This publication contains the official statistics about uses of the Mental Health Act(1) ('the Act') in England during 2018-19. Under the Act, people with a mental disorder may be formally detained in hospital (or 'sectioned') in the interests of their own health or safety, or for the protection of other people. They can also be treated in the community but subject to recall to hospital for assessment and/or treatment under a Community Treatment Order (CTO). In 2016-17, the way we source and produce these statistics changed. Previously these statistics were produced from the KP90 aggregate data collection. They are now primarily produced from the Mental Health Services Data Set (MHSDS). The MHSDS provides a much richer data source for these statistics, allowing for new insights into uses of the Act. However, some providers that make use of the Act are not yet submitting data to the MHSDS, or submitting incomplete data. Improvements in data quality have been made over the past year. NHS Digital is working with partners to ensure that all providers are submitting complete data and this publication includes guidance on interpreting these statistics. Footnotes (1) The Mental Health Act 1983 as amended by the Mental Health Act 2007 and other legislation.

This publication provides the most timely statistics available relating to NHS funded secondary mental health, learning disabilities and autism services in England. This information will be of use to people needing access to information quickly for operational decision making and other purposes. These statistics are derived from submissions made using version 2.0 of the Mental Health Services Dataset (MHSDS). NHS Digital review the quality and completeness of the submissions used to create these statistics on an ongoing basis. More information about this work can be found in the Accuracy and reliability section of this report. Fully detailed information on the quality and completeness of particular statistics in this release is not available due to the timescales involved in reviewing submissions and engaging with data providers. The information that has been obtained at the time of publication is made available in the Provider Feedback sections of the Data Quality Reports which accompany this release. Information gathered after publication is released in future editions of this publication series. More detailed information on the quality and completeness of these statistics and a summary of how these statistics may be interpreted is made available later in our Mental Health Bulletin: Annual Report publication series. All elements of this publication, other editions of this publication series, and related annual publication series' can be found in the Related Links below. The Mental Health Data Hub was launched In February 2018; the hub brings together information on mental health data into a single place and contains visualisations and time series of select data from within this publication. The hub is available here: https://digital.nhs.uk/data-tools-and-services/services/mental-health-data-hub. Included in this months publication is a further exploratory perinatal report. This exploratory analysis is an analysis of women in contact with mental health services who were new or expectant mothers between January 2017 and December 2017. Please note, the Quarter 4 Children and Young People Receiving Second Contact With Services measure will not be included in the June 2018 publication. A validation of this data is currently underway; we expect statistics for the full 2017/18 financial year to be published in the July 2018 publication. MHSDS Monthly: Final January to March 2018 Mental Health Services Selected NHS England Measures Reference Tables has been updated with an additional note, no values have changed. A revised version of Bed days on adult wards for people aged 0-17 and Number of people aged 0-17 on adult wards is available on our supplementary information pages; this file adjusts the measures for known data quality issues to produce the most accurate information possible. A correction has been made to this publication on 10 September 2018. This amendment relates to statistics in the monthly CSV data file; the specific measures effected are listed in the “Corrected Measures” CSV. All listed measures have now been corrected. NHS Digital apologises for any inconvenience caused.

Displayed in this statistic is a distribution of mental health problem types that patients were newly diagnosed with in the UK armed forces in 2018/19. While over *** thousand patients were diagnosed with mood disorders, almost *** thousand patients were affected by neurotic disorders during their service in 2018/19.

This report presents findings from the third (wave 3) in a series of follow up reports to the 2017 Mental Health of Children and Young People (MHCYP) survey, conducted in 2022. The sample includes 2,866 of the children and young people who took part in the MHCYP 2017 survey. The mental health of children and young people aged 7 to 24 years living in England in 2022 is examined, as well as their household circumstances, and their experiences of education, employment and services and of life in their families and communities. Comparisons are made with 2017, 2020 (wave 1) and 2021 (wave 2), where possible, to monitor changes over time.

Publication changes: Please read the section on 'Notes on changes to publications' within the PDF report as this highlights changes to data currently published and potentially the frequency of future reports. In this quarter’s publication an additional table showing the extra detail available from the Tertiary Area of Work field in ESR is included for Trusts and CCGs, together with a document looking at the latest way of defining the Mental Health workforce and a .csv file to allow users to explore a time series of that workforce. This report shows monthly numbers of NHS Hospital and Community Health Service (HCHS) staff groups working in Trusts and CCGs in England (excluding primary care staff). Data is available as headcount and full-time equivalents. This data is an accurate summary of the validated data extracted from the NHS's HR and Payroll system. In addition to the regular monthly reports there are a series of quarterly reports (first published on 26 July 2016 looking at the data for March 2016) which include statistics on staff in Trusts and CCGs and information for NHS Support Organisations and Central Bodies. The quarterly analysis will be published each; September (showing June statistics) December (showing September statistics) March (showing December statistics) June (showing March statistics). Due to their size CSV data are only available for each respective month however data from September 2009 to March 2016 are all available within the March 2016 web page. This is accessible from the 'previous versions of the publication' link within the 'Related Links' section below. Additional healthcare workforce data relating to GPs and Independent Sector workforce are also available; links to this data are available below. We welcome feedback on the methodology and tables within this publication. Please email us with your comments and suggestions, clearly stating Monthly HCHS Workforce as the subject heading, via enquiries@nhsdigital.nhs.uk or 0300 303 5678

