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AIDS Care
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Psychopathology and transmission risk behaviors in patients with HIV/AIDS
Howard Newvilleab; Deborah L. Hallerbc a Ferkauf Graduate School of Psychology, Yeshiva University, Bronx, NY, USA b St. Luke's-Roosevelt Hospital, New York, NY, USA c Columbia University College of Physicians and Surgeons, New York, NY, USA First published on: 16 July 2010
To cite this Article Newville, Howard and Haller, Deborah L.(2010) 'Psychopathology and transmission risk behaviors in
patients with HIV/AIDS', AIDS Care, 22: 10, 1259 — 1268, First published on: 16 July 2010 (iFirst) To link to this Article: DOI: 10.1080/09540121003615111 URL: http://dx.doi.org/10.1080/09540121003615111
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AIDS Care Vol. 22, No. 10, October 2010, 1259 1268
Psychopathology and transmission risk behaviors in patients with HIV/AIDS Howard Newvillea,b and Deborah L. Hallerb,c* a Ferkauf Graduate School of Psychology, Yeshiva University, 1165 Morris Park Avenue, Bronx, NY 10461, USA; bSt. Luke’sRoosevelt Hospital, New York, NY, USA; cColumbia University College of Physicians and Surgeons, New York, NY, USA
(Received 1 September 2009; final version received 12 January 2010)
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The role of psychiatric and substance use disorders in HIV transmission has not been fully elucidated, particularly among those living with the virus. We compared sex and drug risk behaviors for 228 HIV patients in four diagnostic groups: (1) no diagnosis; (2) psychiatric only; (3) substance dependence only; and (4) co-morbid psychiatric and substance dependence. Significant group differences were observed for having multiple sex partners, condom use, and injection drug use (IDU), but not for sharing of injection equipment. Patients with comorbid psychiatric and substance disorders reported multiple sex partners most frequently, while substance dependence contributed to irregular condom use and IDU. Analysis by substance use subgroup (no dependence, alcohol dependence only, drug dependence only, co-morbid alcohol, and drug dependence) showed that alcohol dependence contributed to having multiple sex partners, while alcohol and drug dependence both contributed to irregular condom use. Meanwhile, only drug dependence contributed to drug risk. HIV patients should be screened for psychopathology, with risk reduction interventions tailored to diagnostic group to achieve maximum effect.
Keywords: HIV prevention; sexual risk behavior; drug risk behavior; substance dependence; psychopathology
Introduction The US averages 40,000 new HIV infections each year (Centers for Disease Control and Prevention [CDC], 2007), with 49% attributable to male-to-male sexual contact, 32% to heterosexual contact, and 14% to injection drug use (IDU) (CDC, 2007). While most HIV prevention efforts focus on ‘‘at risk’’ persons, increasing attention is being directed toward positives (Gordon, Forsyth, Stall, & Cheever, 2005). Although some HIV individuals reduce transmission risk behavior post-diagnosis (Weinhardt, Carey, Johnson, & Bickham, 1999), others do not. Ironically, patients on active therapy engage in increased risk behavior (Kelly, Hoffman, Rompa, & Gray, 1998; Kravcik et al., 1998), resulting in higher rates of sexually transmitted infections (Collis & Celum, 2001). Finally, as people live longer with the virus (UNAIDS, 2002), this affords more opportunities for transmission over time. For these reasons, secondary prevention is a critical component of the overall risk reduction strategy. Although studies addressing the interrelationship between mental illness, substance abuse, and ongoing risk behavior among positives are rare, more is known about ‘‘at risk’’ persons (Carey et al., 2004; McKinnon, Cournos, & Herman, 2001; Meade, 2006). Substance abuse is associated with both drug *Corresponding author. Email: dhaller@chpnet.org ISSN 0954-0121 print/ISSN 1360-0451 online # 2010 Taylor & Francis DOI: 10.1080/09540121003615111 http://www.informaworld.com
and sex risk behaviors (Forney, Lombardo, & Toro, 2007; Strathdee et al., 1997; Strathdee, & Sherman, 2003). For instance, stimulant use has been linked to increased sex risk behavior (Plankey et al., 2007; Springer, Peters, Shegog, White, & Kelder, 2007). The fact that alcohol abuse augments HIV risk behavior among methadone clients (Arasteh, Des Jarlais, & Perlis, 2008) suggests that polysubstance use may confer additional risk. Seroconversion rates are higher among the mentally ill (Cournos & McKinnon, 1997; Rosenberg et al., 2001) but relationships are complex. While psychiatric severity is associated with greater drug risk behavior (Stein, Solomon, Herman, Anderson, & Miller, 2003; Woody, Metzger, Navaline, McLellan, & O’Brien, 1997), findings for sex risk are inconsistent. For instance, while depression was associated with sex risk in adolescents and IDUs (Brown et al., 2006; Perdue, Hagan, Thiede, & Valleroy, 2003), it was not a factor among the homeless (Forney et al., 2007). Furthermore, a meta-analysis of 34 studies found no relationship between sex risk and negative affects including depression, anxiety, and anger (Crepaz & Marks, 2001). Secondary prevention interventions have shown promise among selected populations of HIV individuals (Gordon et al., 2005; Kalichman et al.,
