"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 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...

Data are for a study on neurocognitive factors and eating disorder symptoms in college women study. It was hypothesized that women with overlapping inefficiencies in set-shifting and global processing would have higher ED symptoms than those with standalone neurocognitive inefficiencies or no inefficiency. Data include ED symptom dimension scores (EDE-Q) as well as reaction time and error scores for the WCST (set-shifting) and Navon task (global processing). BIS scores were calculated to represent efficiency on the WCST and Navon task. This project contains the data file for all participants used in analyses as well as a codebook for the data. Three responses were missing completely at random. Missing values were imputed using the Expectation Maximization algorithm. Data were screened for univariate outliers using z scores (greater than +/- 3.29). Five univariate outliers were winsorized by replacing with the next within-bound value. Two multivariate outlier was identified using Mahalanobis distance and was removed from the sample, resulting in a final sample of 142 participants.

Median splits of BIS efficiency scores were utilized to classify individuals into one of four neurocognitive profile groups: 1) overlapping (both), 2) central coherence (CC), 3) set-shifting (SS), and 4) no (neither) inefficiencies. Spearkman rank correlation analyses were run between eating disorder symptoms and BIS efficiency scores. Kruskal-Wallis tests were run to compare mean rank WCST and Navon task reaction time and error scores across neurocognitive profile groups. Differences in mean rank eating disorder symptom scores by neurocognitve profile were also tested using Kruskal-Wallis tests. Pairwise comparisons were performed using Dunn’s procedure with Bonferroni correction for multiple comparisons. Results showed that women with overlapping inefficiencies had higher eating disorder symptoms than those with no inefficiency.