Global Trends in Mental Health Disorder

From Schizophrenia to Depression

By Amit [source]

About this dataset

This dataset contains informative data from countries across the globe about the prevalence of mental health disorders including schizophrenia, bipolar disorder, eating disorders, anxiety disorders, drug use disorders, depression and alcohol use disorders. By providing this data in an easy to visualise format you can gain an insight into how these issues are impacting lives; allowing for a deeper understanding of these conditions and the implications. Through this reflection you may be able to answer some important questions: - What are the types of mental health disorder that people around the world suffer? - How many people in each country suffer mental health problems? - Are men or women more likely to have depression? - Is depression linked with suicide and what is the percentage rate? - In which age groups is depression more common?
From exploring patterns between prevalence rates through in-depth data visualisation you’ll be able to further understand these complex issues. The knowledge gained from this dataset can help bring valuable decision making skills such as research grants, policy making or preventative intervention plans across various countries. So if you wish to create meaningful data viz then start with this global prevalence of mental health disorder’s together with accompanying videos for extra context - Deepen your understanding about Mental Health Disorders today!

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How to use the dataset

Using this dataset is quite straightforward. Each row of the table contains information about a certain country or region for a certain year. The following columns are provided: Entity (the country or region name), Code (the code for the country or region), Year (the year the data was collected) Schizophrenia (% - percentage of people with schizophrenia), Bipolar Disorder (%) - percentage of people with bipolar disorder) Eating Disorders (%) - Percentage of individuals with disordered eating patterns Anxiety Disorders (%) - Percentage of individuals with anxiety Drug Use Disorders (%) - Percentage figures for those struggling with substance abuse Depression (%) – Percentages relating to those struggling with depressive illness Alcohol Use Disorders (%) – Percentages relating to those battling alcoholism

Using this dataset requires no special skills; however it is best suited for those comfortable navigating spreadsheets and tables as well as analyzing numerical information quickly and accurately. Many software suites like excel are useful here but simple internet searches will reveal free alternatives if your preference is web-based solutions!

By piecing together these different columns’ values we can get an idea if prevalence rates across different types of mental illnesses increase or decrease over time. For example we could compare depression levels between 2015 and 2018 by creating two separate sets containing information filtered just within our parameters respectively only reading records from 2015 then 2018). From here we can see whether numbers changed very much or stayed stagnant supefying any sort of patterns that could exist

Research Ideas

• Visualizing the prevalence of mental health disorders - Create a data visualization that compares and contrasts the prevalence of depression, anxiety, bipolar disorder, schizophrenia, eating disorders, alcohol use disorder and drug use disorder across different countries. This could provide insight into global differences in mental health and potential causes of those differences.

• Mapping depression rates - Create an interactive map that shows both regional and national variations in depression rates within a specific country or region. This would allow people to easily identify areas with higher or lower than average prevalence of depression which could help inform decision-makers when it comes to policy-making related to mental healthcare services provisioning.

• Developing predictive models for mental health - Use the data from this dataset as part of a larger machine learning project to build predictive models for mental health across countries or regions based on various factors such as demographics, economic indicators etc., This can be helpful for researchers working on understanding populations’ susceptibility towards developing certain disorders so as to craft appropriate preventive strategies accordingly

Acknowledgements

If you use this dataset in your research, please credit the original aut...

This dataset presents the prevalence of depression and anxiety among adults, serving as a key indicator of mental health within the population. It is intended to support monitoring and evaluation efforts aimed at improving mental health outcomes and reducing the burden of common mental disorders. The data is expressed as a percentage, reflecting the proportion of adults experiencing depression and/or anxiety.

Rationale

Mental health is a critical component of overall well-being. Monitoring the prevalence of depression and anxiety in adults helps inform public health strategies, allocate resources effectively, and evaluate the impact of mental health interventions. Reducing the prevalence of these conditions is a priority for improving quality of life and reducing associated social and economic costs.

Numerator

The numerator for this indicator is currently unspecified. It would typically represent the number of adults identified as experiencing depression and/or anxiety within a defined population and time period.

