Demographic and Clinical Profiles of Drug-Induced Psychosis: Age, Gender, and Social Context in a German Cohort
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Nadia1Emailnadia@wildner.info
GabrieleKoller3
KristinaAdorjan4,1
BernhardHaller2
FlorianEyer1
StefanieGeith1Emailstefanie.geith@mri.tum.de
1Division of Clinical Toxicology and Poison Center MunichSchool of Medicine and Health, Technical University of MunichIsmaninger Str. 2281675MunichGermany
2School of Medicine and Health, Technical University of MunichIsmaninger Str. 2281675MunichGermany
3Department of Psychiatry and PsychotherapyLudwig-Maximilians UniversityNußbaumstr. 780336MunichGermany
4Institute of Psychiatric Phenomics and Genomics (IPPG)Ludwig-Maximilians UniversityNußbaumstr. 780336MunichGermany
5Department of Psychiatry and Psychotherapy (UPD)University of BernBolligenstrasse 1113000 Bern 60Switzerland
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Abstract
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Background:Drug-induced psychosis (DIP) represents a complex clinical challenge, particularly among younger substance-using populations. While DIP arises in the context of substance use, distinguishing transient episodes from emerging primary psychotic disorders remains difficult, and data on demographic and clinical variations are limited. \\ \\Methods: We conducted a descriptive cohort study of 340 patients diagnosed with DIP (ICD-10: F1X.5) between 2010 and 2020 at two university hospitals in Germany. Equal numbers of cases (n=170) were included from the toxicology and psychiatry departments; all eligible cases at TUM were included, and the same number were randomly selected from the LMU cohort. Variables assessed included sociodemographic data, psychiatric symptoms, substance use (self-report and toxicology), comorbidities, and family history. Analyses included descriptive statistics, chi-square or Fisher’s exact tests, and Mann–Whitney U tests to examine group differences. \\ \\Results: The cohort was predominantly male (77.4%) with a median age of 27 years. Migration background was present in 27.6%, with admissions peaking in 2015–2018. Psychotic symptoms were dominated by perceptual disturbances (62.3%) and thought disorder (76.3%). Women exhibited higher rates of tactile hallucinations, suicidal ideation, and depression (all p0.05) and tended to have higher serum ethanol levels. Daily substance use was common (69.0%), with polysubstance use exceeding 75%. Cannabis and stimulants predominated in younger patients, while opiates, sedatives, and ethanol were more frequent in older groups. \\ \\Conclusion: DIP showed clear gender- and age-related differences in psychotic symptom profiles and clinical presentation, alongside high rates of daily and polysubstance use, young male predominance, and migration-related patterns. These findings underscore the need for early, individualized, and demographically informed interventions.
Keywords
Drug-induced psychosis
Substance use disorder
Polysubstance use
Age differences
Gender differences
Social determinants
Psychotic symptoms
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Introduction
Drug-induced psychosis (DIP) refers to acute psychotic episodes triggered by psychoactive substances. Symptoms often resemble early manifestations of primary psychotic disorders, complicating differential diagnosis. Traditionally seen as a transient reaction to intoxication or withdrawal, DIP is now considered to signal an underlying vulnerability to chronic psychoses fiorentini2021,kendler2019,bramness2024, underscoring the need for early detection and prevention, particularly in young adults.
Diagnosis is challenging because substance use disorders are common in schizophrenia fiorentini2021, and symptom overlap with primary psychoses is substantial, especially in the acute phase bramness2024,ricci2024. Longitudinal observation is often required to clarify whether symptoms remit with abstinence or progress to chronic psychosis mauri2017. Frequent polysubstance use, especially cannabis, stimulants, tramadol, and ethanol, further complicates causal attribution vallersnes2016,karimi2024,taha2019,roncero2014.
Cannabis is the best studied DIP trigger, linked to higher incidence, more severe symptoms, and elevated risk of transition to schizophrenia spectrum disorders, particularly with early and regular use. Methamphetamine shows a strong dose–response relationship, while opioids, sedatives, and novel psychoactive substances (NPS) add diagnostic complexity.
Age and gender shape DIP risk and presentation. Young men are most vulnerable, while women more often present with affective symptoms rognli2022,irving2021,mendrek2017a, highlighting the need for age- and gender-sensitive approaches.
Given these complexities and DIP's potential to indicate underlying vulnerability, this study addresses four questions:
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Which clinical symptoms and comorbidities occur, and how do these vary by age and gender?
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What substance use patterns, including polysubstance use, are observed, and how do they differ by gender and age?
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How do migration background and social factors influence DIP?
Methods
Study design and setting
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We conducted a retrospective cohort study using data from the Department of Clinical Toxicology (Technical University of Munich, TUM) and the Department of Psychiatry (Ludwig Maximilian University of Munich, LMU), including admissions between August 2010 and August 2020.Fig. 1
Flowchart of patient selection
Participants
A total of 340 patients were included in the study, comprising 170 cases per institution. At TUM, all available cases meeting inclusion criteria were included. At LMU, cases were randomly selected from a predefined pool of patients diagnosed with DIP to match the sample size of the TUM cohort (Figure 1). Random selection was performed by drawing physical patient records from the eligible pool. For patients with multiple admissions, only the first eligible case was included to avoid duplication.
Inclusion criteria were a diagnosis of drug-induced psychotic disorder (ICD-10: F1X.5), admission for psychiatric or toxicological treatment, recent substance use confirmed by self-report and toxicology (urine or serum), and psychotic symptoms consistent with DSM-5. Diagnoses were made by board-certified psychiatrists.
Exclusion criteria were age under 16 years (treated in pediatric settings), absence of verified substance use or psychotic symptoms, and cases where schizophrenia spectrum disorder was judged more likely. Age was stratified into three groups (16–25, 26–40, 41–60 years) to reflect developmental stages and allow subgroup analyses.
The study was approved by the LMU Ethics Committee (No.
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20/773, 27 Aug 2020) and conducted in accordance with the Declaration of Helsinki. A
Patient consent was waived due to the retrospective design.Data collection
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Clinical data were extracted manually from patient records using a standardized protocol. Variables included sociodemographic, clinical, and substance-related information, as well as psychopathological symptoms across several domains. Toxicological screening was performed at both centers using immunoassays and confirmatory methods. Detailed assay descriptions are provided in the Supplementary Material.Statistical analysis
All statistical analyses were performed using IBM SPSS Statistics, Version 29. Descriptive statistics are presented as median (range) for quantitative data (mean
SD for serum ethanol levels) and as absolute and relative frequencies for categorical variables. For each test, a significance level of 5% was used. Group comparisons by sex and age group were conducted using Pearson’s chi-square tests or Fisher-based tests (Fisher’s exact or Fisher–Freeman–Halton, as appropriate) for categorical variables, and Mann–Whitney U tests for continuous non-normally distributed variables. Gender differences in mean serum ethanol levels were tested using Welch’s t-test, with effect sizes reported as Cohen’s d.
Temporal variation in the proportion of patients with migration background was examined descriptively across admission periods (2010–2014, 2015–2018, 2019–2020).
