NHS Digital Data Release Register - reformatted
University Of Exeter projects
- Understanding Time Trends in Child and Adolescent Mental Health: Impact of Covid-19
- A Spatial Microsimulation model of Comorbidity
- Tracking the impact of Covid-19 on the mental health of children, young people and families; follow up of a national longitudinal probability sample: follow-on interviews
- Project 4
48 data files in total were disseminated unsafely (information about files used safely is missing for TRE/"system access" projects).
Understanding Time Trends in Child and Adolescent Mental Health: Impact of Covid-19 — DARS-NIC-424336-T7K7T
Type of data: information not disclosed for TRE projects
Opt outs honoured: Anonymised - ICO Code Compliant (Does not include the flow of confidential data)
Legal basis: Health and Social Care Act 2012 - s261 - 'Other dissemination of information', Health and Social Care Act 2012 s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 s261(2)(b)(ii)
Purposes: No (Academic)
When:DSA runs 2021-07-01 — 2023-06-30
Access method: One-Off
Data-controller type: NATCEN SOCIAL RESEARCH, UNIVERSITY OF CAMBRIDGE, UNIVERSITY OF EXETER
Sublicensing allowed: No
- Mental Health of Children and Young People
- Mental Health of Children and Young People (MHCYP)
The research team, University of Exeter, University of Cambridge, and NatCen Social Research request access to the 2017 Mental Health of Children and Young People (MHCYP) and the 2020 follow-up. This data request derives from a National Institute for Health Research funded fellowship and associated Medical Research Council grant with the purpose of examining the influence of Covid-19 on trends over time in child mental health, the impact of the pandemic, and child mental health service use and support during the coronavirus pandemic.
The pandemic is occurring against a background of deteriorating mental health and health inequalities in young people in the UK. Prior to Covid-19, there has been a trend of rising referrals to child and adolescent mental health services and in the numbers of young people attending emergency departments for self- harm. However, there have also been changes in awareness and recognition of mental health problems, meaning that it is not clear whether and to what extent this is due to an actual increase in the numbers of children and young people experiencing difficulties.
It is important to understand these trends and the impact of Covid-19, in order to plan for the future, target risk factors and those groups most affected, and to improve access to services for children and young people who need them. To this end, this project intends to provide outputs to assist those designing, commissioning, and delivering Child and Adolescent Mental Health Services (CAMHS), providing evidence of the prevalence and nature of mental health service needs; and for education services and others working with the school-age population.
This project aims to answer the following questions in the context of Covid-19:
Have there been changes in psychopathology, socio-economic and family factors between 2017 and 2020?
Have there been changes in the ways that mental health problems affect young people? (e.g. in the impact on them or their families)
Has the mental health of particular groups got worse over time (e.g. in those from more deprived backgrounds, or with certain diagnoses)?
Are children with problems becoming more or less likely to be in contact with sources of help and support?
How does any change between 2017 and 2020 compare with changes between baseline and follow-up in the previous national child mental health surveys is there any evidence that outcomes have worsened?
The Mental Health of Children and Young People survey (MHCYP) 2017 included 9,117 children and young people aged 2 to 19 years old, who were recruited from a stratified probability sample taken from GP registers. The 2020 follow-up included 3,570 children and young people (now aged 5-22) who took part in the 2017 survey, who had agreed to being re-contacted, and who were successfully recruited again in 2020. In each survey, parents reported on younger children, with additional self-report questions for those aged 11-16. Young people aged 17 and over completed their own questionnaires.
The 2017 survey included the Development and Well-being Assessment (DAWBA), a validated standardised diagnostic assessment, which gathers structured data on symptoms and their impact, with semi-structured probes about problems. Both surveys included the Strengths and Difficulties Questionnaire (SDQ), which is a validated dimensional measure of mental health difficulties and impact. In addition, the 2017 survey and the 2020 follow-up also included data on the socio-economic circumstances of the family and the child or young persons contact with services.
The 2017 and 2020 datasets which are requested are uniquely able to address the aim of this project. They are a large national representative probability sample, which has comprehensive pre-pandemic and well characterised social context data. These detailed data on baseline characteristics and mental health in a population sample, allowing examination of the outcomes for these children and young people during the Covid-19 pandemic and to identify which groups did and did not participate in the 2020 follow up. This enables identification of groups that may be theoretically higher risk and the examination of outcomes for those in different diagnostic groups at baseline.
As all of the MHCYP and earlier BCAMHS (British Child and Adolescent Mental Health Surveys) used the Strengths and Difficulties Questionnaire to measure psychopathology, the team will also compare the change in scores between 2017 and 2020 with the change in scores between baseline and follow-up in the previous surveys, allowing examination of how much change may be related to the impact of Covid-19.
The data controllers will be the University of Exeter, University of Cambridge and NatCen. All three will also be data processors. The GDPR lawful basis for the University of Exeter, University of Cambridge and NatCen to process this data is Article 6(1)(e) processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller. The legal basis for the processing of special category data is GDPR Article 9 (2)(j), for research or statistical purposes.The funders are the National Institute for Health Research (NIHR) and the Medical Research Council (MRC).
Outputs from this work can help mitigate the impact of the Covid-19 pandemic on child mental health. This will be achieved through the anticipated impact in three main areas: research, policy and commissioning. Findings will directly impact on policy and commissioning by providing essential information that will inform and improve impact assessments, policy development, workforce development and training and service planning. It will do this in two main ways: (1) identifying groups who experienced poorer mental health outcomes during the Covid-19 pandemic, and examining risk and protective factors; (2) identifying groups who may be experiencing inequalities in terms of access to services during the pandemic and whether these have worsened since 2017.
