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EPIDEMIOLOGY AND SOCIAL

A longitudinal, HIV care continuum

10-year restricted mean time in each care continuum stage after enrollment in care, by history of IDU

Lesko, Catherine R.; Edwards, Jessie K.; Moore, Richard D.; Lau, Bryan

Author Information
doi: 10.1097/QAD.0000000000001183

Abstract

Introduction

Viral suppression is the ultimate goal of HIV care, given the associated reduced morbidity, mortality, and infectiousness [1,2]. The HIV care continuum is a convenient framework for visualizing the path to viral suppression, and is typically presented as the proportion of HIV-infected persons diagnosed, linked to medical care, retained in care, prescribed antiretroviral therapy (ART), and virally suppressed in a given population in a given year [3,4]. The cross-sectional care continuum does not address how individuals move through the continuum over time [5].

Injection drug use (IDU) is associated with poorer HIV-related outcomes [6,7]. Particularly, people who inject drugs (PWID) experience delays linking to HIV medical care, and are less likely to be retained in care, prescribed ART, and virally suppressed [8–10]. PWID also have a higher mortality risk than non-IDU [11,12]. ‘Longitudinal’ analyses of the care continuum are typically serial snapshots, with people who die removed from the denominator for continuum estimates in subsequent years [13], artificially inflating the reported proportion of PWID who are virally suppressed [14,15]. Furthermore, excluding deceased persons precludes consideration of death as a relevant end point for the care continuum.

We present a novel method for visualizing the care continuum for PWID and non-IDU, subsequent to engagement in care, which accounts for differences in survival and shifts consideration of the care continuum from a cross-sectional to a longitudinal perspective.

Methods

Study population

The Johns Hopkins HIV Clinic provides HIV continuity care to HIV-infected persons principally residing in the Baltimore, Maryland area, but also from surrounding states. HIV-infected patients both self-refer and are referred to the clinic by other healthcare providers across the region. Patients are enrolled into care without regard to demographic, socioeconomic status, or medical insurance, and the clinic population reflects the HIV epidemic in Baltimore and the surrounding region. The clinic is staffed by pharmacists, nurses, and caseworkers that supports antiretroviral adherence through methods tailored to the individual patient. The Johns Hopkins HIV Clinical Cohort (JHHCC) consists of all HIV-infected persons age at least 18 years who enroll in care at Johns Hopkins HIV clinic and consent to share their data (>90% of persons enrolled into continuity care). For this study, we included persons enrolled in the JHHCC from January 2000 to August 2015 who were ART naïve (prior exposure to mono or dual therapy only) and not virally suppressed (≤400 copies/ml) at enrollment. Collection of data on patients in the JHHCC, and this analysis, was approved by the Johns Hopkins Hospital Institutional Review Board.

Patients who reported IDU as the likely source of their HIV infection (i.e. who had a history of injection prior to their HIV diagnosis) were classified as PWID. PWID may not have been actively injecting throughout the study period. Furthermore, non-IDU may have been using illicit drugs through routes other than injection (it is possible but unlikely that some patients started injecting drugs after enrollment). Heroin and cocaine are the most commonly injected drugs in this cohort; injection of amphetamine and other drugs is rare. Baseline laboratory values were defined as those measured closest to enrollment, within a window 6 months prior to and 1 month after enrollment. We excluded 14 patients without baseline CD4+ cell count.

Outcomes measurement

Because our study sample was, by definition, linked to care, we focused on care continuum outcomes subsequent to linkage to care: loss to care, ART initiation, viral suppression, and death. We stratified death and loss to care by whether they occurred before or after ART initiation. Thus, there were seven care continuum stages in our framework (listed below; also in Appendix A, Figure 1, http://links.lww.com/QAD/A943). Loss to care was approximated as loss to clinic (LTC), with the understanding that patients lost to the Johns Hopkins HIV clinic may re-engage in care elsewhere. Patients were classified as LTC after 12 months with no HIV laboratory measurements or clinical visits in the HIV outpatient center. Patients re-entered care with any new CD4+ cell count, viral load, or clinical visit. ART initiation was defined as the initiation of at least three antiretroviral medications on the same day. Viral suppression was defined as most recent viral load 400 copies/ml or less. Dates of death were obtained from clinic sources and regular matches against the Social Security Death Index.

