Abstract
BACKGROUND:
Persons with HIV (PWH) and opioid use disorder (OUD) can have poor health outcomes. We assessed whether intensity of behavioral treatment for OUD (BOUD) with and without medication for OUD (MOUD) is associated with improved HIV clinical outcomes.
METHODS:
We used Veterans Aging Cohort Study (VACS) data from 2008 to 2017 to identify PWH and OUD with ≥1 BOUD episode. We assessed BOUD intensity and ≥6 months of MOUD (methadone or buprenorphine) receipt during the 12 months after BOUD initiation. Linear regression models assessed the association of BOUD intensity and MOUD receipt with pre-post changes in log viral load (VL), CD4 cell count, VACS Index 2.0, antiretroviral treatment (ART) initiation, and ART adherence.
RESULTS:
Among 2,419 PWH who initiated BOUD, we identified five distinct BOUD intensity trajectories: single visit (39% of sample); low-intensity, not sustained (37%); high-intensity, not sustained (9%); low-intensity, sustained (11%); and high-intensity, sustained (5%). MOUD receipt was low (17%). Among 709 PWH not on ART at the start of BOUD, ART initiation increased with increased BOUD intensity (p<0.01). Among 1,401 PWH on ART at the start of BOUD, ART adherence improved more in higher-intensity BOUD groups (p<0.01). VL, CD4 count and VACS Index 2.0 did not differ by BOUD or ≥6 months of MOUD treatment.
CONCLUSION:
Among PWH and OUD who initiated BOUD, higher intensity BOUD was associated with improved ART initiation and adherence, but neither BOUD alone nor BOUD plus ≥6 months MOUD was associated with improvements in VL, CD4 count or VACS Index 2.0.
Keywords: HIV, opioid use disorder, veterans, medication treatment, behavioral treatment
1. INTRODUCTION
Opioid use disorder (OUD) is highly prevalent among persons with HIV (PWH) (Lesko et al., 2018; Mimiaga et al., 2013). Untreated OUD in PWH is associated with delayed access and adherence to antiretroviral therapy (ART), lack of achieving and maintenance of viral suppression, decreased retention in care, and increased morbidity and mortality from HIV- and non-HIV-related causes (Altice et al., 2010; Blacker, 2004; Chitsaz et al., 2013; Coviello et al., 2018; Mellins et al., 2009; Tolson et al., 2018).
Although the repercussions of untreated OUD are significant, treatment remains infrequent among PWH (Fiellin et al., 2011; Korthuis et al., 2017; Kraemer et al., 2019; Wyse et al., 2019). Medication for opioid use disorder (MOUD), such as buprenorphine, methadone, are first-line treatments for OUD (National Institute on Drug Abuse, 2018). While behavioral treatment for OUD (BOUD) is not necessary to initiate or maintain MOUD (Crotty et al., 2020), many patients also receive BOUD such as cognitive-behavioral therapy, motivational interviewing, and contingency management in addition to MOUD, and some receive BOUD alone (Sofuoglu et al., 2019). Duration and intensity of BOUD (defined as number of clinic visits and/or days in inpatient treatment with BOUD with or without MOUD per year) varies. Reasons for this variation include OUD severity and/or related consequences (e.g., infectious complications), provider/patient preference, insurance coverage, programmatic regulations/rules associated with maintaining treatment engagement, and geographic access to treatment facilities (Gellad et al., 2017; Oliva et al., 2012; Wyse et al., 2018). Persons with HIV and OUD have high rates of concurrent non-opioid substance use and psychiatric comorbidities, and thus may experience greater benefit from higher levels of OUD treatment intensity than the general population (Card et al., 2018; Mimiaga et al., 2008). It is unknown if increased intensity of BOUD, with or without MOUD, leads to better HIV outcomes. Better understanding of this relationship could guide efforts to promote retention in treatment for PWH and OUD.
The aim of this observational cohort study was to assess whether measurable differences exist in HIV clinical outcomes from pre-BOUD to 12 months after start of treatment by BOUD intensity and MOUD receipt. We hypothesized that higher-intensity BOUD and ≥6 months of MOUD would be associated with improvements in HIV-related outcomes, compared to lower levels of treatment.