Results
Descriptive analysis
Sociodemographic characteristics and migration background
A total of 340 patients were included in the analysis. The median age was 27 years, with most participants being under the age of 40 (see Table \ref{tab:socio1}). The majority were unmarried, while long-term partnerships and parenthood were uncommon. Educational and vocational levels were heterogeneous, with over half of the patients unemployed at the time of admission. Migration background was recorded in 27.6% overall and was more common among men. The proportion of patients with migration background was highest during 2015–2018 (see Figure 2), coinciding with the peak in total admissions, and decreased slightly thereafter.
begin{center}\begin{minipage}{\linewidth}\captionof{table}{Sociodemographic characteristics by gender and age}\vspace{6pt}\label{tab:socio1}\begin{adjustbox}{max width=\linewidth}\renewcommand{\arraystretch}{1.2}\begin{tblr}{ cell{7}{4} = {c=2}{}, cell{7}{6} = {c=2}{}, vline{2,4,9} = {-}{0.05em}, hline{4,8,13,17,21,25,34,42} = {-}{}, hline{5} = {-}{0.05em},}\topruleCharacteristic & Total & & Male & & Female & & p-value & Age & & & & & & p-value\& & & & & & & &
25 & & 26-40 & &
41 & & \& n & (%) & n & (%) & n & (%) & & n & (%) & n & (%) & n & (%) & \& 340 & (100) & 263 & (77.4) & 77 & (22.6) & & 145 & (42.6) & 155 & (45.6) & 40 & (11.8) & \\Age & & & & & & & & & & & & & & \\Median age (range) & 27.0 & & 27.0 & & 27.0 & & 0.842 & & & & & & & \& (16--60) & & (16-–59) & & (17-–60) & & & & & & & & & \\ Family situation & & & & & & & & & & & & & & \\Unmarried & 222 & (92.1) & 171 & (91.4) & 51 & (94.4) & 0.894 & 108 & (97.3) & 91 & (87.5) & 23 & (88.5) & 0.031\\Married & 14 & (5.8) & 12 & (6.4) & 2 & (3.7) & & 2 & (1.8) & 9 & (8.7) & 3 & (11.5) & \\Divorced & 5 & (2.1) & 4 & (2.1) & 1 & (1.9) & & 1 & (0.9) & 4 & (3.8) & 0 & (0.0) & \\Missing & 99 & & 76 & & 23 & & & 34 & & 51 & & 14 & & \\Permanent Relationship & & & & & & & & & & & & & & \\Yes & 58 & (26.6) & 43 & (25.6) & 15 & (30.0) & 0.536 & 17 & (18.3) & 28 & (28.0) & 13 & (52.0) & 0.003 \\No & 160 & (73.4) & 125 & (74.4) & 35 & (70.0) & & 76 & (81.7) & 72 & (72.0) & 12 & (48.0) & \\Missing & 122 & & 95 & & 27 & & & 52 & & 55 & & 15 & & \\Migration background & & & & & & & & & & & & & & \\Without & 246 & (72.4) & 183 & (69.6) & 63 & (81.8) & 0.035 & 108 & (74.5) & 106 & (68.4) & 32 & (80.0) & 0.257 \\With & 94 & (27.6) & 80 & (30.4) & 14 & (18.2) & & 37 & (25.5) & 49 & (31.6) & 8 & (20.0) & \\Missing & 0 & & 0 & & 0 & & & 0 & & 0 & & 0 & & \\Parenthood & & & & & & & & & & & & & & \\Yes & 32 & (16.8) & 21 & (14.6) & 11 & (23.9) & 0.141 & 7 & (8.4) & 19 & (22.4) & 6 & (27.3) & 0.014\\No & 158 & (83.2) & 123 & (85.4) & 35 & (76.1) & & 76 & (91.6) & 66 & (77.6) & 16 & (72.7) & \\Missing & 150 & & 119 & & 31 & & & 62 & & 70 & & 18 & & \\Education & & & & & & & & & & & & & & \\Lower secondary & 69 & (34.7) & 55 & (35.5) & 35 & (53.8) & 0.696& 32 & (30.8) & 23 & (30.7) & 14 & (70.0) & 0.086 \\Intermediate secondary & 45 & (22.6) & 35 & (22.6) & 10 & (15.4) & & 23 & (22.1) & 20 & (26.7) & 2 & (10.0) & \\Higher secondary & 49 & (24.6) & 39 & (25.2) & 10 & (15.4) & & 25 & (24.0) & 21 & (28.0) & 3 & (15.0) & \\Vocational qualification & 9 & (4.5) & 6 & (3.9) & 3 & (4.6) & & 4 & (3.8) & 5 & (6.7) & 0 & (0.0) & \\School ongoing & 8 & (4.0) & 7 & (4.5) & 1 & (1.5) & & 7 & (6.7) & 1 & (1.3) & 0 & (0.0) & \\Other & 3 & (1.5) & 3 & (1.9) & 0 & (0.0) & & 2 & (1.9) & 1 & (1.3) & 0 & (0.0) & \\No certificate & 16 & (8.0) & 10 & (6.5) & 6 & (9.2) & & 11 & (10.6) & 4 & (5.3) & 1 & (5.0) & \\Missing & 141 & & 108 & & 12 & & & 41 & & 80 & & 20 & & \\Qualification level & & & & & & & & & & & & & & \\In training & 47 & (23.2) & 39 & (25.0) & 8 & (17.0) & 0.552 & 38 & (35.8) & 9 & (11.7) & 0 & (0.0) & textless0.001 \\No qualification & 63 & (31.0) & 44 & (28.2) & 19 & (40.4) & & 41 & (38.7) & 18 & (23.4) & 4 & (20.0) & \\Skilled worker & 50 & (24.6) & 39 & (25.0) & 11 & (23.4) & & 12 & (11.3) & 28 & (36.4) & 10 & (50.0) & \\Commercial school & 25 & (12.3) & 21 & (13.5) & 4 & (8.5) & & 13 & (12.3) & 8 & (10.4) & 4 & (20.0) & \\College & 5 & (2.5) & 4 & (2.6) & 1 & (2.1) & & 1 & (0.9) & 4 & (5.2) & 0 & (0.0) & \\University & 13 & (6.4) & 9 & (5.8) & 4 & (8.5) & & 1 & (0.9) & 10 & (13.0) & 2 & (10.0) & \\Missing & 137 & & 107 & & 30 & & & 39 & & 78 & & 20 & & \\Currently working & & & & & & & & & & & & & & \\Yes & 85 & (37.9) & 67 & (38.5) & 18 & (36.0) & 0.748 & 37 & (37.4) & 38 & (39.2) & 10 & (35.7) & 0.935 \\No & 139 & (62.1) & 107 & (61.5) & 32 & (64.0) & & 62 & (62.6) & 59 & (60.8) & 18 & (64.3) & \\Missing & 116 & & 89 & & 27 & & & 46 & & 58 & & 12 & & \\\bottomrule\SetCell[c=15]{l}{\footnotesize p-values based on Pearson’s
test; Fisher’s exact test applied for variables with expected cell counts
5; Mann–Whitney U test used for continuous variables (age).} \end{tblr}\end{adjustbox}\end{minipage}\end{center}
Fig. 2
Percentage of patients with migration background. Data for 2012 included only patients without migration background.
Anamnestic data
Psychiatric comorbidities were common among the study population (see Table \ref{tab:comorbidities}). Substance use disorders were the most frequently documented additional diagnoses, followed by depression and borderline personality disorder (BPD), with smaller numbers presenting with conditions such as panic disorder, post-traumatic stress disorder (PTSD), or attention deficit hyperactivity disorder(ADHD).
