Examining the characteristics and predictors of alcohol withdrawal readmissions and emergency department attendances
University of Hull · Academic
Expired The latest version ended on 30 April 2022. The September 2026 register still lists the agreement, but its term has passed.
- Reference
- DARS-NIC-226185-B6C2J
- Latest version
- v3.4
- Term of latest version
- 1 May 2021 to 30 April 2022
- Start date
- 1 September 2019
- Data controller
- Sole Data Controller
- Commercial purposes
- No
- Sublicensing
- No
- Files released to date
- 2
Why the data was released
Objective for processing
With over 1 million alcohol-related hospital admissions the burden and unmet needs of excessive alcohol consumption and related conditions remain a priority under the NHS 10-year plan and for Public Health England (PHE, 2019).
In 20016/17 there were over 1 million alcohol-related admissions of which 300,000 hospital admissions were wholly attributable to alcohol in England, a rise of 29% over the last decade (PHE, 2018). With 65% of these admissions attributable to mental and behavioural disorders due to alcohol, the characteristics among those experiencing alcohol withdrawal and the relationship with readmission rates are not known.
Unplanned alcohol-related hospital admissions have been associated with physical multi-morbidity, coexisting mental health conditions and socioeconomic deprivation (Payne et al, 2013). Recent research in the US (Yedlapati and Stewart, 2018) has identified hospital readmission rates following alcohol withdrawal are linked to discharge against medical advice (AMA) and the complexity of the patients (i.e. co morbid mental health). Furthermore, being discharged against medical advice is associated with subsequent AMA events (Kraut et al, 2013). Whilst individual factors associated with re admissions rates for unplanned alcohol withdrawal can be identified, incomplete episodes of care for alcohol withdrawal may influence a return to excessive drinking on discharge and subsequent readmission.
The period required to complete a programme of alcohol withdrawal varies according to the needs of the individual although commonly require 5-7 days of clinical monitoring and treatment (NICE 2010,2011). HES-APC data contains primary and secondary diagnostic codes that allow for exploratory analysis of readmission rates amongst those with alcohol dependence including specifically alcohol withdrawal.
Alcohol dependent patients (ICD-10 code: F10.2) who receive unplanned hospital care may experience alcohol withdrawal (F10.3/F10.4) due to the abrupt cessation or substantial reduction in alcohol consumption. Exploring the length of stay and characteristics for those experiencing unplanned alcohol withdrawal and the association with readmission will help to understand the impact of clinical practice and patient factors on their representation rates to A&E and re admissions. This study aims to examine routine hospital data to examine characteristics and predictors of alcohol withdrawal re admissions and ED attendances in England by linking Hospital Episode Statistics Admitted Patient Care (HES APC) data sets with HES Accident & Emergency (HES ED)
A previous study (Phillips et al., 2019) which was a PhD study considered the burden of alcohol disorders on ED and Inpatient Care and is published in Alcohol and Alcoholism (28 June, 2019). The study examined the characteristics of individuals with acute and chronic alcohol disorders using HES-APC and HES -ED data sets from 2009/10. This data that was used for this PhD is no longer accessible as it has been destroyed.
The study only used 2009/10 data. Since this time there have been changes in:
• quality of data reporting to HES
• increases in the recorded levels of alcohol withdrawal admissions from 21,590 in 2009/10 to 27,530 in 2017/18 despite no significant change in community prevalence of alcohol dependence
• significant changes in the commissioning of alcohol treatment which may have impacted on the care pathways for alcohol treatment
• Data published in the US indicating readmission rates for alcohol withdrawal are predicted by length of stay and discharges against medical advice. These areas have not previously been considered in UK populations.
The PhD study did not consider the entire inpatient dataset and differs in the following ways:
• The data requested in the PhD only included admission data following emergency presentations. This Agreement includes admission data for those admitted via emergency presentations, booked and elective admissions. This is to include those cases where an elective admission is planned for and the patient experiences alcohol withdrawal.
• The data used in the PhD only used data where an individual case in the inpatient data could be matched to a coded ED presentation. As significant minority of ED presentations do not receive a coded ED ‘diagnosis’ (i.e. 36%). Hence, the PhD did not interrogate all possible cases if they meet the inclusion criteria set out in the protocol for this study.
• The previous study did not examine predictor of readmission or re-attendance, the impact of length of stay nor discharges against medical advice. The study referenced in this Agreement will examine alcohol withdrawal admissions and consider the characteristics that predict readmission. Principally, the hypothesis will be drawn from the US study that indicates shorter lengths of stay will predict greater likelihood of readmission and ED re-attendance.
To summarise; this study plans to examine the impact of length of stay and discharges against medical advice in predicting readmission and ED re-attendances within 30 days following alcohol withdrawal admissions.
This Agreement will therefore build on the original work completed by Phillips et al (2019) and inform future research aimed at reducing the nature and burden of unplanned alcohol treatment within non-specialist care settings.
The lawful basis for undertaking this research under the General Data Protection Regulation (GDPR) articles are:
• Article 6 (1)(e): processing is necessary for the performance of a task carried out in the public interest, improving the care for people with alcohol dependency and related conditions.
• Article 9 (2)(j): processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) based on Union or Member State law which shall be proportionate to the aim pursued, respect the essence of the right to data protection and provide for suitable and specific measures to safeguard the fundamental rights and the interests of the data subject
The data previously requested will achieve the aim identified above by using encrypted HES ID to cases common to both the ED and APC data sets. No identifiable data will be shared to carry out this process. The ICD-10 codes will be searched to identify those who experienced alcohol diagnoses, including alcohol dependence, alcohol withdrawal and alcohol withdrawal with delirium. Previous research has identified an association between community prevalence and alcohol-related admissions (Brennan et al, 2019), furthermore clinical practice within hospital settings is also highly variable and therefore to obtain a robust understand of the impact of the length of stay for alcohol withdrawal data representing national coverage is required.
Socio-Demographic characteristics that influence clinical outcomes and patient engagement within these groups will be explored to characterise both cases (i.e. those who experience alcohol dependence and withdrawal) and controls (i.e. those without an in year history of alcohol dependence and withdrawal). Overall A&E attendances and admissions (i.e. hospital spells) will be identified. The length of hospital stay (LOS) will be identified for each spell where a patient experienced alcohol withdrawal (i.e. F10.3/4) statistical analysis will explore the association between LOS and subsequent re admissions and A&E attendances.
Common concomitant conditions will be examined and individuals with alcohol withdrawal and a common concomitant condition will be compared to controls without a recorded history of alcohol disorder (i.e. F10) but share the common concomitant condition. This analysis will help identify the relative burden of alcohol withdrawal accounting for the presence of co morbid health problems.
The hypothesis is that a shorter length of stay experienced by those with alcohol withdrawal during hospital admissions will be associated with greater ED re attendance and hospital readmission.
The research study requires demographic, and socioeconomic data to characterise cases and controls and to allow for matching cases using propensity score matching. This will allow the examination of the associations between individual characteristics and readmission.
