CambridgeshireThe current project aims to link HES/Mortality records for individuals who have used CPFT services, and compare the health outcomes to a control population and Peterborough NHS Foundation Trust mental health record linkage with the NHS Hospital Episode Statistics and Mortality records

Cambridgeshire and Peterborough NHS Foundation Trust (CPFT) requests Hospital Episode Statistics (HES) and Mortality data for the purpose of research in the public interest.

Data from the CPFT Research Database has established that patients with a diagnosis of schizophrenia died on average 16.8 years younger than patients known to CPFT without such a coded diagnosis (unpublished data 2005–12, CPFT). This is concordant with data from elsewhere in the UK (Chang et al., 2011) and represents a major public health crisis. Improvement in the physical health of people with mental disorders is highlighted regularly in Government policy (e.g. 'Closing the gap: priorities for essential change in mental health’, 2014) and the monitoring of physical health outcomes is increasingly becoming a metric for mental health Trusts, as well as for national structures such as the National Mental Health Dementia and Neurology Intelligence Networks. However, relatively little is known about the health conditions underlying health inequalities, and the associations between physical and mental health, although this knowledge is clearly important in order to develop interventions to improve the situation. The over-arching objective of this research programme is to provide information that will assist in narrowing the mortality and physical morbidity disadvantage experienced by people with mental disorders.

The lawful basis for processing personal data under the UK GDPR 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 lawful basis for processing special category data under the UK GDPR is:

and Article 9 (2) (j) - processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) based on Union or Member State law which shall be proportionate to the aim pursued, respect the essence of the right to data protection and provide for suitable and specific measures to safeguard the fundamental rights and the interests of the data subject.

The objective of the current data linkage project is to create a research resource to be used for research projects aiming to investigate physical health outcomes (including mortality) and receipt of health care in people with mental disorders attending secondary mental health care services provided by CPFT.

CPFT is an NHS Trust providing secondary mental health care and community services to patients in Cambridgeshire and Peterborough with a catchment area of estimated 1 million residents. CPFT Research Database is a deidentified version of structured and unstructured clinical data from all CPFT patients referred to the services from 2005 onwards (estimated 250,000 individuals), except those who actively opt out of research.

In order to de-identify the patient records, a freely available software called Clinical Records Anonymisation and Text Extraction (CRATE) has been developed, which allows for de-identification of both structured and free-text clinical material for research (Cardinal, 2017, PubMed ID 28441940). Data available for research includes structured information (e.g. data entered by clinicians from drop down lists) such as past and present ICD-10 psychiatric diagnoses, appointments attended, routine outcome measures (e.g. Health of the Nation Outcomes scales) and risk assessment details including risk of self-harm, self-injury, and aggression to others. Natural language processing software is used to enhance these data by extracting information predominately found in clinical progress notes and correspondence that might include more detail about family mental health problems, substance misuse, pharmacotherapy, and symptoms. One of the primary aims of the CPFT Research Database is to use the de-identified data for the purposes of epidemiological research. The database has been made available within secure NHS computing facilities to approved CPFT researchers. Approved researchers using the data are (and will be) required to have a CPFT substantive or honorary contract, letter of access, or a research passport. REC approval for CPFT Research Database was granted in 2012 (12/EE/0407) and renewed in 2022 (17/EE/0442).

Funding for the CPFT Research Database has been provided through NIHR Cambridge Biomedical Research Centre, through the NIHR Clinical Research Network, through CPFT core funding and through the UK Medical Research Council [MRC] Mental Health Data Pathfinder award, ref. MC_PC_17213, Cardinal et al. The current CPFT-HES/Mortality linkage is funded by the MRC Mental Health Data Pathfinder award, ref. MC_PC_17213.