Regarding familial predisposition, a high proportion of patients (75.5%) reported a family history of psychiatric illness, including substance use disorders, affective disorders, and schizophrenia-spectrum disorders (see Table \ref{tab:historynew}).
begin{center}\begin{minipage}{\linewidth}\captionof{table}{Psychiatric comorbidities by gender and age group (new)}\vspace{6pt}\label{tab:comorbidities}\resizebox{\linewidth}{!}{\begin{tblr}{ column{even} = {c}, column{3} = {c}, column{5} = {c}, column{7} = {c}, column{9} = {c}, column{11} = {c}, column{13} = {c}, column{15} = {c}, cell{1}{2} = {c=2}{}, cell{1}{5} = {c=2}{}, cell{1}{7} = {c=2}{}, cell{1}{10} = {c=6}{}, cell{2}{10} = {c=2}{}, cell{2}{12} = {c=2}{}, cell{2}{14} = {c=2}{}, cell{11}{1} = {c=16}{}, cell{12}{1} = {c=16}{}, vline{2,5,10} = {1-10}{}, hline{1,11} = {-}{0.08em}, hline{4} = {-}{0.05em},} & Total (Positive) & & Missing & Male & & Female & & p-value & Age & & & & & & p-value\\Diagnosis & & & & & & & & &
25 & & 26–40 & &
41 & & \& n & (%) & & n & (%) & n & (%) & & n & (%) & n & (%) & n & (%) & \\Psychiatric comorbidity & 255 & (75.2) & 1 & 200 & (76.3) & 55 & (71.4) & 0.372 & 106 & (73.6) & 115 & (74.2) & 34 & (85.0) & 0.310\\Substance use disorder & 196 & (57.8) & 1 & 152 & (58.0) & 44 & (57.1) & 0.892 & 79 & (54.9) & 87 & (56.1) & 30 & (75.0) & 0.063\\Depression & 39 & (11.6) & 3 & 25 & (9.6) & 14 & (18.2) & 0.044 & 18 & (12.5) & 16 & (10.5) & 5 & (12.5) & 0.835\\Panic disorder & 7 & (2.1) & 2 & 6 & (2.3) & 1 & (1.3) & 1.000 & 3 & (2.1) & 4 & (2.6) & 0 & (0.0) & 0.876\\PTSD
& 9 & (2.7) & 2 & 6 & (2.3) & 3 & (3.9) & 0.431 & 4 & (2.8) & 5 & (3.2) & 0 & (0.0) & 0.799\\BPD
& 15 & (4.4) & 2 & 9 & (3.4) & 6 & (7.8) & 0.118 & 8 & (5.6) & 7 & (4.5) & 0 & (0.0) & 0.366\\ADHD
& 21 & (6.2) & 2 & 16 & (6.1) & 5 & (6.5) & 1.000 & 13 & (9.0) & 8 & (5.2) & 0 & (0.0) & 0.094\\
Posttraumatic Stress Disorder,
Borderline Personality Disorder,
Attention Deficit Hyperactivity Disorder. & & & & & & & & & & & & & & & \\p-values based on Pearson’s
test; Fisher’s exact test applied for variables with expected cell counts
5 & & & & & & & & & & & & & & & \end{tblr}}\end{minipage}\end{center}
Substance use patterns
The median age at first substance use was 15 years (range 8--57). Most participants (69.0%) reported daily use, while roughly one-quarter used only a single substance and others up to five or more (Table \ref{tab:socio2}).
Cannabis, stimulants, and ethanol were most frequently reported, whereas NPS were used by approximately one quarter of the sample. Smaller proportions reported opiates, sedatives, or hallucinogens.
Toxicological analyses largely confirmed these patterns, with THC and benzodiazepines most frequently detected, followed by amphetamines, cocaine, and opiates. Serum testing showed ethanol in just over one-fifth of patients at admission.
begin{center}\begin{minipage}{\linewidth}\captionof{table}{Substance use patterns by gender and age group}\vspace{6pt}\label{tab:socio2}\begin{adjustbox}{max width=\linewidth}\renewcommand{\arraystretch}{1.2}\begin{tblr}{ width=\linewidth, colspec={l *{14}{c}}, vline{2,4,9} = {-}{0.05em} }\topruleCharacteristic & Total & & Male & & Female & & p-value & Age & & & & & & p-value\& & & & & & & &
25 & & 26-40 & &
41 & & \& n & (%) & n & (%) & n & (%) & & n & (%) & n & (%) & n & (%) & \\\midrule & 340 & (100) & 263 & (77.4) & 77 & (22.6) & & 145 & (42.6) & 155 & (45.6) & 40 & (11.8) & \\\midruleAge at first consumption & & & & & & & & & & & & & & \\Median age (range) & 15.0 & (8--57) & 15.0 & (12--57) & 14.5 & (8--27) & 0.506 & & & & & & & \\\midruleConsumption pattern & & & & & & & & & & & & & & \\Daily & 225 & (69.0) & 178 & (70.6) & 47 & (63.5) & 0.338 & 91 & (65.5) & 103 & (91.2) & 31 & (79.5) & 0.176 \\Weekly & 27 & (8.3) & 18 & (7.1) & 9 & (12.2) & & 17 & (12.2) & 9 & (8.0) & 1 & (2.6) & \\Irregular & 74 & (22.7) & 56 & (22.2) & 18 & (24.3) & & 31 & (22.3) & 1 & (0.9) & 7 & (17.9) & \\Missing & 14 & & 11 & & 3 & & & 6 & & 42 & & 1 & & \\\midruleNumber of used substances & & & & & & & & & & & & & & \\1 Substance & 79 & (23.6) & 58 & (22.3) & 21 & (28.0) & 0.251 & 33 & (22.9) & 37 & (24.0) & 9 & (24.3) & 0.430 \\2 Substances & 116 & (34.6) & 92 & (35.4) & 24 & (32.0) & & 47 & (32.6) & 57 & (37.0) & 12 & (32.4) & \\3 Substances & 82 & (24.5) & 65 & (25.0) & 17 & (22.7) & & 43 & (29.9) & 32 & (20.8) & 7 & (18.9) & \\4 Substances & 40 & (11.9) & 28 & (10.8) & 12 & (16.0) & & 13 & (9.0) & 19 & (12.3) & 8 & (21.6) & \\
5 Substances & 18 & (5.4) & 17 & (6.5) & 1 & (1.3) & & 8 & (5.6) & 9 & (5.8) & 1 & (2.7) & \\Missing & 5 & & 3 & & 2 & & & 1 & & 1 & & 3 & & \\\midruleSelf-reported substance use & & & & & & & & & & & & & & \\Ethanol & 181 & (53.2) & 141 & (53.6) & 40 & (51.9) & 0.797 & 74 & (51.0) & 82 & (52.9) & 25 & (62.5) & 0.432 \\Sedatives & 52 & (15.3) & 40 & (15.2) & 12 & (16.0) & 0.867 & 12 & (8.3) & 31 & (20.1) & 9 & (22.5) & 0.006 \\Opiates & 65 & (19.1) & 48 & (18.3) & 17 & (22.4) & 0.422 & 17 & (11.7) & 33 & (21.4) & 15 & (37.5) & 0.001 \\Stimulants & 178 & (52.4) & 144 & (54.8) & 34 & (44.7) & 0.124 & 82 & (56.6) & 81 & (52.6) & 15 & (37.5) & 0.102 \\Hallucinogens & 35 & (10.3) & 30 & (11.4) & 5 & (6.6) & 0.287 & 20 & (13.8) & 14 & (9.1) & 1 & (2.5) & 0.090 \\THC & 214 & (62.9) & 165 & (62.7) & 49 & (65.3) & 0.681 & 109 & (75.7) & 89 & (57.8) & 16 & (40.0) & 0.001 \\NPS
& 84 & (24.7) & 68 & (26.0) & 16 & (21.3) & 0.415 & 35 & (24.3) & 39 & (25.5) & 10 & (25.0) & 0.984 \\\midruleToxicological analysis & & & & & & & & & & & & & & \\Ethanol
& 25 & (22.1) & 15 & (17.2) & 10 & (38.5) & 0.031 & 7 & (18.4) & 12 & (20.3) & 6 & (37.5) & 0.272 \\Benzodiazepines & 108 & (38.6) & 87 & (31.1) & 21 & (7.5) & 0.236 & 47 & (16.8) & 48 & (17.1) & 13 & (4.6) & 0.729 \\Opiates & 18 & (6.5) & 16 & (5.6) & 2 & (0.7) & 0.262 & 0 & (0.0) & 10 & (3.6) & 8 & (2.9) & 0.001 \\Cocaine & 34 & (12.2) & 24 & (8.6) & 10 & (3.6) & 0.385 & 10 & (3.6) & 18 & (6.5) & 6 & (2.2) & 0.144 \\Amphetamines & 51 & (18.6) & 41 & (15.0) & 10 & (3.6) & 0.585 & 20 & (7.3) & 27 & (9.9) & 4 & (1.5) & 0.563 \\THC (toxicology) & 131 & (45.8) & 100 & (35.7) & 31 & (10.8) & 1.000 & 63 & (21.9) & 58 & (20.2) & 10 & (3.5) & 0.160 \\\bottomrule\SetCell[c=15]{l}{\footnotesize
Novel Psychoactive Substances,\;
cut-off 0.2\,g/L in serum.}\\\SetCell[c=15]{l}{\footnotesize p-values based on Pearson’s
test; Fisher’s exact test (or Fisher–Freeman–Halton for RxC tables) applied when expected cell counts
5. Mann–Whitney U test used for continuous variables (age).}\\\end{tblr}\end{adjustbox}\end{minipage}\end{center}
Psychiatric symptoms
The most frequently documented clinical symptoms included perceptual disturbances, thought disorder, and restlessness (see Table \ref{tab:historynew}). Hallucinations were common, particularly auditory and visual types, while tactile hallucinations were less frequently observed. Disturbances of self-awareness and affective impairment were also noted in a considerable number of cases, alongside widespread reports of sleep disruption.
