AIDS and Behavior (2021) 25:3883–3897 https://doi.org/10.1007/s10461-021-03271-3 ORIGINAL PAPER Recreational Drug Use in People Living with HIV in Spain: Factors Associated with Drug Use and the Impact on Clinical Outcomes Vanessa Castro‑Granell1,2 · Noé Garin3,4,5 · Ángeles Jaén6 · José Luis Casado7 · Lorna Leal8 · Santiago Cenoz9 · María José Fuster‑RuizdeApodaca10,11 Accepted: 16 April 2021 / Published online: 1 May 2021 © The Author(s) 2021 Abstract We analysed the impact of recreational drug use (RDU) on different outcomes in people living with HIV (PLHIV). A mul- ticentre retrospective cohort study was performed with two cohorts of PLHIV included: people using recreational drugs (PURD) vs. people not using recreational drugs (PNURD). Overall, 275 PLHIV were included. RDU was associated with men having sex with men (OR 4.14, 95% CI [1.14, 5.19]), previous sexually transmitted infections (OR 4.00, 95% CI [1.97, 8.13]), and current smoking (OR 2.74, 95% CI [1.44, 5.19]). While the ­CD4/CD8 ratio increased amongst PNURD during the follow-up year, it decreased amongst PURD (p = 0.050). PURD presented lower scores of self-reported and multi-interval antiretroviral adherence (p = 0.017, and p = 0.006, respectively), emotional well-being (p < 0.0001), and regular follow-up (p = 0.059), but paid more visits to the emergency unit (p = 0.046). RDU worsens clinical, immunological, and mental health outcomes amongst PLHIV. Keywords HIV · Recreational drug use · Antiretroviral therapy · Clinical outcomes Resumen Analizamos el impacto del consumo de drogas recreativas sobre variables relacionadas con la salud en personas con VIH (PVIH). Estudio multicéntrico retrospectivo con dos cohortes de PVIH: consumidores de drogas recreativas (CDR) y no consumidores (NCDR). Se incluyeron 275 PVIH. El consumo de drogas recreativas se asoció al colectivo de hombres que mantienen sexo con hombres (OR 4.14, IC95% [1.14, 5.19]), a infecciones de transmisión sexual previas (OR = 4.00, IC95% Vanessa Castro-Granell and María José Fuster-RuizdeApodaca contributed equally to this work. 5 * Vanessa Castro‑Granell School of Health Science Blanquerna, Universitat Ramon vanemcg@hotmail.com; castro_van@gva.es Llull, Barcelona, Spain 6 Ángeles Jaén Research Unit, Fundació Docència i Recerca mutua ajaen@mutuaterrassa.es Terrassa. Universidad de Barcelona, Terrassa, Barcelona, http://www.mutuaterrassa.cat/fundaciorecerca/ Spain 7 María José Fuster‑RuizdeApodaca Department of Infectious Diseases, Hospital Ramón y Cajal, mjfuster@psi.uned.es Madrid, Spain 8 1 Infectious Diseases‑HIV Unit, Hospital Clínic Doctoral Programme in Pharmacy, Granada University, Barcelona- IDIBAPS, Barcelona, Spain Granada, Spain 9 2 Medical Department, ViiV Healthcare, Tres Cantos, Madrid, Department of Pharmacy, Hospital Marina Baixa, Spain Av. Alcalde En Jaume Botella Mayor, 7, 03570 Villajoyosa, 10 Alicante, Spain Spanish Interdisciplinary AIDS Society (Sociedad Española 3 Interdisciplinaria del Sida, SEISIDA), Madrid, Spain Department of Pharmacy, Hospital Santa Creu i Sant Pau, 11 Universitat Autònoma de Barcelona, Barcelona, Spain Department of Social and Organizational Psychology, 4 Universidad Nacional de Educación a Distancia (UNED), Instituto de Salud Carlos III, Centro de Investigación Madrid, Spain Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain 13 Vol.:(0123456789) 3884 AIDS and Behavior (2021) 25:3883–3897 [1.97, 8.13]) y a ser fumador (OR = 2.74, IC95% [1.44, 5.19]). El ratio C ­ D4/CD8 aumentó entre los NCDR durante el año de seguimiento y disminuyó en los CDR (p = 0.050). Los CDR presentaron peor adherencia al tratamiento antiretroviral medida con dos métodos indirectos (p = 0.017 y p = 0.006, respectivamente), y bienestar emocional (p < 0.0001). Además, visitaron menos al especialista en enfermedades infecciosas (p = 0.059), y más a urgencias (p = 0.046). El consumo de drogas recreativas empeora los resultados clínicos y de salud mental entre las PVIH. Palabras clave VIH · Drogas recreativas · Terapia antiretroviral · Resultados clínicos Abbreviations to the largest study conducted in Spain [3], there were four AIDS Acquired immunodeficiency syndrome consumption patterns. Two were composed mostly of het- ART​ Antiretroviral therapy erosexuals (HTX), and the other two principally of MSM. HIV Human immunodeficiency virus Amongst these studies were two well-differentiated poly- HTX Heterosexual consumption profiles: a group of MSM (whose profile was MSM Men who have sex with men related to recreational drug use) with the highest rate of poly- PLHIV People living with HIV consumption, who showed an association with an increased PWID People who inject drugs risk of sexually transmitted infections (STIs), and a group STIs Sexually transmitted infections of predominantly heterosexual (HTX) individuals (whose RDU Recreational drug use profile was associated with the use of traditional drugs such PURD People who use recreational drugs as heroin), who showed worse adherence to antiretroviral PNURD People who do not use recreational drugs treatment (ART) and suffered worse health outcomes. The other two remaining groups presented a pattern of moder- ate drug use regarding both the frequency and diversity of drugs used. As for the clinical impact of illicit drug use, it Background seems that the pattern associated with PWID presents clear evidence of poor health outcomes [14–17]. However, inter- Advances in knowledge of HIV infection, as well as the est in recreational drugs is relatively recent and less well emergence of new therapies, have revolutionised the natu- known in the field of HIV. Studies have focused primarily on ral history of the infection, which is currently considered the risk of transmission of infectious diseases, mainly STIs, a chronic disease with mortality rates similar to those of caused by a higher prevalence of risk behaviours associated the general population [1]. This paradigm shift requires with consumption [5, 18–26]. the adaptation of approaches