Denominator

The denominator is also unspecified in the current metadata. It would generally be the total number of adults in the population under study during the same time period.

Caveats

At present, the dataset lacks detailed definitions for both the numerator and denominator, as well as the data sources. This limits the interpretability and comparability of the indicator. Users should exercise caution when drawing conclusions or making comparisons based on this data.

External References

No external references have been provided. For further context or methodological guidance, users may refer to national health surveys or reports from organizations such as the World Health Organization.

Localities ExplainedThis dataset contains data based on either the resident locality or registered locality of the patient, a distinction is made between resident locality and registered locality populations:Resident Locality refers to individuals who live within the defined geographic boundaries of the locality. These boundaries are aligned with official administrative areas such as wards and Lower Layer Super Output Areas (LSOAs).Registered Locality refers to individuals who are registered with GP practices that are assigned to a locality based on the Primary Care Network (PCN) they belong to. These assignments are approximate—PCNs are mapped to a locality based on the location of most of their GP surgeries. As a result, locality-registered patients may live outside the locality, sometimes even in different towns or cities.This distinction is important because some health indicators are only available at GP practice level, without information on where patients actually reside. In such cases, data is attributed to the locality based on GP registration, not residential address.

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

Number and proportion of people with neurotic disorders including phobias, depressive episodes, generalised anxiety disorder, obsessive compulsive disorder and panic disorder Source: Department of Health (DoH): National Psychiatric Morbidity Survey Publisher: Mental Health Observatory: North East Public Health Observatory Geographies: Local Authority District (LAD), County/Unitary Authority, Government Office Region (GOR), National, Primary Care Trust (PCT) Geographic coverage: England Time coverage: 2006 Type of data: Modelled data

BackgroundThe rise of depression, anxiety, and suicide rates has led to increased demand for telemedicine-based mental health screening and remote patient monitoring (RPM) solutions to alleviate the burden on, and enhance the efficiency of, mental health practitioners. Multimodal dialog systems (MDS) that conduct on-demand, structured interviews offer a scalable and cost-effective solution to address this need.ObjectiveThis study evaluates the feasibility of a cloud based MDS agent, Tina, for mental state characterization in participants with depression, anxiety, and suicide risk.MethodSixty-eight participants were recruited through an online health registry and completed 73 sessions, with 15 (20.6%), 21 (28.8%), and 26 (35.6%) sessions screening positive for depression, anxiety, and suicide risk, respectively using conventional screening instruments. Participants then interacted with Tina as they completed a structured interview designed to elicit calibrated, open-ended responses regarding the participants' feelings and emotional state. Simultaneously, the platform streamed their speech and video recordings in real-time to a HIPAA-compliant cloud server, to compute speech, language, and facial movement-based biomarkers. After their sessions, participants completed user experience surveys. Machine learning models were developed using extracted features and evaluated with the area under the receiver operating characteristic curve (AUC).ResultsFor both depression and suicide risk, affected individuals tended to have a higher percent pause time, while those positive for anxiety showed reduced lip movement relative to healthy controls. In terms of single-modality classification models, speech features performed best for depression (AUC = 0.64; 95% CI = 0.51–0.78), facial features for anxiety (AUC = 0.57; 95% CI = 0.43–0.71), and text features for suicide risk (AUC = 0.65; 95% CI = 0.52–0.78). Best overall performance was achieved by decision fusion of all models in identifying suicide risk (AUC = 0.76; 95% CI = 0.65–0.87). Participants reported the experience comfortable and shared their feelings.ConclusionMDS is a feasible, useful, effective, and interpretable solution for RPM in real-world clinical depression, anxiety, and suicidal populations. Facial information is more informative for anxiety classification, while speech and language are more discriminative of depression and suicidality markers. In general, combining speech, language, and facial information improved model performance on all classification tasks.