2. METHODS
2.1. Setting and Sample
We used data from PWH enrolled in the Veterans Aging Cohort Study (VACS) from 2008 to 2017 (Justice et al., 2006). Briefly, VACS is a national cohort study of 55,986 PWH who received care within the Veterans Health Administration (VHA) since 1996 (Fultz et al., 2006). We obtained data from the VHA Corporate Data Warehouse (CDW) and the VA Pharmacy Benefits Management database, which captures data from the VHA’s electronic health record and includes addiction-related care (Fultz et al., 2006).
Of the 55,986 PWH in VACS, we identified 2,419 with a “new” BOUD encounter from January 2008 to September 2017. We considered this encounter to be a “new” treatment episode only if it was preceded by a 12-month period without OUD treatment (BOUD or MOUD). We chose 2008 as the starting point as buprenorphine was not widely used within the VHA prior to this time (Oliva et al., 2011, 2012, 2013; Wyse et al., 2018).
Patients met inclusion criteria if they had 1) at least one year of observation time within the VHA prior to their initial BOUD episode and 2) at least one inpatient or outpatient BOUD episode during the study period. BOUD encounters were defined using methodology adapted from Harris and colleagues (Harris et al., 2015) (see Supplementary Tables 1–3). The large majority of MOUD prescribing within the VHA occurs within specialty substance use disorder and mental health settings, thus a behavioral treatment visit would generally occur, regardless of the treatment setting or type of treatment (MOUD or BOUD) (Gordon et al., 2020, Valenstein-Mah et al., 2018, Wyse et al., 2018).
The study excluded patients if they died during the year following their initial BOUD episode, as outcomes were measured at 12 months following BOUD initiation. We defined a BOUD clinical encounter as the first episode of BOUD after enrollment in the VACS database, and thus did not assess individuals at multiple points in time. The project was IRB approved at the VA Connecticut Healthcare System, the VHA, and university affiliates of participating sites (Justice et al., 2006).
2.2. Measures
2.2.1. Behavioral Treatment for OUD (BOUD)
We defined BOUD as any outpatient or inpatient psychosocial therapy encounter, including cognitive behavioral therapy, motivational enhancement therapy, contingency management or behavioral couples therapy.
2.2.2. Defining BOUD Intensity
We determined the number of BOUD days for each month during the 12-month period following the first BOUD episode. We used a semi-parametric group-based trajectory model to fit treatment trajectories (Jones & Nagin, 2013) based on number of treatment days per month over a 12-month period. The trajectory procedure sorts each individual’s set of month treatments into “clusters,” estimates a single model consisting of distinct trajectories from the data, and then assigns the trajectory with the highest probability of membership to each individual. We used a zero-inflated Poisson outcome distribution for all trajectory-based models based on the distribution of the data. Model selection requires determining the number of groups and the trajectory shapes that best describe the data (Jones & Nagin, 2013; Nagin & Odgers, 2010). We examined Bayesian information criterion (BIC), significance of polynomial terms, and the values of average posterior group membership probability. We also calculated, more simply, the number of treatment days over the 12-month period.
2.2.3. Medication for Opioid Use Disorder (MOUD)
We considered opioid agonist treatment with buprenorphine or methadone to be MOUD. We did not include naltrexone in our analysis as: 1) receiving naltrexone requires detoxification before use, 2) use is limited within the VHA, and 3) naltrexone is second-line MOUD treatment (National Institute on Drug Abuse, 2018). For buprenorphine, we calculated the number of days during the 12-month period that were covered by a prescription fill or refill. Our analysis considered one month of retention on buprenorphine to be ≥28 days of medication coverage, and six months of buprenorphine to be ≥170 days of medication coverage. Methadone treatment/retention was determined using opioid agonist clinic codes for outpatient visits and pharmacy fill data from inpatient visits (see Supplementary Table 4). To determine one-month and six-month MOUD retention, we combined the buprenorphine and methadone retention variables. We also created a variable for any MOUD receipt over the 12-month follow-up (at least one buprenorphine prescription or at least one stop code 523) and time until receipt of buprenorphine or methadone.
