This table contains 94080 series, with data for years 2003 - 2003 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (70 items: Newfoundland and Labrador; Health and Community Services Eastern Region; Newfoundland and Labrador; Health and Community Services St. John's Region; Newfoundland and Labrador ...) Age group (14 items: Total; 12 years and over; 12 to 14 years; 12 to 19 years; 15 to 19 years ...) Sex (3 items: Both sexes; Females; Males ...) Probability of depression (4 items: Total population for the variable probability of depression; Probability of depression; 0.9 or greater; Probability of depression; less than 0.9 ...) Characteristics (8 items: Number of persons; High 95% confidence interval; number of persons; Coefficient of variation for number of persons; Low 95% confidence interval; number of persons ...).

This table contains 8400 series, with data for years 1994 - 1996 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (not all combinations are available): Geography (5 items: Territories; Northwest Territories; Yukon; Northwest Territories including Nunavut ...), Age group (14 items: Total; 12 years and over; 12-19 years; 12-14 years; 15-19 years ...), Sex (3 items: Both sexes; Females; Males ...), Risk level of depression (5 items: Total population for the variable risk of depression; No risk of depression; Probable risk of depression; Possible risk of depression ...), Characteristics (8 items: Number of persons; Low 95% confidence interval - number of persons; High 95% confidence interval - number of persons; Coefficient of variation for number of persons ...).

This table contains 18480 series, with data for years 1994 - 1998 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (not all combinations are available): Geography (11 items: Canada; Newfoundland and Labrador; Prince Edward Island; Nova Scotia ...), Age group (14 items: Total; 12 years and over; 15-19 years; 12-19 years; 12-14 years ...), Sex (3 items: Both sexes; Females; Males ...), Risk level of depression (5 items: Total population for the variable risk of depression; Probable risk of depression; No risk of depression; Possible risk of depression ...), Characteristics (8 items: Number of persons; High 95% confidence interval - number of persons; Coefficient of variation for number of persons; Low 95% confidence interval - number of persons ...).

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of depression in adults (aged 18+). Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.ANALYSIS METHODOLOGYThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to depression in adults (aged 18+).This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.The percentage of each MSOA’s population (aged 18+) with depression was estimated. This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of registered patients that have that illness The estimated percentage of each MSOA’s population with depression was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA with depression, within the relevant age range.Each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have depressionB) the NUMBER of people within that MSOA who are estimated to have depressionAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA that are estimated to have depression, compared to other MSOAs. In other words, those are areas where it’s estimated a large number of people suffer from depression, and where those people make up a large percentage of the population, indicating there is a real issue with depression within the population and the investment of resources to address that issue could have the greatest benefits.LIMITATIONS1. GP data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).2. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children (see the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset), we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.3. It was not feasible to incorporate ultra-fine-scale geographic distribution of populations that are registered with each GP practice or who live within each MSOA. Populations might be concentrated in certain areas of a GP practice’s catchment area or MSOA and relatively sparse in other areas. Therefore, the dataset should be used to identify general areas where there are high levels of depression, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of depression.TO BE VIEWED IN COMBINATION WITH:This dataset should be viewed alongside the following datasets, which highlight areas of missing data and potential outliers in the data:Health and wellbeing statistics (GP-level, England): Missing data and potential outliersLevels of obesity, inactivity and associated illnesses (England): Missing dataDOWNLOADING THIS DATATo access this data on your desktop GIS, download the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.DATA SOURCESThis dataset was produced using:Quality and Outcomes Framework data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.GP Catchment Outlines. Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. Data was cleaned by Ribble Rivers Trust before use.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Analysis carried out using data that is: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

This table contains 93984 series, with data for years 2002 - 2002 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (11 items: Canada; Prince Edward Island; Newfoundland and Labrador; Nova Scotia ...), Age group (4 items: 65 years and over;25 to 64 years;15 to 24 years; Total; 15 years and over ...), Sex (3 items: Both sexes; Females; Males ...), Mental health and well-being profile (89 items: Total population for the variable major depressive episode; Major depressive episode; all measured criteria are met; Major depressive episode; measured criteria not met; Major depressive episode; not stated ...), Characteristics (8 items: Number of persons; Coefficient of variation for number of persons; Low 95% confidence interval; number of persons; High 95% confidence interval; number of persons ...).

Mental Health in Tech Survey

Understanding Employee Mental Health Needs in the Tech Industry

By Stephen Myers [source]

About this dataset

This dataset contains survey responses from individuals in the tech industry about their mental health, including questions about treatment, workplace resources, and attitudes towards discussing mental health in the workplace. Mental health is an issue that affects all people of all ages, genders and walks of life. The prevalence of these issues within the tech industry–one that places hard demands on those who work in it–is no exception. By analyzing this dataset, we can better understand how prevalent mental health issues are among those who work in the tech sector.–and what kinds of resources they rely upon to find help–so that more can be done to create a healthier working environment for all.

