Information about the types of eating disorders, some reasons why the military community are at risk, warning signs and how to get help. The Missouri Eating Disorders Council (MOEDC) created this document so support service members, veterans and their families.

"Total number of people with anorexia and bulimia nervosa. This is measured across both sexes and all ages."

https://ourworldindata.org/grapher/number-with-anorexia-and-bulimia-nervosa?country=~OWID_WRL

Photo: https://penntoday.upenn.edu/news/eating-disorders-grow-more-prevalent-and-skew-younger

Global Mental Health Disorders

Prevalence of Common Mental Health Conditions, 2005-2017

By Amit [source]

About this dataset

This dataset contains valuable information about the prevalence of mental health disorders including schizophrenia, bipolar disorder, eating disorders, anxiety disorders, drug use disorders, depression, and alcohol use disorders from various countries across the globe. Mental health is a critical and complex issue which touches us all and this dataset allows a deeper dive into the quantitative understanding of its prevalence and geographical distribution. With this data at hand one can gain insight on questions such as: which countries have rates of mental illness that are higher or lower than average? Which regions are disproportionately dealing with certain types of mental health disruptions? Who is struggling with particular types of illnesses? This data provides answers to those inquiries as well as helping us gain a better understanding of how we can take action towards increasing global awareness, prevention efforts, and access to vital resources that help individuals become healed and empowered

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How to use the dataset

This dataset provides information on the prevalence of mental health disorders globally, with data collected from various countries in a given year. It includes statistics on several types of mental health disorders, such as schizophrenia, bipolar disorder, eating disorders, anxiety disorders, drug use disorders and depression.

Using this dataset can provide useful insights into the prevalence of mental health conditions worldwide. This could be used to better understand how different countries are affected by mental health issues and to identify areas that may need more help or attention. The data is broken down by country or region and year to allow for a better understanding of trends over time.

To use this dataset effectively for research or data analysis purposes it is important to first familiarize yourself with the columns available in the dataset: Entity (country/region), Code (country code), Year (year in which the data was collected), Schizophrenia (%) , Bipolar Disorder (%) , Eating Disorders (%) , Anxiety Disorders (%) , Drug Use Disorders (%) , Depression (%) and Alcohol Use Disorders (%). Each column represents a specific type of mental health disorder and provides information on its prevalence rate in each country/region during that calendar year.

Once you have an understanding of these columns you can begin analyzing the data to gain further insights into global trends related to these mental health conditions. You might perform descriptive analyses such as finding average percentages across different groups (e.g., genders) or time periods, as well as performing inferential analyses like assessing relationships between different variables within your data set (e.g., correlation). Additionally you could create visualizations such as charts, maps or other graphics that help make sense out of large amounts of statistical information easily accessible to a wider audience

Research Ideas

• Creating age-group specific visualizations and infographics that compare the prevalence of mental health disorders in different countries or regions to better understand how the issue of depression or anxiety intersects with factors such as gender, culture, or socioeconomic status.
• Creating a global map visualization that shows the prevalence of different mental health disorders in different countries/regions to demonstrate disparities between places and provide a way for policy makers to better target areas most affected by these issues.
• Developing data visualizations exploring relationships between demographic variables (e.g., gender, age) and prevalence of mental health disorder types such as depression or anxiety disorders in order to gain insight into possible correlations between them

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: Mental health Depression disorder Data.csv | Column name | Description | |:------------------------------|:--------------------------------------------------------------------------------------| | Entity | The name of the country or region. (String) | | Code ...

PurposeThe unique constraints to everyday life brought about by the COVID-19 pandemic have been suggested to negatively impact those with pre-existing mental health issues such as eating disorders. While individuals with eating disorders or disordered eating behaviors likely represent a vulnerable group to the COVID-19 pandemic, the impact of the pandemic is yet to be fully established.MethodsWe systematically examined the impact of the COVID-19 pandemic on eating disorders and disordered eating behaviors. We searched electronic databases MEDLINE, PsycINFO, CINAHL, and EMBASE for literature published until October 2021. Eligible studies were required to report on individuals with or without a diagnosed eating disorder or disordered eating behaviors who were exposed to the COVID-19 pandemic.FindingsSeventy-two studies met eligibility criteria with the majority reporting an increase in eating disorder or disordered eating behaviors associated with the COVID-19 pandemic. Specifically, it appears children and adolescents and individuals with a diagnosed eating disorder may present vulnerable groups to the impacts of the COVID-19 pandemic.DiscussionThis mixed systematic review provides a timely insight into COVID-19 eating disorder literature and will assist in understanding possible future long-term impacts of the pandemic on eating disorder behaviors. It appears that the role of stress in the development and maintenance of eating disorders may have been intensified to cope with the uncertainty of the COVID-19 pandemic. Future research is needed among understudied and minority groups and to examine the long-term implications of the COVID-19 pandemic on eating disorders and disordered eating behaviors.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=284749, PROSPERO [CRD42021284749].

