NHS Digital Data Release Register - reformatted

The University of Manchester

Project 1 — DARS-NIC-147774-MZT95

Opt outs honoured: No - consent provided by participants of research study

Sensitive: Sensitive

When: 2016/09 — 2019/03.

Repeats: Ongoing

Legal basis: Informed Patient consent to permit the receipt, processing and release of data by the HSCIC, Informed Patient consent to permit the receipt, processing and release of data by NHS Digital

Categories: Identifiable

Datasets:

  • MRIS - Cohort Event Notification Report
  • MRIS - Cause of Death Report

Objectives:

Juvenile idiopathic arthritis (JIA) is a chronic disease characterised by the onset of inflammatory arthritis before the 16th birthday. Historically, the treatment of this condition has been limited to non-steroidal anti-inflammatory drugs (eg. ibuprofen), anti-rheumatic drugs, particularly methotrexate (MTX) and corticosteroids. The advent of biologic drugs has revolutionized the treatment of this and other rheumatic diseases. Unlike MTX and other traditional therapies for JIA, which offer general immune suppression in an attempt to control the disease, these new biologic therapies are directed specifically at one specific protein or cell that is felt to be important in driving the arthritis, with a hope of turning off the disease. It was decided to establish the Biologics for Children with Rheumatic Diseases study to monitor the long term safety and efficacy of these new biologic treatments in children with Juvenile Idiopathic Arthritis

Expected Benefits:

This study will document use of biologic drugs in children with Juvenile Idiopathic Arthritis (JIA) and other rheumatic diseases in order to assess their efficacy and safety during routine clinical use. A further aim of the study is to collect samples of DNA (via a blood or saliva sample) of children receiving biologic therapy in the UK to allow us to test whether there are variations in genes which can predict who will respond and who may get serious side effects.

Processing:

This is an observational prospective cohort study to compare the risk of development initially over 5 years, of the endpoint in two cohorts: (i) a group of patients with Juvenile Idiopathic Arthritis newly exposed to a biologic therapy and (ii) A comparison cohort of patients with JIA newly exposed to DMARD therapies (e.g, Methotrexate). We intend to flag both the biologic cohort and the comparison cohort for notification of mortality and cancer registration. A copy of the death certificate will be required for those who die and a copy of the histology for those who develop a malignancy. This will enable us to monitor any adverse events in both cohorts


Project 2 — DARS-NIC-147776-69CX7

Opt outs honoured: No - consent provided by participants of research study (Consent (Reasonable Expectation))

Sensitive: Sensitive

When: 2016/04 (or before) — 2019/03.

Repeats: Ongoing, One-Off

Legal basis: Informed Patient consent to permit the receipt, processing and release of data by the HSCIC, Health and Social Care Act 2012 – s261(2)(c)

Categories: Identifiable

Datasets:

  • MRIS - Cause of Death Report
  • MRIS - Cohort Event Notification Report
  • MRIS - Members and Postings Report

Objectives:

The data supplied by the NHSIC to Salford Royal NHS Foundation Trust will be used only for the approved Medical Research Project identified above

Expected Benefits:

Findings will be disseminated to clinicians through journal publication and presentation at specialist meetings (e.g. annual UKMYONET meeting). Clinician implementation of findings can lead to improved clinical outcomes for patients. Implementation of previously identified clinical association with myositis specific autoantibodies (e.g. Jo-1 association with interstitial ling disease) has led to improved patient outcomes. Improved knowledge of cancer/mortality associations with myositis specific auto antibodies can improve early cancer diagnosis rates and allow implementation of preventative mortality measures in patients with myositis. All patients with myositis (estimated 10,000 people in the UK) will benefit from potential early cancer diagnosis and implementation of measures to prevent early mortality.

Outputs:

A paper has been accepted for publication (in-print) to Rheumatology. This paper utilised cancer data generated from NHS Digital. - Oldroyd, A., Sergeant, J., New, P., McHugh, N. J., Betteridge, Z. E., Lamb, J., Ollier, W., Cooper, R. & Chinoy, H. The temporal relationship between cancer and adult onset anti-transcriptional intermediary factor 1 antibody positive dermatomyositis. Rheumatology. 16 Sep 2018 Several conference abstracts, utilising data from NHS Digital, have been accepted, published and presented at international conferences: - American College of Rheumatology annual scientific meeting 2017 – “Anti-TIF-1 Antibody Positivity Is Associated with a Five-Fold Increase in Cancer Risk in the Idiopathic Inflammatory Myopathies” - British Society of Rheumatology annual conference 2016 – “The Risk of Premature Death of both Cancer Associated and Non-Cancer Associated Myositis in UK Adult-Onset Myositis Patients is Significantly Raised Compared to the General Population” - Annual European Congress of Rheumatology 2015 – “The Standardised Mortality Rate in UK Adult-Onset Myositis Patients is Seven Times Higher than the UK General Population” Further planned analysis will take place to delineate the risk of death premature death and cancer associated with myositis specific autoantibodies. Mortality data from NHS Digital will be utilised. Findings will be submitted for publication in high-impact open-access journals. All outputs, past and present, contain aggregated data in line with NHS Digital guidelines.

Processing:

Under previous iterations of this Data Sharing Agreement, Salford Royal NHS Foundation Trust (SRFT) provided files of identifiers (Patient’s forenames, surnames, date of birth, NHS Number) to the Health and Social Care Information Centre (now known as NHS Digital) for flagging. Prior to this Agreement, a total of 746 individuals had been flagged. Since 2008 NHS Digital has provided linked mortality and cancer data along with the associated participant's forename, surname, date of birth and NHS number to SRFT. SRFT stored the data on a server in the Clinical Science Building on encrypted, password protected Trust computers which can be only accessed at SRFT. This data is accessed only by the study coordinator. A pseudonymised version of this dataset (i.e. containing no patient identifiers other than study ID numbers) is transferred to the University of Manchester where it can be accessed by researchers working on study analysis. Under this Agreement, an additional 550 patients will be sent to NHS Digital and will contain NHS Number, full name, date of birth and pseudonymised study ID. These participants will be flagged and added to the MR1002 cohort that NHS Digital currently hold. Reports containing mortality, cancers, and exits will be sent to SRFT assigned to pseudonymised study ID only and applied to the database at SRFT. The data received from NHS Digital can only be accessed by authorised individuals within SRFT and the University of Manchester for the purposes described, all of whom are substantive employees of one of those organisations. Blood samples from recruiting centres are posted to University of Manchester (UoM) where the samples are processed and stored. UoM carries out genetic analysis on the extracted DNA and then batch transfers the extracted plasma to University of Bath for the purpose of performing antibody analysis. No data from NHS Digital is transferred to the University of Bath. The data has been and will continue to be used to calculate mortality rate in myositis population and compared to norms. Cancer rates were studied in myositis population and compared to norms as well as comparison in between myositis sub groups in order to answer questions such as: ‘Do more people with dermatomyositis develop cancer as a result of cancer associated myositis (CAM) when compared with patients with polymyositis?’ and ’'Is cancer more common in patients with the Tiff 1 antibody in blood sample when compared to absence of tiff 1 antibody?’ Therefore, cancer data from NHS Digital has been linked with antibody data from research blood analysis within the University of Manchester. 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).


Project 3 — DARS-NIC-147811-YTH88

Opt outs honoured: N

Sensitive: Non Sensitive, and Sensitive

When: 2016/04 (or before) — 2017/02.

Repeats: Ongoing

Legal basis: Informed Patient consent to permit the receipt, processing and release of data by the HSCIC

Categories: Identifiable

Datasets:

  • MRIS - Cohort Event Notification Report
  • MRIS - Cause of Death Report
  • MRIS - Scottish NHS / Registration

Objectives:

NOAR started in 1989 and is a long-term study of inflammatory arthritis in the community. The purpose of the Register is to study the natural history of arthritis and to identify genetic and non-genetic factors which may be related to the onset of arthritis, response to treatment, and to long-term outcome. We are also interested in learning more about the effects which arthritis may have on other medical conditions.


Project 4 — DARS-NIC-147916-DPQ3Q

Opt outs honoured: Y, N

Sensitive: Non Sensitive, and Sensitive

When: 2016/04 (or before) — 2018/05.

Repeats: Ongoing

Legal basis: Section 251 approval is in place for the flow of identifiable data, Section 42(4) of the Statistics and Registration Service Act (2007) as amended by section 287 of the Health and Social Care Act (2012)

Categories: Identifiable

Datasets:

  • MRIS - Flagging Current Status Report
  • MRIS - Cause of Death Report
  • MRIS - Cohort Event Notification Report
  • MRIS - Scottish NHS / Registration

Objectives:

Self-harm is a major public health problem, with 220,000 presentations per year at UK Emergency Depts. Reduction in the numbers of suicide following self-harm is an important objective of the National Suicide Prevention Strategy for England, with research showing that suicide prevention is key to achieving this. As well as suicide, self-harm is associated with increased risk of other causes of mortality. Therefore, all-cause mortality is an important outcome to monitor following self-harm.

Expected Benefits:

Data will be stored in locked filing cabinets, or secure standalone server, in secure offices in a secure department accessed by swipe card only. Only research staff from MaSH, with honorary contracts with Manchester Mental Health trust who have been authorised by Dr J Cooper will have access to identifiable data. The data will be stored in accordance with strict security protocols that conform to BS7799 standards and DPA 1998. Electronic files of patient identifiable will be password protected and accessed only by named investigators. Data will be anonymised and transferred to separate database where patients are identified by numerical coding. Named information will be destroyed once analyses are complete.

Outputs:

To investigate mortality as an outcome following self-harm to inform and assess national strategies on self-harm and suicide prevention. Some studies within the mortality investigation will be carried out in Manchester on Manchester data alone, with other studies carried out as part of a multicentre collaborative project. Aims of the multicentre mortality studies are to : determine current risk of suicide following self-harm over time, in gender/age subgroups; identify risk factors for suicide to inform assessment procedures; provide data on risk of death from non-suicidal causes; and provide information on mortality following self-harm in important subgroups highlighted in the National Suicide Prevention Strategy.

Processing:

Patient data collected on approx 20,000 persons by the Manchester Self- Harm Project from 1997 to 2007 will be submitted to MRIS, to flag for long-term mortality follow- up. Local Manchester mortality data will be linked to the existing Manchester Self-Harm database and pseudoanonymised. For the multicentre project, pseudonymised data from each centre will be integrated into the main database at the coordinating centre (Oxford). The mortality data will be used to calculate suicide rates and standardized mortality ratios. Potential risk factors for suicide and other cause of mortality will be investigated using survival analyses. The data will also be analysed by gender and age.


Project 5 — DARS-NIC-147941-XX4JP

Opt outs honoured: N, Yes - patient objections upheld (Mixed)

Sensitive: Sensitive

When: 2016/04 (or before) — 2019/03.

Repeats: Ongoing, One-Off

Legal basis: Informed Patient consent to permit the receipt, processing and release of data by the HSCIC, Health and Social Care Act 2012 – s261(7)

Categories: Identifiable

Datasets:

  • MRIS - Cause of Death Report
  • MRIS - Cohort Event Notification Report
  • MRIS - Scottish NHS / Registration
  • MRIS - Flagging Current Status Report
  • MRIS - Members and Postings Report

Yielded Benefits:

Published data will help clinicians and patients make more informed decisions about psoriasis treatment options. There have been three publications in high impact journals on drug effectiveness. One paper concerned drug survival of biologic treatments for psoriasis received as first-line therapy. Biologics in clinical practice have a good overall survival rate in psoriasis patients but decrease over time; ustekinumab had the highest first-course drug survival in biologic-naive patients, followed by adalimumab. A paper followed on second-line drug survival which wound that 77% of patients who were switched to a second biologic continued on the new treatment for at least 12 months. This shows clearly that patients experiencing treatment failure with one biologic therapy can benefit from switching to another. The results of this study should support clinical decision making when choosing second-line biologic therapy for psoriasis patients. In 2017 a publication was made on the risk of serious infections for patients with psoriasis being treated with biologics. The University of Manchester did not find a statistically significant higher relative risk of serious infections for etanercept, adalimumab and ustekinumab as compared to non-biologic therapies for patients with psoriasis. There was no difference in the risk of serious infections between etanercept, adalimumab and ustekinumab. The risk of serious infection, therefore, should not be a primary concern for patients and clinicians when deciding between non-biologic systemic therapies or these three biologic therapies for psoriasis. There are no direct benefits for participants in BADBIR but the publications that arise from the project will contribute to the knowledge on the long-term safety of these new treatments thus benefiting patients in the future.