Social Anxiety Disorder (SAD) and Alcohol Use Disorder (AUD) are highly prevalent and frequently co-occur. The results of population studies suggest that SAD tends to precede AUD, and the results of laboratory studies suggest that alcohol use facilitates social behaviors in socially anxious individuals. Therefore, we posited that, in a modern context, a tendency to consume alcohol may be positively selected for among socially anxious individuals by its effect on the likelihood of finding a partner and reproducing. We tested the hypothesis that a higher proportion of individuals with a lifetime diagnosis of SAD and AUD reproduce (i.e., have at least one child) relative to individuals with SAD absent AUD in an individual participant meta-analysis based on over 65,000 adults derived from four nationally representative cross-sectional samples. We then cross-validated these findings against the results of a 10-year follow up of one of these surveys. Lifetime history of SAD was not associated with reproduction whereas lifetime history of AUD was positively associated with reproduction. There was no statistically detectable difference in the proportion of individuals with a lifetime history of SAD with or without AUD who reproduced. There was considerable heterogeneity in all of the analyses involving SAD, suggesting that there are likely to be other pertinent variables relating to SAD and reproduction that should be delineated.

BackgroundThe mental well-being of physicians is increasingly recognized as vital, both for their personal health and the quality of care they provide to patients. Physicians face a variety of mental health challenges, including depression, anxiety, and burnout, which have become prevalent issues globally. These mental health concerns are like those found in the general population but are particularly significant in the demanding healthcare setting.ObjectiveThis review aims to explore the prevalence and correlates of depression, anxiety, and burnout among physicians and residents in training.MethodsA comprehensive literature review was conducted, searching databases such as Medline, PubMed, Scopus, CINAHL, and PsycINFO. The review focused on studies published from 2021 to 2024 that addressed the prevalence of these mental health conditions in physicians and residents. The findings, in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, were summarized in detailed tables.ResultsFollowing titles and abstracts screening, 196 publications were selected for full-text review, with 92 articles ultimately included in the analysis. The results revealed significant variability in the prevalence of burnout, depression, and anxiety. Burnout rates among physicians ranged from 4.7 to 90.1% and from 18.3 to 94% among residents. Depression prevalence ranged from 4.8 to 66.5% in physicians and from 7.7 to 93% in residents. Anxiety rates were between 8 and 78.9% in physicians and 10 to 63.9% in residents. Notably, women reported higher rates of all three conditions compared to men. Key factors influencing these mental health conditions included demographics (age, gender, education, financial status, family situation, occupation), psychological conditions, social factors (stigma, family life), work organization (workload, work conditions), and COVID-19-related issues (caring for COVID-19 patients, fear of infection, working in high-risk areas, concerns about personal protective equipment (PPE), and testing positive).ConclusionThis review indicates a high prevalence of burnout, depression, and anxiety among physicians and residents, with female participants consistently showing higher rates than males. These findings can guide policymakers and healthcare administrators in designing targeted programs and interventions to help reduce these mental health issues in these groups.

This is a development key figure, see questions and answers on kolada.se for more information. Number of people aged 65 and older with home care on an individual basis who stated that they have severe problems with anxiety, anxiety or anxiety divided by all people aged 65 and older in ordinary housing with home care on an individual basis who responded to the survey of older people’s perceptions. “Do not know/No opinion” is excluded from the denominator. Data from 2012.

This is a development key figure, see questions and answers on kolada.se for more information. Number of people aged 65 and older living in special public housing who reported having severe anxiety, anxiety or anxiety divided by all persons aged 65 years and older, residents in special housing under public authority who responded to the survey of older people’s perceptions. “Do not know/No opinion” is excluded from the denominator. Data from 2012.

