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The effect of drug use on the age at onset of psychotic disorders in an Australian cohort
SCHRES-05825; No of Pages 6 Schizophrenia Research xxx (2014) xxx–xxx
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The effect of drug use on the age at onset of psychotic disorders in an Australian cohort Nikos C. Stefanis a,b,⁎, Milan Dragovic a,b, Brian D. Power a,b,c, Assen Jablensky a, David Castle d, Vera A. Morgan a a
School of Psychiatry and Clinical Neurosciences, The University of Western Australia, WA Clinical Research Centre, North Metropolitan Health Service — Mental Health, Perth, WA Peel and Rockingham Kwinana Mental Health Service, South Metropolitan Area Health Service, Perth, WA d St. Vincent's Hospital, University of Melbourne, Australia b c
a r t i c l e
i n f o
Article history: Received 17 December 2013 Received in revised form 14 March 2014 Accepted 2 April 2014 Available online xxxx Keywords: Schizophrenia Causality Cannabis Amphetamine Psychosis
a b s t r a c t Background: We aimed to examine the association between illicit substance use and age at onset in psychotic disorders in an Australian cohort. Methods: Retrospectively acquired information on substance use during the year prior to illness onset was collected from 1642 participants enrolled in the Australian National 2010 Survey of High Impact Psychosis study (SHIP), with an ICD-10 diagnosis of schizophrenia spectrum or affective psychosis. Latent class analysis was performed according to illicit substance use, using age as an active covariate; identified classes were subsequently validated. Cox regression was used to examine the independent contribution of the identified substance use classes and several confounding variables to the prediction of age at onset of psychosis. Results: Three classes according to substance use were identified: non-users (n = 803), cannabis predominant users (n = 582), and polysubstance users (n = 257). For participants with schizophrenia spectrum disorders, cannabis predominant users had a higher hazard of earlier age at onset than for non-users (adjusted HR = 1.38, 95% CI = 1.2–1.6); polysubstance users had an even higher hazard (adjusted HR = 1.95, 95% CI = 1.5–2.4). In contrast, for participants with affective psychosis, cannabis predominant users (adjusted HR = 1.10, 95% CI = 0.8–1.4) and polysubstance users (adjusted HR = 0.87, 95% CI = 0.6–1.3) did not have a higher hazard of earlier age at onset compared with non-users. Conclusions: Illicit substance use in the 12 months prior to psychosis onset has a differential effect on age at onset in schizophrenia spectrum and affective psychotic disorders. Our findings are compatible with the notion that illicit drugs bring forward age at onset in schizophrenia spectrum disorders but not affective psychotic disorders. Published by Elsevier B.V.
1. Introduction There is much debate regarding the association between substance use and age at onset (AAO) in psychosis, with a recent meta-analysis reporting an earlier mean AAO in samples with more cannabis users, and arguing in favor of a causality hypothesis (Large et al., 2011) (i.e., that the illness is precipitated by cannabis, possibly in those predisposed to develop psychosis) (Henquet et al., 2006; Stefanis et al., 2004). Others Abbreviations: AAO, age at onset; AIC3, Akaike information criterion 3; APD, affective psychotic disorders; BIC, Bayesian information criterion; DIP, Diagnostic Interview for Psychosis; ICD-10, International Classification of Diseases Version 2010; LCA, latent class analysis; LSD, lysergic acid diethylamide; OPCRIT, Operational Criteria for Psychosis; SSD, schizophrenia spectrum disorders; SHIP, Survey of High Impact Psychosis. ⁎ Corresponding author at: Centre for Clinical Research in Neuropsychiatry, Private Bag No 1, Claremont WA 6910, USA. Tel.: +61 8 9347 6429; fax: +61 8 9384 5128. E-mail addresses: [email protected] (N.C. Stefanis), [email protected] (M. Dragovic), [email protected] (B.D. Power), [email protected] (A. Jablensky), [email protected] (D. Castle), [email protected] (V.A. Morgan).
maintain that the association is likely to be spurious (i.e., younger participants are more likely to use cannabis) (Wade, 2005). Consistent with the hypothesis that cannabis may bring forward psychosis onset, recent studies have demonstrated that age of cannabis initiation was associated with AAO of psychotic symptoms in high risk individuals (Dragt et al., 2012) and directly and linearly associated with age at prodrome onset and AAO in schizophrenia spectrum disorders (SSD) (Galvez-Buccollini et al., 2012; Leeson et al., 2012; Stefanis et al., 2013). However, the complex temporal relationship between substance use and psychotic symptoms in the premorbid period defies simple interpretations that are further hampered by a plethora of confounding factors, including uncertainties regarding the effect of comorbid substance use in addition to cannabis, and the specificity or not of these effects to SSD. Several studies have examined the association between number of illicit substances consumed and AAO in SSD, and have reported disparate findings (Barnes et al., 2006; Gonzalez-Pinto et al., 2008; Dekker et al., 2012; Power et al., 2012). For instance, Dekker et al. (2012)
http://dx.doi.org/10.1016/j.schres.2014.04.003 0920-9964/Published by Elsevier B.V.
Please cite this article as: Stefanis, N.C., et al., The effect of drug use on the age at onset of psychotic disorders in an Australian cohort, Schizophr. Res. (2014), http://dx.doi.org/10.1016/j.schres.2014.04.003
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N.C. Stefanis et al. / Schizophrenia Research xxx (2014) xxx–xxx
examined the effect of substance use in the 12 months prior to interview and over the lifetime, and reported that cannabis use had a similar effect compared with cannabis plus another illicit substance. However, we recently reported that the consumption of illicit substances in addition to cannabis (specifically in the 12 months prior to onset) predicted an earlier AAO by approximately one additional year in schizophrenia (Power et al., 2012); this remained significant after controlling for mean duration of illness, and was independent of family history of schizophrenia and sex. There were a number of limitations imposed on this study by sample size, such that replication is warranted. Few studies have examined the association between substance use (namely alcohol and cannabis) and AAO in affective psychotic disorders (APD) (Strakowski et al., 2005; Fossey et al., 2006; Lin et al., 2006; Strakowski et al., 2007; Ringen et al., 2008; Lagerberg et al., 2011). Although Lin et al. (2006) did not differentiate between the types of substance used, they reported an increased risk of substance abuse for patients with younger onset of bipolar disorder. Other authors, however, have reported a later onset for patients who abuse substances prior to onset of bipolar disorder (Strakowski et al., 2005; Fossey et al., 2006; Strakowski et al., 2007), while excessive cannabis use in the period prior to illness onset has been associated with earlier onset in 151 patients with bipolar disorder (types I and II) (Lagerberg et al., 2011). While methodological differences may explain these discrepancies, Lagerberg et al. (2011) also hypothesize that differences in the sequence of onset of bipolar disorder and onset of substance use disorder may have a bearing on age at onset of illness. Nonetheless, these disparate findings suggest that the association between premorbid substance use and AAO of APD warrants further investigation. Responding to conflicting findings regarding the premorbid effect of substance use on the AAO of SSD and APD, we set out to investigate this association in a large sample of people with psychosis in which information on substance use in the 12 months prior to onset of illness was available. We hypothesized that cannabis consumption in the 12 months prior to illness onset would lead to an earlier AAO of both SSD and APD. We also aimed to independently replicate our initial finding that illicit substance use in addition to cannabis in the 12 months prior to onset would have an additive effect on AAO in schizophrenia, and to examine whether these findings were consistent for SSD and APD. 2. Experimental/materials and methods 2.1. Participants Participants were selected from the 2010 Australian SHIP (Survey of High Impact Psychosis), the methods of which have been described elsewhere (Morgan et al., 2012; Stefanis et al., 2013). In brief, participants were recruited from across seven sites in Australia, employing a twophase sampling design. In phase 1, screening for psychosis was conducted in participants aged 18–64 in contact with public mental health services and non-government organisations supporting people with mental illness. Of the 7955 who screened positive for psychosis, 1825 were randomly selected and engaged in assessment (phase 2); participants provided written informed consent. From the 1825 participants who were able to be interviewed, those who did not meet full criteria for an ICD-10 psychotic disorder in phase 2 were excluded (n = 183), leaving a total of 1642 participants with an established ICD-10 diagnosis of SSD (schizophrenia, schizoaffective, delusional and other non-organic psychotic disorder) or APD (bipolar disorder, depressive disorder with psychotic features).