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2001; Margolin, Avants, Warburton, Hawkins, & Shi, 2003); however, more intensive approaches have been recommended for substance abusers (Gordon et al., 2005). One study demonstrated reductions in risk behavior among men who have sex with men (MSM), including some substance users, although the effects were short-lived (Morin et al., 2008). Among interventions for positives (Gordon et al., 2005), none is designed for use with individuals with psychiatric and/or substance use disorders. Furthermore, while harm reduction is a cornerstone of HIV risk reduction interventions, it is unclear that patients with psychiatric and/or substance use disorders are capable of meaningful behavioral change in the absence of treatment and abstinence. Among persons whose HIV status is unknown, a clear relationship exists between substance use and risk behavior; with the role of psychiatric illness being somewhat less clear. No research has focused on these interrelationships among positives, however. Therefore, the current study was designed to characterize transmission risk behaviors among HIV patients different types of psychopathology: (1) no diagnosis; (2) psychiatric only; (3) substance dependence only; or (4) co-morbid psychiatric and substance dependence (Center for Substance Abuse Treatment [CSAT], 2005; Rosenthal, 1992). Patients with comorbidity were expected to display the most HIV risk behavior and those with no diagnosis the least. We further hypothesized that HIV patients with both alcohol and drug dependence, arguably a more severe addition problem, would exhibit more risk behavior than those with no substance dependence disorder or with alcohol or drug dependence only.
Methods Program In conjunction with an 11-site treatment demonstration project, the Medical College of Virginia Hospitals added comprehensive mental health services to its Infectious Disease (ID) Clinic. These included psychological assessment, psychoeducation, risk-reduction training, HIV adherence counseling, pharmacotherapy, detoxification, and counseling (individual and group) for psychiatric/substance abuse problems. This demonstration project aimed to determine acceptability and feasibility of a ‘‘one-stop shopping approach’’ to address mental health concerns in patients with HIV/AIDS. This program has been described elsewhere in the literature (Haller & Miles, 2003). Support for the project was provided by the Center for Mental Health Services (CMHS) of the Substance Abuse Mental Health Services Administration (SAMHSA),
the National Institute of Mental Health, and the Health Resources Services Administration. Procedures Both active and passive recruitment strategies were utilized. Eighty-eight percent of participants were recruited by trained research assistants stationed in the ID Clinic waiting room or were referred by their HIV primary care providers. Flyers and posters facilitated self-referral. A small number of ID Clinic patients were referred by community providers, AIDS service organizations, and drug treatment programs. Patients were eligible if they were HIV , 17 years of age or older, desired mental health/ substance abuse services, and were willing to sign informed consent and participate in a clinical research study. No patients were excluded for psychiatric reasons. During baseline appointments, participants completed measures to evaluate psychosocial, psychological, substance abuse, and cognitive functioning. Additional measures assessed HIV risk behavior, social support, and functional interference. Both structured clinical interviews and self-report questionnaires were administered by trained interviewers. As compensation for completing baseline measures, participants were provided with free psychological and/or substance treatment. Sample The study sample was comprised of 228 HIV patients who receiving treatment in an ID Clinic, enrolled in an HIV mental health treatment demonstration project and completed all baseline measures (82% of participants). They were similar to patients excluded from analyses on demographic variables including age, gender, race, sexual orientation, education, employment, marital status, HIV transmission factors, and presence of an AIDS diagnosis; participants were aware of their HIV status longer, however (3.7 vs. 2.4 years, t 2.48, p 0.014). Measures University of Michigan Composite International Diagnostic Interview (UM-CIDI) The University of Michigan Composite International Diagnostic Interview (UM-CIDI) was used to assess participants for common Axis I disorders including major depression, dysthymia, generalized anxiety disorder, agoraphobia, panic attack, alcohol, and drug dependence in the past 12 months. Psychotic disorders were not assessed. The UM-CIDI was developed for
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the National Co-morbidity Study (Kessler et al., 1994). The UM-CIDI contains fewer modules, and was administered to reduce burden on patients. Compared to the full version, the UM-CIDI includes commitment and clarification probes in order to increase the accuracy of the response, and demonstrates moderate to excellent sensitivity (89.6%), specificity (93.9%), and total classification accuracy (93.2%; Kessler, Andrews, Mroczek, Ustun, & Wittchen, 1998).