Analysis

We estimated the proportion of the cohort in each stage of the care continuum over time, stratified by history of IDU. Complete details of our approach are available in appendix A, http://links.lww.com/QAD/A943. Briefly, we estimated the cumulative incidence from enrollment to the following events, nonparametrically [14]. Events in bold correspond to transition into a continuum state (regardless of prior state); events in italics correspond to transition out of a continuum state (regardless of future state).

  1. Death before ART initiation
  2. LTC before ART initiation
  3. No longer LTC before ART initiation (composite outcome of return to clinic and death prior to return to clinic)
  4. ART initiation
  5. Viral suppression after ART initiation
  6. No longer virally suppressed after ART initiation (composite outcome including viral load measurement >400 copies/ml, death, or LTC)
  7. LTC after ART initiation
  8. No longer LTC after ART initiation
  9. Death after ART initiation

Note that ART initiation corresponds to transition into the continuum state ‘on ART and not suppressed’ only if ART initiation is not also accompanied by viral suppression or death on the same day. Events other than ART initiation and death could occur more than once in the analysis. The maximum numbers of occurrences of each event in our data are available in Appendix A Table 1, http://links.lww.com/QAD/A943. By estimating cumulative incidence functions, we have appropriately accounted for competing events (i.e. events that preclude occurrence of the event of interest). For example, death prior to ART initiation precludes a patient from ever initiating ART; the cumulative incidence function for ART initiation can only ever go up to (100–D)% where D is the cumulative incidence of death before ART initiation. Competing event(s) for each continuum-related event are listed in Appendix A Table 1, http://links.lww.com/QAD/A943.

The cumulative incidence for events above represents the proportion entering and exiting each of the continuum stages. To estimate the proportion present in each continuum stage, we added and subtracted cumulative incidence curves [16,17] as follows:

  1. Dead before ART initiation = cumulative incidence of death prior to ART initiation
  2. LTC before ART initiation = sum of cumulative incidences for LTC before ART initiation, less the sum of the cumulative incidences for no longer LTC before ART initiation
  3. Dead after ART initiation = cumulative incidence of death after ART initiation
  4. LTC after ART initiation = sum of the cumulative incidences for LTC after ART initiation, less the sum of the cumulative incidences for no longer LTC after ART initiation
  5. On ART, virally suppressed = sum of the cumulative incidences of viral suppression on ART, less the sum of the cumulative incidences of no longer being virally suppressed
  6. On ART and not suppressed = cumulative incidence of ART initiation, less the cumulative incidences of death after ART initiation, LTC after ART initiation, and virally suppressed on ART
  7. In care and not on ART = 1 minus the cumulative incidence of death before ART initiation, minus the proportion LTC before ART initiation (#2 in this list), minus the cumulative incidence of ART initiation

By design, the proportions above sum to 1 at each time point. Thus, we can present the distribution of the cohort over time since enrollment as a set of stacked curves. Integrating the area between adjacent curves (or equivalently, the area under each individual curve graphed separately) gives the (restricted) mean time spent in each continuum stage (or the mean months of life lost) over 10 years of follow-up. Here, the term ’restricted’ is used to clarify that the total follow-up time is capped at (restricted to) 10 years, and thus the average time in each state is a function of the maximum follow-up time allowed in the analysis; for example, the 5-year restricted mean time in a stage will necessarily be smaller than the 10-year restricted mean time, which will be smaller than the overall average time in each state had we been able to follow everyone from enrollment until the last person had died (the unrestricted mean time). We estimated the restricted mean time in each state empirically, and then took the difference in estimates for PWID and non-IDU.

We compare our metric to the difference in the proportion of PWID and non-IDU in each stage of the care continuum at 10 years. This latter quantity is similar to traditional care continuum estimates in that, it provides a snapshot of the population at a specific time; however, it is anchored to time since clinic enrollment, rather than to calendar time.

