Trauma and comorbidity of psychosis with other psychiatric disorders

This Data Sharing Agreement permits the retention and processing of the data provided under previous iterations of this Agreement for an interim period. This is a pragmatic approach to provide an active Agreement whilst enabling the University of Sheffield to complete the necessary actions to enable a subsequent application to extend the Agreement meeting all applicable data sharing standards as published in NHS Digital’s website (see: https://digital.nhs.uk/services/data-access-request-service-dars/dars-guidance).

The University of Sheffield requires the APMS dataset for use in a rolling programme of research which aims to address two scientific questions:

Research question 1: What is the comorbidity between psychosis and other psychiatric conditions, especially bipolar disorder, post-traumatic stress disorder and autism.

Research question 2: What is the relationship between early traumatic experiences, other social and demographic characteristics and the symptoms of psychosis (schizophrenia and bipolar disorder)?

Research question 1 arises from the observation that many psychiatric disorders are comorbid (that is, that diagnoses overlap to an extent that conditions that are usually thought as separate may in fact be different manifestations of the same underlying conditions). There is already evidence that this is the case for ‘schizophrenia’ and ‘bipolar disorder’ (Tamminga et al., 2014) and that ‘autism’ may share some common genetic (Owen, 2012) and psychological mechanisms with psychosis (Craig, Hatton, & Bentall, 2004).

Research question 2 arises as a consequence of previous work showing that trauma in childhood is a strong predictor of severe mental illness (Varese et al., 2012). There is some evidence that specific psychotic symptoms are related to specific childhood adversities e.g (Bentall, Wickham, Shevlin, & Varese, 2012) but this has been disputed (van Nierop et al., 2014).

The data from the Adult Psychiatric Morbidity Survey 2014 (APMS2014) includes variables of childhood trauma, bipolar symptoms, autism, and psychotic symptoms collected from a large representative UK sample, so it will allow these issues to be investigated in the UK population. The data subjects are adults aged 16 or older who contributed to APMS2014.

The analyses of the data will be carried out in accordance with Article 6(1)(e) and 9(2)(j) of the GDPR - the processing is necessary to perform a task in the public interest. In order to inform the treatment of psychiatric disorders, and to develop a principled approach to public mental health, it is necessary to identify how different social determinants are associated with psychiatric disorders. This problem is made difficult by disputes about how psychiatric disorders should be optimally classified.

Ethics approval is not required for secondary analysis of pseudonymised epidemiological datasets.

The applicant on behalf of the University of Sheffield has been conducting research with the aim of answering the two questions above since 1985. Different analyses, utilising new research techniques and methodologies as they evolve, will be undertaken to answer the above questions.

The University of Sheffield conducts independent research but collaborates with a network of other Universities (specifically the Universities of Ulster, Manchester and Liverpool) to share findings and discuss approaches, priorities, etc. Outputs from the processing of the APMS data may be shared with these collaborating organisations but such outputs will contain only aggregated data with appropriate small number suppression.

The University of Sheffield is the sole data controller with sole autonomy for determining the purposes for processing the APMS data and the manner of processing. The data will only be processed by the applicant and his up to 6 PhD students, all of whom are personnel of the University of Sheffield and trained in Data Security. Any postgraduate student using the data will only be permitted to do so under close supervision of the applicant, and only for the purposes indicated in the application.

No other organisation will process the data.

The APMS dataset will be received in a pseudonymised form so there will be no storage of directly identifiable data at any point. Data will be securely stored by the University of Sheffield, and accessed only by authorised personnel. No data will be linked to record patient level data, nor will it be passed onto other organisations. There will be no requirement or attempt to re-identify individuals from the data. Standard statistical and advanced analyses (e.g. networked analyses) will be conducted in SPSS, R or other appropriate statistical packages. Only aggregate data will be used in research reports.

The data will only be permitted to be stored and analysed on university computers. In the event of a student wishing to use the data, they will not be allowed to save it to personal laptops or desktop computers. It is possible that they will seek to analyse the data at their homes, in which case they will be issued with a university-owned laptop computer specifically for this purpose. The students will be asked to undertake not to share the data with anyone else, and not to transfer the data to an unauthorized computer. The files will be password protected. On completion of their projects, students will be required to delete the data from the computer that has been used by them, and checks implemented to ensure that this has been done. It is important to note that the information contained in the APS2014 dataset does not contain any information that would allow any participant to be identified in any case (the data is anonymized).

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).

In order to protect patient confidentiality in publications resulting from analysis of APMS data, researchers will:

• Guarantee that any outputs made available to anyone other than those with whom this agreement is made, will meet required standards, including the guarantee, methods and standards contained in the Code of Practice for Official Statistics and the ONS Statistical Disclosure Control for tables produced from surveys;

• Apply methods and standards specified in the Microdata Handling and Security Guide to Good Practice for disclosure control for statistical outputs.

All outputs will only contain data that is aggregated in line with NHS Digital guidelines.

The data was collected with the sole purpose of enabling researchers to conduct epidemiological research. Use of the data has led to one high-impact research publication, but several of the usages envisaged in the original application require more time.

The research has shown a strong association between autistic traits and risk of psychotic symptoms. In 2021, the findings were published in a high-impact journal: Psychological Medicine: Robust association between autistic traits and psychotic-like experiences in the adult general population: epidemiological study from the 2007 Adult Psychiatric Morbidity Survey and replication with the 2014 APMS.

Together with colleagues and postgraduate students, the researcher will write research papers describing the findings. The first paper on co-morbidity between psychotic symptoms and autism, was published in 2021. Analyses using more advanced approaches such as network analysis are currently being conducted with postgraduate students, and will probably be complete by early 2023. The dataset will also be used to address other questions about the social determinants of psychosis and bipolar disorder during 2023-4. This is an ongoing programme of work which will yield periodic outputs. These will include keynote presentations at relevant national and international conferences and impact journals.

The research will contribute to improving understanding of the structure of psychiatric disorders (e.g. whether psychosis and autism are separate conditions) and of social determinants of severe mental illness. This will inform the treatment of psychiatric disorders and assist in developing a principled approach to public mental health. There will be implications for clinical practice (psycho-diagnostics) and social policy (public mental health) but no set target date for achieving these benefits.

Current National Institute for Health and Care Excellence (NICE) recommendations for cognitive behaviour therapy as a primary psychological treatment for severe mental illness are based on the results of clinical trials and other research in this field and including work which has been undertaken by the applicant. Further studies of the relationship between attachment difficulties and complex trauma are amongst NICE’s current recommendations for research.

The research showed that individuals who scored highly on a measure of autistic traits (as measured by the Autism Questionnaire) had a high risk of meeting the criteria of probable psychosis (as measured by the Psychosis Screening Questionnaire) and, moreover, also a high risk of experiencing individual psychotic symptoms such as hallucinations and delusions (again as measured by the ASQ). Moreover, there was a dose-response relationship between autistic traits and probable psychosis (at the highest quartile of autistic traits, the odds ratio for probable psychosis was 22.5). This is the first time that this association has been demonstrated in an adult general population dataset. These findings are important because it has long been suspected that there are common mechanisms in autism and psychosis. Further analyses are likely to be conducted to find out whether this association could be accounted for by a common exposure to childhood trauma, which is also measured in APMS2014

Understanding the mechanisms involved in psychosis is vital because psychotic disorders (schizophrenia and related conditions) are associated with reduced life expectancy and cause a high social and economic burden to the country. Existing therapies (antipsychotic medication and cognitive behaviour therapy) are of limited effectiveness. Once the mechanisms of disease-causation are understood, it will hopefully be possible to develop targeted therapies of greater effectiveness.