split the sample into four substance dependence subgroups: (1) no substance dependence; (2) alcohol dependence only; (3) drug dependence only; and (4) co-morbid alcohol and drug dependence. Demographics were reported, and participants were compared across quadrants on demographic variables, with Chi-squares for dichotomous variables and ANOVAs for continuous variables. Chi-squares were calculated for HIV risk behaviors by quadrant.
Center for Mental Health Services (CMHS) HIV/ AIDS Risk Questionnaire HIV transmission risk behaviors were assessed using the CMHS HIV/AIDS Risk Questionnaire, consisting of six female-specific and 12 male-specific sexualrisk items, along with five drug-risk items (Table 1). If a participant endorsed a particular risk behavior, he/ she was queried about the frequency of that behavior. For sex risk, we assessed the number of sexual partners each participant reported and whether or not participants had engaged in sex without a condom in the past three months. As the data were heavily skewed toward abstinence, responses were coded as ‘‘abstinent’’, ‘‘one’’ or ‘‘multiple’’ partners. For drug risk, we assessed lifetime and past three months IDU, along with sharing of injection equipment during the past three months. This instrument was developed by consensus of the principle investigators of the treatment demonstration project. Though no validity/ reliability testing was done, it is face valid.
Results
Addiction Severity Index (ASI-5) The Addiction Severity Index (ASI-5; McLellan et al., 1992) is a structured clinical interview that assesses problem severity over the past 30 days in seven areas affected by addiction: medical, employment, alcohol, drug, legal, family, and psychological functioning. For this study, only self-reported drug and alcohol use were considered. Analyses Participants were assigned to one of four diagnostic quadrants based on UM-CIDI diagnoses: (1) no diagnosis; (2) psychiatric diagnoses only; (3) substance dependence diagnoses only; and (4) co-morbid psychiatric and substance dependence diagnoses. The rationale for employing the ‘‘Four Quadrant Model’’ to characterize participants’ psychiatric and substance dependence status is described in Rosenthal (1992) and in SAMHSA TIP 42 (CSAT, 2005). Research has confirmed the utility of the four quadrant model for classifying patients (McGovern, Clark, & Samnaliev, 2007). To determine if there were differences by type of substance dependence, we then
Psychiatric diagnoses and substance use characteristics are presented in Table 2. Based on the UM-CIDI, participants were assigned to one of four diagnostic quadrants: (1) no diagnosis (N 51; 22%); (2) psychiatric diagnoses (N 69; 30%); (3) substance dependence diagnoses (N 17; 8%); or (4) co-morbid psychiatric/substance dependence diagnoses (N 91; 40%). Additionally, four substance use subgroups were formed: (1) no substance dependence (N 120; 53%); (2) alcohol dependence (N 19; 8%); (3) drug dependence (N 51; 22%); and (4) co-morbid alcohol/drug dependence (N 38; 17%). Sample characteristics are displayed in Table 3. No significant differences were observed by quadrant for illness characteristics including time since diagnosis or presence of an AIDS diagnosis. However, fewer participants in the substance dependence and comorbid quadrants were stably housed than those in the no diagnosis and psychiatric quadrants. Participants in the substance dependence and co-morbid quadrants also were more likely to reuse needles than those in the no diagnosis and psychiatric quadrants. In addition, significant differences in race, housing status, education, and needle sharing as a possible transmission vector were observed by substance use subgroup (Table 4). More specifically, participants in the drug and drug/alcohol subgroups were significantly more likely to be African-American, to lack stable housing, to not have finished high school, and to identify needle sharing as a likely HIV transmission factor. Regarding gender differences, 57% of the 54 females reported having sexual intercourse with a man in the past three months. Eighty-seven percent of reported one partner, but 10% had three and 3% had four partners. Thirty-seven percent failed to use condoms regularly and 13% engaged in anal intercourse. Of the 171 male participants, 18% reported sexual intercourse with a woman in the past three months; 80% reported one partner, 7% two, 10% three, and 3% six partners. Of these, 73% used condoms during every sexual encounter. Twenty-five percent of males reported MSM in the preceding
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Table 1. CMHS HIV/AIDS Risk Questionnaire items.