Objective: To evaluate the inappropriate eating behaviors (IEB) of female adolescents over a one-year period.Methods:290 adolescents aged between 11 and 14 years old participated in the three research stages (T1: first four months, T2: second four months and T3: third four months). The Eating Attitudes Test (EAT-26) was applied to assess the IEB. Weight and height were measured to calculate body mass index (BMI) in the three study periods. Analysis of variance for repeated measures was used to analyze the data, adjusted for the scores of the Body Shape Questionnaire and the Brazil Economic Classification Criteria.Results:Girls at T1 showed a higher frequency of IEB compared to T2 (p=0.001) and T3 (p=0.001). The findings also indicated higher values for BMI in T3 in relation to T1 (p=0.04). The other comparisons did not show statistically significant differences.Conclusions:IEB scores of female adolescents declined over one year.

BMI = Body mass index (weight/height2).; HC = Healthy control participants; AN = Anorexia nervosa participants; BN = Bulimia nervosa participants; RecAN = Recovered anorexia nervosa participants. Test statistics are ANOVAs and descriptive statistics are means followed by standard deviations in parentheses.

Background. Most previous mortality research in eating disorders involves individuals attending specialist treatment services. Data linkage across jurisdictional health databases at a population level improves the generalisability of findings. Aims. To investigate mortality risk and causes of death for people with an eating disorder across a large geographic region using administrative health datasets. Method. Using linked hospital, mental health and death records, a retrospective cohort study was conducted including individuals aged 10-59 years who received an eating disorder diagnosis during hospital-based encounters in Australia, over a 10-year period between 2010 and 2019. A contemporary cohort of people accessing community care only were also evaluated. Mortality rates and standardised morality ratios (SMR) compared to the general population were calculated for each state, and by sex and age groups. Cox regression models were used to assess the risk of sociodemographic characteristics on mortality. Results. Mortality in people hospitalised with an eating disorder (N=19,697) was more than four times higher than the general population (SMR: 4.54), and highest in people aged 30-39 years (SMR: 13.32). Men hospitalised for eating disorders had a higher risk of death. Mortality rates in anorexia nervosa were not higher than other eating disorder diagnoses. Almost three-quarters of deaths were caused by suicide/self-harm or cardio/respiratory illness. Conclusions. People accessing hospital care with eating disorders in Australia have a higher risk of premature death regardless of age, sex or eating disorder diagnosis. Gender and age group disparities can inform policy and resource allocation and support the development of targeted interventions.

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 is about books. It has 1 row and is filtered where the book is A psychotherapeutic understanding of eating disorders in children and young people : ways to release the imprisoned self. It features 7 columns including author, publication date, language, and book publisher.

Objective - Theories on emotional eating are central to our understanding of etiology, maintenance, and treatment of binge eating. Yet, findings on eating changes under induced negative emotions in binge-eating disorder (BED) are equivocal. Thus, we studied whether food-cue reactivity is potentiated under negative emotions in BED, which would point toward a causal role of emotional eating in this disorder. Methods - Patients with BED (n = 24) and a control group without eating disorders (CG; n = 69) completed a food picture reactivity task after induction of negative versus neutral emotions. Food-cue reactivity (self-reported food pleasantness, desire to eat [DTE], and corrugator supercilii muscle response, electromyogram [EMG]) was measured for low- and high-caloric food pictures. Results - Patients with BED showed emotion-potentiated food-cue reactivity compared to controls: Pleasantness and DTE ratings and EMG response were increased in BED during negative emotions. This was independent of caloric content of the images. Conclusions - Food-cue reactivity in BED was consistent with emotional eating theories and points to a heightened response to all foods regardless of calorie content. The discrepancy of appetitive ratings with the aversive corrugator response points to ambivalent food responses under negative emotions in individuals with BED.

Eating disorders (EDs) are characterized by disturbances in eating behavior and occur worldwide, with a lifetime prevalence of 2 to 5%. Their etiology is complex and multifactorial, involving a complex interplay between genetic, biological, psychological, sociocultural, and environmental factors. They are more common among females than males and may be associated with medical and psychiatric complications, impaired functioning, and decreased quality of life. This narrative review aims at providing an updated contribution to the current understanding of gender differences in eating disorders (EDs) focusing on male population to foster more targeted and effective clinical interventions. A comprehensive review of the scientific literature was conducted by analyzing several major databases, including PubMed, PsycINFO, and Google Scholar. Only in recent years, there has been increased attention on the male population, revealing multiple differences between genders in terms of prevalence, onset, phenomenology, diagnosis, comorbidities, and outcomes of EDs. Moreover, the relationship between different sexual orientations and/or gender identities and EDs is an emerging field of study. Data suggest an increase in eating disorders (EDs) also among the male population underlines the importance that healthcare personnel of all specialties acquire basic competencies for adequately tackling these disorders in a gender perspective. In particular, prevention and early intervention, especially during critical developmental periods like puberty and adolescence, are crucial to avoid permanent damage. Future research and public health initiatives involving schools and families and targeting males should be addressed to promote a healthy relationship with food and body image, reduce stigma, and encourage people to seek help when needed.