utilization). The diagnostic module (DIP-DM) follows the structure of the Operational Criteria for Psychosis (OPCRIT) (McGuffin et al., 1991), a 90-item checklist which allows the examiner to rate symptoms in a number of domains (present state, past year, and lifetime). Diagnostic classification of cases was made using the OPCRIT diagnostic computer algorithm (McGuffin et al., 1991). In addition, the DIP includes items on substance use (alcohol, tobacco, cannabis, amphetamines, LSD, cocaine, ecstasy), including frequency of use in the 12 months prior to onset of psychosis. Participants were categorized as either substanceusers (ranging from “daily use” to “used less frequently than once per month”), or substance non-users (if they reported “no use”). The DIP also includes items regarding the use of alcohol to determine ICD-10 classifications for alcohol abuse and dependence. 2.3. Definition of AAO AAO was determined after interviewing the participant, and recorded to the nearest year (defined as the earliest age medical advice was sought for psychiatric reasons, or that any psychiatric symptom diagnostic of psychotic or major affective illness began to cause subjective distress or impair functioning). If there were no clear symptoms described, age at first hospital admission was recorded. 2.4. Statistical analysis To investigate whether there existed classes according to illicit substance use within the SHIP (n = 1642), latent class analysis (LCA) was performed using illicit substance use (cannabis, amphetamine, cocaine, LSD, ecstasy) in the 12 months prior to onset of illness as nominal indicators. We used age at interview as an active covariate, thus allowing participants' age to influence the classification probabilities; age categories were determined impartially using the binning procedure with three equal percentiles based on scanned data (age at interview ≤32, 33–43, and ≥44 years). The optimal LCA solution was determined by identifying a point of a convincing convergence of various fit indices. The identified classes were then described using one way analysis of variance or Pearson's chi-square test (χ2). Analyses were performed using IBM SPSS (version 20) and Latent GOLD (version 4.0.4). Based on the substance-using age-adjusted classes of participants identified and externally validated in LCA, we then sought to determine the effect of substance-using status on AAO in both SSD and APD. We used Cox proportional hazards regression models to predict a status (event) variable, namely AAO of psychosis, using latent class membership as the principle predictor. We then investigated the possible influence of several confounding factors to adjust crude hazard rates. The cumulative hazard of AAO was determined and presented graphically according to illicit substance use for both SSD and APD. 3. Results 3.1. Demographic data Table 1 illustrates the demographic data of SHIP participants. There were 1242 participants diagnosed with SSD, and 400 participants with APD. Additional data pertaining to further sociodemographic data, lifestyle variables, mental health profile, medications, service utilization, functioning and quality of life have been reported elsewhere (Morgan et al., 2012). 3.2. Classes according to substance use
2.2. Measures — diagnostic algorithm Participants in phase 2 were assessed using the DIP (Diagnostic Interview for Psychosis), a standardized semi-structured interview for psychosis (Castle et al., 2006). The DIP is comprised of a number of modules (demography and social functioning, diagnostic, service
The best-fitting model produced by the LCA according to substance use and using age as an active covariate was a three-class model: Class 1 (n = 803; 49%), Class 2 (n = 582; 35%) and Class 3 (n = 257; 16%). Inspection of the log likelihood statistic plot indicated that the optimal number of classes was three, being the most clinically parsimonious
Please cite this article as: Stefanis, N.C., et al., The effect of drug use on the age at onset of psychotic disorders in an Australian cohort, Schizophr. Res. (2014), http://dx.doi.org/10.1016/j.schres.2014.04.003
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Table 1 Demographic data of SHIP participants. Variables
Schizophrenia spectrum disorders
Diagnostic categories Schizophrenia Schizoaffective Delusional disorder and other non-organic psychoses Bipolar, mania Depressive psychosis Male sex (N, %) Age (years) (mean ± SD) Length of illness (years) (mean ± SD) Family history of schizophrenia Family history of other psychiatric disorders a b
Affective psychotic disorders
t-Test/chi-square
P-value
319 81 174 (43.5) 40.6 (11.1) 16.1 (10.7) 93 (23.3) 235 (58.8)
71.39a 4.85b 3.26b 6.10a 39.26a
b0.001 b0.001 0.001 0.014 b0.001
857 293 92
834 (67.1) 37.5 (11.0) 14.2 (10.0) 368 (29.6) 507 (40.8)
Chi-square test. t-Test.
and supported by goodness-of-fit measures (degrees of freedom, BIC, AIC3). The partial conditional probabilities per substance were near to zero for Class 1 (Fig. 1), suggesting this class consisted largely of non-users of illicit substances (class identified as “non-users”); participants in this class were more likely to be older at interview compared to participants in other classes. Participants in Class 2 were highly likely to use cannabis and less likely to use other substances (class identified as “cannabis predominant users”); participants in this class were less likely to be older at interview. Participants in Class 3 were highly likely to have used cannabis plus other illicit substances (class identified as “polysubstance users”); participants in this group were likely to be younger at interview. Characteristics of participants in each of the classes are provided in Table 2. Non-users comprised fewer male participants (49.8%) compared with cannabis predominant users (71.5%), and polysubstance users (74.7%). Polysubstance users had an earlier mean AAO (21.4 ± 4.6 years) than the cannabis predominant group (22.5 ± 6.9 years), and non-users (25.0 ± 10.0 years); the variation in the means between the three classes was statistically significant (Table 2).
CI = 1.2–1.6). Polysubstance users also had a higher hazard of earlier AAO than non-users: this remained significant after adjusting for confounding factors (adjusted HR = 1.95, 95% CI = 1.5–2.4). In addition, polysubstance users also had a significantly higher hazard ratio than cannabis predominant users (P b 0.01). We also performed the analysis on those 857 participants with a diagnosis of schizophrenia, and the results were similar (cannabis predominant: adjusted HR = 1.52 (95% CI = 1.3–1.8); polysubstance users: adjusted HR = 2.03 (95% CI = 1.6– 2.5)); a similar trend was observed for schizoaffective disorder (data not shown). While both family history of schizophrenia and illicit substance consumption were independently associated with an earlier AAO in participants with SSD, their interaction was not (see Table 3). In contrast, for participants with APD, there were no significant differences according to illicit substance use (Table 3). Fig. 2 shows the cumulative hazard for earlier AAO over time according to substance use for participants with SSD (panel A) and APD (panel B).