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Items
Responses
For women only (1) Have you had sexual intercourse with a man in the last three months? If NO, go to #19 (2) How many men have you had sexual intercourse with in the last three months? (3) About how many times have you had vaginal intercourse in the last three months? (4) During vaginal intercourse, how often did your partner use a condom? (5) About how many times have you had anal intercourse in the last three months? If 0, go to #19 (6) During anal intercourse, how often did your partner use a condom? Go to #19
N 54 Yes 31 (57%)
For men only
N 174a
(7) Have you had sexual intercourse with a woman in the last three months? If NO, go to #13 (8) How many women have you had sexual intercourse with in the last three months? (9) How many times have you had vaginal intercourse in the last three months? If 0, go to #11 (10) How often did you use a condom during vaginal intercourse in the last three months? (11) How many times have you had anal intercourse in the last three months? If 0, go to #13 (12) How often did you use a condom during anal intercourse in the last three months? (13) Have you had anal intercourse with a man in the last three months? If NO, go to #19 (14) How many men have you had anal intercourse with in the last three months? (15) How many times have you had anal intercourse where the man inserted his penis into your anus? If 0, go to #17 (16) How often did you use a condom when another man was inserting his penis into your anus? (17) How many times have you had anal intercourse when inserting your penis into another man’s anus? If 0, go to #19 (18) How often did you use a condom when inserting your penis into another man’s anus?
Yes 31 (18%)
For men and women
N 228
(19) Have you injected drugs at any time in your life? If 0, end administration (20) Have you injected drugs in the last three months? If 0, end administration (21) How many times have you injected drugs in the last three months? (22) How often did you inject drugs with an outfit/works that someone had already used? (23) How often did someone use a cotton, cooker or rinse water after another drug user had used them?
Yes 63 (28%)
a
M(SD) 1.3 (0.8); range 1 4 M(SD) 9.9 (13.7); range 1 60 Never 9 (29%); sometimes 2 (6%); usually 1 (3%); every time 19 (61%) N 4, M(SD) 0.3 (1.1); range 0 6
Never 2 (50%); sometimes 0 (0%); usually 0 (0%); every time 2 (50%)
M(SD) 1.4 (1.1); range 1 6 M(SD) 23.1 (26.1); range 1 90
Never 3 (10%); sometimes 3 (10%); usually 2 (6%); every time 23 (74%) N 1, M(SD) 0.1 (0.7); range 0 4
Never 1 (100%); sometimes 0 (0%); usually 0 (0%); every time 0 (0%) Yes 43 (25%) M(SD) 2.3 (3.2); range 1 20 M(SD) 9.1 (17.3); range 0 90
Never 8 (19%); sometimes 1 (2%); usually 3 (7%); every time 22 (51%) M(SD) 7.3 (18.2); range 0 104
N 24, Never 6 (25); sometimes 2 (8%); usually 0 (0%); every time 16 (67%)
Yes 15 (7%) M(SD) 42.8 (50.3); range 1 180 Never 11 (73%); sometimes 4 (27%); usually 0 (0%); every time 0 (0%) Never 11 (73%); sometimes 0 (0%); usually 1 (7%); every time 3 (20%)
The three transgendered individuals in the sample took the risk questionnaire as males.