The current data linkage project will contribute towards the following aims set out in the MRC Mental Health Data Pathfinder proposal:

1) Consolidating and extending the reach of the CPFT Research Database in a national context. Data from CPFT Research Database will be linked to other national (and local) data sets, with the aim of creating integrated anonymised healthcare data set to be used by researchers across the UK (and beyond). The CPFT-HES/Mortality dataset will be used for epidemiological research purposes. Those wishing to apply for access will be required to have a contractual relationship with CPFT and will need to submit an application to the CPFT Research Database Oversight Committee for the use of data. Approved researchers will not have access to any identifiable information, and the researchers will need to access the data only through the CPFT network (i.e. none of the data will leave CPFT secure network).

2) Tackling the mortality gap in serious mental illness. Data from the CPFT Clinical Database have previously confirmed what others have reported: life expectancy is reduced by >15 years in CPFT service users with serious mental illness. The causes of this need to be understood in greater detail, along with the need for methods to predict mortality so as to provide early warning and be able to intervene better. The data provided by the linkage and de-identification tools and sophisticated machine learning algorithms will be used to develop new predictive models of outcomes in serious mental illness and to develop new ways of working between mental health services and industry whilst maintaining confidentiality of NHS records.

The data subjects for the purposes of this application are individuals who:

i. have received treatment from the Trust since 2005 and who have not notified CPFT that they wish to opt out of having their data collected and/or linked, and/or

ii. individuals who are or have been resident within CPFT’s geographical catchment area (Cambridgeshire and Peterborough) since 2005 and attended hospital for any reason whilst resident in that catchment area. The individual is defined as a resident in CPFT's catchment area if they are registered with a GP surgery that is part of the Cambridgeshire and Peterborough Clinical Commissioning Group (using the data field GP_PRACTICE=06H). Data from people who have told CPFT they wish to opt out, or who have opted out nationally via the NHS National Data Opt-Out will not be used.

Data spanning the past 15 years is requested to maximise the representativeness of the project sample to a general clinical population and to minimise the risk of de-anonymisation through small cell sizes. This is because the data is likely contain a number of potentially rare exposures and outcomes which include combinations of psychiatric co-morbidity, treatments, and adverse health events (such as suicide attempt or completed suicide).

CPFT is the sole data controller who also process data. Individuals substantively employed by organisations other than CPFT who wish to process the data would do so under honorary contracts or letter of access with CPFT and only for purposes and in a manner CPFT has authorised. The number of honorary contracts/letters of access are estimated to be less than 10 per year. As part of its process for determining the purposes for which data shall be used (within the scope defined in this Agreement), CPFT has appointed the CPFT Research Database Oversight Committee which includes individuals who are not employees of CPFT. However, CPFT has sole autonomy for determining the purposes and the manner in which the data under this Agreement shall be used.

The CPFT Research Database Oversight Committee will consider research proposals to use the linked dataset. The Oversight Committee consists of

(i) service user representation,

(ii) carer representation,

(iii) CPFT Research Database developer (chair),

(iv) CPFT Research and Development Governance Officer,

(v) Clinical research representative,

(vi) Patient and public involvement lead,

(vii) CPFT Research Database manager.

Applications must meet the following criteria:

1) All studies have a purpose in the public interest in the area of medical research. Specifically for the current data CPFT-HES/Mortality data linkage project, all requests need to be within the scope of investigating the associations between specific mental disorders in secondary mental healthcare and physical illnesses or mortality.

2) Requests for data are proportionate, and studies using such data are conducted with due regard for the laws, principles, and methods governing access to sensitive patient-identifiable data (or de-identified versions thereof), including technical security requirements and the requirement for data minimization;

3) Data queries do not carry a significant potential for inadvertent re-identification (e.g. through extremely specific queries and/or relating to very rare diseases;

4) Requests for data involving multiple underlying approvals (e.g. via the Clinical Data Linkage Service; CDLS) meet the conditions of all relevant approvals;

Additional considerations for data linkage studies are the following:

1) No patient shall be re-identified on the basis of CPFT data linked with external sources.

2) Specific data subsets will be created for linkage studies, containing only the data required.