2.3. HIV Treatment and Health Outcomes
The primary outcome was change in log HIV RNA-1 VL (copies/mL) from pre-BOUD to 12 months post-treatment. Our secondary outcomes included changes in pre- to post-treatment CD4 count (cells/mL), VACS Index 2.0, ART initiation (based on pharmacy fill/refill data, for those not on ART at BOUD initiation) (Williams et al., 2019), and adherence to ART (percent of days covered by ART fill/refill data out of 365 days) (Williams et al., 2020). The VACS Index 2.0 is a validated mortality index that uses age, routine indicators of HIV disease and organ system injury and hepatitis C infection to estimate HIV disease severity (Tate et al., 2013). Scores range from 0 to 164, with higher scores reflecting higher disease severity and increased risk of all-cause mortality (McGinnis et al., 2019; Tate et al., 2013). Among PWH on ART at baseline, we defined adherence to ART as percent of days covered by an ART prescription for the 12 months after BOUD initiation. We obtained all outcome variables as close to the initiation (up to 1 year prior) and 12 months post-initiation of BOUD as possible (range from 3-15 months after first BOUD).
2.4. Covariates
We considered demographic and clinical variables thought to be associated with both the exposure and outcomes as potential covariates in our models. These variables were age at start of treatment, gender (male/female), race/ethnicity, residency (urban vs. rural), homelessness, and concurrent mental health and comorbid non-opioid substance use disorders. We used ICD-9 and ICD-10 codes documented from the 12 months prior to the baseline BOUD date to 6 months after enrollment in VACS to measure psychiatric diagnoses, alcohol-related diagnoses, and other drug use. Unhealthy alcohol use was based on having a score of ≥4/3 for men/women on the Alcohol Use Disorders Identification Test Consumption in the VHA Health Factors dataset, using the date closest to first BOUD treatment date (Bradley et al., 2007). Smoking status was based on the most common value (never, past, current) in the VHA Health Factors dataset (McGinnis et al., 2011). Rural versus urban location was based on rural-urban commuting area codes for each 3-digit station at which first treatment occurred. Homelessness was defined based on ever having an ICD-9 or -10 code for homelessness (McGinnis et al., 2019).
2.5. Analysis
Our analysis described patient characteristics and HIV outcomes overall and by BOUD intensity groups using analysis of variance, Kruskal-Wallis, and chi-square tests as appropriate. To understand and portray treatment patterns, we graphed the average number of BOUD days per month by the 12-month treatment intensity trajectory groups.
We ran multivariate linear regression models to assess the adjusted association of BOUD intensity (as a categorical variable) and ≥6 months of MOUD receipt with the pre-post changes in HIV clinical outcomes. We employed two sets of models for each outcome. Model 1 adjusted for baseline demographic characteristics, pre-treatment measures, and time between outcome measures. Model 2 adjusted for these variables and added covariates including residency, hepatitis C, smoking status, unhealthy alcohol use, and other drug use Models were run including all PWH and stratified by VL (suppressed VL ≤50 copies/mL versus detectable VL >50 copies/mL). We used an overall Wald test to test the significance of the association between treatment intensity and change in biomarker outcomes and adherence. We performed all statistical analysis utilizing Stata SE version 15.
2.5.1. Secondary Analysis
Since only a small percentage of the overall sample that initiated BOUD received ≥6 months of MOUD, we ran regression models excluding those who received ≥6 months of MOUD. Additionally, we tested for the effect of BOUD intensity combined with ≥6 months of MOUD by running a model including an interaction term. We also performed a sensitivity analysis using a stricter definition of methadone treatment for our MOUD variable (10 or more opioid agonist clinic visits or days with inpatient medication over the first month and for each month after) (See Supplementary Table 4 for full details). To examine HIV VL in a different way, we ran multivariate logistic regression models to evaluate the association of BOUD intensity and MOUD receipt with HIV VL as a binary variable (suppressed VL ≤50 copies/mL vs. detectable VL >50 copies/mL).
3. RESULTS
Of the 55,986 PWH in VACS, 2,419 PWH and OUD met the criteria for our analysis. The sample was predominantly middle-aged, Black, and male (Table 1). The majority had co-occurring hepatitis C, were currently smoking, and had an alcohol-related diagnosis. At baseline, 71% were on ART, 56% had a suppressed VL, and 85% had a CD4 count above 200 cells/mL.
Table 1.
