Abstract
Introduction
Lifelong antiretroviral therapy (ART) persistence prevents the progression of human immunodeficiency virus (HIV)-related illnesses and reduces HIV transmission. People with HIV who have a mental health disorder or substance use disorder (PWH-MHD/SUD) often face persistence challenges. This real-world study compared ART persistence among PWH-MHD/SUD who restarted various ART regimens after a treatment interruption.
Methods
This observational, retrospective cohort study analyzed US claims data from the HealthVerity Marketplace from January 2015 through February 2024. PWH aged ≥ 18 years who restarted the same ART regimen they had previously discontinued for > 90 days were included. The population of PWH-MHD/SUD was analyzed. Pairwise comparisons were conducted for those who received bictegravir (B)/emtricitabine (F)/tenofovir alafenamide (TAF) versus dolutegravir (DTG)/lamivudine (3TC), DTG/abacavir (ABC)/3TC, and DTG-based multitablet regimens [MTRs; i.e., DTG + F/TAF or DTG + F/tenofovir disoproxil fumarate (TDF)]. Baseline characteristics were balanced using inverse probability of treatment weighting. Time to nonpersistence (i.e., ART regimen discontinuation or switching) was depicted using Kaplan–Meier plots. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using weighted Cox proportional hazards models.
Results
Among all the PWH who restarted a previously discontinued ART regimen (n = 20,623), 43.4% had an MHD or SUD. Compared with PWH-MHD/SUD who received B/F/TAF, those receiving DTG/ABC/3TC and DTG-based MTRs were significantly more likely to be nonpersistent [weighted HR (95% CI) 1.18 (1.09–1.29) and 1.19 (1.06–1.34), respectively], while there was no significant difference for those receiving DTG/3TC. Compared with those receiving B/F/TAF, the risk of switching was significantly higher for PWH-MHD/SUD receiving DTG/3TC, DTG/ABC/3TC, or a DTG-based MTR [weighted HR (95% CI) 1.68 (1.08–2.63), 2.67 (2.23–3.19), and 2.88 (2.32–3.58), respectively]. These results were generally consistent among the broader population of restarters.
Conclusion
For PWH-MHD/SUD who restarted ART after a treatment interruption, B/F/TAF was associated with longer persistence and the lowest risk of switch compared with other guideline-recommended therapies.
Supplementary Information
The online version contains supplementary material available at 10.1007/s12325-025-03379-1.
Keywords: Antiretroviral therapy, B/F/TAF, DTG + F/TAF, DTG + F/TDF, DTG/3TC, DTG/ABC/3TC, Mental health disorder, Persistence, Substance use disorder
Key Summary Points
| Why carry out this study? |
| Preventing human immunodeficiency virus (HIV)-related disease progression and reducing the risk of HIV transmission require persistence with antiretroviral therapy (ART), as people with HIV (PWH) who experience lengthy interruptions in ART are more likely to lose viral suppression, are more likely to transmit the virus to others, and are at increased risk of morbidity and mortality compared with PWH who are persistent with ART. |
| PWH who have a mental health disorder or substance use disorder (PWH-MHD/SUD), in particular, may struggle with persistence, and comprise > 40% of PWH. |
| This study used US claims data to compare real-world treatment persistence (defined as ART regimen discontinuation or switching) across different ART regimens that had been restarted by PWH-MHD/SUD after a treatment interruption. |
| What was learned from the study? |
| A high proportion of PWH who experienced a treatment interruption and restarted the same ART regimen of interest had an MHD or SUD; the hazard of nonpersistence in this population was lower for those who received bictegravir/emtricitabine/tenofovir alafenamide compared with dolutegravir/abacavir/lamivudine and dolutegravir-based multitablet regimens, and the hazard of switch was lower for those who received bictegravir/emtricitabine/tenofovir alafenamide compared with all dolutegravir-based regimens. |
| In the broader population of all restarters, 49–58% of PWH who restarted ART after a treatment interruption discontinued or switched their ART during the follow-up period, regardless of regimen. |
| Further interventions are needed to increase persistence in populations of PWH who are prone to treatment interruptions. |
Introduction
In 2022, the prevalence of human immunodeficiency virus (HIV) among adolescents and adults in the United States was estimated to be 1.2 million individuals, with 31,800 new infections and 4145 new HIV-related deaths [1]. The availability of effective, safe, and tolerable antiretroviral therapy (ART) has led to considerable reductions in HIV-associated morbidity and mortality and, therefore, increases in life expectancy for those who take their medication consistently and correctly [2]. However, achieving the US Centers for Disease Control and Prevention goal of ending the HIV epidemic will require reaching, treating, and maintaining care for people with HIV (PWH), some of whom have historically had difficulty maintaining lifelong persistence and adherence to ART [3].
There are several ART regimens approved for new initiation or for switching among virally suppressed PWH [4]. Modern ART regimens are simplified and have improved tolerability compared with older regimens [4]. PWH have demonstrated strong persistence and adherence to these modern regimens, which can be partially attributed to the low pill burden (e.g., requiring only a single tablet) and simplified dosing (e.g., taken only once daily) [5, 6]. Recent US Department of Health and Human Services (DHHS) regimen recommendations included bictegravir (B)/emtricitabine (F)/tenofovir alafenamide (TAF), dolutegravir (DTG)/lamivudine (3TC), DTG/abacavir (ABC)/3TC, DTG + F/TAF, and DTG + F/tenofovir disoproxil fumarate (TDF) as treatment options for PWH.