This dataset tracks key measures such as age, gender and country to determine overall prevalence, along with responses surrounding employee access to care options; whether mental health or physical illness are being taken as seriously by employers; whether or not anonymity is protected with regards to seeking help; and how coworkers may perceive those struggling with mental illness issues such as depression or anxiety. With an ever-evolving landscape due to new technology advancing faster than ever before – these statistics have never been more important for us to analyze if we hope remain true promoters of a healthy world inside and outside our office walls

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How to use the dataset

In this dataset you will find data on age, gender, country, and state of survey respondents in addition to numerous questions that assess an individual's mental state including: self-employment status, family history of mental illness, treatment status and access or lack thereof; how their mental health condition affects their work; number of employees at the company they work for; remote work status; tech company status; benefit information from employers such as mental health benefits and wellness program availability; anonymity protection if seeking treatment resources for substance abuse or mental health issues ; ease (or difficulty) for medical leave for a mental health condition ; whether discussing physical or medical matters with employers have negative consequences. You will also find comments from survey participants.

To use this dataset effectively: - Clean the data by removing invalid responses/duplicates/missing values - you can do this with basic Pandas commands like .dropna() , .drop_duplicates(), .replace(). - Utilize descriptive statistics such as mean and median to draw general conclusions about patterns of responses - you can do this with Pandas tools such as .groupby() and .describe(). - Run various types analyses such as mean comparisons on different kinds of variables(age vs gender), correlations between different features etc using appropriate statistical methods - use commands like Statsmodels' OLS models (.smf) , calculate z-scores , run hypothesis tests etc depending on what analysis is needed. Make sure you are aware any underlying assumptions your analysis requires beforehand !
- Visualize your results with plotting libraries like Matplotlib/Seaborn to easily interpret these findings! Use boxplots/histograms/heatmaps where appropriate depending on your question !

Research Ideas

• Using the results of this survey, you could develop targeted outreach campaigns directed at underrepresented groups that answer “No” to questions about their employers providing resources for mental health or discussing it as part of wellness programs.
• Analyzing the employee characteristics (e.g., age and gender) of those who reported negative consequences from discussing their mental health in the workplace could inform employer policies to support individuals with mental health conditions and reduce stigma and discrimination in the workplace.
• Correlating responses to questions about remote work, leave policies, and anonymity with whether or not individuals have sought treatment for a mental health condition may provide insight into which types of workplace resources are most beneficial for supporting employees dealing with these issues

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redi...

This table contains 94080 series, with data for years 2003 - 2003 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (70 items: Newfoundland and Labrador; Health and Community Services Eastern Region; Newfoundland and Labrador; Health and Community Services St. John's Region; Newfoundland and Labrador ...) Age group (14 items: Total; 12 years and over; 12 to 14 years; 12 to 19 years; 15 to 19 years ...) Sex (3 items: Both sexes; Females; Males ...) Probability of depression (4 items: Total population for the variable probability of depression; Probability of depression; 0.9 or greater; Probability of depression; less than 0.9 ...) Characteristics (8 items: Number of persons; High 95% confidence interval; number of persons; Coefficient of variation for number of persons; Low 95% confidence interval; number of persons ...).

This table contains 8400 series, with data for years 1994 - 1996 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (not all combinations are available): Geography (5 items: Territories; Northwest Territories; Yukon; Northwest Territories including Nunavut ...), Age group (14 items: Total; 12 years and over; 12-19 years; 12-14 years; 15-19 years ...), Sex (3 items: Both sexes; Females; Males ...), Risk level of depression (5 items: Total population for the variable risk of depression; No risk of depression; Probable risk of depression; Possible risk of depression ...), Characteristics (8 items: Number of persons; Low 95% confidence interval - number of persons; High 95% confidence interval - number of persons; Coefficient of variation for number of persons ...).

This dataset (n=800) explores risk factors for postpartum depression (PPD) in Bangladesh. Data was collected from postpartum women (birth within 24 months) across various locations (health complexes, hospitals, clinics, and rural areas and residential areas in major cities). Participation was voluntary and required informed consent.

The dataset includes sociodemographic (age, residence, education, marital status, occupation, income), familial (husband's education/income, family type, household members, relationship with family), personal health (addiction, children, pregnancy history, abuse, depression history, chronic diseases), and neonatal health-related variables (pregnancy details, birth details, newborn health, postpartum feelings) variables. For depression screening, PHQ-2, EPDS, and PHQ-9 scores are included.