Additionally, data related to diagnosis will be essential in discriminating between cases and controls. Furthermore, being able to match cases and controls on age, gender and shared primary diagnosis (i.e. gastritis) will allow for the examination of the relative burden of alcohol on readmission. This is important, as gastritis may independently be associated with greater readmission rates.
A series of analyses will be undertaken to examine characteristics between cases and controls (i.e. those without alcohol dependence/withdrawal). Demographic factors (age, gender, ethnicity, marital status, living circumstances, IMD, etc), clinical characteristics (length of stay, category of co-morbidity, A&E clinical diagnosis, emergency v elective admissions, previous discharges AMA, etc) and health service use (number of admissions, A&E re-attendances) will be explored against readmission.
Data Previously Requested
i. Access to national data allows for examination of this hypothesis, which is unable to be achieved efficiently through prospective research. If an association is found between LOS and readmission and A&E attendance this will inform policy and future research applications through Research for Patient Benefit Grant or Programme Development grants applications.
ii. The level of data: Pseudonymised was requested to allow for linkage between data sets at a ‘case’ level for the whole of England.
iii. One year of data provided an adequate sample frame
iv. There is significant variation in the prevalence of alcohol disorders across the country and variation in admissions and practice – obtaining a countywide sample allows the study to establish national norms.
Published data for 2017/18 (PHE, 2019) identified that when both secondary and primary diagnosis were considered, alcohol withdrawal, and alcohol withdrawal with delirium accounted for over 28,000 alcohol-related hospital admissions. Previous research has identified an association between community prevalence of alcohol dependence and alcohol-related admissions (Brennan et al, 2019), furthermore, clinical practice within hospital settings is also highly variable and therefore to the most robust examination of the impact of LOS on hospital readmission and ED re-attendance a national spread of data is required.
v. This method of research is the least intrusive option to explore this question and is supported by the recently published Framework for Mental Health Research (DH, 2017), which supports the use of existing data sources to increase or understanding of mental health problems and how healthcare is provided. The fields requested have been kept to a minimum and do not include patient identifiable fields.
The University of Hull (UoH) is the sole Data Controller and also processes the data for the purposes described in this Agreement. The Professor of Nursing (Addictions), University of Hull is the Chief Investigator for the study and is responsible for the overall design, and conduct of the study. An Epidemiologist, employed by the University of Hull, will lead on the data management, and analysis. A Professor of Health Service Research, employed by the University of Kent, is providing methodological support and is a collaborator on the study.
Data shared with the Collaborator (University of Kent) will be exclusively aggregate level summary table data with small numbers suppressed. All data is stored and processed within the AIMES Data Safe Haven (DSH). Researchers (employees of the University of Hull only) access the environment via a secured VPN connection and work on virtual machines within the environment. No data ever leaves the environment nor do researchers have the capability to move the data.
• No data is or will be stored at the University of Hull – in the original Agreement the data was disseminated to the University prior to upload to the AIMES DSH. That data was only ever stored on a single encrypted laptop by the Hull Health Trials Unit Information Systems Manager, and was wiped as soon as the data was uploaded to AIMES DSH.
• All staff handling the data are employees with the University of Hull or have honorary contracts with the University.
Only named UoH researchers will have access to the data. The Collaborator will be given opportunity to make comment on the summary data. Based on those comments further analysis may be considered but the decision as to what analyses to perform will be made exclusively by the research team at UoH.
Importantly the comments will not drive further data requests, change the variables requested or the means of storage, processing or data flow. The decision as to what variables are included in the request to NHS Digital will be made exclusively by the research team at the UoH. Aggregate data will be shared using the Hull Health Trials Unit (HHTU) managed Box cloud storage. University of Kent (The Collaborator) will offer statistical support only and will have access to aggregate tables with small numbers suppressed in line with the HES analysis guide in read-only format only. In summary, the University of Kent has no role in determining the means by which the data are processed or influence over the outputs and dissemination.
Additional Purpose (Version 3)
The aim of the proposed new purpose is to utilise the dataset requested above to characterise risk of patients experiencing alcohol withdrawal when admitted to acute hospitals. This proposed analysis has not been covered as part of the existing analysis which has been approved. Therefore the applicant, who is a PhD student and honorary Research Associate at the University of Hull, is requesting access to the same datasets in order to conduct the analysis for the new purpose.
Those with alcohol use disorders exert a disproportionate impact or burden on the NHS, usually accessing care via emergency departments (Phillips et al, 2019). Recent reports identify that the majority of patients experiencing alcohol dependence, including alcohol withdrawal (AW) receive treatment in acute hospitals – outside of specialist services where these skills and competencies are concentrated (Roberts et al, 2020a). During 2017/18 there were 5,887 planned admissions to specialist inpatient units for alcohol use disorders, and over 80,000 for alcohol dependence and AW across acute hospital trusts in England, with an estimated cost of £1,500 - £2,704 per admission (NICE, 2011; Phillips et al, 2019). There were 36,6000 more patients admitted for alcohol-related reasons in 2018/2019 compared with 2012/2013 (PHE, 2020).
Provision of specialist inpatient services for AW is dwindling year on year, with a concomitant increase in alcohol related hospital admissions in England (Robertson et al, 2017; Drummond, 2017; Roberts et al, 2020b; Phillips et al, 2020a). Roberts et al (2020b) conducted an examination of the relationship between alcohol related admissions, specialist alcohol treatment provision and deprivation levels since 2012. They found that the national rise in alcohol related hospital admissions may be fueled by funding cuts to specialist alcohol services (Roberts et al, 2020b). They also highlighted that there is a statistically significant association between reduction in net expenditure for alcohol misuse treatment per 1000 people and increased rate of alcohol related hospital admissions (Roberts et al, 2020b).
Phillips et al (2020a) assessed the relationship between specialist and non-specialist admissions for alcohol withdrawal and found a strong statistical association between increase in alcohol related hospital admissions and reduction in non-specialist admissions. They highlighted the displacement of cost reductions in specialist services to non-specialist settings, and the need for resource development for non-specialist staff to meet the care needs of this population (Phillips et al, 2020a).
Limited use of validated tools, together with poor identification and inadequate assessment of AW in non-specialist settings (Barnaby et al, 2003; Mitchell et al, 2012) is likely to result in deficient clinical management which has been associated with discharges against medical advice and subsequent readmission (Mullins et al, 2017).
Barriers to the effective implementation of protocols to enhance the identification and assessment of AW within non-specialist settings relate to; nurses feeling inadequately trained, uncomfortable with regards to the high doses of medication patients often require, limited time to assess patients and the need for simpler assessment tools (Roberts & Drummond, 2019; Glann et al, 2019). There are a small number of limited studies which aim to improve identification and management of AW in non-specialist settings – one of which considered a symptom-based tool which is predictive of Alcohol Withdrawal Syndrome, the Prediction of Alcohol Withdrawal Severity Scale (PAWSS; Maldonado et al, 2014). However, the current standards of practice for identifying and managing AW in non-specialist settings has not been studied, and there are no tools which exist to identify and target individuals likely to experience AW. Therefore, the development of a series of tools to identify ‘at risk’ individuals would allow for targeted care and result in better outcomes.