3) De-identified unstructured free text shall not be provided for linkage studies (though structured data, e.g. derived from free text via automated natural language processing, may be, if appropriate).

4) The CPFT Research Database/CDLS shall not be used to link data from external sources without the agreement of the data owner/supplier and relevant overseeing authority (e.g. REC). That is, if CPFT data is linked to data source A and CPFT data is linked separately to data source B, then A shall not be linked to B unless this has been specifically approved.

Additionally:

(a) Researchers using the data are (and will be) required to have a CPFT substantive or honorary contract, letter of access, or a research passport;

(b) researchers must have had appropriate information governance training from CPFT.

The number of honorary contracts is currently estimated to stay below 10 per year.

All research projects are carried out within the CPFT and the linked data will remain within the CPFT NHS firewall at all times.

Approval is only sought for use of linked HES and mortality data incorporating the CPFT linkage (i.e. not for analysis of HES data alone). The studies using the linkage will adopt the following designs:

1. Investigations carried out on HES data from CPFT catchment area, identifying a HES-derived outcome and comparing its occurrence between people with/without a given mental disorder in order to derive standardised morbidity ratios (for example, investigating respiratory disease admissions in people with learning disability compared to the local population);

2. Investigations restricted to people with a given HES-derived outcome and comparing subsequent events between people with/without a given mental disorder (for example, further analyses of people with/without a learning disability who have a respiratory disease admission, comparing duration of hospitalisation and risk of readmission between the two groups);

3. Investigations restricted to people with a given mental disorder investigating one or more HES-derived outcomes in relation to CPFT-derived information (for example, investigating the relationship between mental health symptom profiles and physical health events in people with severe mental illness);

4. Investigations primarily carried out using CPFT data, where HES-derived information is used to provide supplementary information (for example, the ability to adjust for serious physical illness in a number of analyses). This includes the use of mental healthcare data contained on HES for residents in the CPFT catchment to capture mental health service use by providers other than CPFT (e.g. out-of-catchment hospitalisations);

5. Investigations primarily carried out using CPFT data where a HES outcome is used to define the sample (for example, a series of analyses investigating medication and health outcomes before and after the diagnosis of a physical health condition in individuals with pre-existing severe mental illness).

The request also covers Mortality data linked to CPFT Research Database records, in order to describe the mortality rates in people with mental disorders and investigating specific causes of death, as well as other relevant outcomes (e.g. place of death). The analyses may be carried out either within-group (comparing different characteristics as predictors in a survival analysis), between-group (comparing the mortality rates across groups with different mental health conditions), and/or using national data for standardisation.

The data will only be used for purposes relating to the provision of healthcare or the promotion of health in line with the requirements of the Health and Social Care Act 2012 as amended by the Care Act 2014.

The proposed data linkage projects have been approved by the Cambridge Central REC (17/EE/0442, IRAS 236644, Amendment 1) and the Confidentiality Advisory Group (20/CAG/0087).

All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” as defined within the Data Sharing Framework Contract i.e.: employees, agents and contractors of the Data Recipient who may have access to that data).

CPFT would send the following patient identifiers to NHS England for identity verification checks:

- NHS number

- Date of birth,

- Postcode,

- CPFT-specific pseudonym (Study_ID; an arbitrary alphanumeric code) .

NHS England would extract HES and Mortality data for those individuals (and those resident in CPFT’s catchment area but not known to CPFT, as controls) and send the HES/Mortality data securely to CPFT with CPFT’s pseudonym. In the case of control individuals, NHS England would supply a pseudonym that is meaningless to CPFT – making those data fully anonymised from CPFT’s perspective (and by definition not linkable to CPFT data since CPFT has no identity information or data for those individuals).

Patient opt-outs are applied prior to each release of data by NHS England.

All supplied HES and Mortality data are held separately by the CPFT Clinical Data Linkage Service (CDLS; ethical approval to set up CDLS has been granted by the Cambridge Central REC) and are only accessible to a restricted number of approved technical support staff.