The data was digitized after collection. This dataset can be used to investigate the prevalence of PPD, identify risk factors, and develop predictive models. Limitations include self-reported data, screening tools vs. diagnosis, and potential sampling bias. The dataset has shown, about 44% of the participants are in the risk of high level of postpartum depression, based on the EPDS scoring and 28% of the participants are in the risk of sever and moderately severe postpartum depression, based on the PHQ-9 scoring.

This table contains 334320 series, with data for years 2000 - 2000 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (not all combinations are available): Geography (199 items: Canada; Health and Community Services Eastern Region; Newfoundland and Labrador (Peer group D); Newfoundland and Labrador; Health and Community Services St. John's Region; Newfoundland and Labrador (Peer group H) ...), Age group (14 items: Total; 12 years and over; 12-14 years; 15-19 years; 12-19 years ...), Sex (3 items: Both sexes; Females; Males ...), Risk level of depression (5 items: Total population for the variable risk of depression; Possible risk of depression; No risk of depression; Probable risk of depression ...), Characteristics (8 items: Number of persons; Low 95% confidence interval - number of persons; Coefficient of variation for number of persons; High 95% confidence interval - number of persons ...).

These data represent the predicted (modeled) prevalence of Depression among adults (Age 18+) for each census tract in Colorado. Depression is defined as ever being told by a doctor, nurse, or other health professional that you have a depressive disorder.The estimate for each census tract represents an average that was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2014-2017).CDPHE used a model-based approach to measure the relationship between age, race, gender, poverty, education, location and health conditions or risk behavior indicators and applied this relationship to predict the number of persons' who have the health conditions or risk behavior for each census tract in Colorado. We then applied these probabilities, based on demographic stratification, to the 2013-2017 American Community Survey population estimates and determined the percentage of adults with the health conditions or risk behavior for each census tract in Colorado.The estimates are based on statistical models and are not direct survey estimates. Using the best available data, CDPHE was able to model census tract estimates based on demographic data and background knowledge about the distribution of specific health conditions and risk behaviors.The estimates are displayed in both the map and data table using point estimate values for each census tract and displayed using a Quintile range. The high and low value for each color on the map is calculated based on dividing the total number of census tracts in Colorado (1249) into five groups based on the total range of estimates for all Colorado census tracts. Each Quintile range represents roughly 20% of the census tracts in Colorado. No estimates are provided for census tracts with a known population of less than 50. These census tracts are displayed in the map as "No Est, Pop < 50."No estimates are provided for 7 census tracts with a known population of less than 50 or for the 2 census tracts that exclusively contain a federal correctional institution as 100% of their population. These 9 census tracts are displayed in the map as "No Estimate."

International students’ mental health has become an increasing concern in recent years, as more students leave their country for better education. They experience a wide range of challenges while studying abroad that have an impact on their psychological well-being. These challenges can include language obstacles, cultural differences, homesickness, financial issues and other elements that could severely impact the mental health of international students. Given the limited research on the demographic, cultural, and psychosocial variables that influence international students’ mental health, and the scarcity of studies on the use of machine learning algorithms in this area, this study aimed to analyse data to understand the demographic, cultural factors, and psychosocial factors that impact mental health of international students. Additionally, this paper aimed to build a machine learning-based model for predicting depression among international students in the United Kingdom. This study utilized both primary data gathered through an online survey questionnaire targeted at international students and secondary data was sourced from the ’A Dataset of Students’ Mental Health and Help-Seeking Behaviors in a Multicultural Environment,’ focusing exclusively on international student data within this dataset. We conducted data analysis on the primary data and constructed models using the secondary data for predicting depression among international students. The secondary dataset is divided into training (70%) and testing (30%) sets for analysis, employing four machine learning models: Logistic Regression, Decision Tree, Random Forest, and K Nearest Neighbor. To assess each algorithm’s performance, we considered metrics such as Accuracy, Sensitivity, Specificity, Precision and AU-ROC curve. This study identifies significant demographic variables (e.g., loan status, gender, age, marital status) and psychosocial factors (financial difficulties, academic stress, homesickness, loneliness) contributing to international students’ mental health. Among the machine learning models, the Random Forest model demonstrated the highest accuracy, achieving an 80% accuracy rate in predicting depression.

Description The Post-Pandemic Remote Work Health Impact 2025 dataset presents a comprehensive, global snapshot of how remote, hybrid, and onsite work arrangements are influencing the mental and physical health of employees in the post-pandemic era. Collected in June 2025, this dataset aggregates responses from a diverse workforce spanning continents, industries, age groups, and job roles. It is designed to support research, data analysis, and policy-making around the evolving landscape of work and well-being.

