Correlates of Psychotic Like Experiences among Young Adults during the Covid19 Pandemic across Four Countries

Correlates of Psychotic Like Experiences among Young Adults during the Covid19 Pandemic across Four Countries | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Correlates of Psychotic Like Experiences among Young Adults during the Covid19 Pandemic across Four Countries Helin Yilmaz Kafali, İnanç Kabasakal, Dymytro Martsenkovskyi, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7340400/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background We aimed to identify predictors and specific country factors associated with psychotic-like experiences (PLEs), and to examine whether depressive and anxiety symptoms, and sleep problems mediates the relationship between daily reported COVID-19 death number and PLEs. Methods A total of 854 participants completed online questionnaires assessing PLEs, depressive and anxiety symptoms, and sleep problems using Community Assessment of Psychic Experiences-42-Positive Subscale (CAPE-42-Pos), Patient Health Questionnaire (PHQ), Generalized Anxiety Disorder-7 (GAD), and Pittsburg Sleep Quality Index (PSQI). Psychiatric/COVID-19 infection history and cigarette/alcohol/substance use, daily new COVID-19 cases/deaths were noted on survey date. Association rule mining and PROCESS Macro Mediation analysis were applied. Results COVID-19 death toll and PSQI had a non-linear, PHQ and GAD had a linear relationship with PLEs. Cigarette usage increased, but alcohol usage decreased PLEs risk. PHQ-Total score partially mediated the association between COVID-19-related death toll and CAPE-Pos-Total score. Rule mining revealed that in Turkey, substance/cigarette use and suicidality; in Nigeria, sleep problems and depression; in India, suicidality and sleep disturbances; and in Spain, COVID-19 history and psychiatric symptoms were strongly associated with high CAPE scores. Conclusions Reported death number, depressive and anxiety symptoms, and sleep disturbances could be used to predict PLEs. As a mediating factor, depressive symptoms could be an important target for preventing PLEs during pandemics. However, country-specific risk factors should also be considered for targeted interventions. Psychotic-like experiences pandemic COVID-19 data mining cross-cultural young adults Figures Figure 1 Figure 2 Figure 3 Figure 4 OBJECTIVES The COVID-19 pandemic brought unprecedented mental health challenges across the globe, and young adults appeared to be among the groups most affected. Psychotic-like experiences (PLEs) — subclinical symptoms that do not necessarily indicate a psychotic disorder — have been linked to various adverse mental health outcomes. While some studies have addressed PLEs during the pandemic, most have been conducted in single-country settings, limiting understanding of cross-cultural differences. Between July 2020 and July 2021, we collected data from young adults aged 18–24 years in Turkey, Nigeria, Spain, and India, using an online survey format. The aim was to examine both common and country-specific correlates of PLEs during the pandemic. We also investigated whether depressive symptoms, anxiety, and sleep problems might mediate the association between daily COVID-19-related death counts and PLEs. In addition to conventional analyses, a data mining approach (association rule mining) was used to detect patterns that may be complex, and therefore less visible with standard methods. This work was developed within an international collaboration focused on generating culturally informed knowledge to guide prevention and early intervention. The dataset presented here has not previously been reported in full, nor with the current cross-national perspective. We expect that the data will be useful for researchers and policymakers seeking to understand how pandemic-related stressors interact with mental health vulnerabilities in different cultural contexts. Ultimately, such insights may help design more effective, targeted screening and intervention strategies for future public health crises. 1. Background Despite a variety of definitions, psychotic-like experiences (PLEs) are widely conceptualized as subclinical psychotic experiences (unusual thoughts, delusions, hallucinations, and behavioral abnormalities) occurring outside the context of sleep and substance use, which are not necessarily associated with distress, help-seeking behavior, or clinical psychotic disorders [ 1 ]. Initially, the clinical significance of PLEs focused on their association with the future risk of psychotic disorders, assuming a premorbid presentation or continuum phenotype of psychosis [ 1 ]. Subsequent research found that those who reported PLEs during childhood and adolescence were also at increased risk of both concurrent and subsequent non-psychotic disorders, suicidal behavior, multimorbidity, and poor level of functioning [ 2 , 3 ]. The coronavirus disease 2019 (COVID-19) pandemic posed unprecedented risks to the safety, well-being, and mental health of global populations [ 4 ]. Cumulative evidence suggests that pandemic-related restrictions, such as lockdowns, have resulted in an increase in known risk factors for mental health problems and have contributed to the exacerbation of pre-existing vulnerabilities [ 5 ]. Overall, data published to date suggest that while some youths demonstrated resilience, a significant cohort of young adults experienced psychological distress and a consequent increase in the prevalence of clinically significant mental health problems, mainly an elevated level of depression, anxiety, and stress [ 5 ]. Although there is evidence that the pandemic outbreak was a potent adverse environmental factor, its association to increased PLEs occurrence and risk factors is unclear [ 6 – 9 ]. To date, several studies reported a number of risk factors associated with PLEs during the COVID-19 pandemic including female gender, previous mental disorder, having friends/relatives infected with SARS-Cov-2, childhood trauma, suicidal ideation, SARS-CoV-2 infection, insomnia, severe depression and anxiety levels, poor family functioning, and daily COVID-19-related deaths [ 6 – 14 ]. However, the best of our knowledge, these factors have only been investigated in local sample groups, and no studies thoroughly examined country-specific factors related to PLEs during the COVID-19 pandemic. In the current study, our primary objective was to compare countries (Turkey, Nigeria, Spain, and India) regarding PLEs, depressive and anxiety levels, and sleep problems during the pandemic and also identify which variables most strongly predicted PLEs among young adults across the entire sample. Secondly, drawing on the findings of Simor et al. (2021)[ 6 ], which indicated that daily reports of country-specific COVID-19 deaths were predictive of increased negative mood, psychotic-like experiences, and somatic complaints on the same day, as well as diminished subjective sleep quality the following night, this study aimed to investigate whether depressive and anxiety severity, along with sleep problems, serve as mediators in the relationship between daily COVID-19 cases/deaths and PLEs. Thirdly, we sought to determine country-specific risk factors for PLEs in young adults during the pandemic. To uncover hidden and potentially valuable patterns in the data collected from four countries, we employed an association rule mining approach [ 15 ]. 2. Methods 2.1. Participants Using G*power 3.1. program, a power analysis was applied (ANOVA Fixed Effect One-Way, alpha 0.05, power of 0.95, effect size = 0.25, number of groups = 4) to calculate the minimum sample size for the current study. Minimum total sample size required for the study was 280. The convenience sample of 854 participants from India (n = 356), Turkey (n = 264), Spain (n = 135), and Nigeria (n = 99) were recruited using the snowball reference technique between July 2020 and July 2021. With the snowball sampling technique, a method where existing participants refer others from their network, the researchers provided referrals to recruit samples required for the study [ 16 ]. Participants were included in the study if they were young adults aged 18–24 years old, willing to participate and gave informed consent. 176 participants were excluded from the final sample due to the following reasons: (1) Out of age range (India (n = 69), Turkey (n = 9), Spain (n = 13), Nigeria (n = 21)) and (2) not willing to participate (India (n = 12), Turkey (n = 56), Spain (n = 2), Nigeria (n = 2)). 2.2. Assessments and Procedure All eligible participants were asked to fill the web-based consisting of socio-demographic data, the positive psychotic symptoms dimension of the Community Assessment of Psychic Experiences-42 (CAPE-Pos), Patient Health Questionnaire (PHQ), Generalized Anxiety Disorder-7 (GAD-7), and Pittsburg Sleep Quality Index (PSQI). The questionnaire was distributed via social media (WhatsApp, Instagram, or Facebook) and communication networks of universities. Detailed information about the assessment materials can be found in Supplementary Material. 2.3. Statistics Descriptive statistics (mean, standard deviation, median, interquartile range, and frequencies) and group comparison statistics were performed. The normality of the quantitative variables was assessed via histogram, skewness, kurtosis, and normality plots. Country was treated as a grouping variable (n = 4). Between-group comparisons of categorical variables were carried out using χ2 or Fisher's exact test. The Kruskal-Wallis test was utilized to detect significant differences between countries regarding age, CAPE-Pos, GAD, PHQ, and PSQI scores. Dunn-Bonferroni post-hoc test was used to determine significant pairwise comparisons. To examine the predictors of CAPE-Pos-Frequency, we employed a Generalized Additive Model (GAM) that included both continuous and categorical variables. Continuous predictors were the daily number of newly reported SARS-CoV-2 cases, daily COVID-19-related deaths, PHQ scores, GAD scores, and PSQI total scores. Categorical predictors included gender, presence of psychiatric disorders, family history of psychiatric illness, suicidal thoughts or ideation, substance use (including cigarettes, alcohol, or drugs), history of COVID-19 infection, and family history of COVID-19 infection. This model is particularly effective for non-parametric data, as it provides flexibility in modeling non-linear relationships [ 17 ]. To analyze the mediating effects of PHQ-total, GAD-total, and PSQI-total scores on the relationship between daily reported new SARS-CoV-2 cases and COVID-19-related deaths, and the CAPE-Pos-Total score, we employed Partial Least Squares (PLS) Path Analysis using SmartPLS 4 since the data did not satisfy the assumptions of multivariate normality ( https://webpower.psychstat.org/models/kurtosis/results.php?url=7f4b62d3df5877af240c8bd06dc382f6 ). Detailed statistical information regarding the Association Rule Mining algorithm is presented in the Supplementary Material. Data were analyzed with R.Studio 2023.03, SmartPLS-4, and RuleGenerator Software. Statistical significance was set at α < 0.05. 