Hospitalization Discharge Rates in Lake County, Illinois. Explanation of field attributes: Anxiety Disorder - Anxiety disorders are characterized by excessive fear or stress that is difficult to control and negatively and substantially impacts daily functioning. This is a rate per 100,000. Mood Disorder – Mood disorders are characterized by the elevation or lowering of a person's mood, such as depression or bipolar disorder. This is a rate per 100,000. Alcohol Rehabilitation – Alcohol rehabilitation is a term for the medical and/or psychotherapeutic treatment for dependency on alcohol. This is a rate per 100,000. Diabetes – Diabetes is a chronic disease in which blood sugar (glucose) levels are above normal. This is a rate per 100,000. Hypertension – Hypertension is a chronic disease in which blood pressure (the force of the blood flowing blood vessels) is consistently high. This is a rate per 100,000. Asthma - Asthma is a condition in which airways narrow, swell, and produce extra mucus leading to difficulty in breathing. This is a rate per 100,000. Senior Falls Emergency Room Visit – Senior falls refers to individuals who are 65 years or older who have a fall and injure themselves. This is a rate per 100,000. Hospital Discharges – Hospital discharge is defined as the release of a patient who has stayed at least one night in hospital. This is a rate per 100,000. Mental Health Emergency Room Visit – Mental health conditions/ or mental illnesses refer to disorders generally characterized by dysregulation of mood, thought, and/or behavior. This is a rate per 100,000. Total Mental Health – Mental health conditions/ or mental illnesses refer to disorders generally characterized by dysregulation of mood, thought, and/or behavior. This is a rate per 100,000. Total Ambulatory Care Sensitive Conditions – Ambulatory Care Sensitive Conditions (ACSCs) are defined as conditions where effective community care and case management can help prevent the need for hospital admission. This is a rate per 100,000.

ObjectiveCare patterns and Traditional Chinese Medicine (TCM) constitution affects the emotion and health of patients with systemic sclerosis (SSc) while the prevalence of COVID-19 may aggravate such patients’ emotion and health. We investigated the depression and anxiety levels of patients with SSc during the pandemic to identify the correlation between care patterns, TCM constitution, and patients’ emotion.Materials and methodsThis was a cross-sectional study. Patients with SSc and healthy individuals were surveyed using the patient health questionnaire-9, generalized anxiety disorder-7, and constitution in Chinese medicine questionnaire and a modified care pattern questionnaire. Factors correlated with depression and anxiety were screened using univariate and multivariate logistic regression analyses.ResultsA total of 273 patients with SSc and 111 healthy individuals were included in the analysis. The proportion of patients with SSc who were depressed was 74.36%, who had anxiety was 51.65%, and who experienced disease progression during the pandemic was 36.99%. The proportion of income reduction in the online group (56.19%) was higher than that in the hospital group (33.33%) (P = 0.001). Qi-deficiency [adjusted odds ratio (OR) = 2.250] and Qi-stagnation (adjusted OR = 3.824) constitutions were significantly associated with depression. Remote work during the outbreak (adjusted OR = 1.920), decrease in income (adjusted OR = 3.556), and disease progression (P = 0.030) were associated with the occurrence of depression.ConclusionChinese patients with SSc have a high prevalence of depression and anxiety. The COVID-19 pandemic has changed the care patterns of Chinese patients with SSc, and work, income, disease progression, and change of medications were correlates of depression or anxiety in patients with SSc. Qi-stagnation and Qi-deficiency constitutions were associated with depression, and Qi-stagnation constitution was associated with anxiety in patients with SSc.Trial registrationhttp://www.chictr.org.cn/showproj.aspx?proj=62301, identifier ChiCTR2000038796.