The first step in developing these tools is the proposed new purpose which is a detailed analysis of Hospital Episode Statistics (HES) Data to characterise risk of AW based on the information collected on admission to hospital. The findings of this study will underpin a wider programme of research which incorporates observational work, staff interviews and development of interventions/tools to support non-specialist staff in managing AW. It is important to understand the characteristics that place individuals at risk of AW prior to developing tools to support their care.
Data Previously Requested
The data required for the new purpose is the same datasets which were requested (and granted) for the existing study, with an extension until April 2022 to allow time for completion of the new analysis. Access to both HES APC and AED datasets is being requested as required for the proposed analysis.
The Professor of Nursing (Addictions) will remain the Chief Investigator, with the PhD student and honorary Research Associate at the University of Hull, leading on the project for the new purpose. The Professor of Nursing is the primary supervisor for the Research Associate and will provide support and guidance accordingly. The Professor of Health Service Research (University of Kent) will have the same involvement as the existing study – as a Collaborator and provider of methodological support who will only have access to aggregated data with small numbers suppressed. The Research Associate is funded by the Society for the Study of Addiction (SSA), however the SSA will not be involved in analysis or management of the data.
Processing activities
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).
Data has already been provided under v0 of this data sharing Agreement.
Data extracts will be stored and processed with the University of Hull Data Safe Haven (DSH). The DSH is supplied as a service by AIMES management services limited. Their product name is a Trusted Research Environment (TRE).
• The University of Hull DSH is provided as a managed service by AIMES
• Data will be stored within AIMES data centre.
• The environment provided is logically separated for University of Hull and branded as such
• AIMES are a data processor under the contract with University of Hull. University of Hull is the sole data controller for this project.
• The AIMES service was procured under the GCLOUD framework. This is a crown commercial services framework contract between the government and suppliers.
• AIMES have system administrative access to the system but act on the instructions of UoH DSH admin. AIMES are processor for the purposes of provisioning, securing and back-up of the service.
• All processing / analysis of the data is performed by University of Hull staff. AIMES staff will not be processing the data.
• University of Hull researchers who work remotely (within the UK) from home are using university managed devices and as such their network traffic is tunnelled through the Univeristy of Hull via the Global Connect VPN. The connection to AIMES is via a virtualised desktop VMWare Horizon. As such whilst the researcher is accessing from home no data leaves the AIMES datacentre in Liverpool.
• AIMES TRE works via a secured VPN (Virtual Private Network) connection. This means that whilst researcher access the environment from their computers, all activity occurs on virtual machines within the TRE. No data ever leaves the secured environment and there is no ability for researchers to copy or move data themselves.
All research activity will be within the Data Safe Haven.
Only those employed within the Institute for Clinical and Applied Health Research (ICAHR) University of Hull involved in this study, or employed under honorary contract, will be processing the data and have been trained in data protection and confidentiality . Staff will sign specific terms and conditions prior to been given access to the DSH linked to this project DSA.
Data related to this project is logically separated within the DSH meaning only researchers working on this project can access this data.
The project will not involve the flow of data into NHS Digital from the research team.
Existing data variables will be transformed to create dummy variables for use in the analysis which will include categorical variables relating to diagnosis, length of stay, etc. A&E Data will be explored to identify the frequency of A&E attendance and common presentations with linkage between inpatient spells to observe the frequency of A&E attendances in relation to admissions for alcohol withdrawal. Similarly, the frequency of inpatient spells will be calculated.
Data linkage will only occur between HES APC data and HES A&E data using the unique HES ID provided for the study. The use of the A&E attendance dates and admission dates together with the HES ID to identify A&E attendances leading to admission - parameters of age and gender will be used as secondary matching variables. No other data sets will be linked or matched to the HES data.
There will be no requirement or attempt to re-identify individuals
For this project aggregate level derived data will be shared with collaborators throughout the analysis period. Based on feedback and comments an iterative analysis and review process will be followed. Aggregate data will be shared using the HHTU managed Box cloud storage instance. This instance is administered by HHTU admin staff, uses only EU data storage and has additional governance modules allowing specific data residency policies to be applied. All aggregate data with small number suppressed will be shared on a read-only basis.
Data shared with the Collaborator (University of Kent) will be exclusively aggregate level summary table data with small numbers suppressed. No record level data will leave the UoH Data Safe Haven, within which only named UoH researchers will have access to it. The Collaborator will be given opportunity to make comment on the summary data. Based on those comments further analysis may be considered but the decision as to what analyses to perform will be made exclusively by the research team at UoH. Importantly the comments will not drive further data requests, change the variables requested or the means of storage, processing or data flow. The decision as to what variables are included in the DARs will be made exclusively by the research team at the UoH. Aggregate data will be shared using the Hull Health Trials Unit (HHTU) managed Box cloud storage. University of Kent (The Collaborator) will offer statistical support only and will have access to aggregate tables with small numbers suppressed in line with the HES analysis guide in read-only format only. In summary, the University of Kent has no role in determining the means by which the data are processed or influence over the outputs and dissemination.
Aggregate level data is exported from the DSH via the AIMES digital airlock. This is a two-stage process and will include fully auditable approval process ensuring that the data conforms to the DSA. All ingress and egress are auditable via:
• A data egress approval document signed by the project lead (data applicant) authorising that the data to be egressed conforms to the DSA. This will include the destination location outside of the TRE
• A copy of the data egressed from the TRE is retained
• A datetime stamped record of who released the data is retained in a record that cannot be edited by UoH
At the end of the DSA period the entire project can be formally torn down and AIMES will issue a certificate of data destruction as in line with NHS Digital requirements.
There will be no data linkage undertaken with NHS Digital data provided under this Agreement that is not already noted in the Agreement.
Processing activities – New Purpose:
All previously agreed procedures and activities will be adhered to – the Data Sharing Framework Contract will be complied with, and storage, processing and sharing of data will be conducted as above.
The new purpose will involve creation of dummy variables from the existing data to explore characteristics of those experiencing AW. ICD-10 codes for primary and secondary diagnoses, alcohol related diagnoses and cause of injury or poisoning will be used to identify which patients experienced AW (F10.3, F10.4). Demographic and clinical data (including admission time and method) for patients who did and did not experience AW will be explored in order to characterise risk.
Data linkage will only occur between HES APC data and HES A&E data using the unique HES ID provided for the study. The use of the A&E attendance dates and admission dates together with the HES ID will be used to identify A&E attendances leading to admission. No other data sets will be linked or matched to the HES data. There will be no requirement or attempt to re-identify individuals
Finished consultant episodes (FCEs) will be explored for those who did and did not experience alcohol withdrawal. This will allow for consideration of complexity of care and any differences for those patients experiencing AW.
A prognostic model will then be developed to allow for allocation of an alcohol withdrawal risk score to patients on admission to the acute hospital setting. The utility of the prognostic model will be tested using a split-sample approach whereby a random sample of HES data is used to develop the risk score, with the remaining sample used to test the reliability and validity of the generated score. Data will not be accessed or processed by any other third parties not mentioned in this Agreement.