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Table 2. Psychiatric diagnoses and substance use by quadrant. Total (n 228)
No diagnosis (n 51)
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Psychiatric diagnoses (past 12 months; n, %; UM-CIDI) Major depressive 138 (61) 0 (0) episode Generalized anxiety 54 (24) 0 (0) disorder Panic disorder 53 (23) 0 (0) Dysthymia 51 (22) 0 (0) Agoraphobia 38 (17) 0 (0) Drug dependence 89 (39) 0 (0) Alcohol dependence 57 (25) 0 (0) Self-reported substance use (in the past month; n, %) (ASI) Alcohol 107 (47) 22 (43) Heroin 13 (6) 0 (0) Methadone 2 (1) 0 (0) Opiates/analgesics 13 (6) 3 (6) Barbiturates 3 (1) 1 (2) Crack/cocaine 51 (22) 2 (4) Amphetamines 3 (1) 0 (0) Marijuana 62 (27) 8 (16) Inhalants 8 (4) 1 (2)
three months; 65% had one partner, 12% two partners, 9% three partners, 7% four partners, 2% five partners, 2% 10 partners, and 2% 20 partners. Of these, 65% used condoms during every sexual encounter. One male participant (B1%) reported sexual activity with both a man and a woman. When prevalence rates for specific risk behaviors were compared (Table 5), it was noted that participants in the co-morbid quadrant had significantly more sexual partners (x2 17.59, p 0.007) and were more likely to engage in unprotected sex (x2 10.32, p 0.016) compared to those in the no diagnosis and psychiatric diagnoses quadrants. Not surprisingly, more substance dependent participants (alone or in combination with a psychiatric diagnosis) reported lifetime (x2 23.21, p B0.001) and recent IDU (x2 13.75, p 0.003) than those without diagnoses or with psychiatric diagnoses only. Sharing of injection equipment was not significantly different by quadrant (x2 5.08, p 0.166). More participants in the alcohol/drug subgroup reported having multiple sex partners compared to those without substance dependence or with drug dependence only (x2 29.21, pB0.001). Participants in the three substance dependence subgroups also were more likely to engage in unprotected sex than those without substance dependence diagnoses (x2 10.41, p 0.015). Not surprisingly, more participants in the drug and alcohol/drug subgroups reported lifetime (x2 22.93, pB0.001)
Psychiatric diagnoses only (n 69)
Substance diagnoses only (n 17)
Co-morbid diagnoses (n 91)
p
55 (80)
0 (0)
83 (91)
B0.001
25 (36)
0 (0)
29 (32)
B0.001
21 20 16 0 0
(30) (29) (23) (0) (0)
0 0 0 14 5
(0) (0) (0) (82) (29)
32 31 22 75 52
(35) (34) (24) (82) (57)
B0.001 B0.001 B0.001 B0.001 B0.001
29 1 0 3 1 2 1 16 2
(42) (1) (0) (4) (1) (3) (1) (23) (3)
7 2 1 0 0 5 0 2 0
(41) (12) (6) (0) (0) (29) (0) (12) (0)
61 10 1 7 1 42 2 36 5
(67) (11) (1) (8) (1) (46) (2) (40) (6)
0.005 0.011 0.097 0.616 0.932 B0.001 0.693 0.006 0.571
and recent IDU (x2 19.91, p B0.001) than those without substance dependence. Finally, there was a trend for more participants with drug dependence (with/without alcohol dependence) to share injection equipment in the prior 3 months compared to those without drug dependence (x2 6.59, p 0.086), although this finding did not reach significance. Discussion This study explored relationships between psychiatric and/or substance use disorders and transmission risk behaviors among patients receiving HIV primary care. Despite repeated exposure to low intensity risk reduction interventions in the ID Clinic (e.g., psychoeducation, free condoms, care of injection equipment, referrals to drug treatment), rates of sex and drug risk behaviors were higher than expected. Within the sample, however, self-report risk behavior varied by diagnosis (none, psychiatric, substance dependence, both) and by substance use subgroup (none, alcohol, drug, both). Patients without diagnoses endorsed the fewest risk behaviors and those with co-morbid conditions the most, thus establishing the role of ‘‘severity’’ in continued risk taking among positives. In isolation, psychiatric disorders contributed minimally to risk behavior in this population. However, when combined with a substance dependence
