AIDS Behav DOI 10.1007/s10461-016-1303-3
ORIGINAL PAPER
Patterns of Drug Use and Drug-related Hospital Admissions in HIV-Positive and -Negative Gay and Bisexual Men Cecilia L. Moore1 • Heather F. Gidding2 • Fengyi Jin1 • Limin Mao3 • Kathy Petoumenos1 • Iryna B. Zablotska1 • I. Mary Poynten1 • Garrett Prestage1,4 Matthew G. Law1 • Andrew E. Grulich1 • Janaki Amin1
•
Ó Springer Science+Business Media New York 2016
Abstract We aimed to compare rates of illicit drug-related hospitalisations in HIV-negative (HIV-ve) (n = 1325) and HIV-positive (HIV?ve) (n = 557) gay and bisexual men (GBM) with rates seen in the general male population and to examine the association between self-reported illicit drug use and drug-related hospitalisation. Participants were asked how often they used a range of illicit drugs in the previous 6 months at annual interviews. Drug-related hospital admissions were defined as hospital admissions for mental or behavioural disorders due to illicit drug use (ICD 10: F11–16, F18, F19), drug poisoning (T40–T45, T50) or toxic effect of gases (T53, T59, T65). Drug-related hospitalisations were 4.8 times higher in the HIV-ve cohort [SIR 4.75 (95 % CI 3.30–6.91)] and 3.5 times higher in the HIV?ve cohort [SIR 3.51 (1.92–5.88)] compared with the general population. Periods of weekly drug use [IRR 1.86 (1.01–3.46)], poly-drug use [IRR 2.17 (1.07–4.38)] and cannabis use [low use-IRR 1.95 (1.01–3.77), high use-IRR 2.58 (1.29–5.16)] were associated with drug-related hospitalisation in both cohorts, as was being a consistently high meth/amphetamine user throughout follow-up [IRR 3.24 (1.07–9.83)] and being an inconsistent or consistent injecting
& Cecilia L. Moore
[email protected]; http://www.kirby.unsw.edu.au 1
The Kirby Institute, University of New South Wales, Wallace Wurth Building, Sydney, NSW 2052, Australia
2
School of Public Health and Community Medicine, University of New South Wales, Sydney, Australia
3
Centre for Social Research in Health, University of New South Wales, Sydney, Australia
4
Australian Research Centre in Sex, Health and Society, La Trobe University, Melbourne, Australia
drug user throughout follow-up [IRR 3.94 (1.61–9.66), IRR 4.43(1.04–18.76), respectively]. Other risk factors for drugrelated hospitalisation indicated the likelihood of comorbid drug and mental health issues in GBM hospitalised for drug use. Keywords Illicit drug use Homosexual Gay Bisexual Male Hospital admission
Introduction There is substantial evidence that illicit drug use is more prevalent among gay and bisexual men (GBM) globally compared with their heterosexual counterparts [1–4]. Research on drug use in Australian GBM comes from the National Drug Strategy Household Survey (NDSHS) [5] and the Gay Community Periodic Surveys (GCPS) [6] conducted in major capital cities. Both the NDSHS and GCPS have consistently shown significantly higher levels of recent (in the last 6–12 months) meth/amphetamine use (8.6–13.9 %), ecstasy use (12.8–28.8 %), cocaine use (5.9–19.0 %) and cannabis use (30.4–38.6 %) in GBM compared to heterosexual men [5, 7–11]. These rates are higher than other population groups in Australia and are consistent with data from other high income countries [1, 12]. Injecting drug use (IDU) remains generally low in GBM (\5 % in most non-purposive samples), though rates are higher than those found in a comparable heterosexual population [13] and tend to be significantly higher in HIV positive (HIV?ve) compared with HIV negative (HIV-ve) GBM [14]. Methamphetamines and steroids are reported to be the most commonly injected drugs in GBM in Australia [15]. Polydrug use (defined as the consumption of more
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than one illicit drug during a specific time period) has also been identified as being common in GBM [16–18]. Polydrug use has been shown to be both associated with increased risk for toxicity and overdose [19, 20] as well as poorer outcomes in terms of mental and physical health when compared with single drug use. The implications of illicit drug use in GBM are not well understood. While the physical and mental harms of drug abuse are well documented [21–26] and likely to be present in GBM abusing drugs, the difficulty in distinguishing between illicit ‘use’ and problematic ‘abuse’ of drugs is inherently difficult. Drug abuse is generally distinguished from drug use by measurable physical, social, or psychological harm [27]. To-date, much of the substance use research in HIV-ve and HIV?ve GBM has focused on HIV acquisition and transmission, respectively, as the measurable harm. This study benefits from the linkage of two cohorts of GBM, with high levels of illicit drug use [28, 29], to a national hospital data register enabling rates of drug-related hospital admissions to be examined over a long period of follow-up. Furthermore, detailed drug-use questionnaires were completed by cohort participants at annual interviews enabling hospitalisations to be analysed with respect to reported drug use. We aimed to compare rates of illicit drug-related hospitalisations in HIV-ve and HIV?ve GBM with rates seen in the general male Australian population and to examine the association between self-reported illicit drug use and drug-related hospital admissions.