This dataset enables in-depth exploration of:

• The prevalence of mental health conditions (e.g., anxiety, burnout, PTSD, depression) across different work setups.
• The relationship between work arrangements and physical health complaints (e.g., back pain, eye strain, neck pain).
• Variations in work-life balance, social isolation, and burnout levels segmented by demographic and occupational factors.
• Salary distributions and their correlation with health outcomes and job roles.

By providing granular, anonymized data on both subjective (self-reported) and objective (hours worked, salary range) factors, this resource empowers data scientists, health researchers, HR professionals, and business leaders to:

• Identify risk factors and protective factors for employee well-being. Benchmark health impacts across industries and regions.
• Inform organizational policy and future-of-work strategies.

| Column Name Description Example Values | | | Survey_Date Date when the survey response was submitted (YYYY-MM-DD) 2025-06-01 Age Age of the respondent (in years) 27, 52, 40 Gender Gender identity of the respondent Female, Male, Non-binary, Prefer not to say Region Geographical region of employment Asia, Europe, North America, Africa, Oceania Industry Industry sector of the respondent Technology, Manufacturing, Finance, Healthcare Job_Role Specific job title or function Data Analyst, HR Manager, Software Engineer Work_Arrangement Primary work mode Onsite, Remote, Hybrid Hours_Per_Week Average number of hours worked per week 36, 55, 64 Mental_Health_Status Primary self-reported mental health condition Anxiety, Burnout, Depression, None, PTSD Burnout_Level Self-assessed burnout (categorical: Low, Medium, High) High, Medium, Low Work_Life_Balance_Score Self-rated work-life balance on a scale of 1 (poor) to 5 (excellent) 1, 3, 5 Physical_Health_Issues Self-reported physical health complaints (semicolon-separated if multiple) Back Pain; Eye Strain; Neck Pain; None Social_Isolation_Score Self-rated social isolation on a scale of 1 (none) to 5 (severe) 1, 2, 5 Salary_Range Annual salary range in USD $40K-60K, $80K-100K, $120K+ | --- | | | |

BackgroundResearch has shown increased mental health problems and use of prescription drugs among adolescents in recent years and social media use has been linked to poorer mental health. However, trend studies concerning these topics are scarce. The purpose of this study was to analyze gender-specific trends in a) symptoms of depression and loneliness, and b) prescription of antidepressants, hypnotics and sedatives, in relation to the emergence of social media among adolescents in Norway.MethodsThis is an ecological study using data from the ‘Young in Oslo’ surveys from 1996 to 2021. The surveys included approximately 110 000 students, 14–17 years of age, and yielded a response rate varying from 95% in 1996 to 64% in 2021. A self-report questionnaire was used to collect information on symptoms of depression and loneliness. Information on antidepressant and sleep medication prescription was retrieved from the Norwegian Prescription Database for the age group 15 to 19 years. A graphical approach and logistic regression models were used to examine gender-specific time-trends between 1996 to 2021.ResultsWe found a doubling in self-reported symptoms of depression and loneliness among girls between 1996 and 2021, with the steepest increase in the period from 2006 to 2012, when Facebook and other social media were introduced. A similar trend was observed in the prescription of antidepressants among girls, with the steepest increase between 2011 and 2013. Among both boys and girls, ‘worried too much about things’ and ‘had sleep problems’ were the two symptoms with the greatest changes.ConclusionA significant upward trend in self-reported depressive symptoms and medication use was observed over the past 25 years, with variations in the rate of increase, including a steeper trajectory during certain periods immediately after the introduction of social media platforms in Norway.