Sample Characteristics of Veteran Patients Living with HIV and Opioid Use Disorder.
| Behavioral Treatment Intensity | |||||||
|---|---|---|---|---|---|---|---|
| Characteristic | Total | One Visit Only | Minimal | High to None | Low, Sustained | High, Sustained | P-value |
| Number (%) | 2,419 (100) | 943 (39.0) | 889 (36.8) | 213 (8.8) | 258 (10.7) | 116 (4.8) | |
| Age, mean (SD) | 55.3 (7.5) | 55.2 (7.7) | 55.5 (7.6) | 54.7 (7.8) | 55.3 (6.2) | 56.3 (6.8) | 0.27 |
| Sex, Male | 2,346 (97.0) | 918 (97.4) | 859 (96.6) | 205 (96.2) | 251 (97.3) | 113 (97.4) | 0.85 |
| Race/Ethnicity | -- | -- | -- | -- | -- | -- | 0.08 |
| White | 614 (25.4) | 243 (25.8) | 229 (25.8) | 55 (25.8) | 69 (26.7) | 18 (15.5) | -- |
| Black | 1,491(61.6) | 583 (61.8) | 525 (59.1) | 135 (63.4) | 160 (62.0) | 88 (75.9) | -- |
| Latino | 255 (10.5) | 95 (10.1) | 112 (12.6) | 16 (7.5) | 25 (9.7) | 7 (6.0) | -- |
| Other | 59 (2.4) | 22 (2.3) | 23 (2.6) | 7 (3.3) | 4 (1.6) | 3 (2.6) | -- |
| Residency | -- | -- | -- | -- | -- | -- | <0.01 |
| Urban | 2,047 (84.6) | 807 (85.6) | 732 (82.3) | 183 (85.9) | 222 (86.8) | 103 (88.8) | -- |
| Rural | 154 (6.4) | 57 (6.0) | 57 (6.4) | 22 (14.3) | 12 (4.7) | 6 (5.2) | -- |
| Unknown | 218 (9.0) | 79 (8.4) | 100 (11.3) | 8 (3.8) | 24 (9.3) | 7 (6.0) | -- |
| Ever Homeless | 1,719 (71.1) | 649 (68.8) | 618 (69.5) | 171 (80.3) | 191 (74.0) | 90 (77.6) | <0.01 |
| Hepatitis C | 1, 682 (69.5) | 567 (60.1) | 642 (72.2) | 161 (75.6) | 210 (81.4) | 102 (87.9) | <0.01 |
| Psychiatric Diagnosis | -- | -- | -- | -- | -- | -- | -- |
| Depression | 310 (12.8) | 121 (12.8) | 117 (13.2) | 29 (13.6) | 35 (13.6) | 8 (6.9) | 0.41 |
| Anxiety | 165 (6.8) | 68 (7.2) | 57 (6.4) | 14 (6.6) | 20 (7.7) | 6 (5.2) | 0.86 |
| PTSD | 231 (9.6) | 81 (8.6) | 91 (10.2) | 29 (13.6) | 20 (7.8) | 10 (8.6) | 0.16 |
| Bipolar Disorder | 188 (7.8) | 65 (6.9) | 74 (8.3) | 24 (11.3) | 19 (7.4) | 6 (5.2) | 0.19 |
| Schizophrenia | 121 (5.0) | 52 (5.5) | 48 (5.4) | 8 (3.8) | 12 (4.7) | 1 (0.9) | 0.22 |
| Substance Use | -- | -- | -- | -- | -- | -- | -- |
| Current Smoking | 1,938 (80) | 710 (75.6) | 720 (81.1) | 184 (86.4) | 223 (86.4) | 101 (87.8) | <0.01 |
| Unhealthy Alcohol Use | 594 (26.2) | 196 (22.7) | 199 (23.9) | 82 (40.2) | 77 (30.9) | 40 (35.1) | <0.01 |
| HIV Indicators at BL | -- | -- | -- | -- | -- | -- | -- |
| Mean Adherent to ART | 1,683 (70.0) | 698 (74.0) | 600 (67.5) | 140 (65.7) | 176 (68.2) | 69 (59.5) | <0.01 |
| On ART | 1,710 (70.7) | 718 (76.1) | 605 (68.1) | 144 (67.6) | 174 (67.4) | 69 (59.5) | <0.01 |
| Suppressed VL ≤ 50 copies/mL | 1,041 (56.2) | 453 (58.0) | 373 (56.8) | 78 (53.8) | 101 (52.3) | 36 (50.0) | 0.41 |
| Baseline CD4 > 200 cells/mm3 | 1,578 (84.8) | 676 (86.3) | 555 (83.8) | 125 (83.9) | 161 (83.4) | 61 (83.6) | 0.67 |
