 (94.0%) | 546 (91.6%) | |
| Yes | 282 (6.3%) | 232 (6.0%) | 50 (8.4%) | |
| Number of comorbidity | 0.18 | |||
| =0 | 3,447 (77.1%) | 2,982 (77.0%) | 465 (78.0%) | |
| =1 | 782 (17.5%) | 690 (17.8%) | 92 (15.4%) | |
| ≥2 | 241 (5.4%) | 202 (5.2%) | 39 (6.5%) | |
| Baseline viral load | 0.652 | |||
| ≤200 | 322 (7.2%) | 279 (7.2%) | 43 (7.2%) | |
| >200 and ≤10,000 | 1,047 (23.4%) | 917 (23.7%) | 130 (21.8%) | |
| >10,000 and ≤100,000 | 1,801 (40.3%) | 1,548 (40.0%) | 253 (42.4%) | |
| >100,000 | 1,300 (29.1%) | 1,130 (29.2%) | 170 (28.5%) | |
| Baseline CD4 counts | 0.003 | |||
| <200 | 1,125 (25.2%) | 940 (24.3%) | 185 (31.0%) | |
| ≥200 and <350 | 1,076 (24.1%) | 942 (24.3%) | 134 (22.5%) | |
| ≥350 and <500 | 1,015 (22.7%) | 883 (22.8%) | 132 (22.1%) | |
| ≥500 | 1,254 (28.1%) | 1,109 (28.6%) | 145 (24.3%) | |
#VR = Number of viral rebound.
P values were calculated by ANOVA test (Pearson's Chi-squared test).
Number (<10) was hided because of confidentiality.
Figure 1:
Forest plot for odds ratio from logistic regression model.
Discussion
Using data collected from a population of PWH in SC, our study examined the potential risk factors associated with both the time to first VR and the number of VR including HIV related characteristics and other demographic and behavioral factors. We found out that 14.4% of PWH experienced at least one VR which is compatible to previous study (15%) (Benator et al., 2015). We also found that 2.2% of PWH who had at least two VRs, which is lower than the 4% reported in another study that used a less stringent definition of sustained VR (Liu et al., 2023). The stricter criteria and differences in PWH characteristics likely contributed to the lower observed rate in our cohort. Among the HIV related factors, we found that higher CD4 counts were associated with a reduced risk of VR. We used baseline CD4 counts in the Poisson regression model since baseline CD4 counts can provide starting health condition of the PWH (Farhadian et al., 2021), which is important for understanding the occurrence of VR over the follow-up period. PWH with higher baseline CD4 counts tended to experience fewer number of VR. This finding indicates that CD4 counts have significant association with the occurrence of VR, which is consistent with another prior research (Henrich et al., 2012). Individuals with higher CD4 counts may be better equipped effectively control VL and reduce the risk of VR (Min et al., 2020). Special attentions should be given to patients with low CD4 count, as they are more susceptible to experiencing VR. Our study did not show a significant association between baseline VL and time to first VR, or the frequency of VR. One study had similar finding, but they did find that higher baseline VL is significantly associated with higher risk of VR when they restricted to younger PWH (age <29 at first ARV initiation) (Palmer et al., 2018). Another study also showed that baseline VL was not a significant factor associated with VR (Mujugira et al., 2016). Future studies are warranted to confirm this relationship.
Additionally, we found individuals diagnosed with HIV at a younger age had significantly higher risk to experience the first VR and tended to have more VR. Our finding is consistent with previous studies’ results that younger age was significantly associated with the increased risk of VR (Mujugira et al., 2016; Nachega et al., 2009; Weintrob et al., 2008). Younger may individuals have longer follow-up period and have more time to capture VR. We also found that PWH who had timely linkage to care had less VR in the follow-up period, which is consistent with another study (Robertson et al., 2015). PWH with timely linkage to care usually received timely, comprehensive, and ongoing support, including access to treatment, medical monitoring, and psychosocial services. These elements work together to maintain VS and reduce the risk of VR (Miller et al., 2019).
In our cohort, we also found that Black individuals were more likely to both have VR and more VR. Consistent with other prior studies, we observed ongoing disparities in VR among Black PWH after adjusting other factors (Beer et al., 2016). Moreover, consistent with prior study (Min et al., 2020), we found that individuals who are MSM tended to have less VR. MSM communities were concerned about their HIV outcomes and had high level of willingness to participate in related awareness programs or clinical trials (Kwan et al., 2022), which may lead to a better HIV education and understanding of how to maintain VS. The significant interaction term between race (Black) and transmission mode (MSM) also highlighted the importance of conducting subgroup analyses to assess the VR risks among MSM PWH.
