Predictors Of Drug Use Research Articles

Psycho-spatial predictors of drug use among Inner City Youths in Uyo

This study examined psycho-spaal predictors (psychological – opmisc bias and stress: spaal-drug availability, neighborhood density and proximity) of drug use among inner city youths of Uyo. A descripve cross-seconal survey design was used to interview 228 youths in Uyo, Akwa Ibom State. Parcipants, with an average age of 31.46, were 75% male. A standardized quesonnaire, consisng of Drug Use Disorder Idenficaon Test (DUDIT), the Perceived Stress Scale (PSS-10), and the Revised Life Orientaon Test-Revised (LOT-R) were used for data collecon. Drug availability was assessed in this study in terms of ease of difficulty in obtaining the drug ranging from very easy to very difficult. Environmental proximity was assessed by asking the respondents the proximity between their residence and where the drug they used are sold- ranging from “On the same street” to “not available in my neighborhood”. Neighborhood density was assessed by asking the respondents their area of residence in Uyo – as high density or low density. Result showed that drug availability [β= -4.23; t = - 4.22; p < 0.05] stress level [β=0.33; t =2.96; p < 0.05] and opmisc bias [β= - 0.52; t=-3.18; p< 0.05] predicted drug use. The study concluded that these psychospaal variables are very important in understanding drug use among young persons and the need to situate intervenon within this psychospaal domain was emphasized.

Characteristics of Patients in Methadone Maintenance Treatment (MMT) Who Have Difficulties in Emotion Regulation

Disturbances in emotion regulation among opioid users receiving methadone maintenance treatment (MMT) was reported in a few small studies. We aimed to evaluate the prevalence and characteristics of patients in MMT with impaired emotion regulation. We studied a cross-sectional sample of 121 patients by means of the Difficulties in Emotion Regulation Scale (DERS). Data on clinical, sociodemographic and addiction variables were reviewed, including results of random observed urine testing for opioids, alcohol, cocaine, cannabis, and benzodiazepine (BDZ). The prevalence of a high DERS score (> 90) was 39%. Characteristics associated with high DERS scores (logistic regression model) were benzodiazepine misuse and 20 years of opioid use before admission to MMT. Among the subscales, IMPULSE and STRATEGIES were independently related to substance use (logistic regression). Greater cocaine usage was also more prevalent only in univariate analyses. The DERS score correlated inversely with years of education among patients with negative urine test. The DERS subscale score for AWARENESS was lower in females than in males as among patients with suicide attempts or involvement in physical violence. Since the IMPULSE and STRATEGIES scores predict drug use, these aspects of emotional regulation should be targeted first in third-wave cognitive-behavioral intervention in MMT.

A Systematic Review of Artificial Intelligence Used to Predict Loneliness, Social Isolation, and Drug Use During the COVID-19 Pandemic.

This systematic literature review evaluates the role of machine learning, artificial intelligence (AI), and social determinants of health (SDOH) in identifying loneliness during the COVID-19 pandemic. By examining various studies and articles through a comprehensive search of databases EBSCOhost, Medline Complete, Academic Search Complete, Directory of Open Access Journals, and Complementary Index, the research team sought to discern consistent themes and patterns. We identified four constructs central to understanding the impact of the pandemic on societal well-being: (1) the prediction of compliance with COVID-19 measures, (2) the prediction of loneliness and its effects, (3) the prediction of well-being and social inclusion, and (4) the prediction of drug use. Within these constructs, prevalent themes related to opioid overdose, stress levels, mental health, well-being, and cognitive decline emerged. The adherence to the PRISMA 2020 checklist has resulted in a PRISMA flow diagram that categorizes the selected literature. The findings of this review, including the proportion of studies predicting various attributes related to loneliness, demonstrate the critical intersections between machine learning, AI, SDOH, and the psychosocial phenomenon of loneliness amidst a global health crisis. The review results provide a summary of the occurrences and predictive percentages of each construct as determined by the literature, contributing to a nuanced understanding of the pandemic's multifaceted impact on loneliness, social isolation, and drug use. Using AI to predict these constructs has remarkable capabilities in identifying individuals at risk and facilitating timely interventions to mitigate adverse outcomes and promote mental health resilience in the face of challenges such as the COVID-19 pandemic. Moving forward, future research is warranted to refine AI algorithms, validate predictive models and utilize AI-based interventions in healthcare and mental health services while ensuring data security, and individuals' privacy.