3. Results 3.1. Sociodemographic characteristics of the participants The mean age of participants was lowest in Turkey (20.1 ± 1.9 years) and highest in Nigeria (22.4 ± 1.6 years). Significant differences were observed in the distribution of age ( H (3) = 121.876, p < 0.001) and gender ( χ² (5) = 37.171, p < 0.001) across countries. However, the prevalence of diagnosed psychiatric disorders did not significantly differ between countries. The lowest prevalence of psychiatric disorders in first-degree family members was reported in Nigeria (8.1%), while the highest was observed in Spain (21.5%). Turkey reported the highest rate of cigarette use in the past month ( n = 96, 36.7%), while Spain reported the highest alcohol use ( n = 98, 72.6%). Spain also had the highest lifetime illicit drug use ( n = 96, 36.7%). The rates of cigarette, alcohol, and illicit drug use varied significantly between countries (Table 1 ). The highest prevalence of SARS-CoV-2 infection reported in Spain ( n = 27, 20%), and the lowest was in Nigeria ( n = 4, 4%). In India, nearly half of the participants reported a family history of COVID-19 infection. Additionally, significant differences were observed across countries in the prevalence of suicidal thoughts in the past month and lifetime suicide attempts (Table 1 ). The highest rates of suicidal thoughts and lifetime suicide attempts were reported in India, while the lowest were reported in Spain. Table 1 Sociodemographic features of the participants according to their countries Countries Statistics Turkey (TR) (n = 264) India (IN) (n = 356) Spain (ES) (n = 135) Nigeria (NI) (n = 99) H, X 2 p value Age (Mean ± SD) (Median (IQR)) 20±1.9 19 (4) 20.3±1.5 20 (2) 21.8±0.9 21.5 (2) 22.4±1.6 23 (5) 121.876 < 0.001 a Gender (Female) (n(%)) 167 (63.3) 292 (82) 82 (60.7) 68 (68.7) 37.171 < 0.001 b Diagnosed psychiatric disorder (n (%)) 83 (31.4) 89 (25) 28 (20.7) 24 (24.2) 6.285 0.09 b Psychopharmacological treatment (n (%)) 38 (14.4) 20 (5.6) 8 (5.9) 5 (5.1) 17.201 < 0.001 b Psychiatric disorder in first degree family members (n (%)) 54 (20.5) 56 (15.7) 29 (21.5) 8 (8.1) 10.992 0.012 b Cigarette use in past month (n (%)) 96 (36.7) 25 (7) 25 (18.5) 2 (2) 166.106 < 0.001 b Alcohol use in past month (n (%)) 112 (42.4) 38 (10.7) 98 (72.6) 26 (26.3) 508.744 < 0.001 b Life-time illicit drug use (n (%)) 34 (12.9) 39 (11) 92 (68.1) 19 (19.2) 285.725 < 0.001 b History of COVID-19 infection (n (%)) 14 (5.3) 50 (14) 27 (20) 4 (4) 29.260 < 0.001 b History of COVID-19 infection in family (n (%)) 64 (24.1) 141 (52.6) 53 (39.3) 10 (10.1) 46.443 < 0.001 b Suicidal thoughts in last month (n (%)) 41 (15.5) 88 (24.7) 9 (6.7) 13 (13.1) 27.544 < 0.001 b Life-time suicide attempts (n (%)) 45 (17) 62 (17.4) 7 (5.2) 15 (15.2) 15.496 0.001 b CAPE-Pos Total Score (Mean ± SD) (Median (IQR)) 29.1±0.4 24 (11) 30.1±0.3 29 (10) 26.1±0.4 25 (6.5) 29 ±0.7 28 (10) 34.297 < 0.001 a PHQ Total score (Mean ± SD) (Median (IQR)) 11.2±0.4 10 (10) 12±0.4 9 (12) 7:8 ±0.5 6 (8) 7±06 5 (9.5) 39.024 < 0.001 a GAD Total Score (Mean ± SD) (Median (IQR)) 9.6±0.3 10 (8) 8.8±0.3 5.8(10) 6.2 ±0.4 5.1 (5.5) 6 ±0.5 5 (8) 54.090 < 0.001 a PSQI Total Score (Mean ± SD) (Median (IQR)) 6.6 ±0.1 6(5) 6.2±0.1 6(5) 5.4 ±0.2 5(5) 5.3 ±0.3 5(5) 19.365 < 0.001 a a Kruskal Wallis test; b Chi Square test; CAPE-Pos: Community Assessment of Psychic Experiences, Positive subscale; PHQ: Patient Health Questionnaire; GAD: Generalized Anxiety Disorder; PSQI: Pittsburg Sleep Quality Index. 3.2. Comparison of PLEs, depression, anxiety and sleep difficulties between the countries The prevalence of experiencing at least one CAPE-Positive (CAPE-Pos) symptom “often” or “almost always” was 36% in Turkey, 72.7% in Nigeria, 73.9% in India, and 51.1% in Spain. Significant differences were observed between countries in terms of CAPE-Pos ( H = 34.297, p < 0.001), GAD ( H = 54.090, p < 0.001), PHQ ( H = 39.024, p < 0.001), and PSQI ( H = 19.365, p < 0.001) scores (Table 1 ). CAPE-Pos total score did not differ between India, Turkey, and Nigeria. However, Spain significantly had the lowest CAPE-Pos-Total score (Figure S1 ). Additionally, Turkey and India had significantly higher GAD and PHQ total scores than Nigeria and India. Turkey had significantly higher PSQI score compared to Nigeria and Spain. PSQI score of India, Spain, and Nigeria did not significantly differ (Fig. 1 ). 3.3. The most predictive factors of PLEs in the whole sample The GAM model explained 36.8% of the deviance. The model was fitted using Generalized Cross Validation (GCV) as smoothing parameter selection method. The Root Mean Square (RMS) GCV score gradient at convergence was below 0.001, indicating that the model achieved a stable solution. Our model indicated that among the categorical variables cigarette usage significantly increased but alcohol usage significantly decreased the risk of presence of PLEs (Table 2 ). Among the continuous variables, the number of reported new deaths related to COVID-19 infection, PSQI, PHQ, and GAD total scores significantly predicted CAPE-Pos-Frequency (Table 2 , Fig. 2 ). Basis dimension checking results showed that edf values of number of new deaths related to COVID-19 infection (3.82) and PSQI (2.52) was higher than 1 indicating a non-linear relationship between CAPE-Pos and these variables. By analyzing the slope of the fitted curve, we identified change points that indicate areas of significant change in the predicted values. The CAPE-Pos total score increased alongside the daily reported new COVID-19-related deaths, rising from 216 to 2423 (Figure S1 ). However, once the number of daily reported COVID-19-related deaths exceeded 2423, the CAPE-Pos total score began to decrease. For the PSQI score, the CAPE-Pos total score started to rise after the PSQI score reached 4.22. Additionally, the relationship between the CAPE-Pos score and the GAD/PHQ scores appeared to be nearly linear. Table 2 GAM results to investigate predictors of CAPE-Pos-Frequency Statistic β / edf St. Error/Ref df t / F value p value Intercept 28.888 0.361 79.286 < 0.01 a Gender (male) 0.925 0.479 1.930 0.05 a Having a psychiatric disorder -0.348 0.502 -0.693 0.48 a Psychiatric disorder in family -0.259 0.548 -0.473 0.63 a Illicit drug-usage life-time 0.269 0.597 0.451 0.65 a Cigarette usage 1.507 0.595 2.531 0.01* ,a Alcohol usage -1.138 0.535 -2.127 0.03* ,a COVID infection in family -0.139 0.469 -0.296 0.76 a History of COVID infection -0.374 0.685 -0.547 0.58 a Suicidal thought 0.696 0.634 1.099 0.27 a Suicidal attempt (life-time) 0.013 0.620 0.022 0.98 a Daily reported new COVID-19 cases 1.924 2.435 1.574 0.21 b Daily reported new COVID-19 related deaths 3.860 4.720 3.475 0.005* , b PSQI total score 2.694 3.481 3.891 0.007* , b PHQ total score 1.000 1.000 46.028 < 0.001* , b GAD total score 1.847 2.313 6.126 0.001* , b a Categorical values, b Continuous variable, CAPE-Pos: Community Assessment of Psychic Experiences; PHQ: Patient Health Questionnaire; GAD: Generalized Anxiety Disorder; PSQI: Pittsburg Sleep Quality Index. 3.4. Investigating the mediator effect of GAD, PSQI and PHQ on the association between CAPE-Pos and daily reported new COVID-19 related deaths The evaluation of the path coefficients in our research model (Fig. 3 ) showed that GAD, PHQ, and PSQI total scores had a significant direct effect on CAPE-Pos-Total Score (Table 3 ). There was significant direct effect ( B = 0.106, t = 3.563, p < 0.001) and indirect effect ( B = 0.037, t = 1.814, p = 0.03) of the number of daily reported new COVID-19-related deaths on CAPE-Pos-Total score ( B = 0.106, t = 3.563, p < 0.001). Exploring specific indirect effects, we found that only PHQ-Total score significantly mediated ( B = 0.025, t = 1.776, p = 0.03) the association between the number of daily reported new COVID-19-related deaths and CAPE-Pos-Total score. Table 3 Structural model testing for direct and indirect effects and the mean of the bootstrapped samples with bootstrapped t test estimates and p values for CAPE-Pos-Total score Relationships Std. Beta Std. Dev. t value 95% CI 5% CI Decision GAD → CAPE-Pos 0.115 0.047 2.478* 0.192 0.039 Supported PHQ → CAPE-Pos 0.380 0.050 7.591** 0.462 0.297 Supported PSQI → CAPE-Pos 0.131 0.038 3.404** 0.194 0.067 Supported New deaths → GAD → CAPE-Pos 0.004 0.005 0.760 0.018 -0.001 Not supported New deaths → PHQ → CAPE-Pos 0.025 0.014 1.776* 0.049 0.002 Supported New deaths → PSQI → CAPE-Pos 0.008 0.006 0.467 0.012 -0.004 Not supported * p < 0.05, ** p < 0.001, CAPE-Pos: Community Assessment of Psychic Experiences, Positive subscale; PHQ: Patient Health Questionnaire; GAD: Generalized Anxiety Disorder; PSQI: Pittsburg Sleep Quality Index Kruskal Wallis test pairwise comparison, CAPE-Pos: Community Assessment of Psychic Experiences, Positive subscale; PHQ: Patient Health Questionnaire; GAD: Generalized Anxiety Disorder; PSQI: Pittsburg Sleep Quality Index CAPE-Pos: Community Assessment of Psychic Experiences, Positive subscale; PHQ: Patient Health Questionnaire; GAD: Generalized Anxiety Disorder; PSQI: Pittsburg Sleep Quality Index. 3.5. Findings of Rule Mining Algorithm 3.5.1. Turkey In Turkey, 7.6% of the participants had both suicidal thoughts and previous suicidal attempts. The ratio of having both a psychiatric disorder and a high CAPE-Pos-Total score in these patients was 6.19 times higher than the entire group of participants from Turkey. Three quarters (75%) of the participants using cigarettes every day and having suicidal thoughts also reported high PLEs based on CAPE score (OR = 5.19). 7.6% of the participants from Turkey had both a psychiatric disorder and suicidal attempts. 40% of these individuals had high CAPE score and suicidal thoughts (OR = 5.0) (Table 4). 70% of the participants that used alcohol 3–5 times in past month and had suicidal thoughts, also had high CAPE score (OR = 4.8, Table S1 ). 3.5.2. Nigeria In Nigeria, 50% of the participants with severe sleep problems (PSQI > 11) had both high CAPE score and severe depression (OR = 6). 60% of the male participants with severe depression had high CAPE-score (OR = 5.8). 9.2% of the participants from Nigeria had a psychiatric disorder, severe depression, and severe anxiety. 44.4% of these participants also reported high CAPE score (OR = 4.3). 