Objectives:

To establish a 'biologics' register for psoriasis to ascertain whether there is an importantly increased risk of serious adverse events following the introduction of and to follow-up patients treated with biologic agents(not more than 4,000 on each biological agent) so that long term-term safety can be monitored. A subsidiary aim will be to collect information on their long-term efficacy. Anouther of subsidiary questions will be addresses which include the evaluation of differences between these agents of differences between these agents, multiple agents concurrently or in sequence in terms of serious adverse effects

Expected Benefits:

Real world evidence on drug safety and effectiveness is a pivotal part of evidence-based medicine. The results can be used to inform clinical practice and also clinical guidance e.g. NICE The methodology employed will also allow for an evaluation of the potential for the sole use of data collected via routine healthcare to be used for further research of this type. This would potentially result in very substantial efficiency savings for future studies such as: 1. Collection of events (which requires training and employment of skilled staff) will be greatly streamlined 2. The need for trial participants to attend study clinics (which can be onerous and expensive if travel costs are not reimbursed) may be reduced This, in turn will enable such future research to be conducted on a greatly reduced budget, which is vital given the limited funding that national and charity funding bodies can typically offer. This may be particularly important for long term safety studies or trials of generic drugs in common conditions (e.g. aspirin in cancer prevention) which do not currently attract industry funding. Such methodological research is becoming increasingly important to the efficiency, design and data collection strategies of future trials and studies, and hence will be of benefit to public health at home and abroad. In summary, expected benefits include; Researchers/health and social care: 1. Reduced costs 2. Greater efficiency leading to increased throughput of research and associated enhancement of evidence based medicine, with impact upon national clinical guidelines 3. Increased awareness of the potential for routinely collected data to augment existing understanding and knowledge of a therapeutic area Patients: 1. Increased knowledge which will be used to inform healthcare decisions leading to improved quality of patient care 2. Reduction in the demands upon study participants in terms of time and inconvenience in attending study visits

Outputs:

Publication of the results of analyses is ongoing. Results of the analyses are presented at relevant national and international scientific meetings, such as the British Association of Dermatologists, American Academy of Dermatology, International Conference on Pharmacoepidemiology (ICPE) and in peer reviewed journals e.g. British Journal of Dermatology (BJD), Journal of Investigative Dermatology (JID), JAMA Dermatology. These will be targeted to ensure that the results are disseminated widely among the dermatology and the research community, including and the UK Dermatology Clinical Trials Network. The following is a list of publications using BADBIR data in peer-reviewed journals to date (13/09/18): 1. The British Association of Dermatologists Biologic Interventions Register (BADBIR): Design, Methodology and Objectives, A.D. Burden, R.B. Warren, C.E. Kleyn, K. McElhone, C.H. Smith, N.J. Reynolds, A.D. Ormerod, C.E.M. Griffiths, BADBIR Study Group., British Journal of Dermatology, March 2012. 2. Biological therapies for the treatment of severe psoriasis in patients with previous exposure to biological therapy: a cost-effectiveness analysis, L.M. Sawyer, D. Wonderling, K. Jackson, R. Murphy, E.J. Samarasekera, C.H. Smith, PharmacoEconomics, February 2015. 3. Baseline characteristics of patients with psoriasis enrolled in the British Association of Dermatologists' Biologic Interventions Register, I.Y.K Iskandar, Z.N. Yiu, R.B. Warren, K. McElhone, M. Lunt, A.D. Ormerod, N.J. Reynolds, C.H. Smith, C.E.M. Griffiths, D.M. Ashcroft, British Journal of Dermatology, July 2015. 4. Differential Drug Survival of Biologic Therapies for the Treatment of Psoriasis: A Prospective Observational Cohort Study from the British Association of Dermatologists Biologic Interventions Register (BADBIR), R.B. Warren, C.H. Smith, Z.Z.N. Yiu, D.M. Ashcroft, J.N.W.N. Barker, A.D. Burden, M. Lunt, K. McElhone, A.D. Ormerod, C.M.Owen, N.J. Reynolds, C.E.M. Griffiths, Journal of Investigative Dermatology, June 2015. 5. Identification of factors that may influence the selection of first-line biologic therapy for people with psoriasis: a prospective, multi-centre cohort study, N.J. Davison, R.B. Warren, K.J. Mason, K. McElhone, B. Kirby, A.D. Burden, C.H. Smith, K. Payne, C.E.M. Griffiths, British Journal of Dermatology, April 2017. 6. Patterns of biologic therapy use in the management of psoriasis: cohort study from the British Association of Dermatologists Biologic Interventions Register (BADBIR), I.Y.K. Iskandar, D.M. Ashcroft, R.B. Warren, I. Evans, K. McElhone, C.M. Owen, A.D. Burden, C.H. Smith, N.J. Reynolds, C.E.M. Griffiths., British Journal of Dermatology, March 2017. 7. Comparative effectiveness of biologic therapies on improvements in quality of life in patients with psoriasis., I.Y.K. Iskandar, D.M. Ashcroft, R.B. Warren, M. Lunt, K. McElhone, C.H. Smith, N.J. Reynolds, C.E.M. Griffiths, British Journal of Dermatology, March 2017. 8. Differential Drug Survival of Second-Line Biologic Therapies in Patients with Psoriasis: Observational Cohort Study from the British Association of Dermatologists Biologic Interventions Register (BADBIR), I.Y.K. Iskandar, R.B. Warren, M. Lunt, K.J. Mason, I. Evans, K. McElhone, C.H. Smith, N.J. Reynolds, D.M. Ashcroft, C.E.M. Griffiths, Journal of Investigative Dermatology, December 2017 9. Risk of Serious Infection in Patients with Psoriasis Receiving Biologic Therapies: A Prospective Cohort Study from the British Association of Dermatologists Biologic Interventions Register (BADBIR), Z.N. Yiu, C.H. Smith, D.M. Ashcroft, M. Lunt, S. Walton, R. Murphy, N.J. Reynolds, A.D. Ormerod, C.E.M. Griffiths, R.B. Warren, Journal of Investigative Dermatology, October 2017 10. Intentional and Unintentional Medication Non-Adherence in Psoriasis: The Role of Patients' Medication Beliefs and Habit Strength, R.J. Thorneloe, C.E.M. Griffiths, R. Emsley, D.M. Ashcroft, L. Cordingley, Journal of Investigative Dermatology, November 2017 11. Generating EQ-5D-3L Utility Scores from the Dermatology Life Quality Index: A Mapping Study in Patients with Psoriasis, N.J. Davison, A.J. Thompson, A.J. Turner, L. Longworth, K. McElhone, C.E.M. Griffiths, K. Payne. Value in Health, December 2017. 12. Comparison of Drug Discontinuation, Effectiveness, and Safety Between Clinical Trial Eligible and Ineligible Patients in BADBIR, K.J. Mason, J.N.W.N. Barker, C.H. Smith, P.J. Hampton, M. Lunt, K. McElhone, R.B. Warren, Z.Z.N. Yiu, C.E.M. Griffiths, A.D. Burden, JAMA Dermatology, March 2018. 13. Cumulative exposure to biologics and risk of cancer in psoriasis patients: A meta‐analysis of Psonet studies from Israel, Italy, Spain, United Kingdom and Republic of Ireland, I. Garcia‐Doval, M.A. Descalzo, K.J. Mason, A.D. Cohen, A.D. Ormerod, F.J. Gómez‐García, S. Cazzaniga, I. Feldhamer, H. Ali, E. Herrera‐Acosta, C.E.M. Griffiths, R. Stern, L. Naldi, British Journal of Dermatology, May 2018. 14. Identifying demographic, social and clinical predictors of biologic therapy effectiveness in psoriasis: a multicentre longitudinal cohort study, R.B. Warren , A. Marsden, B. Tomenson K.J. Mason, M.M. Soliman, A.D. Burden, N.J. Reynolds, D. Stocken, R. Emsley, C.E.M. Griffiths, C. Smith, British Journal of Dermatology, August 2018. Most of these papers have also been presented at local, regional and international medical, nursing, scientific and patient conferences thus informing evidenced based clinical care. In addition, this “real world” experience of safety and effectiveness of these drugs will contribute to national guidelines of management of psoriasis e.g. NICE The up to date list of all publications arising from BADBIR data is posted on the BADBIR website (http://www.badbir.org) It is expected that other papers will be published in the 2018/2019 including: incidence of suicide in the BABDIR population, risk of keratinocyte cancers in patients treated with biologic therapy and risk of MACE in patients treated with biologic therapy. An up to date publication plan is available on the BADBIR website http://www.badbir.org All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide. Six-monthly summary reports are provided to the BADBIR Data Monitoring Committee to include crude unadjusted rates of specific events of interests. Should a safety signal be identified, then further analysis can be requested and in the event of a significant concern the wider dermatology community will be informed. In addition, this “real world” experience of safety and effectiveness of these drugs will contribute to national guidelines of management of psoriasis e.g. NICE and thereby to the clinical management of patients with psoriasis. A yearly Participant (patient) Newsletter which includes a summary of any published paper is also provided via the BADBIR website and the recruiting dermatology centres. YouTube and Twitter have also been used to disseminate information on BADBIR including two presentations at the Psoriasis Association annual meetings (https://www.youtube.com/watch?v=OtmX-3uDtfY&t=618s and https://www.youtube.com/watch?v=LDYfOIuIGug), a short summary of the keratinocyte carcinoma presentation to the BDNG (https://www.youtube.com/watch?v=AcR4GRLK7l0), a summary of a Psoriasis Association-funded PhD student’s upcoming project (https://www.youtube.com/watch?v=4aoawSeODZM) and two videos by an NIHR doctoral fellow who completed his PhD using BADBIR (https://vimeo.com/251302895 and https://youtu.be/aQ64ePHVRKE). It is expected that other papers will be published in 2019 including: incidence of suicide in the BABDIR population, risk of keratinocyte cancers in patients treated with biologic therapy and risk of MACE in patients treated with biologic therapy. A research grant has been obtained from the Psoriasis Association to explore the risk of solid tumour cancer in patients treated with biologic therapy. It is anticipated that this analysis will be undertaken in 2019/2020. An up to date publication plan is available on the BADBIR website http://www.badbir.org Data dissemination is managed by a data writing group who are responsible to the BADBIR Steering Committee. This group comprises of dermatologists, research scientists, dermatology nurses and patients with psoriasis and guide the potential for output for disseminated data to provide a measurable benefit. The BADBIR Steering Committee is comprised of representatives of the data controller and will therefore contribute to this responsibility.

Processing:

The data will be accessed only by substantive employees of University of Manchester and only for the purposes described in this agreement. All data will be stored securely on servers at the University of Manchester. NHS Digital already hold the cohort data and will link this with mortality, cancer and HES data. The identifiers that were sent to NHS Digital: Study ID, NHS Number, Postcode, Sex, DoB. CAG approval is in place all of these identifiers in addition to Name however Name is not required for the matching and so has not been shared with NHS Digital. The study previously received NHS Number, Supplied Identifiers and Latest Identifiers but has removed these for data minimisation purposes. Data supplied by NHS Digital will be identifiable mortality, cancer data and HES data all supplied with a study ID. The researchers at University of Manchester will link and compare the NHS Digital data with the existing BADBIR database which includes: • Unique study Patient ID • Clinical data on clinical events • Laboratory results • Study treatment In summary, each record from the NHS Digital data will be reviewed against existing study data to avoid duplicate entries. Any new͛ adverse events will then join the main study data for analysis. 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 ie: employees, agents and contractors of the Data Recipient who may have access to that data). There will be no data linkage undertaken with NHS Digital data provided under this agreement that is not already noted in the agreement. Data will only be accessed and processed by substantive employees of The University of Manchester and will not be accessed or processed by any other third parties not mentioned in this agreement.