to other emerging problems Some data suggest that the use of illicit drugs is predic- that can have a decisive impact on the management of HIV, tive of poorer HIV outcomes, although the literature shows including the prevalence of recreational drug use (RDU) inconsistent findings [27]. Few studies have analysed the which has potential implications for people living with HIV impact of recreational drug use on immunological param- (PLHIV) in Spain [2, 3]. eters ­(CD4, ­CD8, and C ­ D4/CD8 ratio), but there are cross- The pattern of illicit drug use in Spain has changed over sectional studies that used self-reported data [28]. Chao time in terms of the substances consumed, the scenarios in et al. did not find evidence that the use of recreational drugs which they are used, and user profiles. As a result, its impact adversely affects ­CD4 or ­CD8 T cell counts in PLHIV, but on people and society as a whole has also changed [4]. Peo- they did not report polydrug use [27]. Moreover, several ple who injected drugs (PWID) in the 1980s and 1990s were studies have focused on the study of traditional drugs often socially marginalised, and had a tenuous connection (cocaine, heroin, and crack), and have found contradictory with the health system [5]. The prevalence of this profile results [29–31]. It is difficult to determine the effect of illicit has decreased from 1994 to 2010, reducing from 3.26 to drug use on ­CD4 and ­CD8 cell count levels. Several factors 0.49 PWID per 1000 inhabitants, thanks to the implementa- could influence these cell counts, and there is no consensus tion of specific harm reduction policies [6]. In return, the in the literature about it. Such factors include the nadir ­CD4 prevalence of drug use in the ludic or recreational environ- [31], time on antiretroviral therapy [31], adherence level [29, ment has increased by about 50% amongst PLHIV, as studies 31], changes in ART [32], age [33], type of ART regimen conducted in Spain and elsewhere in Europe have shown [29], follow-up time [27], history of STIs [27], and smoking [3, 7–11]. These have placed special emphasis on “poly- [27], among others. Thus, the impact of polyconsumption of consumption,” which is especially predominant in men who recreational drugs on C ­ D4 and C ­ D8 counts remains unclear. have sex with men (MSM) with HIV [12, 13]. Besides immunological parameters, illicit drug use could The literature shows considerable variability in the fre- have an impact on other health-related variables. Studies quency of use of different illicit drugs [3, 7–10]. According show that RDU can have a negative impact on adherence to 13 AIDS and Behavior (2021) 25:3883–3897 3885 ART [9, 26, 34, 35]. However, the results have been variable Method and controversial [5, 11, 36–41]. The relationship between RDU and the lack of adherence is complex, and includes Design and Sample intentional and unintentional mechanisms. Adherence is closely linked to viral suppression and is therefore consid- The present study is a part of a broader research project ered a clear predictor of ART success and survival [42]. that aimed to analyse several aspects of the use of illicit However, the clinical relevance of the impact of adherence drugs amongst PLHIV in Spain. We previously conducted on health in PLHIV using recreational drugs remains uncer- a qualitative study interviewing 21 PLHIV who used illicit tain. In fact, some studies suggest that RDU in PLHIV does drugs [2], and a second observational cross-sectional ex-post not affect the viral load level [42, 43]. Certain factors might facto study with 1401 PLHIV [3]. The current third study modulate the final impact of adherence on health outcomes; comprised multicentre observational, retrospective cohort on the one hand, less restrictive adherence cut-off points research, in which two cohorts of PLHIV were formed: (around 80–90%) [44], which achieve viral suppression and, PURD and PNURD. on the other hand, the use of simplified ART regimes [45]. We estimated a minimum required sample of 222 PLHIV, Recreational drug use also has an impact on mental with 111 in each group (i.e., PURD and PNURD), accept- health, although it is difficult to establish causality or direc- ing an alpha risk of 0.05 and a beta risk of 0.2 in a bilateral tionality because they co-occur very frequently [46]. The contrast (and considering a follow-up loss rate of 10%); the negative impact of opioid and stimulant use (including meth- aim was to achieve an expected standardised mean differ- amphetamines) on the mental health dimension of quality ence in ART adherence of the PURD group of 0.395, based of life [47] has been investigated, and it has been found that on the results of the meta-analysis by Langebeek et al. [55]. their use is associated with increased anxiety, depression, This sample size was sufficient to detect small-to-moderate and psychosis [48]. Also, anxiety and depression are par- effect size differences in the other variables under investiga- ticularly prevalent in MSM, and there is a strong association tion. Due to the fact that we have several outcome variables, with drug use [49]. Both are associated with risky sexual we chose ART adherence to estimate sample size; there is practices, the increased risk of HIV transmission, and the more scientific evidence as to what the expected differences more frequent use of medical services [50–52]. between groups would be. RDU can also have consequences for the health econ- The general inclusion criteria for both groups were HIV omy. Some authors claim that the situation may deteriorate positive diagnosis, age over 18 years, taking ART for at because of increased hospitalisations and visits to outpatient least one year, clinical follow-up of more than 1 year at the and emergency centres [52, 53], dose increases or changes in centre, and not having any severe psychiatric or cognitive prescription, or the performance of greater numbers of more disorder. The specific inclusion criteria for the