Social Media Insecurity and Intimate Partner Violence: Drug Use As an Escape Route

ABSTRACT The study examined social media insecurity and intimate partner violence as determinants of drug use. This prospective cohort study purposively recruited 387 married population with 54 of them re-enrolled in intervention therapy. Multilinear regressions and paired sample t-test were employed. Social media insecurity was an independent predictor of drug use; the higher the level of social media insecurity, the more participants used drugs whereas intimate partner violence did not predict drug use. The paired t-test showed a significant reduction in drug use after the eclectic therapy, which involved integrative coping skills, drug counseling/education and family therapy, aimed at reducing harms.

Does prior misconduct predict drug use by police?

ABSTRACT Recent research has yielded important insight into the characteristics and behaviours of officers who commit misconduct across the UK, USA and Australia. However, there remains little research into illicit substance use by sworn police. Considering substance use by police through the lens of police misconduct, this paper employs correlation and regression analyses using a sample of 489 sworn officers, to analyse whether individual types of misconduct, or the overall volume of misconduct committed, were predictive of an officer returning a positive drug test during departmental random drug testing. Findings suggested that neither individual types of misconduct, or the overall number of complaints received by officers were useful in predicting a positive drug test. However, a current or historical association with a known offender held a strong relationship with returning a positive drug test. Pivotally, holding a second job (outside of the agency) was also a significant predictor of a positive drug test. The findings of this research hold important implications for drug testing policy and procedure of policing agencies, and for understanding which factors may suggest that an officer is at risk of drug use.

An Electroencephalogram Signature of Melanin-Concentrating Hormone Neuron Activities Predicts Cocaine Seeking

BackgroundIdentifying biomarkers that predict substance use disorder propensity may better strategize antiaddiction treatment. Melanin-concentrating hormone (MCH) neurons in the lateral hypothalamus critically mediate interactions between sleep and substance use; however, their activities are largely obscured in surface electroencephalogram (EEG) measures, hindering the development of biomarkers. MethodsSurface EEG signals and real-time calcium (Ca2+) activities of lateral hypothalamus MCH neurons (Ca2+MCH) were simultaneously recorded in male and female adult rats. Mathematical modeling and machine learning were then applied to predict Ca2+MCH using EEG derivatives. The robustness of the predictions was tested across sex and treatment conditions. Finally, features extracted from the EEG-predicted Ca2+MCH either before or after cocaine experience were used to predict future drug-seeking behaviors. ResultsAn EEG waveform derivative—a modified theta-delta-theta peak ratio (EEGTDT ratio)—accurately tracked real-time Ca2+MCH in rats. The prediction was robust during rapid eye movement sleep (REMS), persisted through vigilance states, sleep manipulations, and circadian phases, and was consistent across sex. Moreover, cocaine self-administration and long-term withdrawal altered EEGTDT ratio, suggesting shortening and circadian redistribution of synchronous MCH neuron activities. In addition, features of EEGTDT ratio indicative of prolonged synchronous MCH neuron activities predicted lower subsequent cocaine seeking. EEGTDT ratio also exhibited advantages over conventional REMS measures for the predictions. ConclusionsThe identified EEGTDT ratio may serve as a noninvasive measure for assessing MCH neuron activities in vivo and evaluating REMS; it may also serve as a potential biomarker for predicting drug use propensity.

Sensation seeking, drug use, and high contact sports

Research question: Studies report associations between sensation seeking and risky behaviour, including drug use, suggesting sensation seeking may involve a vulnerability to health compromising behaviour. Findings on sports as a protective factor against drug use are mixed. The present study was designed to examine whether young adults who participate in high contact sports have an increased likelihood to use drugs, and if the likelihood of drug use is mediated by sensation seeking. Research methods: This quantitative, correlational study included analysis of 463 individuals’ (218 in high contact sports and 245 in non-contact sports) responses to online surveys. Results and findings: Results show that high contact sport is associated with recreational drug use, particularly for males. Sensation seeking is only related to drug use for those engaging in high contact sport. Implications: Sensation seeking may not be useful in predicting drug use directly but can be useful in predicting drug use in high contact sport