60% of participants with severe depression and severe sleep problems had high CAPE score (OR = 5.8, Table S1 ). 3.5.3. India In India, 13% of the participants had severe sleep problems. 47.3% of these participants had high CAPE score (OR = 3). 81% of the participants with psychiatric disorder, suicidal though, and severe sleep problems had high CAPE score (OR = 5.1). 7% of the participants had severe sleep problems and a psychiatric disorder. 52% of these participants had high CAPE score (OR = 3.2). 56% of the participants who attempted suicide and had severe sleep problems were classified into the high CAPE score (Table S1 ). 3.5.4. Spain In Spain, 37.5% of the participants using psychopharmacological treatment had high CAPE score (OR = 9.9). 7.5% of the participants had severe sleep problems. 30% of those with severe sleep problems had high CAPE score (OR = 7.9). 17.6% of the participants who had COVID-19 infection in family and had severe anxiety had both high CAPE score and severe depression (OR = 7.8). 15.7% of the participants had severe depression and severe anxiety. 14.2% of these participants had COVID-19 in family and high CAPE score (OR = 6.3). 16.6% of the participants with the history of COVID-19 infection in family and severe depression had high CAPE score and severe anxiety (OR = 7.3). Participants who had history of COVID-19 infection in family, those who reported severe anxiety, and severe depression had increased risk of classifying in the high CAPE group (OR = 6.1, Table S1 ). 4. Discussion In the present study, significant cross-national differences were observed in PLEs and mental health indicators. The finding of a higher frequency of PLEs in India, Turkey, and Nigeria compared to Spain aligns with the previous research by Wüsten et al. (2018)[ 18 ], showing higher PLEs in low- and middle-income countries than in high-income ones. In terms of anxiety and depression levels, participants from Turkey and India displayed similar levels, which were higher than those observed in Nigeria and Spain. This finding is consistent with Burkova et al. (2022)[ 19 ], who reported elevated anxiety levels in India and Turkey and lower levels in Nigeria during the pandemic. They suggested that participants from collectivist cultures, where group interests often take precedence over individual ones, tend to experience less anxiety in crisis situations. Moreover, our study identified that sleep problems were most pronounced among participants from Turkey. Although no prior studies directly compare sleep disturbances across these specific countries, Duran and Erkin (2021)[ 20 ] noted that poor sleep quality affected 55.1% of adults in Turkey during the pandemic. Our results indicated a non-linear relationship between daily reported new COVID-19 related deaths, sleep problem severity, and PLEs. Specifically, the CAPE-Pos total score increased when daily COVID-19-related deaths ranged from 216 to 2423; however, beyond 2423 daily deaths, the CAPE-Pos total score began to decline. Although Simor et al. (2021)[ 6 ] demonstrated a positive correlation between PLEs and daily reported new COVID-19 related deaths, they did not investigate the linearity of this relationship. Stevens et al. (2021)[ 21 ] demonstrated that, early in the COVID-19 pandemic, users’ tweets showed a significant increase in anxiety in response to fear-inducing news articles, as measured by the Linguistic Inquiry tool. As the death toll increased, the ability of news articles to provoke anxiety among readers decreased. This phenomenon may also explain the connection between PLEs and the reported COVID-19 death toll here. Our analysis also revealed that depression levels partially mediated the association between PLEs and daily reported new COVID-19 related death toll. To our knowledge, no previous research has identified depression as a partial mediator between daily COVID-19 deaths and PLEs. Due to the cross-sectional design of the current study, causal relationships between variables could not be assessed. However, if future cohort studies confirm our findings, this could suggest that interventions targeting depressive symptoms in the early stages of a pandemic may help reduce the risk of future PLEs. Our results indicated that among the categorical variables, cigarette usage significantly increased the risk of PLEs. In our previous study[ 7 ], we also showed that cigarette usage significantly contributed to the presence of at least one CAPE-Pos “often” or “almost always” frequency during the pandemic. However, unexpectedly, in the current study, we found that alcohol usage significantly decreased the risk of PLEs. Saha et al. (2011)[ 22 ] reported that PLEs were more common among individuals who had a heavy daily tobacco intake. While the pattern of PLEs and alcohol use or dependence was less consistent, those with early onset alcohol use disorders were more likely to show more frequently signs of PLEs. In our study, we did not investigated the alcohol use disorder and only asked whether they used alcohol within the last 30 days. In short-term, alcohol might have caused the alleviation of anxiety, thus, decreasing of PLEs. The association between alcohol usage and PLEs should be further explored in future studies. Daily cigarette use, a diagnosed psychiatric disorder, and alcohol use accompanying suicidal ideation in Turkey, as well as severe sleep problems and/or a diagnosed psychiatric disorder accompanying suicidal ideation in India, were associated with PLEs. The pandemic itself and its consequences, including self-isolation, loneliness, and feeling of entrapment, may have triggered or exacerbated both suicidal ideation and PLEs. Moreover, recent studies found that PLEs before the pandemic predicted suicidal ideation during the pandemic [ 11 , 13 ]. Our study also revealed that having a psychiatric disorder was associated with PLEs when the following concomitant risk factors were present: severe anxiety and depression in Nigeria; severe sleep problems or suicidal thoughts in India; and suicidal attempt in Turkey. Previous research by Giocondo et al. (2021)[ 23 ] highlighted a bidirectional relationship between PLEs and mental disorders, with PLEs predicting subsequent depressive disorder, substance use, and self-harm. Moreover, Lindgren et al. (2022)[ 24 ] reported that PLEs predicted all mental disorders, particularly mood and anxiety disorders, among young adults. Although our study’s cross-sectional design does not allow for causal conclusions, it is notable that risk factors such as severe sleep disturbances, family history of psychiatric disorders, severe anxiety, depression, and suicidal thoughts may elevate the risk of PLEs among individuals with a psychiatric disorder. Several reports have shown that sleep disturbances predicted new onset and persistence of PLEs [ 25 , 26 ]. Supporting this, we showed that the CAPE-Pos total score began to rise after the PSQI score exceeded 4.22. Our results also demonstrated that following factors accompanying sleep problems were associated with PLEs: severe depression in Nigeria, and suicidal attempt, or suicidal thought and/or having a psychiatric disorder in India. Severe sleep problems were observed in 6.1%, 13%, and 7.5% of participants from Nigeria, India, and Spain, respectively. Nearly half of those with sleep problems in Nigeria and India, and 30% of those in Spain, scored high on the CAPE-Pos scale. Therefore, sleep problems may be both a sole and an accompanying factor associated with PLEs. Our results indicated a linear relationship between the severity of anxiety/depression and PLEs. Moreover, we found that severe anxiety was associated with PLEs when accompanied by a psychiatric disorder and severe depression in Nigeria, and by COVID-19 infection in family or severe depression in Spain. Other factors included severe depression accompanied by being male or experiencing sleep difficulties in Nigeria; and COVID-19 infection in family and severe anxiety in Spain. Supporting our findings, Varghese et al. (2011)[ 27 ] reported that young adults with PLEs were at a 4- to 6-fold increased risk of experiencing depression and anxiety compared to those without PLEs. Stochl et al. (2015)[ 28 ] found that PLEs, depression and anxiety are the manifestations of a unitary and latent continuum of common mental distress and PLEs demonstrated more severe consequences. Therefore, screening and monitoring for depression and anxiety are highly relevant for individuals with PLEs. Possible clinical implications can be offered in the light of our findings. First, it seems that during the crisis such as pandemics, PLEs can be associated with different risk factors across different cultural contexts. Secondly, screening for PLEs could help identify individuals who may benefit from mental health interventions during such crisis situations. However, cultural context should be kept in mind and cultural-specific screening programs should be developed. Psychosocial and psychological interventions, such as cognitive behavioral therapy, have been identified as effective options for addressing subthreshold psychotic symptoms [ 29 ]. Thus, thirdly, decreasing stress and enhancing coping mechanisms through these interventions may prevent further impairments such as severe anxiety/depression or suicidal ideation. Moreover, interventions aimed at reducing anxiety/depression in individuals, stress in families, improving sleep quality and closely monitoring individuals with psychiatric disorders may hinder PLEs in young adults. Our finding that PLEs increased particularly during the initial period of heightened mortality suggests that the early phase of the pandemic may represent a more critical period for intervention. 5. Limitations Several limitations should be noted regarding this study. Using a self-report questionnaire might lead to overestimation of PLEs prevalence. Retrospective and online nature of data collection may limit the reliability, generalizability, and may introduce response bias. Limited sample size in countries such as Nigeria and Spain might hinder the generalization of our results. Despite these limitations, our study is the first one that used data mining algorithm to comprehend country-specific risk factors for PLEs during the pandemic. 