Regarding risk behaviors, nearly one-third of patients exhibited signs of self-harm risk, with smaller proportions presenting a risk to others or reporting suicidal ideation.
begin{center} \begin{minipage}{\linewidth} \captionof{table}{Family history and symptom profiles by gender and age group} \vspace{6pt} \label{tab:historynew} \begin{adjustbox}{max width=\linewidth} \renewcommand{\arraystretch}{1.2} \begin{tblr}{ width=\linewidth, colspec={l *{14}{c}}, vline{2,5,10} = {-}{0.05em} } \toprule Characteristic & Total & & Missing & Male & & Female & & p-value & Age & & & & & & p-value\& & & & & & & & &
& & 26--40 & &
& & \& n & (%) & n & n & (%) & n & (%) & & n & (%) & n & (%) & n & (%) & \\ \midrule Family history & & & & & & & & & & & & & & & \\ Substance abuse disorder & 39 & (20.2) & 147 & 27 & (18.2) & 12 & (26.7) & 0.288 & 21 & (20.6) & 15 & (20.5) & 3 & (16.7) & 0.932\\ {Schizophrenia, schizotypal or\\ delusional disorders} & 28 & (14.6) & 148 & 25 & (17.0) & 3 & (6.7) & 0.096 & 16 & (15.7) & 9 & (12.5) & 3 & (16.7) & 0.826\\ Affective disorder & 56 & (29.2) & 148 & 39 & (26.5) & 17 & (37.8) & 0.189 & 36 & (35.3) & 16 & (22.2) & 4 & (22.2) & 0.146\\ {Neurotic, stress-related or \\somatoform disorders} & 6 & (3.1) & 148 & 4 & (2.7) & 2 & (4.4) & 0.626 & 4 & (3.9) & 1 & (1.4) & 1 & (5.6) & 0.463\\ Personality or behavioural disorders & 3 & (1.6) & 148 & 0 & (0.0) & 3 & (6.7) & 0.012 & 2 & (2.0) & 0 & (0.0) & 1 & (5.6) & 0.194\\ Behavioural abnormalities & 7 & (3.6) & 148 & 6 & (4.1) & 1 & (2.2) & 1.000 & 4 & (3.9) & 2 & (2.8) & 1 & (5.6) & 0.727\\ {Behavioural or emotional \\disorders (childhood onset)} & 2 & (1.0) & 148 & 2 & (1.4) & 0 & (0.0) & 1.000 & 1 & (1.0) & 1 & (1.4) & 0 & (0.0) & 1.000\\ Suicide
& 10 & (5.2) & 148 & 9 & (6.1) & 1 & (2.2) & 0.457 & 5 & (4.9) & 4 & (5.6) & 1 & (5.6) & 1.000\\ \midrule Symptoms & & & & & & & & & & & & & & & \\ Perceptual disturbance & 210 & (62.3) & 3 & 159 & (61.2) & 51 & (66.2) & 0.503 & 92 & (63.9) & 92 & (59.7) & 26 & (66.7) & 0.637\\ Visual hallucination & 106 & (31.5) & 3 & 81 & (31.2) & 25 & (32.5) & 0.827 & 45 & (31.2) & 45 & (29.2) & 16 & (41.0) & 0.365\\ Tactile hallucination & 32 & (9.5) & 3 & 20 & (7.7) & 12 & (15.6) & 0.038 & 10 & (6.9) & 16 & (10.4) & 6 & (15.4) & 0.246\\ Olfactory hallucination & 2 & (0.6) & 3 & 1 & (0.4) & 1 & (1.3) & 0.405 & 1 & (0.7) & 1 & (0.6) & 0 & (0.0) & 1.000\\ Auditory hallucination & 135 & (40.1) & 3 & 97 & (37.3) & 38 & (49.4) & 0.058 & 61 & (42.4) & 57 & (37.0) & 17 & (43.6) & 0.573\\ Attention or memory impairment & 147 & (43.9) & 5 & 110 & (42.5) & 37 & (48.7) & 0.337 & 68 & (47.6) & 61 & (39.9) & 18 & (46.2) & 0.394\\ Disorders of thought content or form & 257 & (76.3) & 3 & 193 & (74.2) & 64 & (83.1) & 0.128 & 116 & (80.6) & 111 & (72.5) & 30 & (75.0) & 0.264\\ Thought disorder & 177 & (52.8) & 5 & 135 & (52.3) & 42 & (54.5) & 0.732 & 85 & (59.0) & 73 & (48.0) & 19 & (48.7) & 0.143\\ Paranoid delusion & 163 & (48.5) & 4 & 124 & (47.9) & 39 & (50.6) & 0.669 & 63 & (43.8) & 78 & (51.3) & 22 & (55.0) & 0.292\\ Delusional thinking & 142 & (42.1) & 3 & 108 & (41.5) & 34 & (44.2) & 0.683 & 59 & (41.0) & 64 & (41.8) & 19 & (47.5) & 0.757\\ Affective impairment & 178 & (53.1) & 5 & 132 & (51.2) & 46 & (59.7) & 0.186 & 85 & (59.0) & 76 & (50.0) & 17 & (43.6) & 0.133\\ Impairment of drive or motivation & 273 & (81.0) & 3 & 212 & (81.5) & 61 & (79.2) & 0.624 & 115 & (79.9) & 125 & (81.7) & 33 & (82.5) & 0.892\\ Lack of drive & 134 & (40.0) & 5 & 101 & (39.1) & 33 & (42.9) & 0.560 & 63 & (43.8) & 61 & (40.1) & 10 & (25.6) & 0.123\\ Decline in performance & 72 & (21.5) & 5 & 53 & (20.5) & 19 & (24.7) & 0.438 & 29 & (20.1) & 30 & (19.7) & 13 & (33.3) & 0.159\\ Restlessness & 229 & (67.9) & 3 & 180 & (69.2) & 49 & (63.6) & 0.355 & 91 & (63.2) & 106 & (69.3) & 32 & (80.0) & 0.117\\ Sleep disturbance & 173 & (51.5) & 4 & 135 & (52.1) & 38 & (49.4) & 0.669 & 78 & (54.2) & 70 & (45.8) & 25 & (64.1) & 0.086\\ Disturbance of self-awareness & 112 & (33.5) & 6 & 86 & (33.5) & 26 & (33.8) & 0.961 & 62 & (43.1) & 41 & (27.0) & 9 & (23.7) & 0.005{Anxiety or compulsivity-related \\symptoms} & 166 & (49.3) & 3 & 124 & (47.7) & 42 & (54.5) & 0.302 & 66 & (45.8) & 78 & (51.0) & 22 & (55.0) & 0.500\\ Anxiety & 164 & (48.7) & 3 & 122 & (46.9) & 42 & (54.5) & 0.240 & 65 & (45.1) & 77 & (50.3) & 22 & (55.0) & 0.466\\ Obsessive-compulsive symptoms & 7 & (2.1) & 4 & 6 & (2.3) & 1 & (1.3) & 0.697 & 3 & (2.1) & 2 & (1.3) & 2 & (5.0) & 0.281\\ Harm potential & 123 & (36.3) & 1 & 88 & (33.6) & 35 & (45.5) & 0.060 & 53 & (36.6) & 56 & (36.4) & 14 & (35.0) & 0.983\\ Risk of self-harm & 95 & (28.0) & 1 & 69 & (26.3) & 26 & (33.8) & 0.202 & 41 & (28.3) & 42 & (27.3) & 12 & (30.0) & 0.939\\ Risk of harm to others & 60 & (17.8) & 2 & 49 & (18.8) & 11 & (14.3) & 0.365 & 22 & (15.2) & 31 & (20.3) & 7 & (17.5) & 0.516\\ Suicidal ideation & 48 & (14.4) & 6 & 31 & (12.1) & 17 & (22.1) & 0.028 & 21 & (14.6) & 21 & (13.9) & 6 & (15.4) & 0.968\\ \bottomrule \SetCell[c=16]{l}{\footnotesize
Family history of suicide.}\\ \SetCell[c=16]{l}{\footnotesize p-values based on Pearson’s
test; Fisher’s exact test (or Fisher–Freeman–Halton for RxC tables) applied when expected cell counts
5}\\ \end{tblr} \end{adjustbox} \end{minipage} \end{center}
Gender-specific differences
Age distribution was similar between men and women, with identical median ages (Table \ref{tab:socio1}). While most participants in both groups were unmarried, women more frequently reported a permanent relationship or parenthood, though these differences were not statistically significant.
Men more frequently had a migration background (30.4% vs. 18.2%,
). Educational and employment status showed no significant differences, though women more often reported only lower secondary education or no vocational qualification, while men were more often undergoing training.
Women more often presented with detectable serum ethanol (38.5% vs.\17.2%;
) and tended to show higher mean serum ethanol levels at admission (0.80
1.22 g/L) than in men (0.40
0.94 g/L), although this difference did not reach statistical significance (Welch’s t(34.38) = –1.55,
, Cohen’s