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of obesity, inactivity and inactivity/obesity-related illnesses. Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.The analysis incorporates data relating to the following:Obesity/inactivity-related illnesses (asthma, cancer, chronic kidney disease, coronary heart disease, depression, diabetes mellitus, hypertension, stroke and transient ischaemic attack)Excess weight in children and obesity in adults (combined)Inactivity in children and adults (combined)The analysis was designed with the intention that this dataset could be used to identify locations where investment could encourage greater levels of activity. In particular, it is hoped the dataset will be used to identify locations where the creation or improvement of accessible green/blue spaces and public engagement programmes could encourage greater levels of outdoor activity within the target population, and reduce the health issues associated with obesity and inactivity.ANALYSIS METHODOLOGY1. Obesity/inactivity-related illnessesThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to:- Asthma (in persons of all ages)- Cancer (in persons of all ages)- Chronic kidney disease (in adults aged 18+)- Coronary heart disease (in persons of all ages)- Depression (in adults aged 18+)- Diabetes mellitus (in persons aged 17+)- Hypertension (in persons of all ages)- Stroke and transient ischaemic attack (in persons of all ages)This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.For each of the above illnesses, the percentage of each MSOA’s population with that illness was estimated. This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of patients registered with each GP that have that illness The estimated percentage of each MSOA’s population with each illness was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA with each illness, within the relevant age range.For each illness, each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have that illnessB) the NUMBER of people within that MSOA who are estimated to have that illnessAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA predicted to have that illness, compared to other MSOAs. In other words, those are areas where a large number of people are predicted to suffer from an illness, and where those people make up a large percentage of the population, indicating there is a real issue with that illness within the population and the investment of resources to address that issue could have the greatest benefits.The scores for each of the 8 illnesses were added together then converted to a relative score between 1 – 0 (1 = worst, 0 = best), to give an overall score for each MSOA: a score close to 1 would indicate that an area has high predicted levels of all obesity/inactivity-related illnesses, and these are areas where the local population could benefit the most from interventions to address those illnesses. A score close to 0 would indicate very low predicted levels of obesity/inactivity-related illnesses and therefore interventions might not be required.2. Excess weight in children and obesity in adults (combined)For each MSOA, the number and percentage of children in Reception and Year 6 with excess weight was combined with population data (up to age 17) to estimate the total number of children with excess weight.The first part of the analysis detailed in section 1 was used to estimate the number of adults with obesity in each MSOA, based on GP-level statistics.The percentage of each MSOA’s adult population (aged 18+) with obesity was estimated, using GP-level data (see section 1 above). This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of adult patients registered with each GP that are obeseThe estimated percentage of each MSOA’s adult population with obesity was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of adults in each MSOA with obesity.The estimated number of children with excess weight and adults with obesity were combined with population data, to give the total number and percentage of the population with excess weight.Each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have excess weight/obesityB) the NUMBER of people within that MSOA who are estimated to have excess weight/obesityAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA predicted to have excess weight/obesity, compared to other MSOAs. In other words, those are areas where a large number of people are predicted to suffer from excess weight/obesity, and where those people make up a large percentage of the population, indicating there is a real issue with that excess weight/obesity within the population and the investment of resources to address that issue could have the greatest benefits.3. Inactivity in children and adultsFor each administrative district, the number of children and adults who are inactive was combined with population data to estimate the total number and percentage of the population that are inactive.Each district was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that district who are estimated to be inactiveB) the NUMBER of people within that district who are estimated to be inactiveAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the district predicted to be inactive, compared to other districts. In other words, those are areas where a large number of people are predicted to be inactive, and where those people make up a large percentage of the population, indicating there is a real issue with that inactivity within the population and the investment of resources to address that issue could have the greatest benefits.Summary datasetAn average of the scores calculated in sections 1-3 was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer the score to 1, the greater the number and percentage of people suffering from obesity, inactivity and associated illnesses. I.e. these are areas where there are a large number of people (both children and adults) who are obese, inactive and suffer from obesity/inactivity-related illnesses, and where those people make up a large percentage of the local population. These are the locations where interventions could have the greatest health and wellbeing benefits for the local population.LIMITATIONS1. For data recorded at the GP practice level, data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Levels of obesity, inactivity and associated illnesses: Summary (England). Areas with data missing’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).2. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children, we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.3. It was not feasible to incorporate ultra-fine-scale geographic distribution of

BackgroundPsychological resilience may reduce the impact of psychological distress to some extent. We aimed to investigate the mental health status of the public during the outbreak of coronavirus disease 2019 (COVID-19) and explore the level and related factors of anxiety and depression.MethodsFrom February 8 to March 9, 2020, 3,180 public completed the Zung’s Self-Rating Anxiety Scale (SAS) for anxiety, Zung’s Self-Rating Depression Scale (SDS) for depression, the Connor–Davidson resilience scale (CD-RISC) for psychological resilience, and the Simplified Coping Style Questionnaire (SCSQ) for the attitudes and coping styles.ResultsThe number of people with depressive symptoms (SDS > 53) was 1,303 (the rate was 41.0%). The number of people with anxiety symptoms (SAS > 50) was 1,184 (the rate was 37.2%). The depressed group and anxiety group had less education, more unmarried and younger age, as well as had significant different in SDS total score (P < 0.001), SAS total score (P < 0.001), CD-RISC total score (P < 0.001), and SCSQ score (P < 0.001). The binary logistic regression showed that female (B = -0.261, P = 0.026), strength (B = -0.079, P = 0.000), and the subscales of active coping style in SCSQ (B = -0.983, P = 0.000) remained protective factors and passive coping style (B = 0.293, P = 0.003) and higher SAS score (B = 0.175, P = 0.000) were risk factors for depression. Optimism (B = -0.041, P = 0.015) in CD-RISC was a protective factor, and passive coping styles (B = 0.483, P = 0.000) and higher SDS score (B = 0.134, P = 0.000) were risk factors for anxiety.LimitationsThis study adopted a cross-sectional design and used self-report questionnaires.ConclusionThe mental health of the public, especially females, the younger and less educational populations, and unmarried individuals, should be given more attention. Individuals with high level of mental resilience and active coping styles would have lower levels of anxiety and depression during the outbreak of COVID-19.