Policy and commissioning will be impacted at a national level, with beneficiaries including the Department of Health and Social Care and Department of Education, and the Education and Health and Social Care Select Committees, who have an important role in holding Government to account on child mental health policy. Representatives of the Department of Health and Social Care contributed to the development of the questions asked in the follow on questionnaires and so these analyses will focus on questions that are directly relevant to policy priorities. As such, it is anticipated that there will be an immediate impact of this work on the school age population. Commissioners and practitioners who work with them are desperate to better understand who is at risk and how to help them. Other relevant national bodies include Public Health England, NHS England and think-tanks such as the Education Policy Institute, with whom the team plan to link, as well as regional specialist mental health and child health commissioning networks. Policy briefings will be widely disseminated across these groups.
A major beneficiary will be those designing, commissioning, and delivering Child and Adolescent Mental Health Services (CAMHS), as the research will provide evidence of the prevalence and nature of mental health service needs. Equally, the pathfinder areas with school-based mental health teams and those offering mental health support in schools (school nurses and counsellors for example), will benefit from improved knowledge of the extent of service need and also which groups are particularly vulnerable and could benefit from targeted approaches.
This project aims to contribute to a better understanding of the impact of Covid-19 on child mental health in the context of trends over time. The team will work with partners and PPI groups (through the Royal College of Paediatrics and Child Health & Us programme) including the Office for National Statistics, Association for Child and Adolescent Mental Health, Mental Health Commissioning Network, Department for Education, Department for Health and Social Care, and Public Health England to produce specific outputs. These will include:
- Peer reviewed outputs of international standing e.g. journal articles (Summer 2021), such as Lancet Psychiatry, British Journal of Psychiatry, and the Journal of Psychology and Psychiatry
- Conference presentations to a range of audiences including health and education (Summer-Autumn 2021) such as the International Congress of the Royal College of Psychiatrists, the Festival of Education and the conference of the Faculty of Public Health.
- Blogs and other public facing output, for schools, health professionals and the general public. These will be developed in conjunction with PPI groups and partners as above (Spring-Summer 2021) for networks such as the Mental Elf, Place2Be, Times Educational Supplement, The Conversation and the Association of Child and Adolescent Mental Health.
- Rapid digests (Spring 2021) in the form of short briefings and presentations on key findings such as groups experiencing poorer outcomes, profile of most common psychopathology, and groups who appear to have unmet need for services such as CAMHS or school-based mental health services. These rapid digests will be tailored for different audiences including CAMHS, Department for Education, schools, and pathfinder areas with school mental health teams.
University of Exeter, University of Cambridge and NatCen will receive the whole standard dataset for each survey and their respective follow-up datasets. It is not possible to obtain individual variables. The University of Exeter will be the lead organisation for the Data Sharing Agreement with NHS Digital which will stipulate the length of time for which these data will be kept. This will be for the two years of the research project, after which the data will be securely destroyed according to the DSA unless an extension is applied for and granted.
The datasets received will not be shared with any third parties. The flow of data ends with the University of Exeter, University of Cambridge and NatCen.
The MHCYP survey data is carried out by NatCen Social Research and the Office for National Statistics, the collected data is checked, derived further, minimised and pseudonymised. The pseudonymised data asset is then sent to UK Data Service (UKDS) for agreed dissemination.
The UK Data Service (UKDS) securely transfers the datasets. All data is pseudonymised. The data processors will be the University of Exeter, University of Cambridge and NatCen.
The datasets will be securely transmitted via the UKDS using their approved pathways and then stored electronically in the secure research hubs of all three organisations, which have Data Security and Protection Toolkits (DSPTs). Analysis will also take place within these secure areas.
Data cannot be exported from secure environments without going through a checking procedure to ensure it is not identifiable in any way, and the environments also include logging procedures of who has accessed the data. Only named members of the study team and are substantive employees will have access. All data is regularly backed up on the Universities secure server.
This research will involve the analysis of pseudonymised data provided with the permission of NHS Digital. Personal data such as names, addresses and dates of birth are not included, only the unique serial number used to represent participants. In order to minimise the risk of re-identification in this pseudonymised dataset, the team will also follow the Disclosure control for microdata produced from social surveys guidance set out by the Government Statistical Service.
What will be done with the data:
The pseudonymised dataset will be analysed and only aggregated outputs will be made available to third parties in peer reviewed publications and open access reports. There will be no attempt to identify individuals.
Data from these surveys will be used to understand temporal changes in child and adolescent mental health and risk factors for poor mental health, in the context of Covid-19, and how support and services for children and young people with mental health problems has been affected.
The aims are to:
Examine changes in psychopathology, socio-economic and family factors, and mental health-related contact with services between 2017 and 2020
Compare outcomes in 2020 for different diagnostic groups at baseline
Compare changes in psychopathology between 2017 and 2020 with changes between previous British Child and Adolescent Mental Health (BCAMHS) surveys and their follow-ups, to examine changes in outcomes over time and the impact of the coronavirus pandemic
Data management will be done using a statistical analysis package. All analyses will be conducted using survey weights and controlling for complex survey design where appropriate, and for non-response. Descriptive statistics will initially be used, with stratification by age and gender where appropriate, as well as cross-tabulations and pairwise comparisons. This will be followed by the use of regression models to examine the factors which may explain change over time and the association between factors, adjusting for the effect of measured confounders where possible.
