Substance use disorders (SUDs) occur frequently in patients with psychotic disorders and have been associated with various demographic and clinical correlates. There is an absence of research on the prevalence and clinical correlates of SUDs in psychotic disorders in low-and-middle-income countries (LMICs).
We aimed to determine the prevalence and correlates of SUDs in psychotic disorders.
Patients attending a large secondary-level psychiatric hospital in Cape Town South Africa.
We used the Structured Clinical Interview for DSM-IV (SCID-I) to determine psychiatric and substance use diagnoses, depressive, anxiety, obsessive-compulsive and post-traumatic symptoms. We used logistic regression models to determine significant predictors of SUDs.
In total sample (
This study found a high prevalence and wide distribution of SUDs in patients with psychotic disorders, consistent with previous work from high income countries. Given clinical correlates, in individuals with psychotic disorders and SUDs it is important to assess anxiety symptoms, suicidality and criminal involvement.
Depending on the sample characteristics and setting, the prevalence of substance use disorders (SUDs) in patients with serious mental disorders varies from as low as 10% to as high as 74%.
Variation in the prevalence of difference substance use disorders is influenced by factors such as geographical region, setting, phase of illness (first vs. chronic), diagnostic method and other demographic and clinical characteristics. Some of these clinical characteristics include variations in clinical diagnosis and ethnic grouping, and in some studies stimulant use disorders were more prevalent in patients with affective psychosis,
In patients with psychotic disorders and SUDs, substance rehabilitation treatment is typically reported as to be low.
Whereas most studies report that male sex, younger age, ethnic minority status, low educational attainment, unemployment, single marital status are significantly associated with SUDs,
We aimed to determine the prevalence, and demographic and clinical correlates of co-occurring SUDs in a clinically heterogeneous sample of patients with psychotic disorders. In addition, we aimed to determine in SMI the association of co-occurring SUDs with anxiety, depressive symptoms including suicidality, involvement with police arrests, and prior treatment for SUDs.
We conducted a secondary analysis of a database (
The second study was a pilot randomised controlled trial (“Social inclusion Project-SIP”,
Exclusion criteria for the last two studies were the same as the first study.
A socio-demographic schedule was used across the parent studies to record participants’ demographic details such as age, sex, self-identified ethnicity, level of education, marital status, employment and past drug or alcohol treatment. For self-identified ethnic groups the terms ‘mixed race (coloured)’ ‘black’ and ‘caucasian’ and ‘other’ (Asian), were not intended to reify sociocultural constructs but were instead used to study ongoing health disparities. Across all three studies all participants had to complete the English language version of the Structured Clinical Interview for DSM-IV (SCID-I),
We calculated the 95% confidence intervals of prevalence’s using the normal approximation of the binomial distribution. We constructed six separate dichotomous dependent variables denoting the presence or absence of any lifetime, alcohol, cannabis, methamphetamine, methaqualone and other drug (cocaine and hallucinogens) SUDs (abuse or dependence). We then explored the distribution and relationship between the different SUDs using logistic regression analyses. For the association between SUDs and demographic and clinical variables, we firstly conducted bivariate logistic regression analyses for each dependent variable separately onto each of the different independent demographic and clinical predictor variables. We then constructed multivariable logistic regression models with the dependent variables the various SUDs (any SUDS, alcohol, cannabis, methamphetamine, methaqualone, and other drug use disorders) and entered independent variables that were significant in the bivariate analyses at a
We report the associations between independent variables and dependent variables as adjusted odds ratios (ORs) with their 95% confidence intervals. All analyses were two-tailed and considered significant at the 5% level. We used Stata version 13 for Windows for all analyses.
All participants in the original studies provided written informed consent to participate in the studies and the secondary data analysis was also approved by the Human Research Ethics Committee of the University of Cape Town (HREC 652/2014) with its contributory studies: PRP study (HREC: 332/2008) the SIP study (HREC: 511/2011) and the CIAM study (HREC: 192/2010).
In the total of sample of 248 participants the mean age was 31.5 years (SD = 9.2), with the majority of participants being male (64.5%).