| VACS Index 2.0 (SD) | 58.2 (17.3) | 56.0 (17.0) | 59.2 (17.9) | 57.7 (16.0) | 61.8 (16.5) | 63.4 (15.8) | <0.01 |
| OUD Treatment Location | -- | -- | -- | -- | -- | -- | <0.01 |
| Clinic Only | 1,801 (74.4) | 909 (96.4) | 729 (82.0) | 63 (29.6) | 93 (36.1) | 7 (6.0) | -- |
| Clinic +Medication | 289 (12.0) | 31 (3.3) | 91 (10.2) | 37 (17.4) | 86 (33.3) | 44 (37.9) | -- |
| Clinic +Inpt | 240 (9.9) | 2 (0.2) | 61 (6.9) | 91 (42.7) | 51 (19.7) | 35 (30.2) | -- |
| Clinic +Inpt+Medication | 89 (3.7) | 1 (0.1) | 8 (0.9) | 28 (10.3) | 28 (10.9) | 20 (25.9) | -- |
| MOUD Therapyb | -- | -- | -- | -- | -- | -- | -- |
| Any MOUD | 378 (15.6) | 32 (3.4) | 99 (11.1) | 59 (27.7) | 114 (44.2) | 74 (63.8) | <0.01 |
| Methadone | 258 (10.7) | 25 (2.7) | 69 (7.8) | 30 (14.1) | 74 (28.7) | 60 (51.7) | <0.01 |
| Buprenorphine | 176 (7.3) | 7 (0.7) | 37 (4.2) | 38 (17.8) | 64 (24.8) | 30 (25.9) | <0.01 |
| HIV Outcomes (SD) | |||||||
| Change in Log VL (SD) | −0.16 (1.1) | −0.15 (1.1) | −0.20 (1.1) | −0.05 (1.1) | −0.07 (1.2) | −0.38 (1.0) | 0.04 |
| Change in CD4 (SD) | 11 (191) | 13 (186) | 3 (204) | 27 (174) | 17 (169) | 23 (210) | 0.71 |
| Change in VACS Index 2.0 (SD) | −0.69 (10.7) | −0.83 (10.2) | −0.07 (11.2) | −0.34 (10.2) | −1.70 (11.4) | −2.23 (11.0) | 0.37 |
| Change in Adherence (SD) | 1 (22.7) | −0.03 (20.7) | −0.01 (23.3) | 2.8 (25.7) | 3.4 (25.7) | 6.8 (24.1) | 0.03 |
| ARV Initiation (%) | 220 (31.0) | 52 (23.1) | 86 (30.3) | 27 (39.1) | 36 (42.9) | 19 (40.4) | .003 |
Abbreviations: SD, standard deviation; PTSD, post-traumatic stress disorder; Dx, diagnosis; BL, baseline; VL, viral load; ART, antiretroviral therapy; OUD, opioid use disorder; MOUD, medication for opioid use disorder; Inpt, inpatient; D, day; M, month.
Data cells are N (%) unless otherwise specified.
Participants can have been on more than one type of medication during any given year, e.g., methadone and buprenorphine > 414.
Out of n=1,570 on ARVs
Out of n=709 not on ARV at baseline
3.1. Patterns of BOUD
The majority of PWH and OUD accessed outpatient BOUD only (73%) (Table 1). Most had two or fewer documented BOUD days during the study period (39% had one day, 13% had two days), while only 10% had greater than 45 days in BOUD (Figure 1). Five distinct BOUD trajectories were identified over the one-year period: 1) only one BOUD episode (single visit only; n=943, 39% of sample); 2) very few (range 2-13) BOUD episodes at the beginning of the year (minimal, not sustained; n=889, 36%); 3) many (range 13-120) BOUD episodes at the beginning of the year (high to none; n=213, 9%); 4) few (range 7-99) BOUD episodes dispersed throughout the year (low, sustained; n=258, 11%); and 5) many (range 72-365) BOUD episodes that continued throughout the year (high, sustained; n=116, 5%).
Figure 1.
Mean number of BOUD treatment days per month after start of treatment, by treatment trajectory.