Comparison with the outcomes of previous BCAMHS baseline surveys (see Aim above) will be carried out by analysis of the BCAMHS 1999 and BCAMHS 2004 datasets, which will be obtained through the usual process via the UKDS.
A Spatial Microsimulation model of Comorbidity — DARS-NIC-03716-R3W8Q
Type of data: information not disclosed for TRE projects
Opt outs honoured: Anonymised - ICO Code Compliant (Does not include the flow of confidential data)
Legal basis: Health and Social Care Act 2012 s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 s261(2)(b)(ii)
Purposes: No (Academic)
When:DSA runs 2020-01-01 — 2022-12-31
Access method: One-Off
Data-controller type: UNIVERSITY OF EXETER
Sublicensing allowed: No
- Hospital Episode Statistics Admitted Patient Care
- Hospital Episode Statistics Admitted Patient Care (HES APC)
The University of Exeter has undertaken a project to develop a new way of producing the spatially referenced health data required so that health services can be proactively planned to meet the local health needs. Specifically, the work on the project addressed the current need for a dataset that presents patterns of comorbidity at the small area level in England. Without this spatially referenced data health practitioners and policymakers currently do not have a spatial map of comorbidity for England and therefore areas with very high or very low incidences of comorbidity are not identifiable to clinicians or policymakers.
The University of Exeter previously produced baseline small-area population estimates of co-morbidity outcomes (Cardiovascular Disease (CVD), diabetes and obesity) at the Lower Super Output Area (LSOA) level. These have been simulated for England using spatial microsimulation techniques to combine information from the Census of Population and the Health and Safety Executive (HSE) 2008-2010. To ensure that the data produced by the spatial microsimulation model is robust, the newly produced data needed to be compared to real service use data. This is a standard validation method for area data estimation.
The University of Exeter obtained aggregated admissions data on patients with a diagnosis of CVD, diabetes and obesity from the Hospital Episodes Statistics (HES) Admitted Patient Care data at the LSOA level. Data on admissions for diabetes, CVD and obesity or any combination of these diseases (i.e. patient presenting with both diabetes and CVD) were obtained at the LSOA level and were also broken down by age bands of five years and gender at the LSOA level. The unsuppressed small number data was required to ensure that all individual cases of admissions were captured. As the data was only required for validation, the original data request did not cover further use of the data for other analysis, such as an econometrical analysis of differences in spatial use patterns.
However, on using the HES data for validation, clear spatial differences in expected rates of admission for the diseases of interest and the admissions reported by HES were observed. Mapping these it was increasingly obvious that these differences were particularly apparent in rural areas and more deprived areas, where actual admissions were much lower than predicted admissions. As such, it is believed that rurality and area level deprivation is a barrier to patients with CVD, diabetes, obesity or comorbidity from accessing hospital services.
Based on this hypothesis this Agreement is to extend the use of the already disseminated HES data-set already disseminated to include a further econometrical analysis of the impact of rurality, and area level deprivation on hospital admissions for the diseases noted above. No further data is required.
Funding for the original analysis was provided by the Economic and Social Research Council (ESRC) Secondary Data Analysis Initiative. Moving forward there will be no funders or commissioners involved in this study.
Data will be analysed using standard count data econometrical data and only standard coefficients and confidence intervals will be used in future publications. If maps produced in a Geographical Information System (GIS) are produced the HES data will be only presented at the Medium Super Output Area (MSOA) level in map form. It is expected that many of the requested cells will have small data numbers. However, researchers will not attempt to re-identify the patient data and these data will not be released as part of the research. These small number cells will just be used in the analysis, with results presented across all LSOAs.
East Kent Hospital Trust (EKHT) are interested in the results as their catchment area includes some of the most deprived areas in England. Thus, the EKHT are interested in understanding if area level deprivation is a barrier to hospital use. EKHT are not involved in designing the new research or processing the data and are therefore not a data controller. The University of Exeter is the only organisation processing the data for the purposes described here.
The University of Exeter are processing the data being accessed under this agreement as part of their public task around research under Article 6(1)(e) and 9(2)(j) of the GDPR.
Article 6(1)(e) - processing is necessary for the performance of a task carried out in the public interest.
Article 9(2)(j) - processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes.
The hypothesis is that while people living in the most deprived areas will have the highest health care service needs, in fact they will have lower admittance rates to hospital. This will be linked to factors such as access to hospital services, access to GP services, individual education levels and income (although the NHS is free, getting there, taking time off work, getting someone to look after children is difficult/expensive).
Additional hypothesis is that rural areas will have particularly low level of admittance relative to their actual need.
As originally planned, the rates of comorbidity at the small area level produced by the spatial microsimulation model has been validated against the HES data. This work found that the simulated data is representative of the pattern of admissions observed in the HES. Given that the spatial distribution is correct, this means that the data controller can now use the outputs of the spatial microsimulation model to examine different policy questions, such as equitable access to health care services based on need. Furthermore, based on the spatial patterns of admissions observed during the validation process, permission to undertake additional analysis is being sought to explore whether deprivation and location-based inequalities exist in accessing hospital care for people with diabetes, CVD, obesity or a combination of these diseases in England. The analysis of the data has not yet been completed due to the teaching commitments of the researcher.
People are increasingly presenting with a number of chronic diseases such as diabetes and obesity or CVD and diabetes. With regard to the provision of health services, recent research (Morrissey et al., (2016) A Multinomial Model for Comorbidity in England of Long-standing Cardiovascular Disease, Diabetes, and Obesity. Health & Social Care in The Community) indicates that in England single disease management approach is no longer suitable for a large number of patients. Since comorbidity is significantly related to increased levels of mortality and decreased functional status and quality of life, health care should shift its focus from specific diseases, to multiple pathologies, worsening functional status, increasing dependence of care and the increased risk of mental and social problems.