3.3. Illicit substance use and age at onset
4.1. Findings
Table 3 summarizes the association between substance use and AAO according to diagnostic spectrum. For participants with SSD, cannabis predominant users had a higher hazard of earlier AAO than non-users, and this remained statistically significant after adjusting for confounding factors such as family history of schizophrenia and other psychiatric disorders, gender, tobacco and alcohol use (adjusted HR = 1.38, 95%
In a large sample of people with psychosis, we identified retrospectively three age-adjusted classes according to illicit substance use in the 12 months prior to onset of psychosis: non-users, predominantly cannabis users, and polysubstance users. Further, those participants who were on the pathway to develop SSD were at increased hazard of earlier AAO if they were predominantly cannabis users in the 12 months prior to illness onset, and this hazard further increased for those participants who were polysubstance users. No such association was found for substance-users who were on the pathway to develop APD. These findings from an Australian-wide study are in keeping with our previous findings from the Western Australian Family Study of Schizophrenia (Power et al., 2012), where we reported that polysubstance use (mainly cannabis, amphetamine and cocaine) in the year prior to illness onset was associated with an earlier AAO in schizophrenia than that seen for cannabis consumption alone. Notwithstanding that increased cannabis use in the context of polysubstance use may in fact account for the additive effects observed in both studies, our findings are compatible with the notion of a common mechanism underlying the association between both cannabis and polysubstance use and earlier AAO in SSD. Membership of the two substance using classes (cannabis predominant users and polysubstance users) in SSD was associated with a higher hazard of earlier AAO even after adjusting for other confounders. Further, familial loading for schizophrenia did not modify the effect of cannabis and substance use on AAO. In the same cohort we recently reported a temporal direct association between age at initiation of cannabis use and AAO of psychosis, with a premorbid exposure to cannabis trend of 7–8 years (i.e., regardless of age at which cannabis was initiated) (Stefanis et al., 2013). Taken together, these observations
Partial conditional probabilities
1.20
Class 1 Class 2
1.00
Class 3
0.80 0.60 0.40 0.20
Age =>44
Age 33-43
Age <=32
Ecstasy
LSD
Cocaine
Amphetamines
Cannabis
0.00
Fig. 1. Partial conditional probabilities for the three-class model (LCA response profiles).
4. Discussion
Please cite this article as: Stefanis, N.C., et al., The effect of drug use on the age at onset of psychotic disorders in an Australian cohort, Schizophr. Res. (2014), http://dx.doi.org/10.1016/j.schres.2014.04.003
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Table 2 Characteristics of SHIP participants assigned to classes following latent class analysis according to substance use, and using age as a covariate. Variables
Class 1: Non-users, n = 803 (49%)
Class 2: Cannabis predominant users, n = 582 (35%)
Class 3: Polysubstance users, n = 257 (16%)
Male sex (N, %) Age at onset (years) (mean ± SD) Positive family history of schizophrenia and other psychiatric disorders (N, %) Life time smoking history (N, %) Age of initiation to tobacco (mean ± SD) Current smoker (N, %) Alcohol abuse/dependency with psychopathology (N, %) Alcohol/drug abuse YPa (N, %) Single, never married (N, %) Employed at onset (N, %) Poor premorbid social adjustment (N, %) Premorbid personality disorder (N, %)
400 (49.8) 25.0 ± 10.0 219 (27.3)
416 (71.5) 22.5 ± 6.9 158 (27.1)
192 (74.7) 21.4 ± 4.6 84 (32.7)
535 (66.6) 17.5 ± 6.2 392 (49.2) 216 (26.9) 186 (23.2) 438 (54.5) 588 (73.2) 301 (37.5) 100 (12.5)
564 (96.9) 15.7 ± 4.8 484 (83.2) 316 (54.3) 431 (74.1) 397 (68.2) 397 (68.2) 208 (35.7) 80 (13.7)
247 (96.9) 15.2 ± 4.0 227 (90.1) 154 (59.9) 209 (81.3) 199 (77.4) 169 (65.8) 78 (30.4) 41 (16.0)
a
F/χ2
Pvalues
89.62 b0.001 26.48 b0.001 3.21 0.201 255.20 22.97 246.00 145.36 466.86 94.78 7.04 4.31 2.11
b0.001 b0.001 b0.001 b0.001 b0.001 b0.001 0.030 0.116 0.348
YP = year prior (substance use in the 12 months prior to the onset of psychiatric symptoms).
may collectively indicate that, between competing causal and reverse causality arguments, the balance may be shifted towards the former, suggesting that substance use can precipitate or potentially have some causal involvement in earlier manifestation of SSD. Our findings suggest that illicit substance use in the 12 months prior to illness onset was not an important determinant for AAO in APD. Taken together with the strong effect of family history on AAO in these disorders (see Table 3), this might suggest that genetic, but not proximal environmental factors (such as drug exposure in the prodromal period) largely determine AAO in APD, such as bipolar disorder (Birmaher et al., 2009). In light of strong biological plausibility underlying the effect of cannabis and other substances on psychosis outcomes, the absence of an association between substance use and AAO in APD is challenging in its interpretation, even more so in the presence of such an observed association in SSD. The majority of prospective studies indicate that substance abuse (including alcohol and cannabis) prior to the onset of bipolar disorder is associated with either no effect or a delayed effect on AAO, although this finding has been interpreted as a milder form of illness that is precipitated by substance use (Strakowski et al., 2007). While longitudinal population studies provide little assurance for the association between cannabis and susceptibility to affective spectrum (Manrique-Garcia et al., 2012), even if this was substantiated it would
not necessarily imply that cannabis brings forward the onset of bipolar disorder, since risk factors for a disease are not by default important predictors of AAO. Notwithstanding that the differences in AAO between APD and SSD may be attributable to patterns of use (duration of exposure, severity of use, etc.), it is tempting to postulate that quantitative differences between bipolar disorder and schizophrenia at the genetic, developmental, or environmental exposure level (Howes et al., 2007; Demjaha et al., 2012) underpin our findings. While we cannot entirely exclude the possibility of reverse causality, our findings do not support the view that people on the pathway to develop psychosis at a young age are simply more likely to use all substances (for example, as a form of self-medication), since under this assumption alcohol use would be associated with an earlier AAO both in SSD and APD, which is not supported from our data (see Table 3). The interpretations of the opposite effect of illicit substances and alcohol on AAO in the prodrome of SSD is compatible with a bi-directional and partial causal argument based on experience sampling methodology employed in ambulatory day patients with schizophrenia (Birmaher et al., 2009; Henquet et al., 2010): while motivations for substance use may include social acceptance, enhancement of positive affect, and coping with negative affect and dysphoria (Spencer et al., 2002), the immediate or medium term consequences were determined
Table 3 Crude and adjusted hazard ratios for earlier age at onset according to age-adjusted substance use classes for schizophrenia spectrum and affective psychotic disorders. N Schizophrenia spectrum disorders Non-users Cannabis predominant users Polysubstance users Family history of schizophrenia Family history of other psychiatric disorders Sex (female) Smoking (lifetime history) Alcohol (recreational) Alcohol (regular) FH scz +ve Class 2 FH scz +ve Class 3 Affective psychotic disorders Non-users Cannabis predominant users Polysubstance users Family history of schizophrenia Family history of other psychiatric disorders Sex (female) Smoking (lifetime history) Alcohol (recreational) Alcohol (regular)
1242 568 456 218 368 507 408 1027 235 609 136 113 400 235 126 39 235 93 226 319 68 172
Crude HR (95% CI)
P-value
Adjusted HR (95% CI)
P-value
1.00 1.51 (1.3–1.7) 1.94 (1.6–2.3) – – – – – – – –
– b0.001 b0.001 – – – – – – – –
1.00 1.38 (1.2–1.6) 1.95 (1.5–2.4) 1.36 (1.1–1.7) 0.99 (0.9–1.1) 0.75 (0.7–0.9) 1.11 (0.9–1.3) 0.82 (0.7–1.0) 0.78 (0.7–0.9) 1.09 (0.8–1.4) 0.81 (0.6–1.2)
–
1.00 1.04 (0.8–1.3) 0.88 (0.6–1.2) – – – – – –
– 0.702 0.478 – – – – – –
1.00 1.10 (0.8–1.4) 0.87 (0.6–1.3) 0.87 (0.7–1.1) 1.86 (1.5–2.3) 1.72 (1.4–2.1) 1.36 (1.0–1.8) 0.65 (0.5–0.9) 0.74 (0.6–0.9)
–
0.001 b0.001 0.003 0.891 b0.001 0.287 0.026 0.001 0.561 0.228
0.476 0.459 0.275 b0.001 b0.001 0.047 0.005 0.017
Confounding factors: Family history of schizophrenia and other psychiatric disorders, sex, lifetime history of smoking, alcohol consumption; HR = hazard ratio; 95% CI = 95% of the confidence interval. Regular use = daily/almost daily in the year prior to onset; recreational use = 1–2 days/week or less in the year prior to onset. FH+ scz = family history of schizophrenia present (testing interaction between family history and substance use for each class).