Expected output
Original Purpose (Version 0):
All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.
Outputs will be in the form of peer-review publications and conference presentations accessed by academics, commissioners and clinicians, with lay summaries made available for service users and the public. Summaries of findings will be used to inform patient and public involvement (PPI) and clinical staff focus groups supporting a programme grant application.
Peer-review publication: The University of Hull has identified the Journal Alcohol and Alcoholism as a key journal which is affiliated to the Medical Council on Alcohol, a national body which supports the translation of evidence to practice. All publications from the University of Hull are promoted through websites, blogs, and other social media (Twitter, LinkedIn)
The University of Hull aim to target National conferences (i.e. MCA Annual Conference, Society for the Study of Addiction (SSA) Annual Symposium) and international conferences (i.e. Research Society of Alcohol (RSA), USA). The latter publishes accepted abstracts in Alcohol: Clinical Experimental Research, a recognised peer-review journal.
The Lead Researcher is a member of a number of national expert groups, including the Alcohol Treatment Expert Group, Public Health England, which publishes guidance for providers and clinicians. The outcomes of this study will be reported to this group to support the recently published guidance by Public Health England, which considers the patient pathways between secondary and specialist care for alcohol dependent patients.
The Lead Researcher is also supporting the Humber, Coast and Vale Sustainability and Transformation Partnership (STP) in defining health priorities. The findings from this national study will also inform the work with a variety of NHS Trusts who are developing strategic responses to increasing alcohol-related admissions as part of the implementation of the NHS Long-Term Plan.
Analysis of data to inform publication is estimated to take 6 months with publications submitted in 8-10 months after commencement of data processing. Conference presentations will commence at the end of 2020 (subject to change and availability due to COVID-19). This has been achieved in the form of the funding which has been awarded by the Society for the Study of Addiction for the PhD Scholarship which encompasses working on the proposed new purpose:
• The COVID-19 pandemic has caused limitations and subsequent delays in achieving the initially stated outputs – this is due to challenges of working from home, internet access and staffing issues.
• The output tables for the existing purpose are currently being finalised, and this extension will allow for this to complete and for outputs to be generated as initially anticipated.
• In addition to this, the new purpose for the data will be completed within timelines as per the amendment application (please see attached protocol for the new purpose).
Additionally, the data will support the development of postgraduate, postdoctoral and research grant applications aimed at addressing the burden and unmet needs of alcohol patients receiving unplanned care.
Additional Purpose (Version 3):
Outputs of the new purpose will be as above, as the topic and aims are aligned, and predictors of alcohol withdrawal complement the work on length of stay, clinical competencies and readmission for those experiencing alcohol withdrawal.
In addition to the above, the new purpose will be published as part of a PhD Thesis, as part of a PhD Scholarship funded by the Society for the Study of Addiction (SSA), who will be credited on the resulting work. This work will support development of postdoctoral opportunities, and will form the basis of the PhD students application for postdoctoral funding following completion of her PhD in Autumn 2023.
Analysis of data to inform publication is estimated to take 6-9 months, with publications submitted approximately one year after commencement of data processing. Conference presentations of initial data will commence as early as Summer 2021.
Expected measurable benefits
The outputs of this study will add to the growing literature and research related to the reduction of alcohol-related hospital burden which highlights the need to tackle unmet needs of patients with alcohol disorders to reduce the overall burden on health service provision. This study will identify the impact of length of stay on alcohol-related readmission rates.
The Lead Investigator retains membership of the Expert Group on Alcohol Treatment, Public Health England and has advised on the development of guidance, Developing pathways for referring patients from secondary care to specialist alcohol treatment care pathways (PHE, 2018). These analyses will further inform the development of improved care pathways for those experiencing unplanned alcohol withdrawal through the publication of peer-reviewed evidence and additional research projects designed to examine the service users experience and outcomes of specialist interventions (i.e. medically assisted alcohol withdrawal) in the non-specialist settings. The findings will be shared with Public Health Leads for alcohol and unplanned care.
The University of Hull works closely with commissioners of services within Yorkshire & The Humber and is also NIHR CRN Speciality Lead for Mental Health. Outcomes of this study will be shared with key stakeholders across the locality and region to inform service development. This will include working with the newly developed Yorkshire & Humber Applied Research Collaborative funded by the NIHR which is focused on reducing demands on Emergency Departments.
Additional Purpose (Version 3)
The new purpose will also contribute to the body of research created as part of the research conducted by the Chief Investigator at the University of Hulls Institute for Clinical and Applied Health Research (ICAHR). Understanding of the impact of length of admission on readmission rates will be complemented by understanding characteristics which predict alcohol withdrawal in the first place.
NHS England’s Long Term Plan has a number of key themes, and within the prevention theme Alcohol Care Teams (ACTs) are identified as a priority. The Chief Investigator is part of the national ACT Working Group, and outputs of this and other studies will inform wider government strategy development (following peer review)
In addition to the above, the new purpose will add to existing research on alcohol withdrawal in the acute hospital setting. It will also provide an opportunity to develop a risk score for alcohol withdrawal for patients admitted to acute hospitals. This will be utilised as part of the Research Associates wider programme of research which aims to develop tools to support clinicians in the acute hospital setting to manage alcohol withdrawal more effectively. This will lead to improvement in understanding of caring for this particular patient group in this setting.
This study is funded by the Society for the Study of Addiction (SSA) as part of an internationally competitive scholarship awarded to the Research Associate. There are strict reporting and submission requirements as part of this scholarship, and the Research Associate will aim to publish this study in a peer reviewed journal by the end of 2022.
Benefits reported so far
The analysis which has been conducted so far is supporting development of peer review publications. The Principal Investigator for the data study is part of the NHS England Alcohol Care Team working group, and his engagement in this work is supported by access to the data.
The data from this study has supported a successful bid for funding of an Alcohol Care Team at Hull Royal Infirmary.
The data has also supported development of a grant application focussing on alcohol withdrawal admissions which result in self discharge (or discharge against medical advice)
Datasets on the latest version
Legal basis for provision: Health and Social Care Act 2012 - s261 - 'Other dissemination of information'
| Dataset | Type of data | Sensitivity | Frequency | Confidential data |
|---|---|---|---|---|
| Hospital Episode Statistics Accident and Emergency (HES A and E) | Anonymised - ICO Code Compliant | Non-Sensitive | One-Off | Does not include the flow of confidential data |
| Hospital Episode Statistics Admitted Patient Care (HES APC) | Anonymised - ICO Code Compliant | Non-Sensitive | One-Off | Does not include the flow of confidential data |
Files released
Files released counts only files released externally by DARS. Access granted in NHS England's own systems, such as its Secure Data Environment, is not included.
Patient opt-outs were not applied to any of the 2 files released under this agreement, across every version. About opt-outs
No files recorded as released under the latest version. 2 were released under earlier versions, shown in the version history.