IntroductionHuman Immunodeficiency Virus/Acquired Immunodeficiency Syndrome (HIV/AIDS) is a major public health concern globally. Due to advancements in Anti-Retroviral Treatment (ART) therapy, more people with HIV are living longer with about 1.4 million infected people in Uganda. Anxiety disorders are often unrecognized and undetected in older persons living with HIV (PLWH) yet they impair an elderly person’s physical health and decrease the ability to perform daily activities.ObjectiveTo determine the prevalence and factors associated with probable anxiety disorders among elderly PLWH at Mulago Immune Suppression Syndrome (ISS) clinic.MethodsA cross-sectional study was conducted at Mulago ISS clinic among 273 systematically selected participants living with HIV/AIDS on antiretroviral therapy for at least 6 months between April and May 2024. Interviews were conducted using the Generalized Anxiety Disorder 7-item (GAD-7) screening tool to help identify individuals who may be at risk for anxiety disorders and structured questionnaires for socio-demographics, and psychological factors. Drug and clinical factors data were extracted from records, entered into Epidata, and later to STATA version 17 for analysis. Prevalence was reported as a percentage and modified Poisson regression analysis was used to determine the factors associated with anxiety disorders.ResultsWe enrolled 273 participants with a median age (Interquartile range) was 56 (52, 61.5) years. 54.9% were females, 56.8% didn’t have a partner and 53.8% were employed. The prevalence of probable anxiety disorders was 16.8% (95% CI 12.5–21.6). Employment status (aPR- 2.113, 95% CI 1.252–3.567), family history of mental health disorder (aPR-2.041, 95% CI 1.228–3.394), stigma (aPR-2.564, 95% CI 1.544–4.257) and family support (aPR-2.169, 95% CI 1.272–3.699) were significantly associated with having probable anxiety disorders.ConclusionOne in every six elderly persons living with HIV may have a probable anxiety disorder. Being unemployed, having a family history of mental health disorders, having stigma and having inadequate family support were significantly associated with having a probable anxiety disorder. Healthcare workers should provide comprehensive anxiety screening and patient-centered care for elderly persons with HIV. At the same time, the government develops financial empowerment strategies and supports mental health through family groups, and public campaigns to reduce HIV stigma and educate families on effective support.

IntroductionHepatitis B is a highly contagious viral infection that has long been a significant global health concern. Given its adverse effects on the course of the disease, evaluating psychiatric outcomes is important. Despite indications of an increased risk of psychological outcomes among those with hepatitis B, the extent of this association remains unclear.MethodsThis PRISMA-adherent systematic review (PROSPERO: CRD42024564246) searched PubMed, Embase, Cochrane, and PsycINFO for all studies evaluating the prevalence and risk of anxiety and depressive symptoms in individuals with hepatitis B. Random effects meta-analyses and meta-regression were used for primary analysis.ResultsA total of 31 studies were included. We identified a high prevalence of depressive symptoms (Proportion=19%, 95% CI: 11-31) and anxiety (Proportion=30%, 95% CI: 18-45) among individuals with hepatitis B. There was also a significantly increased risk of depressive symptoms (RR=1.45, 95% CI: 1.00-2.09, P=0.049) and anxiety (RR=1.40, 95% CI: 1.11-1.78) in individuals with hepatitis B compared to controls. Subgroup analyses indicated that older age and chronic hepatitis B infection were associated with a higher prevalence of anxiety and depressive symptoms. The systematic review found that being single, unemployed, having a lower income, a lower education level, high comorbidities, and a family history of mental illness were significant risk factors for poorer psychological outcomes.ConclusionOur study highlights an increased vulnerability to anxiety and depressive symptoms among individuals with hepatitis B. We emphasize the urgent need for early detection and additional support for this at-risk group.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42024564246.

BackgroundDigital interventions present potential solutions for aftercare and relapse prevention in anxiety and depressive disorders. This systematic review synthesizes evidence on the efficacy of internet- and mobile-based interventions for post-acute care in these conditions.MethodsA systematic search was conducted in electronic databases (MEDLINE, CENTRAL, Scopus, Web of Science, PsycINFO, PsycARTICLES, PsycEXTRA, ProQuest Dissertations and Theses Open, Open Access Theses and Dissertations, and Open Grey) for randomized controlled trials evaluating digital aftercare or relapse prevention interventions for adults with anxiety or depressive disorders. Primary outcomes included symptom severity, relapse rates, recurrence rates, and rehospitalization. Secondary outcomes included general quality of life and adherence to primary treatment. Risk of bias was assessed using the Cochrane tool.ResultsNineteen studies (3,206 participants) met the inclusion criteria. Interventions included cognitive-behavioral therapy, mindfulness-based approaches, and supportive text messaging. Most studies focused on depression, with limited evidence for anxiety disorders. Notably, fourteen studies that reported on depressive symptoms demonstrated significant improvements following digital interventions, with effect sizes ranging from small (Cohen’s d = 0.20) to large (Cohen’s d = 0.80). Five studies investigated relapse or recurrence rates, yielding mixed results. Adherence rates varied significantly across studies, ranging from 50 to 92.3%, highlighting the variability in participant engagement. Methodological quality was also variable, with allocation concealment and blinding being common limitations.ConclusionInternet- and mobile-based interventions show promise for aftercare and relapse prevention in depression, with limited evidence for anxiety disorders. Future research should focus on optimizing engagement, personalizing interventions, standardizing outcome measures, and conducting larger trials with longer follow-up periods. These findings have important implications for integrating digital tools into existing care pathways to improve long-term outcomes for individuals with anxiety and depressive disorders.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020151336, CRD42020151336.