two cohorts invasive diagnostic tests [54]. To the best of our knowledge, were: PURD (consumption ≥ one drug ≥ 10 times a year, no Spanish studies have examined this issue in PLHIV. excluding the use of cannabis as a single drug) vs. PNURD Thus, it appears that existing evidence of the impact of (including the consumption of cannabis ≤ 10 times a year). RDU on variables related to physical and psychological The exclusion criteria for both groups were the consumption health is limited; most of the studies are cross-sectional, of methadone or heroin and dependence on alcohol (current while the majority focus on the increased risk of STIs, are or in the previous 5 years) (Fig. 1). aimed at the study of traditional drugs and report limited data about the impact of polyconsumption on clinical mark- Procedure ers. Thus, the present study, through a retrospective cohort design, had the main objective of examining the impact of Data from the study participants were collected from four recreational drug polyconsumption on several health-related University Hospitals in three of the most populous Spanish variables in a PLHIV cohort in Spain. It specifically aimed regions (Madrid, Barcelona, and Alicante) between April to explore: (1) the variables associated with RDU; (2) poten- 2017 and May 2018. Two cohorts of PLHIV were recruited tial differences in health outcomes between PLHIV people according to the inclusion and exclusion criteria for each using recreational drugs (PURD) and people not using recre- group (PURD vs. PNURD) during regular clinical visits. ational drugs (PNURD), including their immunological sta- Healthcare providers explained the study’s goals to the par- tus evolution during the follow-up period; and (3) potential ticipants, requesting their participation and obtained their differences in their use of healthcare services and resources. informed consent. In the same clinical visit, participants responded to a cross-sectional online survey containing the self-reported variables. The survey was self-administered with the support of tablet computers. 13 3886 AIDS and Behavior (2021) 25:3883–3897 Fig. 1  Cohorts of participants, inclusion and exclusion criteria. General inclusion criteria: General exclusion criteria: PURD people using recreational Age ≥ 18 years old Heroin use or opioid substitution drugs, PNURD people not using VIH (+) therapy current and/or previous five years recreational drugs ART ≥ 12 months Patients with addiction to alcohol No severe psyquiatric disorder current and/or previous five years Clinical follow-up ≥ 12 months PURD PNURD Specific inclusion criteria: Specific inclusion criteria: Taking ≥ on erecreational drug ≥ 10 Not taking any drug apart from cannabis times last year ≤ 10 times a year Specific exclusion criteria: Cannabis used as unique drug N=413 participants screened N=67 participants did not meet selection criteria N=346 participants invited N=71 participants refused to participate N=275 participants included N= 12 participants excluded by protocol deviation N=263 participants analysed (Final sample) PURD PNURD N=135 N=128 (51.3%) (48.7%) Fig. 2  Study sampling. PURD people using recreational drugs, PNURD people not using recreational drugs Healthcare providers performed an initial screening of sample (N = 263) was composed of 135 PURD (51.3%) and 413 patients during clinical visits. Of them, 67 did not fulfil 128 PNURD (48.7%) (Fig. 2). the selection criteria. Among the remaining 346 patients, 71 Next, the collaborating researchers collected retrospective refused to participate. Finally, 275 patients were included clinical data of the previous 12 months from clinical records. in the study after they signed the informed consent. The The Ethics Committee of the Hospital Clínico of acceptance rate of participation in the study was 79.4%. Out Valencia approved the research protocol. Written of the 275 PLHIV included in the study (146 PURD and informed consent was obtained from all participants. All 129 PNURD), 12 participants were excluded due to protocol of the procedures of the study followed the 1964 Dec- deviation detected during depurating data. The final analysed laration of Helsinki (revised in 1996), as well as the guidelines for good clinical practice. The Spanish AIDS 13 AIDS and Behavior (2021) 25:3883–3897 3887 multidisciplinary Society (SEISIDA) coordinated the vs. another route). The continuous covariates included were study. age, level of education, years since HIV diagnosis, monthly incomes, and frequency of drinking alcohol. Measures Next, we used a linear mixed model for repeated meas- ures over time to analyse the change between the first (base- We collected the following retrospective last-12-month clini- line) and the last determination during the follow-up year cal data from clinical records: ART adherence (pharmacy using the following immunological parameters: percentage refill), ­CD4, ­CD4/CD8 ratio, viral load, resistance to and of ­CD4 and ­CD8 cells/mm3 and ­CD4/CD8 ratio (within- adverse effects of ART, previous clinical conditions, clini- subject dependent variable) between PURD and PNURD cal events in the last year, and the use of health services in (between-group factor). We included the following covari- the last year. ates in the model: age, time since HIV diagnosis, number or Participants responded to a cross-sectional online survey ART changes and ART adherence (pharmacy refill) during containing the patient-reported outcome measures. The pro- the follow-up year. cedures for designing the survey that was used are described Finally, to examine the differences in other health-related in Fuster-RuizdeApodaca et al. [3]. Variables used in the variables collected once during the follow-up period, we present analysis were: used Student’s t or χ2, depending on the nature of the data. The analysis was also checked with non-parametric tests; we Use of Illicit Drugs and Other Substances considered a p value < 0.05 to be significant. The analysis was performed