Beyond regimen-related considerations, PWH may encounter various barriers to persistence, and many experience ART interruptions [7–9]. Notably, certain comorbidities that disrupt usual life routines and may thereby negatively impact adherence, such as mental health disorders (MHDs) and substance use disorders (SUDs), are prevalent among PWH [10–15]. Approximately 55% of PWH in the United States and Canada have been diagnosed with ≥ 1 MHD, with approximately 24% experiencing MHD multimorbidity [16]. Estimates of SUDs in various populations of PWH range from 25% in primary care settings [17] to 48% in urban clinic settings [12]. Many of these conditions also occur simultaneously within a nexus of adverse social determinants of health, including unemployment and housing insecurity [18, 19].
Suboptimal patterns of ART uptake can include intermittently low daily adherence and lack of persistence, where ART is discontinued altogether, discontinued and restarted after a gap in therapy, or regimens are switched. Challenges to medication adherence have been demonstrated and well-characterized in PWH who have an MHD or SUD (PWH-MHD/SUD) [13, 15]. There is a need to study medication persistence among PWH who restart ART to better understand if certain regimens are stopped altogether in a population with a history of ART interruption.
ART interruptions are prevalent among PWH [20, 21], and many of those who discontinue ART resume at some point, frequently on the same regimen [22]. Treatment-experienced PWH who face viral rebound after a treatment interruption can achieve viral re-suppression after restarting ART [23, 24] but this requires subsequent persistence. It is unclear whether there are differences in persistence between modern US DHHS-recommended regimens among individuals who are restarting ART after a treatment interruption. The primary objective of this study was to compare real-world treatment persistence among PWH-MHD/SUD who restarted B/F/TAF or different DTG-based regimens after an ART interruption in the same regimen of > 90 days, and among the broader population of restarters.
Methods
Study Design and Data Source
This was an observational, retrospective cohort study conducted using real-world data from the HealthVerity Marketplace. The dataset included closed medical and pharmacy claims from January 1, 2015, through February 29, 2024, and represented individuals in the United States insured under commercial, Medicare Advantage, or Medicaid plans. The study population (described in detail below) was followed from the index date (date individuals restarted a previously discontinued ART regimen of interest and met all eligibility criteria) to the earliest of the following events: end of persistence (defined below in the Outcomes section), evidence of pregnancy, disenrollment, or end of study period (February 29, 2024). The baseline period was defined as the 365 days prior to the index date.
Study Population
The study population consisted of PWH who restarted an ART regimen of interest (B/F/TAF, DTG/3TC, DTG/ABC/3TC, DTG + F/TAF, or DTG + F/TDF). To be eligible for inclusion, individuals were required to restart the same ART regimen they had previously discontinued, with no other intervening ART and a gap of > 90 days between the end of the prior regimen and the index date. There were 2 identification periods based on when the regimens being compared were approved by the US Food and Drug Administration: (1) the identification period for the comparison of B/F/TAF versus DTG/3TC was from April 8, 2019 (approval date for DTG/3TC), to November 30, 2023; and (2) the identification period for the comparison of B/F/TAF versus DTG/ABC/3TC and B/F/TAF versus DTG-based multitablet regimens (MTRs) was from February 7, 2018 (approval date for B/F/TAF), to November 30, 2023.
Additional eligibility criteria included ≥ 1 claim with an HIV diagnosis [International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes: 042, V08; International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes: B20, Z21, O98.711, O98.712, O98.713, O98.719, O98.72, O98.73] during the study period; ≥ 365 days of continuous medical and pharmacy enrollment prior to the index date (i.e., the baseline period) and ≥ 90 days after the index date; age of ≥ 18 years on the index date; nonmissing age or sex; no HIV-2 diagnosis during the baseline period; no evidence of pregnancy during the baseline period or ≤ 90 days after the index date; and > 30 days of persistence on the index regimen.
PWH-MHD/SUD were identified by the presence of ≥ 1 ICD-10-CM diagnosis code for an MHD (e.g., depression, anxiety, mania, mood/affective disorders, personality disorders, psychosis, schizophrenia, bipolar disorder, insomnia/sleep disorders, stress reaction disorders, attention-deficit hyperactivity disorder) or SUD (e.g., opioid abuse, alcohol abuse, stimulant abuse, hallucinogen abuse, inhalant abuse) during the baseline period. ICD-10-CM codes for MHDs and SUDs were selected based on recent publications regarding PWH-MHD/SUD [16, 25] and manual adjudication of related ICD-10-CM codes F01-F99 and T40.X.
ART Regimens
The ART regimen start date was defined as the date of the first closed pharmacy claim (if self-administered) or medical claim (if provider-administered) for a core agent during the study period. Regimens were defined by all ART agents (core, backbone, and boost) filled within the first 14 days on or after the regimen start date, which allowed for short delays in filling the components of MTRs. ART agents filled prior to the regimen start date were also evaluated; if they were refilled within 90 days of their supply period, they were considered part of the regimen. Switch was defined as the start of a new ART agent outside of the 14-day regimen definition window, identified by a closed medical or pharmacy claim for any ART agent that was not a part of the initial regimen. In the case of switch, the remaining days’ supply of a prior regimen’s agent(s) were disregarded. Duration of medication use was defined as the days’ supply on the pharmacy claim or an assumed duration of benefit for medical claims. All subsequent regimens were defined similarly, each starting with the first medical or pharmacy claim for a core agent after the end of the previous regimen, as described above.