Version history
The register lists each renewal of this agreement as a separate row. This site has 4 versions.
DARS-NIC-226185-B6C2J-v3.4 1 May 2021 to 30 April 2022
- Title
- Examining the characteristics and predictors of alcohol withdrawal readmissions and emergency department attendances
- Commercial
- No
- Sublicensing
- No
- Datasets
- 2
- Files released
- 0
Datasets: Hospital Episode Statistics Accident and Emergency (HES A and E); Hospital Episode Statistics Admitted Patient Care (HES APC)
What changed from DARS-NIC-226185-B6C2J-v2.2
Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.
| Field | Was | Became |
|---|---|---|
| Start date | 2021-05-01 | |
| End date | 2022-04-30 | |
| Hospital Episode Statistics Accident and Emergency (HES A and E): legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' | |
| Hospital Episode Statistics Admitted Patient Care (HES APC): legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' |
Objective for processing
[3 paragraphs unchanged]
The period required to complete a programme of alcohol withdrawal varies according
[31 words unchanged]
of readmission rates amongst those with alcohol dependence including specifically alcohol withdrawal.
Alcohol dependent patients (ICD-10 code: F10.2) who receive unplanned hospital care may experience alcohol withdrawal (F10.3/F10.4) due to the abrupt cessation or substantial reduction in alcohol consumption. Exploring the length of stay and characteristics for those experiencing unplanned alcohol withdrawal and the association with readmission will help to understand the impact of clinical practice and patient factors on their representation rates to A&E and re admissions. This study aims to examine routine hospital data to examine characteristics and predictors of alcohol withdrawal re admissions and ED attendances in England by linking Hospital Episode Statistics Admitted Patient Care (HES APC) data sets with HES Accident & Emergency (HES ED)
Alcohol dependent patients (ICD-10 code: F10.2) who receive unplanned hospital care may experience alcohol withdrawal (F10.3/F10.4) due to the abrupt cessation or substantial reduction in alcohol consumption. Exploring the length of stay and characteristics for those experiencing unplanned alcohol withdrawal and the association with readmission will help to understand the impact of clinical practice and patient factors on their representation rates to A&E and re admissions. This study aims to examine routine hospital data to examine characteristics and predictors of alcohol withdrawal re admissions and ED attendances in England by linking Hospital Episode Statistics Admitted Patient Care (HES APC) data sets with HES Accident & Emergency (HES ED)
[15 paragraphs unchanged]
The data
previously
requested will achieve the aim identified above by using encrypted HES ID
[83 words unchanged]
length of stay for alcohol withdrawal data representing national coverage is required.
[3 paragraphs unchanged]
The research study requires demographic, and socioeconomic data to characterise cases and controls and to allow for matching cases using propensity score matching. This will allow the examination of the associations between individual characteristics and readmission. Additionally, data related to diagnosis will be essential in discriminating between cases and controls. Furthermore, being able to match cases and controls on age, gender and shared primary diagnosis (i.e. gastritis) will allow for the examination of the relative burden of alcohol on readmission. This is important, as gastritis may independently be associated with greater readmission rates. A series of analyses will be undertaken to examine characteristics between cases and controls (i.e. those without alcohol dependence/withdrawal). Demographic factors (age, gender, ethnicity, marital status, living circumstances, IMD, etc), clinical characteristics (length of stay, category of co-morbidity, A&E clinical diagnosis, emergency v elective admissions, previous discharges AMA, etc) and health service use (number of admissions, A&E re-attendances) will be explored against readmission.
The research study requires demographic, and socioeconomic data to characterise cases and controls and to allow for matching cases using propensity score matching. This will allow the examination of the associations between individual characteristics and readmission.
Data Requested
Additionally, data related to diagnosis will be essential in discriminating between cases and controls. Furthermore, being able to match cases and controls on age, gender and shared primary diagnosis (i.e. gastritis) will allow for the examination of the relative burden of alcohol on readmission. This is important, as gastritis may independently be associated with greater readmission rates.
i. Access to national data allows for examination of this hypothesis, which is unable to be achieved efficiently through prospective research. If an association is found between LOS and readmission and A&E attendance this will inform policy and future research applications through Research for Patient Benefit Grant or Programme Development grants applications. Therefore, the study request access to HES A&E data and APC data.
A series of analyses will be undertaken to examine characteristics between cases and controls (i.e. those without alcohol dependence/withdrawal). Demographic factors (age, gender, ethnicity, marital status, living circumstances, IMD, etc), clinical characteristics (length of stay, category of co-morbidity, A&E clinical diagnosis, emergency v elective admissions, previous discharges AMA, etc) and health service use (number of admissions, A&E re-attendances) will be explored against readmission.
ii. The level of data: Pseudonymised is requested to allow for linkage between data sets at a ‘case’ level for the whole of England.
Data Previously Requested
iii. One year of data would provide an adequate sample frame
i. Access to national data allows for examination of this hypothesis, which is unable to be achieved efficiently through prospective research. If an association is found between LOS and readmission and A&E attendance this will inform policy and future research applications through Research for Patient Benefit Grant or Programme Development grants applications.
ii. The level of data: Pseudonymised was requested to allow for linkage between data sets at a ‘case’ level for the whole of England.
iii. One year of data provided an adequate sample frame
[1 paragraph unchanged]
Recently published
Published
data for 2017/18 (PHE, 2019) identified that when both secondary and primary
[59 words unchanged]
hospital readmission and ED re-attendance a national spread of data is required.
[1 paragraph unchanged]
The University of Hull (UoH) is the
sole
Data Controller and
also processes
the
Data Processor.
data for the purposes described in this Agreement.
The Professor of Nursing (Addictions), University of Hull is the Chief Investigator for the study and is responsible for the overall design, and conduct of the study.
The
An
Epidemiologist,
employed by the
University of
Hull
Hull,
will lead on the data management, and analysis.
The
A
Professor of Health Service Research,
employed by the
University of
Kent
Kent,
is providing methodological support and is a collaborator on the study.
Data shared with the Collaborator (University of Kent) will be exclusively aggregate level summary table data with small numbers suppressed. No record level data will leave University of Hull - Hull Health Trials Unit. Only named UoH researchers will have access to the data. The Collaborator will be given opportunity to make comment on the summary data. Based on those comments further analysis may be considered but the decision as to what analyses to perform will be made exclusively by the research team at UoH. Importantly the comments will not drive further data requests, change the variables requested or the means of storage, processing or data flow. The decision as to what variables are included in the request to NHS Digital will be made exclusively by the research team at the UoH. Aggregate data will be shared using the Hull Health Trials Unit (HHTU) managed Box cloud storage. University of Kent (The Collaborator) will offer statistical support only and will have access to aggregate tables with small numbers suppressed in line with the HES analysis guide in read-only format only. In summary, the University of Kent has no role in determining the means by which the data are processed or influence over the outputs and dissemination.
Data shared with the Collaborator (University of Kent) will be exclusively aggregate level summary table data with small numbers suppressed. All data is stored and processed within the AIMES Data Safe Haven (DSH). Researchers (employees of the University of Hull only) access the environment via a secured VPN connection and work on virtual machines within the environment. No data ever leaves the environment nor do researchers have the capability to move the data.