Methods Study Population Our study cohorts included participants recruited to the Health in Men (HIM) (HIV-ve) and Positive Health (pH) (HIV?ve) studies who provided informed consent for their study data to be used for data linkage. Both studies have been described in detail elsewhere [30, 31]. Briefly, men were recruited from Sydney, Australia using similar community-based methods. The majority of participants in both studies were recruited through gay community events and venues. Other sources of recruitment included direct recruitment from participants in other relevant studies, personal networking, ‘snowballing’ through friends and acquaintances, direct referrals from medical practitioners, gay press and HIV-positive publications [32, 33]. Participants were interviewed face-to-face annually. Enrolment in HIM occurred from 2001 to 2004 and active follow-up ceased in 2007. Enrolment in pH occurred from 1998 to 2006 and follow up ceased in 2007. The HIV-serostatus of participants in both cohorts was confirmed by serological
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testing at intake and in HIV-ve participants through annual testing thereafter. All participants in both studies either had sexual contact with at least one man during the previous 5 years or self-identified as gay, homosexual, queer or bisexual. In both studies the overwhelming majority of participants identified as gay, homosexual or queer with the remaining largely identifying as bisexual [32, 34]. Drug Use Measures Participants were asked how often they used a range of drugs in the previous 6 months for recreational purposes at annual face-to-face interviews. Drugs assessed included cannabis; amyl nitrate, Viagra or other erection medications, cocaine, amphetamines or methamphetamines, MDMA or other forms of MDA, psychedelics or hallucinogens (lysergic acid diethylamide [LSD], mescaline, or phencyclidine [PCP]); downers (barbiturates, tranquilisers or sedatives), Rohypnol (flunitrazepam) or ketamine, heroin or other opiates (including methadone), and gamma hydroxybutyrate [GHB] (only in the HIV?ve cohort). Slang terms were in some cases used in addition to drug names (e.g. ‘Angel dust’ for PCP). Participants were also asked how often they injected drugs (other than steroids) for other than health reasons in the previous 6 months. The possible frequency of use options for illicit and injecting drug use were never, once or twice, about once a month, about once a week, more than once a week, or every day. Frequency of use was re-categorised into low use (once or twice to about once a month) and high use (about once a week to everyday). Polydrug use for the purposes of this analysis was defined as using more than one drug about once a week or more frequently (i.e. answered ‘more than once a week’ or ‘every day’). GHB was excluded from the polydrug use measure as it was not measured in both cohorts. Use of Viagra and other erection medications were also excluded from this measure as they were used ubiquitously to enhance penile function during sexual intercourse and examination of illicit drug use was our primary aim. Registries and Data linkage Data linkage included all consenting participants. Probabilistic linkage methods [35] were used to link individuals to the data sources described below. (i)
The New South Wales (NSW) Admitted Patient Data Collection (APDC) includes all inpatient admissions from all public (including psychiatric), private and repatriation hospitals, private day procedure centres and public nursing homes in
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(ii)
(iii)
NSW, Australia. Diagnosis and procedure fields are coded according to the 10th revision of the International Classification of Disease-Australian Modification (ICD-10-AM). Patient name has only been recorded since 1 July 2000, so we restricted analysis to admissions from 1 July 2000. The Registry of Births, Deaths and Marriages (RBDM) which reports fact of death was used to censor person-years of observation. The HIV administrative database is a register of HIV, notified to the NSW Department of Health by laboratories, hospitals, and medical practitioners. In addition to annual serological testing in the HIM cohort, seroconversions were identified through linkage of participants to the HIV registry.
First name, surname, address, postcode, date of birth and date of last contact were used to probabilistically link participants from the study cohorts to the APDC and RBDM registries using ChoiceMaker software (ChoiceMaker Technologies Inc., New York, US). Deterministic linkage was used to link participants to the HIV/AIDS notifications using two-character surname and given name codes, date of birth, sex and postcode. Linkage was conducted by the NSW Centre for Health Record Linkage, independent of the study investigators. Full details of the linkage process are outlined at (http://www.cherel.org.au/ how-record-linkage-works). Individual consent for data linkage was optional and was collected in addition to consent to participate in the study. Only data from participants who consented to data linkage were included in this analysis (93 % of HIM and 74 % of pH participants). We found no significant differences between those who consented versus those who declined linkage in examined cohort characteristics. Examined cohort characteristics included ethnicity, education, employment, income, sexual identity, previous exposure to an STI or hepatitis C, self-reported health, Kessler 6 score of psychological distress, relationship status and HIV-serostatus, sexual risk taking behaviour, frequency of exercise, experiences of discrimination and alcohol use (see Appendix). Ethics approval was granted by the University of NSW and the NSW Population and Health Services Research Ethics Committee. Drug-Related Hospital Admissions A hospital admission was defined as an episode of care ending with hospital discharge, death or transfer to another type of care. Duplicate and nested hospital admissions (admissions within the date range of another admission) were excluded (n = 55) to ensure only one principal
diagnostic code for each admission. Drug-related hospital admissions were defined as hospital admissions with a principal or secondary ICD-10 diagnosis code: mental or behavioural disorders due to drug use (F11–16, F18, F19); drug poisoning (T40-T43, T50); or toxic effect of gases (T53, T59, T65). Statistical Methods Time at risk commenced at entry into the study cohort or the opening of database for hospital admissions (1 July 2000), whichever was latest. Incidence rates of events were determined using person-years (PYs) methods with data right censored at death or the end of the follow-up period (31 December 2007). Data from HIV-ve participants who seroconverted (n = 51) were excluded from analysis as exact date of seroconversion was unattainable. The number of hospitalisations with drug use as a primary diagnosis in the cohorts was compared with the expected number using rates for the Australian male population in 2001 (standard population) [36] and summarized as standardised incidence ratios (SIRs). Numbers were calculated in 10 years age groups (18–24, 25–29, etc. to 85? years), thus adjusting for age. To adjust for correlation between hospitalisations in the same individual, 95 % confidence intervals (CIs) for the SIRs were calculated using the method by Stukel et al. [37]. Random-effects Poisson methods were used to assess the relationship between self-reported drug use, other risk factors for drug-related hospital attendance and drug-related hospital attendance. Each risk factor was assessed for its association with drug-related hospitalisation independently in univariate analyses. Drug use was assessed as a patient-level and observation-level, time-updated variable. For the patient-level analysis, participants were categorized as nonusers, inconsistent users (moving between use and non-use), consistently low users (once or twice to about once per month) and consistently high users (about once per week or more frequent) according to their longitudinal pattern of drug use for each drug type. Participants needed to have at least two rounds of interviews to be included in the patient-level analysis otherwise they were included in the analysis as a missing category. For the observationlevel, time-updated analysis, participants were categorised on the basis of their self-reported drug use for each drug type in a particular interview year, which was then tested for its association with drug-related hospitalisations in the same year. Other risk factors assessed as fixed effects at entry into the cohort included: ethnicity, country of birth, highest level of education, employment, income, presence of antibodies to hepatitis C, smoking, sexual identity, selfreported general health, prior use of mental health counselling services, and Kessler 6 score of psychological
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distress. Other risk factors assessed as time-updated covariates included: age, frequency of exercise, alcohol consumption, frequency of gay social engagement, number of gay friends, serostatus of regular relationship, number of sexual partners and frequency of unprotected anal intercourse. The interactions between risk factors for drug-related hospitalisation and HIV status (cohort) were tested. If a significant association was found, indicating that the effect of the risk factor differed by cohort then stratified analyses were done and separate results presented for HIV?ve and HIV-ve GBM cohorts. Missing data were included in the analysis as a separate category. Analyses were performed using STATA (version 13; StataCorp LP, College Station, Texas, USA) and SAS (version 9.3; SAS Institute INC., North Carolina, USA).