Project 6 — DARS-NIC-147993-QY3ZL

Opt outs honoured: N

Sensitive: Sensitive, and Non Sensitive

When: 2016/04 (or before) — 2017/02.

Repeats: Ongoing

Legal basis: Informed Patient consent to permit the receipt, processing and release of data by the HSCIC

Categories: Identifiable, Anonymised - ICO code compliant

Datasets:

  • MRIS - Cause of Death Report
  • MRIS - Cohort Event Notification Report
  • MRIS - Scottish NHS / Registration

Objectives:

Rheumatoid arthritis (RA) affects about 0.8% of the adult population in the UK. RA is also associated with increased and premature cardiovascular (CV) mortality. Almost half of all deaths in RA (and about 35 - 40% of the excess deaths) are due to cardiovascular disease (CVD). High levels of inflammation in RA sufferers can cause damage to blood vessels. Statins are drugs that can help to prevent diseases of the heart and blood vessels by reducing cholesterol and possibly inflammation. This trial will investigate whether the statin called Atorvastatin (Lipitor) can reduce the occurrence of conditions such as heart attack and stroke in patients with RA. The trial also aims to investigate whether Atorvastatin can reduce the level of inflammation in the joints in people who have significant RA disease activity at the time that they are included into the trial.


Project 7 — DARS-NIC-148247-CH0Z6

Opt outs honoured: No - consent provided by participants of research study (Consent (Reasonable Expectation))

Sensitive: Sensitive

When: 2017/03 — 2019/03.

Repeats: Ongoing, One-Off

Legal basis: Informed Patient consent to permit the receipt, processing and release of data by the HSCIC, Health and Social Care Act 2012 – s261(7)

Categories: Identifiable

Datasets:

  • MRIS - Cause of Death Report
  • MRIS - Cohort Event Notification Report

Objectives:

This study will document use of biologic drugs in patients with Systemic Lupus Erythematosus (SLE) in order to assess their efficacy and safety during routine clinical use. 2 cohorts will be recruited: a cohort treated with biologic therapy and a biologically naïve group. The primary aim of establishing the BILAG Biologics Prospective Cohort is to ascertain whether using biologics in the routine treatment of SLE is associated with an increased risk of being hospitalised for infection, compared to SLE patients with similar disease activity receiving conventional therapies. The secondary purpose of the BILAG Biologics Prospective Cohort is to determine the long-term efficacy of biological therapies in the treatment of SLE. A further aim of the study is to collect biological samples from patients receiving biologic therapy in the UK to allow us to test whether there are variations in genes which can predict who will respond and who may get serious side effects.

Expected Benefits:

There have been very few major advances in the treatment of Systemic Lupus Erythematosus (SLE) over the past 35 years. In the past 5 years however, there has been an explosion of interest in developing new molecules for the treatment of SLE. A number of approaches have been proposed and are currently in various stages of development including B-cell depleting therapies, IL6 and IL10 blockade as well as inhibition of co-stimulatory molecules, TNF-blockade and lymphodepletion. As these drugs become available for diseases such as RA, off-licence use in SLE is already underway and it is likely that several of these products will gain licences for use in SLE over the next 5 years. However, clinical trials are limited by patient numbers and study duration and therefore are under powered to study potentially important adverse events. In addition, clinical trials tend to exclude patients who have been exposed to other biological therapies in the past and therefore the potential medium-term interactions between various therapeutic approaches cannot be adequately studied. It was therefore decided to establish the BILAG Biologics Prospective Cohort to monitor the long-term safety and efficacy of these new biologic treatments in patients with SLE

Processing:

This is an observational prospective cohort study to compare the risk of development initially over 3 years, of the endpoint in two cohorts: (i) a group of patients with SLE newly exposed to a biologic therapy and (ii) A comparison cohort of patients with SLE newly exposed to conventional, non-biologic therapy, with an equivalent disease severity. We intend to flag both the biologic cohort and the comparison cohort for notification of mortality and cancer registration. A copy of the death certificate will be required for those who die and a copy of the histology for those who develop a malignancy. This will enable us to monitor any adverse events in both cohorts


Project 8 — DARS-NIC-148264-XXC29

Opt outs honoured: N

Sensitive: Sensitive

When: 2016/04 (or before) — 2016/11.

Repeats: Ongoing

Legal basis: Informed Patient consent to permit the receipt, processing and release of data by the HSCIC

Categories: Identifiable

Datasets:

  • MRIS - Cause of Death Report
  • MRIS - Cohort Event Notification Report

Objectives:

European Male Ageing study: Prevalence, Incidence and Geographical Distribution of Symptoms of Ageing in Men and their Endocrine, Genetic and Psychosocial Correlates. Title appearing in all publications: the European Male Ageing study (EMAS). Identify and quantify any disparities in the prevalence, incidence, nature and severity of symptoms and disabilities of ageing in the general male population from different regions of the EU. Explain regional differences in the health status of ageing men on the basis of the decline in endocrine (hormonal) functions. Elucidate the relationships between clinical features associated with ageing and the attendant hormonal changes. Characterise threshold hormone levels for symptomatic and asymptomatic deficiency states in elderly men (i.e. identify the low hormone levels at which older men may become unwell or impaired). Identify potentially modifiable region-specific and/or race/ethnicity-specific risk factors for the evolution and progression of symptoms, disabilities, changes in body composition and hormonal decline associated with ageing in the EU. Inform European policy-makers and industry and formulate recommendations for screening, prevention, diagnosis and treatment of individual functional disabilities or an ageing-related clinical syndrome in men if it is found to exist.


Project 9 — DARS-NIC-148353-G88Q7

Opt outs honoured: N

Sensitive: Non Sensitive, and Sensitive

When: 2016/04 (or before) — 2017/02.

Repeats: Ongoing

Legal basis: Informed Patient consent to permit the receipt, processing and release of data by the HSCIC, Health and Social Care Act 2012

Categories: Anonymised - ICO code compliant, Identifiable

Datasets:

  • MRIS - Cohort Event Notification Report
  • MRIS - Cause of Death Report
  • MRIS - Scottish NHS / Registration
  • MRIS - Members and Postings Report
  • MRIS - Personal Demographics Service

Objectives:

The study aims to test the hypothesis that biologic therapy (Anti-TNF) in patients with rheumatic diseases increases the risk of malignancy, important co-morbidity and severe infection. Primary endpoints include malignancy, infection requiring hospitalisation, serious co-morbiditity and death. Subsidary hypotheses include (1) To test whether an increased risk is related to dose/duration of therapy, (2) there are identifiable disease characteristics that act synergistically to increase the risk, and (3) therapy with multiple biologic agents act synergistically to increase the risk.


Project 10 — DARS-NIC-148412-BC33Q

Opt outs honoured: Y

Sensitive: Sensitive, and Non Sensitive

When: 2016/04 (or before) — 2018/09.

Repeats: Ongoing

Legal basis: Section 42(4) of the Statistics and Registration Service Act (2007) as amended by section 287 of the Health and Social Care Act (2012), Approved researcher accreditation under section 39(4)(i) and 39(5) of the Statistical Registration Service Act 2007

Categories: Identifiable, Anonymised - ICO code compliant

Datasets:

  • MRIS - Cause of Death Report
  • MRIS - Cohort Event Notification Report

Objectives:

The data supplied will be used only for the approved medical research project - MR739: Rates of Cognitive Changes Preceding Death in Later Life


Project 11 — DARS-NIC-179285-7RS6G

Opt outs honoured: N

Sensitive: Sensitive

When: 2016/04 (or before) — 2017/02.

Repeats: Ongoing

Legal basis: Informed Patient consent to permit the receipt, processing and release of data by the HSCIC

Categories: Identifiable

Datasets:

  • MRIS - Cohort Event Notification Report
  • MRIS - Scottish NHS / Registration

Objectives:

The BSPAR Etanercept Cohort Study Aims; 1) Safety monitoring of biological therapies and new drugs in compliance with drug licences and NICE guidelines, and methotrexate (current standard therapy) in juvenile idiopathic arthritis patients, with adverse event monitoring. 2) Collection of information on long term course of the condition, long term outcomes and use of therapeutics in a cohort of children treated with biologic agents, methotrexate or other new drugs for rheumatic disease. Objectives; 1) To establish a register patients with severe juvenile arthritis. 2) To collect and record data on children and young people prescribed biological therapies or new drugs for Juvenile ldiopathic Arthritis (JlG) including demographic data, disease type and activity, outcomes and safety data. 3) To collect data on appropriate methotrexate treated controls. 4) To be able to extend data collection to incorporate new treatments 5) To provide for the further collection of data on longer term outcomes through flagging with the NHS Central Register (now the HSCIC)


Project 12 — DARS-NIC-179438-WCHHZ

Opt outs honoured: Y

Sensitive: Non Sensitive, and Sensitive

When: 2016/04 (or before) — 2016/11.

Repeats: Ongoing

Legal basis: Section 251 approval is in place for the flow of identifiable data

Categories: Identifiable

Datasets:

  • MRIS - Flagging Current Status Report
  • MRIS - List Cleaning Report

Objectives:

Tarn is a national audit for trauma care across England and Wales and has been commissioned by the Department of Health to look at the long terms outcomes of injured patients. This request is to access mortality data in order to assess the long term outcomes of patients and obtain a better picture of the overall impact of traumatic injury ("the purpose").


Project 13 — DARS-NIC-186860-T7H5K

Opt outs honoured: No - data flow is not identifiable (Does not include the flow of confidential data)

Sensitive: Non Sensitive

When: 2019/03 — 2019/03.

Repeats: One-Off

Legal basis: Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii)

Categories: Anonymised - ICO code compliant

Datasets:

  • Hospital Episode Statistics Admitted Patient Care
  • HES:Civil Registration (Deaths) bridge

Objectives:

This agreement from the University of Manchester (UoM) relates to a National Institute for Health Research School for Social Care Research (NIHR SSCR) funded study entitled ‘Effective Healthcare Support to Care Homes’. By this the University of Manchester mean the support that visiting healthcare professionals such as GPs, district nurses, geriatricians, pharmacists, palliative care staff and specialist care home support teams provide to meet the physical healthcare needs of older, long-term care home residents. The research is being undertaken by staff from the Personal Social Services Research Unit (PSSRU). The Personal Social Services Research Unit (PSSRU) was originally established at the University of Kent in 1974. The branch at Manchester was established in 1996 and there are now three branches – Manchester, LSE and Kent. For the purpose of this agreement data will only be accessed by substantive employees of University of Manchester and record level data will only be stored and processed at the university of Manchester. In England, over 375,000 older people live in care homes. These individuals have multiple and complex needs, many of which stem from progressive, chronic conditions including neuro-degenerative, musculoskeletal and cardio-respiratory disease. Visual and hearing deficits are common; about 40% of residents are depressed; and the majority of residents have dementia. Median life expectancy in care homes is just 15 months. Surveys suggest that the healthcare support that care homes receive varies across the country and that the majority of care homes do not have access to all the services and support that they require. It can thus be difficult for staff to obtain timely and appropriate diagnosis and treatment for their residents. In consequence, care home residents have high rates of potentially avoidable admissions to hospital, with significant implications for both NHS costs and residents’ well-being. At present, around a third of residents with unplanned admissions to hospital die during their stay. A report by the National Audit Office suggests that many of these people could be supported at their care home if appropriate services were available. International evidence indicates that specialist assessment and management of care home residents (including enhanced primary care services) can improve resident outcomes and save money. Nevertheless, although a range of new models of care home support are emerging, little is known about the most effective ways to structure and deliver this input. This includes evidence on the precise organisational forms, staff mix and clinical processes associated with reducing avoidable hospital admissions and enabling residents to remain in the care home at the end of life. Against this background, this study will investigate the association between different forms of healthcare support provided for long-term care home residents and both unplanned (emergency) hospital admissions and place of death. A 2017/18 survey of care homes in Greater Manchester undertaken by the PSSRU has already collected information on the healthcare support that these facilities receive and subsequent analysis has identified seven different ways (Care Home Support Models 1 to 7) of classifying this. This has enabled the research team to categorise the care homes into subgroups of homes receiving similar forms of support. For example, one model groups the homes according to the mix of professionals that provide support, another according to the frequency of the input they receive and a third according to the nature of the support that they receive and whether this includes care home staff training as well as advice on individual care home residents. This strand of the research did not involve any NHS Digital data. The information now sought from NHS Digital will be used to explore the extent to which these different models of support are associated with the unplanned hospital admission and place of death of care home residents. This data will enable the researchers to move beyond a simple description of the patterns of service delivery to answer the following research question: ‘What are the outcomes of different arrangements for healthcare support to care homes?’ There is no other way the PSSRU can access this data.