We used inverse probability weights [18–20] to adjust for sex, black race, MSM transmission risk, and baseline age, CD4+ cell count, HIV1 viral load, AIDS diagnosis, and prior antiretroviral exposure. Adjustment served to balance the distribution of these covariates among PWID and non-IDU ‘at clinic enrollment,’ such that adjusted results are not because of differences in demographics or initial care-seeking behavior of PWID and non-IDU.

To examine changes in the continuum over calendar time, we stratified the cohort according to year of enrollment (2000–2003, 2004–2007, and 2008–2014) and compared the difference in 5-year restricted mean time spent in each stage of the care continuum associated with IDU for each enrollment cohort.

We formed 95% Wald confidence intervals using standard error estimated by the SD of estimates based on 200 nonparametric bootstrap resamples of the data [21]. We conducted all analyses in SAS 9.4 (SAS Institute Inc., Cary, North Carolina, USA).

Results

Of 1443 ART-naive, virally unsuppressed, HIV-infected persons enrolled in the JHHCC between January 2000 and August 2015, the majority was men (65%), black (77%), and heterosexual (57%). Median age at enrollment was 40 years [interquartile range (IQR): 34, 46], median CD4+ cell count was 284 cells/μl (IQR: 119, 477), and median log10 HIV viral load was 4.4 copies/ml (IQR: 3.8, 5.0)]. One-third of persons (33%) had prior exposure to antiretroviral medications and 21% had a prior AIDS diagnosis. PWID composed 32% of the study sample. PWID were slightly older than non-IDU and fewer reported MSM or heterosexual sex as another possible risk factor for HIV infection (Table 1).

Table 1
Table 1:
Characteristics of 1443 antiretroviral therapy-naive, virally unsuppressed, HIV-infected persons who enrolled in the Johns Hopkins HIV Clinical Cohort from 2000 to 2014, stratified by self-report of IDU as their likely route of HIV acquisition.

During the 10 years following enrollment in the JHHCC, both non-IDU and PWID spent the most time in any one continuum stage, on ART, and virally suppressed (44.9 and 27.9 months, respectively) (Fig. 1, Table 2). However, this represented only 37 and 23%, respectively, of total person-time for non-IDU and PWID. On average, PWID spent 17.0 fewer months [95% confidence interval (CI: −21.2, −12.8) on ART and virally suppressed compared with non-IDU. In contrast, PWID lost 8.9 more months of life, spent 5.0 more months on ART but not virally suppressed (95% CI: 2.4, 7.6), and spent 5.5 more months in care but not on ART (95% CI: 3.0, 7.9) (Table 3). PWID spent 4.9 fewer months lost to clinic after ART initiation, but that may not be attributable to better retention; PWID had lower incidence of ART initiation and higher incidence of death after ART initiation. Results from the weighted analysis were similar to unweighted results.

Fig. 1
Fig. 1:
Proportion of N = 1443 antiretroviral therapy-naïve, virally unsuppressed, HIV-infected PWID and non-IDU in each compartment of the HIV care continuum following enrollment in the Johns Hopkins HIV Clinical Cohort to 10-year follow-up.ART, antiretroviral therapy (3+ drugs); drop-out/LTC, lost-to-clinic, defined as 12 months without an HIV laboratory measurement or HIV clinic visit; PWID, people who inject drugs; viral suppression defined as ≤ 400 cells/μl. Adjusted curves adjusted for sex, age at enrollment, race, male-male sexual contact as an HIV acquisition risk factor, history of any antiretroviral use (mono- or dual-therapy), prior AIDS diagnosis and CD4+ cell count and HIV viral load at enrollment.
Table 2
Table 2:
Crude and adjusteda average number of months over 10 years of follow-up spent in each of the HIV care continuum stages by antiretroviral therapy-naïve, virally unsuppressed people who inject drugs and those who do not (non-IDU) who enrolled in the Johns Hopkins HIV Clinical Cohort, 2000–2014.
Table 3
Table 3:
Crude average number of months and difference comparing people who inject drugs and non-IDU over 5 years of follow-up spent in each of the HIV care continuum stages, for antiretroviral therapy-naïve, virally unsuppressed, HIV-infected persons who enrolled in the Johns Hopkins HIV Clinical Cohort, stratified by calendar period of enrolment.