using SPSS v.22 software. The survey included items that measured the type of illicit drugs used during the last year, and the frequency and route of consumption. A list of 18 illicit drugs was included. Results ART Adherence Characteristics of the Participants We used the Questionnaire to Evaluate the Adherence to Table 1 shows the socio-demographic, clinical, and epide- HIV Therapy (CEAT-VIH) [3, 56]. Higher scores indicate miological characteristics of the participants, and differ- higher treatment adherence. ences between PURD and PNURD. The PURD were sin- gle, employed, and living in urban areas mostly. They were Psychological Well‑Being younger, and had been diagnosed and taking ARV within a shorter time frame than the PNURD. They also contained a We used the 12-item validated Spanish version of the Gen- higher proportion of MSM. eral Health Questionnaire (GHQ-12). The higher the score, The most prevalent recreational drugs consumed by the the worse the psychological health. Normative data in the PURD were poppers, cocaine, and cannabis. The average Western population indicates that scores > 14 in the GHQ-12 number of different drugs used per participant in the last are indicative of poor psychological health [57, 58]. year was 5.5 (SD = 3.2; range 1–14). Injected and rectal The survey also collected several socio-demographic administration routes accounted for 10.4% and 9.6% of the characteristics: age, gender, sexual orientation, education cases, respectively (Table 2). level, employment status, financial resources, and city of residence. Predictors of RDU Data Analysis The full logistic regression model containing the pre- dictors was statistically significant, χ2 (df = 11, n = 229, We carried out multivariate logistic regression to assess the PURD = 114, PNURD = 115) = 79.32, p < 0.0001, indicating socio-demographic/clinical factors associated with RDU. that the model distinguished between PURD and PNURD. We selected the independent variables (covariates) based The model explained the following percentage of variance: both on a theoretically based approach (see previous findings ­R 2 = 0.29 (Cox & Snell), 0.390 (Nagelkerke), and 0.25 in the literature review) and significance at a level of < 0.1 in (Homer & Lemeshow). Based on odds ratio or effect size, the bivariate analysis. The categorical covariates included in MSM compared with HTX, and those who had contracted the model were: sexual orientation (MSM vs. HTX), STIs in STIs in the previous year compared with those who had not the previous year (yes/no), current smoking (yes/no), previ- were around four times more likely to be PURD. Moreover, ous clinical conditions (yes/no), employment status (work- current smoking was a positive predictor, whereas age was ing vs. not working), and route of HIV transmission (sexual 13 3888 AIDS and Behavior (2021) 25:3883–3897 Table 1  Differences between participants who used and who did not use recreational drugs in the variables under study Total PURD PNURD p-value (n = 263) (n = 135, 51.3%) (n = 128, 48.7%) (Contrast statistic) Patients’ characteristics Age, mean (SD; years) 45.79 (10.86) 42.21 (8.24) 49.56 (11.99) < 0.0001 (t =  − 5.76) Gender 0.003 (χ2 = 11.45) Male, % (n) 93.5 (246) 98.5 (133) 88.3 (113) Female, % (n) 6.1 (16) 1.5 (2) 10.9 (14) Transgender, % (n) 0.4 (1) 0 0.8 (1) Sexual orientation < 0.0001 (χ2 = 21.85) Heterosexual, % (n) 12.9 (34) 4.4 (6) 21.9 (28) Homosexual, % (n) 78.7 (207) 89.6 (121) 67.2 (86) Bisexual, % (n) 5.7 (15) 3.7 (5) 7.8 (10) Others, % (n) 2.7 (7) 2.2 (3) 3.1 (4) Current relationship 0.034 (χ2 = 4.48) Yes, % (n) 40.3 (106) 34.1 (46) 46.9 (60) No, % (n) 59.7 (57) 65.9 (89) 53.1 (68) Educational level 0.080 (χ2 = 6.77) No studies, % (n) 1.2 (3) 0 2.4 (3) Primary, % (n) 17.5 (45) 13.6 (18) 21.6 (27) Secondary, % (n) 41.6 (107) 42.4 (56) 10.8 (51) University degree, % (n) 31.7 (102) 43.9 (58) 35.2 (44) Work situation < 0.0001 (χ2 = 19.88) Working, % (n) 66.2 (174) 76.3 (103) 55.5 (71) Unemployed, % (n) 20.2 (53) 17.8 (24) 22.7 (29) Retired or disability, % (n) 12.5 (33) 4.4 (6) 21.1 (27) Other, % (n) 1.1 (3) 1.5 (2) 0.8 (1) Monthly ­incomea 2.77 ± 0.84 2.66 ± 0.82 2.88 ± .84 0.035 (t =  − 2.12) Residence 0.015 (χ2 = 5.92) ­Ruralb, % (n) 4.6 (12) 1.5 (2) 7.8 (10) Urban, % (n) 94.7 (249) 98.5 (131) 92.2 (118) < 0.0001 (t =  − 5.76) HIV health-related variables Time diagnosed, M ± SD (years) 13.28 ± 7.59 11.70 ± 5.78 14.93 ± 8.80 0.001 (t =  − 3.37) Time on ART, M ± SD (years) 10.67 ± 6.27 9.92 ± 5.66 12.01 ± 7.20 0.014 (t =  − 2.47) Previous clinical conditions, % (n) 75.7 (199) 82 (105) 69.6 (94) 0.019 (χ2 = 5.48) M ± SD 2.56 ± 1.80 2.88 ± 2.08 2.20 ± 1.35 0.006 (t = -2.76) Route of transmission 0.051 (χ2 = 3.80) Sexual intercourse, % (n) 81.4 (214) 85.9 (116) 76.6 (98) Other routes, % (n) 18.6 (49) 14.1 (19) 23.4 (30) HIV stage, % (n) 0.005 (χ2 = 21.87) A1 40.9 (79) 24 (18) 51.7 (61) A2 28 (54) 37.3 (28) 22 (26) A3 9.3 (18) 10.7 (8) 8.5 (10) B1 3.1 (6) 4 (3) 2.5 (3) B2 4.1 (8) 2.7 (2) 5.1 (6) B3 2.1 (4) 4 (3) 0.8 (1) C1 2.6 (5) 2.7 (2) 2.5 (3) C2 1 (2) 0 1.7 (2) C3 8.8 (17) 14.7 (11) 5.1 (6) STIs, % (n) 34.6 (91) 51.9 (70) 16.4 (21) < 0.0001 (χ2 = 36.48) M ± SD 1.89 ± 1.02 2.04 ± 1.05 1.38 ± 0.74 0.009 (t = 2.67) Current smoker, % (n) 49.4 (130) 62.2 (84) 35.9 (46) < 0.0001 (χ2 = 18.16) c Frequency drinking ­alcohol , M ± SD 3.98 ± 1.83 4.22 ± 1.68 3.73 ± 1.95 0.032 (t = 2.17) PURD people using recreational drugs, PNURD people not using recreational drugs a The item ranged from 1 (none); 2 (≤ 1000 €); 3 (1001–2000 €); 4 (2001–6000 €) and 5 (> 6001 €) 13 AIDS and Behavior (2021) 25:3883–3897 3889 Table 1  (continued) b “urban” if having more than 10,000 inhabitants c Frequency of drinking alcohol responses ranged from 1 (never); 2 (sometimes a year); 3 (once a month); 4 (sometimes a month); 5 (once a week); 6 (sometimes a week) and 7 (daily) Table 2  Type and frequency of recreational drugs used and consumption routes Drugs % (N) M ± SDa 100 (135) 5.5 ± 3.2 Poppers 81.5 (110) 2.5 ± 1.5 Cocaine (powder) 72.6 (98) 1.9 ± 1.2 Cannabis 63.0 (85) 2.9 ± 2.1 GHB/GBL 57.8 (78) 1.7 ± 1.3 MDMA (pills) 50.4 (68) 1.5 ± 0.8 MDMA (crystal) 45.9 (62) 1.4 ± 0.7 Ketamine 40.7 (55) 1.4 ± 1.0 Mephedrone 40.7 (55) 1.4 ± 0.9 Speed 37.0 (50) 1.4 ± 0.8 Methamphetamine 36.3 (49) 2.1 ± 1.7 Cocaine (base) 5.9 (8) 2.1 ± 1.6 Spice drugs 5.9 (8) 1.5 ± 0.9 LSD 5.2 (7) 1.0 ± 0.0 Mushrooms 3.0 (4) 1.0 ± 0.0 Other hallucinogenic plants 1.5 (2) 1.0 ± 0.0 2C-B nexus 0.7 (1) 1.0 Routes of consumption % (N)b Sniff 81.5 (110) Smoke 74.8 (101) Oral 70.4 (95) Inhaled 58.5 (79) Injection 10.4 (14) Anal 9.6 (13) a Frequency of consumption ranged from 1 (occasionally during the last 12 months); 2 (once a month); 3 (sometimes a month); 4 (once a week); 5 (sometimes a week) and 6 (daily) b Score for each route of consumption was calculated by summing the use of the route in each drug a negative predictor of being in the PURD group (Table 3). between the first and the last determination during the The model correctly classified 73.4% of the cases (Table 4). follow-up year (F (1, 209) = 0.2, p = 0.138). Nevertheless, after controlling the effect of the covariates, the interaction Evolution of Immunological Status During between group membership and the change in the percentage the Follow‑Up Year of ­CD4 cells/mm3 during the follow-up year was marginally significant (F (1, 209) = 3.56, p = 0.060) (Fig. 3). The results of the main effects of the linear mixed model Moreover, the results did not show a significant main for repeated measures did not show a significant difference effect in the percentage of ­C D 8 cells/mm 3 between the between the PURD and PNURD in the percentage of C ­ D4 PURD and PNURD (F (1, 188) = 0.26, p = 0.609). Also, (F [1, 209] = 2.63, p = 0.106). The covariates, number of none of the covariates showed significant effects in the ART changes and years since HIV diagnosis were margin- ­C D 8 cells/mm 3 of the participants. Likewise, there was ally related to the participant’s percentage of C ­ D4 (F (1, no within subject effects because there was no signifi- 209) = 3.01, p = 0.084 and F (1, 209) = 3.08, p = 0.080, cant change in percentage of C ­ D8 cells/mm3 during the respectively). Also, there was no within subject effects follow-up year (F (1, 188) = 2.23, p = 0.137). Neverthe- because there was no significant change in C ­ D4 cell/mm3 less, after controlling for the effect of covariates, there 13 3890 AIDS and Behavior (2021) 25:3883–3897 Table 3  Logistic regression Parameter β SE β Wald’s χ2 p eβ (odds ratio) analysis of variables related to (df) [95% CI] recreational drug use Constant − 1.03 1.35 0.59 (1) 0.355 NA MSM (vs. HTX) 1.42 0.66 4.68 (1) 0.031 4.14 [1.14, 5.19] STIs in the previous year (vs. no) 1.39 0.36 14.76 (1) < 0.0001 4.00 [1.97, 8.13] Current smoker (vs. no) 1.00 0.33 9.50 (1) 0.002 2.74 [1.44, 5.19] Frequency of alcohol c­ onsumptiona 0.18 0.09 3.59 (1) 0.058 1.19 [0.99, 1.44] Age − 0.05 0.02 7.65 (1) 0.006 0.94 [0.91, 0.98] Previous clinical conditions (vs. no) − 0.34 0.39 0.74 (1) 0.390 0.71 [0.33, 1.54] Incomeb 0.15 0.25 0.36 (1) 0.548 1.16 [0.71, 1.91] Education ­levelc 0.05 0.25 0.04 (1) 0.842 1.05 [0.64, 1.73] Working (vs. no working) 0.39 0.42 0.85 (1) 0.356 1.48 [0.64, 3.39] Transmission through sexual inter- − 0.16 0.47 0.12 (1) 0.731 0.85 [0.34, 2.13] course (vs. other routes) Years since HIV diagnosis 0.00 0.03 0.03 (1) 0.870 1.00 [0.95, 1.06] Model fit NA not applicable, MSM men who have sex with men, HTX heterosexual, STIs sexually transmitted infec- tions a Frequency of alcohol consumption responses ranged from 1 (never); 2 (sometimes a year); 3 (once a month); 4 (sometimes a month); 5 (once a week); 6 (sometimes a week) and 7 (daily) b Monthly income responses ranged from 1 (none); 2 (≤ 1000 €); 3 (1001–2000 €); 4 (2001–6000 €) and 5 (> 6001 €) c Level of education responses ranged from 1 (no studies); 2 (primary); 3 (secondary) and 4 (university degree) Table 4  Classification table: the observed and predicted frequencies a significant interaction between intragroup membership of predictors of drug use estimated by logistic regression and the ­C D 4/CD 8 ratio (F (1, 188) = 3.84, p = 0.050). Predicted % ­Correcteda While the ­CD4/CD8 ratio increased amongst the PNURD during the follow-up year, it decreased amongst the PURD Observed PURD PNURD (Fig. 5). PURD 86 28 75.4 Table 5 presents the marginal means, standard errors and PNURD 33 82 71.3 confidence intervals in the immunological variables in both Overall % correct 73.4 groups. PURD people using recreational drugs, PNURD people not using rec- reational drugs Differences in Health Outcomes and Use a Sensitivity = 86/(86 + 28) = 75.4%. Specificity = 82/ of Healthcare Services and Resources (33 + 82) = 71.3%. False positive = 33/(33 + 86) = 27.7%. False nega- tive = 28(28 + 82) = 25.4% Both the PURD and PNURD maintained undetectable viral loads throughout the follow-up year without group differ- ences. However, there was a higher percentage of PURD was a significant interaction between group membership than PNURD who had experienced changes in their ART and the percentage of ­CD8 cells/mm3 (F (1, 188) = 4.52, regimen during the follow-up year, mainly because of p = 0.035). While the percentage of C ­ D 8 cells/mm 3 adverse effects and drug-drug interactions. Moreover, a decreased amongst the PNURD, it remained stable in marginally higher number of PURD than PNURD presented PURD (Fig. 4). non-AIDS related events. Finally, we did not find the main effects between and We observed a higher proportion and number of analyti- within subject because there were no significant differ- cal tests performed amongst the PURD. A higher percentage ences between the PURD and PNURD in the C ­ D 4/CD 8 of PURD visited emergency units and a lower percentage ratio (F (1, 188) = 0.45, p = 0.503), or in the change in ratio visited specialised care units. In addition, PURD participants during the follow-up year (F (1, 188) = 0.03, p = 0.954). presented lower scores in the two measures of ART adher- Also, none of the covariates showed significant effects in ence (i.e., self-report measured through a validated ques- the ­CD4/CD8 ratio of the participants. However, we found tionnaire and multi-interval adherence calculated through 13 AIDS and Behavior (2021) 25:3883–3897 3891 Fig. 3  Evolution of ­CD4% 35 cells/mm3 in the follow-up year. Note The y axis does not 34.5 start from 0; this is to facili- tate visualisation of the graph. PURD people using recreational 34 drugs, PNURD people not using recreational drugs 33.5 Percentage 33 PURD 32.5 PNURD 32 31.5 31 30.5 %CD4_First %CD4_Last Fig. 4  Evolution of ­CD8% 41.5 cells/mm3 in the follow-up year. Note The y axis does not start from 0; this is to facili- tate visualisation of the graph. 