illicit drug monthly was reported in 24 % of interviews, and using an illicit drug at least weekly was reported in 32 % of interviews (Table 2). HIV?ve GBM reported no illicit drug use in 17 % of interviews, using an illicit drug once or twice was reported in 19 % of interviews as was using an illicit drug about monthly, using an illicit drug at least weekly was reported in 46 % of interviews. The most consistently used drugs in both cohorts of men were cannabis, amyl nitrate, MDMA or other MDA and meth/amphetamines. One percent of HIV-ve and 6 % of HIV?ve men reported consistent injecting drug use with 5 % of HIV-ve men and 13 % of HIV?ve men reporting intermittent injecting drug use. The most commonly used drugs in polydrug use (at least weekly use of 2 or more drugs) were cannabis and amyl nitrate in both cohorts followed by meth/amphetamines, MDMA or other MDA and downers, Rohypnol or ketamine (Fig. 1).
Results Drug-Related Hospitalisation Rates Study Participants A total of 1882 participants were included in the analysis; 1325 HIV-ve GBM and 557 HIV?ve GBM. At baseline, HIV-ve participants were aged from 18 to 75 years with a median age of 35 years (Table 1). HIV?ve participants were aged from 21 to 69 with a median age of 41 years. Seventy-four percent (73.6 %) of HIV-ve men and 60.0 % of HIV?ve men had attained some form of tertiary education after secondary school. The majority of men in both cohorts identified as Anglo-Australian or Anglo-Celtic (74.3 % of HIV-ve, 76.1 % of HIV?ve) and had been born in Australia (68.9 % of HIV-ve, 70.4 % of HIV?ve). The majority of men self-identified as gay, queer or homosexual (95.2 % of HIV-ve, 92.1 % of HIV?ve). Of the HIV?ve participants, 44.7 % reported a recent CD4 count above 500 cells/mm3, the majority were receiving antiretroviral therapy (74.2 %) and most participants (76.7 %) were diagnosed with HIV prior to 1996 (preHAART era). There were a total of 5455 interviews completed in the HIV-ve group and 1887 interviews completed in the HIV?ve group. Of the 1325 HIV-ve participants included in the analysis, all men completed at least 1 interview following entry into the study; 89 % two interviews, 79 % three interviews, 68 % four interviews, 48 % five, 28 % the maximum possible of six interviews. Of the 557 HIV?ve participants included in the analysis, all men completed at least one interview; 78 % complete two interviews, 56 % completed three interviews, 43 % four interviews, 32 % five, 18 % six, 11 % the maximum number of seven interviews. HIV-ve GBM reported no illicit drug use in 24 % of interviews, using an illicit drug once or twice was reported in 21 % of interviews, using an
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The maximum follow-up time in the HIV-ve and HIV?ve cohort was 6.6 and 7.5 years with a mean follow-up time of 5.4 and 5.8 years, respectively (Table 3). There were 3 deaths in the HIV-ve cohort and 25 in the HIV?ve cohort during follow-up. We observed 76 drug-related hospitalisations in 1325 HIV-ve men [rate p. 100PYs 1.07 (95 % CI 0.85–1.34)] and 83 drug-related hospitalisations in 557 HIV?ve men [rate p. 100PYs 2.56 (95 % CI 2.06–3.17)]. There was a significantly higher rate of drug-related hospital admissions in the HIV?ve compared with HIV-ve cohort [IRR 3.02 (95 % CI 1.67–5.46); p value \0.001]. Hospitalisation rates with a drug-related primary diagnosis were 4.8 times higher in the HIV-ve cohort [SIR 4.75 (95 % CI 3.30–6.91)] and 3.5 times higher in the HIV?ve cohort [SIR 3.51 (95 % CI 1.92–5.88)] compared with the Australian male population (Table 3). Hospitalisation for mental and behavioural disorders due to stimulant use was prevalent in both cohorts. Hospitalisation for opioids and cannabinoids was more prevalent in the HIV?ve cohort compared with the HIV-ve cohort. Hospitalisations due to ICD-10 code ‘‘poisoning by antiepileptic, sedative-hypnotic and anti-Parkinsonism drugs’’, which were primarily for benzodiazepine use [N = 18 (82 %)] were more prevalent in the HIV-ve cohort. Self-Reported Drug Use as a Risk Factor for Hospitalisations Periods of any weekly single drug use (p value = 0.048) and poly-drug use (2 or more drugs: p value = 0.031) were found to be significantly associated with drug-related hospitalisation in both cohorts, as were periods of injecting
AIDS Behav Table 1 Baseline characteristics of 557 HIV?ve and 1325 HIV-ve gay and bisexual men recruited in Sydney, Australia
Age (years), median (IQR)
HIV-ve cohort (n = 1325)
HIV?ve cohort (n = 557)
35.3 (29.6–42.0)
40.9 (36.0–46.7)
985 (74.3)
424 (76.1)
913 (68.9)
392 (70.4)
975 (73.6)
334 (60.0)
83 (6.3)
216 (38.8)
266 (20.1)
267 (47.9)
1088 (95.2)
443 (92.1)
42 (3.7)
17 (3.5)
9 (0.8)
21 (4.4)
52 (3.9)
69 (12.4)
420 (31.7)
292 (52.42)
242 (18.3)
68 (12.2)
62 (4.7)
31 (5.6)
Ethnicity Anglo-Australian/Anglo-Celtic Country of birth Australia Education Tertiary Employment Unemployed or receiving pension/disability pension Income \500 per week/\26,000 per year Sexual identity Gay, queer or homosexual Bisexual Other Exposure to hepatitis C Yes Daily smoker Yes Average number of drinks when drinking 5 to 8 [9 Year diagnosed HIV positive 1980–1986
151 (27.1)
1987–1991
153 (27.5)
1991–1996
123 (22.1)
1996–2006 Last CD4 cell count
118 (21.2)
Less than 100
26 (4.7)
100–200
28 (5.0)
201–350
89 (16.0)
351–500
105 (18.9)
Over 500
249 (44.7)
Antiretroviral therapy history Never taken Currently taking Past but now stopped