Individuals meeting the eligibility criteria were grouped by the regimen of interest they reinitiated on the index date. Due to low sample size in the DTG + F/TDF group, the DTG + F/TAF and DTG + F/TDF groups were combined into a single DTG-based MTR group. Pairwise comparisons of interest were B/F/TAF versus DTG/3TC, B/F/TAF versus DTG/ABC/3TC, and B/F/TAF versus DTG-based MTR.
Outcomes
Persistence (defined as time on index regimen until discontinuation or switch) was evaluated during the follow-up period and was measured from the index date to the time to discontinuation (defined as the last day with index regimen prior to a gap of > 90 days in any of the index regimen agents), switch (during follow-up; defined as the last day with index regimen prior to the appearance of an ART agent not included in the index regimen > 30 days after the index date), or censoring (i.e., disenrollment, end of study period, or pregnancy-related claim).
Covariates
Baseline demographics, including age, sex, cohort entry year, region, and insurance type, were assessed on the index date. Clinical characteristics, including comorbidities and comedications, were assessed during the baseline period using diagnosis and medication codes. Baseline adherence was measured as the number of days an individual had supply of any ART (not limited to their index regimen) during the baseline period divided by 365 days × 100.
Statistical Analysis
For descriptive analyses, categorical variables were summarized using counts and proportions, and continuous variables were summarized using means with standard deviations (SDs).
For comparative analyses among PWH-MHD/SUD and the overall population of restarters, inverse probability of treatment weighting (IPTW) was used to control for confounding. Propensity scores were calculated for PWH using multivariable logistic regression to determine the probability of treatment with DTG/3TC, DTG/ABC/3TC, or a DTG-based MTR versus B/F/TAF. Covariates in the propensity score models included the demographic and clinical characteristics reported here, as well as baseline ART use. Weights were calculated separately for each regimen comparison and re-estimated within each subgroup. Absolute standardized mean differences (ASDs) of the baseline measures included in the weighting were assessed before and after weighting. An ASD ≤ 0.10 was considered an indicator of balance between the groups; ASDs > 0.10 were evaluated on a case-by-case basis to determine clinical relevance. After weighting, time to nonpersistence (i.e., discontinuation or switch), discontinuation alone, or switch alone was depicted graphically using Kaplan–Meier plots. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using weighted Cox proportional hazards models. For switch models, individuals were censored at evidence of a discontinuation. For discontinuation models, individuals were censored at evidence of a switch.
All analyses were performed using the Aetion Substantiate application and R version 4.2.2.
Ethical Approval
This study was conducted using retrospectively collected data and received a formal institutional review board exemption. The data contained no personal identification fields. No informed consent was obtained to participate in this secondary analysis of existing data.
Results
Study Population
Between January 1, 2015, and February 29, 2024, a total of 958,882 individuals in the HealthVerity dataset had ≥ 1 closed claim with an HIV diagnosis (Table 1). Of these, 301,027 PWH were adults aged ≥ 18 years with no evidence of HIV-2 who received ART and had the minimum number of days required to meet the continuous enrollment criteria. Among this population, 20,623 PWH were indexed as restarters on an ART regimen of interest after a treatment interruption of > 90 days and met all of the selection requirements relative to their index date. The characteristics of this population can be found in Table S1. Of note, there was a small proportion of individuals with missing insurance type (1.1%). These PWH were combined with the PWH with Medicaid insurance in subsequent analyses.
Table 1.
Study population attrition
| Criteria | Overall population |
|---|---|
| HIV-1 diagnosis between Jan 1, 2015, and Feb 29, 2024 | 958,882 |
| No diagnosis of HIV-2 between Jan 1, 2015, and Feb 29, 2024 | 942,507 |
| ≥ 1 claim for any ART between Feb 7, 2018, and Nov 30, 2023 | 479,088 |
| Age ≥ 18 at any point between Feb 7, 2018, and Nov 30, 2023 | 476,923 |
| ≥ 455 days of continuous enrollment between Feb 7, 2017, and Feb 29, 2024 | 301,027 |
| Included in the overall population of restarters meeting all study inclusion criteria | 20,623 |
ART antiretroviral therapy, HIV human immunodeficiency virus
MHD and SUD Among PWH Who Restarted ART
Among all restarters, a total of 8953 (43.4%) had any MHD or SUD (Table S1). Specifically, 7807 (37.9%) had any MHD, 5857 (28.4%) had a severe MHD, and 4015 (19.5%) had an SUD. The proportion of PWH-MHD/SUD differed by cohort, ranging from 39.7% in the DTG/3TC group to 48.6% in the DTG-based MTR group (Tables S2 and S3). When comparing PWH who received B/F/TAF versus DTG/3TC, a greater proportion of PWH who received B/F/TAF had a severe MHD (28.1% vs. 23.8%, respectively; P = 0.006) or an SUD (20.2% vs. 13.1%, respectively; P < 0.001) at baseline. Conversely, a lower proportion of PWH who received B/F/TAF had a severe MHD (28.1%) compared with PWH who received a DTG-based MTR (33.3%; P < 0.001); this was also true for those with an SUD (20.1% vs. 25.2%, respectively; P < 0.001). Other baseline differences between regimens for the overall population and balance between groups after IPTW are shown in Tables S2 and S3, respectively.