Please cite this article as: Stefanis, N.C., et al., The effect of drug use on the age at onset of psychotic disorders in an Australian cohort, Schizophr. Res. (2014), http://dx.doi.org/10.1016/j.schres.2014.04.003
N.C. Stefanis et al. / Schizophrenia Research xxx (2014) xxx–xxx
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Fig. 2. Cox-regression cumulative survival curves for the three identified classes of illicit substance use in schizophrenia spectrum and affective psychotic disorders.
by the known psychoactive properties of the substances used (i.e., whereas illicit substances eventually exacerbated psychotic symptoms, alcohol acting as a central nervous system depressant did not) (Swendsen et al., 2011). While further prospective studies during the prodrome are urgently required in order to decipher the subclinical temporal association between substance use and symptoms (and their relation with AAO), retrospective studies have strongly suggested that cannabis use may have a significant effect in hastening onset of florid prodromal symptoms among first-episode patients with non-affective psychosis (Compton et al., 2009; Leeson et al., 2012). The association between alcohol use or abuse and later rather than earlier onset of bipolar disorder is firmly established (Strakowski et al., 2005) and the most parsimonious explanation for our findings is that alcohol use amongst young participants on the pathway to develop a psychotic disorder (either SSD or APD) exerts a slight onset delaying effect due to its depressant effects, and a general lack of psychotogenic effect. 4.2. Comparison with previous findings While our findings are in keeping with previous work from our group in a cohort of patients with schizophrenia (Power et al., 2012), a number of other studies have failed to report an additional effect of other substances on AAO in SSD (Barnes et al., 2006; Gonzalez-Pinto et al., 2008; Dekker et al., 2012), and it is likely that key methodological differences may underpin these discrepancies. These include the differences in sample sizes (resulting in small numbers of participants who had used illicit substances in addition to cannabis), definition of substance use (e.g., ever used, recreational or regular use, abuse, dependence), and the period of substance use under investigation (e.g., lifetime use, use in the 12 months prior to illness onset, use in the last 12 months, current use). For instance, in a sample of 131 patients hospitalised with first episode psychosis, which identified 67 cannabis users (and an unstated number of patients using an illicit substance in addition to cannabis) Gonzalez-Pinto et al. (2008) used multivariate analysis and reported that additional substances did not mediate the association between cannabis and age at onset; it is quite possible that their findings were limited by a small number of illicit substance users. Nonetheless, Dekker et al. (2012) examined 785 patients presenting consecutively as in-patients and out-patient with SSD (67% of whom had schizophrenia), and identified substance use in the past year or lifetime (with 223 cannabis users and 281 cannabis plus other illicit substance users identified); they reported that when cannabis use was taken into account, lifetime or recent use of another illicit substance did not have an additional effect on age at onset. The discrepancy between our findings and those of Dekker et al. (2012) may, in part,
be explained by the differences in the period of exposure to additional substance use: our study specifically focused on substance use on the 12 months prior to illness onset, whereas Dekker and colleagues focused on substance use in the 12 months prior to interview (with illness onset in the last 10 years as an inclusion criteria) or lifetime. Our findings that illicit substance use in affective psychotic illnesses was not associated with hazard of earlier AAO are not in keeping with findings from recent studies (De Hert et al., 2011; Lagerberg et al., 2011), most likely due to methodological limitations of these studies. Notably, Lagerberg et al. (2011) focused on bipolar disorders, and only “excessive” (at least weekly) illicit substance use such that only a very small number of participants in their cannabis using group used other substances to excess (n = 13). De Hert et al. (2011) reported that lifetime cannabis use was associated with a greater reduction in the AAO in 16 cannabis-using patients with bipolar disorder, compared with 247 cannabis-using patients with schizophrenia; this result is difficult to interpret in the context of insufficient power in the bipolar group. 4.3. Limitations The study is limited in its scope as it addresses the consumption of illicit substances in the 12 months prior to the onset of psychiatric symptoms, and does not consider the age of first use, pattern or frequency of lifetime use, or current use. Further, the study does not consider the frequency of use of a particular substance in the 12 month period under investigation. We acknowledge we have not fully investigated the different disorders within SSD and APD, and anticipate pursuing further analyses to ascertain the trends in AAO in specific disorders (e.g., schizophrenia vs. schizoaffective vs. delusional disorder). Recall bias is a further limitation of the study; like many similar studies we relied on retrospective data on substance use (i.e., in the 12 months prior to the onset of psychiatric symptoms) that was obtained by self-report, and this was not corroborated by an informant. Although Barnes et al. (2006) make an argument for the reliability and validity of substance use data by self-report, we acknowledge that the majority of participants included in our study had to recall their substance use many years prior to interview. Therefore, in an effort to address recall bias in this study, we used age of participants as an active covariate in constituting our substance-using classes, such that they were age-adjusted. In conclusion, illicit substance use in the 12 months prior to psychosis onset has a differential effect on AAO in SSD and APD. Our findings are compatible with the notion that illicit drugs bring forward age at onset in SSD but not APD.
Please cite this article as: Stefanis, N.C., et al., The effect of drug use on the age at onset of psychotic disorders in an Australian cohort, Schizophr. Res. (2014), http://dx.doi.org/10.1016/j.schres.2014.04.003
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Role of funding source The SHIP was funded under contract to the Australian Government Department of Health and Ageing. Contributors V.M., D.C. and A.J. designed the study and wrote the protocol. B.P., N.S. and M.D. undertook the statistical analysis, and wrote the first draft of the article. All authors contributed to and have approved the final article. Conflict of interest The authors have declared that there are no conflicts of interest in relation to the subject of this study. Acknowledgements SHIP acknowledgement: This publication is based on data collected in the framework of the 2010 Australian National Survey of High Impact Psychosis. The members of the õSurvey of High Impact Psychosis Study Group are: V. Morgan (National Project Director), A. Jablensky (Chief Scientific Advisor), A. Waterreus (National Project Coordinator), R. Bush, V. Carr, D. Castle, M. Cohen, C. Galletly, C. Harvey, B. Hocking, A. Mackinnon, P. McGorry, J. McGrath, A. Neil, S. Saw, and H. Stain. Ethics approvals for the study were obtained from relevant institutional human research ethics committees. The study was funded by the Australian Government Department of Health and Ageing. This report acknowledges, with thanks, the hundreds of mental health professionals who participated in the preparation and conduct of the survey and the many Australians with psychotic disorders who gave their time and whose responses form the basis of this publication.