6. Conclusion This study uniquely applied rule-mining analysis to uncover complex, cross-national risk patterns for PLEs during the pandemic. By identifying both linear and non-linear associations with mental health indicators and contextual factors, our findings emphasize the value of data-driven approaches in shaping culturally sensitive prevention strategies. Declarations Funding: None. Conflict of Interest: The Authors have declared that there are no conflicts of interest in relation to the subject of this study. Author Contribution: Conceptualization: HYK, VP, TT; Methodology: İK, PFC, MSA, TT; Analysis: İK, HYK; Data collecting: HYK, DM, SS, İU, JSV, OPO, PFC, GE, ÖK, MSA, AB, VP; Writing – original draft: HYK, İK; Writing – review and editing: HYK, İK, DM, SS, İU, JSV, OPO, PFC, GE, ÖK, MSA, AB, VP, VP, TT. Data Availability Statement: The data supporting the findings of this study are openly available in the PLE_Project_Data repository at the Open Science Framework (OSF), https://doi.org/10.17605/OSF.IO/UGYNS. Acknowledgment: None. Decleration of Generative AI usage: During the preparation of this work the author(s) used ChatGPT in order to improve the readability and language of the manuscript. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article. Ethics Statement: Identifiable information of the responders was protected by the survey administrator and only used the data for statistical group analysis. Informed consent was acquired from each responder before beginning the online questionnaire. The Ethics Committee of the Ministry of Health Ankara City Hospital approved the current study (E1-20-1011). The study procedure adhered to the principles of the Declaration of Helsinki. Consent to Participate and Publish: All participants provided informed consent for participation and publication before completing the online questionnaire. References Hinterbuchinger B, Mossaheb N. 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Psychotic-Like Experiences in Major Depression and Anxiety Disorders: A Population-Based Survey in Young Adults. Schizophr Bull. 2011;37(2):389–93. Stochl J, Khandaker GM, Lewis G, Perez J, Goodyer IM, Zammit S, et al. Mood, anxiety and psychotic phenomena measure a common psychopathological factor. Psychol Med. 2015;45(7):1483–93. Yilmaz Kafali H, Solerdelcoll M, Vujinovic L, Martsenkovskyi D, Awhangansi S, Noel C, et al. Evaluating the tendencies of community practitioners who actively practice in child and adolescent psychiatry to diagnose and treat DSM-5 attenuated psychotic syndrome. Eur Child Adolesc Psychiatry. 2022;31(10):1635–44. Additional Declarations No competing interests reported. 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3","display":"","copyAsset":false,"role":"figure","size":111242,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization of the non-linear relationship between the Pittsburg Sleep Questionnaire Index (PSQI) or Daily New Covid-19 related deaths, and Community Assessment of Psychic Experiences (CAPE-Pos)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7340400/v1/7a71e1a22f32630c6c5fb32e.png"},{"id":95322294,"identity":"f5c07432-4896-4dfc-b693-d1abf5e055a8","added_by":"auto","created_at":"2025-11-06 16:52:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":27305,"visible":true,"origin":"","legend":"\u003cp\u003ePathway analysis to investigate the mediator role of anxiety/depressive symptoms and sleep problems on the association between psychotic like experiences and number of new daily COVID-19 related deaths\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7340400/v1/20c3f412bc6295df040c2c17.png"},{"id":102745439,"identity":"7ca86c3b-ae72-42a9-b9e7-f80689a3e3d0","added_by":"auto","created_at":"2026-02-16 08:50:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1467998,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7340400/v1/17e47e85-f437-42d4-93c4-58871b23cfe0.pdf"},{"id":95523558,"identity":"f8c2d73b-77a7-4dfa-9c22-eab464297e90","added_by":"auto","created_at":"2025-11-10 09:58:07","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":4161767,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-7340400/v1/6c339e13165d5a7c3e0201a4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correlates of Psychotic Like Experiences among Young Adults during the Covid19 Pandemic across Four Countries","fulltext":[{"header":"OBJECTIVES","content":"\u003cp\u003eThe COVID-19 pandemic brought unprecedented mental health challenges across the globe, and young adults appeared to be among the groups most affected. Psychotic-like experiences (PLEs) — subclinical symptoms that do not necessarily indicate a psychotic disorder — have been linked to various adverse mental health outcomes. While some studies have addressed PLEs during the pandemic, most have been conducted in single-country settings, limiting understanding of cross-cultural differences.\u003c/p\u003e\n\u003cp\u003eBetween July 2020 and July 2021, we collected data from young adults aged 18–24 years in Turkey, Nigeria, Spain, and India, using an online survey format. The aim was to examine both common and country-specific correlates of PLEs during the pandemic. We also investigated whether depressive symptoms, anxiety, and sleep problems might mediate the association between daily COVID-19-related death counts and PLEs. In addition to conventional analyses, a data mining approach (association rule mining) was used to detect patterns that may be complex, and therefore less visible with standard methods.\u003c/p\u003e\n\u003cp\u003eThis work was developed within an international collaboration focused on generating culturally informed knowledge to guide prevention and early intervention. The dataset presented here has not previously been reported in full, nor with the current cross-national perspective.\u003c/p\u003e\n\u003cp\u003eWe expect that the data will be useful for researchers and policymakers seeking to understand how pandemic-related stressors interact with mental health vulnerabilities in different cultural contexts. Ultimately, such insights may help design more effective, targeted screening and intervention strategies for future public health crises.\u003c/p\u003e"},{"header":"1. Background","content":"\u003cp\u003eDespite a variety of definitions, psychotic-like experiences (PLEs) are widely conceptualized as subclinical psychotic experiences (unusual thoughts, delusions, hallucinations, and behavioral abnormalities) occurring outside the context of sleep and substance use, which are not necessarily associated with distress, help-seeking behavior, or clinical psychotic disorders [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Initially, the clinical significance of PLEs focused on their association with the future risk of psychotic disorders, assuming a premorbid presentation or continuum phenotype of psychosis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Subsequent research found that those who reported PLEs during childhood and adolescence were also at increased risk of both concurrent and subsequent non-psychotic disorders, suicidal behavior, multimorbidity, and poor level of functioning [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe coronavirus disease 2019 (COVID-19) pandemic posed unprecedented risks to the safety, well-being, and mental health of global populations [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Cumulative evidence suggests that pandemic-related restrictions, such as lockdowns, have resulted in an increase in known risk factors for mental health problems and have contributed to the exacerbation of pre-existing vulnerabilities [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Overall, data published to date suggest that while some youths demonstrated resilience, a significant cohort of young adults experienced psychological distress and a consequent increase in the prevalence of clinically significant mental health problems, mainly an elevated level of depression, anxiety, and stress [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although there is evidence that the pandemic outbreak was a potent adverse environmental factor, its association to increased PLEs occurrence and risk factors is unclear [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. To date, several studies reported a number of risk factors associated with PLEs during the COVID-19 pandemic including female gender, previous mental disorder, having friends/relatives infected with SARS-Cov-2, childhood trauma, suicidal ideation, SARS-CoV-2 infection, insomnia, severe depression and anxiety levels, poor family functioning, and daily COVID-19-related deaths [\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10 CR11 CR12 CR13\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, the best of our knowledge, these factors have only been investigated in local sample groups, and no studies thoroughly examined country-specific factors related to PLEs during the COVID-19 pandemic.\u003c/p\u003e\u003cp\u003eIn the current study, our primary objective was to compare countries (Turkey, Nigeria, Spain, and India) regarding PLEs, depressive and anxiety levels, and sleep problems during the pandemic and also identify which variables most strongly predicted PLEs among young adults across the entire sample. Secondly, drawing on the findings of Simor et al. (2021)[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], which indicated that daily reports of country-specific COVID-19 deaths were predictive of increased negative mood, psychotic-like experiences, and somatic complaints on the same day, as well as diminished subjective sleep quality the following night, this study aimed to investigate whether depressive and anxiety severity, along with sleep problems, serve as mediators in the relationship between daily COVID-19 cases/deaths and PLEs. Thirdly, we sought to determine country-specific risk factors for PLEs in young adults during the pandemic. To uncover hidden and potentially valuable patterns in the data collected from four countries, we employed an association rule mining approach [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Participants\u003c/h2\u003e\u003cp\u003eUsing G*power 3.1. program, a power analysis was applied (ANOVA Fixed Effect One-Way, alpha 0.05, power of 0.95, effect size\u0026thinsp;=\u0026thinsp;0.25, number of groups\u0026thinsp;=\u0026thinsp;4) to calculate the minimum sample size for the current study. Minimum total sample size required for the study was 280. The convenience sample of 854 participants from India (n\u0026thinsp;=\u0026thinsp;356), Turkey (n\u0026thinsp;=\u0026thinsp;264), Spain (n\u0026thinsp;=\u0026thinsp;135), and Nigeria (n\u0026thinsp;=\u0026thinsp;99) were recruited using the snowball reference technique between July 2020 and July 2021. With the snowball sampling technique, a method where existing participants refer others from their network, the researchers provided referrals to recruit samples required for the study [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Participants were included in the study if they were young adults aged 18\u0026ndash;24 years old, willing to participate and gave informed consent. 