HIV Viral Suppression Trends Over Time Among HIV-Infected Patients Receiving Care in the United States, 1997 to 2015

Approximately 1.2 million adults are living with HIV in the United States, with a disproportionate burden among men who have sex with men and among African Americans (1). Since 1996, the availability of potent antiretroviral therapy (ART) has led to large decreases in HIV-related mortality and morbidity. Antiretroviral therapy has transformed HIV from a fatal disease into a manageable chronic illness for people living with HIV (PLWH) who are aware of their infection and can access and adhere to ART. Furthermore, ART regimens have become increasingly better tolerated and easier to take (2). Achieving and maintaining HIV viral suppression with ART can optimize health outcomes among PLWH and limit transmission to others (3, 4). Monitoring viral suppression is thus an important aspect of HIV care. Identifying predictors of nonsuppression and groups at increased risk for it can enhance intervention efforts. Reports from individual clinics suggest high levels of viral suppression (5), but current large-scale estimates from across the United States are limited. One of the best assessments was from the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD), which found peak viral suppression of 72%; however, key potential predictors, such as adherence and substance use, were not examined (6). Furthermore, this analysis only included data through 2008 and did not examine potential improvements in viral suppression due to recent changes in ART regimens, such as increased use of integrase strand transfer inhibitors (ISTIs). These drugs may have superior tolerability, reduced pill burden, and improved virologic and immune outcomes (715). Up-to-date evaluations are needed to assess improvements in HIV care, specifically viral suppression, over the past decade. The Centers for AIDS Research Network of Integrated Clinical Systems (CNICS) is a cohort collaboration that integrates data from a large and diverse population of PLWH across the United States (16). CNICS captures a broad range of information associated with the rapidly changing course of HIV disease management. Our objective was to examine changes in viral suppression over time and identify associated factors. We were particularly interested in the association of viral suppression with changing demographic and clinical characteristics, such as substance use patterns, medication adherence, and ISTI use. Methods Data Source CNICS is a dynamic cohort of more than 32000 PLWH who have had 2 or more HIV clinical care visits at 8 sites across the United States (Appendix Figure 1). Each CNICS clinic is at a Centers for AIDS Research site. The CNICS data repository integrates comprehensive clinical data, including laboratory test results; ART and other medications; diagnoses; demographic data; and historical information, including prior ART use, collected at initial clinic visits via standardized intake processes (16). In addition, in recent years, sites have initiated the CNICS clinical assessment of patient-reported measures and outcomes, including illicit drug use, alcohol use, adherence, and depression (1720). During routine clinical care visits, PLWH use touchscreen tablets equipped with Web-based survey software to complete the CNICS clinical assessments (17, 21). Those who appear intoxicated; have cognitive impairment; or do not speak English, Spanish, or Amharic are not asked to complete the assessment. Appendix Figure 1. Map of CNICS sites. CNICS = Centers for AIDS Research Network of Integrated Clinical Systems. Institutional review boards at each site approved CNICS protocols. Study Participants The overall study sample included all CNICS participants aged 18 years or older who had a viral load (VL) measure as part of clinical care between 1997 and 2015. We also examined key subgroups, including those who were receiving ART. We included a subgroup of PLWH receiving ART in or after 2010 to minimize the effect of changes in treatment initiation guidelines. Additional subgroup sensitivity analyses were done among PLWH who had completed the CNICS clinical assessment of patient-reported measures, such as adherence and illicit drug use, and among those known to be ART-naive at initiation of CNICS care. Outcomes The primary outcome was viral suppression versus detection, which we defined as a VL of no more than 400 versus greater than 400 copies/mL to exclude clinically insignificant VL blips (22) and account for changing thresholds of detection over time. In addition, we examined a secondary outcome of VL (in copies/mL) at each time point as a continuous variable, which we log-transformed (in base10) due to skew. We then back-transformed VL coefficients by raising them to a power of 10. We calculated relative VL values, which we defined as the ratio of VL for PLWH with and without each characteristic of interest (such as ISTI use). Predictors We examined age, race/ethnicity, sex, HIV transmission