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The Active Lives Children and Young People Survey, which was established in September 2017, provides a world-leading approach to gathering data on how children engage with sport and physical activity. This school-based survey is the first and largest established physical activity survey with children and young people in England. It gives anyone working with children aged 5-16 key insight to help understand children's attitudes and behaviours around sport and physical activity. The results will shape and influence local decision-making as well as inform government policy on the PE and Sport Premium, Childhood Obesity Plan and other cross-departmental programmes. More general information about the study can be found on the Sport England Active Lives Survey webpage and the Active Lives Online website, including reports and data tables.

The Active Lives Children and Young People survey is a school-based survey (i.e., historically always completed at school as part of lessons). Academic years 2020-2021 and 2019-20 have both been disrupted by the coronavirus pandemic, resulting in school sites being closed to many pupils for some of the year (e.g., during national lockdown periods, and during summer term for 2019-20). Due to the closure of school sites, the Active Lives Children and Young People Survey, 2020-2021 was adapted to allow at-home completion. Despite the disruption, the survey has still received a sufficient volume of responses for analysis.

The adaptions involved minor questionnaire changes (e.g., to ensure the wording was appropriate for those not attending school and to enable completion at home), and communication changes. For further details on the survey changes, please see the accompanying User Guide document. Academic year 2020-21 saw a more even split of responses by term across the year, compared to 2019-20 which had a reduced proportion of summer term responses due to the disruption caused by Covid-19. It is recommended to analyse the data within term, as well as at an overall level, because of the changes in termly distribution.

The survey identifies how participation varies across different activities and sports, by regions of England, between school types and terms, and between different demographic groups in the population. The survey measures levels of activity (active, fairly active and less active), attitudes towards sport and physical activity, swimming capability, the proportion of children and young people that volunteer in sport, sports spectating, and wellbeing measures such as happiness and life satisfaction. The questionnaire was designed to enable analysis of the findings by a broad range of variables, such as gender, family affluence and school year.

The following datasets have been provided:

1) Main dataset – this file includes responses from children and young people from school years 3 to 11, as well as responses from parents of children in years 1-2. The parents of children in years 1-2 provide behavioural answers about their child’s activity levels, they do not provide attitudinal information. Using this main dataset, full analyses can be carried out into sports and physical activity participation, levels of activity, volunteering (years 5 to 11), etc. Weighting is required when using this dataset (wt_gross / wt_gross.csplan files are available for SPSS users who can utilise them).

2) Year 1-2 dataset – this file include responses from children in school years 1-2 directly, providing their attitudinal responses (e.g. whether they like playing sport and find it easy). Analysis can be carried out into feelings towards swimming, enjoyment for being active, happiness etc. Weighting is required when using this dataset (wt_gross / wt_gross.csplan files are available for SPSS users who can utilise them).

3) Teacher dataset – this file includes response from the teachers at schools selected for the survey. Analysis can be carried out into school facilities available, length of PE lessons, whether swimming lessons are offered, etc. Weighting was formerly not available, however, as Sport England have started to publish the Teacher data, from December 2023 we decide to apply weighting to the data. The Teacher dataset now includes weighting by applying the ‘wt_teacher’ weighting variable.

For further information about the variables available for analysis, and the relevant school years asked survey questions, please see the supporting documentation. Please read the documentation before using the datasets. More general information about the study can be found on the Sport England Active Lives Survey webpages.

Latest edition information

For the second edition (January 2024), the Teacher dataset now includes a weighting variable (‘wt_teacher’). Previously, weighting was not available for these...

SUMMARYTo be viewed in combination with the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.This dataset shows where there was no data* relating to one of more of the following factors:Obesity/inactivity-related illnesses (recorded at the GP practice catchment area level*)Adult obesity (recorded at the GP practice catchment area level*)Inactivity in children (recorded at the district level)Excess weight in children (recorded at the Middle Layer Super Output Area level)* GPs do not have catchments that are mutually exclusive from each other: they overlap, with some geographic areas being covered by 30+ practices.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. This dataset identifies areas where data from 2019/20 was used, where one or more GPs did not submit data in either year (this could be because there are rural areas that aren’t officially covered by any GP practices), 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.Results of the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ analysis in these areas should be interpreted with caution, particularly if the levels of obesity, inactivity and associated illnesses appear to be significantly lower than in their immediate surrounding areas.Really small areas with ‘missing’ data were deleted, where it was deemed that missing data will not have impacted the overall analysis (i.e. where GP data was missing from really small countryside areas where no people live).See also Health and wellbeing statistics (GP-level, England): Missing data and potential outliers dataDATA 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.- National Child Measurement Programme: 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. - Active Lives Survey 2019: Sport and Physical Activity Levels amongst children and young people in school years 1-11 (aged 5-16). © Sport England 2020.- Active Lives Survey 2019: Sport and Physical Activity Levels amongst adults aged 16+. © Sport England 2020.- 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.- Administrative boundaries: Boundary-LineTM: Contains Ordnance Survey data © Crown copyright and database right 2021. Contains public sector information licensed under the Open Government Licence v3.0.- MSOA boundaries: © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021.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; © Sport England 2020; © Office for National Statistics licensed under the Open Government Licence v3.0. Contains Ordnance Survey data © Crown copyright and database right 2021. Contains public sector information licensed under the Open Government Licence v3.0.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

Proportion of the population who, when surveyed, reported that they had eaten the recommended 5 portions of fruit and vegetables on a usual day. This is the weighted number of respondents aged 16 and over, with valid responses to the question 'How many portions of fruit and vegetables did you eat yesterday?', eating at least 5 portions of fruit and vegetables in the previous day.