41 PURD people using recreational drugs, PNURD people not using recreational drugs 40.5 Percentage PURD PNURD 40 39.5 39 %CD8_First %CD8_Last pharmacy refills) than PNURD. Finally, the PURD presented First, we examined the variables associated with RDU poorer psychological well-being than the PNURD (Table 6). in PLHIV. We found that the strongest associations were: being MSM, having suffered STIs during the previous year, and being a regular smoker. The frequency of con- Discussion sumption of alcoholic beverages and being younger were also associated with RDU, albeit at a lower intensity. To the best of our knowledge, the present study is the first to These results confirm the findings of other studies, where evaluate the association between RDU and clinical variables a higher prevalence of RDU amongst those of a younger of physical and psychological health, as well as variables of age [3, 59, 60] and a higher incidence of STIs in the HIV health economy, in a PLHIV cohort in Spain. population [3, 61–63] were found. Legal drugs such as 13 3892 AIDS and Behavior (2021) 25:3883–3897 Fig. 5  Evolution of the ­CD4/ 0.94 CD8 ratio in the follow-up year. Note The y axis does not from 0; this is to facilitate visu- 0.92 alisation of the graph. PURD people using recreational drugs, PNURD people not using rec- reational drugs 0.9 0.88 Percentage PURD PNURD 0.86 0.84 0.82 0.8 %CD4/CD8_First %CD4/CD8_Last alcohol and tobacco are the most commonly used drugs linear mixed model results did not find significant differ- amongst MSM, according to the European MSM Internet ences in the percentage of C­ D4, ­CD8, or ­CD4/CD8 between Survey [8]. Most studies include alcohol and tobacco when the PURD and PNURD. The epidemiological and clinical studying the effects of drugs, and show that a high percent- variables that characterised the PURDs, such as being HIV age of patients consume recreational drugs and alcohol positive for a shorter time, being younger, or having fewer combined with tobacco [43, 64]. Therefore, it makes sense clinical antecedents [3], might have influenced the lack of to think that alcohol or tobacco use is related to RDU. difference in the cross-sectional measures between both Following this, we examined differences in health out- groups. The results did not show any differences within- comes as a function of using recreational drugs or not. The subjects because there were no significant changes in the immune parameters during the follow-up year. However, we Table 5  Marginal means, standard errors, and 95% confidence inter- found significant interaction effects between the evolution vals in the dependent variables in PURD and PNURD of immune parameters in the follow-up period and group membership. While the PURD group experienced a decrease Dependent Group (Fac- Measure Statistics variable tor) in the ­CD4/CD8 ratio throughout the follow-up, it increased M SE 95% CI in the PNURD group. This increase was associated with a CD4 cells/ PURD Baseline 34.66 0.91 32.86, 36.47 decrease in the percentage of ­CD8 and an increase in the mm3 (%) percentage of ­CD4 during the follow-up year in the PNURD PNURD 32.05 0.86 30.35, 3376 group, whereas the PURD group remained stable both in the PURD Last 34.51 0.86 32.80, 36.22 evolution of C­ D4 and ­CD8. We found the previous results PNURD 33.15 0.82 31.53, 34.77 controlling some covariates which the literature shows could CD8 cells/ PURD Baseline 39.73 1.11 37.54, 41.93 influence immune parameters such as age, time since diag- mm3 (%) nosis, adherence level, and ART changes [29, 31–33]. How- PNURD 41.17 1.07 39.04, 43.29 ever, our results should be interpreted with caution because PURD Last 39.93 1.01 37.93, 41.93 other potential variables might influence the results. Fur- PNURD 40.04 0.98 38.11, 41.97 thermore, it has been suggested that being MSM could be CD4/CD8 ratio PURD Baseline 0.92 0.44 0.84, 1.01 associated with a change in the C­ D4/CD8 ratio because bac- PNURD 0.85 0.04 0.77, 0.93 terial and viral agents responsible for STIs, which are more PURD Last 0.90 0.04 0.82, 0.98 common among MSM, may increase immune activation and PNURD 0.89 0.04 0.81, 0.97 result in a continuous expansion of the C­ D8 population [32]. PURD people using recreational drugs, PNURD people not using rec- The ­CD4/CD8 ratio represents a predictor of age-related dis- reational drugs eases and non-HIV-associated events (e.g., ischemic heart 13 AIDS and Behavior (2021) 25:3883–3897 3893 Table 6  Differences between HIV health-related variables PURD PNURD p-value (χ2) PURD and PNURD in health- (n = 135, 51.3%) (n = 128, 48.7%) related variables Viral load undetectable in all tests of the follow-up 83.0 (112) 84.4 (108) 0.868 (χ2 = 0.09) period, % (n) Non-AIDS related events, % (n) 30.4 (41) 20.3 (26) 0.061(χ2 = 3.50) Opportunistic infection, % (n) 4.4 (6) 6.3 (8) 0.514 (χ2 = 0.42) Adverse events, % (n) 14.1 (19) 10.9 (14) 0.443 (χ2 = 0.58) Change in ART regimen, % (n) 35.6 (48) 21.9% (28) 0.014 (χ2 = 5.98) Resistance tests, % (n) 3 (4) 3.9 (5) 0.674 (χ2 = 0.17) Use of healthcare resources Diagnostic tests, % (n) 64.4 (87) 64.8 (83) 0.946 (χ2 = 0.00) Radiological tests, % (n) 45.9 (62) 55.5 (71) 0.122 (χ2 = 2.39) Analytical tests, % (n) 26.7 (36) 