Expected Benefits:

The study is designed to increase understanding of the extent to which different models of care home support are associated with better resident outcomes. In particular, it will explore differences in the unplanned admission to hospital and place of death of residents of care homes receiving different forms of support, controlling for care home size. As such it will add to the limited existing knowledge base about models of support for care homes, and will yield information with direct implications for: i/ Health and social care planners and commissioners seeking to promote the delivery of timely and appropriate healthcare services for care home residents and the more efficient use of acute hospital beds. ii/ Managers and senior staff in provider organisations, enabling them to identify gaps in access to specialist care and negotiate improvements. iii/ Front line care home staff, care home managers and members of healthcare support services by identifying those care home residents at greatest risk of inappropriate hospitalisation and facilitating the planning of anticipatory care. iv/ Care home residents, who may achieve increased well-being through the provision of more appropriate health care. In addition, to shape the influence of this study upon the way care services are delivered, further subsequent research funding will be sought to enable the research team to explore changes in service arrangements and care home resident outcomes over the next five years. These findings will also identify locations where care outcomes for residents are particularly good, highlighting where more in-depth case studies can be undertaken to further identify key service components associated with better quality and safety. These are the elements needed for commissioners to arrange better care for care home residents. The anticipated dates of the publication of the analysis and plans for its wider dissemination are given in the previous section. The fact that Manchester is likely to have a more diverse range of models than some other places is designed as a strength of the study. Furthermore, the findings are likely to be of additional interest locally in the context of aims to reduce the number of hospital admissions by 60,000 per year. The research findings will contribute to that debate.

Outputs:

The production of outputs utilising the requested data will begin soon after data receipt and will continue for up to five years. In all cases, all outputs will be at aggregated level and small numbers will be suppressed in line with NHS Digital guidelines. Specific outputs are described below. A final report on the wider study (including brief details of the methodology, findings and conclusions of this analysis) will be submitted to the study’s funders (the NIHR SSCR) in the spring of 2019. Following peer review, a publicly available version of this report will be posted on the websites of the funder (https://www.sscr.nihr.ac.uk/) and the University (http://research.bmh.manchester.ac.uk/pssru/). Given the nature of the final report (a short summary document) further analysis of the data will be reported in two academic papers detailing the association between the different models of care home support and the hospital admission and place of death of care home residents respectively. The main audience for these papers will comprise commissioners, managers and providers (locally, nationally and internationally) as well as other academics. These papers will thus be submitted to peer-reviewed journals such as Age and Ageing, Primary Healthcare Research and Development and the Journal of the American Medical Directors Association (JAMDA) and will be available via open access where possible. Target publication date: Summer 2020. The findings of the analysis will also be presented at a national conference such as the SSCR’s Annual Conference, and at local care home owner, manager and staff forums (regular meetings between commissioners and providers) within Greater Manchester. Target presentation date: Spring 2019. The applicants will also produce a short, evidence-based briefing document on the study’s findings, available free of charge as a web-based document. This will form part of the PSSRU at Manchester Expert Briefing series which is designed to inform service development, both nationally and locally. As such it is expected to be of interest to personnel who commission and deliver healthcare support to care homes, as well as care home providers. Target publication date: Autumn 2019. In addition, a PSSRU Research and Policy Update summary document detailing the key research findings will be distributed to clinical commissioning groups and local authorities in England, CQC, major care home providers and umbrella organisations for care homes through our national mailing lists. Target publication date: Winter 2019/20. All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.

Processing:

Only substantive employees of the University of Manchester (UoM) will have access to the data, and only for the purposes described. The data will not be shared with third parties or linked to any other datasets. Below is a summary of the dataflow and processes; 1. The University of Manchester will send NHS Digital a spreadsheet containing the names and postcodes of the 418 care homes in Greater Manchester along with their first line of their addresses. NHS Digital will run that information through Personal Demographic Service (PDS) and extract the NHS numbers and use these to identify people at each of these addresses. They will then add the seven variables classifying the external healthcare support received by their care home (Care Home Support Models 1 to 7) and eight further variables relating to the care home type (Care Home Type 1 to 8) and append these to the data. NHS Digital will not disseminate PDS data, they will use it purely to identify any individual living at the care home addresses over the age of 75. No individual care home resident identifying information will be provided. 2. NHS Digital will link the requested HES data and Mortality data to the provided postcodes, Care Home Support Models 1 to 7 and Care Home Type variables 1 to 8. 3. NHS Digital will remove the postcodes and first line of address from the linked data before delivering this to the UoM. No individual patient identifying information will be provided. The data will be pseudonymised at record level. The linked HES and Mortality data will be entered into two statistical software suites [Statistical Package for the Social Sciences (SPSS) and Statistical Analysis System (SAS)] and a range of descriptive, bivariate and multivariate statistics as well as generalised linear models will be employed. The analyses will profile the full sample of admissions from care homes in Greater Manchester and will explore differences in unplanned hospital admissions and place of death between residents of care homes receiving different models of support, controlling for care home size. All outputs will only contain aggregate level data and small numbers will be suppressed in line with Hospital Episode Statistics (HES) analysis guidance. Data will be stored in the University of Manchester Data Safe Haven (Joule House), which has been built following ISO27001 standards and NHS Digital security requirements. The Data Safe Haven is located on a separate premise (Joule House) to that where the data will be accessed and analysed (Crawford House). 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 ie: employees, agents and contractors of the Data Recipient who may have access to that data).


Project 14 — DARS-NIC-23410-W8N9L

Opt outs honoured: N, Y

Sensitive: Sensitive, and Non Sensitive

When: 2016/12 — 2017/05.

Repeats: Ongoing, One-Off

Legal basis: Health and Social Care Act 2012, Section 42(4) of the Statistics and Registration Service Act (2007) as amended by section 287 of the Health and Social Care Act (2012)

Categories: Anonymised - ICO code compliant, Identifiable

Datasets:

  • Patient Reported Outcome Measures (Linkable to HES)
  • Hospital Episode Statistics Accident and Emergency
  • Hospital Episode Statistics Outpatients
  • Hospital Episode Statistics Admitted Patient Care
  • Office for National Statistics Mortality Data (linkable to HES)
  • Office for National Statistics Mortality Data

Objectives:

The University of Manchester has been awarded research funding from the Department of Health Policy Research Programme to analyse the impact of the Care Quality Commission's new inspection and rating system on provider performance. The data will be used to uncover the relative impact of healthcare regulation, compared to other influences, on hospital performance. It would not be possible to measure hospital performance, and therefore evaluate the CQC, without the datasets requested. The research sets out to examine how providers, the public and other stakeholders respond to provider ratings, and what impacts inspection and ratings have on the quality of care and on improving performance. Understanding how ratings work, as part of the wider inspection process, is essential if the Department of Health are to increase the potential benefits for the quality of healthcare, and reduce the costs and any adverse or unintended consequences. There are four main questions in the research: - How is the system of ratings meant to work, and bring about improvements in the quality of care? - How does it work in practice, and what do providers, the public and others do in response to ratings? - What impact does the system of ratings have on measures of the quality of care and provider performance? - How could the system of ratings be improved? Hospital performance will be measured in the following way: - Mortality [within hospital/out of hospital/within 30 days] - Readmissions within 30 days - Length of stay - Waiting times - Occurrence of adverse events - Cancellations of surgery - PROMs The data provided by NHS Digital will only be used by the members of the research team detailed in this application. The analysis conducted on these data will feed into the evaluation of the CQC which also includes qualitative researchers based at the University of Manchester and The Kings Fund. Only employees of the University of Manchester will process the data. Only aggregated outputs will be shared with researchers outside of the University and small numbers will be suppressed in line with the HES Analysis Guide

Expected Benefits:

The Care Quality Commission developed its system of provider ratings as part of its new regulatory model and inspection regime in 2013, piloted it in the NHS acute care sector in 2013-14 and is now developing it for use in other sectors. The new approach is radically different from the model it replaced. It uses larger and more expert inspection teams, a wider range of data and fieldwork, and produces provider ratings in five domains (safe, effective, caring, responsive and well-led) using a four-point rating scale (inadequate, requires improvement, good or outstanding) as part of a detailed narrative inspection report. The key purpose of these provider ratings is to drive improvements in the quality of care and in provider performance, but the ways in which this might happen are complex, and there is potential for unintended consequences. This research will help CQC, the Department of Health and other stakeholders to understand how the system of provider ratings works, and to find ways to increase its impact on the quality of care, minimise costs and prevent or reduce any adverse or unintended effects. This will allow the Department of Health to impact on the future direction of policy, and also contribute to a wider body of knowledge on regulation/inspection and performance ratings. The final report to the Department of Health will be submitted in December 2017. The dissemination of the research findings will include journal articles published after the final report. The University will continue to work on the data following the final report due to the lengthy peer review/revisions stage for journal articles. The CQC and the Department of Health will both receive regular updates on the research methods and outputs. Several meetings have already been held with the CQC to establish links by which the research can be disseminated within the CQC. These updates are in addition to the final report and journal articles.

Outputs:

The purpose of the data processing activities performed on these data is to produce statistical outputs to be used in peer-reviewed journal articles, presentations and reports for the Department of Health Policy Research Programme. These statistical outputs will be of three types: 1) descriptive tables summarising the data, 2) graphs and figures summarising the data, and 3) regression results tables. Outputs will contain only data which is aggregated, with any small numbers suppressed in line with the HES Analysis Guide. The overall research project includes a number of work packages. At the completion of these, seminars will be held for invited participants from the research case studies and interested organisations, for feedback on early and emerging findings and member checking as well as to promote knowledge mobilisation. For case studies, a short report will be produced and the findings may be presented locally to stakeholders in the “system of care”. The number of outputs, in terms of articles/presentations/reports, will be determined by the findings of the analysis and by the academic peer-review process. There is a PPI (Patient and Public Involvement) forum who will be engaged in producing these reports and their dissemination. The aim is to produce a series of journal articles investigating hospital performance and how it is affected by regulation and inspection. Journals that will be targeted for these publications will be: 'Health Economics', 'The Journal of Health Economics', 'Health Services Journal', 'Journal of Health Services Research & Policy'. Target journal publication dates are as follows: c. Journal Article 1 submission target: May 2017 d. Journal Article 2 submission target: June 2017 e. Journal Article 3 submission target: July 2017 Presentations will be given at national and international conferences to audiences of academics and health professionals. Typical conferences are the UK Health Economists' Study Group. Reports will be provided for the Department of Health Policy Research Programme on the progress and outcome of the research. Target report publication dates are as follows: a. Department of Health interim report 2: April 2017 b. Department of Health final report: August 2017 Reports and journal articles will be published online and made available to all. Submitted versions of all journal articles will be freely available on the University of Manchester library website.