Abstract: Although depression and anxiety are known to result in disabilities and workplace and health system losses, population-based studies on this problem are rare in Brazil. The current study assessed the prevalence of mental disorders in adolescents, youth, and adults and the relationship to sociodemographic characteristics in five birth cohorts (RPS) in Ribeirão Preto (São Paulo State), Pelotas (Rio Grande do Sul State), and São Luís (Maranhão State), Brazil. Major depressive episode, suicide risk, social phobia, and generalized anxiety disorder were assessed with the Mini International Neuropsychiatric Interview. Bootstrap confidence intervals were estimated and prevalence rates were stratified by sex and socioeconomic status in the R program. The study included 12,350 participants from the cohorts. Current major depressive episode was more prevalent in adolescents in São Luís (15.8%; 95%CI: 14.8-16.8) and adults in Ribeirão Preto (12.9%; 95%CI: 12.0-13.9). The highest prevalence rates for suicide risk were in adults in Ribeirão Preto (13.7%; 95%CI: 12.7-14.7), and the highest rates for social phobia and generalized anxiety were in youth in Pelotas, with 7% (95%CI: 6.3-7.7) and 16.5% (95%CI: 15.4-17.5), respectively. The lowest prevalence rates of suicide risk were in youth in Pelotas (8.8%; 95%CI: 8.0-9.6), social phobia in youth in Ribeirão Preto (1.8%; 95%CI: 1.5-2.2), and generalized anxiety in adolescents in São Luís (3.5%; 95%CI: 3.0-4.0). Mental disorders in general were more prevalent in women and in individuals with lower socioeconomic status, independently of the city and age, emphasizing the need for more investment in mental health in Brazil, including gender and socioeconomic determinants.

Business Context

Anxiety and stress make your heart work harder. When you’re under stress your body’s “fight or flight” response is triggered i.e. your body tenses, your blood pressure rises and your heart beats faster. Stress hormones may damage the lining of the arteries. In the current scenario post-covid, since most of us are indoors, stress levels are at an all time high due to increasing anxieties which is leading to a higher heart rate. And your body's response to stress may be a headache, back strain, or stomach pains. Stress can also zap your energy, wreak havoc on your sleep and make you feel cranky, forgetful and out of control.

Higher heart rate is not always better since pathological conditions can lead to an increased heart rate. Tachycardia refers to a fast resting heart rate, usually over 100 beats per minute. Tachycardia can be dangerous, depending on its underlying cause and on how hard the heart has to work.

An optimal level of heart rate is associated with health and self-regulatory capacity, and adaptability or resilience. Higher levels of resting vagally-mediated heart rate are linked to performance of executive functions like attention and emotional processing by the prefrontal cortex.

Higher heart rates are usually connected with higher stress levels. When stress is excessive, it can contribute to everything from high blood pressure , also called hypertension, to asthma to ulcers to irritable bowel syndrome.

Stress may affect behaviors and factors that increase heart disease risk: high blood pressure and cholesterol levels, smoking, physical inactivity and overeating. Some people may choose to drink too much alcohol or smoke cigarettes to “manage” their chronic stress, however these habits can increase blood pressure and may damage artery walls.

Thus, heart rate can be used to monitor your stress levels and keep it under check as it is a useful indicator of good health.