Sample sociodemographic characteristics (
Sociodemographics | % | |
---|---|---|
18-29 | 122 | 49.2 |
30-44 | 97 | 39.1 |
45-65 | 29 | 11.7 |
Female | 88 | 35.5 |
Male | 160 | 64.5 |
Mixed race (coloured) | 141 | 56.9 |
Black | 72 | 29.0 |
Caucasian | 28 | 11.3 |
Other |
7 | 2.8 |
Never married | 198 | 79.8 |
Married or cohabiting | 33 | 13.3 |
Previously married | 17 | 6.9 |
≤ 7 | 39 | 15.7 |
8-11 | 119 | 48.0 |
12 | 64 | 25.8 |
> 12 | 26 | 10.5 |
No | 168 | 67.7 |
Yes | 80 | (32.3 |
CIAM study (outpatients) | 103 | 41.5 |
PRP study (inpatients) | 86 | 34.7 |
SIP study (inpatients) | 59 | 23.8 |
, Other = Asian.
Sample clinical characteristics (
Clinical characteristics | % | |
---|---|---|
Schizophrenia spectrum disorder |
132 | 53.2 |
Bipolar type I disorder | 51 | 20.6 |
Schizoaffective disorder | 33 | 13.3 |
Substance induced psychotic disorder | 32 | 12.9 |
Lifetime MDE |
51 | 20.6 |
Lifetime MDE |
138 | 55.7 |
No suicidality | 171 | 69.0 |
Ideation or plans | 54 | 21.8 |
Attempt | 23 | 9.3 |
Lifetime anxiety |
31 | 13 |
Lifetime anxiety |
62 | 25 |
Lifetime OCD |
2 | 0.8 |
Lifetime OCS |
13 | 5.2 |
Lifetime PTSD |
10 | 4.0 |
Lifetime PTSD |
23 | 9.3 |
No arrests | 167 | 67.3 |
Serious/violent crime | 22 | 8.9 |
Major crime | 20 | 8.1 |
Other crime | 39 | 15.7 |
Any rehabilitation | 33 | 13.3 |
Inpatient | 23 | 9.3 |
Outpatient | 18 | 7.3 |
, Schizophrenia, schizophreniform disorder, brief psychotic disorder, psychotic disorder NOS
, MDE = Major depressive episode
, Symptoms or disorders: Panic disorder, Agoraphobia without a history of panic, specific phobia, social phobia, generalised anxiety disorder
, OCD = Obsessive compulsive disorder. OCS = obsessive compulsive symptoms
, PTSD = Posttraumatic stress disorder.
, Legal involvement: Serious violent crime (assault, rape, murder, armed robbery), Major crime (shoplifting, vandalism, parole violation, forgery, weapons offense, burglary, arson, contempt of court, domestic violence), Other crime (possession of illegal substances, weapons offense, prostitution, disorderly conduct in public, major driving violation, driving under the influence of substances).
In the total sample of 248 participants, the prevalence of any substance use disorder (abuse or dependence) was 55.6% (95% CI = 49.2% - 62.0%). For the individual SUDs in the total sample, the most common SUD was cannabis use disorders with a prevalence of 34.3% (95% CI: 28.3% - 40.5%), followed by alcohol use disorders (30.6%; 95% CI = 25.0% - 36.7%), methamphetamine use disorder (27.4%; 95% CI = 22.0% - 33.4%) and methaqualone (sedative-hypnotic) use disorders (10.4%; 95% CI = 6.9% - 14.9%). Other drug use disorders occurred at a much lower frequency with cocaine use disorder occurring in only 4.4% (95% CI = 2.2% - 7.7%) and hallucinogens (MDMA and LSD) only in 1.6% (95% CI = 0.4%- 4.0%). All participants with SUDs fulfilled criteria for more than one SUD, with 4% abusing more than one substance and 22.9% fulfilling criteria for more than one substance dependence syndrome. There was a significant association between having a cannabis and alcohol use disorder (OR = 2.0,
Patterns and relationship between substance use disorders (
SUD |
Alcohol ( |
Cannabis ( |
Methamphetamine ( |
Methaqualone ( |
Other ( |
|||||
---|---|---|---|---|---|---|---|---|---|---|
Adjusted OR | 95% CI | Adjusted OR | 95% CI | Adjusted OR | 95% CI | Adjusted OR | 95% CI | Adjusted OR | 95% CI | |
Alcohol | - | - | 2.0 |
(1.0 - 3.7) | 1.2 | (0.6 - 2.5) | 2.4 | (1.0 - 6.2) | 2.1 | (0.6 - 7.5) |
Cannabis | 2.0 |
(1.1 - 3.7) | - | - | 4.4 |
(2.3 - 8.5) | 5.1 |
(1.7 - 15.0) | 1.9 | (0.5 - 7.6) |
Methamphetamine | 1.3 | (0.6 - 2.5) | 4.4 |
(2.3 - 8.4) | - | - | 5.2 |
(1.9 - 14.1) | 2.0 | (0.5 - 7.5) |
Methaqualone | 2.1 | (0.8 - 5.2) | 4.7 |
(1.6 - 14.1) | 4.7 |
(1.7 - 12.8) | - | - | 3.3 | (0.8 - 13.7) |
Other | 2.1 | (0.6 - 7.3) | 1.8 | (0.4 - 7.7) | 1.7 | (0.5 - 6.5) | 3.7 | (0.9 - 14.7) | - | - |
, SUD = substance use disorders.