).Although no significant gender differences emerged regarding age at first use, consumption patterns, or the number of substances used, certain trends were apparent: women more often reported single-substance use, whereas men more frequently reported stimulant and hallucinogen use.
Clinical profiles were largely comparable, but women more often reported tactile hallucinations (15.6% vs. 7.7%,
) and suicidal ideation (22.1% vs. 12.1%,
) (Table \ref{tab:historynew}). Family history was similar across groups, except that only women reported personality or behavioral disorders (6.7% vs. 0.0%,
).
Age group-specific differences
Distinct sociodemographic and substance use patterns emerged across age groups (Tables \ref{tab:socio1}, \ref{tab:socio2}). Younger patients (
years) were significantly more often single (p = 0.031), while older patients more frequently reported a stable relationship or parenthood. Vocational training was most common among the youngest and employment rates were similar across groups.
Age at first use did not differ, but daily consumption peaked in the 26-40 group. Older patients more often reported multiple substances, though not significantly. THC detection declined with age, whereas opiates and sedatives were more common in older participants.
Clinically, disturbances of self-awareness were significantly more frequent in the youngest group, while no further robust age-related differences appeared in psychopathology or family history (Table \ref{tab:historynew}).
Discussion
This study offers a descriptive analysis of a large clinical DIP sample from psychiatric and toxicological settings. A major strength is the integration of self-reported and toxicologically verified substance data, including serum ethanol levels, enabling a more nuanced characterization of use patterns than studies relying solely on retrospective reports.
Most patients were young men (median age 27), consistent with epidemiological data showing that DIP predominates in this group kendler2019,rognli2022,kirkbride2012,hobbs2018,bonnynoach2017,murrie2020,taha2019. Scandinavian registry studies report 75-80% male cases with median ages around 30, similar to UK cohorts rognli2022,hobbs2018. Young men are also at elevated risk of progression to schizophrenia-spectrum disorders kendler2019,rognli2023, and recent data suggest decreasing age of onset, particularly for cannabis- and stimulant-related cases rognli2022, underscoring the need for early prevention.
Low educational attainment and unemployment in our cohort align with known social determinants of psychosis martinotti2021, and similar associations between socioeconomic adversity and polysubstance use have been reported internationally lal2018,paruk2016. The peak in migration background during 2015-2018 coincides with the European refugee crisis unhcr2015 and reflects evidence of increased psychosis risk among migrants and refugees selten2019,hollander2016,odonoghue2020. This pattern is consistent with socio-developmental models linking migration-related adversity and structural disadvantage to increased psychosis risk morgan2019.
In 2019–2020, the proportion of patients with migration background decreased slightly, consistent with declining migration flows during this period. Despite being recorded only as a binary variable, migration remains an important factor underscoring the need for culturally sensitive, trauma-informed care in urban settings bourque2011,zarafonitis2019,smyth2023,pignon2020.
Polysubstance use was common, with cannabis, stimulants, and ethanol most frequent, consistent with international findings wearne2018methamphetamine,paruk2016,lal2018,vallersnes2016. Age gradients were evident: THC use was highest in patients
25 and declined with age, whereas sedatives and opiates increased among older groups. These patterns parallel international data roncero2014 and general population trends, where benzodiazepine prescribing peaks in midlife while misuse is more common in young adults maust2019. Taken together, our results suggest that sedative and opiate use in DIP largely reflects prescribing and long-term use, whereas cannabis remains more accessible in youth.
Gender differences in substance use were less pronounced. Although the difference did not reach statistical significance, women more often tested positive for ethanol and showed higher mean serum levels (0.8\,g/L vs.\0.4\,g/L), consistent with sex-specific pharmacokinetics and neurobiology sugarman2009,mccaul2019,white2020,Erol2015,vatsalya2023. These factors plausibly explain the observed trend.
Clinical presentation in our cohort was dominated by positive psychotic symptoms, particularly perceptual disturbances, thought disorder, and restlessness, consistent with prior descriptions of acute DIP fiorentini2021,arunogiri2018,radhakrishnan2014. Younger patients more frequently exhibited disturbances of self-awareness, aligning with previous evidence of limited insight and ongoing neurocognitive maturation in early psychosis kim2019. These features highlight the typically acute, affectively charged, and fluctuating nature of DIP presentations, which may complicate initial diagnostic differentiation and require developmentally tailored treatment approaches.
Women differed from men mainly in affective and somatic symptoms, with higher rates of tactile hallucinations, suicidal ideation, and depression. These findings support the notion of an affective subtype of DIP in women, characterized by greater affective burden and suicidality caton2015,mazza2021,salvade2024,Kuehner2017,riecher2018,mahoney2010. Elevated ethanol levels may further increase suicide risk, consistent with evidence that alcohol is a strong predictor of suicidal behavior in females lange2022. Longitudinal data suggest that women with DIP are more likely to progress to bipolar disorder, whereas men more often convert to schizophrenia-spectrum disorders mendrek2017a,irving2021. Notably, only women reported a family history of personality or behavioral disorders, which may indicate gender-specific familial transmission pathways skoglund2019,ma2016. However, as multiple comparisons were performed, these differences should be interpreted with caution.