Researchers do not have access to the raw HES or Mortality data.

The HES and Mortality data will not be linked with patient identifiers from CPFT electronic patient record and no attempt will be made to identify individuals in the data under any circumstances.

Researchers wishing to use the linked data need to gain approval for their projects from the CPFT Research Database Oversight Committee. Additionally, (a) researchers must hold a CPFT substantive contract or honorary contract/letter of access or a research passport; (b) researchers must have had appropriate information governance training from CPFT. See Section 5a for further information on the requirements that need to be satisfied in order to gain approval from the Oversight Committee for the use of linked data.

When an application has been approved by the CPFT Research Database Oversight Committee, technical staff, all of whom are substantive employees of CPFT, will assemble bespoke de-identified linked databases meeting the approved requirements of the research study. These will be held securely in a dedicated controlled research space within the CPFT network. Approved researchers can only access the data within the CPFT network.

All research databases remain within the CPFT firewall at all times on the CPFT network. Removal of data from this environment is expressly forbidden other than in the form of aggregated summary data with small numbers suppressed in line with the HES Analysis Guide.

For each research database created, a different encoded identifier variable (anonym) is assigned, meaning there are no common identifiers or pseudo-IDs across different databases, making it impossible for researchers to link their database with source CPFT or HES/Mortality data. This system uses a one-way encryption method following which anonyms cannot be reverse-engineered.

At the completion of research projects, the databases used are removed from accessibility to approved researchers and destroyed once the DSA comes to an end.

In addition to the CPFT-HES/Mortality data linkage, CPFT has obtained approval from the Cambridge Central REC (17/EE/0442, IRAS 236644, Amendment 1) to carry out the following data linkage projects: 1) CPFT and National Pupil Database (NPD) record linkage; 2) CPFT and National Cancer Registration and Analysis Service (NCRAS) record linkage. All supplied datasets are held separately by the CPFT Clinical Data Linkage Service (CDLS). HES/Mortality data will only be linked to CPFT data, and this will take place on a project-specific basis. There will be no further linkages between HES/Mortality data and other datasets.

DATA MINIMISATION:

This data request attempts to balance the following principles:

1) principles of data minimisation;

2) maximise the representativeness of the project sample to a general clinical population, while minimising the risk of de-anonymisation through small cell sizes.

Only data adequate, relevant and limited to what is necessary will be requested, i.e. A&E, Inpatient, Outpatient, and Mortality data. Irrelevant and excessive data (e.g. neonatal records) will not be requested.

As the CPFT holds electronic patient records from 2005 onwards, the current data request will cover the same years (2005 to present).

The control sample (people with HES/Mortality records, but no CPFT records) are narrowed geographically to patient records within the CPFT's catchment area (as defined by the data field GP_PRACTICE=06H) and will be limited to the same time period (2005 – present).

The primary output of the linkage is the production and maintenance of a research resource that will be used in informative research analyses. The results from the analyses will be published in peer-reviewed journals and other standard routes of academic dissemination (e.g. conference presentations).

All secondary outputs (whether tables or visuals) will only include aggregated data in line with the HES Analysis Guide.

CPFT expects that a minimum of three research papers would be published per year using the proposed data linkages. The content of the papers will likely focus on the following research questions:

1. What is the incidence of specific physical illnesses (ischaemic heart disease, stroke, accidents requiring inpatient admission, respiratory disease and diabetes) in people with the mental disorders of interest (e.g. schizophrenia, dementia) and how do these compare with what is expected in the control population (people without mental health disorders)?

2. Are there specific sub-groups of people with mental disorder who are at higher risk than others (for example, by virtue of symptom profile or medication use) of adverse health outcomes?

3. Are there differences in processes of healthcare (length of stay, readmission likelihood) between people with mental disorders of interest and controls from the same source population?

4. What are the levels of acute care admission cost associated with the mental disorders of interest?

5. What are the mortality rates, and the causes of mortality in people with mental health disorders of interest, and how do these compare with what is expected in the control population?