Comparing the proportion of PWID and non-IDU in each stage of the care continuum at 10 years after enrollment resulted in similar substantive conclusions: PWID were less likely to be on ART and virally suppressed. However, the relative magnitude of differences between PWID and non-IDU differed. For example, while PWID spent 5% more person-time over 10 years of follow-up in care but not yet on ART (Table 2), at the end of 10 years, 4% fewer PWID were in care but not yet on ART (Supplemental Table 1, http://links.lww.com/QAD/A943). In contrast, while PWID spent 4% more person-time over 10 years on ART but not virally suppressed, at the end of 10 years, the proportion of PWID on ART but not virally suppressed was 7% higher than among non-IDU.

There were increases in the 5-year restricted mean time spent on ART and virally suppressed for both PWID and non-IDU from the 2000–2003 enrollment cohort (9.8 and 17.8 months, respectively) to the 2008–2014 enrollment cohort (17.6 and 28.0 months, respectively). However, differences in 5-year restricted mean time in each continuum stage comparing PWID with non-IDU were similar across all enrollment cohorts (Fig. 2, Table 3). For example, the difference in months spent on ART and virally suppressed was −8.0 (95% CI: −10.9, −5.2) for the 2000–2003 enrollment cohort, and −10.3 (95% CI: −14.5, −6.1) for the 2008–2014 enrollment cohort. Thus, while overall HIV care continuum outcomes improved, the disparity in outcomes between PWID and non-IDU did not.

Fig. 2
Fig. 2:
Proportion of antiretroviral therapy-naïve, virally unsuppressed, HIV-infected PWID and non-IDU in each compartment of the HIV care continuum following enrollment in the Johns Hopkins HIV Clinical Cohort to 5-year follow-up, stratified by enrollment cohort.ART, antiretroviral therapy (3+ drugs); drop-out/LTC, lost-to-clinic, defined as 12 months without an HIV laboratory measurement or HIV clinic visit; PWID, people who inject drugs; viral suppression defined as ≤ 400 cells/μl. Adjusted curves adjusted for sex, age at enrollment, race, male-male sexual contact as an HIV acquisition risk factor, history of any antiretroviral use (mono- or dual-therapy), prior AIDS diagnosis and CD4+ cell count and HIV viral load at enrollment.

Discussion

PWID and non-IDU spent only 23 and 37%, respectively, of 10-years of follow-up on ART and virally suppressed. If we exclude years lost to death from the denominator, PWID and non-IDU spent only 28 and 41%, respectively, of alive follow-up on ART and virally suppressed. If we exclude person-time lost to death or LTC (include only person-time in care), 56% (44.9/79.8 months) and 38% (27.9/73.3 months) of follow-up time was spent on ART and virally suppressed by non-IDU and PWID, respectively. In contrast, excluding persons who were dead or LTC from the denominator, 79% of non-IDU and 65% of PWID were on ART and virally suppressed at 10 years after clinic enrollment. In a traditional cross-sectional cascade analysis for 2009, 40% of non-IDU and 34% of PWID in the United States who were HIV-infected and who had initiated HIV care were virally suppressed on ART [22]. Although our analysis reached substantively similar conclusions for comparing non-IDU and PWID, the two quantities measure different constructs (person-time versus proportion of a population) in different populations (a clinical cohort versus a population). Recently, McNairy et al. compared the care continuum in four African countries using traditional continuum estimates and a new ‘comprehensive HIV care continuum’ that shared several features with our approach (namely following persons longitudinally from when they are ‘at risk’ for care continuum outcomes) [23]. Their conclusions comparing the four countries were substantively different depending on the approach used, demonstrating that longitudinal and traditional approaches yield different but complementary information [23].