https://www.newworldencyclopedia.org/d/images/thumb/2/2b/Mental_Disorder_Silhouette.png/220px-Mental_Disorder_Silhouette.png" alt="">

Context

A psychiatric disorder is a mental illness diagnosed by a mental health professional that greatly disturbs your thinking, moods, and/or behavior and seriously increases your risk of disability, pain, death, or loss of freedom. In addition, your symptoms must be more severe than expected in response to an upsetting event, such as normal grief after the loss of a loved one. A large number of psychiatric disorders have been identified. Chances are that, whether or not you or someone close to you has been diagnosed with a psychiatric disorder, you know something about one or more of the following examples:

• Depression
• Personality disorders
• Anxiety disorders
• Schizophrenia
• Eating disorders
• Addictive behaviors

Content

EEG Dataset with approx 1k attributes for identifying psychiatric disorders.

Acknowledgements

Park, S. M. (2021, August 16). EEG machine learning. Retrieved from osf.io/8bsvr Please credit the original author if you want to use this dataset for your research The Source of dataset is here

The HealthLink BC Mental Health and Substance Use (MHSU) data set includes the following: Programs that offer early intervention, transitional care or other services that supplement and facilitate primary and adjunctive therapies; which offer community mental health education programs; or which link people who are in need of treatment with appropriate providers. Programs that provide preventive, diagnostic and treatment services in a variety of community and hospital-based settings to help people achieve, maintain and enhance a state of emotional well-being, personal empowerment and the skills to cope with everyday demands without excessive stress or reliance on alcohol or other drugs. Treatment may include emotional support, introspection and problem-solving assistance using a variety of modalities and approaches, and medication, as needed, for individuals who have a substance use disorder involving alcohol and/or other drugs or for people who range from experiencing difficult life transitions or problems in coping with daily living to those with severe, chronic mental illnesses that seriously impact their lives. Multidisciplinary programs, often offered on an inpatient basis with post-discharge outpatient therapy, that provide comprehensive diagnostic and treatment services for individuals who have anorexia nervosa, binge-eating disorder, bulimia or a related eating disorder. Treatment depends on the specific type of eating disorder involved but typically involves psychotherapy, nutrition education, family counseling, medication and hospitalization, if required, to stabilize the patient's health. Alliance of Information & Referral Systems (AIRS) / 211 LA County taxonomy is the data classification used for all HealthLink BC directory data, including this MHSU data set (https://www.airs.org/i4a/pages/index.cfm?pageid=1). AIRS taxonomy and data definitions are protected by Copyright by Information and Referral Federal of Los Angeles County, Inc (https://211taxonomy.org/subscriptions/#agreement)

Dataset Overview

This dataset contains mental health assessment data for 120 patients with various psychological symptoms and expert diagnoses. The dataset is designed to identify patterns in mental health disorders based on symptom presentations.

Dataset Statistics

• Total Records: 120 patients
• Total Features: 19 columns
• Symptom Features: 17 behavioral/psychological indicators
• Target Variable: Expert Diagnose
• File Format: CSV (UTF-8 encoded)

Column Descriptions

📈 Key Findings Summary

Most Important Insights

1. Symptom Patterns: The analysis identifies unique symptom combinations that characterize different mental health conditions