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Please cite this article as: Stefanis, N.C., et al., The effect of drug use on the age at onset of psychotic disorders in an Australian cohort, Schizophr. Res. (2014), http://dx.doi.org/10.1016/j.schres.2014.04.003
Contents lists available at ScienceDirect
Schizophrenia Research journal homepage: www.elsevier.com/locate/schres
The effect of drug use on the age at onset of psychotic disorders in an Australian cohort Nikos C. Stefanis a,b,⁎, Milan Dragovic a,b, Brian D. Power a,b,c, Assen Jablensky a, David Castle d, Vera A. Morgan a a
School of Psychiatry and Clinical Neurosciences, The University of Western Australia, WA Clinical Research Centre, North Metropolitan Health Service — Mental Health, Perth, WA Peel and Rockingham Kwinana Mental Health Service, South Metropolitan Area Health Service, Perth, WA d St. Vincent's Hospital, University of Melbourne, Australia b c
a r t i c l e
i n f o
Article history: Received 17 December 2013 Received in revised form 14 March 2014 Accepted 2 April 2014 Available online xxxx Keywords: Schizophrenia Causality Cannabis Amphetamine Psychosis
a b s t r a c t Background: We aimed to examine the association between illicit substance use and age at onset in psychotic disorders in an Australian cohort. Methods: Retrospectively acquired information on substance use during the year prior to illness onset was collected from 1642 participants enrolled in the Australian National 2010 Survey of High Impact Psychosis study (SHIP), with an ICD-10 diagnosis of schizophrenia spectrum or affective psychosis. Latent class analysis was performed according to illicit substance use, using age as an active covariate; identified classes were subsequently validated. Cox regression was used to examine the independent contribution of the identified substance use classes and several confounding variables to the prediction of age at onset of psychosis. Results: Three classes according to substance use were identified: non-users (n = 803), cannabis predominant users (n = 582), and polysubstance users (n = 257). For participants with schizophrenia spectrum disorders, cannabis predominant users had a higher hazard of earlier age at onset than for non-users (adjusted HR = 1.38, 95% CI = 1.2–1.6); polysubstance users had an even higher hazard (adjusted HR = 1.95, 95% CI = 1.5–2.4). In contrast, for participants with affective psychosis, cannabis predominant users (adjusted HR = 1.10, 95% CI = 0.8–1.4) and polysubstance users (adjusted HR = 0.87, 95% CI = 0.6–1.3) did not have a higher hazard of earlier age at onset compared with non-users. Conclusions: Illicit substance use in the 12 months prior to psychosis onset has a differential effect on age at onset in schizophrenia spectrum and affective psychotic disorders. Our findings are compatible with the notion that illicit drugs bring forward age at onset in schizophrenia spectrum disorders but not affective psychotic disorders. Published by Elsevier B.V.
1. Introduction There is much debate regarding the association between substance use and age at onset (AAO) in psychosis, with a recent meta-analysis reporting an earlier mean AAO in samples with more cannabis users, and arguing in favor of a causality hypothesis (Large et al., 2011) (i.e., that the illness is precipitated by cannabis, possibly in those predisposed to develop psychosis) (Henquet et al., 2006; Stefanis et al., 2004). Others Abbreviations: AAO, age at onset; AIC3, Akaike information criterion 3; APD, affective psychotic disorders; BIC, Bayesian information criterion; DIP, Diagnostic Interview for Psychosis; ICD-10, International Classification of Diseases Version 2010; LCA, latent class analysis; LSD, lysergic acid diethylamide; OPCRIT, Operational Criteria for Psychosis; SSD, schizophrenia spectrum disorders; SHIP, Survey of High Impact Psychosis. ⁎ Corresponding author at: Centre for Clinical Research in Neuropsychiatry, Private Bag No 1, Claremont WA 6910, USA. Tel.: +61 8 9347 6429; fax: +61 8 9384 5128. E-mail addresses: [email protected] (N.C. Stefanis), [email protected] (M. Dragovic), [email protected] (B.D. Power), [email protected] (A. Jablensky), [email protected] (D. Castle), [email protected] (V.A. Morgan).
maintain that the association is likely to be spurious (i.e., younger participants are more likely to use cannabis) (Wade, 2005). Consistent with the hypothesis that cannabis may bring forward psychosis onset, recent studies have demonstrated that age of cannabis initiation was associated with AAO of psychotic symptoms in high risk individuals (Dragt et al., 2012) and directly and linearly associated with age at prodrome onset and AAO in schizophrenia spectrum disorders (SSD) (Galvez-Buccollini et al., 2012; Leeson et al., 2012; Stefanis et al., 2013). However, the complex temporal relationship between substance use and psychotic symptoms in the premorbid period defies simple interpretations that are further hampered by a plethora of confounding factors, including uncertainties regarding the effect of comorbid substance use in addition to cannabis, and the specificity or not of these effects to SSD. Several studies have examined the association between number of illicit substances consumed and AAO in SSD, and have reported disparate findings (Barnes et al., 2006; Gonzalez-Pinto et al., 2008; Dekker et al., 2012; Power et al., 2012). For instance, Dekker et al. (2012)
http://dx.doi.org/10.1016/j.schres.2014.04.003 0920-9964/Published by Elsevier B.V.
Please cite this article as: Stefanis, N.C., et al., The effect of drug use on the age at onset of psychotic disorders in an Australian cohort, Schizophr. Res. (2014), http://dx.doi.org/10.1016/j.schres.2014.04.003
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examined the effect of substance use in the 12 months prior to interview and over the lifetime, and reported that cannabis use had a similar effect compared with cannabis plus another illicit substance. However, we recently reported that the consumption of illicit substances in addition to cannabis (specifically in the 12 months prior to onset) predicted an earlier AAO by approximately one additional year in schizophrenia (Power et al., 2012); this remained significant after controlling for mean duration of illness, and was independent of family history of schizophrenia and sex. There were a number of limitations imposed on this study by sample size, such that replication is warranted. Few studies have examined the association between substance use (namely alcohol and cannabis) and AAO in affective psychotic disorders (APD) (Strakowski et al., 2005; Fossey et al., 2006; Lin et al., 2006; Strakowski et al., 2007; Ringen et al., 2008; Lagerberg et al., 2011). Although Lin et al. (2006) did not differentiate between the types of substance used, they reported an increased risk of substance abuse for patients with younger onset of bipolar disorder. Other authors, however, have reported a later onset for patients who abuse substances prior to onset of bipolar disorder (Strakowski et al., 2005; Fossey et al., 2006; Strakowski et al., 2007), while excessive cannabis use in the period prior to illness onset has been associated with earlier onset in 151 patients with bipolar disorder (types I and II) (Lagerberg et al., 2011). While methodological differences may explain these discrepancies, Lagerberg et al. (2011) also hypothesize that differences in the sequence of onset of bipolar disorder and onset of substance use disorder may have a bearing on age at onset of illness. Nonetheless, these disparate findings suggest that the association between premorbid substance use and AAO of APD warrants further investigation. Responding to conflicting findings regarding the premorbid effect of substance use on the AAO of SSD and APD, we set out to investigate this association in a large sample of people with psychosis in which information on substance use in the 12 months prior to onset of illness was available. We hypothesized that cannabis consumption in the 12 months prior to illness onset would lead to an earlier AAO of both SSD and APD. We also aimed to independently replicate our initial finding that illicit substance use in addition to cannabis in the 12 months prior to onset would have an additive effect on AAO in schizophrenia, and to examine whether these findings were consistent for SSD and APD. 2. Experimental/materials and methods 2.1. Participants Participants were selected from the 2010 Australian SHIP (Survey of High Impact Psychosis), the methods of which have been described elsewhere (Morgan et al., 2012; Stefanis et al., 2013). In brief, participants were recruited from across seven sites in Australia, employing a twophase sampling design. In phase 1, screening for psychosis was conducted in participants aged 18–64 in contact with public mental health services and non-government organisations supporting people with mental illness. Of the 7955 who screened positive for psychosis, 1825 were randomly selected and engaged in assessment (phase 2); participants provided written informed consent. From the 1825 participants who were able to be interviewed, those who did not meet full criteria for an ICD-10 psychotic disorder in phase 2 were excluded (n = 183), leaving a total of 1642 participants with an established ICD-10 diagnosis of SSD (schizophrenia, schizoaffective, delusional and other non-organic psychotic disorder) or APD (bipolar disorder, depressive disorder with psychotic features).