176 participants were excluded from the final sample due to the following reasons: (1) Out of age range (India (n\u0026thinsp;=\u0026thinsp;69), Turkey (n\u0026thinsp;=\u0026thinsp;9), Spain (n\u0026thinsp;=\u0026thinsp;13), Nigeria (n\u0026thinsp;=\u0026thinsp;21)) and (2) not willing to participate (India (n\u0026thinsp;=\u0026thinsp;12), Turkey (n\u0026thinsp;=\u0026thinsp;56), Spain (n\u0026thinsp;=\u0026thinsp;2), Nigeria (n\u0026thinsp;=\u0026thinsp;2)).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Assessments and Procedure\u003c/h2\u003e\u003cp\u003eAll eligible participants were asked to fill the web-based consisting of socio-demographic data, the positive psychotic symptoms dimension of the Community Assessment of Psychic Experiences-42 (CAPE-Pos), Patient Health Questionnaire (PHQ), Generalized Anxiety Disorder-7 (GAD-7), and Pittsburg Sleep Quality Index (PSQI). The questionnaire was distributed via social media (WhatsApp, Instagram, or Facebook) and communication networks of universities. Detailed information about the assessment materials can be found in Supplementary Material.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Statistics\u003c/h2\u003e\u003cp\u003eDescriptive statistics (mean, standard deviation, median, interquartile range, and frequencies) and group comparison statistics were performed. The normality of the quantitative variables was assessed via histogram, skewness, kurtosis, and normality plots. Country was treated as a grouping variable (n\u0026thinsp;=\u0026thinsp;4). Between-group comparisons of categorical variables were carried out using χ2 or Fisher's exact test. The Kruskal-Wallis test was utilized to detect significant differences between countries regarding age, CAPE-Pos, GAD, PHQ, and PSQI scores. Dunn-Bonferroni post-hoc test was used to determine significant pairwise comparisons.\u003c/p\u003e\u003cp\u003eTo examine the predictors of CAPE-Pos-Frequency, we employed a Generalized Additive Model (GAM) that included both continuous and categorical variables. Continuous predictors were the daily number of newly reported SARS-CoV-2 cases, daily COVID-19-related deaths, PHQ scores, GAD scores, and PSQI total scores. Categorical predictors included gender, presence of psychiatric disorders, family history of psychiatric illness, suicidal thoughts or ideation, substance use (including cigarettes, alcohol, or drugs), history of COVID-19 infection, and family history of COVID-19 infection. This model is particularly effective for non-parametric data, as it provides flexibility in modeling non-linear relationships [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo analyze the mediating effects of PHQ-total, GAD-total, and PSQI-total scores on the relationship between daily reported new SARS-CoV-2 cases and COVID-19-related deaths, and the CAPE-Pos-Total score, we employed Partial Least Squares (PLS) Path Analysis using SmartPLS 4 since the data did not satisfy the assumptions of multivariate normality (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://webpower.psychstat.org/models/kurtosis/results.php?url=7f4b62d3df5877af240c8bd06dc382f6\u003c/span\u003e\u003cspan address=\"https://webpower.psychstat.org/models/kurtosis/results.php?url=7f4b62d3df5877af240c8bd06dc382f6\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Detailed statistical information regarding the Association Rule Mining algorithm is presented in the Supplementary Material.\u003c/p\u003e\u003cp\u003eData were analyzed with R.Studio 2023.03, SmartPLS-4, and RuleGenerator Software. Statistical significance was set at α\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Sociodemographic characteristics of the participants\u003c/h2\u003e\n \u003cp\u003eThe mean age of participants was lowest in Turkey (20.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9 years) and highest in Nigeria (22.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6 years). Significant differences were observed in the distribution of age (\u003cem\u003eH\u003c/em\u003e(3)\u0026thinsp;=\u0026thinsp;121.876, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and gender (\u003cem\u003e\u0026chi;\u0026sup2;\u003c/em\u003e(5)\u0026thinsp;=\u0026thinsp;37.171, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) across countries. However, the prevalence of diagnosed psychiatric disorders did not significantly differ between countries. The lowest prevalence of psychiatric disorders in first-degree family members was reported in Nigeria (8.1%), while the highest was observed in Spain (21.5%). Turkey reported the highest rate of cigarette use in the past month (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;96, 36.7%), while Spain reported the highest alcohol use (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;98, 72.6%). Spain also had the highest lifetime illicit drug use (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;96, 36.7%). The rates of cigarette, alcohol, and illicit drug use varied significantly between countries (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The highest prevalence of SARS-CoV-2 infection reported in Spain (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;27, 20%), and the lowest was in Nigeria (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4, 4%). In India, nearly half of the participants reported a family history of COVID-19 infection. Additionally, significant differences were observed across countries in the prevalence of suicidal thoughts in the past month and lifetime suicide attempts (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The highest rates of suicidal thoughts and lifetime suicide attempts were reported in India, while the lowest were reported in Spain.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSociodemographic features of the participants according to their countries\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eCountries\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eStatistics\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTurkey (TR)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;264)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndia (IN)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;356)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpain (ES)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;135)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNigeria (NI)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;99)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eH, X\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (Mean \u0026plusmn; SD)\u003c/p\u003e\n \u003cp\u003e(Median (IQR))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026plusmn;1.9\u003c/p\u003e\n \u003cp\u003e19 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.3\u0026plusmn;1.5\u003c/p\u003e\n \u003cp\u003e20 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.8\u0026plusmn;0.9\u003c/p\u003e\n \u003cp\u003e21.5 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.4\u0026plusmn;1.6\u003c/p\u003e\n \u003cp\u003e23 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e121.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender (Female) (n(%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e167 (63.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e292 (82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82 (60.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68 (68.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosed psychiatric disorder (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83 (31.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePsychopharmacological treatment (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePsychiatric disorder in first degree family members (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54 (20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56 (15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCigarette use in past month (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96 (36.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e166.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlcohol use in past month (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112 (42.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98 (72.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e508.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLife-time illicit drug use (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92 (68.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (19.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e285.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistory of COVID-19 infection (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistory of COVID-19 infection in family (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64 (24.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141 (52.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53 (39.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSuicidal thoughts in last month (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88 (24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLife-time suicide attempts (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAPE-Pos Total Score\u003c/p\u003e\n \u003cp\u003e(Mean \u0026plusmn; SD) (Median (IQR))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.1\u0026plusmn;0.4\u003c/p\u003e\n \u003cp\u003e24 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.1\u0026plusmn;0.3\u003c/p\u003e\n \u003cp\u003e29 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.1\u0026plusmn;0.4\u003c/p\u003e\n \u003cp\u003e25 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 \u0026plusmn;0.7\u003c/p\u003e\n \u003cp\u003e28 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHQ Total score\u003c/p\u003e\n \u003cp\u003e(Mean \u0026plusmn; SD) (Median (IQR))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.2\u0026plusmn;0.4\u003c/p\u003e\n \u003cp\u003e10 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u0026plusmn;0.4\u003c/p\u003e\n \u003cp\u003e9 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7:8 \u0026plusmn;0.5\u003c/p\u003e\n \u003cp\u003e6 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u0026plusmn;06\u003c/p\u003e\n \u003cp\u003e5 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGAD Total Score\u003c/p\u003e\n \u003cp\u003e(Mean \u0026plusmn; SD) (Median (IQR))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.6\u0026plusmn;0.3\u003c/p\u003e\n \u003cp\u003e10 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.8\u0026plusmn;0.3\u003c/p\u003e\n \u003cp\u003e5.8(10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.2 \u0026plusmn;0.4\u003c/p\u003e\n \u003cp\u003e5.1 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 \u0026plusmn;0.5\u003c/p\u003e\n \u003cp\u003e5 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePSQI Total Score\u003c/p\u003e\n \u003cp\u003e(Mean \u0026plusmn; SD) (Median (IQR))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.6 \u0026plusmn;0.1\u003c/p\u003e\n \u003cp\u003e6(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.2\u0026plusmn;0.1\u003c/p\u003e\n \u003cp\u003e6(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.4 \u0026plusmn;0.2\u003c/p\u003e\n \u003cp\u003e5(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.3 \u0026plusmn;0.3\u003c/p\u003e\n \u003cp\u003e5(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003ea Kruskal Wallis test; b Chi Square test; CAPE-Pos: Community Assessment of Psychic Experiences, Positive subscale; PHQ: Patient Health Questionnaire; GAD: Generalized Anxiety Disorder; PSQI: Pittsburg Sleep Quality Index.