In 2018/19, approximately **** thousand girls aged between eleven and fifteen years in England were in contact at least once with NHS mental health services. In addition there were **** thousand boys in the eleven to fifteen years age group who were in contact with mental health services in this year.

This dataset contains responses from an online survey of 2187 participants primarily located in the UK. All participants stated that they had used the UK National Health Service (NHS) at some time in their lives. The data were collected between December 2018 and August 2019. Participants' views on data sharing - this dataset contains information about people's willingness to share mental and physical health data for research purposes. It also includes information on willingness to share other types of data, such as financial information. The dataset includes participants' responses to questions relating to mental health data sharing, including the trustworthiness of organisations which use such data, how much the presence of different governance measures (such as deidentification, opt-out, etc.) would alter their views, and whether they would be less likely to access NHS mental health services if they knew their data might be shared with researchers. Participants' satisfaction and interaction with UK mental and physical health services - the dataset includes information regarding participants' views on and interaction with NHS services. This includes ratings of satisfaction at first contact and in the previous 12 months, frequency of use, and type of treatment received. Information about participants - the dataset includes information about participants' mental and physical health, including whether or not they have experience with specific mental health conditions, and how they would rate their mental and physical health at the time of the survey. There is also basic demographic information about the participants (e.g. age, gender, location etc.). ## This item has been replaced by the one which can be found at https://hdl.handle.net/10283/4467 ##

This statistic presents the rate of diagnoses of mental health problems in the UK armed forces in 2018/19, by service. The Royal Marines showed the lowest rate of psychological problems compared to the three other services. The Air Force and the Army had the highest rates of service personnel having been diagnosed with some sort of mental disorder during their service in 2018/19, with the RAF noting a rate of ****.

In 2017/18, ***** per 100,000 15 to 17 year olds were admitted to hospital in England as a result of mental health conditions. While among the age group 10 to 14 years there were **** admissions per 100,000 in 2017/18. The number of hospital admissions in the 15 to 17 years age group has increased since the start of the provided time interval, while admissions has fallen for 10 to 14 year olds.

This statistic shows the rate of newly diagnosed mental health disorder cases per 1,000 strength in the British Royal Marines from 2007/08 to 2018/19. Between 2007 and 2011 the data shows a high variability instead of a clear trend. However, from 2012/13 onwards, the diagnosis rate increases considerably, resulting in a rate of almost ** diagnoses per 1,000 strength in 2018/19.

Out of Area Mental Health Placements

Data from NHS & Independent Providers in England

By data.world's Admin [source]

About this dataset

This dataset provides a comprehensive look into the Out of Area Placements (OAPs) happening in the mental health services in England. It gives insight on placements from both NHS and independent providers, giving an overall picture of how these placements are happening across the country.

By taking a closer look at this report we can gain understanding into what is going on with OAPs around us – like which questions are being asked, breakdowns of how it’s divided and number to back it up. With this data we can better understand issues that affect our community and do our part to help support those in need

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How to use the dataset

This dataset provides information on out of area placements in mental health services in England from both NHS and independent providers. The dataset contains data related to the number placements, as well as breakdowns by region and provider. With this data you can explore the trends for out of area placements in your region and compare those trends with national level figures.

This guide will show you how to get started exploring this dataset.

Step 1: Understand The Data Set Structure

The first step for getting started is to get a good understanding of the structure of the dataset itself in order to better understand what types of questions we can ask our data with. This dataset has several columns which have been listed below:

• Publication Type : This column provides information on what type of report is being referenced such as statistical bulletin or key facts & figures etc

• Publication Period: This column represents a period within a year moment which periods are expressed by either month, quarter or financial year etc..

• Publication Date: This column informs us when the publication was made available online expressed as a date format e.g 2018-04-02)

• Question : Here we will find measurements such as people waiting an average or median length times such that they answer certain question asked by officials.