87 (15.6) 413 (74.2) 47 (8.4)
All numbers are N (%) unless otherwise stated IQR interquartile range
drug use (p value = 0.017) (Table 4). Periods of low cannabis use (p value = 0.046) and high cannabis use (p value = 0.008) were also found to be associated with drug-related admissions as were, to a lesser extent, periods of high use of downers, Rohypnol or ketamine (p value = 0.053). Both low (p value = 0.001) and high (p value = 0.027) meth/amphetamine use was associated with drugrelated hospital admission in the HIV-ve cohort but not
the HIV?ve cohort. There was also a strong indication that missing drug-use data for a particular interview year was associated with drug-related hospitalisation (see Table 4). There was evidence that participants who engaged in consistent high users of meth/amphetamine were more likely to be hospitalised for drug-related reason than nonusers (p value = 0.037). Participants who were inconsistent users of heroin or other opiates (p value = 0.020)
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AIDS Behav Table 2 Comparison of prevalence of drug use (%) reported in 5455 interviews in 1325 HIV-ve and 1887 interviews in 557 HIV?ve gay and bisexual men recruited in Sydney, Australia HIV-ve cohort (n = 1325; 5455 interviews)
HIV?ve cohort (n = 557; 1887 interviews)
% of interviews: none
23.7
16.6
% of interviews: once or twice % of interviews: about 1/month
20.6 23.9
18.6 19.0
% of interviews: about 1/week or more
31.8
45.8
% of interviews: 0 drugs
68.2
54.2
% of interviews: 1 drugs
22.9
35.3
8.9
10.5
10.3
4.3
p value
Any recreational drug usea
\0.001
Weekly drug use
% of interviews: 2 drugs or more
\0.001
Any drug use Nonuser Intermittent user of 1 drug Intermittent user of [1 drug Consistent user of 1 drug Consistent user of 1 drug, intermittent user of other drugs Consistent user of [1 drug Consistent user of [1 drug, intermittent user of other drugs Use of cannabis Nonuser
7.1
7.2
13.8
14.0
2.6
3.8
16.3
18.5
2.3
3.4
36.2
21.9
28.8
19.0
Intermittent user
33.0
25.0
Consistent user
26.9
34.1
Nonuser
24.9
23.0
Intermittent user
31.9
31.6
Consistent user
31.9
23.5
Nonuser
51.3
47.2
Intermittent user
27.6
19.8
Consistent user
9.7
11.0
Nonuser
54.5
55.1
Intermittent user
27.7
21.2
Consistent user
6.5
1.8
38.6
36.8
\0.001
\0.001
Use of amyl nitrate
0.076
Use of Viagra/erection pills
0.038
Use of cocaine
Use of meth/amphetamines Nonuser Intermittent user
32.5
25.0
Consistent user
17.7
16.3
Nonuser
29.0
32.3
Intermittent user
24.2
25.7
Consistent user
35.6
20.1
Nonuser
73.6
61.9
Intermittent user
14.0
14.4
Consistent user
1.1
1.8
50.6
76.5
0.219
0.219
Use of MDMA or other MDA
\0.001
Use of psychedelics or hallucinogens
Use of downers, Rohypnol or ketamine Nonuser
123
0.111
AIDS Behav Table 2 continued HIV-ve cohort (n = 1325; 5455 interviews)
HIV?ve cohort (n = 557; 1887 interviews)
Intermittent user
29.1
16.0
Consistent user
16.4
2.2
Nonuser Intermittent user
86.2 2.3
74.5 3.1
Consistent user
0.2
0.5
p value
\0.001
Use of heroin or other opiates
0.157
Use of GHB Nonuser
–
65.2
Intermittent user
–
10.6
Consistent user
–
2.3
Injecting drug use Nonuser
83.2
57.6
Intermittent user
4.5
12.9
Consistent user
1.1
5.8
\0.001
All numbers are % of participants (n) unless otherwise stated GHB gamma hydroxybutyrate a
Excludes use of Viagra/other erection medications
were also found to be significantly more likely to be hospitalised for drug-related reasons. Consistent use of heroin/ other opiate was extremely infrequent and did not reach statistical significance. While not reaching statistical significance there was some indication that inconsistent users of cocaine (p value = 0.067) and consistent high users of cannabis (p value = 0.080) were more likely to be hospitalised for drug-related reasons. Being an inconsistent or consistent injecting drug user was also associated with drug-related hospitalisation (p value = 0.003, p value = 0.044, respectively). Again there was also a strong indication that missing longitudinal drug data (i.e. only attending one interview post enrolment) was associated with drug-related hospitalisation (see Table 4). Other risk factors for drug-related hospitalisation included being unemployed at entry to the cohort (p value \ 0.001), having been previously exposed to hepatitis C (p value = 0.006), having previously sought counselling for mental health issues (p value = 0.002), having a high (p value = 0.006) or moderate (p value = 0.004) score on the Kessler 6 Scale of psychological distress compared to low score, being a daily smoker (p value = 0.015) and drinking more than 9 drinks in one sitting compared to not drinking (p value = 0.036). Being in a HIV-sero-discordant relationship compared to being in a concordant relationship was also found to be associated with drug-related hospitalisation (p value = 0.003) as was having 6–10 sexual partners compared to 0–1 partners in the previous 6 months (p value = 0.044). Being older than 55 years (p value = 0.016) and earning more than 500$AUD per week
(500–999: p value = 0.001, 1000–1499: p value = 0.001, C1500: p value = 0.003), and self-reporting excellent health (p value = 0.024) was found to be protective for drug-related hospitalisation. Having tertiary education in the HIV-ve cohort (p value = 0.033) and having university or postgraduate level of education in the HIV?ve cohort (p value 0.001) compared to 10 years or less of highschool was also found to be protective for drug-related hospitalisation.