Population Characteristics of PWH-MHD/SUD
Unweighted demographic and baseline characteristics of PWH-MHD/SUD are summarized in Table 2, with corresponding ASDs in Table S4.
Table 2.
Unweighted demographic and baseline characteristics of PWH-MHD/SUD who received B/F/TAF versus DTG-based comparator regimens
| n (%) | B/F/TAF vs. DTG/3TC | B/F/TAF vs. DTG/ABC/3TC | B/F/TAF vs. DTG-based MTR | ||||||
|---|---|---|---|---|---|---|---|---|---|
| B/F/TAF (n = 5627) | DTG/3TC (n = 354) | P value | B/F/TAF (n = 5632) | DTG/ABC/3TC (n = 2014) | P value | B/F/TAF (n = 5643) | DTG-based MTR (n = 1008) | P value | |
| Age, years, mean (SD) | 43.1 (12.3) | 44.0 (12.3) | 0.197 | 43.1 (12.3) | 45.1 (12.5) | < 0.001 | 43.1 (12.3) | 44.9 (12.1) | < 0.001 |
| Age category | 0.778 | < 0.001 | < 0.001 | ||||||
| 18–39 years | 2457 (43.7) | 148 (41.8) | 2466 (43.8) | 741 (36.8) | 2473 (43.8) | 367 (36.4) | |||
| 40–54 years | 1230 (21.9) | 75 (21.2) | 1235 (21.9) | 440 (21.8) | 1234 (21.9) | 239 (23.7) | |||
| 55–64 years | 1738 (30.9) | 116 (32.8) | 1730 (30.7) | 745 (37.0) | 1734 (30.7) | 358 (35.5) | |||
| ≥ 65 years | 202 (3.6) | 15 (4.2) | 201 (3.6) | 88 (4.4) | 202 (3.6) | 44 (4.4) | |||
| Sex | 0.693 | 0.732 | < 0.001 | ||||||
| Female | 1527 (27.1) | 100 (28.2) | 1525 (27.1) | 554 (27.5) | 1526 (27.0) | 346 (34.3) | |||
| Male | 4100 (72.9) | 254 (71.8) | 4107 (72.9) | 1460 (72.5) | 4117 (73.0) | 662 (65.7) | |||
| Cohort entry year | < 0.001 | < 0.001 | < 0.001 | ||||||
| 2018 | – | – | 17 (0.3) | 385 (19.1) | 17 (0.3) | 214 (21.2) | |||
| 2019 | 230 (4.1) | 0 | 269 (4.8) | 377 (18.7) | 273 (4.8) | 212 (21.0) | |||
| 2020 | 797 (14.2) | 18 (5.1) | 789 (14.0) | 416 (20.7) | 788 (14.0) | 159 (15.8) | |||
| 2021 | 1337 (23.8) | 62 (17.5) | 1322 (23.5) | 323 (16.0) | 1328 (23.5) | 178 (17.7) | |||
| 2022 | 1707 (30.3) | 123 (34.7) | 1693 (30.1) | 325 (16.1) | 1696 (30.1) | 156 (15.5) | |||
| 2023 | 1556 (27.7) | 151 (42.7) | 1542 (27.4) | 188 (9.3) | 1541 (27.3) | 89 (8.8) | |||
| Region | 0.072 | 0.018 | 0.169 | ||||||
| Northeast | 1310 (23.3) | 71 (20.1) | 1307 (23.2) | 448 (22.2) | 1318 (23.4) | 211 (20.9) | |||
| Midwest | 1029 (18.3) | 54 (15.3) | 1025 (18.2) | 321 (15.9) | 1028 (18.2) | 164 (16.3) | |||
| South | 2002 (35.6) | 136 (38.4) | 2011 (35.7) | 738 (36.6) | 2009 (35.6) | 384 (38.1) | |||
| West | 1250 (22.2) | 87 (24.6) | 1252 (22.2) | 484 (24.0) | 1252 (22.2) | 240 (23.8) | |||
| Other | 5 (0.1) | 1 (0.3) | 5 (0.1) | 6 (0.3) | 5 (0.1) | 2 (0.2) | |||
| Missing | 31 (0.6) | 5 (1.4) | 32 (0.6) | 17 (0.8) | 31 (0.5) | 7 (0.7) | |||
| Insurance type | < 0.001 | < 0.001 | < 0.001 | ||||||
| Commercial | 1297 (23.1) | 117 (33.1) | 1313 (23.3) | 551 (27.4) | 1310 (23.2) | 149 (14.8) | |||
| Medicaid/missinga | 3810 (67.7) | 192 (54.2) | 3798 (67.4) | 1227 (60.9) | 3813 (67.6) | 725 (71.9) | |||
| Medicare Advantage | 520 (9.2) | 45 (12.7) | 521 (9.3) | 236 (11.7) | 520 (9.2) | 134 (13.3) | |||
| Comorbidity | |||||||||
| CCI score (excluding HIV) | 0.180 | 0.227 | < 0.001 | ||||||
| 0 | 2789 (49.6) | 193 (54.5) | 2791 (49.6) | 959 (47.6) | 2797 (49.6) | 421 (41.8) | |||
| 1 or 2 | 2053 (36.5) | 114 (32.2) | 2056 (36.5) | 749 (37.2) | 2059 (36.5) | 387 (38.4) | |||
| ≥ 3 | 785 (14.0) | 47 (13.3) | 785 (13.9) | 306 (15.2) | 787 (13.9) | 200 (19.8) | |||
| Condition | |||||||||
| Any MHD | 4896 (87.0) | 319 (90.1) | 0.107 | 4908 (87.1) | 1771 (87.9) | 0.381 | 4913 (87.1) | 862 (85.5) | 0.198 |