Peramalan Kebutuhan Obat Menggunakan Metode Single Exponential Smoothing

Ogan Ilir Regional General Hospital (RSUD) tries to maintain the availability of medicines to meet patient needs. The Pharmacy Department of Ogan Ilir Regional Hospital always monitors existing drug stocks before placing orders with distributors of pharmaceutical supplies. The drug procurement process is carried out in a planned manner to reduce the risk of loss from the method of drug procurement and to identify the need for highly used drugs. This research aims to determine the forecasting of drug needs using the Single Exponential Smoothing method. The data used is drug use data for the period 2021 to 2022. Based on the research results, it was found that based on the highest MAPE score it was at 22% for alpha 0.1, and the highest MAPE was at 12% for alpha 0.5. These results show that the prediction of drug use is successful and has good value because the MAPE score does not exceed 20%.

Predicting Students’ Illicit Drug Use Intentions: An Application of the Theory of Planned Behavior

Background: Substance use disorders are a significant global public health issue, causing considerable suffering, trauma, and financial burden. With the increasing prevalence of drug use and its detrimental consequences, it is crucial to identify predictors and implement preventive measures to address these problems. The main aim of the present study was to test the utility of the Theory of Planned Behaviour (TPB) to predict university students’ illicit drug use intentions in the Indian context.
 Method: The study used a cross-sectional research design and included a sample of 1254 students (male=473, female=81) aged 15-29 (Median age=22.61). Participants completed the measures of attitude, perceived norms, refusal self-efficacy and drug use intentions. Structural Equation Modeling was utilised to analyse the effectiveness of TPB constructs to predict university students’ drug use intentions.
 Results: The results indicated that positive attitude and perceived norms were positively related with drug use intentions, while refusal self-efficacy was negatively related. The proposed model accounted for (30%) of the variance in students’ illicit drug use intentions. The analysis of Regression weight estimates and critical ratios represented that the perceived norms and perceived behavioural control significantly predicted drug use intention. However, contrary to expectations, attitude did not predict drug use intentions.
 Conclusion: The findings of the present study highlighted the usefulness of TPB in predicting drug use intentions in the Indian context. These findings will benefit drug use intervention programmes.

Perceived Stress Predicts Drug Use in Transgender Women: The Mediating Role of Loneliness.

Drug use is quite common in transgender women, but the mechanisms of the drug use remain unclear. The purpose of this study was to investigate the mediating role of loneliness in perceived stress and the drug use among transgender women. A cross-sectional survey was conducted from April to September 2018 in Shenyang and Kunming, China, among 247 transgender women. Chinese Perceived Stress Scale, the Chinese version of 8-item UCLA Loneliness Scale, a drug use questionnaire, and a demographic characteristic questionnaire were included in the survey. Descriptive analysis, logistic regression, and mediation analysis were done using SPSS Statistics version 26.0 and Mplus version 8.3. The mean age of transgender women in the study was 33.04 years (standard deviation=8.14). Around 33.20% (n=82) of transgender women reported ever using one kind of drugs, the most commonly used drugs were rush (79.20%), ice (8.50%), and 5-MeO-DIPT (8.50%). Univariate logistic regression showed that perceived stress (odds ratio [OR]=1.73, 95% bootstrap confidence intervals [CI]=1.27-2.36) and loneliness (OR=1.62, 95% CI=1.23-2.13) had a significant association with drug use, all p [ps] <0.01. The effect of perceived stress on drug use was partially mediated by feeling of loneliness (estimate=0.09, standard error=0.04, p<0.05), the mediating effect (0.09) accounted for 27.27% of the total effect (0.33). Perceived stress can positively predict drug use. Loneliness plays an important role in drug use among transgender women.

Substance use under a restrictive and prohibitive policy regime in secondary schools in Uganda: The convergence of motives, contexts and student characteristics