Processing:

- Data will only flow from the HSCIC to the University of Manchester. - No data will be shared with third parties. - The data will be analysed in order to measure hospital performance. This will not involve linking data to other datasets (apart from the linkage to PROMs and ONS provided by NHS Digital). Trends in hospital performance over the period of time requested will be analysed. - Requested are the years prior to the introduction of the CQC inspection in order to accurately account for historic trends in performance. This is a standard approach and required in order to measure changes in these trends, which may be due to the CQC inspection. The data will be analysed using the statistical packages Stata and R. Regression methods will be used which will output regression results tables. No record level data will be produced as an output at any stage, only aggregated date (with small numbers suppressed in line with the HES Analysis Guide). Descriptive statistics tables of the data will be produced which will aggregate the data by year. Graphs will be produced to describe the data and these graphs will also aggregate the data by year. The outputs produced cannot be used to identify patients or sensitive information.


Project 15 — DARS-NIC-317873-H3L1R

Opt outs honoured: N

Sensitive: Non Sensitive

When: 2017/03 — 2017/05.

Repeats: One-Off

Legal basis: Health and Social Care Act 2012

Categories: Anonymised - ICO code compliant

Datasets:

  • Hospital Episode Statistics Accident and Emergency
  • Hospital Episode Statistics Admitted Patient Care
  • Hospital Episode Statistics Outpatients

Objectives:

The purpose of this application is to obtain data to be used in specific academic research projects evaluating the impact of financial and organisational reforms of the English National Health Service. Over the past 10 years, multiple reforms have aimed at creating incentives for performance improvements amongst providers, and higher quality of care for patients. The aim of this research is to assess the extent to which the structure and design of these reforms has led to the intended improvements in productivity and outcomes. The data will be used in regression analysis using advanced econometric modelling (including linear and non-linear regressions models, multilevel models and difference-in-differences designs) to generate causal inferences on the impact on health policy and service structure and design on health outcomes. The data will only be used for the following projects: Project 1: The effects of non-payment for performance Since 2011 the English National Health Service (NHS) has aimed at reducing readmission rates by not paying for individual readmissions occurring within 30 days of discharge from hospital (Department of Health, 2011). In a similar spirit, the “never events” policy framework withholds payment for hospitalisations leading to undesired patient outcomes e.g. pressure ulcers during hospitalisation and wrong side surgery. The objective of this project is to conduct academic research on the effect of the non-payment policies on patients’ health outcomes directly and indirectly affected by those policies. Project 2: The effects of the ‘payment by results for drug recovery’ The aim of this project is to evaluate the effects of the effect of the Payment by Results for Drug Recovery (PbRDR) Pilot Programme on patient health and hospital outcomes. Providers of services for substance misusers have their payments linked to the ‘national outcomes framework’. PbRDR was introduced to ‘facilitate better care leading to better outcomes for service users’ (DH 2010: 7) including patients’ health and wellbeing. The project will examine whether the introduction of PbRDR pilots led to changes in emergency hospital admissions and associated costs for conditions that indicate complications of drugs misuse. Project 3: An economic evaluation of the Advancing Quality pay-for-performance scheme Pay-for-performance (P4P) programmes link financial payments by purchasers to the quality of care supplied by health care providers, and have grown in popularity over recent years. Advancing Quality (AQ) was the first hospital-based P4P scheme to be introduced in England in October 2008. The objective of processing this data is to conduct academic research to investigate the effects of AQ on health outcomes of importance to patients: mortality, length of stay, and readmissions rates. This will be compared to the costs of the programme to the NHS to evaluate whether the AQ P4P programme represents a cost-effective use of NHS resources. No data will be made available to third parties. Project 4: The future of 24/7 care: investigating the links between staffing levels, patient access and inequalities in health outcomes There are long-standing concerns that patients admitted to hospital at night and at weekends, when staffing levels are lower and some services are not available, suffer higher complication and mortality rates that patients admitted at times when the hospital is fully operational. Salford Royal Foundation Trust (SRFT) has been gradually extending fully-operational service provision since 2007. The objective of processing this data is to conduct academic research to investigate outcomes for patients admitted to SRFT at the weekend and compare with those admitted to other hospitals in England that have not yet extended service provision. Outcomes of interest are length of stay, mortality, complications and readmissions. Analysis will estimate the effect of fully-operational hours on the quality of care provided to patients, and any improvements in outcomes as a result of increased quality. Data will not be made available to any third parties.

Expected Benefits:

Please see the individual project descriptions for details. The results from the research projects described above are expected to be published by 2018 and will shed important new light on how the framing and design of organisational performance incentives affect the outcome of such reforms. In addition to its academic value, the research will thus inform policy makers on efficient design of health care service organisation and performance incentives and how to best allocate scarce health care resources in the English NHS for the benefit of patients. Project 1: The effects of non-payment for performance The non-payment policies analysed in this project represent a genuine novelty in the approach to incentivising higher quality care in the English NHS. Where previous initiatives to improve health care quality has relied on paying a bonus for improved care quality – so called Pay for performance (for example Best Practice Tariffs) - the reimbursement policies analysed in this project relies on financial penalties for “poor performance” – the occurrence of ‘never events’ and high readmission rate – to improve quality. England first introduced a non-payment policy for so-called ‘Never Events’ in 2009. A Never Event has been defined by the National Patient Safety Agency (NPSA) as “[a] serious, largely preventable patient safety incident that should not occur if the available preventative measures have been implemented by healthcare providers.” (NPSA, 2009). The list of Never Events is updated regularly, and for 2012/13 contained 25 events including wrong site surgery, severe scalding of patients, and unintended retention of a foreign object in a patient after surgical intervention (Department of Health, 2012a). If a Never Event occurs, providers must initiate an investigation into the causes of the event. In addition, the provider is not reimbursed for both the episode of care that involved the event, and for the costs of consequential treatment (Department of Health, 2012b). In April 2011, England introduced a policy (Department of Health, 2011) according to which hospitals would no longer be reimbursed for emergency readmissions occurring within 30 days of discharge from an elective admission. Around 40% of all readmissions, including those for children under four years of age, maternity, childbirth and cancer patients, and those who self-discharge against clinical advice, were however excluded from these non-payment rules. The policy was expanded after its first year of operation, and now mandatorily applies to both emergency and elective first admissions (Department of Health, 2012c). Any savings made by commissioners due to non-payment for readmissions must be reinvested in post-discharge reablement services which support rehabilitation, reablement, and the prevention of future readmissions. Nonpayment policies are potentially more cost effective than pay for performance because there is no cost in terms of bonus payments. However, although these policies that apply to all hospital in-patients have been a part of the Department of Health’s hospital reimbursement policies for years, the intended and unintended effects of the policies on patients’ health outcomes and on hospital performance is hitherto unknown. The research outcome of this project will thus shed important light on the effect of non-payment policies on the intended and unintended effects of NP4P on patients’ health outcomes, and assess whether non-payment are effective in improving hospital performance on the targeted areas. In addition to publishing the findings in academic research journals that are also read by policymakers, the findings from the project will be disseminated at conferences such as the UK Health Economic Study Group and the International Health Economics Association. These conferences are regularly attended by representatives from the Department of Health and NHS England, and the relevant actors in NHS policy making can learn about the results through these channels. In addition, when publishing research findings that are thought to be of importance to the public and policy makers, the University of Manchester generally issues press releases to ensure a wider dissemination of research findings. If the findings are positive, policy makers in NHS England and the Department of Health involved in the design of payment policies for health care may consider applying non-payment policies to other areas of the health service or expand to other outcomes than readmission rates and never events. If the results indicate unintended consequences for patients’ health outcomes, or hospital gaming of the policies, policymakers may consider changing the design of the non-payment policies to avoid adverse consequences and gaming. The results have thus potentially important impact on both the patients using the health care service and the future design of health care reimbursement policies. References: Department of Health, 2011. Payment by Results Guidance for 2011-12. Leeds. Department of Health, 2012a. The “Never Events” list 2012/13. London. Department of Health, 2012b. The Never Events Policy Framework: an update to the never events policy. London. Department of Health, 2012c. Payment by Results Guidance for 2012-13. Leeds. NPSA, 2009. Never Events Framework 2009/10. National Reporting and Learning Service - National Patient Safety Agency. Project 2: The effects of the ‘payment by results for drug recovery’ It is an important requirement of projects funded by the NIHR and the DH that applicants make a convincing case for the expected benefits of the research. This is assessed by expert reviewers and the funding panels at the award stage. Projects that do not offer benefits to health and social care are not funded by NIHR and the DH. Project 3: An economic evaluation of the Advancing Quality pay-for-performance scheme It is an important requirement of projects funded by the NIHR and the DH that applicants make a convincing case for the expected benefits of the research. This is assessed by expert reviewers and the funding panels at the award stage. Projects that do not offer benefits to health and social care are not funded by NIHR and the DH. Project 4: The future of 24/7 care: investigating the links between staffing levels, patient access and inequalities in health outcomes The research will provide evidence on how best to allocate scare NHS resources in order to obtain the maximum benefits in terms of patient health. This will inform policy makers on the costs and benefits of extending fully-operational hours for hospital services in England, and aid the efficient organization of NHS hospital services.

Outputs:

In summary, the research output from all projects will be used for academic research papers that will be submitted to peer reviewed academic journals in health policy, economics, health economics and health services organisation and delivery. In addition, the research will be included in PhD projects (project 2,3 and 4), and for reports made as part of work undertaken for the Department of Health (project 2) work funded by the NIHR HS & DR programme (project 4). Please see specific details in each project. All outputs will consist of aggregate data only with small numbers suppressed in line with HES analysis guide. Project 1: The effects of non-payment for performance The project outcome is expected to be 2 publications in peer reviewed academic journals in the fields of health economics and health policy. • A first version of the paper on the readmission policy will be presented at the Royal Economic Society conference in March 2016. The target submission date is 1st August 2016, target journal American Economic Journal: Economic Policy or similar, • Paper on Never Events, target submission date 1st December 2016, target journal Health Economics or similar Project 2: The effects of the ‘payment by results for drug recovery’ • A report was submitted to the Department of Health on the evaluation of the policy; initially submitted Autumn 2015 - currently awaiting peer review; further changes and use of the data may be required after peer review. • A chapter for the PhD thesis of a PhD student which will be submitted to The University of Manchester, with a target submission date of January 2017. Project 3: An economic evaluation of the Advancing Quality pay-for-performance scheme The specific outputs expected are two publications in peer-reviewed academic journals in the field of health economics and health policy, and a PhD thesis, which will contain one of these papers. This will follow three previous peer-reviewed publications which have already been produced as a result of the project, making significant contributions to the literature on the effects of P4P on patient health outcomes: • Sutton et al. (2012). Reduced mortality with hospital pay for performance in England. New England Journal of Medicine, 367, 1821-1828. • Meacock et al. (2014). Paying for improvements in quality: a recent experience in the NHS in England. Health Economics, 23, 1-13. • Kristensen et al. (2014). Long-term effect of hospital pay for performance on mortality in England. New England Journal of Medicine, 371, 540-548. One paper, which assesses the impact of AQ on mortality for patients affected by the policy, has already been submitted to Medical Decision Making and is currently being revised for resubmission to the journal. Project 4: The future of 24/7 care: investigating the links between staffing levels, patient access and inequalities in health outcomes The specific outputs expected are four publications in peer-reviewed academic journals in the fields of health economics and health policy, and an overall project report to be submitted to the National Institute for Health Research (NIHR). These will follow one previous peer-reviewed publication which has already been produced as a result of the project, making significant contributions to the literature on the costs and benefits of extending weekend hospital services: • Meacock et al. (2015). What are the costs and benefits of providing comprehensive seven-day services for emergency hospital admissions? Health Economics, 24, 907-912. Papers will investigate the following questions: 1. What is the impact of changes to fully operational hours on access to services for different population groups? 2. How do service re-configurations affect quality of care and patient outcomes, and their