12.5 (16) 0.004 (χ2 = 8.31) Pathological anatomy tests, % (n) 9.6 (13) 13.3 (17) 0.352 (χ2 = 0.86) Surgical procedure tests, % (n) 0.7 (1) 3.9 (5) 0.086 (χ2 = 2.95) Exploratory tests, % (n) 0 3.9 (5) NA Electromagnetic tests, % (n) 4.4 (6) 7.8 (10) 0.253 (χ2 = 1.30) Other tests % (n) 3.7 (5) 6.3 (8) 0.341 (χ2 = 0.90) Use of healthcare services Primary care visits, % (n) 5.9 (8) 1.6 (2) 0.064 (χ2 = 3.42) Specialised care visits, % (n) 83.7 (113) 91.4 (117) 0.059 (χ2 = 3.55) Emergency room visits, % (n) 61.8 (42) 38.2 (26) 0.046 (χ2 = 3.99) Hospitalisation, % (n) 3.7 (5) 6.3 (8) 0.341 (χ2 = 0.90) ART adherence Multi-interval adherence (pharmacy refill), M ± SD 90.9 ± 14.2 94.5 ± 9.2 0.017 (t =  − 2.40) Self-reported adherence (CEAT-HIV), M ± SD 87.8 ± 9.3 90.6 ± 6.7 0.006 (t =  − 2.74) Psychological well-beinga, M ± SD 12.88.1 ± 6.3 10.62 ± 4.3 0.0001 (t = 3.38) PURD people using recreational drugs, PNURD people not using recreational drugs, ART​ antiretroviral treatment, NA not applicable a The scores ranged from 0 to 36. Means and standard deviations were calculated using the number of patients with presence (yes) in the variable. All mean differences were also tested through non-parametric tests disease, stroke, and chronic kidney failure [65] and higher prior to current ART therapy which determines the less mortality [66]. Moreover, C ­ D4/CD8 ratio recently has been strict adherence to cut-off points [44]. Thus, our sample’s shown to be a more sensitive immune prognostic marker of adherence level does not appear to have impacted their adverse outcomes than the ­CD4 cell count which frequently virological suppression because a small percentage of par- normalises with effective ART [32]. Thus, further longitu- ticipants presented detectable viral loads. In this regard, dinal studies that analyse longer follow-up times and more some studies claim that RDU in PLHIV does not nega- potential covariates that the current research, should deepen tively impact the viral load [42, 43], and argue that the the impact of the polyconsumption of recreational drugs on most influential aspect in terms of health impact is not immune parameters. specifically drug use itself, but the degree of dependence Additionally, we found that the adherence rate meas- on drug use. It should be noted that although a small per- ured by two indirect evaluation methods was lower in the centage presented a detectable viral load (about 15% of PURD group. Our results are consistent with evidence patients), the impact could be transcendent at the level showing that people who use drugs are more likely to of HIV transmissibility, especially in the MSM group, experience adherence problems [9, 26, 34, 35]. However, who are involved in more extreme sexual practices (i.e., adherence does not seem to be determinant in the changes chemsex). It is to be hoped that, over time, the difference we found in the immune markers’ evolution, in agreement between the groups will be more pronounced and may with other studies [33]. On the contrary, previous studies have a greater impact on patients’ viral load. According to found that adherence levels influence the decline of these published studies, adherence remains at more stable levels markers [29, 31]. However, these studies were conducted for the first 24 months of treatment and decreases by 5% 13 3894 AIDS and Behavior (2021) 25:3883–3897 every 6 months [42]. Adherence to ART is paramount to in light of the extensive research conducted. We carried out controlling HIV infection, and is associated with positive various types of triangulation, obtaining different types of clinical outcomes [67, 68]. data through different methodologies. This will have given The present study also found that the PURD had expe- our conclusions greater validity. rienced more ART treatment changes during the follow-up year. Moreover, the linear mixed model results found a mar- ginal association with it and participant’s percentage of C ­ D4. Conclusions According to published studies, ART changes are a predictor of viral suppression failure [69]. The side effects and toxic- According to our results, RDU in PLHIV who are in ART ity associated with treatment and interactions were the main treatment has clinical implications for health-related vari- causes of ART changes. Some studies have found a high ables, such as immune status, adherence to ART treat- prevalence of interactions between ART and recreational ment, psychological well-being, and the use of healthcare drugs, in some cases with severe consequences [26]. Thus, resources. close attention should be paid to adequately addressing fac- The long-term impact of using recreational drugs on tors associated with ART changes. PLHIV’s health could be more extensive than our results Likewise, we found that the PURD had worse psycho- suggest. Patients belonging to the PURD group presented logical health than the PNURD. Anxiety and depression are better health status than the PNURD group, who had a two of the most prevalent symptoms amongst PLHIV [48, higher rate of associated comorbidities. Thus, interven- 49], and both may be at the root and the result of problem- tions are needed to reduce the negative impact of drug atic drug use [70]. Drug use is also related to dependence. use on health. These interventions should provide patients This is a complex relationship that can cause mental health with adequate information on the potential risks related problems, stigmatisation, and sexual acceptance, amongst to RDU: drug-drug interactions, adverse effects, and the others [71]. impact on physical and psychological health. Moreover, In our study, a higher proportion of patients visiting appropriate ART is necessary for patients who use drugs, emergency units was found amongst the PURDs (1.6 times so that changes due to interactions and side effects that higher than the PNURD), which did not include patients may produce resistance and therapeutic failure can be with severe mental morbidity—according to