6. What factors in the mental health data set (e.g. diagnosis, psychotropic medication) predict outcomes relating to self-harm and suicidality involving attendance at the emergency department?

Most of the work using the linked information is best classified as research. However, where an investigation is primarily evaluating these outcomes in a single service, evaluating its performance with a view to improving this, it is likely to be more appropriately categorised as clinical audit.

Additionally, classical statistical predictors and machine-learning algorithms to predict premature mortality or major morbidity early will be developed based on these data, with the aim of eventually developing risk prediction/decision support tools for patients and clinicians. The anticipated endpoint is a predictive algorithm for mortality in serious mental illness. The output was published in 2021 (PubMed ID 34880262).

Another aim of this project is to develop a data query platform suitable for use, in whole or in modular fashion. The linked data was ready to be used by approved CPFT researchers in March 2022.

Individual research teams in CPFT and partner research organisations will publish the results of their research in peer-reviewed journals and through their public information systems.

CPFT will provide regular updates on research involving the CPFT Research Database to those who have actively chosen this.

CPFT will organise patient and public involvement programmes in Cambridge, where the ongoing projects, and the results from previous projects will be introduced.

The data being requested will only be used for the purpose described. Any proposed changes will be submitted to NHS England for approval before implementation.

Publication targets will clearly depend on the nature of individual findings and the potential audience envisaged. Where possible, CPFT will target general medical and/or public health journals with a broad audience, because analyses are likely to cross disciplines; however, we specialist journals within the mental health field as well as the individual medical specialties implicated will be considered. Dissemination at national and international conferences will adopt a similar strategy of aiming for as broad as possible a reach. They will include mental health focused meetings such as the Royal College of Psychiatrists and European Psychiatric Association congresses, and psychiatric epidemiology meetings such as the International Federation of Psychiatric Epidemiology (IFPE) but CPFT will also seek presentations at medical specialty conferences where results have relevance to those audiences, as well as meetings where commissioners are likely to be represented.

The research aims to produce findings which can directly inform healthcare policy and treatment guidelines. Timescales for this type of impact are likely to be relatively rapid, ideally within 5-10 years of the output. Result summaries will be fed back to relevant organisations such as the National Institute for Health and Care Excellence (NICE), and promoted locally with the aim of directly impacting NHS policy and current patient care. This would be assisted through support of local collaborators with expertise in research dissemination at the University of Cambridge.

Periodic updates on the Clinical Informatics for Mind and Brain Health (CLIMB) webpage will provide up-to-date information to interested parties.

Relevant academic papers:

Cardinal RN (2017) Clinical records anonymisation and text extraction (CRATE): an open-source software system. BMC Med Inform Decis Mak 17:50.

Chang C-K, Hayes RD, Perera G, Broadbent MTM, Fernandes AC, Lee WE, Hotopf M, Stewart R (2011) Life expectancy at birth for people with serious mental illness and other major disorders from a secondary mental health care case register in London. PLoS ONE 6:e19590.

European Parliament and Council (2016) Regulation (EU) 2016/679 (General Data Protection Regulation). Official Journal of the European Union L119:1–88. Fok ML-Y, Hayes RD, Chang C-K, Stewart R, Callard FJ, Moran P (2012) Life expectancy at birth and all-cause mortality among people with personality disorder. J Psychosom Res 73:104–107.

Fok ML-Y, Stewart R, Hayes RD, Moran P (2014) Predictors of natural and unnatural mortality among patients with personality disorder: evidence from a large UK case register. PLoS ONE

9:e100979.

Fok M, Hotopf M, Stewart R, Hatch S, Hayes R, Moran P (2014) Personality disorder and self-rated health: a population-based cross-sectional survey. J Pers Disord 28:319–333.

Goldner EM, Hsu L, Waraich P, Somers JM (2002) Prevalence and incidence studies of schizophrenic disorders: a systematic review of the literature. Can J Psychiatry 47:833–843.