A recent study speaks about effects of stress on increased heart attacks amongst 30-40 year olds:

https://economictimes.indiatimes.com/magazines/panache/heart-attacks-on-the-rise-among-30-40-year-olds-diabetes-hypertension-are-contributing-factors/articleshow/66997025.cms

Dataset

The data comprises various attributes taken from signals measured using ECG recorded for different individuals having different heart rates at the time the measurement was taken. These various features contribute to the heart rate at the given instant of time for the individual.

There are total of 6 CSV files with the names as follows: time_domain_features_train.csv - This file contains all time domain features of heart rate for training data frequency_domain_features_train.csv - This file contains all frequency domain features of heart rate for training data heart_rate_non_linear_features_train.csv - This file contains all non linear features of heart rate for training data

time_domain_features_test.csv - This file contains all time domain features of heart rate for testing data frequency_domain_features_test.csv - This file contains all frequency domain features of heart rate for testing data heart_rate_non_linear_features_test.csv - This file contains all non linear features of heart rate for testing data

Following is the data dictionary for the features you will come across in the files mentioned: MEAN_RR - Mean of RR intervals MEDIAN_RR - Median of RR intervals SDRR - Standard deviation of RR intervals RMSSD - Root mean square of successive RR interval differences SDSD - Standard deviation of successive RR interval differences SDRR_RMSSD - Ratio of SDRR / RMSSD pNN25 - Percentage of successive RR intervals that differ by more than 25 ms pNN50 - Percentage of successive RR intervals that differ by more than 50 ms KURT - Kurtosis of distribution of successive RR intervals SKEW - Skew of distribution of successive RR intervals MEAN_REL_RR - Mean of relative RR intervals MEDIAN_REL_RR - Median of relative RR intervals SDRR_REL_RR - Standard deviation of relative RR intervals RMSSD_REL_RR - Root mean square of successive relative RR interval differences SDSD_REL_RR - Standard deviation of successive relative RR interval differences SDRR_RMSSD_REL_RR - Ratio of SDRR/RMSSD for relative RR interval differences KURT_REL_RR - Kurtosis of distribution of relative RR intervals SKEW_REL_RR - Skewness of distribution of relative RR intervals uuid - Unique ID for each patient VLF - Absolute power of the very low frequency band (0.0033 - 0.04 Hz) VLF_PCT - Principal component transform of VLF LF - Absolute power of the low frequency band (0.04 - 0.15 Hz) LF_PCT - Principal component transform of LF LF_NU - Absolute power of the low frequency band in normal units HF - Absolute power of the high frequency band (0.15 - 0.4 Hz) HF_PCT -...

Additional file 1. VisitsOverTime.csv, data showing percentage of patients with a visit to each specialty by year and antidepressant type (used for Fig. 1).

Restrictions affecting mental health and wellbeing can significantly affect vulnerable populations that include university students. Therefore, we undertook a scoping review by searching LitCovid, the WHO Covid-19 database, Google Scholar and bibliographies of retrieved articles for systematic reviews that included data on depression and anxiety in university students during the COVID-19 pandemic. We found nine systematic reviews (two were preprints) that varied from five included studies to 89. The quality of the short-term evidence was rated low to moderate, and evidence for the medium to long term impact was low (the prevalence estimates may change substantially if further high-quality evidence becomes available).Reviews consistently reported high prevalence rates of anxiety and depression amongst university and college students. Rates of depression and anxiety were higher in those with financial difficulties, in non-Chinese students, in older students and females. In the most extensive review to date the pooled prevalence of depression was 34% (95%CI: 30-38%, 52 studies, n=1,277,755, I2 100%). The prevalence of anxiety was 32% (95% CI: 26-38%, 69 studies I2 100%). Also, anxiety was found at higher rates in older students, in those living alone, and in female students. Only one review concluded the evidence does not suggest a widespread negative effect on mental health in COVID-19 compared with previous years.The overall impact of COVID-19 on the mental health and wellbeing of university students is substantial.AppendicesFull Report - Effects of COVID-19 Restrictions on University Students Mental HealthFiguresFigure 1. PRISMA Flow ChartFigure 2. Included ReviewsFigure 3. Deng 2021 ResultsTablesTable 1. Review Populations and Impacts