published stud- avoided. These actions should preferably target MSMs ies, this seems to be a determinant of hospital morbidity [51, with HIV and young people who have previously expe- 72, 73]. We also found a high frequency of emergency visits rienced STIs. (175 visits per 100 patients per year), although there were no differences between the PURD and PNURD. This might be Acknowledgements This paper has been researched and written by the authors on behalf of the Study Group on the Use of Recreational Illicit explained by the fact that the PNURD had longer medical Drugs Amongst PLHIV in Spain: Castro, V. (Hospital de la Marina histories. According to a meta-analysis [53], the combined Baixa de Villajoyosa, Universidad de Granada); Leal, L., Laguno, M., rate in the studies covered was 151 emergency visits per 100 Martínez-Rebollar, M. (Hospital Clínic-IDIBAPS, Universidad de Bar- people per year in the general drug-using population. Our celona); Casado, JL., Rojo, AM., Vizcarra, P. (Hospital Ramón y Cajal de Madrid); Garin, N., Aguilera, V., Saavedra, M., Gras, L., Gutierrez, study reflects a high demand for emergency care in the drug M., Mateo, G. (Hospital de la Santa Creu i Sant Pau), Jaén, A. (Fun- consuming PLHIV population. Predictors of risk of hospi- dació Docència i Recerca MutuaTerrassa, University of Barcelona); tal emergency services by drug-consuming patients include Fuster-RuizdeApodaca, M. J. (SEISIDA). We thank the collaborating patients diagnosed with HIV, mental health problems, and healthcare providers at the recruitment sites Hospital Clínic and Hos- pital de la Santa Creu i Sant Pau, Barcelona; Hospital Ramón y Cajal, polyconsumption [14, 51, 74–79]. Madrid; and Hospital de la Marina Baixa, Villajoyosa. This paper is Our study has some limitations. Because it is observa- part of the doctoral thesis of Vanesa María Castro Granell, who is on tional, the relationships may be influenced by confound- the Doctoral Program in Pharmacy, Granada University, Spain. ing variables. Although we use a longitudinal design, our findings are limited by the short follow-up period. Further- Author Contributions VC and MJF-RdA were the main analysts and writers of the manuscript. AJ contributed to the design of the research, more, because of the retrospective study design, we could data analysis, and improvements in the writing of the paper. NG, JC, not collect relevant data that are potentially related to our LL contributed to the design and development of the research, review- findings, such as the frequency and number of drugs con- ing other studies and improving the writing of the paper. All authors sumed throughout the follow-up period. Also, some of the read and approved the final manuscript. data presented come from a self-reported questionnaire, and Funding This study was funded by ViiV Healthcare. there was no way to verify the veracity of the information. It is possible that respondents may have attenuated their Data Availability Data are available from https://​doi.​org/​10.​6084/​m9.​ answers with regard to drug use, which would have led to figsh​are.​13090​142.​v1 information bias. However, our results should be interpreted 13 AIDS and Behavior (2021) 25:3883–3897 3895 Declarations in Spain: a cross-sectional study. PLoS ONE. 2019;14(6):1–18. https://​doi.​org/​10.​1371/​journ​al.​pone.​02112​52. 4. Sánchez PL. Situación actual y evolución de los consumos de Conflict of interest This study was funded by ViiV Healthcare (www. drogas ilícitas en España. Trastor Adict. 2001;3(2):85–94. viivhealthcare.com). ViiV was involved in the study design and 5. Coll J, Fumaz CR. Drogas recreativas y sexo en hombres que preparation of the manuscript, but did not influence the data collec- tienen sexo con hombres: chemsex. Riesgos, problemas de salud tion and analysis or the decision to publish. All listed authors meet asociados a su consumo, factores emocionales y estrategias de the criteria for authorship set forth by the International Committee of intervención. Rev Enf Emerg. 2016;15(2):77–84. Medical Journal Editors. All authors had full access to the data and all 6. Barrio G, Bravo MJ, Brugal MT, et al. Harm reduction interven- are responsible for the veracity and completeness of the reported data. tions for drug injectors or heroin users in Spain: expanding cover- SCG is employed full-time in ViiV Healthcare. MJF-RdA, and AJ, age as the storm abates. Addiction. 2012;107(6):1111–22. https://​ are members of the Spanish Interdisciplinary Aids Society (SEISIDA) doi.​org/​10.​1111/j.​1360-​0443.​2011.​03759.x. Executive Board. SEISIDA has received grants from MSD, ViiV, Jans- 7. World Health Organisation. Consolidated guidelines on HIV sen, and Gilead. MJF-RdA has provided consultancy services to ViiV, prevention, diagnosis, treatment and care for key populations. Gilead, MSD, and Janssen, and has received payments for lectures or Geneva: WHO; 2014. educational presentations from MSD, Gilead, Janssen, and ViiV. JC 8. EMIS Network. EMIS 2010: the European men-who-have-sex- has provided consultancy services to ViiV, Gilead, and Janssen, and with-men internet survey. Findings from 38 countries. Stockholm: has received payments for lectures or educational presentations from European Centre for Disease Prevention and Control; 2013. http://​ MSD, Gilead, Janssen, and ViiV. NG has received payments for lec- www.​emis-​proje​ct.​eu. tures or educational presentations from ViiV. LL has provided consul- 9. Daskalopoulou M, Rodger A, Phillips AN, et al. Recreational drug tancy services and has received payments for lectures or educational use, polydrug use, and sexual behaviour in HIV-diagnosed men presentations from Gilead. These interests do not alter our adherence who have sex with men in the UK: results from the cross-sectional to AIDS and behaviour policies on sharing data and materials. ASTRA study. Lancet. 2014;3018(14):1–10. https://​doi.​org/​10.​ 1016/​S2352-​3018(14)​70001-3. 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