| Literature DB >> 31206550 |
Maria Jose Fuster-RuizdeApodaca1, Vanessa Castro-Granell2,3, Noé Garin4,5,6, Ana Laguía7, Ángeles Jaén8, Carlos Iniesta1,9, Santiago Cenoz10, María José Galindo1,11.
Abstract
This study assessed the prevalence and patterns of drug use among people living with HIV (PLHIV) in Spain. We conducted an observational cross-sectional study including 1401 PLHIV. Data were collected through 33 sites across Spain using an online computer-assisted self-administered interview. The survey measured use of illicit drugs and other substances, treatment adherence and health-related variables. To analyse patterns of drug use we performed cluster analysis in two stages. The most frequently consumed substances were: alcohol (86.7%), tobacco (55.0%), illicit drugs (49.5%), other substances (27.1%). The most prevalent illicit drugs used were cannabis (73.8%), cocaine powder (53.9%), and poppers (45.4%). Results found four clusters of PLHIV who used drugs. Two of them were composed mainly of heterosexuals (HTX): Cluster 1 (n = 172) presented the lowest polydrug use and they were mainly users of cannabis, and Cluster 2 (n = 84) grouped mostly men who used mainly heroin and cocaine; which had the highest percentage of people who inject drugs and presented the lowest level of treatment adherence (79.8±14.2; p < .0001). The other two clusters were composed mainly of men who have sex with men (MSM), who were mostly users of recreational drugs. Cluster 3 (n = 285) reported moderate consumption, both regarding frequency and diversity of drugs used, while Cluster 4 (n = 153) was characterized by the highest drug polyconsumption (7.4±2.2; p < .0001), and 4 grouped MSM who injected recreational drugs, and who reported the highest frequency of use of drugs in a sexual context (2.6±0.8; p < .0001) and rates of sexually transmitted infections (1.8±1.1; p < .01). This is the largest multi-centre cross-sectional study assessing the current prevalence and patterns of drug use among PLHIV in Spain. The highest prevalence of drug use was found among MSM, although HTX who used heroin and cocaine (Cluster 2) had the most problems with adherence to HIV treatment and the worst health status.Entities:
Mesh:
Substances:
Year: 2019 PMID: 31206550 PMCID: PMC6576760 DOI: 10.1371/journal.pone.0211252
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Fig 1Prevalence and frequency of use of illicit drugs among PLHIV using drugs.
N = 694.
Characteristics of the participants (n = 1,401).
| Variables | n (%) |
|---|---|
| Cis-men | 1,100 (78.5) |
| Cis-women | 280 (20) |
| Transgender | 21 (1.5) |
| Heterosexual | 580 (41.4) |
| Homosexual | 713 (50.9) |
| Bisexual | 61 (4.4) |
| Others | 28 (2) |
| Prefer not to answer | 19 (1.4) |
| Sexual intercourse | 946 (67.5) |
| Sharing needles | 272 (19.4) |
| Various practices concur | 141 (10.1) |
| Other | 42 (3) |
| No studies | 57 (4.1) |
| Primary | 383 (27.4) |
| Secondary | 484 (34.6) |
| University degree | 439 (31.4) |
| Other | 37 (2.6) |
| Working | 685 (48.9) |
| Unemployed | 257 (18.3) |
| Retired or occupational disability | 325 (23.2) |
| Other | 134 (9.6) |
| None | 185 (13.2) |
| ≤ 1,000 € | 539 (38.5) |
| 1,000−1,500 € | 405 (28.9) |
| 1,500−2,000 € | 104 (7.4) |
| > 2,000 € | 168 (12) |
| 14.1±9.6 | |
| 11.6±8.4 | |
| 45.3±10.1 |
Note: Data provided in percentages, except where specified. ART: antiretroviral therapy. SD: standard deviation.
Fig 2Frequencies of consumption routes used for the different types of illicit drugs.
N = 694.
Fig 3Percentage of PLHIV who used other medicines or substances.