Discussion We determined the incidence of drug-related hospitalisation over approximately 6–7 years of follow-up among HIV?ve and HIV-ve GBM recruited in Sydney, Australia and found an increased number of drug-related hospitalisations in both cohorts compared to the general Australian male population. A significant proportion of these were for stimulant use and cannabinoids use in both cohorts. We found a significantly greater incidence of drug-related hospitalisation in the HIV?ve compared to the HIV-ve group. There was also significantly more hospitalisation for opioid use and a greater degree of injecting drug use in the HIV?ve cohort. We further examined patterns of drug use as a risk factor for drug-related hospitalisations in HIV-ve and HIV?ve GBM. There were high levels of self-reported cannabis use, amyl nitrate use, MDMA and other MDA use and meth/ amphetamine use in both cohorts. This accords with illicit
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Use of MDMA or other MDA
Use of amphetamines or methamphetamines Use of cocaine
Use of psychedelics or hallucinogens Use of cannabis
Use of downers, Rohypnol or ketamine
Percent of Interviews with Recorded Use
100% Use of heroin or other opiates
Use of amyl nitrate
90% 80% 70% 60% 50% 40% 30% 20% 10% 0%
Frequency of Interviews with Recorded Use
HI V-ve
HI V+ ve
Use of heroin or other opiates
3
16
Use of MDMA or other MDA
8
24
Use of amphetamines or methamphetamines
22
59
Use of cocaine
2
5
Use of psychedelics or hallucinogens
1
4
485
170
Use of cannabis Use of downers, Rohypnol or ketamine Use of amyl nitrate
9
32
138
126
Fig. 1 Polydrug use (at least weekly use of 2 or more drugs) by type of drug in 5455 interviews in 1325 HIV-ve and 1887 interviews in 557 HIV?ve gay and bisexual men recruited in Sydney, Australia
drug use data published in other cohorts of men who have sex with men globally [2, 38–40]. There was also significant polydrug use in both cohorts, with participants reporting using 2 or more drugs weekly in around 10 % of interviews. There was evidence in both cohorts that periods of cannabis use, both low use and high use, periods of polydrug use and periods of injecting drug use were associated with a greater degree of drug-related morbidity. There was also evidence that consistent meth/amphetamine users and both inconsistent and consistent injecting drugs users were more likely to be hospitalised than nonusers. While heroin and other opiate use and downers, Rohypnol and ketamine use was low in both cohorts, there was some indication that both were associated with drug-related hospitalisation. Other significant risk factors for drug-related hospitalisation indicate the likelihood of comorbid drug and mental health issues in drug-using GBM hospitalised for drug use. Both a higher Kessler distress score and having previously sought mental health counselling were found to be significantly associated with drug-related hospitalisation. Rates of drug-related hospitalisation significantly reduced in the over 55 years age group suggesting that treatment services for
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drug-using GBM should be targeting towards this under 55 years age bracket. Unsurprisingly, exposure to hepatitis C was positively correlated with drug-related hospitalisation. There is evidence that both injecting drug use and non-injecting illicit drug use are associated with hepatitis C seroconversion [41]. Smoking was also shown to be positively correlated with drug-related hospitalisation which is supported by previous literature which has linked smoking and illicit drug use [5]. There was also some indication that being more sexually adventurous was associated with drug-related hospitalisation. This perhaps is unsurprising considering the link between drug use and sexual activity previously described in cohorts of men who have sex with men [42, 43]. However, it does suggest support services should be aimed at GBM who have a high number of sexual partners ([6 in 6 months) and who also engage in other risky drug use behaviour to reduce drug-related harm. Interestingly being in a HIV-serodiscordant relationship compared to a concordant relationship was found to be positively associated with drugrelated hospitalisation. Possibly the heightened stressors associated with being in a HIV-serodiscordant relationship contribute to engagement in risky drug use. Further research is required to more critically examine this association.
AIDS Behav Table 3 Comparison of drug-related hospitalisation admission in 1325 HIV-ve and 557 HIV?ve gay and bisexual men recruited in Sydney, Australia HIV-ve (n = 1325)
HIV?ve (n = 557)
Total follow-up time, years
7101
3243
Mean follow-up time, years
5.4
5.8
Number of drug-related discharges, N (%) Primary diagnosis drug-related
76 (3.2) 48
83 (4.8) 14
28
69
Secondary diagnosis drug-related Median length of Stay, days (IQR)
2 (1–6)
4 (2–9)
Number of participants with drug-related discharge, N (%)
37 (2.8)
48 (8.6)
Rate p. 100PYs (95 % CI)
1.07 (0.85–1.34)
2.56 (2.06–3.17)
4.75 (3.30–6.91)
3.51 (1.92–5.88)
Reference
3.02 (1.67–5.46)
F11: Mental and behavioural disorders due to use of opioids
1 (1.3)
18 (21.7)
F12: Mental and behavioural disorders due to use of cannabinoids
10 (13.2)
27 (32.5)
F13: Mental and behavioural disorders due to use of sedatives or hypnotics
0
4 (4.8)
F14: Mental and behavioural disorders due to use of cocaine
0
2 (2.4)
F15: Mental and behavioural disorders due to use of other stimulants, including caffeine F16: Mental and behavioural disorders due to use of hallucinogens
11 (14.5) 0
16 (19.3) 4 (4.8)
F18: Mental and behavioural disorders due to use of volatile solvents
0
0
F19: Mental and behavioural disorders due to multiple drug use and use of other psychoactive substances
7 (9.2)
5 (6.0)
T40: Poisoning by narcotics and psychodysleptics
2 (2.6)
0
Compared with general Australian male population SIR (95 % CI)a HIV?ve compared with the HIV-ve Cohort IRR (95 %CI)b By diagnosis, N (%)