• Breakdown1, BreakDown1Code, ‘Breakdown1Description’ : These columns provide extra context into specific highlights from results in further detail eg Breakdowns include areas like Age Group ,Nationality (for immigration statistics) gender for population statistics etc... where code values may appear something like “OAP_AGE_All” and descriptions appear like “Waiting Times All Ages respectively .

• BreakDown2, BreakDown2Code, 'Breakdown2Description':These are data attributes similar top BreakDown 1 but at even more granular level eg Doctor Specialty/Department, Treatment Type, Indicators (for regional/local analysis), Countries ..etc . It's important not note here that breakdown 2 has deeper break down against Breakdown 1 depending further detail asked while investigating deeper under specified parameters /results .Eg You might want drill down ages into age groups 0–4, 5–14 ,15-29....etc excluding 65+ corresponding breakdown codes might be OAP_AGE_0

Research Ideas

• Creating insight into regional differences in mental health out of area placements in order to identify if more funding is needed and implement programs to address the predisposing risk factors for those regions with higher out of area placement rates.
• Comparing the amount of expenditure allocated on out of area placements between different areas and provinces, so that extra funding may be given to areas which need it more.
• Examining the correlation between changes in funding or policy and its effects on out of area placements at both a national and local level, in order to assess whether certain policies are successful or not at curbing them such as introducing preventative measures before placement outside an individual's region is necessary

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 redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a...

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of physical and mental illnesses that are linked with obesity and inactivity. 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:- 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 area- Of 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.LIMITATIONS1. GPs do not have catchments that are mutually exclusive from each other: they overlap, with some geographic areas being covered by 30+ practices. This dataset should be viewed in combination with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset to identify where there are areas that are covered by multiple GP practices but at least one of those GP practices did not provide data. Results of the analysis in these areas should be interpreted with caution, particularly if the levels of obesity/inactivity-related illnesses appear to be significantly lower than the immediate surrounding areas.2. 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).3. 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.4. 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 obesity/inactivity-related illnesses, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of these illnesses. 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 outliersDOWNLOADING 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.

In 2017/18, there were more than ************ females aged between 10 and 24 years in England who were admitted to hospital with a primary diagnosis of an eating disorder, while *** male admissions for eating disorders also occurred in this age group. For both genders, 2017/18 had the highest amount of hospital admissions for eating disorders in the provided time interval.

SUMMARYTo be viewed in combination with the following datasets and their accompanying metadata:- Levels of obesity, inactivity and associated illnesses (England): Summary-Levels of obesity and inactivity related illnesses (physical and mental illnesses): Summary -Levels of obesity and inactivity related illnesses (physical illnesses only): Summary-Obesity in adults (ages 18+)-Depression in adults (aged 18+)-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)-Diabetes mellitus (in persons aged 17+)-Hypertension (in persons of all ages)-Stroke and transient ischaemic attack (in persons of all ages)For the aforementioned analyses, 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 identifies 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.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. Produced 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 publication provides the most timely statistics available relating to NHS funded secondary mental health, learning disabilities and autism services in England. This information will be of use to people needing access to information quickly for operational decision making and other purposes. These statistics are derived from submissions made using version 3.0 of the Mental Health Services Dataset (MHSDS). This edition includes final statistics for June 2018 and provisional statistics for July 2018. NHS Digital review the quality and completeness of the submissions used to create these statistics on an ongoing basis. More information about this work can be found in the Accuracy and reliability section of this report. Fully detailed information on the quality and completeness of particular statistics in this release is not available due to the timescales involved in reviewing submissions and engaging with data providers. The information that has been obtained at the time of publication is made available in the Provider Feedback sections of the Data Quality Reports which accompany this release. Information gathered after publication is released in future editions of this publication series. More detailed information on the quality and completeness of these statistics and a summary of how these statistics may be interpreted is made available later in our Mental Health Bulletin: Annual Report publication series. All elements of this publication, other editions of this publication series, and related annual publication series' can be found in the Related Links below. Please be aware, the data quality reports (and associated CSVs) that usually accompany the statistical reports within this publication have not been published. We have identified errors within the data quality reports and as such will not be able to publish them at this time. The files will be made available as soon as possible. NHS Digital apologises for any inconvenience caused. Also included this month is the first regular data release of perinatal mental health. This analysis is an analysis of women in contact with mental health services who were new or expectant mothers between July 2017 and June 2018. Please note: AMH04 (People in contact with adult mental health services on CPA at the end of RP with HoNOS recorded) and MHS-DIM03 (The number of SNOMED stop clock codes recorded in Procedure code for Eating Disorder referrals where Age at Service Referral Received Date is under 19) has not been included in this publication. NHS Digital will issue AMH04 and MHS-DIM03 as soon as possible. NHS Digital apologises for any inconvenience caused.