Processing:

The data will be stored within an access restricted data share on the University’s network storage infrastructure which is the recommended location for storing sensitive or critical University data. The storage infrastructure is hosted across two data centres (Kilburn Building and Reynolds House (approx. 2KM apart)) for resilience and disaster recovery purposes. The research group are based in offices on the 4th floor of the Jean McFarlane Building and the data will be hosted on a strictly controlled data share within the University’s network storage infrastructure to which only six designated members of research group staff will have access permissions. The data will only be used for the purpose listed in the four projects above and not for any additional projects without further permission. All five projects are addressing the same theme but there will be no cross-project sharing of data. Only the University of Manchester will have access to the data provided and no data will be shared with a third party or used for commercial purposes. Project 1: The effects of non-payment for performance The effect of the policies on patients’ health outcomes and resource use will be assessed using difference in differences analysis and duration analysis using advanced econometric methods appropriate for linear and nonlinear multilevel data. The outcomes analysed will be readmission rates within 30 and 90 days, time to readmission, resource use in the inpatient, outpatient and A&E setting, and the probability of experiencing a never event. All analysis will include controls for patient and provider characteristics. Project 2: The effects of the ‘payment by results for drug recovery’ The effect of the policy will be assessed using difference-in-differences regression analysis, comparing risk-adjusted emergency admission rates between pilot and non-pilot areas over time. Project 3: An economic evaluation of the Advancing Quality pay-for-performance scheme The effect of the policy on the three outcomes listed above will be assessed using difference-in-differences regression analysis. Survival analysis will also be performed on the mortality data. Project 4: The future of 24/7 care: investigating the links between staffing levels, patient access and inequalities in health outcomes The effect of the policy on the three outcomes listed in the objective for processing will be assessed using difference-in-differences regression analysis.


Project 16 — DARS-NIC-326033-G1P7Q

Opt outs honoured: N, Y

Sensitive: Non Sensitive

When: 2016/09 — 2017/02.

Repeats: One-Off

Legal basis: Health and Social Care Act 2012, Section 251 approval is in place for the flow of identifiable data

Categories: Anonymised - ICO code compliant, Identifiable

Datasets:

  • Hospital Episode Statistics Admitted Patient Care

Objectives:

The Trauma Audit and Research Network (TARN) is a non-commercial organisation affiliated with the University of Manchester and funded by membership fees from participant trusts. TARN is the mandated organisation for the audit of trauma care in England, as set out in and endorsed by Standard ISB 1606. TARN holds Europe's largest database of traumatic injury, with hospitals within participating trusts entering details of relevant cases into TARN’s online system. Any trusts receiving trauma patients are participants. The aim of TARN is to support service improvement by providing analytical feedback to trusts, in reports such as the major trauma dashboards. TARN is the means by which funding mechanisms such as Best Practice Tariff operate. The TARN site hosts the Best Practice Tariff report, which major trauma centres use to report to commissioners and receive payment. To ensure that analyses are as accurate as possible, TARN needs to ensure that its dataset is as complete as possible. TARN requires HES data in order to measure completeness of submission to the TARN database for individual trusts and hospitals. Trusts are fed back results for their individual trust and information on completeness for all trusts is also displayed on TARN’s website. HES data helps TARN to help trusts ensure that all cases that are eligible for payment are submitted and can be reported on.

Expected Benefits:

The ultimate aim of TARN is to improve patient care. TARN carries out a number of planned and ad hoc analyses for hospitals and trusts to highlight good performance and areas where performance could be improved. A key element of TARN’s work is analysing rates of survival to identify hospitals with excess deaths, based on injury and demographic profiling. This work is dependent on the quality/accuracy of information emanating from the trusts so the use of HES data to monitor and improve completion rates contributes towards this overall aim. Using HES data to calculate data completeness assists in determining whether apparent poor performance is related to poor data collection, or whether other issues exist that need to be examined further. Using the HES data allows TARN to derive a denominator of expected cases, which in turn allows TARN to identify which sites may be missing patients. TARN can then work with those sites on case identification. An additional benefit of improving case identification is Best Practice Tariff, where Major Trauma Centres receive payment for meeting national standards on a patient by patient basis. The notification to trusts of potentially eligible patients that were not reported by the provision of patient lists linked to the TARN dataset using NHS number has resulted in significant improvements in data completeness. Improvements ranged from 5 to 50% with an average improvement of roughly 20%.

Outputs:

TARN’s outputs relevant to this application are: • Data completeness measures at site and trust level which are published on the TARN website and are accessible to the public. The number of TARN cases for a given time period is expressed as a percentage of the denominator derived from the processed HES data. The site is updated three times a year, in March, July and November. See the Performance Comparison section at https://www.tarn.ac.uk/Content.aspx?ca=15 for details. • Data completeness measures at site level which are published in the Major Trauma Dashboard. Circulated securely and confidentially to Major Trauma Centres in February, May, August and November. • Data completeness measures at site level which are published in the Trauma Unit Major Trauma Dashboard. Circulated securely and confidentially to Trauma Units in February, May, September and November. • Data completeness measures at site and trust level which are published in the TARN clinical reports. Circulated securely and confidentially to TARN member hospitals in March, July and November. Data completeness measures contain no identifiable or record level personal data. All published data is aggregated with small numbers suppressed in line with the HES Analysis Guide. • Patient lists sent securely and confidentially to sites for assistance with case identification. TARN sends hospitals lists of patients identified as TARN eligible from HES data for comparison with lists produced by local systems and procedures. These contain identifiable data as detailed above.

Processing:

TARN requires HES data from NHS Digital for patients with at least one ICD-10 code relating to traumatic injury. Any of these patients are potentially eligible for the TARN audit, which is concerned only with traumatically injured patients. TARN does not audit all traumatically injured patients: TARN further processes the received data to create a reduced cohort that correlates with TARN inclusion criteria. Records would be linked to hospitals and trusts using Organisation Data Service (ODS) codes. The purpose of this is to derive a number of TARN-eligible cases for each participating hospital and trust. This is then used to assist hospitals with case identification and to measure completeness of data. TARN requires 2 different copies of the HES data: 1. A pseudonymised version containing no identifiable information from which no data will be removed because of Type 2 patient objections. 2. An identifiable version containing NHS Number for all eligible patients and excluding data for all patients who registered Type 2 patient objections; Both datasets will be filtered by ICD-10 codes to identify cases that appear to be eligible for the TARN audit. The pseudonymised dataset containing all potentially eligible episodes will be used to determine the total number of eligible cases used in data completeness (case ascertainment) calculations. The identifiable dataset containing NHS number will be used to notify trusts of potentially eligible cases that were not reported by linking to the TARN database using NHS number. This is achieved through the provision of lists of patients (containing NHS number, arrival date, age, length of stay in hospital, discharge destination and the first 5 ICD-10 codes) sent securely and confidentially to the hospital that provided the treatment. Only data relevant to the specific hospital is sent to each hospital and no provider will receive data about individuals for whom they did not provide treatment. The data sent is derived from the HES data supplied by NHS Digital but is data already held by the trusts to which they are sent as the HES data originated from the trusts in the first place. Data will only be processed as described above and access will be restricted to substantive employees of the University of Manchester and the hospitals that provided the treatment with which data may be shared. No data will be sent to other organisations other than to hospitals about the patients they have treated. The HES data that TARN currently holds will be destroyed as it potentially contains identifiable information about patients who have opted out. Each year TARN will request the latest available year of final HES data and destroy the oldest year of data that TARN have if older than 3 years to ensure that TARN never holds more than 3 years at any one time.


Project 17 — DARS-NIC-333021-B6W2C

Opt outs honoured: Yes - patient objections upheld

Sensitive: Non Sensitive

When: 2018/03 — 2018/12.

Repeats: One-Off

Legal basis: Section 251 approval is in place for the flow of identifiable data, National Health Service Act 2006 - s251 - 'Control of patient information'.

Categories: Anonymised - ICO code compliant

Datasets:

  • Hospital Episode Statistics Admitted Patient Care
  • Hospital Episode Statistics Critical Care

Objectives:

Rheumatoid arthritis (RA) and its subset inflammatory arthritis (IP) are chronic auto-immune diseases with a prevalence of about 1.5% in the adult population. Patients with IP are at increased risk to develop cardiovascular diseases, lung disorders and certain types of cancer. However, limited data are available on risk factors for developing these outcomes. It is therefore important to follow patients since disease onset is over a long period of time. The Norfolk Arthritis Register (NOAR) includes patients with early IP and follows these patients over time. The purpose of the Norfolk Arthritis Register is to identify genetic and nongenetic factors which may be related to the onset of inflammatory arthritis, response to treatment and its long-term outcomes. The principal research objective of this substudy is to establish the frequency and risk factors for certain medical conditions known to be linked to inflammatory polyarthritis in particular coronary heart disease, stroke, certain types of cancer and infection. Data obtained from HSCIC will enable the University of Manchester to identify which patients have experienced the outcomes of interest: - co-morbidities associated with rheumatoid arthritis; cardiovascular disease, lung disorders, cancer and potentially other co-morbidities the University of Manchester plan to investigate. Based on the information the University of Manchester have already collected as part of the NOAR study they will establish whether cumulative disease activity, degree of physical activity, treatment or other disease related factors are risk factors or protective factors for the different outcomes of interest. The prevalence of RA in the UK is about 1.5% and patients with RA are at increased risk of cardiovascular comorbidities: eg unrecognized myocardial infarction (HR 2.13, 95%CI 1.13-4.03), and heart failure (HR 1.87, 95%CI 1.47-2.39). In addition, it has been estimated that over 50% of premature deaths in RA are due to cardiovascular disease. The output from this study will inform health professionals working in the field of rheumatology about the occurrence of the most common co-morbidities in patients with IP. Identification of possible (modifiable) risk factors will also enable early intervention to prevent the development of these comorbidities in the future.

Expected Benefits:

A better understanding of risk factors of comorbidities including cardiovascular diseases, lung disorders and cancer will directly impact of better intervention resulting in less morbidities and reduced likelihood of premature death, better quality of life for the patient and less direct and indirect costs for the NHS and society. The research the University of Manchester intend to carry out, utilising HES data, has the potential to influence the National Institute for Health and Care Excellence (NICE) guidelines and other guidelines regarding clinical practice in rheumatology based on any outputs. The overall aim of the research is to improve the health care of patients with IP/RA resulting in better quality of life of these patients. For most of the studies investigating the occurrence and development of co-morbidities, utilising HES data, the University of Manchester will develop prediction models identifying possible demographic (e.g. age, gender, smoking status) and clinical predictors of these co-morbidities. Identification of these predictors will inform health care professionals on how to manage IP/RA with an aim to reduce the risk of the development of co-morbidities. In addition, if certain lifestyle factors are identified, the study will seek further collaborations with patient organisations to provide better information for patients about these risk factors. Although the University of Manchester overall aim is to improve the health care of patients with IP/RA, it may take a number of years before the impact of any changes in management can be measured. Benefits achieved to date To date the University of Manchester already have shown that there is still an increased risk of mortality due to rheumatoid arthritis associated co-morbidities such as cardiovascular diseases and the prevalence of obstructive lung disease is higher in the rheumatoid arthritis compared to the general population based on spirometry tests. The University of Manchester have also shown that early treatment and achieving early remission is associated with less functional disability and improved survival in patients with inflammatory polyarthritis. Future expected benefits The University of Manchester envisage outcomes from the proposed research will also feed into NICE guidelines and published guidelines by the British Society of Rheumatology on management of arthritis and prevention of co-morbidities.