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Comparison of PLEs, depression, anxiety and sleep difficulties between the countries\u003c/h2\u003e\n \u003cp\u003eThe prevalence of experiencing at least one CAPE-Positive (CAPE-Pos) symptom \u0026ldquo;often\u0026rdquo; or \u0026ldquo;almost always\u0026rdquo; was 36% in Turkey, 72.7% in Nigeria, 73.9% in India, and 51.1% in Spain. Significant differences were observed between countries in terms of CAPE-Pos (\u003cem\u003eH\u003c/em\u003e\u0026thinsp;=\u0026thinsp;34.297, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), GAD (\u003cem\u003eH\u003c/em\u003e\u0026thinsp;=\u0026thinsp;54.090, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), PHQ (\u003cem\u003eH\u003c/em\u003e\u0026thinsp;=\u0026thinsp;39.024, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and PSQI (\u003cem\u003eH\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19.365, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) scores (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). CAPE-Pos total score did not differ between India, Turkey, and Nigeria. However, Spain significantly had the lowest CAPE-Pos-Total score (Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Additionally, Turkey and India had significantly higher GAD and PHQ total scores than Nigeria and India. Turkey had significantly higher PSQI score compared to Nigeria and Spain. PSQI score of India, Spain, and Nigeria did not significantly differ (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. The most predictive factors of PLEs in the whole sample\u003c/h2\u003e\n \u003cp\u003eThe GAM model explained 36.8% of the deviance. The model was fitted using Generalized Cross Validation (GCV) as smoothing parameter selection method. The Root Mean Square (RMS) GCV score gradient at convergence was below 0.001, indicating that the model achieved a stable solution. Our model indicated that among the categorical variables cigarette usage significantly increased but alcohol usage significantly decreased the risk of presence of PLEs (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Among the continuous variables, the number of reported new deaths related to COVID-19 infection, PSQI, PHQ, and GAD total scores significantly predicted CAPE-Pos-Frequency (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Basis dimension checking results showed that \u003cem\u003eedf\u003c/em\u003e values of number of new deaths related to COVID-19 infection (3.82) and PSQI (2.52) was higher than 1 indicating a non-linear relationship between CAPE-Pos and these variables. By analyzing the slope of the fitted curve, we identified change points that indicate areas of significant change in the predicted values. The CAPE-Pos total score increased alongside the daily reported new COVID-19-related deaths, rising from 216 to 2423 (Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). However, once the number of daily reported COVID-19-related deaths exceeded 2423, the CAPE-Pos total score began to decrease. For the PSQI score, the CAPE-Pos total score started to rise after the PSQI score reached 4.22. Additionally, the relationship between the CAPE-Pos score and the GAD/PHQ scores appeared to be nearly linear.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGAM results to investigate predictors of CAPE-Pos-Frequency\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eStatistic\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta; / edf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSt. Error/Ref df\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e \u003cstrong\u003e/\u003c/strong\u003e \u003cstrong\u003eF\u003c/strong\u003e \u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e \u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender (male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHaving a psychiatric disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePsychiatric disorder in family\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIllicit drug-usage life-time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCigarette usage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01*\u003csup\u003e,a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlcohol usage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03*\u003csup\u003e,a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOVID infection in family\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistory of COVID infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSuicidal thought\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSuicidal attempt (life-time)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDaily reported new COVID-19 cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDaily reported new COVID-19 related deaths\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005*\u003csup\u003e, b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePSQI total score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007*\u003csup\u003e, b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHQ total score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003csup\u003e, b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGAD total score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001*\u003csup\u003e, b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003csup\u003ea\u003c/sup\u003e Categorical values, \u003csup\u003eb\u003c/sup\u003e Continuous variable, CAPE-Pos: Community Assessment of Psychic Experiences; PHQ: Patient Health Questionnaire; GAD: Generalized Anxiety Disorder; PSQI: Pittsburg Sleep Quality Index.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.4. Investigating the mediator effect of GAD, PSQI and PHQ on the association between CAPE-Pos and daily reported new COVID-19 related deaths\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe evaluation of the path coefficients in our research model (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) showed that GAD, PHQ, and PSQI total scores had a significant direct effect on CAPE-Pos-Total Score (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). There was significant direct effect (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.106, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.563, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and indirect effect (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.814, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) of the number of daily reported new COVID-19-related deaths on CAPE-Pos-Total score (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.106, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.563, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Exploring specific indirect effects, we found that only PHQ-Total score significantly mediated (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.776, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) the association between the number of daily reported new COVID-19-related deaths and CAPE-Pos-Total score.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStructural model testing for direct and indirect effects and the mean of the bootstrapped samples with bootstrapped t test estimates and p values for CAPE-Pos-Total score\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRelationships\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd. Beta\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd. Dev.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% \u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e5% \u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDecision\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGAD \u0026rarr; CAPE-Pos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.478*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHQ \u0026rarr; CAPE-Pos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.591**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePSQI \u0026rarr; CAPE-Pos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.404**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNew deaths \u0026rarr; GAD \u0026rarr; CAPE-Pos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot supported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNew deaths \u0026rarr; PHQ \u0026rarr; CAPE-Pos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.776*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNew deaths \u0026rarr; PSQI \u0026rarr; CAPE-Pos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot supported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e*\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, CAPE-Pos: Community Assessment of Psychic Experiences, Positive subscale; PHQ: Patient Health Questionnaire; GAD: Generalized Anxiety Disorder; PSQI: Pittsburg Sleep Quality Index\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eKruskal Wallis test pairwise comparison, CAPE-Pos: Community Assessment of Psychic Experiences, Positive subscale; PHQ: Patient Health Questionnaire; GAD: Generalized Anxiety Disorder; PSQI: Pittsburg Sleep Quality Index\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eCAPE-Pos: Community Assessment of Psychic Experiences, Positive subscale; PHQ: Patient Health Questionnaire; GAD: Generalized Anxiety Disorder; PSQI: Pittsburg Sleep Quality Index.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. Findings of Rule Mining Algorithm\u003c/h2\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e3.5.1. Turkey\u003c/h2\u003e\n \u003cp\u003eIn Turkey, 7.6% of the participants had both suicidal thoughts and previous suicidal attempts. The ratio of having both a psychiatric disorder and a high CAPE-Pos-Total score in these patients was 6.19 times higher than the entire group of participants from Turkey. Three quarters (75%) of the participants using cigarettes every day and having suicidal thoughts also reported high PLEs based on CAPE score (OR\u0026thinsp;=\u0026thinsp;5.19). 