• No data is or will be stored at the University of Hull – in the original Agreement the data was disseminated to the University prior to upload to the AIMES DSH. That data was only ever stored on a single encrypted laptop by the Hull Health Trials Unit Information Systems Manager, and was wiped as soon as the data was uploaded to AIMES DSH.
• All staff handling the data are employees with the University of Hull or have honorary contracts with the University.
Only named UoH researchers will have access to the data. The Collaborator will be given opportunity to make comment on the summary data. Based on those comments further analysis may be considered but the decision as to what analyses to perform will be made exclusively by the research team at UoH.
Importantly the comments will not drive further data requests, change the variables requested or the means of storage, processing or data flow. The decision as to what variables are included in the request to NHS Digital will be made exclusively by the research team at the UoH. Aggregate data will be shared using the Hull Health Trials Unit (HHTU) managed Box cloud storage. University of Kent (The Collaborator) will offer statistical support only and will have access to aggregate tables with small numbers suppressed in line with the HES analysis guide in read-only format only. In summary, the University of Kent has no role in determining the means by which the data are processed or influence over the outputs and dissemination.
Additional Purpose (Version 3)
The aim of the proposed new purpose is to utilise the dataset requested above to characterise risk of patients experiencing alcohol withdrawal when admitted to acute hospitals. This proposed analysis has not been covered as part of the existing analysis which has been approved. Therefore the applicant, who is a PhD student and honorary Research Associate at the University of Hull, is requesting access to the same datasets in order to conduct the analysis for the new purpose.
Those with alcohol use disorders exert a disproportionate impact or burden on the NHS, usually accessing care via emergency departments (Phillips et al, 2019). Recent reports identify that the majority of patients experiencing alcohol dependence, including alcohol withdrawal (AW) receive treatment in acute hospitals – outside of specialist services where these skills and competencies are concentrated (Roberts et al, 2020a). During 2017/18 there were 5,887 planned admissions to specialist inpatient units for alcohol use disorders, and over 80,000 for alcohol dependence and AW across acute hospital trusts in England, with an estimated cost of £1,500 - £2,704 per admission (NICE, 2011; Phillips et al, 2019). There were 36,6000 more patients admitted for alcohol-related reasons in 2018/2019 compared with 2012/2013 (PHE, 2020).
Provision of specialist inpatient services for AW is dwindling year on year, with a concomitant increase in alcohol related hospital admissions in England (Robertson et al, 2017; Drummond, 2017; Roberts et al, 2020b; Phillips et al, 2020a). Roberts et al (2020b) conducted an examination of the relationship between alcohol related admissions, specialist alcohol treatment provision and deprivation levels since 2012. They found that the national rise in alcohol related hospital admissions may be fueled by funding cuts to specialist alcohol services (Roberts et al, 2020b). They also highlighted that there is a statistically significant association between reduction in net expenditure for alcohol misuse treatment per 1000 people and increased rate of alcohol related hospital admissions (Roberts et al, 2020b).
Phillips et al (2020a) assessed the relationship between specialist and non-specialist admissions for alcohol withdrawal and found a strong statistical association between increase in alcohol related hospital admissions and reduction in non-specialist admissions. They highlighted the displacement of cost reductions in specialist services to non-specialist settings, and the need for resource development for non-specialist staff to meet the care needs of this population (Phillips et al, 2020a).
Limited use of validated tools, together with poor identification and inadequate assessment of AW in non-specialist settings (Barnaby et al, 2003; Mitchell et al, 2012) is likely to result in deficient clinical management which has been associated with discharges against medical advice and subsequent readmission (Mullins et al, 2017).
Barriers to the effective implementation of protocols to enhance the identification and assessment of AW within non-specialist settings relate to; nurses feeling inadequately trained, uncomfortable with regards to the high doses of medication patients often require, limited time to assess patients and the need for simpler assessment tools (Roberts & Drummond, 2019; Glann et al, 2019). There are a small number of limited studies which aim to improve identification and management of AW in non-specialist settings – one of which considered a symptom-based tool which is predictive of Alcohol Withdrawal Syndrome, the Prediction of Alcohol Withdrawal Severity Scale (PAWSS; Maldonado et al, 2014). However, the current standards of practice for identifying and managing AW in non-specialist settings has not been studied, and there are no tools which exist to identify and target individuals likely to experience AW. Therefore, the development of a series of tools to identify ‘at risk’ individuals would allow for targeted care and result in better outcomes.
The first step in developing these tools is the proposed new purpose which is a detailed analysis of Hospital Episode Statistics (HES) Data to characterise risk of AW based on the information collected on admission to hospital. The findings of this study will underpin a wider programme of research which incorporates observational work, staff interviews and development of interventions/tools to support non-specialist staff in managing AW. It is important to understand the characteristics that place individuals at risk of AW prior to developing tools to support their care.
Data Previously Requested
The data required for the new purpose is the same datasets which were requested (and granted) for the existing study, with an extension until April 2022 to allow time for completion of the new analysis. Access to both HES APC and AED datasets is being requested as required for the proposed analysis.
The Professor of Nursing (Addictions) will remain the Chief Investigator, with the PhD student and honorary Research Associate at the University of Hull, leading on the project for the new purpose. The Professor of Nursing is the primary supervisor for the Research Associate and will provide support and guidance accordingly. The Professor of Health Service Research (University of Kent) will have the same involvement as the existing study – as a Collaborator and provider of methodological support who will only have access to aggregated data with small numbers suppressed. The Research Associate is funded by the Society for the Study of Addiction (SSA), however the SSA will not be involved in analysis or management of the data.
Processing activities
[2 paragraphs unchanged]
Data extracts will be stored and processed with the
UoH
University of Hull
Data Safe Haven (DSH). The DSH is supplied as a service by AIMES management services limited. Their product name is a Trusted Research Environment (TRE).
• The
UoH
University of Hull
DSH is provided as a managed service by AIMES
[1 paragraph unchanged]
• The environment provided is logically separated for
UoH
University of Hull
and branded as such
• AIMES
act as
are
a data processor under the contract with
UoH. UoH are
University of Hull. University of Hull is the sole
data controller for this project.
[2 paragraphs unchanged]
• All processing / analysis of the data is performed by
UoH
University of Hull
staff. AIMES staff will not be processing the
data for research purposes
data.
• AIMES TRE works via a secured VPN (Virtual Private Network) connection. This means that whilst researcher access the environment from their computers, all activity occurs on virtual machines within the TRE. No data ever leaves the secured environment and there is no ability for researchers to copy or move data themselves. The only exception is the initial processing by the Information Systems Manager at UoH, who will upload the data from local storage as per the previous approved agreement to the AIMES TRE. The local copy of data will then be deleted.
• University of Hull researchers who work remotely (within the UK) from home are using university managed devices and as such their network traffic is tunnelled through the Univeristy of Hull via the Global Connect VPN. The connection to AIMES is via a virtualised desktop VMWare Horizon. As such whilst the researcher is accessing from home no data leaves the AIMES datacentre in Liverpool.