All analyses for this indicator have been weighted to be representative of the population of England. These indicators use data from the Sport England Active Lives (AL) survey. These indicators were not intended to be, and should not be, compared directly with other sources of diet data. The AL questions are more simplistic than those used in other sources e.g., National Diet and Nutrition Survey (NDNS) or Health Survey for England (HSE). Estimates from the different surveys are not directly comparable as the data collection methodologies are different.

For the HSE, participants are asked numerous questions, including separate questions about fruits, vegetables, and pulses, and portion sizes. For the NDNS, data are collected using food diaries. Foods are then broken down to their component parts and fruit and vegetable portions are calculated. The method used in AL enables responses from a very large sample to be collected which means we can present data for each local authority which cannot be done using other sources.

The main purpose of the data set used for the PHOF indicators is to enable comparisons between areas and to measure progress. Latest update presents data from Active Lives for the period mid-November to mid-November. The current estimates (2020 to 2021 onwards) are not comparable with the previous estimates (between 2015 to 2016 and 2019 to 2020) as the current estimates are consistently around 20 percentage points lower than the previous estimates. Although the estimates of those meeting the ‘5-a-day’ fruit and vegetable consumption recommendations are significantly lower from the single question, the ranking order across local authorities or inequality groups is similar.

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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

The number of Instagram users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 2.1 million users (+7.02 percent). After the ninth consecutive increasing year, the Instagram user base is estimated to reach 32 million users and therefore a new peak in 2028. Notably, the number of Instagram users of was continuously increasing over the past years.User figures, shown here with regards to the platform instagram, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

The number of Pinterest users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 0.3 million users (+3.14 percent). After the ninth consecutive increasing year, the Pinterest user base is estimated to reach 9.88 million users and therefore a new peak in 2028. Notably, the number of Pinterest users of was continuously increasing over the past years.User figures, shown here regarding the platform pinterest, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

The number of LinkedIn users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 1.5 million users (+4.51 percent). After the eighth consecutive increasing year, the LinkedIn user base is estimated to reach 34.7 million users and therefore a new peak in 2028. User figures, shown here with regards to the platform LinkedIn, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

Latest monthly statistics on Learning Disabilities and Autism (LDA) from the Assuring Transformation (AT) collection and Mental Health Services Data Set (MHSDS). There is a slight difference in scope between the two data collections. The MHSDS data is from providers based in England and includes care provided in England but may be commissioned outside England. Whereas the AT dataset is provided by English commissioners with healthcare typically be provided in England, but also includes data on care commissioned in England and provided elsewhere in the UK. There are differences in the inpatient figures between the MHSDS and AT data sets and work has been ongoing to better understand these. NHS Digital are now reviewing the future of the LDSM publication series in terms of content. Further detail on the comparisons can be found in the Resources link below. The release comprises: Assuring Transformation Publication: This statistical release published by NHS Digital makes available the most recent data relating to patients with learning disabilities and/or autistic spectrum disorder receiving inpatient care commissioned by the NHS in England. MHSDS LDA Publication: This publication provides statistics relating to NHS funded secondary mental health, learning disabilities and autism services in England. These statistics are derived from submissions made using version 4.1 of the Mental Health Services Dataset (MHSDS). Prior to May 2018 the LDA service specific statistics were included in the main MHSDS publication. Each publication consists of the following documents: • A report which presents England level analysis of key measures. • A monthly CSV file which presents key measures at England level. • A metadata file to accompany the CSV file, which provides contextual information for each measure. • An easy read version of both main reports highlighting key findings in an easy-to-understand way. A number of changes to NHS organisations were made operationally effective from 1 April 2020. These changes included: 74 former Clinical Commissioning Groups (CCGs) merging to form 18 new CCGs; alterations to commissioning hubs; provider mergers; and the incorporation of Sustainability and Transformation Partnerships (STPs) into the NHS commissioning hierarchy. The Organisation Data Service (ODS) is responsible for publishing organisation and practitioner codes, along with related national policies and standards. A series of ODS data amendments are required to support the introduction of these changes. This would normally result in a number of organisations becoming ‘legally’ closed including the 74 former CCGs. However, to minimise any burden to the NHS during the COVID-19 pandemic and remove any non-critical activity, these organisations remain open within ODS data. ODS aim to both legally and operationally close predecessor organisations involved in April 2020 reconfiguration on 30 September 2020. As all former and current organisations remain legally open, both may be presented within this publication. Activity may be recorded against either former or current organisations, depending on data providers and processors ability to transition to the new organisation codes at this time. The same activity will not be recorded against both former and current organisations. NHS Digital are working to understand the implications of this on how you can interpret these statistics and will provide more information in future editions of this publication series. Due to the coronavirus illness (COVID-19) disruption, it would seem that this is now starting to affect the quality and coverage of some of our statistics, such as an increase in non-submissions for some datasets. We are also starting to see some different patterns in the submitted data. For example, fewer patients are being admitted to and discharged from hospital. Therefore, data should be interpreted with care over the COVID-19 period.