This table contains 334320 series, with data for years 2000 - 2000 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (not all combinations are available): Geography (199 items: Canada; Health and Community Services Eastern Region; Newfoundland and Labrador (Peer group D); Newfoundland and Labrador; Health and Community Services St. John's Region; Newfoundland and Labrador (Peer group H) ...), Age group (14 items: Total; 12 years and over; 12-14 years; 15-19 years; 12-19 years ...), Sex (3 items: Both sexes; Females; Males ...), Risk level of depression (5 items: Total population for the variable risk of depression; Possible risk of depression; No risk of depression; Probable risk of depression ...), Characteristics (8 items: Number of persons; Low 95% confidence interval - number of persons; Coefficient of variation for number of persons; High 95% confidence interval - number of persons ...).

BackgroundAnxiety disorders, depression and schizophrenia are the focus of global mental health attention, resulting in a significant number of disability-adjusted life years and a considerable social and economic burden. It’s can affect the socioeconomic landscape as a result of experiencing a global epidemic. And rarely, different Socio-demographic Index (SDI) levels and Age-Period-Cohort (APC) have been used to evaluate the prevalence of mental disorders worldwide.MethodsUsing data from the Global Burden of Disease 2021 (GBD) database, this study assessed trends in the incidence and prevalence of anxiety disorders, depression, and schizophrenia in countries with different SDI levels from 1990 to 2021. Joinpoint and periodic cohort (APC) models were used to sort out the effects of age, period and cohort on incidence. Data were categorized into 5-year age groups and 95% uncertainty intervals (UI) were calculated to account for data variability.ResultsIn countries with different SDI levels, the age-standardized average annual percentage change (AAPC) in the incidence of anxiety were all shown to be increasing, and there were large gender differences between the different SDI levels, with a maximum of 0.97 (0.76–1.18) for females in countries with a high SDI level, Age-standardized more rates per 100,000 people in high SDI countries, from 658.87 in 1990 to 841.56 in 2021, and the largest gender differences in countries with a low to moderate SDI level, with AAPCs for males and females of 0.04 (0.04–0.05), 0.86 (0.63–1.09); for depression, only the countries with medium-high SDI levels were statistically significant compared to the countries with medium-low SDI levels, with AAPCs of 0.05 (0.04–0.07), 0.04 (0.04–0.05); for schizophrenia in addition to the AAPCs of the countries with medium-high SDI levels showed an increase of 0.16 (0.13–0.18); the rest decreased.ConclusionThis study highlights the current status of global incidence and prevalence of mental disorders and examines the complex interactions between the period of onset and cohort of onset of mental disorders using APC modeling, with differences in gender differences in mental disorders in countries with different SDIs, and significant differences in countries with low to medium SDI levels, requiring further exploration of the mechanisms by which socio-economic development influences gender-specific mental health. Countries with different SDI levels have responded to unique trends within their specific socioeconomic, cultural, and historical contexts, suggesting the need for contextualized public health strategies to effectively respond to and manage the incidence and prevalence of mental disorders in these different settings. Prevalence of mental disorders. This points the way to more in-depth future research on treatments and interventions for mental disorders.

Objectives: To examine trends in strong opioid prescribing in a primary care population in Wales and identify if factors such as age, deprivation and recorded diagnosis of depression or anxiety may have influenced any changes noted.

Design: Trend, cross-sectional and longitudinal analyses of routine data from the Primary Care General Practice database and accessed via the Secure Anonymised Information Linkage (SAIL) databank. Setting: A total of 345 Primary Care practices in Wales.

Participants: Anonymised records of 1,223,503 people aged 18 or over, receiving at least one opioid pre- scription between 1 January 2005 and 31 December 2015 were analysed. People with a cancer diagnosis (10.1%) were excluded from the detailed analysis.