Outputs:

Outputs achieved to date A NOAR research forum took place at Norfolk and Norwich University Hospital September 2013 - presentations by NOAR consultant rheumatologist and senior research fellow; alongside other consultants and researchers. An abstract including HES data was presented at the international EULAR conference in 2014 (respiratory morbidity and mortality in early rheumatoid arthritis, S Ramanujam, DPM Symmons, T Marshall, J Chipping, IN Bruce, SMM Verstappen). The University of Manchester are awaiting HES follow-up data to finalise the manuscript. To date one abstract looking at the development of lung disorders in patients with inflammatory polyarthritis (IP)/rheumatoid arthritis (RA) was presented at EULAR in 2014. However, the number of patients with IP developing lung disorders was too small and the University of Manchester are currently awaiting follow-up HES data to increase the number of cases and write the manuscript on this topic. Future outputs Abstracts of outputs will be submitted for presentation at national (e.g. British Society for Rheumatology (BSR)) and international conferences (e.g. European League Against Rheumatism (EULAR) and American College of Rheumatology (ACR)). In general, the University of Manchester endeavour to write a manuscript of all abstracts presented at national and international conferences. The University of Manchester plan to target a number of publications looking at the occurrence and development of co-morbidities in patients with IP based on the information provided by HES and linked with NOAR data. Manuscripts will be submitted to high impact peer-reviewed journals in the field of rheumatology (e.g. Annals of Rheumatic Diseases, Arthritis & Rheumatology) or to general medical journals (e.g. The Lancet, Annals of Internal Medicine). Outputs will contain only aggregate level data with small numbers suppressed. Within the Arthritis Research UK Centre for Epidemiology all manuscripts containing data from the Norfolk Arthritis Register (NOAR) will be open access publications and thus freely available for everyone to access. Once manuscripts are accepted a lay summary will be written and uploaded on the Centre for Musculoskeletal Research (CfMR) website. Funders of the University of Manchester research, Arthritis Research UK and the NIHR, will be informed about the publication and all publications will be referenced in the annual reports of these funding bodies. After publication, clinically relevant publications may be cited on websites of charity organisations such as Arthritis Research UK and the National Rheumatoid Arthritis Society or the British Society for Rheumatology website. If the University of Manchester feel that the output is of high significance they will inform the press officer of the University of Manchester as well. It is very likely that one of these manuscripts will be submitted for REF (Research Excellence Framework - a system for assessing the quality of research in UK higher education institutions). The University of Manchester work closely with patient organisations (Arthritis Research UK and National Rheumatoid Arthritis Society) to implement their research findings into patient management information leaflet or website blogs. Outputs will contain only aggregate level data with small numbers suppressed in line with HES analysis guide.

Processing:

In the Norfolk Arthritis Register (NOAR) all patients have a unique ID number and after linkage with hospitalisation data all identifying data will be removed. Data will be linked with information obtained as part of the NOAR study including patients with IP. For linkage of HES data with clinical data obtained by NOAR the following procedures will be followed: 1) The University of Manchester will submit NHS number, patient ID, postcode, gender and DOB and their date of study entry to HES. 2) NHS Digital will return to the University of Manchester an episode level report of all admissions for these patients since 2000 or the date of NOAR study entry, depending on inclusion date. HES data will be stored in a separate password protected database to which only key personnel to the study have access. 3) As part of the NOAR study the University of Manchester collect data on dob, gender and postcode to determine socioeconomic status. This information is stored in the NOAR database. However, using the NOAR ID number the University of Manchester will link the NOAR demographic and clinical data with the HES data. This linked information will be stored on a standalone encrypted and password protected computer with limited access to researchers involved in data analysis of HES data. Before linking data between NOAR and HES, the University of Manchester will generate age and deprivation scores and remove dob and postcode from the NOAR database to be linked with HES data. 4) No individual identifiable patient data will be published. For publications the University of Manchester will aggregate data and events with small numbers (N<5 events) will not be published. All analyses are performed on groups of patients. Only eligible researchers and the NOAR data management team working within the Centre for Musculoskeletal Research who are substantive employees of the University of Manchester, will have access to the HES data and the University of Manchester will not share HES data with other organisations. Researchers analysing HES data linked with NOAR data are researchers working within the Centre for Musculoskeletal Research, University of Manchester. All data are saved on secure servers and researchers are not allowed to copy HES data on external computers/laptops. The University of Manchester will not share HES data with 3rd parties.


Project 18 — DARS-NIC-33318-X4Q1B

Opt outs honoured: N

Sensitive: Non Sensitive

When: 2017/06 — 2017/11.

Repeats: One-Off

Legal basis: Health and Social Care Act 2012

Categories: Anonymised - ICO code compliant

Datasets:

  • Hospital Episode Statistics Accident and Emergency
  • Hospital Episode Statistics Admitted Patient Care
  • Hospital Episode Statistics Outpatients

Objectives:

The Economic and Social Research Council (ESRC) have funded the Universities of Manchester and Lancaster to investigate the impact of hospital staff training in best care for patients with dementia, on key hospital outcomes for patients with dementia. This objective is part of a larger 5-year programme of ESRC-funded research aimed at improving the lived experience of people with dementia across many areas of their lives (http://www.neighbourhoodsanddementia.org/). There is a mix of training packages in dementia care being delivered in NHS general hospital settings as shown from the findings from the 2010 and 2012 NAD national surveys. Given the rapid development in this area much more understanding is needed about the impact of training on hospital outcomes for patients with dementia compared to those without dementia, the cost-benefits of training, and what types of training hospital staff find most useful. To the best of the University of Manchester and Lancaster's knowledge this is the first investigation of the effect of hospital training on hospital outcomes for patients with dementia using a large national sample of hospital admissions data. The study will use HES data to construct measures of key hospital outcomes (see below) for patients with dementia, and also for matched patients without dementia, and compare these to hospital-level information on staff training from the National Audit on Dementia (NAD) administered in 2010, 2012 and 2016, plus the University of Manchester and Lancaster's own national training survey which captures information about availability of training at an individual hospital level. The survey was administered in January 2017. The University of Manchester and Lancaster will also undertake an in-depth survey of random samples of individual staff at 20-30 hospitals and will be comparing the hospital-level HES-based outcomes data to that data for these specific hospitals, to examine relationships to staff knowledge and confidence in caring for patients with dementia. The focus of the statistical and Health Economics (HE) analyses of admissions data will be on the financial years 2010/11 and 2012/13 in the first instance. The analysis will be repeated including the final 2016/17 data when it becomes available. The HE analyses will in addition use A&E and OP data for these financial years. HES APC from 2005-6 (and the following 4 years) is requested as part of the process for identifying patients with dementia in 2010. Dementia is not routinely coded when a person enters hospital, therefore the usual process for determining dementia is to look for an ICD10 code for dementia in one of the diagnosis fields for any spell in the previous 5 years: this method has been previously used by the Care Quality Commission and the Centre for Health Economics at York. For this reason University of Manchester have requested APC data starting from 2005 to correspond to the first analysis year 2010/11. This includes spells in mental health hospitals as well as acute hospitals. However, the only data they are asking for over this period is the diagnosis code data and fields to identify spell dates. The statistical and health economics analyses of HES APC data will only be on the financial years 2010/11, 2012/13 and 2016/17 to correspond with the hospital-level information on staff training from the National Audit on Dementia surveys and also the University's own national hospital-level survey of dementia administered in January 2017. The statistical analysis will be based on comparing outcomes for patients with and without dementia in acute hospitals during these three years. For the statistical methods a matched cohort study of APC patients in acute hospitals with dementia will be matched to controls using several variables such as age and sex. The other variables requested will be used to construct outcomes such as length of stay or used as covariates in the statistical models where it is important to control for confounding. Only the three analysis years (i.e., 2010/11, 2012/13 and 2016/17) are required for the HES OP and HES A&E data where only health economics analyses will be undertaken. All the data handling and analysis will be conducted in the UK at the University of Manchester. None of the HES data provided by NHS Digital will be made available to third parties including Lancaster University.

Expected Benefits:

According to a NAD dementia survey carried out by the Royal College of Psychiatrists a quarter of acute hospital beds are occupied by people with dementia (2013). However, a high percentage of dementia is not recognised at admission and most hospital staff lack the knowledge of how best to care for patients with dementia. In 2013 Health Education England (HEE) was mandated to ensure training is made available so that all NHS staff looking after patients with dementia have foundation level dementia training, and a number of other local and national training initiatives have been launched. However, there is no available evidence, apart from anecdotal, on the impact such training is having on hospital outcomes for patients with dementia, on the cost-effectiveness of the training being delivered, or on what aspects of dementia care training hospital staff find most valuable. This study aims to produce that evidence, and in addition to identify those components of hospital staff training in dementia care that are particularly influential in affecting outcomes. However, it may be that it is found the impact of the training being delivered to be negligible relative to all other internal and external factors impinging on patient outcomes. In either case, it is envisaged that the published findings will inform policy at both the national and Hospital Trust level. Research findings will help Trusts to ensure that the dementia training they provide is cost-effective and best meets the needs of both staff and patients. The results will also identify Trusts where care outcomes for patients with dementia are particularly poor or particularly good, providing a sampling frame for developing more in-depth case studies to further increase knowledge of the role of training, other dementia initiatives, and other factors, in influencing quality of care. Consideration will be taken on how to sensitively feed the findings back to specific hospitals, in a way that can best help those struggling to provide good quality of care to patients with dementia. At the national policy level, it is envisaged the research will feed into the development of guidance on dementia awareness training and the care of dementia patients in hospital, and into the development of recommendations around future research.

Outputs:

Outputs will include a final report to the research programme funder ESRC, papers in peer-reviewed medical journals, and presentations at appropriate health research conferences. The final report will be submitted towards the end of the funded programme, in Spring 2019. There will be three main peer-reviewed journal publications: the first concerning the analysis of the 2010/11 and 2012/13 linked datasets and submitted in late 2017; the second extending the analysis to include the 2016/17 time-point, to be submitted in late 2018; and the third on the findings of the hospital staff survey. These papers will be published in open-access journals where they will be publically and freely available. Target journals will be PlosOne and BMC Public Health and target conferences will include the Health Services Research UK Meeting, the British Society of Gerontology conference, the joint Royal College of Nursing/British Geriatrics Society conference, the UK Dementia Congress conference and Kings Fund conferences. The University will set up a ‘Neighbourhoods and Dementia’ project website hosted at the University of Manchester and provide regular postings and invites for project engagement and interaction. This will include a blog and will allow for comments to be posted and the principal investigator will take responsibility for coordinating inputs onto the site. The research programme will take advantage of other social media outlets, such as twitter. The University will monitor access and record comments for evidence of impact. The University will disseminate the project through a variety of publication resources from high impact peer reviewed journals through to practice and professional outputs, such as briefing updates distributed through INVOLVE, DeNDRoN, Age UK, the Alzheimer’s Society and Alzheimer Scotland. The University will present the work at international, national and local conferences, sharing the conference stage with people with dementia and carers at every opportunity. The University will engage with television and media outlets and will look to develop a series of features on ‘neighbourhoods’ work on dementia in national newspapers, e.g. the Guardian’s Society page. In Sweden, similar media outlets and impacts will be sought. The University will host an international conference on ‘Dementia-Friendly Neighbourhoods’ at the end of the research programme to bring each work programme together; people with dementia and carers will be planners, coordinators and speakers at this event. In addition to the above, the research programme also includes a Dementia Use Involvement stream, which has provided a co-researcher education programme to a number of people living with dementia to enable them to participate as co-researchers in the study and to facilitate their further participation as co-developers of user-engagement outputs. An Impact on Policy conference is also planned for the end of the research programme with the Rt Hon Hazel Blears and Prof Alistair Burns. Outputs will report only results aggregated across all patients in any particular analysis, for example in the form of means, variances and regression coefficients. Graphs such as scatterplots may display derived values for individual hospitals, but without any hospital identifying information. Small numbers will be suppressed in line with HES analysis guidelines. Only the statisticians and health economists from the University of Manchester will have access to the HES data. Their University of Lancaster collaborative colleagues will have access to the aggregated outputs for the purpose of journal and conference abstract submissions. These outputs will not be given to a third party.