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Table 3. Demographics by quadrant. Total (n 228)
No diagnosis (n 51)
Gender (n, %) Male Female Transgendered Age (mean (SD))
171 (75) 54 (24) 3 (1) 36.0 (7.5)
41 (80) 9 (18) 1 (2) 38.1 (8.8)
49 (71) 19 (28) 1 (1) 35.4 (8.1)
12 (71) 5 (29) 0 (0) 36.1 (5.5)
69 (76) 21 (23) 1 (1) 35.4 (6.5)
Sexual orientation (n, %) Heterosexual Homosexual Bisexual Other
113 93 19 1
26 20 4 0
30 33 4 1
13 2 2 0
44 38 9 0
Race (n, %) White, Non-Hispanic Black, Non-Hispanic Other
68 (30) 149 (65) 5 (2)
17 (33) 31 (61) 1 (2)
25 (36) 39 (57) 1 (1)
Marital status (n, %) Married Separated Never married
43 (19) 178 (78) 1 (0)
11 (22) 36 (71) 0 (0)
106 (46) 91 (40) 26 (11)
Housing (n, %) Own home or apartment Family or friends home or apartment Unstable housinga Education (n, %) Less than high school High school graduate or GED Some college Completed college or more Employed (n, %) Disability (n, %) Time since HIV diagnosis (years, SD) AIDS diagnosis? (n, %) Transmission factor (n, %) Needle exchange Homosexual contact Heterosexual contact with a bisexual partner Heterosexual contact with an IV drug user Heterosexual, unknown risk Blood products Other
(50) (41) (8) (0)
(51) (39) (8) (0)
Psychiatric Substance only (n 69) only (n 17)
(43) (48) (6) (1)
(76) (12) (12) (0)
Co-morbid (n 91)
p
0.891
0.167
(48) (42) (10) (0)
0.293
0 (0) 17 (100) 0 (0)
26 (29) 62 (68) 3 (3)
0.082
16 (23) 51 (74) 0 (0)
2 (12) 15 (88) 0 (0)
14 (15) 76 (84) 1 (1)
0.640
28 (55) 15 (29)
41 (59) 24 (35)
6 (35) 7 (41)
31 (34) 45 (49)
0.006
4 (8)
3 (4)
4 (24)
15 (16)
77 (34) 61 (27) 53 (23) 32 (14) 56 (25) 71 (31) 3.7 (3.3)
6 (12) 16 (31) 14 (27) 12 (24) 16 (31) 16 (31) 3.6 (3.5)
25 (36) 19 (28) 15 (22) 9 (13) 19 (28) 23 (33) 3.4 (3.3)
7 (41) 5 (29) 3 (18) 2 (12) 4 (24) 8 (47) 3.5 (3.5)
39 (43) 21 (23) 21 (23) 9 (10) 17 (19) 24 (26) 4.0 (3.1)
0.060
54 (24)
17 (33)
19 (28)
2 (12)
16 (18)
0.094
46 (20) 115 (50) 27 (12)
6 (12) 29 (57) 2 (4)
8 (12) 36 (52) 12 (17)
7 (41) 4 (24) 2 (12)
25 (27) 46 (51) 11 (12)
0.006 0.115 0.157
95 (42)
13 (25)
33 (48)
7 (41)
42 (46)
0.056
100 (44) 18 (8) 3 (1)
21 (41) 3 (6) 0 (0)
26 (38) 5 (7) 2 (3)
8 (47) 3 (18) 0 (0)
45 (49) 7 (8) 1 (1)
0.524 0.475 0.525
0.240 0.352 0.727
a
Unstable housing includes rooming houses, single room hotels, shelters, halfway houses, group homes, institutions or living on the street.
disorder, a synergistic effect was observed, thus highlighting the importance of co-morbidity. The relationship between substance dependence on risk behavior was more straightforward. Individuals with substance dependence diagnoses (with/without co-morbid psychiatric disorders) were less likely to reliably use
condoms. Among substance abusers, those with drug dependence (with/without co-morbid alcohol dependence) evidenced the highest rates of both sex and drug risk behaviors. Accordingly, substance abusers, particularly drug abusers, must be specifically targeted for secondary prevention interventions. Interestingly,
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Table 4. Demographics by substance use quadrant.