3.2. Patterns of MOUD
Of the 2,419 PWH and OUD who received BOUD for at least one day, only 378 (16%) received at least one month of MOUD during the year of treatment: 11% methadone, 7% buprenorphine (Table 1). MOUD was significantly associated with BOUD intensity (Table 1). The majority of PWH on MOUD (n=238, 63%) received medication within 30 days of BOUD initiation, while 28% (n=107) did not receive MOUD until >90 days after their initial BOUD episode. Of the 378 individuals who received MOUD, 54% (n= 225) received MOUD for at least 30 days and 35% (n=146) for at least 6 months. The mean time to MOUD was 71 days (SD 98.5).
3.3. Patient Characteristics Associated with BOUD Intensity
In univariate analyses, residency, homelessness, hepatitis C, tobacco, alcohol, and other drug use diagnoses, VACS Index 2.0, ART receipt and initiation, MOUD, time to MOUD, 6-month retention on MOUD, and location of OUD treatment were all associated with BOUD intensity (Table 1).
3.4. Association of BOUD Intensity and MOUD with HIV Outcomes
In univariate analyses, log VL, CD4 count, VACS Index 2.0 improved over the 12-month follow-up period for all BOUD intensity groups (Table 1). In adjusted models, change in log VL, CD4, and VACS Index 2.0 outcomes did not differ by BOUD intensity or receiving ≥6 months of MOUD (Table 2).
Table 2.
Association of opioid treatment intensity group and change in log VL, CD4 count, VACS Index 2.0, ART adherence, and ART initiation among patients living with HIV and OUD using multivariate linear regression.
| Model 1 | Model 2 | |||
|---|---|---|---|---|
| Treatment Intensity | Coefficient (95% CI)b | P-Value | Coefficient (95% CI)c | P-Value |
| Log VL | N= 1,706 | N= 1,459 | ||
| ≥6 Months of MOUD | 0.01 (−0.24, 0.26) | 0.92 | −0.01 (−0.22, 0.20) | 0.92 |
| One Visit Only (reference group) | -- | -- | ||
| Minimal | −0.05 (−0.17, 0.07) | 0.19 | −0.05 (−0.15, 0.06) | |
| High to None | 0.10 (−0.10, 0.30) | −0.04 (−0.14, 0.22) | 0.28 | |
| Low, sustained | 0.07 (−0.12, 0.26) | 0.07 (−0.10, 0.24) | ||
| High, sustained | −0.24 (−0.53, −0.05) | −0.17 (−0.42, −0.08) | ||
| CD4 Cell Count | N= 1,691 | N= 1,449 | ||
| ≥6 Months of MOUD | 5.86 (−37.67, 49.39) | 0.79 | 11.54 (−31.75, 54,83) | 0.60 |
| One Visit Only (reference group) | -- | -- | ||
| Minimal | −9.92 (−30.84, 11.00) | −10.00 (−32.23, 12.24) | ||
| High to None | 11.16 (−24.14, 46.45) | 0.76 | 9.09 (−27.30, 45.47) | 0.80 |
| Low, sustained | 2.29 (−30.95, 35.54) | −7.81 (−42.04, 26.41) | ||
| High, sustained | 6.60 (−44.43, 57.63) | 4.12 (−46.48, 54.72) | ||
| VACS Index 2.0 | N= 1,593 | N= 1,363 | ||
| ≥6 Months of MOUD | 1.23 (−1.16, 3.62) | 0.31 | 0.83 (−1.57, 3.23) | 0.50 |
| One Visit Only (reference group) | -- | -- | ||
| Minimal | 0.76 (−0.45, 1.98) | 0.81 (−0.49, 2.11) | ||
| High to none | 0.53 (−1.52, 2.57) | 0.14 | 0.14 (−2.00, 2.28) | 0.41 |
| Low, sustained | −1.29 (−3.19, 0.62) | −0.67 (−2.64, 1.30) | ||
| High, sustained | −2.02 (−4.76, 0.72) | −1.33 (−4.07, 1.40) | ||
| ART Adherence | N= 1570 | N=1,326 | ||
| ≥6 Months of MOUD | −0.02 (−0.08, 0.3) | 0.46 | −0.01 (−0.05, 0.04) | 0.79 |
| One Visit Only (reference group) | -- | |||
| Minimal | 0.00 (−0.03, 0.03) | −0.01 (−0.03, 0.02) | ||
| High to none | 0.03 (−0.01, 0.07) | 0.05 | 0.01 (0-.02, 0.05) | 0.02 a |
| Low, sustained | 0.04 (−0.00, 0.07) | 0.03 (−0.01, 0.07) | ||
| High, sustained | 0.08 (0.01, 0.14) | 0.08 (0.02, 0.13) | ||
| ART Initiation d | N = 709 | N = 592 | ||
| ≥6 Months of MOUD | 1.02 (0.51,2.05) | 0.06 | 0.85 (0.40,1.81) | 0.68 |
| One Visit Only (reference group) | -- | -- | ||
| Minimal | 1.50 (1.00, 2.25) | 1.35 (0.86, 2.13) | ||
| High to none | 2.16 (1.20, 3.88) | 0.007a | 2.18 (1.14, 4.15) | 0.009a |
| Low, sustained | 2.55 (1.46, 4.44) | 2.85 (1.53, 5.28) | ||
| High, sustained | 2.25 (1.06, 4.78) | 2.12 (0.95, 4.74) | ||
Abbreviations: VL, viral load; ART, antiretroviral therapy; OUD, opioid use disorder; MOUD, medication for opioid use disorder.