Cluster’s profile according to demographics, health-related data, use of drugs and other medicines.
| Characteristics | C1 (24.8%, n = 172) | C2 (12.1%, n = 84) | C3 (41.1%, n = 285) | C4 (22.0%, n = 153) |
|---|---|---|---|---|
| Mostly cis-men but the highest percentage of cis-women among clusters | Mainly cis-men but some cis-women and transgender | Mainly cis-men but some transgender | Only cis-men | |
| ~ 50 years | ~ 50 years | ~ 40 years | < 40 years | |
| Mainly HTX | Mainly HTX | Mainly MSM | Mainly MSM | |
| Low level | The lowest level | High level | The highest level | |
| Most retired or unemployed | Most retired and the highest percentage of unemployed | Most working | Most working, the lowest percentage of retired | |
| Low | The lowest | Middle−high | The highest | |
| Mainly injection | Mainly (the highest) injection | Mainly sexual | Mainly sexual | |
| More than 20 years | More than 20 years | Near to 10 years | Less than 10 years | |
| Mainly users of cannabis and some of them cocaine powder | Mainly users of cannabis, heroin and cocaine | Mainly users of cannabis, cocaine and poppers | The highest percentage of using most types of drugs except heroin | |
| Low | Medium | Medium | High | |
| Mainly smoked, some sniffed | Mainly smoked and sniffed, highest % of PWID | Mainly smoked, sniffed, inhaled and around 1/3 oral | All routes. Some PWID. Anal route | |
| Most sedatives and around 1/3 antidepressants. Some methadone and erection enhancers | Mainly sedatives, methadone or other opioids. Half of them antidepressants | Mainly erection enhancers and near to 1/3 sedatives | Most erection enhancers (the highest %), good few sedatives. The highest % of anabolic users | |
| Mainly relaxing or avoiding worries | Mainly relaxing or avoiding worries and negative feelings | Mainly to enjoy and sexual purposes | Mainly to enjoy and sexual purposes |
N = 694. HTX: heterosexual; MSM: men who have sex with other men.
Cluster’s profile according to socio-demographic and health data.
| Variable | C1 | C2 | C3 | C | PLHIV who did not use drugs | Contrast statistic |
|---|---|---|---|---|---|---|
| χ2 = 186.97 | ||||||
| Cis-men | 61 | 78.6 | 96.1 | 100 | 71 | |
| Cis-women | 38.4 | 19 | 0 | 0 | 28 | |
| Transgender | 0.6 | 2.4 | 3.9 | 0 | 1 | |
| χ2 = 547.10 | ||||||
| Heterosexual | 86.6 | 85.7 | 6 | 0 | 48.4 | |
| Homosexual | 2.9 | 3.6 | 87 | 94.8 | 44.1 | |
| Bisexual | 6.4 | 6 | 4.6 | 3.3 | 3.8 | |
| Others | 1.7 | 1.2 | 2.1 | 2 | 2.1 | |
| Prefer not to answer | 2.3 | 3.6 | 0.4 | 0 | 1 | |
| χ2 = 458.65 | ||||||
| Sexual intercourse | 31.4 | 16.7 | 87 | 92.2 | 69.2 | |
| Sharing needles | 57.6 | 75 | 0 | 0 | 15.6 | |
| Various practices concur | 9.9 | 6 | 10.2 | 6.5 | 11.3 | |
| Other | 1.2 | 2.4 | 2.8 | 1.3 | 4 | |
| χ2 = 273.76 | ||||||
| No studies | 8.7 | 14.5 | 1.1 | 0 | 3.8 | |
| Primary | 50.6 | 61.4 | 11.6 | 8.5 | 28.1 | |