Each substance in columns adjusted for the effects of all other substances.
After adjustment for demographic and clinical covariates in multivariable models some variables remained significantly associated with the presence of the various SUDs (
Adjusted demographic and clinical association with any, alcohol and cannabis use disorders. (
Variable | Any SUD ( |
Alcohol ( |
Cannabis ( |
|||
---|---|---|---|---|---|---|
Adjusted OR | 95% CI | Adjusted OR | 95% CI | Adjusted OR | 95% CI | |
18-29(ref) | 1 | (ref) | 1 | (ref) | 1 | (ref) |
30-44 | 0.9 | (0.4 - 1.8) | 0.8 | (0.4 - 1.5) | 0.6 | (0.3 - 1.1) |
45-65 | 0.6 | (0.2 - 1.9) | 1.0 | (0.3 - 3.5) | 1.0 | (0.3 - 3.1) |
Male:Female | 3.9 |
(1.9 - 8.3) | 2.8 |
(1.3 - 6.0) | 4.7 |
(2.0 - 11.1) |
Mixed race (coloured) (ref) | - | - | - | - | 1 | (ref) |
Black | - | - | - | - | 0.5 | (0.2 - 1.1) |
Caucasian | - | - | - | - | 0.7 | (0.2 - 2.2) |
Other | - | - | - | - | 1.2 | (0.2 - 6.8) |
≤ 7 | 1 | (ref) | - | - | 1 | (ref) |
8-11 | 0.6 | (0.2 - 1.5) | - | - | 0.5 | (0.2 - 1.3) |
12 | 0.6 | (0.2 - 1.8) | - | - | 0.4 | (0.1 - 1.0) |
> 12 | 0.6 | (0.2 - 2.3) | - | - | 0.1 |
(0.0 - 0.8) |
Employed | - | - | - | - | 0.8 | (0.4 - 1.7) |
Never married | 1 | (ref) | 1 | (ref) | - | - |
Married or cohabiting | 0.7 | (0.3 - 1.9) | 0.7 | (0.2 - 2.0) | - | - |
Previously married | 1.0 | (0.3 - 3.8) | 0.3 | (0.1 - 1.7) | - | - |
Schizophrenia spectrum disorder |
1 | (ref) | - | - | 1 | (ref) |
Bipolar type I disorder | 1.1 | (0.5 - 2.5) | - | - | 1.7 | (0.7 - 4.0) |
Schizoaffective disorder | 0.9 | (0.3 - 2.4) | - | - | 0.9 | (0.3 - 2.9) |
Substance induced psychotic disorder | 12.0 |
(3.3 - 43.6) | - | - | 3.3 |
(1.1 - 9.4) |
Lifetime MDE | 0.5 | (0.2 - 1.2) | - | - | 0.9 | (0.4 - 2.4) |
Lifetime MDE symptoms | - | - | 1.6 | (0.7 - 3.3) | - | - |
Lifetime anxiety symptoms | - | - | 2.5 |
(1.2 - 5.1) | 0.5 | (0.2 - 1.1) |
No ideation | - | - | 1 | (ref) | - | - |
Ideation or plan | - | - | 1.0 | (0.4 - 2.5) | - | - |
Attempt | - | - | 3.3 |
(1.1 - 9.8) | - | - |
No arrests | 1 | (ref) | 1 | (ref) | 1 | (ref) |
Serious violent crime | 3.2 |
(1.0 - 9.8) | 1.6 | (0.6 - 4.5) | 2.0 | (0.7 - 5.7) |
Major crime | 2.7 | (0.9 - 8.4) | 2.3 | (0.8 - 7.0) | 3.5 |
(1.2 - 10.2) |
Other crime | 11.9 |
(3.4 - 42.2) | 2.8 |
(1.2 - 6.5) | 3.4 |
(1.5 - 8.0) |
CIAM study (outpatients) | - | - | 1 | (ref) | - | - |
PRP study (inpatients) | - | - | 1.6 | (0.8 - 3.2) | - | - |
SIP study (inpatients) | - | - | 0.3 |
(0.1 - 0.9) | - | - |
, Schizophrenia, schizophreniform disorder, brief psychotic disorder, psychotic disorder NOS.