utilization). The diagnostic module (DIP-DM) follows the structure of the Operational Criteria for Psychosis (OPCRIT) (McGuffin et al., 1991), a 90-item checklist which allows the examiner to rate symptoms in a number of domains (present state, past year, and lifetime). Diagnostic classification of cases was made using the OPCRIT diagnostic computer algorithm (McGuffin et al., 1991). In addition, the DIP includes items on substance use (alcohol, tobacco, cannabis, amphetamines, LSD, cocaine, ecstasy), including frequency of use in the 12 months prior to onset of psychosis. Participants were categorized as either substanceusers (ranging from “daily use” to “used less frequently than once per month”), or substance non-users (if they reported “no use”). The DIP also includes items regarding the use of alcohol to determine ICD-10 classifications for alcohol abuse and dependence. 2.3. Definition of AAO AAO was determined after interviewing the participant, and recorded to the nearest year (defined as the earliest age medical advice was sought for psychiatric reasons, or that any psychiatric symptom diagnostic of psychotic or major affective illness began to cause subjective distress or impair functioning). If there were no clear symptoms described, age at first hospital admission was recorded. 2.4. Statistical analysis To investigate whether there existed classes according to illicit substance use within the SHIP (n = 1642), latent class analysis (LCA) was performed using illicit substance use (cannabis, amphetamine, cocaine, LSD, ecstasy) in the 12 months prior to onset of illness as nominal indicators. We used age at interview as an active covariate, thus allowing participants' age to influence the classification probabilities; age categories were determined impartially using the binning procedure with three equal percentiles based on scanned data (age at interview ≤32, 33–43, and ≥44 years). The optimal LCA solution was determined by identifying a point of a convincing convergence of various fit indices. The identified classes were then described using one way analysis of variance or Pearson's chi-square test (χ2). Analyses were performed using IBM SPSS (version 20) and Latent GOLD (version 4.0.4). Based on the substance-using age-adjusted classes of participants identified and externally validated in LCA, we then sought to determine the effect of substance-using status on AAO in both SSD and APD. We used Cox proportional hazards regression models to predict a status (event) variable, namely AAO of psychosis, using latent class membership as the principle predictor. We then investigated the possible influence of several confounding factors to adjust crude hazard rates. The cumulative hazard of AAO was determined and presented graphically according to illicit substance use for both SSD and APD. 3. Results 3.1. Demographic data Table 1 illustrates the demographic data of SHIP participants. There were 1242 participants diagnosed with SSD, and 400 participants with APD. Additional data pertaining to further sociodemographic data, lifestyle variables, mental health profile, medications, service utilization, functioning and quality of life have been reported elsewhere (Morgan et al., 2012). 3.2. Classes according to substance use
2.2. Measures — diagnostic algorithm Participants in phase 2 were assessed using the DIP (Diagnostic Interview for Psychosis), a standardized semi-structured interview for psychosis (Castle et al., 2006). The DIP is comprised of a number of modules (demography and social functioning, diagnostic, service
The best-fitting model produced by the LCA according to substance use and using age as an active covariate was a three-class model: Class 1 (n = 803; 49%), Class 2 (n = 582; 35%) and Class 3 (n = 257; 16%). Inspection of the log likelihood statistic plot indicated that the optimal number of classes was three, being the most clinically parsimonious
Please cite this article as: Stefanis, N.C., et al., The effect of drug use on the age at onset of psychotic disorders in an Australian cohort, Schizophr. Res. (2014), http://dx.doi.org/10.1016/j.schres.2014.04.003
N.C. Stefanis et al. / Schizophrenia Research xxx (2014) xxx–xxx
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Table 1 Demographic data of SHIP participants. Variables
Schizophrenia spectrum disorders
Diagnostic categories Schizophrenia Schizoaffective Delusional disorder and other non-organic psychoses Bipolar, mania Depressive psychosis Male sex (N, %) Age (years) (mean ± SD) Length of illness (years) (mean ± SD) Family history of schizophrenia Family history of other psychiatric disorders a b
Affective psychotic disorders
t-Test/chi-square
P-value
319 81 174 (43.5) 40.6 (11.1) 16.1 (10.7) 93 (23.3) 235 (58.8)
71.39a 4.85b 3.26b 6.10a 39.26a
b0.001 b0.001 0.001 0.014 b0.001
857 293 92
834 (67.1) 37.5 (11.0) 14.2 (10.0) 368 (29.6) 507 (40.8)
Chi-square test. t-Test.
and supported by goodness-of-fit measures (degrees of freedom, BIC, AIC3). The partial conditional probabilities per substance were near to zero for Class 1 (Fig. 1), suggesting this class consisted largely of non-users of illicit substances (class identified as “non-users”); participants in this class were more likely to be older at interview compared to participants in other classes. Participants in Class 2 were highly likely to use cannabis and less likely to use other substances (class identified as “cannabis predominant users”); participants in this class were less likely to be older at interview. Participants in Class 3 were highly likely to have used cannabis plus other illicit substances (class identified as “polysubstance users”); participants in this group were likely to be younger at interview. Characteristics of participants in each of the classes are provided in Table 2. Non-users comprised fewer male participants (49.8%) compared with cannabis predominant users (71.5%), and polysubstance users (74.7%). Polysubstance users had an earlier mean AAO (21.4 ± 4.6 years) than the cannabis predominant group (22.5 ± 6.9 years), and non-users (25.0 ± 10.0 years); the variation in the means between the three classes was statistically significant (Table 2).
CI = 1.2–1.6). Polysubstance users also had a higher hazard of earlier AAO than non-users: this remained significant after adjusting for confounding factors (adjusted HR = 1.95, 95% CI = 1.5–2.4). In addition, polysubstance users also had a significantly higher hazard ratio than cannabis predominant users (P b 0.01). We also performed the analysis on those 857 participants with a diagnosis of schizophrenia, and the results were similar (cannabis predominant: adjusted HR = 1.52 (95% CI = 1.3–1.8); polysubstance users: adjusted HR = 2.03 (95% CI = 1.6– 2.5)); a similar trend was observed for schizoaffective disorder (data not shown). While both family history of schizophrenia and illicit substance consumption were independently associated with an earlier AAO in participants with SSD, their interaction was not (see Table 3). In contrast, for participants with APD, there were no significant differences according to illicit substance use (Table 3). Fig. 2 shows the cumulative hazard for earlier AAO over time according to substance use for participants with SSD (panel A) and APD (panel B).