| Severe MHD | 3637 (64.6) | 212 (59.9) | 0.080 | 3646 (64.7) | 1349 (67.0) | 0.074 | 3651 (64.7) | 692 (68.7) | 0.017 |
| SUD | 2615 (46.5) | 117 (33.1) | < 0.001 | 2600 (46.2) | 805 (40.0) | < 0.001 | 2609 (46.2) | 522 (51.8) | 0.001 |
| Cardiovascular disease | 2662 (47.3) | 168 (47.5) | 1.000 | 2662 (47.3) | 986 (49.0) | 0.201 | 2673 (47.4) | 531 (52.7) | 0.002 |
| Chronic kidney disease | 374 (6.6) | 36 (10.2) | 0.015 | 376 (6.7) | 176 (8.7) | 0.003 | 375 (6.6) | 77 (7.6) | 0.277 |
| Hypertension | 2103 (37.4) | 146 (41.2) | 0.161 | 2103 (37.3) | 831 (41.3) | 0.002 | 2113 (37.4) | 422 (41.9) | 0.009 |
| Lipid disorders | 1416 (25.2) | 126 (35.6) | < 0.001 | 1428 (25.4) | 604 (30.0) | < 0.001 | 1425 (25.3) | 267 (26.5) | 0.429 |
| Overweight or obese | 1210 (21.5) | 105 (29.7) | < 0.001 | 1217 (21.6) | 446 (22.1) | 0.639 | 1209 (21.4) | 202 (20.0) | 0.343 |
| Comedication | |||||||||
| Antidepressants | 2021 (35.9) | 130 (36.7) | 0.803 | 2027 (36.0) | 756 (37.5) | 0.226 | 2019 (35.8) | 392 (38.9) | 0.063 |
| Antipsychotics | 1365 (24.3) | 89 (25.1) | 0.755 | 1369 (24.3) | 456 (22.6) | 0.140 | 1368 (24.2) | 268 (26.6) | 0.121 |
| Benzodiazepines | 739 (13.1) | 67 (18.9) | 0.003 | 745 (13.2) | 320 (15.9) | 0.003 | 739 (13.1) | 158 (15.7) | 0.031 |
The DTG-based MTR cohort included individuals who received DTG + F/TDF and individuals who received DTG + F/TAF
aDue to the small number of PWH with missing insurance type, the missing category was combined with Medicaid
3TC lamivudine, ABC abacavir, B bictegravir, CCI Charlson Comorbidity Index, DTG dolutegravir, F emtricitabine, MHD mental health disorder, MTR multitablet regimen, PWH people with HIV, PWH-MHD/SUD people with HIV who have a mental health disorder or substance use disorder, SUD substance use disorder, TAF tenofovir alafenamide, TDF tenofovir disoproxil fumarate
Compared with PWH-MHD/SUD who received B/F/TAF, those who received DTG/3TC had a similar mean age [mean (SD) age, 43.1 (12.3) vs. 44.0 (12.3) years, respectively; P = 0.197] and sex distribution (72.9% vs. 71.8% male, respectively; P = 0.693; Table 2). A greater proportion of PWH-MHD/SUD who received B/F/TAF were on Medicaid or had missing insurance type compared with those who received DTG/3TC (67.7% vs. 54.2%, respectively; P < 0.001). The proportion of PWH-MHD/SUD who were overweight or had obesity at baseline was lower for those who received B/F/TAF compared with DTG/3TC (21.5% vs. 29.7%, respectively; P < 0.001), as were the proportions of PWH-MHD/SUD with chronic kidney disease (6.6% vs. 10.2%, respectively; P = 0.015) and lipid disorders (25.2% vs. 35.6%, respectively; P < 0.001).
The mean (SD) age of PWH-MHD/SUD who received B/F/TAF was lower compared with those who received DTG/ABC/3TC [43.1 (12.3) vs. 45.1 (12.5) years, respectively; P < 0.001], while the sex distribution was similar (72.9% vs. 72.5% male, respectively; P = 0.732; Table 2). A greater proportion of PWH-MHD/SUD who received B/F/TAF were on Medicaid or had missing insurance type compared with those who received DTG/ABC/3TC (67.4% vs. 60.9%, respectively; P < 0.001). The proportions of PWH-MHD/SUD with chronic conditions, including chronic kidney disease (6.7% vs. 8.7%; P = 0.003), hypertension (37.3% vs. 41.3%; P = 0.002), and lipid disorders (25.4% vs. 30.0%; P < 0.001), were lower among PWH-MHD/SUD who received B/F/TAF compared with DTG/ABC/3TC.