Omitted variables did not reach significance at
Adjusted demographic and clinical association with methamphetamine, metaqualone and other substance use disorders. (
Variable | Methamphetamine ( |
Methaqualone ( |
Other ( |
|||
---|---|---|---|---|---|---|
Adjusted OR | 95% CI | Adjusted OR | 95% CI | Adjusted OR | 95% CI | |
18-29(ref) | 1 | (ref) | - | - | - | - |
30-44 | 0.7 | (0.3 - 1.6) | - | - | - | - |
45-65 | 0.1 |
(0.0 - 0.7) | - | - | - | - |
Male:Female | 1.9 | (0.7 - 5.3) | 2.6 | (0.8 - 8.4) | 2.7 | (0.4 - 16.0) |
Mixed race (coloured) | 1 | (ref) | - | - | 1 | (ref) |
Black | 0.3 |
(0.1 - 0.7) | - | - | 0.5 | (0.1 - 3.3) |
Caucasian | 0.6 | (0.2 - 2.4) | - | - | 2.0 | (0.4 - 9.5) |
Asian | 0.5 | (0.1 - 4.5) | - | - | 20.9 |
(2.7 - 162.6) |
≤ 7 | 1 | (ref) | - | - | - | - |
8-11 | 0.7 | (0.2 - 2.0) | - | - | - | - |
12 | 0.5 | (0.1 - 1.5) | - | - | - | - |
> 12 | 0.2 | (0.0 - 1.8) | - | - | - | - |
Employed | 0.7 | (0.3 - 1.7) | - | - | - | - |
Never married | - | - | 1 | (ref) | - | - |
Married or cohabiting | - | - | 0.4 | (0.1 - 2.5) | - | - |
Previously married | - | - | 4.4 |
(1.1 - 16.6) | - | - |
Schizophrenia spectrum disorder |
1 | (ref) | - | - | - | - |
Bipolar type I disorder | 0.5 | (0.2 - 1.6) | - | - | - | - |
Schizoaffective disorder | 0.4 | (0.1 - 1.6) | - | - | - | - |
Substance induced psychotic disorder | 26.5 |
(7.1 - 98.6) | - | - | - | - |
Lifetime MDE | 0.6 | (0.3 - 1.2) | 0.4 | (0.2 - 1.0) | - | - |
Lifetime anxiety symptoms | - | - | - | - | 2.7 | (0.8 - 9.3) |
No arrests | 1 | (ref) | 1 | (ref) | 1 | (ref) |
Serious violent crime | 2.9 | (0.9 - 9.7) | 4.4 |
(1.3 - 15.1) | 6.7 |
(1.4 - 32.0) |
Major crime | 1.0 | (0.2 - 4.1) | 2.4 | (0.7 - 8.8) | 0.2 | (0.0 - 10.1) |
Other crime | 4.8 |
(1.8 - 12.8) | 1.3 | (0.4 - 4.1) | 3.4 | (0.8 - 14.5) |
CIAM study (outpatients) | - | - | 1 | (ref) | - | - |
PRP study (inpatients) | - | - | 3.3 |
(1.1 - 10.0) | - | - |
SIP study (inpatients) | - | - | 2.5 | (0.7 - 8.6) | - | - |
, Schizophrenia, schizophreniform disorder, brief psychotic disorder, psychotic disorder NOS.