T41: Poisoning by anaesthetics and therapeutic gases
5 (6.6)
1 (1.2)
T42: Poisoning by antiepileptic, sedative-hypnotic and antiparkinsonism drugs (includes barbiturates, benzodiazepines)
22 (28.9)
3 (3.6)
T43: Poisoning by psychotropic drugs, not elsewhere classified T50: Poisoning by diuretics and other and unspecified drugs, medicaments and biological substances
10 (13.2) 2 (2.6)
3 (3.6) 0
T53: Toxic effect of halogen derivatives of aliphatic and aromatic hydrocarbons
5 (6.6)
0
T59: Toxic effect of other gases, fumes and vapours
1 (1.3)
0
T65: Toxic effect of other and unspecified substances
0
0
a
Adjusted for different age distributions in the HIV-ve cohort, HIV?ve cohort and the general male Australian population. SIRs for primary diagnosis only, hospital rates for secondary drug diagnoses were unavailable in the general population
b
IRRs are age-adjusted
n number of individuals, SIR standardized incidence ratio, 95 % CI 95 % confidence interval, N number of hospitalisations, PYs person years, IQR interquartile range, PYs person-years; IRR incidence rate ratio
There are some limitations of this research that must be taken into consideration. Hospitalisation and drug use were assessed from 2000 to 2007 and may not reflect all of current drug use practices in GBM. It is a cohort of GBM recruited in Sydney, Australia and thus the findings may not be applicable in other settings. The patterns of drug use found, however, very much mimic those shown in other cohorts of MSM globally [13, 41]. While the method of recruitment in both cohorts was similar and is a strength of the study, the representativeness of the cohort to the wider
HIV?ve and HIV-ve homosexual population in Australia is not known. The HIV?ve cohort was largely comprised of GBM who had lived long-term with HIV and who may not represent more recently diagnosed HIV?ve gay men. Representative samples of gay and other homosexually active men are difficult to attain [32]. Despite these limitations, investigation of baseline characteristics in both cohorts showed similarity to those described in other cohorts of HIV?ve and HIV-ve GBM in Australia [6]. Also when we looked at baseline drug use in our HIV-ve
123
AIDS Behav Table 4 Univariate analysis of risk factors for drug-related hospitalisations in 1325 HIV-ve and 557 HIV?ve gay and bisexual men recruited in Sydney Australia Risk factor
Category
N
PYs
IRR
95 % CI
p value
Overall p value
Observation level, time-updated drug useb Number of drugs used weekly or more
Injecting drug use
Use of cannabis
Use of meth/amphetamines in HIV-vea
Use of meth/amphetamines in HIV?vea
0
31
4084
1
1 2?
26 17
1554 526
1.86 2.17
1.01–3.46 1.07–4.38
0.048 0.031
1.17–3.18
0.010
Missing
85
4180
1.93
No
55
5320
1
Yes
13
310
2.43
1.17–5.04
0.017
Missing
91
4713
1.44
0.95–2.20
0.073
No
24
3102
1
Low use
24
1921
1.95
1.01–3.77
0.046
High use
26
1139
2.58
1.29–5.16
0.008
Missing
85
4181
2.19
1.26–3.76
0.005
No
14
3004
1
Low use
23
1570
3.77
1.69–8.39
0.001
High use
7
203
3.56
1.15–11.00
0.027
Missing
32
2324
3.28
1.52–7.06
0.002
No
20
907
1
8 2
424 54
0.63 1.10
0.25–1.62 0.21–5.82
0.342 0.904
0.46–1.59
0.628
Low use High use Use of downers, Rohypnol or ketamine
Missing
53
1857
0.86
No
51
4458
1
Low use
15
1543
0.83
0.43–1.62
0.589
High use
8
163
2.41
0.99–5.89
0.053
Missing
85
4181
1.35
0.89–2.06
0.158
0.0555
0.0375
0.0267
0.0071
0.7985
0.0885
Patient level drug usec Use of cannabis
Nonusers
32
2659
1
Inconsistent users
25
3243
0.72
0.33–1.56
0.405
8
842
0.71
0.23–2.22
0.560
Consistently low users
Use of cocaine
Use of meth/amphetamines
Use of heroin or other opiates
Consistently high users
53
2199
1.98
0.92–4.24
0.080
Missing
41
1401
2.67
1.14–6.24
0.024
Nonusers
64
5703
1
Inconsistent users
54
2701
0.96–3.33
0.067
Consistently low users
0
466
Consistently high users Missing
0 41
73 1401
1.32–6.02
0.007
123
– – 2.82
Nonusers
34
3985
1
Inconsistent users
50
3235
1.61
0.81–3.19
0.172
Consistently low users
16
1132
1.43
0.56–3.66
0.452
Consistently high users
18
591
3.24
1.07–9.83
0.037
Missing
41
1401
3.45
1.51–7.89
0.003
Nonusers
93
8627
1
Inconsistent users
18
281
5.21
1.29–21.00
0.020
1
11
14.69
0.38–573.00
0.151
Missing
41
1401
2.85
1.40–5.79
0.004
Nonusers
74
7891
1
Inconsistent users
32
786
3.94
1.61–9.66
0.003
Consistent users
12
246
4.43
1.04–18.76
0.044
Consistent users Injecting drug use
1.79
0.0085
0.0810
0.0302
0.0066
AIDS Behav Table 4 continued Risk factor
Category Missing
N 41
PYs
IRR
95 % CI
p value
1420
3.14
1.55–6.38
0.001
Overall p value 0.0003
Other risk factors Age
18–35
43
2949
1
34–45 44–55
67 48
4007 2472
1.42 1.11
0.85–2.37 0.60–2.05
0.177 0.747
0.01–0.62
0.016
0.19–3.97
0.848
?55 Education in HIV-vea
1
916
0.07
10 years of high-school or less
14
759
1
Completed high school
17
1108
Tertiary education
Education in HIV?vea
5
1521
0.17
0.03–0.87
0.033
University or Postgraduate
40
3707
0.56
0.15–2.06
0.385
10 years of high-school or less
35
801
Income per week ($AUD)
Self-reported health
Reported as having sought counselling for mental health Kessler 6 scale (psychological distress)
Daily smoker
Number of Alcoholic drinks consumed in one sitting (on average)
1
10
540
0.47
0.16–1.43
0.184
Tertiary education
29
753
1.07
0.43–2.67
0.888
9
1149
0.17
0.06–0.47
0.001
0.0015
2.19–8.06
\0.001
0.0001
0.17–0.64 0.11–0.59
0.001 0.001
Employed
86
8348
1
Unemployed
73
1812
4.21
\500
91
3055
1
500–999 1000–1499
37 15
3559 1946
0.33 0.26
[1500
15
1585
0.27
0.12–0.64
0.003
1
199
0.16
0.01–1.94
0.148
No
110
8741
Yes
36
720
3.81
1.47–9.86
0.006
Missing
13
884
1.05
0.40–2.80
0.915
Poor
23
707
1
Fair
46
2939
0.47
0.16–1.36
0.164
Good
72
4539
0.47
0.17–1.32
0.154
0.08–0.84
0.024
0.2680
1.40–4.52
0.002
0.0087
Missing Had antibodies to hepatitis C
0.2079
Completed high school University or Postgraduate Employment
0.86
0.0314
0.0009
1
Excellent
17
2126
0.26
No
84
7438
1
Yes
75
2898
2.51
Low
46
5329
1
Moderate
44
1841
2.86
1.41–5.83
0.004
High
39
1833
2.79
1.34–5.82
0.006
1.37–8.26