Drug use among young people is increasing in Africa. To prevent drug use, it is essential to understand the motivations, environments, and characteristics of users. Although there are established risk factors for drug use, little is known about the reasons why adolescents and young people take drugs. This study investigated the reasons given by a sample of secondary school pupils for using drugs and other substances, specifically, how adolescents circumvent the restrictive and prohibitive policy regime and rationalize and account for drug use. This study used a cross-sectional mixed-methods design. For quantitative data, descriptive and regression analyses were run, and for qualitative data, thematic analysis was used. About 13 per cent (n=41) met criteria for moderate to severe drug use. Coping (Mean = 4.13, SD 0.89), social (Mean = 3.71, SD 0.97), and enhancement (Mean = 3.09, SD 0.92) motives were highly endorsed as motives for drug use. The extent to which motives predicted drug use ranged from β = 0.55 (95% CI: 0.39, 0.72) for coping to β = 0.18 (95% CI: 0.08, 0.28) for expansion motives. Students employed ingenious and frequently risky ways to circumvent the rigorous and prohibitive regulatory regimes governing drug use in schools. Academic stress, limited recreational activities, poor stress management, peer influence, poor adult supervision and dysfunctional family backgrounds, all interrelate in complex ways with motives, contexts, and student characteristics to create a conducive environment for the rationalisation, medicalisation, and veneration of drug use. Further research on contexts, motives and characteristics of adolescents that shape drug use is needed.

Biopsychosocial predictors of drug dependence among Filipino drug users in community-based drug rehabilitation

This study examines the biopsychosocial predictors of drug use and dependence among Filipino drug users enrolled in community-based drug rehabilitation. Data from 925 clients revealed that the severity of drug use, cigarette and alcohol use, recovery skills, and mental health problems predict drug dependence. Family support, life skills, and psychological well-being are indirect predictors of severity of use. Results also revealed differences in predictors by sex, level of use, and type of clients. These findings highlight the importance of a client-centered approach to treatment and suggest what might be critical elements in a community-based drug rehabilitation program in the Philippines.

A Study of Addictive Behavior among University Students

Drug abuse is a public health issue, particularly among adolescents and young adults. Therefore, the purpose of our study is to determine the prevalence rate of drugs use and to identify certain factors that can serve as predictors of drug use. A total of 431 students from Mohammed V University in Rabat participated in this descriptive cross-sectional survey. The prevalence of drug use (regular users and experimenters) was 32.9%. Cannabis has a frequency of 13%, whereas other substances and other psychotropics have a prevalence of 3.8%. The most common average age of onset of substance use was 15. Males had the greatest likelihood of becoming drug addicts. We discovered a high consumption of drugs among university students at the end of this work, implying the need to establish a programme to combat drug addiction, the starting point of which will undoubtedly be prevention at the primary and secondary school levels, requiring the participation of all those involved in the field of education.

SIMULASI MONTE CARLO DALAM MEMPREDIKSI PEMAKAIAN OBAT PENYAKIT GIGI DAN MULUT PADA RUMAH SAKIT

The use of drugs in patients with dental disease is a necessity that needs to be considered by the hospital in providing medical services to patients. Adequate and well-managed drug supply prevents shortages or excess drug stocks. So it needs good planning in managing and monitoring drug stocks appropriately. This study aims to make predictions in the use of dental disease drugs by using a monte carlo simulation. The data used is data on the use of drugs for dental diseases from 2020 to 2022. The data on drug use processed were 12 types of drugs. The data will be processed based on the Monte Carlo simulation stages. The results of using the Monte Carlo Simulation are to obtain predictions of the use of dental disease drugs with an accuracy value reaching 89.14%. Based on the accuracy value obtained, the Monte Carlo simulation can be used to predict drug use in the future. So that the supply of dental disease medicine is maintained.

Prediksi Kuantitas Penggunaan Obat pada Layanan Kesehatan Menggunakan Algoritma Backpropagation Neural Network

Prediction of the amount of drug use at public health centers is needed to ensure the availability of drugs for patients in service quality management. A good prediction of the amount of medicine needed helps the quality of development planning in the health sector. Scientific developments in the field of Artificial Intelligence (AI) deliver a variety of the best techniques for making predictions. By adopting the workings of neural networks (neurons) in the human brain or Artificial Neural Network (ANN), the Backpropagation Neural Network (BPNN) algorithm is one of the best algorithms in making predictions, including predicting drug use in health services. The problem of this research is how to design the best architectural model such as the number of neurons in the input layer, hidden layer and other parameters so as to produce predictions with optimal accuracy. This study aims to develop an ANN architectural design with the Backpropagation algorithm to predict the need for drug use. The data used is data on drug use reports from 2015 to 2021 at the Andalas Community Health Center (Puskesmas) Padang City. The steps taken to predict are; collect data, pre-process data and perform analysis, design ANN architecture, make predictions. Learning using the backpropagation algorithm through the initial weight initialization process, activation stage, weight training (weight change) and iteration stage. The proportion of the amount of data used for training is 70% data and 30% for testing data. The results of this study indicate that the best ANN architecture is 12-12-1 with an accuracy of predicting the quantity of drug use reaching 97.87% for paracetamol with a Mean Absolute Percentage Error (MAPE) of 2.13%. The prediction results become a reference for the Puskesmas and the Health Office for service planning and development.