PWH-MHD/SUD who received B/F/TAF were younger on average than those who received a DTG-based MTR [mean (SD) age, 43.1 (12.3) vs. 44.9 (12.1) years, respectively; P < 0.001], and the proportion that was male was higher (73.0% vs. 65.7%, respectively; P < 0.001; Table 2). A lower proportion of PWH-MHD/SUD who received B/F/TAF were on Medicaid or had missing insurance type compared with those who received a DTG-based MTR (67.6% vs. 71.9%, respectively, P < 0.001). Similarly, a smaller proportion of PWH-MHD/SUD who received B/F/TAF were on Medicare Advantage compared with those who received a DTG-based MTR (9.2% vs. 13.3%, respectively; P < 0.001). A greater proportion of PWH-MHD/SUD who received B/F/TAF had a Charlson Comorbidity Index score (excluding the HIV component) of 0 at baseline compared with those who received a DTG-based MTR (49.6% vs. 41.8%, respectively; P < 0.001). The proportions of PWH-MHD/SUD who received B/F/TAF who had cardiovascular disease (47.4% vs. 52.7%; P = 0.002) and hypertension (37.4% vs. 41.9%; P = 0.009) were lower than the proportions of those who received a DTG-based MTR.
In terms of mental health conditions, the proportion of PWH-MHD/SUD with a severe MHD tended to be lower among those who received B/F/TAF compared with DTG/ABC/3TC (64.7% vs. 67.0%, respectively; P = 0.074) and DTG-based MTRs (64.7% vs. 68.7%, respectively; P = 0.017), but was higher compared with those who received DTG/3TC, although this difference did not reach statistical significance (64.6% vs. 59.9%, respectively; P = 0.080; Table 2). The proportion of PWH-MHD/SUD who had an SUD at baseline was higher for those who received B/F/TAF compared with DTG/3TC (46.5% vs. 33.1%, respectively; P < 0.001) or DTG/ABC/3TC (46.2% vs. 40.0%, respectively; P < 0.001). Finally, the proportion of PWH-MHD/SUD who had a claim for benzodiazepines at baseline was lower for those who received B/F/TAF compared with DTG/3TC (13.1% vs. 18.9%, respectively; P = 0.003).
Postweighting Balance Assessment
After weighting, balance was achieved between treatment comparator groups among PWH-MHD/SUD, with all ASDs < 0.10 except for 5 characteristics with ASDs < 0.20 and 1 characteristic (cohort entry year 2019) with an ASD of 0.275 in the comparison between B/F/TAF and DTG/3TC (Table S4). These characteristics were not hypothesized to be strong confounders of the relationship between the index regimen and outcomes. Among all restarters, balance was also achieved between the B/F/TAF groups and the pairwise comparator DTG/ABC/3TC and DTG-based MTR groups, with ASDs < 0.10 for all characteristics (Table S3). There were 2 characteristics with ASDs > 0.10 for the comparison between B/F/TAF and DTG/3TC (cohort entry year 2019 and diagnosis of any cardiovascular disease during the baseline period).
Weighted Persistence Outcomes After Restarting ART
After weighting, the average follow-up in the data was > 1.5 years for all groups and was relatively similar between comparators (Table 3). The weighted proportions of PWH who were nonpersistent were higher among PHW-MHD/SUD compared with the overall population (range, 50.3–60.4% vs. 48.7–58.1%, respectively; Table S5). Among PWH-MHD/SUD, overall nonpersistence was similar for those who received B/F/TAF compared with DTG/3TC (51.0% vs. 50.3%, respectively) or a DTG-based MTR (53.0% vs. 57.7%, respectively) and lower for those who received B/F/TAF compared with DTG/ABC/3TC (53.6% vs. 60.4%, respectively). Notably, the weighted proportion of PWH-MHD/SUD who switched was lower for those who received B/F/TAF compared with those who received any of the 3 DTG-based regimens [DTG/3TC (6.4% vs. 10.4%, respectively), DTG/ABC/3TC (6.6% vs. 16.6%, respectively), or a DTG-based MTR (6.6% vs. 17.1%, respectively)]. Moreover, among the PWH-MHD/SUD who received B/F/TAF who subsequently switched, their mean time to switch was longer than for all 3 comparators. While a higher proportion of PWH-MHD/SUD who received B/F/TAF discontinued compared with those who received DTG-based ART regimens, the time to discontinuation for those who received B/F/TAF was longer compared with those who received DTG/3TC (262.7 vs. 241.0 days, respectively) or a DTG-based MTR (270.0 vs. 248.1 days, respectively) and similar compared with those who received DTG/ABC/3TC (276.3 vs. 276.5 days, respectively).
Table 3.