| Secondary | 31.4 | 13.3 | 40.4 | 29.4 | 36.6 | |
| University degree | 7 | 8.4 | 43.5 | 58.2 | 29.3 | |
| Other | 2.3 | 2.4 | 3.5 | 3.9 | 2.1 | |
| χ2 = 198.77 | ||||||
| Working | 23.8 | 13.1 | 69.1 | 69.3 | 46.7 | |
| Unemployed | 22.1 | 34.5 | 16.1 | 20.9 | 15.8 | |
| Retired or occupational disability | 41.3 | 41.7 | 8.4 | 3.9 | 26.7 | |
| Other | 12.8 | 10.7 | 6.3 | 5.9 | 10.7 | |
| χ2 = 148.76 | ||||||
| None | 14.5 | 21.4 | 10.5 | 12.4 | 13.2 | |
| ≤ 1,000 € | 61 | 60.7 | 23.5 | 17.6 | 40.9 | |
| 1,000−1,500 € | 15.1 | 9.5 | 41.8 | 39.2 | 27.2 | |
| 1,500−2,000 € | 3.5 | 4.8 | 8.8 | 13.1 | 6.9 | |
| > 2,000 € | 5.8 | 3.6 | 15.4 | 17.6 | 11.9 | |
| χ2 = 28.23 | ||||||
| < 200 CD4 mm | 9.6 | 7.2 | 3.5 | 1.7 | 6.6 | |
| 200−400 CD4 mm | 14.4 | 17.4 | 7.1 | 6 | 15.4 | |
| > 400 CD4 mm | 76 | 75.4 | 89.4 | 92.2 | 78 | |
| 93.3 | 87.8 | 94.4 | 97.4 | 93 | χ2 = 9.02; | |
| 22.5±8.3 | 23.2±6.1 | 9.2±7.3 | 7.0±5.1 | 17.7±9.5 | ||
| 18.0±7.2 | 18.2±6.8 | 7.8±6.8 | 5.6±4.5 | 12.4±8.4 | ||
| 84.1±10.5 | 79.8±14.3 | 87.4±8.7 | 86.3±9.1 | 88.3±8.5 | ||
| 50.9±6.4 | 49.5±5.5 | 41.1±9.4 | 36.8±8.3 | 47.1±10.3 | ||
| 10.5 (1.3±0.5) | 13.1 (1.4±1.2) | 30.9 (1.4±0.7) | 60.8 (1.81±1.1) | 16 (1.4±0.8) |
Note: Data provided in percentages, except where specified. ART: antiretroviral therapy. STIs: sexually transmitted infections suffered in the last year (* percentage of people in each cluster who had suffered any STI in the last year and mean of number of STIs suffered in the last year).
a All differences were significant at p < .0001 except when it is specified in the table.
b Variables included in two-stage cluster analysis.
1HSD Tukey results found differences between C1 and C2, C3, C4 and PLHIV who did not use illicit drugs (p < .0001); differences between C3 and C4 were marginally different (p = .069)
2Differences were found between C1, C2, C3, C4 and PLHIV who did not use illicit drugs (p < .0001), and between C3 and C4 (p < .05)
3Differences were found between C1 and: C2 (p = .005),C3 (p = .002) and PLHIV who did not use illicit drugs (p < .0001), and between C2 and C3, C4 and PLHIV who did not use illicit drugs (p < .0001)
4There were differences between all the groups (p < .0001) except between C1 and C2, and C2 and PLHIV who did not use illicit drugs
5 Significant differences were found between C4 and both C3 and PLHIV who did not use illicit drugs.
Cluster’s profile according to the type and frequency of drugs used, routes of consumption and polyconsumption.
| C1 (n = 172) | C2 (n = 84) | C3 (n = 285) | C4 (n = 153) | Contrast statistic | |
|---|---|---|---|---|---|
| Cannabis | 86.6 (4.4±1.8) | 78.6 (4.1±2.0) | 63.9 (3.1±2.0) | 75.2 (3.1±2.0) | χ2 = 30.32 |
| Cocaine (powder) | 22.7 (1.6±0.9) | 60.7 (2.5±1.6) | 54.0 (1.6±0.9) | 85.0 (1.9±1.1) | χ2 = 128.49 |
| Cocaine (base) | 0 | 3.6 (2.7±1.6) | 0 | 15.7 (2.1±0.8) | χ2 = 75.45 |