Can substance abuse media literacy increase prediction of drug use in students?

ObjectiveThe present study aimed to determine the predictors of drug use among students based on the Extended Prototype willingness model (EPWM).MethodsThis cross-sectional study was performed at Hamadan universities. The participants were selected via multi-stage sampling. Finally, out of 721 students participated. The questionnaire consisted of four sections: (a) socio-demographic characteristics, (b) Questionnaire about the prototype willingness model constructs, (c) Questionnaire about the status of drug use behavior, (d) Substance Abuse Media Literacy (SAML) scale. Data were analyzed using a structural equations partial least squares confirmatory composite approach and SPSS-23.ResultsThe mean age of students was of 23.47 ± 4.14 years. Based on results, behavioral willingness (β = 0.420, t-value = 9.010, p < 0.001) and behavioral intention (β = 0.291, t-value = 6.957, p < 0.001) significantly predicted drug use. The presented results of analysis, 66% of the variance of the drug use, 62% of the variable of the behavioral willingness, and 56% of the behavioral intention could be explained by the EPWM.ConclusionThe present study demonstrated that EPWM could predict drug use in students. Therefore, designing and implementing educational and promotional intervention programs to reduce behavioral willingness and increase students’ skills in substance abuse media literacy is necessary to prevent drug use behavior.

Conceptualizing the Socio-Built Environment: An Expanded Theoretical Framework to Promote a Better Understanding of Risk for Nonmedical Opioid Overdose Outcomes in Urban and Non-Urban Settings.

Nonmedical opioid (NMO) use has been linked to significant increases in rates of NMO morbidity and mortality in non-urban areas. While there has been a great deal of empirical evidence suggesting that physical features of built environments represent strong predictors of drug use and mental health outcomes in urban settings, there is a dearth of research assessing the physical, built environment features of non-urban settings in order to predict risk for NMO overdose outcomes. Likewise, there is strong extant literature suggesting that social characteristics of environments also predict NMO overdoses and other NMO use outcomes, but limited research that considers the combined effects of both physical and social characteristics of environments on NMO outcomes. As a result, important gaps in the scientific literature currently limit our understanding of how both physical and social features of environments shape risk for NMO overdose in rural and suburban settings and therefore limit our ability to intervene effectively. In order to foster a more holistic understanding of environmental features predicting the emerging epidemic of NMO overdose, this article presents a novel, expanded theoretical framework that conceptualizes "socio-built environments" as comprised of (a) environmental characteristics that are applicable to both non-urban and urban settings and (b) not only traditional features of environments as conceptualized by the extant built environment framework, but also social features of environments. This novel framework can help improve our ability to identify settings at highest risk for high rates of NMO overdose, in order to improve resource allocation, targeting, and implementation for interventions such as opioid treatment services, mental health services, and care and harm reduction services for people who use drugs.

Selective Optogenetic Dopamine Terminal Stimulation Reveals Role of GABAergic Signaling in Individual Differences in Dopamine Release