Health practitioners and policymakers currently do not have a spatial map of comorbidity for England. This means that areas with very high or very low incidences of comorbidity are not identifiable to clinicians or policymakers. As such appropriate services, both preventative and disease management focused cannot be spatially targeted to the populations with the highest needs. Using the simulated data validated against the HES data, policymakers and clinicians will be given insight on what areas need to be targeted in terms of the rising incidence of comorbidity so that they can plan services to meet expected demand. The methodology, but not the data will be freely available from the authors so that other interested stakeholders may utilise the model for their specific research question. The method outlined is a well established method to examine hospital admissions data. It is envisioned that the methodology will be widely used by the academic community that currently undertake Small Area Estimation (University of Exeter, University of Leeds, University of Southampton, University of Liverpool, University of South East Anglia) and policymakers at both the national and regional level.
As the methodology will be freely available to be used by other groups, it is anticipated that this work will reduce the need for future flows of patient information.
The HES data was originally sought for validation, to check the rates of comorbidity simulated through the spatial microsimulation model against rates observed in the HES. This work has been completed. Based on the spatial pattern of admissions that was observed during the validation process, an extension to use the data is being sought to examine and explore the impact of deprivation and rurality in accessing hospital care for people with diabetes, CVD, obesity or a combination of these diseases.
From this research one academic paper will be published discussing the impact of deprivation and rurality on admission rates for diabetes, CVD, obesity or a combination of these diseases to the Journal of Health & Place by December 2020. This publication will only focus on the impact of deprivation and rurality on admission rates for diabetes, CVD, obesity or a combination of these diseases. No further analysis will be included.
The applicant's hypothesis of deprivation on hospital usage is that while people living in the most deprived areas will have the highest health care service needs, in fact they will have lower admittance rates to hospital. This will be linked to factors such as access to hospital services, access to GP services, individual education levels and income (although the NHS is free, getting there, taking time off work, getting someone to look after dependents may be difficult/expensive).
Shortly after completion of the analysis (late 2020), further presentations will be made to clinicians and policymakers from East Kent Hospital Trust, the Royal Cornwall Hospital Trust and the Royal Devon and Exeter Trust. It is also expected that the results of the analysis will be presented at a number of academic conferences, specifically the Social Science Medicine conference and the British and Irish Regional Science conference in 2021.
All outputs will be aggregated with small numbers suppressed in accordance with the HES analysis guide.
The aggregated HES data will be stored on the University of Exeters server located at the University of Exeter medical school, St. Lukes Campus and accessed remotely by the sole applicant at the Universitys Medical School located at the Royal Cornwall Hospital. Only one individual will have access to the unsuppressed HES data, and that person is substantively employed by the University of Exeter. The dataset has been used to validate the simulated data on CVD, diabetes, obesity and their comorbidity created by a spatial microsimulation model. Within the spatial microsimulation literature this process is referred to as external validation. This process has been successfully completed.
However based on the spatial patterns of admissions observed during the validation process, permission to undertake additional analysis, is being sought to explore whether deprivation and location-based inequalities exist in accessing hospital care for people with diabetes, CVD, obesity or a combination of these diseases in England.
The University of Exeter is intending to analyse this data using standard statistical analysis, specifically regression analysis. If GIS based maps are produced, the HES data will be visualised at the MSOA geographical level which will prevent anybody from being identified. If GIS based maps are presented as a means of visualising the spatial differences in admission, the HES data will be only mapped at the MSOA level in map form, in range form (10-15%), rather than point estimates. Thus, if mapping of the data does occur LSOAs with small numbers will be automatically aggregated to a higher geography. Only data obtained from the simulated data will be released as a map within a data range rather than point results.
HES data from NHS Digital will be linked to the Index of Multiple Deprivation for 2015, which is publicly available data.
To mitigate the risk of re-identification, data will not be presented at the individual level but as average results across respondents. If GIS based maps are produced, the HES data will be visualised at the MSOA geographical level which will prevent anybody from being identified.
No automated decision-making algorithm will be used on this data.
Tracking the impact of Covid-19 on the mental health of children, young people and families; follow up of a national longitudinal probability sample: follow-on interviews — DARS-NIC-402080-N3V5Z
Type of data: information not disclosed for TRE projects
Opt outs honoured: Identifiable, No (Consent (Reasonable Expectation))
Legal basis: Health and Social Care Act 2012 s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 - s261 - 'Other dissemination of information', Health and Social Care Act 2012 s261(2)(b)(ii), Health and Social Care Act 2012 - s261(5)(d)
Purposes: No (Academic)
Sensitive: Non-Sensitive, and Sensitive
When:DSA runs 2021-04-15 — 2022-04-14 2021.07 — 2022.01.
Access method: One-Off
Data-controller type: UNIVERSITY OF CAMBRIDGE, UNIVERSITY OF EXETER
Sublicensing allowed: No
- Mental Health of Children and Young People
- Mental Health of Children and Young People (MHCYP)
The objective for processing is for the research team from the University of Exeter and University of Cambridge to contact participants from the Mental Health of Children and Young People in England (MHCYP) 2020 follow-up survey (also known as the National Study of Health and Wellbeing: Children and Young People 2020) to invite them to take part in a follow-up research interview study. This follow-up research study forms part of a wider project called: "Tracking the impact of Covid-19 on the mental health of children, young people and families; follow up of a national longitudinal probability sample". The follow-up interviews are referred to as the RESHAPE study (REflecting on the impactS of covid-19 on cHildren And young People in England: exploring experiences of lockdown, service access and education) as this is more accessible for participants.