| Heroin | 0 | 69 (2.3±1.7) | 0.4 (1.0±0.0) | 0.7 (1.0±0.0) | χ2 = 441.50 |
| Poppers | 0 | 0 | 62.5 (1.8±1.2) | 89.5 (2.2±1.3) | χ2 = 366.59 |
| MDMA (crystal) | 1.2 (1.0±0.0) | 6 (1.8±1.7) | 12.6 (1.0±0.2) | 75.2 (1.4±0.7) | χ2 = 314.70 |
| MDMA (pills) | 0 | 16.7 (3.0±2.4) | 11.9 (1.0±0.2) | 72.5 (1.5±0.9) | χ2 = 285.87 |
| Speed | 4.1 (1.1±0.3) | 21.4 (2.2±1.6) | 8.1 (1.04±0.2) | 67.3 (1.3±0.7) | χ2 = 249.55 |
| Methamphetamine | 0.6 (1.0±0.0) | 2.4 (2.0±1.4) | 3.9 (1.0±0.0) | 43.8 (1.5±1.0) | χ2 = 197.53 |
| GHB | 0 | 2.4 (1.0±0.0) | 19.3 (1.1±0.4) | 77.1 (1.7±1.0) | χ2 = 305.12 |
| Mephedrone | 0 | 0 | 11.6 (1.0±0.3) | 62.1 (1.6±0.9) | χ2 = 260.60 |
| Ketamine | 0.6 (1.0±0.0) | 4.8 (1.0±0.0) | 3.9 (1.0±0.0) | 45.8 (1.4±0.8) | χ2 = 202.55 |
| LSD | 0 | 8.3 (1.3±0.7) | 0.7(1.0±0.0) | 9.8 (1.0±0.0) | χ2 = 37.08 |
| Opium | 0 | 7.1 (1.8±2.0) | 0 | 2 (1.0±0.0) | χ2 = 28.95 |
| Spice drugs | 0 | 2.4 (1.5±0.7) | 0.4 (1.0±0.0) | 2 (1.0±0.0) | χ2 = 6.77(ns) (F = 1.0, ns) |
| Mushrooms | 0 | 3.6 (1.0±0.0) | 0 | 15.7 (1.0±0.0) | χ2 = 75.45 |
| Other hallucinogenic plants | 0 | 8.3 (1.0±0.0) | 0.4 (1.0±0) | 2.6 (1.0±0.0) | χ2 = 28.47 |
| 2C-B nexus | 0 | 1.2 (6.0±0) | 0 | 1.3 (1.0±0.0) | χ2 = 5.82(ns) (—) |
| 1.2±0.5 | 3.4±2.0 | 2.5±1.4 | 7.4±2.2 | F = 461.06 | |
| 3.5 (1.7±1.2) | 3.6 (2.0±1.0) | 38.9 (1.8±0.7) | 61.4 (2.6±0.7) | F = 16.67 | |
| Smoke | 85.5 | 89.3 | 65.6 | 82.4 | χ2 = 37.55 |
| Sniff | 21.5 | 52.4 | 62.5 | 95.4 | χ2 = 185.79 |
| Oral | 5.2 | 31 | 35.1 | 98 | χ2 = 304.21 |
| Injection | 1.2 | 32.1 | 1.1 | 9.8 | χ2 = 111.20 |
| Anal | 0 | 0 | 0 | 14.4 | χ2 = 80.33 |
| Inhaled | 0.6 | 6 | 41.4 | 62.7 | χ2 = 183.15 |
| Erection enhancers | 19.4 | 18.8 | 62 | 92.2 | χ2 = 89.86 |
| Methadone | 19.4 | 59.4 | 0 | 0 | χ2 = 113.99 |
| Morphine | 3.2 | 9.4 | 0 | 1 | χ2 = 11.84 |
| Other opioids | 9.7 | 28.1 | 5.4 | 4.9 | χ2 = 18.59 |
| Anabolic | 0 | 0 | 2.2 | 18.6 | χ2 = 24.87 |
| Sedatives | 61.3 | 93.8 | 30.4 | 40.2 | χ2 = 42.65 |
| Antidepressants | 32.3 | 50 | 15.2 | 14.7 | χ2 = 22.54 |
a Measure of the importance of the categorical (χ2 = Pearson’s chi square) and continuous variables (one-way ANOVA) within each cluster. All χ2 differences were significant at p < .0001 except when it is specified in the table. (—) F was not calculated in drugs for which only one cluster had the variance calculated.
b The frequency of consumption ranges from 1 to 6 (1: sometimes in the last 12 months; 2: once a month; 3: several times in a month; 4:once a week; 5: several times a week; 6: daily).
c The frequency of the scale ranges from 1: sometimes, to 4: always.
d The score of each route of consumption was calculated summing the use of the route in each drug.
e Variables included in the two-step cluster analysis.
* p < .05
** p < .01
*** p < .0001.
1HSD Tukey results found differences between all the clusters except between C2 and C3.
2 HSD Tukey results found differences between CL4 and CL1 and CL3.