7.6% of the participants from Turkey had both a psychiatric disorder and suicidal attempts. 40% of these individuals had high CAPE score and suicidal thoughts (OR\u0026thinsp;=\u0026thinsp;5.0) (Table 4). 70% of the participants that used alcohol 3\u0026ndash;5 times in past month and had suicidal thoughts, also had high CAPE score (OR\u0026thinsp;=\u0026thinsp;4.8, Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e3.5.2. Nigeria\u003c/h2\u003e\n \u003cp\u003eIn Nigeria, 50% of the participants with severe sleep problems (PSQI\u0026thinsp;\u0026gt;\u0026thinsp;11) had both high CAPE score and severe depression (OR\u0026thinsp;=\u0026thinsp;6). 60% of the male participants with severe depression had high CAPE-score (OR\u0026thinsp;=\u0026thinsp;5.8). 9.2% of the participants from Nigeria had a psychiatric disorder, severe depression, and severe anxiety. 44.4% of these participants also reported high CAPE score (OR\u0026thinsp;=\u0026thinsp;4.3). 60% of participants with severe depression and severe sleep problems had high CAPE score (OR\u0026thinsp;=\u0026thinsp;5.8, Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003e3.5.3. India\u003c/h2\u003e\n \u003cp\u003eIn India, 13% of the participants had severe sleep problems. 47.3% of these participants had high CAPE score (OR\u0026thinsp;=\u0026thinsp;3). 81% of the participants with psychiatric disorder, suicidal though, and severe sleep problems had high CAPE score (OR\u0026thinsp;=\u0026thinsp;5.1). 7% of the participants had severe sleep problems and a psychiatric disorder. 52% of these participants had high CAPE score (OR\u0026thinsp;=\u0026thinsp;3.2). 56% of the participants who attempted suicide and had severe sleep problems were classified into the high CAPE score (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003e3.5.4. Spain\u003c/h2\u003e\n \u003cp\u003eIn Spain, 37.5% of the participants using psychopharmacological treatment had high CAPE score (OR\u0026thinsp;=\u0026thinsp;9.9). 7.5% of the participants had severe sleep problems. 30% of those with severe sleep problems had high CAPE score (OR\u0026thinsp;=\u0026thinsp;7.9). 17.6% of the participants who had COVID-19 infection in family and had severe anxiety had both high CAPE score and severe depression (OR\u0026thinsp;=\u0026thinsp;7.8). 15.7% of the participants had severe depression and severe anxiety. 14.2% of these participants had COVID-19 in family and high CAPE score (OR\u0026thinsp;=\u0026thinsp;6.3). 16.6% of the participants with the history of COVID-19 infection in family and severe depression had high CAPE score and severe anxiety (OR\u0026thinsp;=\u0026thinsp;7.3). Participants who had history of COVID-19 infection in family, those who reported severe anxiety, and severe depression had increased risk of classifying in the high CAPE group (OR\u0026thinsp;=\u0026thinsp;6.1, Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn the present study, significant cross-national differences were observed in PLEs and mental health indicators. The finding of a higher frequency of PLEs in India, Turkey, and Nigeria compared to Spain aligns with the previous research by W\u0026uuml;sten et al. (2018)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], showing higher PLEs in low- and middle-income countries than in high-income ones. In terms of anxiety and depression levels, participants from Turkey and India displayed similar levels, which were higher than those observed in Nigeria and Spain. This finding is consistent with Burkova et al. (2022)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], who reported elevated anxiety levels in India and Turkey and lower levels in Nigeria during the pandemic. They suggested that participants from collectivist cultures, where group interests often take precedence over individual ones, tend to experience less anxiety in crisis situations. Moreover, our study identified that sleep problems were most pronounced among participants from Turkey. Although no prior studies directly compare sleep disturbances across these specific countries, Duran and Erkin (2021)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] noted that poor sleep quality affected 55.1% of adults in Turkey during the pandemic.\u003c/p\u003e\u003cp\u003eOur results indicated a non-linear relationship between daily reported new COVID-19 related deaths, sleep problem severity, and PLEs. Specifically, the CAPE-Pos total score increased when daily COVID-19-related deaths ranged from 216 to 2423; however, beyond 2423 daily deaths, the CAPE-Pos total score began to decline. Although Simor et al. (2021)[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] demonstrated a positive correlation between PLEs and daily reported new COVID-19 related deaths, they did not investigate the linearity of this relationship. Stevens et al. (2021)[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] demonstrated that, early in the COVID-19 pandemic, users\u0026rsquo; tweets showed a significant increase in anxiety in response to fear-inducing news articles, as measured by the Linguistic Inquiry tool. As the death toll increased, the ability of news articles to provoke anxiety among readers decreased. This phenomenon may also explain the connection between PLEs and the reported COVID-19 death toll here. Our analysis also revealed that depression levels partially mediated the association between PLEs and daily reported new COVID-19 related death toll. To our knowledge, no previous research has identified depression as a partial mediator between daily COVID-19 deaths and PLEs. Due to the cross-sectional design of the current study, causal relationships between variables could not be assessed. However, if future cohort studies confirm our findings, this could suggest that interventions targeting depressive symptoms in the early stages of a pandemic may help reduce the risk of future PLEs.\u003c/p\u003e\u003cp\u003eOur results indicated that among the categorical variables, cigarette usage significantly increased the risk of PLEs. In our previous study[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], we also showed that cigarette usage significantly contributed to the presence of at least one CAPE-Pos \u0026ldquo;often\u0026rdquo; or \u0026ldquo;almost always\u0026rdquo; frequency during the pandemic. However, unexpectedly, in the current study, we found that alcohol usage significantly decreased the risk of PLEs. Saha et al. (2011)[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] reported that PLEs were more common among individuals who had a heavy daily tobacco intake. While the pattern of PLEs and alcohol use or dependence was less consistent, those with early onset alcohol use disorders were more likely to show more frequently signs of PLEs. In our study, we did not investigated the alcohol use disorder and only asked whether they used alcohol within the last 30 days. In short-term, alcohol might have caused the alleviation of anxiety, thus, decreasing of PLEs. The association between alcohol usage and PLEs should be further explored in future studies.\u003c/p\u003e\u003cp\u003eDaily cigarette use, a diagnosed psychiatric disorder, and alcohol use accompanying suicidal ideation in Turkey, as well as severe sleep problems and/or a diagnosed psychiatric disorder accompanying suicidal ideation in India, were associated with PLEs. The pandemic itself and its consequences, including self-isolation, loneliness, and feeling of entrapment, may have triggered or exacerbated both suicidal ideation and PLEs. Moreover, recent studies found that PLEs before the pandemic predicted suicidal ideation during the pandemic [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur study also revealed that having a psychiatric disorder was associated with PLEs when the following concomitant risk factors were present: severe anxiety and depression in Nigeria; severe sleep problems or suicidal thoughts in India; and suicidal attempt in Turkey.\u003c/p\u003e\u003cp\u003ePrevious research by Giocondo et al. (2021)[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] highlighted a bidirectional relationship between PLEs and mental disorders, with PLEs predicting subsequent depressive disorder, substance use, and self-harm. Moreover, Lindgren et al. (2022)[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] reported that PLEs predicted all mental disorders, particularly mood and anxiety disorders, among young adults. Although our study\u0026rsquo;s cross-sectional design does not allow for causal conclusions, it is notable that risk factors such as severe sleep disturbances, family history of psychiatric disorders, severe anxiety, depression, and suicidal thoughts may elevate the risk of PLEs among individuals with a psychiatric disorder.\u003c/p\u003e\u003cp\u003eSeveral reports have shown that sleep disturbances predicted new onset and persistence of PLEs [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Supporting this, we showed that the CAPE-Pos total score began to rise after the PSQI score exceeded 4.22. Our results also demonstrated that following factors accompanying sleep problems were associated with PLEs: severe depression in Nigeria, and suicidal attempt, or suicidal thought and/or having a psychiatric disorder in India. Severe sleep problems were observed in 6.1%, 13%, and 7.5% of participants from Nigeria, India, and Spain, respectively. Nearly half of those with sleep problems in Nigeria and India, and 30% of those in Spain, scored high on the CAPE-Pos scale. Therefore, sleep problems may be both a sole and an accompanying factor associated with PLEs.\u003c/p\u003e\u003cp\u003eOur results indicated a linear relationship between the severity of anxiety/depression and PLEs. Moreover, we found that severe anxiety was associated with PLEs when accompanied by a psychiatric disorder and severe depression in Nigeria, and by COVID-19 infection in family or severe depression in Spain. Other factors included severe depression accompanied by being male or experiencing sleep difficulties in Nigeria; and COVID-19 infection in family and severe anxiety in Spain. Supporting our findings, Varghese et al. (2011)[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] reported that young adults with PLEs were at a 4- to 6-fold increased risk of experiencing depression and anxiety compared to those without PLEs. Stochl et al. (2015)[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] found that PLEs, depression and anxiety are the manifestations of a unitary and latent continuum of common mental distress and PLEs demonstrated more severe consequences. Therefore, screening and monitoring for depression and anxiety are highly relevant for individuals with PLEs.