Data is currently stored as per the arrangements described in the previous agreement (v1) on an encrypted device. As per the agreement the data has not been unzipped.
• AIMES TRE works via a secured VPN (Virtual Private Network) connection. This means that whilst researcher access the environment from their computers, all activity occurs on virtual machines within the TRE. No data ever leaves the secured environment and there is no ability for researchers to copy or move data themselves.
Data will be uploaded into the DSH by the Information Systems Manager and then all locally held copies will be deleted.
All research activity will be within the Data Safe Haven.
All research activity will be within the DSH
Only those employed within the Institute for Clinical and Applied Health Research (ICAHR) University of Hull involved in this study, or employed under honorary contract, will be processing the data and have been trained in data protection and confidentiality . Staff will sign specific terms and conditions prior to been given access to the DSH linked to this project DSA.
Only those employed within the Institute for Clinical and Applied Health Research (ICAHR) University of Hull, involved in this study who have been trained in data protection and confidentiality will be processing the data. Staff will sign specific terms and conditions prior to been given access to the DSH linked to this project DSA.
[13 paragraphs unchanged]
Data will only be accessed and processed by substantive employees of the University of Hull and will not be accessed or processed by any other third parties not mentioned in this agreement.
Processing activities – New Purpose:
All previously agreed procedures and activities will be adhered to – the Data Sharing Framework Contract will be complied with, and storage, processing and sharing of data will be conducted as above.
The new purpose will involve creation of dummy variables from the existing data to explore characteristics of those experiencing AW. ICD-10 codes for primary and secondary diagnoses, alcohol related diagnoses and cause of injury or poisoning will be used to identify which patients experienced AW (F10.3, F10.4). Demographic and clinical data (including admission time and method) for patients who did and did not experience AW will be explored in order to characterise risk.
Data linkage will only occur between HES APC data and HES A&E data using the unique HES ID provided for the study. The use of the A&E attendance dates and admission dates together with the HES ID will be used to identify A&E attendances leading to admission. No other data sets will be linked or matched to the HES data. There will be no requirement or attempt to re-identify individuals
Finished consultant episodes (FCEs) will be explored for those who did and did not experience alcohol withdrawal. This will allow for consideration of complexity of care and any differences for those patients experiencing AW.
A prognostic model will then be developed to allow for allocation of an alcohol withdrawal risk score to patients on admission to the acute hospital setting. The utility of the prognostic model will be tested using a split-sample approach whereby a random sample of HES data is used to develop the risk score, with the remaining sample used to test the reliability and validity of the generated score. Data will not be accessed or processed by any other third parties not mentioned in this Agreement.
Expected output
Original Purpose (Version 0): [6 paragraphs unchanged] Analysis of data to inform publication is estimated to take 6 months [16 words unchanged] the end of 2020 (subject to change and availability due to COVID-19). This has been achieved in the form of the funding which has been awarded by the Society for the Study of Addiction for the PhD Scholarship which encompasses working on the proposed new purpose: • The COVID-19 pandemic has caused limitations and subsequent delays in achieving the initially stated outputs – this is due to challenges of working from home, internet access and staffing issues. • The output tables for the existing purpose are currently being finalised, and this extension will allow for this to complete and for outputs to be generated as initially anticipated. • In addition to this, the new purpose for the data will be completed within timelines as per the amendment application (please see attached protocol for the new purpose). [1 paragraph unchanged] Additional Purpose (Version 3): Outputs of the new purpose will be as above, as the topic and aims are aligned, and predictors of alcohol withdrawal complement the work on length of stay, clinical competencies and readmission for those experiencing alcohol withdrawal. In addition to the above, the new purpose will be published as part of a PhD Thesis, as part of a PhD Scholarship funded by the Society for the Study of Addiction (SSA), who will be credited on the resulting work. This work will support development of postdoctoral opportunities, and will form the basis of the PhD students application for postdoctoral funding following completion of her PhD in Autumn 2023. Analysis of data to inform publication is estimated to take 6-9 months, with publications submitted approximately one year after commencement of data processing. Conference presentations of initial data will commence as early as Summer 2021.
Expected measurable benefits
[3 paragraphs unchanged] Additional Purpose (Version 3) The new purpose will also contribute to the body of research created as part of the research conducted by the Chief Investigator at the University of Hulls Institute for Clinical and Applied Health Research (ICAHR). Understanding of the impact of length of admission on readmission rates will be complemented by understanding characteristics which predict alcohol withdrawal in the first place. NHS England’s Long Term Plan has a number of key themes, and within the prevention theme Alcohol Care Teams (ACTs) are identified as a priority. The Chief Investigator is part of the national ACT Working Group, and outputs of this and other studies will inform wider government strategy development (following peer review) In addition to the above, the new purpose will add to existing research on alcohol withdrawal in the acute hospital setting. It will also provide an opportunity to develop a risk score for alcohol withdrawal for patients admitted to acute hospitals. This will be utilised as part of the Research Associates wider programme of research which aims to develop tools to support clinicians in the acute hospital setting to manage alcohol withdrawal more effectively. This will lead to improvement in understanding of caring for this particular patient group in this setting. This study is funded by the Society for the Study of Addiction (SSA) as part of an internationally competitive scholarship awarded to the Research Associate. There are strict reporting and submission requirements as part of this scholarship, and the Research Associate will aim to publish this study in a peer reviewed journal by the end of 2022.
Benefits reported
Not stated in the previous version; added here.
The analysis which has been conducted so far is supporting development of peer review publications. The Principal Investigator for the data study is part of the NHS England Alcohol Care Team working group, and his engagement in this work is supported by access to the data.
The data from this study has supported a successful bid for funding of an Alcohol Care Team at Hull Royal Infirmary.
The data has also supported development of a grant application focussing on alcohol withdrawal admissions which result in self discharge (or discharge against medical advice)
DARS-NIC-226185-B6C2J-v2.2 2 May 2020 to 1 May 2021
- Title
- Examining the characteristics and predictors of alcohol withdrawal readmissions and emergency department attendances
- Commercial
- No
- Sublicensing
- No
- Datasets
- 2
- Files released
- 0
Datasets: Hospital Episode Statistics Accident and Emergency (HES A and E); Hospital Episode Statistics Admitted Patient Care (HES APC)
What changed from DARS-NIC-226185-B6C2J-v1.2
Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.
| Field | Was | Became |
|---|---|---|
| Start date | 2020-05-02 | |
| End date | 2021-05-01 |
Objective for processing
*Data has already been disseminated to the University of Hull. Upon receipt of the data, and before transfer into the Data Safe Haven - the University of Hull notified NHS Digital that the Data Safe Haven was not ready to receive the extracts. Therefore an interim solution is being proposed by the University of Hull - which has been subsequently approved by NHS Digital Security Consultant. Data will be transferred to encrypted storage devices, with a secure code, stored in a locked cupboard within the secure service of the Hull Health Trials Unit. Once the Data Safe Haven is ready, or an alternative processing centre has been sourced - then a further agreement will be issued by NHS Digital to the University of Hull that permits the processing of data. During this interim period - the only processing that is permitted is storing the data on the encrypted devices until the processing centre is ready to receive extracts.*
[12 paragraphs unchanged]
• The data used in the PhD only used data where an
[37 words unchanged]
cases if they meet the inclusion criteria set out in the protocol
fr
for
this study.