Denotes statistically significant values
Model 1 adjusts for age, race/ethnicity, gender, medication for opioid use disorder (MOUD) treatment.
Model 2 adjusts for age, race/ethnicity, gender, MOUD treatment, hepatitis C status, smoking status, unhealthy alcohol use, drug use, time between measures and pre-measure.
ART initiation only includes patients who were not on ARV at baseline, also reported in odds ratio (OR), other values are coefficients.
Among the PWH and OUD on ART at BOUD initiation, adherence improved more in the highest-intensity BOUD groups in univariate analyses (Table 1) and in both multivariate models (Table 2). Among the PWH and OUD not on ART at BOUD initiation, greater BOUD intensity was associated with ART treatment initiation in both univariate analysis (Table 1) and in both multivariable models (Table 2).
3.5. Secondary Analyses
We observed no differences in outcomes when we stratified results based on VL status nor when we ran multivariate logistic regression models with HIV VL as a binary variable. Our results were similar when we excluded those who did not receive MOUD, as well as when we used a stricter definition of methadone treatment for MOUD. After testing for interaction between BOUD intensity and MOUD, we found no difference in the relationship between BOUD intensity and CD4 count and VACS Index 2.0. A significant interaction between BOUD intensity and log VL was present among those with ≥6 months of MOUD. However, only a few individuals received ≥6 months of MOUD in the lower three trajectory groups (n=18 in all three groups combined).
4. DISCUSSION
In this large, observational cohort study of PWH and OUD, we identified five distinct trajectories of BOUD. Of those on BOUD, only a small number also received MOUD. Higher intensity BOUD in the 12 months following BOUD initiation was associated with improved ART initiation, and sustained high-intensity BOUD was associated with ART adherence. Neither BOUD alone nor BOUD plus MOUD for ≥6 months were associated with improvements in log HIV VL, CD4 count or VACS Index 2.0 among people initiating BOUD.
Our results strengthen prior literature that demonstrates ART adherence and initiation of ART improve with consistent OUD treatment (Low et al., 2016; Simeone et al., 2017; Springer et al., 2010). This is especially true when OUD treatment is integrated into HIV care (Oldfield et al., 2019). However, some studies have shown MOUD to not be associated with improved ART initiation (Altice et al., 2011). PWH in our cohort who received higher-intensity BOUD may have had greater access to VHA services than PWH not in treatment and, consequently, had increased access to HIV primary care visits and increased rates of ART initiation and adherence. It is less clear why we observed no improvements in VL, CD4 count, or VACS Index 2.0. Prior work indicated improvements in VL suppression for PWH who engage in OUD treatment (Adams et al., 2020; Low et al., 2016; Palepu et al., 2006). Many participants in our study were already on ART with suppressed VL; thus, it might have been difficult to demonstrate improvements. Our study may have had too short of a follow-up period to see noticeable changes in CD4 count or VACS Index 2.0 and/or was underpowered to detect associations.