0.008
Missing
30
1341
3.36
No
65
6219
1
Yes
92
3986
2.01
1.15–3.51
0.015
Missing
2
139
1.45
0.21–10.25
0.708
Non-drinker
2
105
1
0.0224
0.0022
0.0505
1 or 2
6
1812
0.43
0.08–2.38
0.332
3 or 4 5 to 8
16 8
2046 643
0.87 1.28
0.18– 4.28 0.24–6.86
0.868 0.774
11
132
6.37
1.13–35.88
0.036
116
5606
2.09
0.77–9.23
0.329 \0.0001
[9 Missing
123
AIDS Behav Table 4 continued Risk factor Serostatus of regular relationship
Number of sexual partners, 6 months prior
a
Category
N
PYs
IRR
95 % CI
p value
0.85–3.47
0.131
No regular partner
31
2155
1.71
Concordant
15
2297
1
Discordant
15
699
3.51
1.54–7.98
0.003
Unknown Missing
1 97
116 5077
1.02 2.29
0.13–8.28 1.22–4.31
0.986 0.010
0–1
10
1421
1
2–5 men
8
1147
0.90
0.33–2.50
0.847
6–10 men
21
1007
2.46
1.02–5.92
0.044
11–50 men
14
1396
1.45
0.57–3.69
0.433
[50 men
13
734
2.44
0.89–6.68
0.082
Overall p value
0.0296
0.1598
Significant p value for interaction term
b
Time-updated use of amyl nitrate, viagra/erection pills, cocaine, psychedelics or hallucinogens, heroin/other opiates and GHB had p values [0.1
c
Patient-level use of amyl nitrate, viagra/erection pills, psychedelics or hallucinogens, downers/rohypnol/ketamine, and GHB all had p values [0.1 N number of drug-related hospital admissions, PYs person-years, 95 % CI 95 % confidence intervals
and HIV?ve cohort, it was consistent with the prevalence of drug use reported in the same time period in Sydney GCPS [14, 44]. A further limitation is that we were unable to exclude GBM from the general population estimates. However, the proportion of men identifying as gay or bisexual is low in the general Australian male population (1.6 and 0.9 % respectively) [45] and would only have biased our relative estimates towards the null. Rates of admissions with a secondary drug-related diagnosis code were also unavailable in the general Australian male population which precluded this comparison. Furthermore participant drug use was measured at annual interviews, which could have varied across the calendar year. Self-reported drug use in the cohorts could also have been affected by social desirability bias. Researchers in the United States, however, found self-reports about recent marijuana use and cocaine use in a cohort of MSM were valid [46]. There was a strong indication that participants lost to study follow-up who thus had missing self-reported drug use data were those at greatest risk for drug-related hospitalisation. Fortunately record linkage ensured that the outcomes of participants with missing data could be measured. A major advantage of this study was the availability of both detailed self-reported drug use data and cause-specific hospital admission data in GBM. These enabled the enumeration of drug-related harms in GBM and the investigation of what types of drug use patterns predicted these harms. Drug practices in GBM have long been a public health concern yet to-date limited research has estimated
123
the impact of heightened drug use in GBM on health outcomes unrelated to HIV acquisition or transmission. This study suggests that GBM do suffer greater drug-related morbidity as a result of their drug use. However, due to high prevalence of illicit drug use in GBM, support and interventions in this area need to be tailored to those at greatest risk of harm. Our findings suggest that the greatest at-risk group for drug-related harm are GBM who selfreport risky drug use, particularly cannabis and meth/amphetamine use, injecting drug use and weekly single substance or polydrug use, and who are under 55 years old, have comorbid mental health issues, have poorer socioeconomic indicators, consume alcohol at high levels and/or are engaging in high risk sexual activity. Acknowledgments The authors would like to thank the participants, the dedicated pH and HIM study teams and the participating doctors and clinics for their contribution to the HIM and pH studies. The authors would also like to acknowledge the assistance of the New South Wales Centre for Health Record Linkage in the conduct of this study. The Kirby Institute and the Centre for Social Research in Health are funded by the Australian Government Department of Health and Ageing. The Health in Men Cohort study was funded by the National Institutes of Health, a component of the USA Department of Health and Human Services (NIH/NIAID/DAIDS: HVDDT Award N01-AI-05395), the National Health and Medical Research Council in Australia (Project Grant #400944), the Australian Government Department of Health and Ageing (Canberra) and the New South Wales Health Department (Sydney). The Positive Health Cohort study was funded by the Australian Government Department of Health and Ageing (Canberra) and the New South Wales Health Department (Sydney). HFG is also supported by a National Health and Medical Research Council Fellowship.
AIDS Behav
Appendix See Table 5. Table 5 Comparison of patient characteristics by willingness to consent to record linkage HIV-ve cohort Risk factor
Category
Ethnicity
AngloAustralian/ Anglo-Celtic Other
Highest level of education
Consented to linkage
HIV?ve cohort Did not consent to linkage
p value
Consented to linkage
Did not consent to linkage
p value
324 (95.3)
16 (4.7)
0.199
430 (79.8)
109 (20.2)
0.344
112 (76.2)
35 (23.8)
131 (78.4)
36 (21.6)
1023 (96.8)
34 (3.2)
10 years of high-school or less
149 (99.3)
1 (0.7)
Completed high school
220(96.9)
7 (3.1)
96 (76.2)
30 (23.81)
295 (95.8)
13 (4.2)
137 (80.6)
33 (19.4)
710 (96.0)
30 (4.1)
203 (77.8)
58 (22.2)
1286 (96.6)
46 (3.5)
298 (80.8)
71 (19.2)
224 (76.5)
69 (23.6)
Tertiary education University or Postgraduate Employment
Unemployed
88 (94.6)
5 (5.4)
Weekly income
\500
278 (95.5)
13 (4.5)
500–999
520 (97.7)
1000–1499 [1500
Employed
Sexual identity
Self-reported as having had any STI (other than HIV)
Gay, queer or homosexual
Kessler 6 scale (psychological distress)
In a relationship/have a regular male sex partner Sero-status of regular relationship (concordant, discordant, unknown etc.)