risk factors, and CD4 cell count at ART initiation. Self-reported adherence to ART (23, 24), illicit drug use (including and excluding marijuana) (2529), and depression (30, 31) were collected as part of the CNICS clinical assessment (Appendix Table 1), which was integrated into clinical care between 2005 and 2011 at 7 CNICS sites. Appendix Table 1. Categories and Instruments Used for Key Covariates Statistical Analysis We used 2 tests for categorical variables and t tests for continuous variables to assess differences in demographic and clinical characteristics among persons with and without viral suppression based on VL at the end of follow-up. To understand VL trends in unadjusted ecological analyses (1997 to 2015), we determined the percentage of VL tests showing no more than 400 copies/mL each year for all patients receiving care that year (overall). We then examined VL trends among PLWH categorized by various factors, including age, sex, race/ethnicity, and HIV transmission risk factor. We examined time to viral suppression for the majority (>50%) of VL tests by year of ART initiation. People living with HIV typically had multiple measures in all years since CNICS enrollment. Because we were concerned that those who were lost to follow-up may have been less likely to have viral suppression, we repeated trend analyses limiting tests to 1 random VL result per calendar year per person and accounting for possible differential loss to follow-up using inverse probability of censoring weights based on prior VL values and demographic characteristics (age, sex, and race/ethnicity) to rebalance the sample on the basis of these parameters (32). To operationalize this approach, separate logistic models (with age, sex, and race as covariates) were fit to each time point to estimate the weights. This limited potential selection bias due to loss to follow-up that differed on the basis of characteristics of participants who were lost to follow-up. Our primary analysis was a joint longitudinal and survival model that examined associations between demographic and clinical characteristics and having a detectable VL among persons receiving ART from 1997 to 2015 while accounting for loss to follow-up (33). In this model, we specified the longitudinal process as a mixed logistic model with detectable VL as the outcome. We specified the time-to-event process as loss to follow-up (due to death or leaving the cohort) using a Weibull distribution. This approach was selected because of known limitations with less complex models (34). In particular, there was concern that the loss-to-follow-up process was related to the detectable VL process, and so the models for these 2 processes needed to be estimated jointly. Using this model allowed us to account for potential informative dropout due to loss to follow-up that was related to both the covariates included in the model and the detectable VL measures. We repeated the analyses and limited them to patients known to be ART-naive at enrollment. We also conducted sensitivity analyses that added adherence, illicit drug use (opiates or heroin, cocaine or crack, and methamphetamine or crystal methamphetamine), and depression among the subset who completed the CNICS clinical assessment and included current substance use categorized by class (opiates or heroin, cocaine or crack, methamphetamine or crystal methamphetamine, marijuana, and hazardous alcohol use) with adherence. To minimize the effect of changes in treatment initiation guidelines, we conducted a subgroup analysis for 2010 to 2015, during which time the guidelines changed little after having been expanded to include initiation of treatment independent of CD4 cell count. Additional sensitivity analyses were conducted that included CD4 cell count at ART initiation, added a quadratic term for calendar time (year of cohort entry, centered around 2010), excluded site, and used a different VL cut point (50 copies/mL). Finally, we repeated the analyses using linear mixed models with VL (log10) as a continuous outcome because these models inherently handle unbalanced numbers and times of observations between patients and clustering by participant (35). All models were adjusted for age, sex, race/ethnicity, site, ISTI use, calendar time, and years of follow-up. Statistical models were fit using Stata, version 14 (StataCorp), with the GSEM package used to fit the joint longitudinal and survival models. Role of the Funding Source CNICS is funded by grants from the National Institutes of Health. The funders had no role in the design, conduct, or analysis or the decision to publish this manuscript. Results We included 31930 PLWH from 8 CNICS sites (Appendix Figure 2) who had at least 1 HIV VL measurement after 1 January 1997 (mean number of measurements per person, 17 [SD, 16]), of whom 47% were alive and receiving care at the end of the study period. Eighty-two percent were men, 55% were nonwhite, and the mean number of HIV care visits was 22. Table 1 shows demographic and clinical chara