Though our results are in line with prior literature on OUD treatment for PWH, a recent systematic review found the majority of prior studies evaluated PWH only receiving MOUD, not BOUD (Oldfield et al., 2019). In contrast to our findings, prior randomized controlled trials comparing PWH receiving MOUD and medical management versus MOUD plus BOUD found no improvement in HIV or OUD outcomes in patients receiving adjunctive BOUD (Pisu et al., 2010; Sullivan et al., 2006; Tetrault et al., 2012). Reasons for these differences may include type and location of BOUD, as well as location of HIV care, and the known efficacy of MOUD along with medical management (Amato et al., 2011). Unlike many prior studies, treatment for OUD and HIV within the VHA often occurs within the same facility; thus, more intensive BOUD in other settings might not necessarily lead to greater access to non-OUD-related healthcare.
The majority of PWH in our cohort received little to no medication or behavioral treatment for OUD. Seventy-five percent (n=1,832) of participants were within the “one visit only” or “minimal, not sustained” BOUD trajectories. Few PWH in our cohort received MOUD, the gold standard treatment for OUD (n=378, 16%). Though evidence suggests BOUD should rarely be used without MOUD (Sofuoglu et al., 2019), we found that the majority of patients on BOUD received this therapy without concurrent MOUD. It is also unclear why MOUD receipt was so low for our population of PWH. PWH within the VHA are less likely to receive timely initiation of MOUD than their non-HIV-infected counterparts (Wyse et al., 2019). This may be due to higher rates of comorbid SUD, psychiatric disorders, and other risk factors such as homelessness in veterans with HIV than in those without HIV. Our low numbers of PWH on MOUD also likely reflect the fact that our denominator was PWH who received any BOUD, not those who initiated MOUD, which is the denominator in other studies reporting retention. Low MOUD receipt may also be related to where PWH receive care within the VHA (i.e., HIV clinics), highlighting the critical need to improve uptake of MOUD within both BOUD and HIV clinics in the VHA.
Our study has limitations. As an observational study, we cannot exclude possible unmeasured confounders. We did not assess severity of OUD, which may introduce confounding by indication. PWH with more severe OUD may have received more treatment and/or had worse outcomes. We did not assess individuals at multiple points in time, nor did we capture individuals who received only MOUD. We excluded patients who died during the year after BOUD initiation, which may make our results less generalizable to this potentially sicker group. We were able to include data only through 2017, and more patients may be on MOUD now than there were during the time period studied. Our analysis included only PWH in treatment at the VHA, which may not be applicable to non-VHA populations. Finally, we did not capture OUD treatment that patients may have received outside the VHA. Still, it is unlikely that the 75% of people who received no or minimal treatment in the VA all engaged in non-VA OUD treatment. In future work, we plan to compare outcomes based on severity of OUD, as well as type of BOUD received. We also plan to assess whether intensity of OUD treatment has an impact on OUD outcomes. This study highlights the lack of MOUD treatment for PWH and OUD within the VHA, and the need for BOUD programs to offer MOUD to their patients.
5. CONCLUSION
Among PWH and OUD within the VHA, we found five distinct trajectories of BOUD. Only a small proportion of individuals on BOUD received MOUD. Higher BOUD intensity was associated with ART adherence and ART initiation, but BOUD with and without MOUD was not associated with improvements in log VL, CD4 count and VACS Index 2.0. Future research on treatment for OUD in PWH should focus on improving uptake of MOUD for veterans and increasing the intensity of BOUD combined with MOUD.
Supplementary Material
Highlights:
Persons with HIV and opioid use disorder (OUD) can have poor health outcomes
Unknown if intense behavioral treatment for OUD (BOUD) improves HIV outcomes
We found that higher intensity BOUD improved ART initiation and adherence
There were no improvements in viral load suppression
Acknowledgments
We wish to thank Ethan Lennox, MA, Division of General Internal Medicine, University of Pittsburgh School of Medicine, for his help with manuscript editing. Preliminary results from this study were presented in abstract form at the Addiction Health Services Research Conference, Salt Lake City, UT, October 2019, and the Association for Medical Education and Research in Substance Abuse National Conference, Boston, MA, November 2019.
Funding
This work was supported by the National Institute on Alcohol Abuse and Alcoholism at the National Institutes of Health [R01-AA022886, U10-AA13566, U24-AA020794, U01-AA020790] and the Veterans Health Administration. The first author was supported by a HRSA T32 [T32HP22240] training grant for the duration of the data analysis and writing of the manuscript. The views expressed in this paper are those of the authors. No official endorsement by the National Institutes of Health or the Department of Veterans Affairs is intended or should be inferred.
Footnotes
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Declaration of Competing Interest
The authors declare no conflict of interest.
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