An estimated 70.5% of Americans aged 12 or older report having used an illicit substance at least once, while about 10‐20% of those individuals ultimately develop a substance use disorder (SUD). These data highlight substantial individual variability in the risk of developing SUD following initial drug use. Identifying markers of increased risk provides a significant opportunity to identify at‐risk populations and to aid in the prevention of the development of SUD. In preclinical rodent models, locomotor response to a novel environment predicts drug use vulnerability. Rodents that demonstrate higher locomotor response to an inescapable novel environment (high responders; HR) acquire self‐administration (SA) of drugs more rapidly and at lower doses compared to low responders (LR). Striatal dopamine (DA) signaling is critical for the acquisition of drug SA and tonic/phasic firing patterns of DA neurons encode information about salient environmental stimuli and rewards. HR rats show increased phasic DA signaling to reward‐predictive cues compared to LR rats. Dopamine release dynamics are tightly controlled by local modulation within the nucleus accumbens (NAc), including by nicotinic acetylcholine receptors (nAChRs), but the mechanisms by which this local modulation may contribute to differential DA release is not fully understood. Our lab has previously shown that locomotor response to a novel environment predicts nAChR modulation of phasic DA signals in the NAc. Specifically, desensitization or blockade of α6β2‐containing (α6β2*) nAChRs within the NAc was found to augment phasic DA signals in brain slices of HR rats, and reduce phasic DA signals in LR rats. However, these studies utilized non‐specific electrical stimulation to record DA release. In the present study, we used ex vivo fast‐scan cyclic voltammetry with an optogenetic approach to selectively stimulate DA neuron terminals or cholinergic interneurons (CINs) within the NAc to examine the specific contributions of acetylcholine (ACh) to the overall DA response in HR and LR rats. We find that, contrary to previous results with electrical stimulation, selective DA phasic stimulation results in lower DA release in HRs compared to LRs, while blockade of α6β2* nAChRs using light stimulation results in higher DA release in HRs. This suggests that additional circuitry is likely involved in differential DA signaling within the NAc. Given the tonic activity of CINs and presence of nAChRs on GABA interneurons, we next used pharmacological manipulations to examine the possibility of GABA signaling contributing to individual differences in DA release. Here, we find that antagonism of GABA receptors blocks the previously seen differential effects of α6β2* nAChR antagonism on DA release, suggesting that GABA interneurons do play a critical role in differential DA signaling in HRs versus LRs. In sum, these data help form a clearer understanding of the mechanisms underlying individual differences in DA signaling, which may be a primary driver of differences in sensitivity to the reinforcing effects of drugs of abuse. Investigating these mechanisms allows us to better identify behavioral and neurochemical markers of SUD vulnerability in humans and to ultimately stimulate development of individualized and effective treatments.

Illicit drug use and exposure in disadvantaged neighborhoods in Czechia: policy representations and evidence

This study examines the lifetime prevalence of illicit drug use and illicit drug exposure in disadvantaged (“Roma”) and more affluent neighborhoods in Czechia. The results of a survey among populations of both types of neighborhoods suggest no statistically significant difference between the two in terms of the overall lifetime prevalence of illicit drug use; however, lifetime prevalence of methamphetamine use proved higher in disadvantaged neighborhoods. The population of disadvantaged neighborhoods has also lower chances to use LSD during their lifetime. Further differences were identified in drug exposure, with the population of more affluent neighborhoods being more frequently exposed to illicit drugs than the population of disadvantaged neighborhoods. The predictors of drug use and drug exposure were partially different for both populations. In the disadvantaged population, drug use was revealed, among other predictors, to be associated with housing conditions.

Childhood adversity subtypes and young adulthood mental health problems: Unpacking effects of maltreatment, family dysfunction, and peer victimization

Associations between adverse childhood experiences (ACEs) and mental health problems are substantiated. Yet, research has not thoroughly examined the differential effects of ACEs subtypes (e.g., adults’ reported childhood maltreatment versus family/household dysfunction), and additional childhood adversities (e.g., adults’ reported peer victimization) on multiple mental health problems. This study unpacked effects of these three childhood adversities on depression, anxiety, and post-traumatic stress disorder (PTSD) symptoms; aggressive behavior; and alcohol and drug use problems in U.S. young adults from 47 states. Participants (N = 550, M = 27.07 years, SD = 4.65, range = 18–35; 53.27% female, 44.55% male, 2.18% gender non-conforming; 68.36% White, 9.45% Black, 8.90% Asian/Pacific Islander, 8.18% Latinx, 3.63% biracial/multiracial, 1.48% other/prefer not to answer) completed standardized instruments on traditional ACEs and peer victimization, young adulthood mental health problems, and demographics. With all three adversities accounted for together, higher levels of childhood maltreatment and peer victimization predicted higher levels of depression, anxiety, and PTSD symptoms; and aggressive behavior. Family dysfunction only predicted PTSD symptoms, and only maltreatment predicted drug use problems. After removing influential cases, no adversity subtype predicted alcohol use problems. Compared to childhood family dysfunction, childhood maltreatment and peer victimization may more broadly relate to young adults’ mental health problems.