In our MSM subgroup analyses, we found that PWH who had higher baseline CD4 counts were less likely to experience VR, which is consistent to our finding in main cohort but is contradicted to another MSM HIV study (Tanner et al., 2016). Consistent to what we found in main cohort and previous study (Liu et al., 2023), we identified that MSM diagnosed at younger age and Black MSM were more likely to experience VR in their follow-up period. In addition, we found illicit drug use were positively associated with the risk of having VR for MSM. An illicit drug use had adverse associations with medication adherence and optimal health-care utilization, which leads to accelerated HIV progression and improved risk of VR (Baum et al., 2010). Moreover, MSM with one comorbidity before or at the time of HIV diagnosis had lower risk of experience VR compared to those without any comorbidity. This might be due to that MSM with one comorbidity in SC longer duration with HIV and longer follow-up time which may lead to more chance to capture VR. Additional research is needed to better understand the association between comorbidity and the risk of VR. It may be important to prioritize the development of adherence interventions tailored for individuals in MSM community who are Black, diagnosed HIV at younger age and engaging illicit drug use to reduce differences in VR (Craw et al., 2020).
Our study had several strengths that contributed to its robustness. First, it benefitted from a large sample size and being a statewide HIV population-based study, encompassing data collected over nearly two decades. The findings could be generalized to a similar population. Moreover, the longitudinal assessments of VL outcomes provided valuable insights into changes over time. An innovative aspect of our approach was the methods of assessing the risks of VR. We consider the hazards of first being VR after the HIV diagnosis by using Cox PH model. Then not only focusing solely on the occurrence of VR once but considering the number of VR as well by using Poisson regression model. This approach allowed for a more comprehensive evaluation of the stability of VS throughout the entire follow-up period. Meanwhile, time-dependent CD4 counts were used to measure the longitudinal information in the study. Additionally, we also incorporated MSM subgroup analyses to find potential risk factors that were uniquely associated with the increased/decreased risk of VR, which provides the targeted insights into the challenges of the MSM community.
Our study had several limitations that need to be considered when interpreting the results. One major limitation was that we did not include ART information when defining VR due to the incompleteness of this data. Although we assumed that individuals with suppressed VL were on ART, there might have been cases of other factors contributing to VS, such as being an elite controller. In this case, we tried to overcome this concern by setting a one-year time lag after the first VS occurrence. Additionally, no information related to drug resistance in our dataset. Moreover, follow-up time and the number of visits varied among different PWH, which could introduce bias into the findings because of this inconsistency in data collection. Finally, some important psychosocial factors affecting VR were not considered in the model, such as depression and anxiety. Existing studies find that depression and anxiety were risk factors of VR (Craw et al., 2020; Lampe et al., 2010) while others did not find such a relationship (Dessie et al., 2020; Liu et al., 2023). PWH with self-reported anxiety/depression were less likely to achieve optimal ART adherence. Poor adherence may be associated with an increased risk of having VR (Dessie et al., 2020). However, the more frequent psychiatric diagnosis may partially reflect the regular disease monitoring, which could promote ART adherence. Given the complexity of this relationship, further research is warranted to further understand the mechanism between VR and psychosocial factors.
In conclusion, our study emphasized the alarming risks of VR among PWH in SC. We found that CD4 counts level was associated with both the risk of experiencing the VR and the number of VR. PWH with low CD4 counts should be paid more attention to reduce the risk of VR. Additionally, our research shed light on other relevant factors, including age at HIV diagnosis, timely linkage to care, comorbidity, substance use, and mode of transmission, which helped us identify individuals at the high risk for VR. This understanding facilitates enhanced monitoring and the deployment of tailored interventions, aiming towards more effective and targeted public health strategies concerning VR in PWH in South Carolina.
Acknowledgment
JZ, XY conceived this study. JX, XY, YM, and JZ designed the study. JX, YM analyzed data and produced figures and tables. JX wrote the first draft of the paper. JX, XY, YM, OB, SW, XL, and JZ revised the paper. All authors have read and approved the final manuscript. The authors thank the SC Department of Health and Environmental Control (DHEC), the office of Revenue and Fiscal Affairs (RFA), and other SC agencies for contributing the data in South Carolina.
Funding
The work was supported by the [National Institute of Allergy and Infectious Diseases of the National Institutes of Health] under [grant number R01AI164947]
Footnotes
Disclosures statement
No potential conflict of interest was reported by the author(s).
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