\u003c/p\u003e\u003cp\u003ePossible clinical implications can be offered in the light of our findings. First, it seems that during the crisis such as pandemics, PLEs can be associated with different risk factors across different cultural contexts. Secondly, screening for PLEs could help identify individuals who may benefit from mental health interventions during such crisis situations. However, cultural context should be kept in mind and cultural-specific screening programs should be developed. Psychosocial and psychological interventions, such as cognitive behavioral therapy, have been identified as effective options for addressing subthreshold psychotic symptoms [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Thus, thirdly, decreasing stress and enhancing coping mechanisms through these interventions may prevent further impairments such as severe anxiety/depression or suicidal ideation. Moreover, interventions aimed at reducing anxiety/depression in individuals, stress in families, improving sleep quality and closely monitoring individuals with psychiatric disorders may hinder PLEs in young adults. Our finding that PLEs increased particularly during the initial period of heightened mortality suggests that the early phase of the pandemic may represent a more critical period for intervention.\u003c/p\u003e"},{"header":"5. Limitations","content":"\u003cp\u003eSeveral limitations should be noted regarding this study.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eUsing a self-report questionnaire might lead to overestimation of PLEs prevalence.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eRetrospective and online nature of data collection may limit the reliability, generalizability, and may introduce response bias.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eLimited sample size in countries such as Nigeria and Spain might hinder the generalization of our results.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eDespite these limitations, our study is the first one that used data mining algorithm to comprehend country-specific risk factors for PLEs during the pandemic.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study uniquely applied rule-mining analysis to uncover complex, cross-national risk patterns for PLEs during the pandemic. By identifying both linear and non-linear associations with mental health indicators and contextual factors, our findings emphasize the value of data-driven approaches in shaping culturally sensitive prevention strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e None.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003e The Authors have declared that there are no conflicts of interest in relation to the subject of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution:\u003c/strong\u003e Conceptualization: HYK, VP, TT; Methodology: İK, PFC, MSA, TT; Analysis: İK, HYK; Data collecting: HYK, DM, SS, İU, JSV, OPO, PFC, GE, ÖK, MSA, AB, VP; Writing – original draft: HYK, İK; Writing – review and editing: HYK, İK, DM, SS, İU, JSV, OPO, PFC, GE, ÖK, MSA, AB, VP, VP, TT.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eThe data supporting the findings of this study are openly available in the PLE_Project_Data repository at the Open Science Framework (OSF), https://doi.org/10.17605/OSF.IO/UGYNS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment:\u003c/strong\u003e None.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDecleration of Generative AI usage:\u0026nbsp;\u003c/strong\u003eDuring the preparation of this work the author(s) used ChatGPT in order to improve the readability and language of the manuscript. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement:\u0026nbsp;\u003c/strong\u003eIdentifiable information of the responders was protected by the survey administrator and only used the data for statistical group analysis. Informed consent was acquired from each responder before beginning the online questionnaire. The Ethics Committee of the Ministry of Health Ankara City Hospital approved the current study (E1-20-1011). The study procedure adhered to the principles of the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate and Publish:\u003c/strong\u003e All participants provided informed consent for participation and publication before completing the online questionnaire.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHinterbuchinger B, Mossaheb N. Psychotic-Like Experiences: A Challenge in Definition and Assessment. Front Psychiatry. 2021;12:582392.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKelleher I, Connor D, Clarke MC, Devlin N, Harley M, Cannon M. Prevalence of psychotic symptoms in childhood and adolescence: a systematic review and meta-analysis of population-based studies. Psychol Med. 2012;42(09):1857\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHealy C, Brannigan R, Dooley N, Coughlan H, Clarke M, Kelleher I, et al. 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Psychiatry Res. 2022;315:114702.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSimor P, Polner B, B\u0026aacute;thori N, Sifuentes-Ortega R, Van Roy A, Albajara S\u0026aacute;enz A et al. Home confinement during the COVID-19: day-to-day associations of sleep quality with rumination, psychotic-like experiences, and somatic symptoms. Sleep. 2021;zsab029.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYilmaz Kafali H, Turan S, Akpınar S, Mutlu M, \u0026Ouml;zkaya Parlakay A, \u0026Ccedil;\u0026ouml;p E, et al. Correlates of psychotic like experiences (PLEs) during Pandemic: An online study investigating a possible link between the SARS-CoV-2 infection and PLEs among adolescents. Schizophr Res. 2022;241:36\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang D, Zhou L, Chen C, Sun M. Psychotic-like experiences during COVID-19 lockdown among adolescents: Prevalence, risk and protective factors. 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Front Psychol. 2022;13:805586.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDuran S, Erkin \u0026Ouml;. Psychologic distress and sleep quality among adults in Turkey during the COVID-19 pandemic. Prog Neuropsychopharmacol Biol Psychiatry. 2021;107:110254.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStevens HR, Oh YJ, Taylor LD. Desensitization to Fear-Inducing COVID-19 Health News on Twitter: Observational Study. JMIR Infodemiology. 2021;1(1):e26876.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaha S, Scott JG, Varghese D, Degenhardt L, Slade T, McGrath JJ. The association between delusional-like experiences, and tobacco, alcohol or cannabis use: a nationwide population-based survey. BMC Psychiatry. 2011;11(1):202.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGiocondo JG, Salum GA, Gadelha A, Argolo FC, Simioni AR, Mari JJ, et al. Psychotic-like Experiences and Common Mental Disorders in Childhood and Adolescence: Bidirectional and Transdiagnostic Associations in a Longitudinal Community-based Study. Schizophrenia Bull Open. 2021;2(1):sgab028.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLindgren M, Numminen L, Holm M, Therman S, Tuulio-Henriksson A. Psychotic-like experiences of young adults in the general population predict mental disorders. Psychiatry Res. 2022;312:114543.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang D, Ma Z, Scherffius A, Liu W, Bu L, Sun M, Fan F. Sleep disturbance is predictive of psychotic-like experiences among adolescents: A two-wave longitudinal survey. Sleep Med. 2023;101:296\u0026ndash;304.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSheaves B, Bebbington PE, Goodwin GM, Harrison PJ, Espie CA, Foster RG, et al. 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Evaluating the tendencies of community practitioners who actively practice in child and adolescent psychiatry to diagnose and treat DSM-5 attenuated psychotic syndrome. Eur Child Adolesc Psychiatry. 2022;31(10):1635\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Psychotic-like experiences, pandemic, COVID-19, data mining, cross-cultural, young adults","lastPublishedDoi":"10.21203/rs.3.rs-7340400/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7340400/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe aimed to identify predictors and specific country factors associated with psychotic-like experiences (PLEs), and to examine whether depressive and anxiety symptoms, and sleep problems mediates the relationship between daily reported COVID-19 death number and PLEs.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 854 participants completed online questionnaires assessing PLEs, depressive and anxiety symptoms, and sleep problems using Community Assessment of Psychic Experiences-42-Positive Subscale (CAPE-42-Pos), Patient Health Questionnaire (PHQ), Generalized Anxiety Disorder-7 (GAD), and Pittsburg Sleep Quality Index (PSQI). Psychiatric/COVID-19 infection history and cigarette/alcohol/substance use, daily new COVID-19 cases/deaths were noted on survey date. Association rule mining and PROCESS Macro Mediation analysis were applied.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCOVID-19 death toll and PSQI had a non-linear, PHQ and GAD had a linear relationship with PLEs. Cigarette usage increased, but alcohol usage decreased PLEs risk. PHQ-Total score partially mediated the association between COVID-19-related death toll and CAPE-Pos-Total score. Rule mining revealed that in Turkey, substance/cigarette use and suicidality; in Nigeria, sleep problems and depression; in India, suicidality and sleep disturbances; and in Spain, COVID-19 history and psychiatric symptoms were strongly associated with high CAPE scores.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eReported death number, depressive and anxiety symptoms, and sleep disturbances could be used to predict PLEs. As a mediating factor, depressive symptoms could be an important target for preventing PLEs during pandemics. However, country-specific risk factors should also be considered for targeted interventions.\u003c/p\u003e","manuscriptTitle":"Correlates of Psychotic Like Experiences among Young Adults during the Covid19 Pandemic across Four Countries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-06 16:52:47","doi":"10.21203/rs.3.rs-7340400/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"98ca6f54-ae18-4cb0-bc9d-847522fb0d63","owner":[],"postedDate":"November 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-11T12:57:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-06 16:52:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7340400","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7340400","identity":"rs-7340400","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}