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No substance dependence (n 120)
Alcohol dependence only (n 19)
Drug dependence only (n 51)
Co-morbid alcohol and drug dependence (n 38)
Gender (n, %) Male Female Transgendered Age (mean, SD)
90 (75) 28 (23) 2 (2) 36.5 (8.5)
17 (89) 2 (12) 0 (0) 35.2 (4.0)
34 (67) 16 (31) 1 (2) 35.7 (6.3)
30 (79) 8 (21) 0 (0) 35.5 (7.4)
Sexual orientation (n, %) Heterosexual Homosexual Bisexual Other
56 53 8 1
8 11 0 0
30 18 3 0
19 11 8 0
Race (n, %) White, Non-Hispanic Black, Non-Hispanic Other
42 (35) 70 (58) 2 (2)
11 (58) 6 (32) 2 (12)
Marital status (n, %) Married Separated Never married
27 (23) 87 (73) 0 (0)
Housing (n, %) Own home or apartment Family or friends home or apartment Unstable housinga Education (n, %) Less than high school High school graduate or GED Some college Completed college or more Employed (n, %) Disability (n, %) Time since HIV diagnosis (years, SD) AIDS diagnosis? (n, %) Transmission factor (n, %) Needle exchange Homosexual Contact Heterosexual contact with a bisexual partner Heterosexual contact with an IV drug user Heterosexual, unknown risk Blood products Other
(47) (44) (7) (1)
(59) (35) (6) (0)
0.559
0.781
(50) (29) (21) (0)
0.086
8 (26) 43 (84) 0 (0)
7 (18) 30 (79) 1 (3)
B0.001
5 (26) 14 (74) 0 (0)
6 (12) 45 (88) 0 (0)
5 (13) 32 (84) 1 (3)
0.148
69 (58) 39 (33)
10 (53) 7 (37)
17 (33) 26 (51)
10 (26) 19 (50)
0.001
7 (6)
2 (12)
8 (16)
9 (24)
6 (32) 2 (12) 8 (42) 3 (16) 5 (26) 4 (21) 4.0 (3.3)
22 (43) 15 (29) 5 (10) 8 (26) 9 (18) 16 (31) 3.9 (3.0)
18 (47) 9 (24) 11 (29) 0 (0) 7 (18) 11 (29) 3.8 (3.4)
0.010
36 (30)
5 (26)
6 (12)
7 (18)
0.063
14 (12) 65 (54) 14 (12)
1 (5) 11 (58) 2 (12)
17 (33) 21 (41) 3 (6)
14 (37) 18 (47) 8 (21)
B0.001 0.423 0.204
46 (38)
7 (37)
24 (47)
18 (47)
0.628
47 (39) 8 (7) 2 (2)
8 (42) 2 (12) 0 (0)
22 (43) 5 (10) 1 (2)
23 (61) 3 (8) 0 (0)
0.155 0.873 0.814
31 (26) 35 (29) 29 (24) 21 (18) 35 (29) 39 (33) 3.5 (3.4)
(42) (58) (0) (0)
p
0.230 0.716 0.827
a
Unstable housing includes rooming houses, single room hotels, shelters, halfway houses, group homes, institutions or living on the street.
IDU was not as prominent a problem as alcohol abuse. While drug dependent patients evidenced the most injection risk behavior, most drug abusers did not inject as they were crack/cocaine users. While
needle hygiene remains an important approach for injectors, other approaches are needed to address sexual risk taking behaviors among addicts. For patients meeting dependence criteria, this likely means
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Table 5. Risk by quadrant.