ObjectivesTo explore the correlation between the incidence rates of depression and anxiety and cerebral glucose metabolism in cancer patients.MethodsThe experiment subjects consisted of patients with lung cancer, head and neck tumor, stomach cancer, intestinal cancer, breast cancer and healthy individuals. A total of 240 tumor patients and 39 healthy individuals were included. All subjects were evaluated by the Hamilton depression scale (HAMD) and Manifest anxiety scale (MAS), and were examined by whole body Positron Emission Tomography/Computed Tomography (PET/CT) with 18F-fluorodeoxyglucose (FDG). Demographic, baseline clinical characteristics, brain glucose metabolic changes, emotional disorder scores and their relations were statistically analyzed.ResultsThe incidence rates of depression and anxiety in patients with lung cancer were higher than those in patients with other tumors, and Standard uptake values (SUVs) and metabolic volume in bilateral frontal lobe, bilateral temporal lobe, bilateral caudate nucleus, bilateral hippocampus, left cingulate gyrus were lower than those in patients with other tumors. We also found that poor pathological differentiation, and advanced TNM stage independently associated with depression and anxiety risk. SUVs in the bilateral frontal lobe, bilateral temporal lobe, bilateral caudate nucleus, bilateral hippocampus, left cingulate gyrus were negatively correlated with HAMD and MAS scores.ConclusionThis study revealed the correlation between brain glucose metabolism and emotional disorders in cancer patients. The changes in brain glucose metabolism were expected to play a major role in emotional disorders in cancer patients as psychobiological markers. These findings indicated that functional imaging can be applied for psychological assessment of cancer patients as an innovative method.

BackgroundLittle is known about the mental health consequences of human rights violations in forcibly displaced populations. Objective: The objectives of this systematic review are to examine: (1) the prevalence of mental health conditions among forcibly displaced persons; (2) to investigate methodological factors contributing to mental health conditions; and (3) associations between mental health conditions and human rights violations.MethodsWe conducted a systematic review with meta-analyses on the prevalence of anxiety, depression, and posttraumatic stress disorder among forcibly displaced populations and factors contributing to it by searching in databases MEDLINE (Ovid), Embase, Web of Science Core Collection (Clarivate), PsycINFO (EBSCO), Sociological Abstracts (ProQuest), and PTSDPubs (ProQuest). Additionally, we assessed the Global Peace Index. Pooled associations were calculated using a random-effects meta-analysis model. Subgroup analyses were performed for the Global Peace Index, sampling methodology, also we assessed risk of bias.ResultsOf the 4,175 records screened, 55 with n = 31,573 participants met the inclusion criteria (n = 15,714 males, females, n = 15,859 females). Most studies were cross-sectional (n = 49). The pooled prevalence rates were 38.90% (95% CI: 29.63; 48.17) for anxiety, 38.16% (95% CI: 32.16; 44.15) for depression and 39.62% (95% CI: 32.87; 46.36) for posttraumatic stress disorder. Analyses by level of human rights violations show anxiety, and depression prevalence rates were higher in countries with low Global Peace Index than countries with high, moderate and low Global Peace Index (39.84% vs. 16.09%; 41.07% vs. 26.67%). Analyses by risk of bias indicate that the prevalence rate of PTSD was higher in studies with a high risk of bias compared to those with a very high risk of bias (49.27% vs. 29.79%). For anxiety, the prevalence rate was greater with random sampling compared to convenience sampling (44.71% vs. 36.87%). Depression and PTSD prevalence rates were higher with convenience sampling than with random sampling (38.67% vs. 37.70%; 42.83% vs. 35.50%).ConclusionOur review suggests that systematic continuous human rights violations are associated with mental health conditions in forcibly displaced persons. To prevent mental health conditions, it is necessary to reduce exposure to human rights violations in the countries forcibly displaced persons come from.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42017076535, PROSPERO registration: CRD42017076535.

Adjusted odds of psychosocial risk factors, general physical health indicators, medication use, and high-risk health behaviours among individuals with depression, anxiety, and comorbidity compared to those without mental illness a.