Despite improvements in nearly all stages in the HIV care continuum across enrollment cohorts, there was an apparent increase in the restricted mean time spent LTC after ART initiation in 2008–2014. We believe that to be, in part, an artifact of the data, and in part, because of changing clinical practice. Clinical guidelines released in 2013 suggested that people who were adherent to ART and virally suppressed could be seen in clinic every 6 months and have viral load and CD4+ cell count monitored every 6–12 months [24]. With traditional markers of being ‘in care’ assessed less frequently, the potential for people who are on ART and virally suppressed to be classified as LTC will increase [13]. We do not have reason to suspect that missing visit data or changes in clinical management are differential according to history of IDU, however, and relative differences in restricted mean time should still lead to correct inference.

Once persons are virally suppressed, their ability to transmit their infection is negligible [1,25]. One strength of this analysis is that it yields a measure of person-time spent virally unsuppressed, which may give an idea of the relative potential impact of PWID and non-IDU on the HIV epidemic through their ability to transmit infection. If we assume that individuals lost to the Johns Hopkins HIV Clinic were not in care elsewhere, we would estimate that that PWID spent 6.02 years on average out of 10, capable of transmitting infection (12.1 + 25.9 + 19.5 + 14.7 months/(12 months/year), from Table 3); we would estimate that non-IDU spent 5.35 years on average out of 10, capable of transmitting infection. If we then multiply the average time spent infections per person by the number of people in the study sample, we estimate that PWID and non-IDU enrolled in the JHHCC contributed 2744 and 5280 infectious person-years to the Baltimore community. If we instead assume that individuals lost to clinic were in care, on ART, and virally suppressed elsewhere (i.e., transferred care to another clinic), on average PWID and non-IDU spent 3.78 and 2.92 of 10 years capable of transmitting infection, and contributed 1725 and 2879 total infectious person-years to the community (these two assumptions represent the lower and upper bounds for infectious person-years). Thus, although on average, an individual PWID spends more time capable of transmitting virus, greatest potential for transmission overall comes from non-IDU.

There is increasing appreciation of limitations of the care continuum as currently estimated [13,26,27] in that it does not capture patients’ transition through continuum stages nor follow the same population over time. Furthermore, there is movement toward developing a complementary, longitudinal assessment of the HIV care continuum [23,26]. Our work is one of the first patient-centric, longitudinal presentations of the HIV care continuum and, while similar to other models, has unique strengths. Our approach summarizes patients’ experience across all of follow-up rather than at only one point in time. Furthermore, our approach corresponds directly to a public health priority: to reduce the amount of time people spend capable of transmitting HIV infection. Our approach is based on a multistate model framework. Although it is relatively easy to implement, its utility for informing future mathematical models of the continuum may be limited compared with a more explicit multistate model approach. We compare our approach with other longitudinal continuum analyses [28,29] appendix B, http://links.lww.com/QAD/A943.

Our analysis remains limited in that we could not follow patients after LTC to appropriately stage them in the care continuum (other than for mortality). It is possible that patients LTC from the JHHCC entered HIV care elsewhere. Applying this approach to a population-based cohort may improve upon this potential misclassification error, but even population-based data may misclassify persons lost to-follow-up as out of care if they migrate out of the catchment area [30].

We present a novel, patient-centric, longitudinal approach to estimating progression through the HIV care continuum. Information from this approach provides supplemental information for evaluating health systems’ success at retaining patients in care, initiating them on ART, and achieving viral suppression. There are not differences in the amount of time PWID and non-IDU spend LTC after enrolling in the JHHCC. Rather PWID in care are spending less time on ART, and once on ART, they spend less time virally suppressed. Efforts to increase viral suppression among PWID should focus on increasing ART initiation and improving adherence to therapy.

Acknowledgements

This work was supported by NIH grants U01 DA036935, and P30 AI094189, R01 AI100654 and DP2 HD084070. The funders have had no influence on the design of this analysis or reporting of results.

Conflicts of interest

C.R.L. reports receiving a speaker honorarium from Gilead Sciences, Inc. for work unrelated to this analysis. The remaining authors declare no conflicts of interest.

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Keywords:

competing risks; HIV care continuum; IDU; survival analysis

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