Participants include 600-1000 parents of children and young people themselves aged 5-22 who previously consented to be contacted for further research in the survey. The main aims of RESHAPE are:
To explore the experiences of children, young people and parents of lockdown during the pandemic and the impact of school closures on children, young people and their families (led by the University of Cambridge)
To examine mental health related service contacts in children and young people, both pre-pandemic and during the pandemic, and to explore the barriers and facilitators to seeking and receiving help (led by the University of Exeter)
The processing involves the names and contact details of participants in the MHCYP 2020 who are eligible to take part in these interviews being securely passed by the National Centre for Social Research (NatCen) to the Universities of Cambridge and Exeter. The purpose is for the University teams to contact these participants to invite them to take part in the research interviews. The University teams will then take separate informed consent for participation in the follow-on research study interviews.
These research interviews form part of a project funded by the Medical Research Council through the UK Research and Innovation (UKRI) 2020 Covid-19 research call. The findings of the interviews will be shared in reports for policymakers such as the Department for Health and Social Care, the Department for Education, NHS England, Public Health England and Office of the Childrens Commissioner, Royal College of Psychiatrists and Royal College of Paediatrics and Child Health, and in publications for peer review.
Better information about the impact of Covid-19 on children and young peoples mental health, and how the lockdown affected them is crucial to mitigate the effects, and to improve services and support for children and young people if there are further waves of Covid-19 or lockdowns in the future. Conducting patient and public involvement activities throughout this research will ensure the project is relevant and accessible, and that outcomes are accessible to schools and services. There are considerable benefits to collecting further data from the participants in MHCYP 2020, as this is the only well-characterised national probability sample of this age group carried out during Covid-19, rather than being a convenience sample.
The data controllers for this application will be the University of Cambridge and the University of Exeter. The data processors will be NatCen, the University of Cambridge and the University of Exeter. The GDPR lawful basis for the University of Exeter and the University of Cambridge to process this data is Article 6(1)(e) processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller. The legal basis for the processing of special category data is GDPR Article 9 (2)(j), for research or statistical purposes. The funder is the Medical Research Council (MRC).
NatCen are a partner in this project, and act as data processors for the MHCYP 2020 survey for NHS Digital. All participants in MHCYP 2020 were asked for consent to be contacted about further research and surveys.
Only those who agreed to contact about future research will be contacted initially by NatCen with information (a brief flyer) about taking part in RESHAPE. The flyer will explain that unless the person opts-out, their contact details (name, phone number, address and email address) will be securely passed to the research team to allow the researchers to contact them with more information about the follow-on research. They will then be able to give separate informed consent about whether to participate in the interview study. People will have the choice to opt-out of having their contact details passed to the research team, by using a Freephone NatCen number.
It is necessary for the University teams to have contact details in order to directly contact participants to complete recruitment and take consent for the interviews. Good research practice suggests that those carrying out the research should take consent so that it is fully informed. It also replicates the method previously used in the follow-ups of the 1999 survey, which were acceptable across over 800 interviews. Participants in MHCYP 2020 have already given consent to being contacted about further related research; hence, requiring that they opt-in consent to contact again on this occasion (within 1 year of the survey) represents an additional and unnecessary burden to them, which may result in a low response rate. The latter would jeopardise the main strength of the sample (i.e. its representative nature). The study team have full ethical approval from the University of Cambridge Psychology Research Ethics Committee for this procedure.
This research has been funded in a competitive application to UK Research and Innovation (UKRI)/ Medical Research Council (MRC) as part of the 2020 Covid-19 research call, which demonstrates its value. Project partners on the application included the Department of Health and Social Care, NHS England, Public Health England, the Children and Young Peoples Mental Health Coalition, the Royal College of Psychiatrists and the Royal College of Paediatrics and Child Health, and the Department for Education. Their contributions have ensured that this research is designed to benefit those providing services and support to children, young people, and families. Involvement of these stakeholder groups in the projects design is important as it will improve accessibility and relevance of the outcome reports.
Through this research, the Universities of Cambridge and Exeter will aim to:
Understand experiences of mental health help-seeking during the pandemic, and the barriers and facilitators to receiving help
Explore the impact of Lockdown, particularly of school closures on children, young people, and families, including on their mental health and their engagement with education, as well as describing education practices which were helpful and unhelpful during this period, focussing on mental health support and impacts.
Provision of the contact details of consenting individuals from the populations of interest is vital in enabling the direct collection of further data to achieve these aims.
A better understanding of the impact of lockdown on childrens mental health, education and access to services should directly benefit the provision of mental health care for children and young people. As above, reports will be produced on access to services. These will include an exploration of the barriers and facilitators to seeking help for mental health concerns during the pandemic, as well as a description of the services that children, young people and parents used most frequently and their experiences of service contact (e.g. face to face and/or virtual). These reports will provide a framework for those planning and running services across health, education and social care to address these factors and improve access to care for the most vulnerable. It is hoped that this will contribute to more accessible and acceptable services being offered should there be further waves of Covid-19, or should we be preparing for a future pandemic.
Similarly, brief reports will be produced on educational experiences and engagement and their impact on mental health and wellbeing. These will be directly relevant to education policy-makers and schools wishing to plan for and mitigate the impact of any future closures. The published report to UKRI is beneficial to funders by detailing methods, project delivery and outcomes during the Covid-19 pandemic. It is hoped that subsequent peer-reviewed publications will allow other researchers to learn from this research and build upon it, to design further research to benefit children and young people. Summaries of the study's findings will be prepared for separate stakeholders, focussed on practitioners and service planners in a variety of accessible formats such as presentations, blogs, podcasts and evidence- briefings.