37 (20.6)
302 (96.8)
10 (3.2)
80 (87.0)
12 (13.0)
252 (96.7)
14 (5.3)
57 (78.7)
14 (19.7)
1308 (96.3)
50 (3.7)
525 (78.5)
144 (21.5)
18 (85.7)
3 (14.3)
24 (75.0)
8 (25.0)
0.379
134 (77.0)
40 (23.0)
87 (70.7)
36 (29.3)
0.521
468 (77.4)
137 (22.6)
71 (83.5)
14 (16.5)
12 (100)
0 (0)
459 (97.0)
14 (3.0)
Never
0.123
0.630
1(1.9)
917 (96.1)
37 (3.9)
1191 (96.6)
42 (3.4)
54 (98.2)
1 (1.8)
Poor/fair
119 (96.0)
5 (4.0)
Good
439 (97.6)
11 (2.4)
Very good
552 (95.0)
29 (5.0)
290 (79.7)
74 (20.3)
Excellent Low
265 (97.8) 1029 (96.6)
6 (2.2) 36 (3.4)
125 (78.2) 215 (79.3)
40 (24.2) 56 (20.7)
Moderate
324 (95.9)
14 (4.1)
297 (78.8)
80 (21.2)
512 (79.0)
136 (21.0)
High Daily smoker
143 (79.4)
52 (98.1)
Yes Self-reported general health
87 (24.1)
12(2.3)
Other
Yes Had antibodies to hepatitis C
0.334
274 (75.9)
Bisexual No
0.195
19 (95.0)
1 (5.0)
No
936 (96.7)
32 (3.3)
Yes
440 (95.9)
19 (4.1)
Yes
765 (96.1)
31 (3.9)
0.087
0.760
0.428 0.467
25 (73.5)
9 (26.5)
121 (78.6)
33 (21.4)
257 (77.9)
73 (22.1)
301 (79.0)
80 (21.0)
265 (77.7)
46 (22.3)
115 (75.7)
37 (24.3)
265 (77.7)
76 (22.3)
No
610 (96.8)
20 (3.2)
No regular partner
646 (96.4)
24 (3.6)
Concordant
496 (95.9)
21 (4.1)
115 (75.7)
37 (24.3)
Discordant
136 (96.5)
5(3.6)
143 (80.3)
35 (19.7)
0.451
0.826
0.178 0.133
0.644
0.222
0.197 0.685
0.864
0.716 0.776
0.776
123
AIDS Behav Table 5 continued HIV-ve cohort Risk factor
Number of male partners (last 6 months)
Category
Consented to linkage
Unknown
62 (100)
0 (0)
0
22 (100)
0 (0)
1
Frequency of exercise in a week
Number of gay/homosexual male friends
0.190
Consented to linkage
Did not consent to linkage
6 (75.0)
2 (25.0)
11 (84.6)
2 (15.4)
244(97.6)
6 (2.4)
35 (87.5)
5 (12.5)
7 (1.9)
54 (78.3)
15 (21.7)
6–10
240 (94.1)
15 (5.9)
46 (88.5)
6 (11.5)
11–50
398 (95.7)
18 (4.3)
63 (86.3)
10 (13.7)
51–200
103 (96.3)
4 (3.7)
22 (91.7)
2 (8.3)
[200
12 (92.3)
1 (7.7)
3 (75.0)
1 (25.0)
Low
73 (97.3)
2 (2.7)
447 (97.0)
–
–
14 (3.0)
–
–
78 ? 9 (96.2) 64 (94.1)
31 (3.8) 4 (5.9)
– –
– –
No
607 (97.3)
17 (2.7)
–
–
Yes
769 (95.8)
34 (4.2)
57 (93.4)
4(6.6)
1 or 2
415 (95.2)
3 or 4
568 (96.9)
5 to 8 [9
253 (97.3) 67 (98.5)
Non-drinker
None
5 (100)
0.634
0.128
–
18 (21.7)
21 (4.8)
206 (78.9)
55 (21.1)
18 (3.1)
188 (21.3)
51 (21.3)
7 (2.7) 1 (1.5)
68 (80.0) 31 (14.1)
17 (20.0) 14 (31.1)
–
–
119 (98.4)
2 (1.7)
–
–
Some
373 (96.1)
15 (3.9)
–
–
Most
816 (96.0)
34 (4.0)
–
–
63 (100)
0 (0)
–
–
References 1. Chakraborty A, McManus S, Brugha TS, Bebbington P, King M. Mental health of the non-heterosexual population of England. British J Psychiatry. 2011;198(2):143–8. 2. Cochran SD, Ackerman D, Mays VM, Ross MW. Prevalence of non-medical drug use and dependence among homosexually active men and women in the US population. Addiction. 2004;99(8):989–98. 3. Fredriksen-Goldsen KI, Kim HJ, Barkan SE, Muraco A, HoyEllis CP. Health disparities among lesbian, gay, and bisexual older adults: results from a population-based study. Am J Public Health. 2013;103(10):1802–9. 4. Stall R, Wiley J. A comparison of alcohol and drug use patterns of homosexual and heterosexual men: the San Francisco Men’s Health Study. Drug Alcohol Depend. 1988;22(1–2):63–73. 5. Welfare AIoHa. National Drugs Strategy Household Surveys (NDSHS) (AIHW): Highlights from the 2013 Survey Canberra: AIHW; 2013 [cited 2014 19/09/2014]. Available from: http:// www.aihw.gov.au/.
0.359
p value
0.621
–
65 (78.3)
0 (0)
0.253
A few
All
123
p value
356 (98.1)
Active Very active
Number of alcoholic drinks consumed in one sitting (on average)
Did not consent to linkage
2–5
Moderate
Ever reported as experiencing discrimination or harassment
HIV?ve cohort
0.633
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