Hayes RD, Chang C-K, Fernandes AC, Begum A, To D, Broadbent M, Hotopf M, Stewart R (2012) Functional status and all-cause mortality in serious mental illness. PLoS One 7:e44613.

Patel R, Jayatilleke N, Broadbent M, Chang C-K, Foskett N, Gorrell G, Hayes RD, Jackson R, Johnston C, Shetty H, Roberts A, McGuire P, Stewart R (2015) Negative symptoms in schizophrenia: a study in a large clinical sample of patients using a novel automated method. BMJ Open 5:e007619.

Saarni SI, Viertiö S, Perälä J, Koskinen S, Lönnqvist J, Suvisaari J (2010) Quality of life of people with schizophrenia, bipolar disorder and other psychotic disorders. Br J Psychiatry 197:386–394. UK (2012) Health and Social Care Act 2012. Available at: http://www.legislation.gov.uk/ukpga/2012/7/contents.

UK (2014) Care Act 2014. Available at: https://www.legislation.gov.uk/ukpga/2014/23/contents/enacted.

UK Department of Health (2014) Closing the gap: priorities for essential change in mental health. Available at: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/281250/Closing_the_gap_V2_-_17_Feb_2014.pdf.

UK National Institute for Health and Care Excellence (2013) Guide to the methods of technology appraisal 2013. Available at: https://www.nice.org.uk/process/pmg9/chapter/foreword [Accessed June 27, 2020].

US Central Intelligence Agency (CIA) (2013) The World Factbook. Langley, Virginia: CIA. Available at: https://www.cia.gov/library/publications/the-world-factbook/.

Wu CY, Chang CK, Hayes RD, Broadbent M, Hotopf M, Stewart R (2012) Clinical risk assessment rating and all-cause mortality in secondary mental healthcare: the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLAM BRC) Case Register. Psychol Med 42:1581–1590.

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Update 18/9/2023: We are regrettably behind on the publication track due to COVID-19-related disruption and research staff turnover. However, related work in our teams (via a different DARS authorization) has been published, e.g. PubMed ID 37208979, with others in preparation.

Update March 2024

Data has recently been supplied from NHS England and there have been some delays at our end, for which we apologise; no publications have arisen from this linkage to date.

At present, the CPFT Research Database includes information only from CPFT clinical records. A number of important studies are intrinsically limited by this, hindering research for patient benefit. For example, work with CPFT Research Database has established that patients with a diagnosis of schizophrenia died on average 16.8 years younger than patients known to CPFT without such a coded diagnosis (unpublished data 2005–12, CPFT). This is concordant with data from elsewhere in the UK (Chang et al., 2011) and represents a major public health crisis – extrapolating to ~72,000 life-years lost per year in the UK, and to a theoretical UK willingness to pay ~£1.8bn/y to solve this tragedy. (Assumptions: UK population 63m and birth rate 12.3/1000 [US Central Intelligence Agency, 2013]; schizophrenia lifetime prevalence 0.55% [Goldner et al., 2002]; quality of life 0.84 [Saarni et al., 2010]; £30k per quality-adjusted life-year [UK National Institute for Health and Care Excellence, 2013].) However, relatively little is known about the health conditions underlying these inequalities, although this knowledge is clearly important in order to develop interventions to improve the situation.

The over-arching objective of this research programme is to provide information that will assist in narrowing the mortality and physical morbidity disadvantage experienced by people with mental disorders. Improvement in the physical health of people with mental disorders is highlighted regularly in Government policy (e.g. ’Closing the gap: priorities for essential change in mental health’ 2014) and the monitoring of physical health outcomes is increasingly becoming a metric for mental health Trusts, as well as for national structures such as the PHE Mental Health Intelligence Network.

The research CPFT enables will provide novel and important information to inform policy initiatives. Physical health disadvantages are likely to cross multiple disorders and multiple levels of morbidity: from mortality to nonfatal conditions, and from the individual impact of serious health conditions to the wider economic impacts of increased secondary care use, longer hospitalisations, and increased risk of readmission. There is therefore a need for a coordinated series of analyses to inform on specific areas of inequality in order to target interventions to improve health.