2. Risk Indicators: Critical symptoms like suicidal thoughts are flagged for immediate attention

3. Comorbidity: Strong co-occurrence patterns between certain symptoms suggest underlying connections

4. Predictive Features: Machine learning identifies the most important symptoms for accurate diagnosis

5. Statistical Significance: Multiple symptoms show statistically significant associations with specific diagnoses

BackgroundPrevious studies have documented that disordered eating is associated with a wide range of impaired physical and mental health conditions among children and adolescents. The relationship between disordered eating and health-related quality of life (HRQOL) has been predominantly examined in children and adolescents who are overweight or obese or suffer from chronic illnesses. In the last decade, several studies have been conducted to investigate the relationship between disordered eating and HRQOL among school and community children and adolescents. No systematic review or meta-analysis has synthesized the findings from these population-based studies. The purpose of this systematic review and meta-analysis was to synthesize the relationship between disordered eating and HRQOL among the general population of children and adolescents.MethodsWe performed a computer search for the English language literature using the databases PUBMED, EMBASE and PSYCINFO to retrieve eligible studies published between 1946 and August 9, 2018. We also searched the relevant articles using PubMed related article search features and manually examined the reference lists of the retrieved full text articles selected from the database search. The association between disordered eating and HRQOL was synthesized using both a qualitative method and a meta-analysis. The review was conducted adhering to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines.ResultsWe identified eight studies that met the inclusion criteria and were included in the final synthesis. The studies included six cross-sectional studies and two longitudinal studies. The systematic review found that disordered eating attitudes and behaviors were associated with lower HRQOL among children and adolescents. Children and adolescents with bulimia nervosa (BN), binge eating disorder (BED), purging disorder (PD) and other eating disorder symptoms had poorer HRQOL than their healthy peers without the eating disorder conditions. The meta-analysis using four out of the eight studies showed that disordered eating was significantly associated with poor psychosocial health and lower overall HRQOL among children and adolescents.ConclusionThe present review reveals that disordered eating behaviors and eating disorders are associated with decreased HRQOL in children and adolescents. More prospective studies are needed to ascertain the directions in the relationship between disordered eating and HRQOL among children and adolescents. The findings of this review suggest that health programs for promoting healthy eating and reducing disordered eating behaviors among school children and adolescents may help to enhance the HRQOL and overall health status of these individuals.

Eating disorders significantly impact the quality of life of the persons they affect, as well as their involvement in school bullying. People with bulimia and binge-eating disorders are known to be more likely to be victims of bullying; however, studies provide mixed evidence on the connection between bullying and anorexia. Therefore, in this paper, we suggest an explanation for the bullying victimization of people with anorexia. Our theoretical framework is based on psychoanalytical research on eating disorders, and we illustrate our arguments with the results of biographical interviews with 50 girls who have been diagnosed with anorexia. We show that a hostile family environment may influence the girls’ proneness to fall victim to school bullying. Therefore, school staff hoping to address the involvement of girls with anorexia in bullying should be aware of the role that family members play in bullying victimization and tailor interventions accordingly.

Datasets for: Arend, A.-K., Schnepper, R., Lutz, A., Eichin, K., N., & Blechert, J. (2022). Prone to food in bad mood – Emotion potentiated food cue reactivity in patients with binge eating disorder. International Journal of Eating Disorders, 55(4), 564-569. https://doi.org/10.1002/eat.23683 Objective - Theories on emotional eating are central to our understanding of etiology, maintenance, and treatment of binge eating. Yet, findings on eating changes under induced negative emotions in binge-eating disorder (BED) are equivocal. Thus, we studied whether food-cue reactivity is potentiated under negative emotions in BED, which would point toward a causal role of emotional eating in this disorder.Methods - Patients with BED (n = 24) and a control group without eating disorders (CG; n = 69) completed a food picture reactivity task after induction of negative versus neutral emotions. Food-cue reactivity (self-reported food pleasantness, desire to eat [DTE], and corrugator supercilii muscle response, electromyogram [EMG]) was measured for low- and high-caloric food pictures.Results - Patients with BED showed emotion-potentiated food-cue reactivity compared to controls: Pleasantness and DTE ratings and EMG response were increased in BED during negative emotions. This was independent of caloric content of the images.Conclusions - Food-cue reactivity in BED was consistent with emotional eating theories and points to a heightened response to all foods regardless of calorie content. The discrepancy of appetitive ratings with the aversive corrugator response points to ambivalent food responses under negative emotions in individuals with BED.: R data (corrugator)

Objective: The aim was to investigate whether a computer-based evaluative conditioning intervention improves body image in adolescents with an eating disorder. Positive effects were found in earlier studies in healthy female students in a laboratory and a field setting. This study is the first to test evaluative conditioning in a clinical sample under less controlled circumstances. Method: Fifty-one adolescent girls with an eating disorder and a healthy weight were randomly assigned to an experimental condition or a placebo-control condition. The computerized intervention consisted of six online training sessions of 5 min, in which participants had to click on pictures of their own and other people’s bodies. Their own pictures were systematically followed by portraits of friendly smiling faces. In the control condition, participants were shown the same stimuli, but here, a stimulus was always followed by another stimulus from the same category, so that own body was not paired with smiling faces. Before, directly after, three weeks after, and 11 weeks after the intervention, self-report measures of body image and general self-esteem were administered. Automatic self-associations were also measured with an Implicit Association Test. Results:In contrast to our hypotheses, we did not find an effect of the intervention on self-report questionnaires measuring body satisfaction, weight and shape concern, and general self-esteem. In addition, the intervention did not show positive effects on implicit associations regarding self-attractiveness. Conclusions: These findings do not support the use of evaluative conditioning in its present form as an intervention for adolescents in clinical practice.