Comorbidity rates were high, particularly for substance use disorders, consistent with diagnostic overlap and shared vulnerability in DIP populations roncero2014. Beyond depression, no other comorbidities differed by gender, reflecting the higher prevalence of depressive disorders in women albert2015,Tang2022,salk2017, likely due to hormonal, psychosocial, and trauma-related factors riecher2018,Kuehner2017,mazza2021. These patterns support the view that DIP in women may represent an affective subtype, while men’s risk is more closely linked to psychotic trajectories. Compared to first-episode psychosis, where men more often meet criteria for substance use disorders salvade2024, this suggests divergent exposure pathways.
Our study has several methodological strengths, including a large sample size, dual-center design, and the integration of toxicological, clinical, and demographic data. Nevertheless, certain limitations must be acknowledged. Its retrospective design entails reliance on clinical documentation, with incomplete toxicological testing and short detection windows for substances such as GHB, LSD, and psilocybin, which may have led to missed exposures. Consequently, part of the data relied on self-report, subject to recall bias and underreporting. Diagnostic assessment was based on clinical ICD-10 judgments without standardized instruments, potentially reducing reliability, although all cases were evaluated by board-certified psychiatrists. The imbalanced gender distribution (77% male) limits power for female-specific analyses. Finally, the absence of follow-up prevents evaluation of diagnostic stability or long-term trajectories, in line with previous studies reporting low diagnostic stability of DIP and frequent conversion to primary psychotic disorders degenhardt2012,mauri2017,bramness2024,bramness2024b].
Clinically, our findings highlight the need for individualized care that considers developmental stage, gender, and migration-related vulnerabilities. The high prevalence of polysubstance use and diagnostic uncertainty, particularly with NPS, call for broader toxicological screening, longitudinal follow-up, and closer psychiatry–toxicology collaboration.
A
Author Contribution
N.B. and S.G. drafted the main manuscript text. B.H. provided statistical advice and contributed to data interpretation. All authors critically revised the manuscript and approved the final version.
Declarations
A
Competing Interests
Professor Adorjan serves as an editor for the European Archives of Psychiatry and Clinical Neuroscience. She will have no involvement in the editorial handling or peer-review process for this manuscript. The remaining authors declare that they have no competing interests.
References:
Fiorentini, Alessio and Cant ù, Filippo and Crisanti, Camilla and Cereda, Guido and Oldani, Lucio and Brambilla, Paolo (2021) Substance-Induced Psychoses: An Updated Literature Review. Frontiers in Psychiatry 12 https://doi.org/10.3389/fpsyt.2021.694863, Frontiers Media SA, December, 1664-0640
Kendler, Kenneth S. and Ohlsson, Henrik and Sundquist, Jan and Sundquist, Kristina (2019) Prediction of Onset of Substance-Induced Psychotic Disorder and Its Progression to Schizophrenia in a Swedish National Sample. American Journal of Psychiatry 176(9): 711--719 https://doi.org/10.1176/appi.ajp.2019.18101217, American Psychiatric Association Publishing, September, 1535-7228
Ricci, Valerio and Martinotti, Giovanni and Maina, Giuseppe (2024) Substance-Induced Psychosis: Diagnostic Challenges and Phenomenological Insights. Psychiatry International 5(4): 759--772 https://doi.org/10.3390/psychiatryint5040052, MDPI AG, October, 2673-5318
Irving, Jessica and Colling, Craig and Shetty, Hitesh and Pritchard, Megan and Stewart, Robert and Fusar-Poli, Paolo and McGuire, Philip and Patel, Rashmi (2021) Gender differences in clinical presentation and illicit substance use during first episode psychosis: a natural language processing, electronic case register study. BMJ Open 11(4): e042949 https://doi.org/10.1136/bmjopen-2020-042949, BMJ, April, 2044-6055
Vallersnes, Odd Martin and Dines, Alison M. and Wood, David M. and Yates, Christopher and Heyerdahl, Fridtjof and Hovda, Knut Erik and Giraudon, Isabelle and Dargan, Paul I. (2016) Psychosis associated with acute recreational drug toxicity: a European case series. BMC Psychiatry 16(1) https://doi.org/10.1186/s12888-016-1002-7, Springer Science and Business Media LLC, August, 1471-244X
Karimi, Isaac and Pooyanmehr, Nioosha (2024) Substances led to Psychosis: A Systematic Review. The Open Psychology Journal 17(1) https://doi.org/10.2174/0118743501297735240510161825, Bentham Science Publishers Ltd., May, 1874-3501
Rognli, Eline Borger and Taipale, Heidi and Hjorth øj, Carsten and Mittendorfer-Rutz, Ellenor and Bramness, J ørgen G. and Heiberg, Ina H. and Niemel ä, Solja (2022) Annual incidence of substance-induced psychoses in Scandinavia from 2000 to 2016. Psychological Medicine 53(11): 5246--5255 https://doi.org/10.1017/s003329172200229x, Cambridge University Press (CUP), August, 1469-8978
Rognli, Eline B. and Heiberg, Ina H. and Jacobsen, Bjarne K. and H øye, Anne and Bramness, J ørgen G. (2023) Transition From Substance-Induced Psychosis to Schizophrenia Spectrum Disorder or Bipolar Disorder. American Journal of Psychiatry 180(6): 437--444 https://doi.org/10.1176/appi.ajp.22010076, American Psychiatric Association Publishing, June, 1535-7228
Mendrek, Adrianna and Fattore, Liana (2017) Sex differences in drug-induced psychosis. Current Opinion in Behavioral Sciences 13: 152--157 https://doi.org/10.1016/j.cobeha.2016.11.019, Elsevier BV, February, 2352-1546
Murrie, Benjamin and Lappin, Julia and Large, Matthew and Sara, Grant (2019) Transition of Substance-Induced, Brief, and Atypical Psychoses to Schizophrenia: A Systematic Review and Meta-analysis. Schizophrenia Bulletin 46(3): 505--516 https://doi.org/10.1093/schbul/sbz102, Oxford University Press (OUP), October, 1745-1701
Roncero, Carlos and Daigre, Costanza and Grau-L ópez, Lara and Barral, Carmen and P érez-Pazos, Jesus and Mart ínez-Luna, Nieves and Casas, Miquel (2014) An International Perspective and Review of Cocaine-Induced Psychosis: A Call to Action. Substance Abuse 35(3): 321--327 https://doi.org/10.1080/08897077.2014.933726, SAGE Publications, July, 1547-0164
Mazza, Marianna and Caroppo, Emanuele and De Berardis, Domenico and Marano, Giuseppe and Avallone, Carla and Kotzalidis, Georgios D. and Janiri, Delfina and Moccia, Lorenzo and Simonetti, Alessio and Conte, Eliana and Martinotti, Giovanni and Janiri, Luigi and Sani, Gabriele (2021) Psychosis in Women: Time for Personalized Treatment. Journal of Personalized Medicine 11(12): 1279 https://doi.org/10.3390/jpm11121279, MDPI AG, December, 2075-4426