Processing:

The data will only be accessed by substantive employees of the University of Manchester and only for the purposes described in this document. HES admissions data will be used to derive the following patient-level outcomes: length of stay (LoS); emergency re-admission within 30 days after discharge and measures of care whilst in hospital, such as in-hospital falls and potentially avoidable conditions (eg UTIs and bed sores). In addition, NHS Digital provide an outcome variable (completed for the last episode) indicating whether a patient died in hospital or within 30 days after discharge. The use of hospital services data (LoS, A&E and OP visits) will be costed using the currency and service codes and the NHS Reference costs for the cost analysis models. A number of essential covariates will also be defined, including age, gender, additional diagnoses, and place of residence (e.g., care home). These variables will then be linked at the hospital level to the NAD training data and the University of Manchester and Lancaster's own national training survey in order that the analyses can be undertaken. A matched cohort study will be used to compare outcomes and costs for patients with a known diagnosis of dementia to matched patients without a known diagnosis, with a focus on how hospital training scores relate to differences in outcomes between these two groups. For financial years 2010/11 and 2012/13 and 2016/17, any patient with a dementia diagnosis (based on the 20 ICD10 codes for each episode) or a dementia report in any acute hospital or NHS Mental Health Hospital in England during the previous 5 years prior to their admission will be considered as having dementia. Control patients will be those with no recorded dementia diagnosis in the last five years. A subset of control patients matched on age, sex and other covariates to the group of dementia patients will be selected for the main analysis. Diagnosis classification will be carried forward to subsequent A&E, admissions and OP visits. Data for the same patient across time-points and products will be linked by using a common encrypted HESID. Descriptive statistics (mean, SD, median and interquartile range) will be produced to summarize each outcome and cost for each financial year 2010/11 and 2012/13 and 2016/17 for those with dementia versus matched control patients. Summary results will also be split by key variables including level of dementia care training, elective versus emergency admission, geographical region and discharge destination (e.g., care home). An additional descriptive analysis will examine the trend in identification of patients with dementia at admission across the period 2010/11 to 2016/17. Multi-level models will be used for the main statistical analyses, which will investigate the impact of training variables on outcomes and costs for dementia patients compared to those without dementia, controlling for confounding covariates. The LoS outcome will be analysed using a multi-level model for continuous outcome data or a survival approach, as appropriate. The mortality and re-admissions outcomes will be analysed by multi-level models for binary outcomes. The cost models will include the costs of the admission and associated use of A&E and OP visits. The costs will be analysed using the best fit distribution for skewed data (eg gamma or poisson) For all statistical and cost models the regression parameters (coefficients or odds ratios), confidence intervals and associated P-values will be reported. Only data aggregated at the hospital level will be reported; no patient-record level data will be produced as an output at any stage. For the analysis of the smaller survey of staff at 20-30 hospitals, the HES-based measures will be aggregated to the hospital-level, and reported descriptively. Individual hospitals will not be identified in any publication. Other than those already specified in the agreement The University of Manchester will not link this data to any other patient level dataset or attempt to re-identify patients or attempt to calculate dates of death using the data supplied by NHS Digital.


Project 19 — DARS-NIC-365623-T3W4S

Opt outs honoured: Y

Sensitive: Sensitive

When: 2017/09 — 2017/11.

Repeats: One-Off

Legal basis: Section 251 approval is in place for the flow of identifiable data

Categories: Identifiable

Datasets:

  • Hospital Episode Statistics Admitted Patient Care

Objectives:

The primary aim of the study is to establish the number and rate of sudden unexplained deaths (SUD) among psychiatric in-patients. National Confidential Inquiry into Suicide and Homicide by People with Mental Illness (NCISH) also conduct a detailed examination of circumstances leading up to a SUD in order to inform the process of improving quality of care. There has been growing concern about the incidence of sudden and unexplained death among psychiatric in-patients and little is known about any associations between clinical factors and these deaths. The aims of the Sudden Unexplained Death (SUD) study are to firstly determine the number and rate of sudden unexplained death among psychiatric in-patients in England and Wales, and secondly to conduct a detailed examination of the social and clinical circumstances leading up to the SUD. The data requested from HES are all psychiatric in-patient discharges with the discharge code of 4 (i.e. deceased), the local patient ID or NHS number, date of birth, the provider code, and the consultant code. This data enables the University of Manchester to identify the NHS trust where the death took place and enquire about whether the patient death falls into the study criteria of SUD. If it does, the University of Manchester then send a questionnaire to the consultant who was responsible for the patient’s care, in order to collect information on the antecedents of the death. The SUD study is part of the National Confidential Inquiry into Suicide and Homicide by People with Mental Illness (NCISH) and study findings are included in the NCISH annual report published each July. The team who carry out the SUDs are based at the Centre for Suicide Prevention and also work on other projects for the National Confidential Inquiry into Suicide and Homicide by People with Mental Illness (NCISH). Only personnel listed as working on the SUDs study have access to the SUDs data, which is kept on a separate computer system and which is not backed up by the NCISH server. The Inquiry was initially funded directly by the Department of Health in 1999 to collect and report sudden unexplained death of psychiatric in-patients with regard to identifying whether there was an association with poly-pharmacy (prescription of more than one anti-psychotic drug) and because of heightened public concern at the time about sudden death following restraint - particularly in respect to restraint of Black and Minority Ethnic (BME) patients. The Secretary of State for Health at that time stated that these numbers should be collected and reported annually which the Inquiry was commissioned to do. Currently, the Inquiry is commissioned by the Healthcare Quality Improvement Partnership, on behalf of NHS England and the Department of Health, to continue its collection of a national case series of suicides, homicides and the sudden unexplained deaths of in-patients cared for under mental health services. The current contract extends to March 2018 and the extract of in-patient deaths we are requesting is crucial for this year’s annual report. To date the findings of the study have contributed to major developments in mental health policy – recently the increased emphasis on the importance of in-patient services providing for physical health care needs as well as mental health needs and in the guidance from the DoH and Royal College of Nursing regarding safer restraint practices.

Expected Benefits:

NCISH are able to monitor the occurrence of SUD in England and Wales and establish any trends (as the study is on-going since it began in 1999). NCISH aim to increase the knowledge of the clinical antecedents of SUD, including the role of restraint, anti-psychotic drugs taken prior to death and any ECG abnormalities, to ultimately inform prevention efforts. In the annual reports NCISH provide clinical implications from the findings which are viewed as output for clinicians. NCISH’s findings from the SUD study are presented in annual reports and NCISH give recommendations and key messages for services that may help in prevention of these deaths. NCISH have also published a paper from the SUDs study which was published in the Journal of Psychopharmacology. This journal has a wide readership, including psychiatrists, pharmacists, nurses, and other clinicians who would gain knowledge into our findings on sudden unexplained deaths. Data collected since 1999 has enabled examination of trends of sudden unexplained death in England over time. The University of Manchester are also able to describe the characteristics of SUD cases and determine if any particular groups are at high risk. This data is presented in the annual reports which are available on the NCISH website. The University of Manchester also have a “report launch” event which, like a conference, the report findings are discussed by senior members of the team. This enables dissemination of the results to clinicians, policy makers, commissioners, and service users. The University of Manchester have also written an article on SUD which was published in a peer-reviewed journal. NCISH also actively seek dissemination of the findings and encourage discussion through daily tweeting on the NCISH twitter account. Input from service users and families and carers has been a feature of NCISH since it began. The original study questionnaires were developed following consultation with service user and family/carer organisations and many of the current items reflect their concerns. NCISH continue to invite suggestions for areas of further study from individual service users and representative organisations. NCISH issue a general call to service users and specifically invite national charities to contribute to their selection of topics to investigate in detail. NCISH also have a secure portal on their website where service users and family/carers can tell them about their experiences so that these can be investigated in their studies. In terms of dissemination, the NCISH website has a new page for service users, entitled “What our findings mean for your care”, based on the 2015 annual report. Their use of social media has helped them reach service users far more widely – people who are not attached to any organisation. Much of social media response to the annual reports, including the 2014 Q&A and the 2015 panel questions, has come from service users. This has helped them to develop a style on social media that is suited to a non-professional audience. In terms of other benefits, NCISH are in a unique position in being able to determine the number of SUD cases at a national level. Findings from the most recent annual report found that a number of SUD in younger psychiatric in-patients continue to occur. NCISH intend to study these deaths more closely for possible antecedents and background risk. Recommendations already made by NCISH are firstly that these deaths should always be subject to investigation and reporting by the mental health trust and to coroner referral. Secondly that wards should take precautionary measures including physical health assessment as soon as practicable after admission. Thirdly, avoidance where possible of high drug dosage and polypharmacy. These recommendations were disseminated at the NCISH report launch which was attended by commissioners, policy-makers, service users, clinicians, and other relevant groups. The recommendations have also been regularly cited on their twitter account. NCISH partnership group includes the anti-stigma campaign Time to Change and its constituent mental health charities Mind and Rethink. These organisations are now in a position to comment on the future priorities of NCISH more directly and to prepare public comment on their publications before they appear in print. NCISH are currently working with their funders and Independent Advisory Group on a Strategy for Engagement summarising recent work, current plans and future developments. This strategy includes a section on service user engagement.

Outputs:

The data is requested to establish the number and rate of SUD in psychiatric in-patients in England and to get a better understanding of the circumstances leading up to a sudden unexplained death. The study is on-going. As covered, Identifiers will only be shared with the relevant consultant responsible for the individual’s care in order to obtain the further information specified. Once received NHS Digital data is deleted. All outputs from analysis will be in aggregate form with small numbers suppressed in line with the HES Analysis guide and therefore all sudden unexplained death (SUD) cases are always anonymised and there is no risk of re-identification. Information on the number, rate and trends of SUD as well as key social and clinical characteristics are provided in NCISH annual reports publically available on the NCISH website. NCISH have also written a publically available paper published in the Journal of Psychopharmacology, available at: http://jop.sagepub.com/content/25/11/1533.short

Processing:

Receiving admission and discharge data of patients under mental health services enables the University of Manchester to identify those who have died as a psychiatric in-patient. Through the NHS hospital number or Local Patient Identifier, these patient details are then followed up with NHS Trusts (using the Provider Code) in order to establish whether the death would fulfil the criteria for the SUD study. For those deaths that do meet the SUD criteria, the University of Manchester then ask the clinician responsible for their care (using Consultant Code) to complete a detailed questionnaire on the clinical circumstances and antecedents of the death. The University of Manchester also collect information on whether restraint was involved, the anti-psychotic drugs taken prior to death, and any electrocardiogram abnormalities. As part of NCISH, the SUDs study based at University of Manchester has National Information Governance Board for Health and Social Care (NIGB) Section 251 approval. All sensitive data collected from HES is protected by the following strict processes: the data is stored on a password protected standalone network accessible only to staff engaged on the project; the network computers are in rooms which are locked when unoccupied and are on a corridor accessible by swipe card only; the rooms are on the 2nd floor of a building with 24 hour security; electronic copies of data are stored in a locked room in a locked filing cabinet. Only individuals who are substantively employed by the University of Manchester and are directly involved in NCISH will have access to the data. The HES data which is processed is minimised at NHS Digital using the filters: The HES data requested is filtered to only those episodes of care which are finished consultant episodes where the patient has been discharged from hospital with a discharge method of ‘4’ – died. Furthermore, the data is filtered by ICD10 code to only include episodes where the primary diagnosis begins with ‘F’ – Mental and behavioural disorders or where the primary diagnosis contains a code in the ‘Z’ chapter of ICD10 which is “Factors influencing health status and contact with health services”, specifically those “persons with potential health hazards related to socioeconomic and psychosocial circumstances” and where the Main Specialty/Treatment Specialty of the consultant is in the field of learning disability, mental illness, psychiatry or psychotherapy.