The impact of the reports, papers, and stakeholder engagement will be monitored through altmetrics, website hits and downloads, as well as noting contributions to policy meetings, committees and citation in policy documents. These benefits, in terms of impact on service planning, are expected to begin to be achieved during the first 12 months of this UKRI grant and to continue for the 12-24 months afterwards.
The Universities of Cambridge and Exeter are also conscious of the high public interest, and will work with young people and parents to co-develop accessible public content, again in the form of blogs and engagement with the media.
The patient and public involvement (PPI) panel for the wider research study will assist in refining interview schedules and participant facing materials for the study. Any emerging themes and findings will be presented for feedback by the PPI panel. The panel will co-design the key messages for children, families, teachers and young people with the study team, as well as the public-facing outputs from the research.
The outputs from the research interviews are likely to include the following:
Reports focussed on education and on access to services during Covid-19, aimed at stakeholders including the Department for Health and Social Care, the Department for Education, NHS England, Public Health England and Office of the Childrens Commissioner, Royal College of Psychiatrists and Royal College of Paediatrics and Child Health (target date, autumn 2021)
Briefings for Schools and Mental Health services and practitioners, containing key messages for service planning and learning to improve practice
Published report for the funder UK Research and Innovation (UKRI)/ Medical Research Council (MRC) (target date, spring 2022)
Submissions to peer reviewed journals including health and education journals. These may include: Child and Adolescent Mental Health, Lancet Psychiatry, School Mental Health, British Educational Research Journal (likely to be 3-4 papers, from autumn 2021-spring 2022)
Presentations to stakeholders as named above
Conference presentations to education, child health, and mental health conferences
Blogs and newsletter articles for practitioners and service planners, e.g. through the Association for Child and Adolescent Mental Health (ACAMH)
A lay summary of the major study findings on the public-facing website: https://dev.psychiatry.cam.ac.uk/reshape/
The outputs will include both quantitative and qualitative data the quantitative data will be presented as aggregate outputs with any smaller numbers suppressed in line with statistical disclosure guidance.
The MHCYP 2020 dataset is held on behalf of NHS Digital at NatCen and securely stored. Analysts from NatCen will run an agreed algorithm to identify eligible participants for research interviews based on their characteristics as recorded in the MHCYP 2020 dataset. These characteristics include participants responses to questions about their/their childs contact with services, their/their childs education participation and status, and their/their childs experience of lockdown. These responses will determine whether MHCYP 2020 participants are invited to take part in the University of Cambridge interview about education and lockdown experiences, or the University of Exeter interview about experiences of seeking help during the pandemic and contact with services.
As the contact details in the dataset were last updated in June 2020, NatCen will firstly transfer serial numbers, first names and surnames of eligible participants to NHS Digital using secure file transfer protocols for a check through the Personal Demographics Service (PDS). The serial numbers, names, most recent addresses, telephone numbers and email addresses alongside fact of death (if applicable) will be returned to NatCen. This will ensure that contact details are up-to-date, and that study invitation letters are not addressed to anyone who has passed away. This process will occur under the established data controller-data processor contract between NHS Digital and NatCen.
These data will only be accessed by individuals within NatCen who have authorisation to access the data for the purpose described, all of whom are employed by NatCen. NatCen will then use the contact details as recorded in the MHCYP 2020 dataset to send information (a flyer) about the research interviews to these eligible participants, explaining the study. The flyer will explain that unless the person opts-out, their contact details (name, phone number, email address, and postal address) will be securely passed to the research team to allow the researchers to contact them with more information about the follow-on research. They will then be able to give separate informed consent about whether to participate in the interview study. People will have the choice to opt-out of having their contact details passed to the research team, by using a Freephone NatCen number. NatCen will then delete the names and data of participants who opt-out of their contact details being passed to the research teams, from their list of eligible participants. The Universities of Cambridge and Exeter have discussed this process with NatCen who agree that it is feasible.
The contact details of eligible participants who do not opt out will be securely passed to the research teams at the University of Cambridge and the University of Exeter, electronically via NatCens secure File Transfer Protocol (FTP) server. The University of Exeter will receive contact details for all participants meeting the criteria for the service contact interviews. The University of Cambridge will receive contact details for all remaining participants eligible for the education/lockdown interviews. These data will be the minimum data necessary to effectively contact participants in relation to the planned purposive sample; names, telephone numbers, addresses and email addresses. NatCen will also indicate whether the participant is a young person aged 17-22 or a parent, in order for the Universities of Cambridge and Exeter to send them the appropriate information about taking part (an information sheet about the study and a consent form). No other data collected from the survey will be passed on with these details i.e. there will be no data on participants characteristics, mental health, socio-economic background etc. from the survey accompanying the contact details. Participants contacted using their provided email addresses will be via the secure NHS Mail service.
The University of Exeter and the University of Cambridge will store this data securely until potential interview participants have been approached and consented/not consented and not share it with any third parties. This data will be stored in the University of Exeter and University of Cambridge secure data environments, which can be securely accessed remotely. Data cannot be exported from these environments without going through a checking procedure to ensure it is not identifiable in any way, and the environments also include logging procedures of who has accessed the data. Only named members of the study team who are substantive employees of the university and have been appropriately trained in data protection and confidentiality will have access to the data. The secure environments allow data to be kept in password protected subfolders, to which access can only be gained by authorised members of the team who require access to that subfolder. All data is regularly backed up on the Universitys secure servers.