Caton, Carol L. M. and Xie, Haiyi and Drake, Robert E. and McHugo, Gregory (2014) Gender Differences in Psychotic Disorders With Concurrent Substance Use. Journal of Dual Diagnosis 10(4): 177--186 https://doi.org/10.1080/15504263.2014.961882, Informa UK Limited, October, 1550-4271
Martinotti, Giovanni and Merino Del Villar, Cristina and Garcia Cordoba, Andr és and Andr és Tubau, Llu ís and Castro S ánchez, Ivan and Di Carlo, Francesco and Chiappini, Stefania and Pettorruso, Mauro and Schifano, Fabrizio and Di Giannantonio, Massimo (2021) Club Drugs and Psychiatric Sequelae: An Issue of Vulnerability and Previous Psychiatric History. International Journal of Environmental Research and Public Health 18(13): 6944 https://doi.org/10.3390/ijerph18136944, MDPI AG, June, 1660-4601
Hobbs, Melissa and Kalk, Nicola J and Morrison, Paul D and Stone, James M (2018) Spicing it up - synthetic cannabinoid receptor agonists and psychosis - a systematic review. European Neuropsychopharmacology 28(12): 1289--1304 https://doi.org/10.1016/j.euroneuro.2018.10.004, Elsevier BV, December, 0924-977X
Bonny-Noach, Hagit and Sagiv-Alayoff, Moran (2017) Rescuing Israeli travellers: effects of substance abuse, mental health, geographic region of rescue, gender and age of rescuees. Journal of Travel Medicine 24(5) https://doi.org/10.1093/jtm/tax045, Oxford University Press (OUP), July, 1708-8305
Degenhardt, Louisa and Hall, Wayne (2012) Extent of illicit drug use and dependence, and their contribution to the global burden of disease. The Lancet 379(9810): 55--70 https://doi.org/10.1016/s0140-6736(11)61138-0, Elsevier BV, January, 0140-6736
Zarafonitis-M üller, Susanna and Leopold, Karolina and Batra, Marcel and Kallenbach, Mich èle and Burkhardt, Eva and J äckel, Dorothea and Bechdolf, Andreas (2018) Service Use of an Early Intervention Centre, Clinical and Sociodemographic Characteristics of Young Adults with Early Psychosis with and without Migration Background. Psychiatrische Praxis 46(01): 13--19 https://doi.org/10.1055/s-0043-124050, Georg Thieme Verlag KG, March, 1439-0876
Pignon, Baptiste and Eaton, Scott and Sch ürhoff, Franck and Sz öke, Andrei and McGorry, Patrick and O ’Donoghue, Brian (2021) Temporal variation in the incidence of treated psychotic disorders in young people. Schizophrenia Research 231: 221--226 https://doi.org/10.1016/j.schres.2021.03.011, Elsevier BV, May, 0920-9964
Radhakrishnan, Rajiv and Wilkinson, Samuel T. and D'Souza, Deepak Cyril (2014) Gone to Pot - A Review of the Association between Cannabis and Psychosis. Frontiers in Psychiatry 5 https://doi.org/10.3389/fpsyt.2014.00054, Frontiers Media SA, May, 1664-0640
Selten, Jean-Paul and van der Ven, Els and Termorshuizen, Fabian (2019) Migration and psychosis: a meta-analysis of incidence studies. Psychological Medicine 50(2): 303--313 https://doi.org/10.1017/s0033291719000035, Cambridge University Press (CUP), February, 1469-8978
Smyth, Emily and Steel, Craig and Ellett, Lyn (2023) The prevalence of non-affective psychosis in refugee populations: A systematic review. Schizophrenia Research 260: 99--112 https://doi.org/10.1016/j.schres.2023.08.011, Elsevier BV, October, 0920-9964
Morgan, Craig and Knowles, Gemma and Hutchinson, Gerard (2019) Migration, ethnicity and psychoses: evidence, models and future directions. World Psychiatry 18(3): 247--258 https://doi.org/10.1002/wps.20655, Wiley, September, 2051-5545
O ’Donoghue, Brian and Downey, Linglee and Eaton, Scott and Mifsud, Nathan and Kirkbride, James B. and McGorry, Patrick (2020) Risk of psychotic disorders in migrants to Australia. Psychological Medicine 51(7): 1192--1200 https://doi.org/10.1017/s0033291719004100, Cambridge University Press (CUP), January, 1469-8978
Hollander, Anna-Clara and Dal, Henrik and Lewis, Glyn and Magnusson, Cecilia and Kirkbride, James B and Dalman, Christina (2016) Refugee migration and risk of schizophrenia and other non-affective psychoses: cohort study of 1.3 million people in Sweden. BMJ : i1030 https://doi.org/10.1136/bmj.i1030, BMJ, March, 1756-1833
Kirkbride, James B. and Errazuriz, Antonia and Croudace, Tim J. and Morgan, Craig and Jackson, Daniel and Boydell, Jane and Murray, Robin M. and Jones, Peter B. (2012) Incidence of Schizophrenia and Other Psychoses in England, 1950 –2009: A Systematic Review and Meta-Analyses. PLoS ONE 7(3): e31660 https://doi.org/10.1371/journal.pone.0031660, Public Library of Science (PLoS), Scott, James G., March, 1932-6203
Yadav, Prateek and Banyal, Navdeep and Bhattacharyya, Debajyoti (2018) Study to determine the prevalance of substance use and factors associated with it, in first-episode of psychosis. Industrial Psychiatry Journal 27(2): 264 https://doi.org/10.4103/ipj.ipj_86_18, Medknow, 0972-6748
Taha, Mai and Taalab, Yasmeen M. and Abo-Elez, Warda F. and Eldakroory, Sahar A. (2019) Cannabis and Tramadol are Prevalent among the First Episode Drug-Induced Psychosis in the Egyptian Population: Single Center Experience. Reports 2(2): 16 https://doi.org/10.3390/reports2020016, MDPI AG, May, 2571-841X
Arunogiri, Shalini and Foulds, James A and McKetin, Rebecca and Lubman, Dan I (2018) A systematic review of risk factors for methamphetamine-associated psychosis. Australian & New Zealand Journal of Psychiatry 52(6): 514--529 https://doi.org/10.1177/0004867417748750, SAGE Publications, January, 1440-1614
Paruk, Saeeda and Jhazbhay, Khatija and Singh, Keshika and Sartorius, Benn and Burns, Jonathan K (2016) A comparative study of socio ‐demographic and substance use correlates in early ‐onset psychosis. Early Intervention in Psychiatry 12(3): 339--347 https://doi.org/10.1111/eip.12330, Wiley, April, 1751-7893
Bramness, J ørgen G. and Hjorth øj, Carsten and Niemel ä, Solja and Taipale, Heidi and Rognli, Eline Borger (2024) Discussing the concept of substance-induced psychosis (SIP). Psychological Medicine 54(11): 2852--2856 https://doi.org/10.1017/s0033291724001442, Cambridge University Press (CUP), August, 1469-8978
Bramness, J ørgen G. and Hjorth øj, Carsten and Niemel ä, Solja and Taipale, Heidi and Rognli, Eline B. (2024) Problematic diagnosis of substance-induced disorders in ICD-11. European Psychiatry 67(1) https://doi.org/10.1192/j.eurpsy.2024.1763, Royal College of Psychiatrists, 1778-3585
Mauri, M.C. and Di Pace, C. and Reggiori, A. and Paletta, S. and Colasanti, A. (2017) Primary psychosis with comorbid drug abuse and drug-induced psychosis: Diagnostic and clinical evolution at follow up. Asian Journal of Psychiatry 29: 117--122 https://doi.org/10.1016/j.ajp.2017.04.014, Elsevier BV, October, 1876-2018
{United Nations High Commissioner for Refugees (UNHCR)}. The Sea Route to Europe: The Mediterranean Passage in the Age of Refugees. Accessed: 2025-10-17. https://www.unhcr.org/us/media/sea-route-europe-mediterranean-passage-age-refugees, UNHCR, 2015