Results: During the study period, 26,180,200 opioid prescriptions were issued to 1,223,503 individuals (55.9% female, 89.9% non-cancer diagnoses). The greatest increase in annual prescribing was in the 18–24 age group (10,470%), from 0.08 to 8.3 prescriptions/1000 population, although the 85+ age group had the highest prescribing rates across the study period (from 149.9 to 288.5 prescriptions/1000 popu- lation). The number of people with recorded diagnoses of depression or anxiety and prescribed strong opioids increased from 1.2 to 5.1 people/1000 population (328%). The increase was 366.9% in areas of highest deprivation compared to 310.3 in the least. Areas of greatest deprivation had more than twice the rate of strong opioid prescribing than the least deprived areas of Wales.

Conclusion: The study highlights a large increase in strong opioid prescribing for non-cancer pain, in Wales between 2005 and 2015. Population groups of interest include the youngest and oldest adult age groups and people with depression or anxiety particularly if living in the most deprived communities. Based on this evidence, development of a Welsh national guidance on safe and rational prescribing of opioids in chronic pain would be advisable to prevent further escalation of these medicines.

Methods Data extracted from the Secure Anonymised Information Linkage databank (SAIL). SQL code used to extract annualised totals for each subset of data.

Excel and SPSS25 used to analyse data using descriptive statistical methods.

Excel used to produce trend graphs and totals.

The National Institute of Mental Health (NIMH) Research Volunteer (RV) Data Set

A comprehensive dataset characterizing healthy research volunteers in terms of clinical assessments, mood-related psychometrics, cognitive function neuropsychological tests, structural and functional magnetic resonance imaging (MRI), along with diffusion tensor imaging (DTI), and a comprehensive magnetoencephalography battery (MEG).

In addition, blood samples are currently banked for future genetic analysis. All data collected in this protocol are broadly shared in the OpenNeuro repository, in the Brain Imaging Data Structure (BIDS) format. In addition, task paradigms and basic pre-processing scripts are shared on GitHub. This dataset is unprecedented in its depth of characterization of a healthy population and will allow a wide array of investigations into normal cognition and mood regulation.

This dataset is licensed under the Creative Commons Zero (CC0) v1.0 License.

Release Notes

Release v2.0.0

This release includes data collected between 2020-06-03 (cut-off date for v1.0.0) and 2024-04-01. Notable changes in this release:

1. 769 new participants have been added along with re-evaluation data for 15 participants. Total unique participants count is now 1859.
2. visit and age_at_visit columns added to phenotype files to distinguish between visits and intervals between them.
3. Follow-up online survey data included.
4. Replaced Beck Anxiety Inventory (BAI) and Beck Depression Inventory-II (BDI-II) with General Anxiety Disorder-7 (GAD7) and Patient Health Questionnaire 9 (PHQ9) surveys, respectively.
5. Discontinued the Perceived Health rating survey.
6. Added Brief Trauma Questionnaire (BTQ) and Big Five personality survey to online screening questionnaires.
7. MRI:
   • Replaced ADNI-3 resting state sequence with a multi-echo sequence with higher spatial resolution.
   • Replaced field map scans with a shorter reversed-blipped EPI scan.
8. MEG:
   • Some participants have 6-minute empty room data instead of the shorter duration empty room acquisition.

See the CHANGES file for complete version-wise changelog.

Participant Eligibility

To be eligible for the study, participants need to be medically healthy adults over 18 years of age with the ability to read, speak and understand English. All participants provided electronic informed consent for online pre-screening, and written informed consent for all other procedures. Participants with a history of mental illness or suicidal or self-injury thoughts or behavior are excluded. Additional exclusion criteria include current illicit drug use, abnormal medical exam, and less than an 8th grade education or IQ below 70. Current NIMH employees, or first degree relatives of NIMH employees are prohibited from participating. Study participants are recruited through direct mailings, bulletin boards and listservs, outreach exhibits, print advertisements, and electronic media.

Clinical Measures

All potential volunteers visit the study website, check a box indicating consent, and fill out preliminary screening questionnaires. The questionnaires include basic demographics, the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0), the DSM-5 Self-Rated Level 1 Cross-Cutting Symptom Measure, the DSM-5 Level 2 Cross-Cutting Symptom Measure - Substance Use, the Alcohol Use Disorders Identification Test (AUDIT), the Edinburgh Handedness Inventory, and a brief clinical history checklist. The WHODAS 2.0 is a 15 item questionnaire that assesses overall general health and disability, with 14 items distributed over 6 domains: cognition, mobility, self-care, “getting along”, life activities, and participation. The DSM-5 Level 1 cross-cutting measure uses 23 items to assess symptoms across diagnoses, although an item regarding self-injurious behavior was removed from the online self-report version. The DSM-5 Level 2 cross-cutting measure is adapted from the NIDA ASSIST measure, and contains 15 items to assess use of both illicit drugs and prescription drugs without a doctor’s prescription. The AUDIT is a 10 item screening assessment used to detect harmful levels of alcohol consumption, and the Edinburgh Handedness Inventory is a systematic assessment of handedness. These online results do not contain any personally identifiable information (PII). At the conclusion of the questionnaires, participants are prompted to send an email to the study team. These results are reviewed by the study team, who determines if the participant is appropriate for an in-person interview.