The University of Cambridge will store all personally identifiable data in the Secure Data Hosting Service (SDHS), managed by the Clinical School Computing Service in collaboration with the Information Governance Office. The SDHS is accessed via a VPN with 2 factor authentication. Data can only be transferred to and from the SDHS via the secure air lock. All remote device access to the SDHS is screen view only.
Ingress and egress of confidential information to the University of Exeters Secure Data Research Hub (SDRH) are via an upload and download folder. No other ingress or egress is available. Remote access to the University of Exeters secure data environment is via multi-factor authentication VPN and is screen view only.
Contact detail data will only be used to contact potential participants by email, telephone or post about the studies and invite them to interview. The study information sheet sent to potential participants will ask them to telephone or email the research team if they are interested in participating or in finding out more. After a ten day period, if they have not contacted the team, the team will email or telephone them with a reminder about the study. They will be reminded again after another two weeks, and again after another week. If they have not responded by that point, it will be assumed that they are not interested in participation.
Where participants consent to take part in the interviews, further data will be gathered from them which does not fall under this data sharing agreement and which will be managed in the usual way for research data in accordance with the General Data Protection Regulation, Data Protection Act, NHS Caldicott Principles, the Research Governance Framework for Health and Social Care, and all conditions of the Research Ethics Committee Approval. The contact data will not be linked with any other data from the MHCYP 2020 survey or that gathered by the research team nor will it be made available to any third parties.
Where participants do not consent, the contact details data will be securely deleted. The remaining contact details data will be held only for the length of the data sharing agreement with NHS Digital, and then deleted. Deletion of data will be logged in the audit trail that is kept by the Universities of Exeter and Cambridge in relation to this agreement, and data will be securely destroyed beyond ability to rebuild and reuse, following each Universitys procedures, to assure this.
Project 4 — DARS-NIC-374485-Y2X9C
Type of data: information not disclosed for TRE projects
Opt outs honoured: N ()
Legal basis: Health and Social Care Act 2012
Sensitive: Non Sensitive
When:2017.03 — 2017.05.
Access method: One-Off
- Hospital Episode Statistics Accident and Emergency
- Hospital Episode Statistics Admitted Patient Care
The aim of processing HES A&E Admissions and HES Inpatient Admissions data is to model relationships between exposure to natural environments and health outcomes, and to develop methods for economic valuation of public health benefits and dis-benefits of these environments. Data processing will be in connection with three sub-projects of the NIHR-funded Health Protection Research Unit (HPRU) on Environmental Change and Health, which is conducted in collaboration with the Met Office, London School of Hygiene and Tropical Medicine and University College London (partner organisations in the HPRU).
The projects are linked to Theme 3 of this HPRU – ‘Health and the Natural Environment’ - and are all led by staff at the European Centre for Environment and Human Health (www.ecehh.org) at University of Exeter Medical School. Specifically, the data processing aims to:
1) improve understanding of the relationships between exposure to land cover types and to allergenic pollen and other air pollutants, and allergic disease and respiratory health outcomes (Project 3.3);
2) improve understanding of the relationships between residential area exposure to green space and other natural environments, and mental and physical health and well-being outcome measures (Project 3.5); and
3) improve understanding of the potential human health impacts of harmful algal bloom (HAB) occurrences, including on respiratory, digestive and neurologic illnesses (Project 3.7).
In all three cases, the objective will be to develop an understanding of the health implications of environmental factors, especially in relation to climate change.
The objectives of Project 3.3 are linked to another Theme 3 project of this HPRU (Project 3.4) led by staff at the Met Office which aims to produce spatial-temporal models of the distribution of allergenic pollens across the UK. The objectives of Project 3.5 build on earlier work carried out by the current team and financed by the ESRC under the Secondary Data Analysis Initiative (Award No. ES/K002872/1) in which environmental exposure variables were derived for Lower-level Super Output Areas using GIS and relationships to health and wellbeing measures were examined in Census 2011, the British Household Panel Survey and the UK Household Longitudinal Survey.
The objectives for processing HES data in relation to Project 3.5 also relate to data analysis which will be carried out using data from UK Biobank as part of this same HPRU project. The objectives of Project 3.5 also link to the HPRU Theme 2 projects (Healthy Sustainable Cities). The objectives of Project 3.7 are linked to a larger collaboration between the University of Exeter Medical School; the UK Met Office; Public Health England; Centre for Environment, Fisheries and Aquaculture Science (CEFAS); and the Scottish Association for Marine Sciences (SAMS).
The wider collaborations use climate projections to explore likely changes in occurrences of HABs driven by future marine environmental conditions and is funded by MRC, NERC and MEDMI in addition to NIHR HPRU on Environmental Change and Health. Project 3.7 also builds on over 2 decades of work by Professor Fleming on the exposures and health effects of HABs in the US and globally.
The work will provide high quality scientific evidence to help Public Health England implement effective public health interventions to reduce the burden of ill health associated with unsustainable development, more specifically the health protection activities outlined in UK’s National Adaptation Programme (2013) and the Sustainable Development Strategy for the Health and Social Care System (2014-2020). The work will also produce research of relevance to other government departments, including DEFRA, EA and CEFAS regarding the health co-benefits of environmental policies (particularly adaptation to and mitigation of climate change) and the protection of the natural environment.