Omitted variables did not reach significance at
In this study, one of the few from a LMIC context such as South Africa, we confirmed the high prevalence of SUDs in patients with a range of different psychotic disorders. We found prevalence for any SUD of 55.6%, similar to other studies in similar populations across the world, and very close to the previously found prevalence of 51% in a similar sample from Cape Town.
Consistent with other studies the association between methamphetamine and younger age remained significant in the multivariable models.
Similarly to other studies we found a significant association between SUD and a diagnosis of substance induced psychosis.
With the exception of alcohol use disorder, all categories of SUDs including any SUDs had significantly elevated occurrence of legal involvement, including serious and violent crime with an even stronger association with the ‘other crime’ category denoting police arrests for crimes relating to illegal drug possession, prostitution, driving violations and disorderly conduct in public. In addition, cannabis users also were significantly more likely to get arrested for major crimes (shoplifting, vandalism, parole violation, forgery, weapons offense, burglary, arson, domestic violence not involving assault) and methaqualone and ‘other drug users’ (predominantly cocaine users) were significantly more likely to be involved with serious violent offenses (assault, rape, murder, armed robbery). These findings are consistent with those from high income countries
Our results confirm the clinical profile of participants with SUDs as being more likely to be male, have a younger age (in particular methamphetamine users) and having a diagnosis of a substance induced psychosis. In particular, those with alcohol use disorders were more likely to experience anxiety symptoms (i.e. panic, generalised and social anxiety) and significantly more likely to have attempted suicide. This underscores the importance of screening for anxiety and suicide risk assessments in patients with co-occurring alcohol use disorders. For most substances, involvement in crimes relating to drug possession, prostitution, disorderly conduct and driving violations (‘other crimes’ category) were significantly more likely; and involvement in major crimes, serious violent crimes was also significantly elevated for cannabis, methaqualone and other (predominantly cocaine) users. Practitioners who manage patients with co-occurring disorder are likely to need to liaise with criminal justice institutions, state prosecutors and police.
Several limitations should be acknowledged. First, is the absence of biological verification of substance use.
Although urine tests for substances such as cannabis and methamphetamine conducted on some patients who were admitted to short-stay psychiatric units were taken into consideration during patient assessment and interviews, tests for alcohol and other drugs are not routinely conducted, neither were such tests recorded consistently so as to allow for use in this study. Short detection windows of most biological tests are also likely to result in false negative tests in participants who used recently but were tested only days after last use. As participants are more likely to underreport substances this would have led to an underestimation of substance use in this study. In addition, self-report of substances have been shown to yield accurate results, with other techniques like hair samples often being problematic in multi-ethnic samples.
This study found high prevalence of substance use disorders and multiple substance use in patients with psychotic disorders in a LMIC context. This underscores the importance of a thorough clinical assessment for various substance use disorders, and when present, for anxiety, suicidality and risky behaviours involving clashes with the law.
The authors would like to thank Jennifer Hsieh, Nyameka Dayakalashe, Lungiswa Mankayi, and Ziyanda Gemashe for their assistance with data collection. We would like to thank Peter Milligan and Valkenberg hospital senior management for their assistance and advice on study operations.
The authors declare that no competing interests exist.
H.S.T. and D.J.S. were involved in the conceptualization, design, analysis and write-up of the PRP study. H.S.T., G.S. and D.J.S. conceptualised and contributed to the design, analysis and write-up of the SIP study. F.M.H. and H.S.T. were involved in the design and conceptualization, data collection and write up of the CIAM study. S.M. contributed to the data management and editing of the current manuscript.
The Presentation and Risk Factors in the Psychobiology of Psychosis (PRP) study received funding from the University of Cape Town, Department of Psychiatry and Mental Health Research Committee. The Cortical Inhibition and Attentional Modulation (CIAM) study received funding from the National Research Foundation of South Africa and the University of Cape Town, Department of Psychiatry and Mental Health Research Committee. The Social Inclusion in Psychosis study (SIP) received funding from the World Psychiatric Association and the University of Cape Town, Department of Psychiatry and Mental Health Research Committee. DJS is supported by the South African Medical Research Council (MRC).
Data for this study is available from the corresponding author upon reasonable request and permission by the University of Cape Town.
The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of any affiliated agency of the authors.