In order to improve morbidity and mortality through health and social care interventions, it is important both to have information on the adverse outcomes potentially underlying disadvantages and to be able to characterise groups most at risk of these outcomes.

The HES and mortality outcomes will address the first information need, through the proposed analyses investigating the most common reasons for acute hospitalisation in people with mental disorders, and their relative risk in relation to the local population. The analyses will also cover adverse outcomes during and following hospitalisation such as longer duration of hospitalisation, lower intervention receipt (where intervention coding is available, e.g. for surgical procedures), and higher risk of readmissions with the physical condition in question or due to a recognised complication, as well as mortality for different causes of death.

CPFT (mental healthcare) data will in turn be used to address the second information need - i.e. allowing the definition of mental health characteristics of people most at risk of adverse physical health outcomes. For example, researchers at South London and Maudsley NHS Foundation Trust (SLaM) have demonstrated that all-cause mortality is more strongly predicted by functional impairment than general symptom severity in severe mental illness (Hayes et al., 2012; Fok et al., 2014) and more by clinician-appraised risk of self-neglect than by appraised risk of suicide or violence (Wu et al., 2012). This is important because mental healthcare priorities (on symptom improvement and risk of suicide/violence) to date have not been optimally focused for mortality prevention and there has consequently been a shift in emphasis towards wider health promotion.

In relation to clarifying populations at risk, rapid advances in text-mining and their implementation in CPFT allow detailed information to be gathered for analyses not only on mental disorder diagnoses, demographic factors and service contacts (i.e. what might be available administrative data in any other Mental Health Trust), but also on pharmacotherapeutic and psychotherapeutic interventions, detailed symptom profiles, risk lifestyles (e.g. smoking, illicit drug use), and adverse drug events. For example, ’negative’ symptoms of schizophrenia have been ascertained through text-mining to predict worse mental healthcare outcomes (Patel et al., 2015), but it is not yet known whether and how psychotic symptoms predict physical health outcomes, including mortality. Although symptom profiles are recognised to be important predictors of psychosis outcomes and are the primary focus for mental healthcare interventions, they are ‘invisible’ in routine healthcare data because they are recorded in text rather than structured fields. The proposed analyses against HES and mortality outcomes are thus only possible at the moment in the CPFT and similar resources - hence they are uniquely positioned to provide influential investigations of physical health outcomes in mental disorders at a level of detail which would not be possible through any other route.

The information on physical health outcomes from HES and mortality will allow policies and interventions to be developed and evaluated within acute and primary care services to serve better people with mental disorders, while information from the mental health record is particularly relevant to mental health services in clarifying patient groups who are most vulnerable. Dissemination targets are therefore broad and cut across primary, acute and mental health care sectors, as well as involving multiple levels from clinicians and Trusts delivering care to commissioners and NHS structures overseeing this, as well as bodies such as PHE with broader health improvement oversight. Furthermore, audiences differ by age-ranges and disorders of interest (e.g. those interested in physical disorders in young adults with severe mental illness may be different from those interested in the acute care impact of dementia in later life).

EXPECTED BENEFITS UPDATE 2024

Public benefits are already accruing from related but separate linkage work between CPFT and NHS England data under section 251 approval 18/CAG/0015, e.g. a finding of reduced mortality associated with some medications used for Lewy body dementia:

(Chen et al. 2022, PubMed ID 36472984) and an examination of causes of mortality across subtypes of dementia (Kershenbaum et al. 2023, PubMed ID 37208979).

Most benefits are to follow, but CPFT work with NHS England Hospital Episode Statistics has established proximal causes of death in subtypes of dementia (2023) and discovered a protective association between cholinesterase inhibitor use in Lewy body dementia and reduced mortality/shorter duration of hospital admissions (2022).