Weighted persistence outcomes in PWH-MHD/SUD who received B/F/TAF versus DTG-based comparator regimens
| B/F/TAF vs. DTG/3TC | B/F/TAF vs. DTG/ABC/3TC | B/F/TAF vs. DTG-based MTR | ||||
|---|---|---|---|---|---|---|
| B/F/TAF | DTG/3TC | B/F/TAF | DTG/ABC/3TC | B/F/TAF | DTG-based MTR | |
| Follow-up durationa, days, mean (SD) | 594.1 (381.6) | 576.6 (333.3) | 660.1 (433.6) | 680.8 (460.4) | 637.2 (417.7) | 636.3 (429.0) |
| Persistence | ||||||
| PWH who were nonpersistent, % | 51.0 | 50.3 | 53.6 | 60.4 | 53.0 | 57.7 |
| Discontinued | 44.6 | 39.9 | 47.0 | 43.7 | 46.4 | 40.6 |
| Switched | 6.4 | 10.4 | 6.6 | 16.6 | 6.6 | 17.1 |
| Time to nonpersistence, days, mean (SD) | 266.8 (233.3) | 238.1 (193.2) | 285.7 (253.2) | 292.0 (261.5) | 276.8 (245.0) | 255.3 (229.3) |
| Time to discontinuation, days, mean (SD) | 262.7 (226.5) | 241.0 (197.8) | 276.3 (238.4) | 276.5 (245.3) | 270.0 (233.7) | 248.1 (210.9) |
| Time to switch, days, mean (SD) | 295.5 (274.3) | 227.0 (178.8) | 352.4 (333.6) | 332.5 (297.0) | 325.3 (310.1) | 272.3 (268.2) |
The DTG-based MTR cohort included individuals who received DTG + F/TDF and individuals who received DTG + F/TAF
aIndividuals were followed from the index date to the earliest of the following events: evidence of pregnancy, disenrollment, or end of data
3TC lamivudine, ABC abacavir, B bictegravir, DTG dolutegravir, F emtricitabine; MTR multitablet regimen, PWH people with HIV, PWH-MHD/SUD people with HIV who have a mental health disorder or substance use disorder, SD standard deviation, TAF tenofovir alafenamide, TDF tenofovir disoproxil fumarate
Kaplan–Meier curves for persistence outcomes are shown in Fig. 1. Based on Cox proportional hazards models, those who received DTG/ABC/3TC or a DTG-based MTR had a significantly higher likelihood of nonpersistence compared with those who received B/F/TAF [weighted HR (95% CI) 1.18 (1.09–1.29) and 1.19 (1.06–1.34), respectively; Fig. 2]. The hazards of switching were significantly higher for PWH-MHD/SUD who received DTG/3TC, DTG/ABC/3TC, or a DTG-based MTR compared with those who received B/F/TAF [weighted HR (95% CI) 1.68 (1.08–2.63), 2.67 (2.23–3.19), and 2.88 (2.32–3.58), respectively].
Fig. 1.
Kaplan–Meier survival analysis of time to (I) nonpersistence, (ii) discontinuation, and (iii) switch after weighting in PWH-MHD/SUD who received B/F/TAF versus DTG-based comparator regimens. The DTG-based MTR cohort included individuals who received DTG + F/TDF and individuals who received DTG + F/TAF. No. at risk reflect the IPTW population and may not match unweighted cohort counts. 3TC lamivudine, ABC abacavir, B bictegravir, DTG dolutegravir, F emtricitabine, MTR multitablet regimen, PWH-MHD/SUD people with HIV who have a mental health disorder or substance use disorder, TAF tenofovir alafenamide, TDF tenofovir disoproxil fumarate
Fig. 2.
Risk of nonpersistence for DTG-based ART regimens compared with B/F/TAF by population estimated from weighted Cox proportional hazards models. *P < 0.05, **P < 0.01, ***P < 0.001. The DTG-based MTR cohort included individuals who received DTG + F/TDF and individuals who received DTG + F/TAF. 3TC lamivudine, ABC abacavir, ART antiretroviral therapy, B bictegravir, CI confidence interval, DTG dolutegravir, F emtricitabine, HR hazard ratio, MHD mental health disorder, MTR multitablet regimen, SUD substance use disorder, TAF tenofovir alafenamide, TDF tenofovir disoproxil fumarate
For the overall population of restarters, the weighted persistence outcomes are shown in Table S5 and the Kaplan–Meier curves for persistence outcomes are shown in Figure S1. Among all PWH who restarted a previously discontinued ART regimen, those who received DTG/ABC/3TC or a DTG-based MTR had a significantly higher hazard of nonpersistence compared with those who received B/F/TAF [weighted HR (95% CI) 1.20 (1.14–1.27) and 1.26 (1.16–1.36), respectively; Fig. 2]. The hazards of discontinuation were similar between those who received B/F/TAF and those who received a comparator regimen. The hazards of switching were significantly higher for PWH who received DTG/ABC/3TC or a DTG-based MTR compared with those who received B/F/TAF [weighted HR (95% CI) 2.74 (2.43–3.09) and 2.94 (2.53–3.41), respectively] but similar between PWH who received B/F/TAF and those who received DTG/3TC [1.26 (0.92–1.73)].
Discussion
Throughout their use of ART, many PWH experience treatment interruptions that are long enough to allow unsuppressed viral replication [20, 21, 23]. For viremic PWH prone to such interruptions, the ability to restart ART and persist with treatment over time is critical to re-establish viral suppression. However, there is a paucity of evidence related to treatment persistence patterns among PWH who restart ART regimens following a treatment interruption. This retrospective, observational cohort study utilized US medical and pharmacy claims data to compare real-world persistence among PWH who had restarted B/F/TAF, DTG/3TC, DTG/ABC/3TC, or a DTG-based MTR after a > 90-day treatment interruption in the same regimen. Within each regimen group, > 35% of PWH had an MHD or SUD at baseline. In both the population of PWH-MHD/SUD and the overall population of restarters, analyses of persistence after restarting identified significant differences between those who received B/F/TAF versus some DTG-based regimens.
Numerous studies have demonstrated that MHD and SUD in PWH pose challenges to maintaining ART persistence and are strongly correlated with nonadherence [26–30]. While treatment interruptions in this population are often assessed over short periods, research indicates that nonadherence among PWH-MHD/SUD is relatively prevalent, with 20–45% of PWH-MHD/SUD reporting missed doses in the week prior to their assessment [27, 30, 31]. Our findings suggest that persistence challenges for PWH-MHD/SUD are commonly recurrent, as over half were eventually nonpersistent on their index regimen after restarting.