3.3. Illicit substance use and age at onset
4.1. Findings
Table 3 summarizes the association between substance use and AAO according to diagnostic spectrum. For participants with SSD, cannabis predominant users had a higher hazard of earlier AAO than non-users, and this remained statistically significant after adjusting for confounding factors such as family history of schizophrenia and other psychiatric disorders, gender, tobacco and alcohol use (adjusted HR = 1.38, 95%
In a large sample of people with psychosis, we identified retrospectively three age-adjusted classes according to illicit substance use in the 12 months prior to onset of psychosis: non-users, predominantly cannabis users, and polysubstance users. Further, those participants who were on the pathway to develop SSD were at increased hazard of earlier AAO if they were predominantly cannabis users in the 12 months prior to illness onset, and this hazard further increased for those participants who were polysubstance users. No such association was found for substance-users who were on the pathway to develop APD. These findings from an Australian-wide study are in keeping with our previous findings from the Western Australian Family Study of Schizophrenia (Power et al., 2012), where we reported that polysubstance use (mainly cannabis, amphetamine and cocaine) in the year prior to illness onset was associated with an earlier AAO in schizophrenia than that seen for cannabis consumption alone. Notwithstanding that increased cannabis use in the context of polysubstance use may in fact account for the additive effects observed in both studies, our findings are compatible with the notion of a common mechanism underlying the association between both cannabis and polysubstance use and earlier AAO in SSD. Membership of the two substance using classes (cannabis predominant users and polysubstance users) in SSD was associated with a higher hazard of earlier AAO even after adjusting for other confounders. Further, familial loading for schizophrenia did not modify the effect of cannabis and substance use on AAO. In the same cohort we recently reported a temporal direct association between age at initiation of cannabis use and AAO of psychosis, with a premorbid exposure to cannabis trend of 7–8 years (i.e., regardless of age at which cannabis was initiated) (Stefanis et al., 2013). Taken together, these observations
Partial conditional probabilities
1.20
Class 1 Class 2
1.00
Class 3
0.80 0.60 0.40 0.20
Age =>44
Age 33-43
Age <=32
Ecstasy
LSD
Cocaine
Amphetamines
Cannabis
0.00
Fig. 1. Partial conditional probabilities for the three-class model (LCA response profiles).
4. Discussion
Please cite this article as: Stefanis, N.C., et al., The effect of drug use on the age at onset of psychotic disorders in an Australian cohort, Schizophr. Res. (2014), http://dx.doi.org/10.1016/j.schres.2014.04.003
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Table 2 Characteristics of SHIP participants assigned to classes following latent class analysis according to substance use, and using age as a covariate. Variables
Class 1: Non-users, n = 803 (49%)
Class 2: Cannabis predominant users, n = 582 (35%)
Class 3: Polysubstance users, n = 257 (16%)
Male sex (N, %) Age at onset (years) (mean ± SD) Positive family history of schizophrenia and other psychiatric disorders (N, %) Life time smoking history (N, %) Age of initiation to tobacco (mean ± SD) Current smoker (N, %) Alcohol abuse/dependency with psychopathology (N, %) Alcohol/drug abuse YPa (N, %) Single, never married (N, %) Employed at onset (N, %) Poor premorbid social adjustment (N, %) Premorbid personality disorder (N, %)
400 (49.8) 25.0 ± 10.0 219 (27.3)
416 (71.5) 22.5 ± 6.9 158 (27.1)
192 (74.7) 21.4 ± 4.6 84 (32.7)
535 (66.6) 17.5 ± 6.2 392 (49.2) 216 (26.9) 186 (23.2) 438 (54.5) 588 (73.2) 301 (37.5) 100 (12.5)
564 (96.9) 15.7 ± 4.8 484 (83.2) 316 (54.3) 431 (74.1) 397 (68.2) 397 (68.2) 208 (35.7) 80 (13.7)
247 (96.9) 15.2 ± 4.0 227 (90.1) 154 (59.9) 209 (81.3) 199 (77.4) 169 (65.8) 78 (30.4) 41 (16.0)
a
F/χ2
Pvalues
89.62 b0.001 26.48 b0.001 3.21 0.201 255.20 22.97 246.00 145.36 466.86 94.78 7.04 4.31 2.11
b0.001 b0.001 b0.001 b0.001 b0.001 b0.001 0.030 0.116 0.348
YP = year prior (substance use in the 12 months prior to the onset of psychiatric symptoms).
may collectively indicate that, between competing causal and reverse causality arguments, the balance may be shifted towards the former, suggesting that substance use can precipitate or potentially have some causal involvement in earlier manifestation of SSD. Our findings suggest that illicit substance use in the 12 months prior to illness onset was not an important determinant for AAO in APD. Taken together with the strong effect of family history on AAO in these disorders (see Table 3), this might suggest that genetic, but not proximal environmental factors (such as drug exposure in the prodromal period) largely determine AAO in APD, such as bipolar disorder (Birmaher et al., 2009). In light of strong biological plausibility underlying the effect of cannabis and other substances on psychosis outcomes, the absence of an association between substance use and AAO in APD is challenging in its interpretation, even more so in the presence of such an observed association in SSD. The majority of prospective studies indicate that substance abuse (including alcohol and cannabis) prior to the onset of bipolar disorder is associated with either no effect or a delayed effect on AAO, although this finding has been interpreted as a milder form of illness that is precipitated by substance use (Strakowski et al., 2007). While longitudinal population studies provide little assurance for the association between cannabis and susceptibility to affective spectrum (Manrique-Garcia et al., 2012), even if this was substantiated it would
not necessarily imply that cannabis brings forward the onset of bipolar disorder, since risk factors for a disease are not by default important predictors of AAO. Notwithstanding that the differences in AAO between APD and SSD may be attributable to patterns of use (duration of exposure, severity of use, etc.), it is tempting to postulate that quantitative differences between bipolar disorder and schizophrenia at the genetic, developmental, or environmental exposure level (Howes et al., 2007; Demjaha et al., 2012) underpin our findings. While we cannot entirely exclude the possibility of reverse causality, our findings do not support the view that people on the pathway to develop psychosis at a young age are simply more likely to use all substances (for example, as a form of self-medication), since under this assumption alcohol use would be associated with an earlier AAO both in SSD and APD, which is not supported from our data (see Table 3). The interpretations of the opposite effect of illicit substances and alcohol on AAO in the prodrome of SSD is compatible with a bi-directional and partial causal argument based on experience sampling methodology employed in ambulatory day patients with schizophrenia (Birmaher et al., 2009; Henquet et al., 2010): while motivations for substance use may include social acceptance, enhancement of positive affect, and coping with negative affect and dysphoria (Spencer et al., 2002), the immediate or medium term consequences were determined
Table 3 Crude and adjusted hazard ratios for earlier age at onset according to age-adjusted substance use classes for schizophrenia spectrum and affective psychotic disorders. N Schizophrenia spectrum disorders Non-users Cannabis predominant users Polysubstance users Family history of schizophrenia Family history of other psychiatric disorders Sex (female) Smoking (lifetime history) Alcohol (recreational) Alcohol (regular) FH scz +ve Class 2 FH scz +ve Class 3 Affective psychotic disorders Non-users Cannabis predominant users Polysubstance users Family history of schizophrenia Family history of other psychiatric disorders Sex (female) Smoking (lifetime history) Alcohol (recreational) Alcohol (regular)
1242 568 456 218 368 507 408 1027 235 609 136 113 400 235 126 39 235 93 226 319 68 172
Crude HR (95% CI)
P-value
Adjusted HR (95% CI)
P-value
1.00 1.51 (1.3–1.7) 1.94 (1.6–2.3) – – – – – – – –
– b0.001 b0.001 – – – – – – – –
1.00 1.38 (1.2–1.6) 1.95 (1.5–2.4) 1.36 (1.1–1.7) 0.99 (0.9–1.1) 0.75 (0.7–0.9) 1.11 (0.9–1.3) 0.82 (0.7–1.0) 0.78 (0.7–0.9) 1.09 (0.8–1.4) 0.81 (0.6–1.2)
–
1.00 1.04 (0.8–1.3) 0.88 (0.6–1.2) – – – – – –
– 0.702 0.478 – – – – – –
1.00 1.10 (0.8–1.4) 0.87 (0.6–1.3) 0.87 (0.7–1.1) 1.86 (1.5–2.3) 1.72 (1.4–2.1) 1.36 (1.0–1.8) 0.65 (0.5–0.9) 0.74 (0.6–0.9)
–
0.001 b0.001 0.003 0.891 b0.001 0.287 0.026 0.001 0.561 0.228
0.476 0.459 0.275 b0.001 b0.001 0.047 0.005 0.017
Confounding factors: Family history of schizophrenia and other psychiatric disorders, sex, lifetime history of smoking, alcohol consumption; HR = hazard ratio; 95% CI = 95% of the confidence interval. Regular use = daily/almost daily in the year prior to onset; recreational use = 1–2 days/week or less in the year prior to onset. FH+ scz = family history of schizophrenia present (testing interaction between family history and substance use for each class).