Analyses related to the HPRU Project 3.3 are conducted in collaboration with the Met Office (a partner organisation in the HPRU), and aim to contribute to the knowledge base required for genera-specific allergenic pollen forecasts which, for example, would potentially help the UK's c.5 million asthma sufferers in the management of their condition.
Project 3.5 aims to contribute to the knowledge base concerning the potential health and wellbeing effects for both physical and mental health benefits of interacting with the natural environment, research that might decrease the impacts of obesity, cardiovascular disease, diabetes and mental health issues on the NHS and the economy. Work in this area already conducted by the current team was discussed as being a key strand of evidence in the Parliamentary Office of Science and Technology POSTnote ‘Urban Green Infrastructure’ (#448) and was presented orally at the POSTnote launch in the House of Commons in May 2014.
Project 3.7 aims to explore if HABs (which have been shown to exacerbate asthma and other respiratory diseases for example in the US) are having any direct impacts on respiratory, digestive and/or neurologic health in the UK. The project aims to contribute to the knowledge base required to identify potentially vulnerable human populations for targeted interventions; and to model potential provision of early HAB warning systems.
Journal papers and associated conference presentations will be the primary outputs. Results will concern samples in large geographical areas, such as admissions for clusters of ICD10 codes in England and in Greater London; data published will be sample means etc., and no data on specific observations of individuals or small groups of specific observations of individuals will be published. An initial paper on pollen and A&E hospital admissions (related to Project 3.3) will be submitted by May 2016; an initial paper on land cover and physical health (related to Project 3.5) will be submitted by December 2016; an initial paper on valuation of land cover for health co-benefits (related to Project 3.5) will be submitted by December 2016; an initial paper on health impacts of harmful algal blooms (related to Project 3.7) will be submitted by December 2017.
Any data included in the outputs will be aggregated with small numbers suppressed in line with the HES Analysis Guidelines.
Further papers as well as presentations will follow from all 3 projects.
The initial processing activity is the same for all three projects and requires data at the individual level. Individual level hospital episode data will be supplied by the HSCIC to the University of Exeter and it will then be linked to residential area environmental factors through the non-sensitive Lower-level Super Output Area (field soal). Area level data to be linked on soal include: Generalised Land Use Database; Land Cover Map 2007; rural-urban classification; tree cover density; coastal proximity; distance to inland waterways; daily, monthly and annual mean exposure to air pollutant concentrations, temperature and rainfall; smoking rates estimated at Postcode District and distributed to soal; genera-specific pollen concentrations; oceanographic data; HAB data, both organisms and toxin levels. This linkage activity will involve no data transfer flows (no data will be supplied to HSCIC for linkage) since the area level environmental datasets are stored on the same secure server that the HES data will be stored on. These environmental datasets are publicly available, or have been derived by the University of Exeter from publicly available sources, or are available for this research under a user agreement with the Met Office; or are made available for this research by CEFAS and SAMS.
The data are physically stored on a secure server located in a University of Exeter secure datacentre. The server, which operates Active Directory Kerberos authentication, will be accessed for the linkage activity and for all other processing activities from the European Centre for Environment and Human Health, University of Exeter Medical School, Knowledge Spa, Royal Cornwall Hospital, Truro. No data supplied by the HSCIC will be shared with or accessible to third parties or used for any purposes outside of the 3 projects outlined above.
Post-linkage processing activities in relation to Project 3.3 (see above) will follow two methodologies which respectively address the health impacts of relatively dynamic and relatively time-invariant (or 'slowly changing') environmental exposures. In the first case, time series regression will be used to analyse daily counts of episodes of potentially allergic diseases against daily variations in environmental airborne exposures, adjusting for seasonal and long-term trend. For example, daily A&E admission counts for respiratory conditions and for allergy, and daily In-patient admission for asthma, will be regressed against current daily (and lagged daily) pollen, weather and air pollutant measures. In the second case, area level standardised period prevalence rates will be calculated by aggregating daily episode data to period, and rates will be regressed against single time point measures of environmental exposures, and against period mean measures of time varying factors. For example, area A&E admission rates 2007/8-2013/4 for respiratory conditions will be regressed against area green space as measured in 2005, social deprivation as measured in 2011, and area mean pollutant concentrations over the period.
Processing activities in the analysis phase in relation to Project 3.5 (see above) will mainly examine relationships between relatively time-invariant environmental exposures and area episode rates using the same approach described above. Research hypotheses on environment/health relationships will assume mechanisms mediated by both the engagement in physical activity (which is stimulated by environmental settings), and cognitive attention restoration (which is stimulated by environmental settings). For example, area level standardised period prevalence rates for in-patient admissions for diseases related to circulatory health will be regressed against single time point measures of environmental exposures, and against period mean measures of time varying factors.
Processing activities in the analysis phase in relation to Project 3.7 (see above) will use time series and cross-sectional regression models to explore how time varying marine environmental factors (such as nutrient loading, temperature and salinity) as well as identified HAB blooms in marine and freshwaters relate to diseases possibly associated with HAB occurrences, including respiratory, digestive and neurologic illnesses, after controlling for exposures to non-time varying environmental factors.
For clarity, all data analyses in connection with the three projects described will either be time series, using days, or aggregations of days, as the unit of analysis, or will be ecological, using geographical areas as the unit of analysis, and in both cases results will be estimations of rates of events associated with environmental conditions. However, data is required at the individual level, rather than at any aggregated level, in order to make possible the linkage of spatial-temporal environmental data to hospital episodes. Published outputs will not specify any information about individuals, nor about groups of individuals, but will be limited to abstracted statistical associations between environmental factors and rates of hospital episodes, and to whole sample descriptive statistics without geographic disaggregation.