Employment, unemployment and economic inactivity levels and rates by age group, UK, rolling three-monthly figures, seasonally adjusted. Labour Force Survey. These are official statistics in development.

Census 2021 data on religion by economic activity status, by sex, by age, and religion by occupation, by sex, by age, England and Wales combined. This dataset provides Census 2021 estimates that classify usual residents in England and Wales by ethnic group. The estimates are as at Census Day, 21 March 2021.

The religion people connect or identify with (their religious affiliation), whether or not they practise or have belief in it.
This question was voluntary and the variable includes people who answered the question, including “No religion”, alongside those who chose not to answer this question.

Total counts for some population groups may not match between published tables. This is to protect the confidentiality of individuals' data. Population counts have been rounded to the nearest 5 and any counts below 10 are suppressed, this is signified by a 'c' in the data tables.

This dataset shows population counts for usual residents aged between 16 to 64 years old only. This is to focus on religious affiliation differences among the working age. Population counts in these tables may be different from other publications which use different age breakdowns.

Quality notes can be found here

Quality information about Labour Market can be found here

The Standard Occupation Classification 2020 code used can be found here

Religion

The 8 ‘tickbox’ religious groups are as follows:

• Buddhist
• Christian
• Hindu
• Jewish
• Muslim
• No religion
• Sikh
• Other religion

This publication provides the most timely picture available of people using NHS funded secondary mental health, learning disabilities and autism services in England. These are experimental statistics which are undergoing development and evaluation. This information will be of use to people needing access to information quickly for operational decision making and other purposes. More detailed information on the quality and completeness of these statistics is made available later in our Mental Health Bulletin: Annual Report publication series. • COVID-19 and the production of statistics Due to the coronavirus illness (COVID-19) disruption, it would seem that this is now starting to affect the quality and coverage of some of our statistics, such as an increase in non-submissions for some datasets. We are also starting to see some different patterns in the submitted data. For example, fewer patients are being referred to hospital and more appointments being carried out via phone/telemedicine/email. Therefore, data should be interpreted with care over the COVID-19 period. • Early release of statistics To support the ongoing COVID-19 work July 2020 monthly statistics were made available early and presented on our supplementary information pages. https://digital.nhs.uk/data-and-information/supplementary-information/2020/provisional-july-2020-mental-health-statistics • Changing existing measures The move to MHSDS version 4.1 from April 2020 has brought with it changes to the dataset; the construction of a number of measures have been changed as a result. Improvements in the methodology of reporting delay of discharge has also resulted in a change in the construction of the measure from the April 2020 publication onwards. Full details of these changes are available in the associated Metadata file. • New measures A number of new measures have been included from the April 2020 publication onwards: • MHS76 Count of people subject to restrictive interventions • MHS77 Count of restrictive interventions • MHS78 Discharges from adult acute beds eligible for 72 hour follow up in the reporting period • MHS79 Discharges from adult acute beds followed up within 72 hours in the reporting period • MHS80 Proportion of discharges from adult acute beds eligible for 72 hour follow up - followed up in the reporting period Full details of these are available in the associated Metadata file. • CCG and STP changes A number of changes to NHS organisations were made operationally effective from 1 April 2020. These changes included: 74 former Clinical Commissioning Groups (CCGs) merging to form 18 new CCGs; alterations to commissioning hubs; provider mergers; and the incorporation of Sustainability and Transformation Partnerships (STPs) into the NHS commissioning hierarchy. The Organisation Data Service (ODS) is responsible for publishing organisation and practitioner codes, along with related national policies and standards. A series of ODS data amendments are required to support the introduction of these changes. This would normally result in a number of organisations becoming ‘legally’ closed including the 74 former CCGs. However, to minimise any burden to the NHS during the COVID-19 pandemic and remove any non-critical activity, these organisations remain open within ODS data. ODS aim to both legally and operationally close predecessor organisations involved in April 2020 Reconfiguration on 30 September 2020. Activity may be recorded against either former or current organisations, depending on data providers and processors ability to transition to the new organisation codes at this time. The same activity will not be recorded against both former and current organisations. There is no impact on this statistics presented here as CCG is derived in all cases within this publication. • Women in contact with mental health services who were new or expectant mothers Please be aware on 19 November 2020 the quarterly women in contact with mental health services who were new or expectant mothers analysis was published following an investigation into an issue that prevented publication on the originally announced date. NHS Digital apologises for any inconvenience caused.