(n, %) Sex risk Multiple sex partners (past three months) Any sex without a condom (past three months) Drug risk Any injection drug use (lifetime) Any injection drug use (past three months) Sharing injection equipment (last three months)
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(n, %)
Sex risk Multiple sex partners (past three months) Any sex without a condom (past three months) Drug risk Any injection drug use (lifetime) Any injection drug use (past three months) Sharing injection equipment (last three months)
Psychiatric diagnosis only (n 69)
Substance dependence diagnosis only (n 17)
Co-morbid diagnoses (n 91)
x2
1 (2)
4 (6)
1 (6)
18 (20)
17.59
0.007
5 (10)
6 (9)
4 (24)
23 (25)
10.32
0.016
8 (16) 0 (0)
10 (14) 1 (1)
10 (59) 2 (12)
35 (38) 12 (13)
23.21 13.75
B0.001 0.003
0 (0)
0 (0)
1 (6)
3 (3)
5.08
0.166
No diagnosis (n 51)
Number of substance dependence (n 120)
Alcohol dependence only (n 19)
Drug dependence only (n 51)
p
Co-morbid dependence diagnoses (n 38)
x2
p
5 (4)
3 (16)
4 (8)
12 (32)
29.21
B0.001
11 (9)
5 (26)
12 (24)
10 (26)
10.41
0.015
18 (15) 1 (1)
5 (26) 0 (0)
23 (45) 8 (16)
17 (45) 6 (16)
22.93 19.91
B0.001 B0.001
0 (0)
0 (0)
2 (4)
2 (5)
6.59
0.086
abstinence-based drug treatment, although recreational users and those with abuse diagnoses may respond to lesser interventions. In the current study, patients with alcohol dependence diagnoses evidenced very high rates of sex risk behavior. Similar to drug dependent patients, it is unrealistic to assume that patients with full blown alcoholism could ‘‘control’’ their drinking in sexual situations. Although interventions targeting specific behavior chains (e.g., excessive drinking leading to faulty judgment and unprotected sex) can be effective for those with less severe drinking problems, individuals with alcohol dependence are likely to require substance abuse treatment to establish abstinence and effect meaningful reductions in sex risk behavior. Whether substance abuse treatment for HIV patients should be delivered separately or as part of an integrated transmission risk reduction interventions is unclear, although the most effective prevention programs are comprehensive, approaching all aspects of the problem (Nation et al., 2003). For instance, drug treatment can be an
efficacious method of reducing HIV transmission risk (Sorensen & Copeland, 2000), although more intensive interventions (in addition to drug treatment) have an even greater effect (Margolin et al., 2003). By classifying patients according to psychiatric status, interventions may be properly tailored to each patient’s needs. To summarize, our data suggest that HIV patients with severe alcohol and/or drug abuse problems (reflected in a dependence diagnosis) are at increased risk for continued risk behavior, despite active participation in HIV primary care. Formal drug treatment thus is indicated. Both substance abuse counseling and pharmacotherapy should be considered. While this study focused on patients with severe addiction problems, it is important to understand that many participants were actively drinking and/or using drugs but did not receive a dependence diagnosis. These patients were assigned to the no diagnosis quadrant, making findings even more striking. Although recreational users displayed lower levels of transmission risk behavior than those meeting diagnostic criteria,
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AIDS Care this sub-group might benefit brief motivational interventions that include personalized feedback about potentially hazardous substance and the relationship between substance use and transmission, along with advice to change. This study has several limitations. All participants were engaged in HIV primary care and had received standard risk reduction interventions; patients not in care may have even higher rates of risk behavior. Because the project provided free psychological and substance abuse services, the patients who chose to enroll in the study may have had greater acuity and/ or motivation to change. The impact of these factors is unknown and more research is needed to confirm findings and ascertain generalizability to out of care populations. Due to limitations of the diagnostic tool, we assessed for depression and anxiety only; bi-polar and psychotic disorders were not included. Their inclusion may or may not have altered results for patients in the psychiatric quadrant. In contrast, McGovern et al. (2007) included patients with serious mental illness in the psychiatric and co-morbid quadrants, further classifying patients with anxiety disorders as ‘‘low severity’’. Future studies should include a broader range of diagnoses if possible. Additionally, the UM-CIDI assesses for alcohol and drug dependence, but not abuse; heavy users and those with abuse diagnoses were thus included in the no diagnosis and psychiatric groups. While this classification scheme provided valuable information about the impact of severe substance use disorders, future studies should address substance use ‘‘severity’’ in a continuous manner if possible. Finally, our risk questionnaire was ‘‘homegrown’’ and somewhat limited in terms of the number and types of risk behaviors assessed. For instance, sex while under the influence of alcohol or drugs and trading sex for drugs was not measured. Neither did the questionnaire query as to reasons for engaging in risky behavior. While some of these are intuitive (e.g., risky sex when intoxicated), others are more complex, requiring closer scrutiny to fully understand relationships. A more comprehensive risk assessment tool will be needed to fully understand these preliminary findings. The current study provides much needed, albeit preliminary, information about HIV risk behavior among positives with psychiatric and/or substance dependence disorders. Unfortunately, many HIV clinics and community service organizations do not routinely assess for mental health problems and thus may be oblivious to their presence and potential impact. Brief screening tools could be helpful in determining which patients might require more intensive interventions, thereby increasing their
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chances for successfully decreasing risk behavior. Instruments like the CIDI are a starting point, although others may be more appropriate for use in the HIV this population. Acknowledgements This work was supported by Substance Abuse Mental Health Services Administration (SAMHSA) Grant #UD5SM51689.
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