Please cite this article as: Stefanis, N.C., et al., The effect of drug use on the age at onset of psychotic disorders in an Australian cohort, Schizophr. Res. (2014), http://dx.doi.org/10.1016/j.schres.2014.04.003
N.C. Stefanis et al. / Schizophrenia Research xxx (2014) xxx–xxx
5
Fig. 2. Cox-regression cumulative survival curves for the three identified classes of illicit substance use in schizophrenia spectrum and affective psychotic disorders.
by the known psychoactive properties of the substances used (i.e., whereas illicit substances eventually exacerbated psychotic symptoms, alcohol acting as a central nervous system depressant did not) (Swendsen et al., 2011). While further prospective studies during the prodrome are urgently required in order to decipher the subclinical temporal association between substance use and symptoms (and their relation with AAO), retrospective studies have strongly suggested that cannabis use may have a significant effect in hastening onset of florid prodromal symptoms among first-episode patients with non-affective psychosis (Compton et al., 2009; Leeson et al., 2012). The association between alcohol use or abuse and later rather than earlier onset of bipolar disorder is firmly established (Strakowski et al., 2005) and the most parsimonious explanation for our findings is that alcohol use amongst young participants on the pathway to develop a psychotic disorder (either SSD or APD) exerts a slight onset delaying effect due to its depressant effects, and a general lack of psychotogenic effect. 4.2. Comparison with previous findings While our findings are in keeping with previous work from our group in a cohort of patients with schizophrenia (Power et al., 2012), a number of other studies have failed to report an additional effect of other substances on AAO in SSD (Barnes et al., 2006; Gonzalez-Pinto et al., 2008; Dekker et al., 2012), and it is likely that key methodological differences may underpin these discrepancies. These include the differences in sample sizes (resulting in small numbers of participants who had used illicit substances in addition to cannabis), definition of substance use (e.g., ever used, recreational or regular use, abuse, dependence), and the period of substance use under investigation (e.g., lifetime use, use in the 12 months prior to illness onset, use in the last 12 months, current use). For instance, in a sample of 131 patients hospitalised with first episode psychosis, which identified 67 cannabis users (and an unstated number of patients using an illicit substance in addition to cannabis) Gonzalez-Pinto et al. (2008) used multivariate analysis and reported that additional substances did not mediate the association between cannabis and age at onset; it is quite possible that their findings were limited by a small number of illicit substance users. Nonetheless, Dekker et al. (2012) examined 785 patients presenting consecutively as in-patients and out-patient with SSD (67% of whom had schizophrenia), and identified substance use in the past year or lifetime (with 223 cannabis users and 281 cannabis plus other illicit substance users identified); they reported that when cannabis use was taken into account, lifetime or recent use of another illicit substance did not have an additional effect on age at onset. The discrepancy between our findings and those of Dekker et al. (2012) may, in part,
be explained by the differences in the period of exposure to additional substance use: our study specifically focused on substance use on the 12 months prior to illness onset, whereas Dekker and colleagues focused on substance use in the 12 months prior to interview (with illness onset in the last 10 years as an inclusion criteria) or lifetime. Our findings that illicit substance use in affective psychotic illnesses was not associated with hazard of earlier AAO are not in keeping with findings from recent studies (De Hert et al., 2011; Lagerberg et al., 2011), most likely due to methodological limitations of these studies. Notably, Lagerberg et al. (2011) focused on bipolar disorders, and only “excessive” (at least weekly) illicit substance use such that only a very small number of participants in their cannabis using group used other substances to excess (n = 13). De Hert et al. (2011) reported that lifetime cannabis use was associated with a greater reduction in the AAO in 16 cannabis-using patients with bipolar disorder, compared with 247 cannabis-using patients with schizophrenia; this result is difficult to interpret in the context of insufficient power in the bipolar group. 4.3. Limitations The study is limited in its scope as it addresses the consumption of illicit substances in the 12 months prior to the onset of psychiatric symptoms, and does not consider the age of first use, pattern or frequency of lifetime use, or current use. Further, the study does not consider the frequency of use of a particular substance in the 12 month period under investigation. We acknowledge we have not fully investigated the different disorders within SSD and APD, and anticipate pursuing further analyses to ascertain the trends in AAO in specific disorders (e.g., schizophrenia vs. schizoaffective vs. delusional disorder). Recall bias is a further limitation of the study; like many similar studies we relied on retrospective data on substance use (i.e., in the 12 months prior to the onset of psychiatric symptoms) that was obtained by self-report, and this was not corroborated by an informant. Although Barnes et al. (2006) make an argument for the reliability and validity of substance use data by self-report, we acknowledge that the majority of participants included in our study had to recall their substance use many years prior to interview. Therefore, in an effort to address recall bias in this study, we used age of participants as an active covariate in constituting our substance-using classes, such that they were age-adjusted. In conclusion, illicit substance use in the 12 months prior to psychosis onset has a differential effect on AAO in SSD and APD. Our findings are compatible with the notion that illicit drugs bring forward age at onset in SSD but not APD.
Please cite this article as: Stefanis, N.C., et al., The effect of drug use on the age at onset of psychotic disorders in an Australian cohort, Schizophr. Res. (2014), http://dx.doi.org/10.1016/j.schres.2014.04.003
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N.C. Stefanis et al. / Schizophrenia Research xxx (2014) xxx–xxx
Role of funding source The SHIP was funded under contract to the Australian Government Department of Health and Ageing. Contributors V.M., D.C. and A.J. designed the study and wrote the protocol. B.P., N.S. and M.D. undertook the statistical analysis, and wrote the first draft of the article. All authors contributed to and have approved the final article. Conflict of interest The authors have declared that there are no conflicts of interest in relation to the subject of this study. Acknowledgements SHIP acknowledgement: This publication is based on data collected in the framework of the 2010 Australian National Survey of High Impact Psychosis. The members of the õSurvey of High Impact Psychosis Study Group are: V. Morgan (National Project Director), A. Jablensky (Chief Scientific Advisor), A. Waterreus (National Project Coordinator), R. Bush, V. Carr, D. Castle, M. Cohen, C. Galletly, C. Harvey, B. Hocking, A. Mackinnon, P. McGorry, J. McGrath, A. Neil, S. Saw, and H. Stain. Ethics approvals for the study were obtained from relevant institutional human research ethics committees. The study was funded by the Australian Government Department of Health and Ageing. This report acknowledges, with thanks, the hundreds of mental health professionals who participated in the preparation and conduct of the survey and the many Australians with psychotic disorders who gave their time and whose responses form the basis of this publication.
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Please cite this article as: Stefanis, N.C., et al., The effect of drug use on the age at onset of psychotic disorders in an Australian cohort, Schizophr. Res. (2014), http://dx.doi.org/10.1016/j.schres.2014.04.003