Participants who meet all inclusion criteria are scheduled for an in-person screening visit to determine if there are any further exclusions to participation. At this visit, participants receive a History and Physical exam, Structured Clinical Interview for DSM-5 Disorders (SCID-5), the Beck Depression Inventory-II (BDI-II), Beck Anxiety Inventory (BAI), and the Kaufman Brief Intelligence Test, Second Edition (KBIT-2). The purpose of these cognitive and psychometric tests is two-fold. First, these measures are designed to provide a sensitive test of psychopathology. Second, they provide a comprehensive picture of cognitive functioning, including mood regulation. The SCID-5 is a structured interview, administered by a clinician, that establishes the absence of any DSM-5 axis I disorder. The KBIT-2 is a brief (20 minute) assessment of intellectual functioning administered by a trained examiner. There are three subtests, including verbal knowledge, riddles, and matrices.

Biological and physiological measures

Biological and physiological measures are acquired, including blood pressure, pulse, weight, height, and BMI. Blood and urine samples are taken and a complete blood count, acute care panel, hepatic panel, thyroid stimulating hormone, viral markers (HCV, HBV, HIV), c-reactive protein, creatine kinase, urine drug screen and urine pregnancy tests are performed. In addition, three additional tubes of blood samples are collected and banked for future analysis, including genetic testing.

Imaging Studies

Participants were given the option to enroll in optional magnetic resonance imaging (MRI) and magnetoencephalography (MEG) studies.

MRI

On the same visit as the MRI scan, participants are administered a subset of tasks from the NIH Toolbox Cognition Battery. The four tasks asses attention and executive functioning (Flanker Inhibitory Control and Attention Task), executive functioning (Dimensional Change Card Sort Task), episodic memory (Picture Sequence Memory Task), and working memory (List Sorting Working Memory Task). The MRI protocol used was initially based on the ADNI-3 basic protocol, but was later modified to include portions of the ABCD protocol in the following manner:

1. The T1 scan from ADNI3 was replaced by the T1 scan from the ABCD protocol.
2. The Axial T2 2D FLAIR acquisition from ADNI2 was added, and fat saturation turned on.
3. Fat saturation was turned on for the pCASL acquisition.
4. The high-resolution in-plane hippocampal 2D T2 scan was removed, and replaced with the whole brain 3D T2 scan from the ABCD protocol (which is resolution and bandwidth matched to the T1 scan).
5. The slice-select gradient reversal method was turned on for DTI acquisition, and reconstruction interpolation turned off.
6. Scans for distortion correction were added (reversed-blip scans for DTI and resting state scans).
7. The 3D FLAIR sequence was made optional, and replaced by one where the prescription and other acquisition parameters provide resolution and geometric correspondence between the T1 and T2 scans.

MEG

The optional MEG studies were added to the protocol approximately one year after the study was initiated, thus there are relatively fewer MEG recordings in comparison to the MRI dataset. MEG studies are performed on a 275 channel CTF MEG system. The position of the head was localized at the beginning and end of the recording using three fiducial coils. These coils were placed 1.5 cm above the nasion, and at each ear, 1.5 cm from the tragus on a line between the tragus and the outer canthus of the eye. For some participants, photographs were taken of the three coils and used to mark the points on the T1 weighted structural MRI scan for co-registration. For the remainder of the participants, a BrainSight neuro-navigation unit was used to coregister the MRI, anatomical fiducials, and localizer coils directly prior to MEG data acquisition.

Specific Survey and Test Data within Data Set

NOTE: In the release 2.0 of the dataset, two measures Brief Trauma Questionnaire (BTQ) and Big Five personality survey were added to the online screening questionnaires. Also, for the in-person screening visit, the Beck Anxiety Inventory (BAI) and Beck Depression Inventory-II (BDI-II) were replaced with the General Anxiety Disorder-7 (GAD7) and Patient Health Questionnaire 9 (PHQ9) surveys, respectively. The Perceived Health rating survey was discontinued.

1. Preliminary Online Screening Questionnaires