In this study, PWH-MHD/SUD who received B/F/TAF were significantly less likely to switch ART regimens compared with those who received DTG-based regimens. As 1 aspect of persistence, switching can be captured in real-world data, as it represents a discrete, trackable event (i.e., a claim for a new medication). PWH may switch regimens for a variety of reasons, including treatment resistance, inadequate viral suppression on their current regimen, co-infection with hepatitis B, or concerns related to safety or tolerability [32, 33]. Switching regimens may also occur as treatment guidelines change or as new therapies come to market; we attempted to reduce the impact of this by indexing comparators only starting from years when both regimens were on the market. PWH-MHD/SUD may also need to switch ART regimens to reduce side effects related to coexisting conditions and concomitant medications [34]. For example, some adverse effects of DTG-based regimens, such as sleep or neuropsychiatric disorders [35], may be particularly problematic in those with MHDs or SUDs. As such, the US DHHS guidelines provide specific considerations for prescribers who are selecting ART regimens for PWH with SUDs; these considerations include potential barriers to adherence, comorbidities that could impact treatment, potential drug–drug interactions, and adverse events associated with ART regimens [4]. Therefore, it is possible that the differential switching patterns observed in this study may be related to these special guidelines for PWH with SUDs. Ultimately, additional studies are needed to identify the most common reasons for switching and the most common switches that occur for each regimen among PWH facing barriers to treatment persistence.
This study had several limitations that should be noted. First, claims data are primarily collected for reimbursement purposes rather than clinical research; therefore, reliance on diagnosis codes to identify comorbidities and outcomes for this study may have led to some misclassifications. Secondly, the presence of a claim for a filled prescription may not necessarily indicate that the medication was taken as prescribed. Claims data may also be miscoded, leading to potential measurement error. The HealthVerity dataset did not include information on race and other demographic characteristics, such as socioeconomic status. Furthermore, channeling bias may have affected the study results; specifically, PWH with a history of poor adherence may have been more likely to be prescribed B/F/TAF [33, 36]. Additionally, discontinuation may have been misclassified as a PWH may have been categorized as discontinued if they began filling prescriptions outside of their pharmacy insurance. While IPTW was used to control for confounding, it was not able to control for unobserved characteristics that were not in the database, such as income, education, race, ethnicity, neighborhood, access to transportation and health care facilities, and stigma, which may have impacted the study findings. It should also be noted that sample size varied widely by regimen, and the power to detect differences may have been particularly limited for the DTG/3TC group due to the low sample size. Finally, the population had a minimum of 455 days of continuous medical and pharmacy enrollment to assess baseline comorbidities and comedications, which may limit the generalizability of the findings, particularly among PWH-MHD/SUD who may be less likely to have access to health insurance.
Conclusion
Subsequent discontinuation or switching is common among PWH who restart ART after a treatment interruption. These results demonstrate that B/F/TAF may be a preferred treatment choice for PWH restarting ART, including those with MHDs or SUDs. Improved solutions are needed to reduce the overall likelihood of ART interruptions, particularly in those with a history of treatment gaps.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgments
Medical Writing, Editorial, and Other Assistance
Medical writing and editorial support were provided by Catherine Bautista, PhD, of Lumanity Communications Inc. (Yardley, PA, USA), and were funded by Gilead Sciences, Inc.
Author Contributions
Conceptualization: Mary J Christoph, Uche Mordi, and Travis Lim. Methodology: Amanda M Kong, Jacqueline Lucia, Mary J Christoph, Uche Mordi, Daisha Joseph, Gulce Askin, Daniela Yucuma, and Travis Lim. Formal analysis: Amanda M Kong, Jacqueline Lucia, Daisha Joseph, Gulce Askin, and Daniela Yucuma. Data curation: Amanda M Kong, Jacqueline Lucia, Daisha Joseph, Gulce Askin, and Daniela Yucuma. Writing – review and editing: Amanda M Kong, Jacqueline Lucia, Mary J Christoph, Uche Mordi, Daisha Joseph, Gulce Askin, Daniela Yucuma, Neia Prata Menezes, Peter McMahon, and Travis Lim. Supervision: Travis Lim. Project administration: Travis Lim.
Funding
This study, along with the journal’s Rapid Service and Open Access Fees, were funded by Gilead Sciences, Inc.
Data Availability
The dataset generated during the current study is not publicly available as it is a proprietary dataset.
Declarations
Conflict of Interest
Amanda M Kong, Jacqueline Lucia, Daisha Joseph, Gulce Askin, and Daniela Yucuma are employees of and may own stock options in Aetion, Inc., which received funding from Gilead Sciences, Inc., to conduct this analysis. Mary J Christoph, Uche Mordi, Neia Prata Menezes, Peter McMahon, and Travis Lim are employees of and may own stock or stock options in Gilead Sciences, Inc.
Ethical Approval
This study was conducted using retrospectively collected data and received a formal institutional review board exemption. The data contained no personal identification fields. No informed consent was obtained to participate in this secondary analysis of existing data.
Footnotes
Prior Presentation: The data in this manuscript have been presented, in part, at IDWeek 2025 (October 19–22, 2025; Atlanta, GA, USA).
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The dataset generated during the current study is not publicly available as it is a proprietary dataset.