Albert, Paul R. (2015) Why is depression more prevalent in women?. Journal of Psychiatry and Neuroscience 40(4): 219--221 https://doi.org/10.1503/jpn.150205, Canadian Science Publishing, July, 1488-2434
Tang, Jun and Zhang, Tianyi (2022) Causes of the male-female ratio of depression based on the psychosocial factors. Frontiers in Psychology 13 https://doi.org/10.3389/fpsyg.2022.1052702, Frontiers Media SA, November, 1664-1078
Riecher-R össler, Anita (2017) Sex and gender differences in mental disorders. The Lancet Psychiatry 4(1): 8--9 https://doi.org/10.1016/s2215-0366(16)30348-0, Elsevier BV, January, 2215-0366
Kuehner, Christine (2017) Why is depression more common among women than among men?. The Lancet Psychiatry 4(2): 146--158 https://doi.org/10.1016/s2215-0366(16)30263-2, Elsevier BV, February, 2215-0366
Salk, Rachel H. and Hyde, Janet S. and Abramson, Lyn Y. (2017) Gender differences in depression in representative national samples: Meta-analyses of diagnoses and symptoms.. Psychological Bulletin 143(8): 783--822 https://doi.org/10.1037/bul0000102, American Psychological Association (APA), August, 0033-2909
Mahoney, James J. and Hawkins, Rollin Y. and De La Garza, Richard and Kalechstein, Ari D. and Newton, Thomas F. (2010) Relationship between gender and psychotic symptoms in cocaine-dependent and methamphetamine-dependent participants. Gender Medicine 7(5): 414--421 https://doi.org/10.1016/j.genm.2010.09.003, Elsevier BV, October, 1550-8579
Maust, Donovan T. and Lin, Lewei A. and Blow, Frederic C. (2019) Benzodiazepine Use and Misuse Among Adults in the United States. Psychiatric Services 70(2): 97--106 https://doi.org/10.1176/appi.ps.201800321, American Psychiatric Association Publishing, February, 1557-9700
{Deutsche Suchthilfestatistik (DSHS)}. Jahresbericht 2023 der Deutschen Suchthilfestatistik – Kapitel 3.1: Klientel der ambulanten Suchtberatung und -behandlung. Kapitel 3.1, Accessed July 2025. https://www.suchthilfestatistik.de, 2023
Wearne, Travis A. and Cornish, Jennifer L. (2018) A Comparison of Methamphetamine-Induced Psychosis and Schizophrenia: A Review of Positive, Negative, and Cognitive Symptomatology. Frontiers in Psychiatry 9 https://doi.org/10.3389/fpsyt.2018.00491, Frontiers Media SA, October, 1664-0640
Salvad é, Aude and Golay, Philippe and Abrahamyan, Lilith and Bonnarel, Vincent and Solida, Alessandra and Alameda, Luis and Ramain, Julie and Conus, Philippe (2024) Gender differences in first episode psychosis: Some arguments to develop gender specific treatment strategies. Schizophrenia Research 271: 300--308 https://doi.org/10.1016/j.schres.2024.07.046, Elsevier BV, September, 0920-9964
Kim, Jeong-Youn and Jeon, Hyeonjin and Kwon, Aeran and Jin, Min Jin and Lee, Seung-Hwan and Chung, Young-Chul (2019) Self-Awareness of Psychopathology and Brain Volume in Patients With First Episode Psychosis. Frontiers in Psychiatry Volume 10 - 2019 https://doi.org/10.3389/fpsyt.2019.00839, https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2019.00839, Frontiers Media SA, November, 1664-0640
Balda çara, Leonardo and Ramos, Artur and Castaldelli-Maia, Jo ão Maur ício (2023) Managing drug-induced psychosis. International Review of Psychiatry 35(5 –6): 496--502 https://doi.org/10.1080/09540261.2023.2261544, Informa UK Limited, August, 1369-1627
McCaul, Mary E. and Roach, Deidra and Hasin, Deborah S. and Weisner, Constance and Chang, Grace and Sinha, Rajita (2019) Alcohol and Women: A Brief Overview. Alcoholism: Clinical and Experimental Research 43(5): 774--779 https://doi.org/10.1111/acer.13985, Wiley, March, 1530-0277
White, A (2020) Gender Differences in the Epidemiology of Alcohol Use and Related Harms in the United States. Alcohol Research: Current Reviews 40(2) https://doi.org/10.35946/arcr.v40.2.01, National Institute on Alcohol Abuse and Alcoholism, 2169-4796
Sugarman, Dawn E. and DeMartini, Kelly S. and Carey, Kate B. (2009) Are Women at Greater Risk? An Examination of Alcohol-Related Consequences and Gender. The American Journal on Addictions 18(3): 194--197 https://doi.org/10.1080/10550490902786991, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2909382/, PMC2909382
Erol, Almila and Karpyak, Victor M. (2015) Sex and gender-related differences in alcohol use and its consequences: Contemporary knowledge and future research considerations. Drug and Alcohol Dependence 156: 1--13 https://doi.org/10.1016/j.drugalcdep.2015.08.023, Elsevier BV, November, 0376-8716
Vatsalya, Vatsalya and Byrd, Nia D. and Stangl, Bethany L. and Momenan, Reza and Ramchandani, Vijay A. (2023) Influence of age and sex on alcohol pharmacokinetics and subjective pharmacodynamic responses following intravenous alcohol exposure in humans. Alcohol 107: 144--152 https://doi.org/10.1016/j.alcohol.2022.08.010, Elsevier BV, March, 0741-8329
Bourque, F. and van der Ven, E. and Malla, A. (2010) A meta-analysis of the risk for psychotic disorders among first- and second-generation immigrants. Psychological Medicine 41(5): 897--910 https://doi.org/10.1017/s0033291710001406, Cambridge University Press (CUP), July, 1469-8978
Skoglund, Charlotte and Tiger, Annika and R ück, Christian and Petrovic, Predrag and Asherson, Philip and Hellner, Clara and Mataix-Cols, David and Kuja-Halkola, Ralf (2019) Familial risk and heritability of diagnosed borderline personality disorder: a register study of the Swedish population. Molecular Psychiatry 26(3): 999--1008 https://doi.org/10.1038/s41380-019-0442-0, Springer Science and Business Media LLC, June, 1476-5578
Ma, Guorong and Fan, Hongying and Shen, Chanchan and Wang, Wei (2016) Genetic and Neuroimaging Features of Personality Disorders: State of the Art. Neuroscience Bulletin 32(3): 286--306 https://doi.org/10.1007/s12264-016-0027-8, Springer Science and Business Media LLC, April, 1995-8218
Lange, Shannon and Roerecke, Michael and Orpana, Heather and Bagge, Courtney and Rehm, J ürgen (2022) Alcohol use and the gender-specific risk of suicidal behavior: a systematic review and meta-analysis protocol. Systematic Reviews 11(1) https://doi.org/10.1186/s13643-022-02159-0, Springer Science and Business Media LLC, December, 2046-4053
{Beckman Coulter, Inc.}. AU480 Chemistry Analyzer. Accessed: 2025-09-02. https://www.beckmancoulter.com/de/products/chemistry/au480, 2025
{Thermo Fisher Scientific Inc.}. Indiko Plus Clinical Chemistry Analyzer. Accessed: 2025-09-02. https://www.thermofisher.com/order/catalog/product/98640000, 2025
Data Availability Statement
The data that support the findings of this study are available from the corresponding author, upon reasonable request.
Declarations
Professor Kristina Adorjan serves as an editor for the European Archives of Psychiatry and Clinical Neuroscience. She had no role in the editorial decision-making or peer review process for this manuscript. The remaining authors declare that they have no competing interests.
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