[2 paragraphs unchanged]
This agreement will therefore build on the original work completed by
Phillps
Phillips
et al (2019) and inform future research aimed at reducing the nature and burden of unplanned alcohol treatment within non-specialist care settings.
[17 paragraphs unchanged]
Processing activities
[1 paragraph unchanged]
Data has already been disseminated to the University of Hull. Upon receipt of the data, and before transfer into the Data Safe Haven - the University of Hull notified NHS Digital that the Data Safe Haven was not ready to receive the extracts. Therefore an interim solution is being proposed by the University of Hull - which has been subsequently approved by NHS Digital Security Consultant. Data will be transferred to encrypted storage devices, with a secure code, stored in a locked cupboard within the secure service of the Hull Health Trials Unit. Once the Data Safe Haven is ready, or an alternative processing centre has been sourced - then a further agreement will be issued by NHS Digital to the University of Hull that permits the processing of data. During this interim period - the only processing that is permitted is storing the data on the encrypted devices until the processing centre is ready to receive extracts.
Data has already been provided under v0 of this data sharing agreement.
*Below is wording from the previous approved iteration of this agreement.:*
Data extracts will be stored and processed with the UoH Data Safe Haven (DSH). The DSH is supplied as a service by AIMES management services limited. Their product name is a Trusted Research Environment (TRE).
Data extracts will be stored and processed within The Hull Health Trials Unit (HHTU) at the new Institute for Clinical and Applied Health Research (ICAHR), University of Hull.
• The UoH DSH is provided as a managed service by AIMES
The HHTU is among a few university departments across the country that has a dedicated Data Safe Haven (DSH) meeting the NHS Toolkit Approvals. Lead investigators for this project are co-located and will conduct data management, cleansing, and processing within the DSH suite in accordance with, and overseen by the Information Systems Manager at the HHTU. All processing activities will be undertaken in Stata 15SE statistical software package. Alcohol-attributable diagnosis will be considered as primary and secondary diagnoses. Hospital Admission will be calculated using Information Centre Method 4 and re admissions calculated initially using the NHS Digital IAS Ref Code: IAP00333. The study will consider the impact of length of stay on those admitted with alcohol dependence (including alcohol withdrawal) and A&E re attendance and readmission.
• Data will be stored within AIMES data centre.
• The environment provided is logically separated for UoH and branded as such
• AIMES act as a data processor under the contract with UoH. UoH are data controller for this project.
• The AIMES service was procured under the GCLOUD framework. This is a crown commercial services framework contract between the government and suppliers.
• AIMES have system administrative access to the system but act on the instructions of UoH DSH admin. AIMES are processor for the purposes of provisioning, securing and back-up of the service.
• All processing / analysis of the data is performed by UoH staff. AIMES staff will not be processing the data for research purposes
• AIMES TRE works via a secured VPN (Virtual Private Network) connection. This means that whilst researcher access the environment from their computers, all activity occurs on virtual machines within the TRE. No data ever leaves the secured environment and there is no ability for researchers to copy or move data themselves. The only exception is the initial processing by the Information Systems Manager at UoH, who will upload the data from local storage as per the previous approved agreement to the AIMES TRE. The local copy of data will then be deleted.
Data is currently stored as per the arrangements described in the previous agreement (v1) on an encrypted device. As per the agreement the data has not been unzipped.
Data will be uploaded into the DSH by the Information Systems Manager and then all locally held copies will be deleted.
All research activity will be within the DSH
Only those employed within the Institute for Clinical and Applied Health Research (ICAHR) University of Hull, involved in this study who have been trained in data protection and confidentiality will be processing the data. Staff will sign specific terms and conditions prior to been given access to the DSH linked to this project DSA.
Data related to this project is logically separated within the DSH meaning only researchers working on this project can access this data.
[1 paragraph unchanged]
Once agreed the data will be received from the NHS Digital SFTP System directly into the Hull Health Trials Unit (HHTU), Date Safe Haven (DSH) by the Information Systems Manager. The storage, preservation, processing and statistical analysis will be conducted within the HHTU DSH suite with redacted tables made available for panel discussions and review informing and refining statistical analyses. The final report will only include aggregate data with small number suppression applied in line with the HES analysis guide.
Only those employed within the ICAHR, University of Hull, involved in this study who have been trained in data protection and confidentiality will be processing the data.
[3 paragraphs unchanged]
The HES data and all record level manipulations will be processed exclusively in the HHTU Data Safe Haven. This is a secure environment which is disconnected from the wider university network other than when importing and exporting datasets where a temporary connection is made to a white list of URLs. This service utilises dedicated DSH servers which are managed independently to the wider university network by specific DSH staff. The DSH has its own active directory where users and environments are provisioned specific to each research project. User access to the DSH is via dedicated thin clients machines housed in a secure specific DSH rooms which are managed by the HHTU. As part of gaining access to the environment staff will sign usage terms and conditions which will include a check on a member of staff’s IG training. Data entering and leaving the DSH is controlled by HHTU DSH admins and users have no ability to save or output data themselves. Data leaving the DSH will only be aggregate data and users will sign to confirm that their output meets the terms specified in their data sharing agreement.
[2 paragraphs unchanged]
Aggregate level data is exported from the DSH via the AIMES digital airlock. This is a two-stage process and will include fully auditable approval process ensuring that the data conforms to the DSA. All ingress and egress are auditable via:
• A data egress approval document signed by the project lead (data applicant) authorising that the data to be egressed conforms to the DSA. This will include the destination location outside of the TRE
• A copy of the data egressed from the TRE is retained
• A datetime stamped record of who released the data is retained in a record that cannot be edited by UoH
At the end of the DSA period the entire project can be formally torn down and AIMES will issue a certificate of data destruction as in line with NHS Digital requirements.
[2 paragraphs unchanged]
Expected output
*Data has already been disseminated to the University of Hull. Upon receipt of the data, and before transfer into the Data Safe Haven - the University of Hull notified NHS Digital that the Data Safe Haven was not ready to receive the extracts. Therefore an interim solution is being proposed by the University of Hull - which has been subsequently approved by NHS Digital Security Consultant. Data will be transferred to encrypted storage devices, with a secure code, stored in a locked cupboard within the secure service of the Hull Health Trials Unit. Once the Data Safe Haven is ready, or an alternative processing centre has been sourced - then a further agreement will be issued by NHS Digital to the University of Hull that permits the processing of data. During this interim period - the only processing that is permitted is storing the data on the encrypted devices until the processing centre is ready to receive extracts*
[6 paragraphs unchanged]
Analysis of data to inform publication is estimated to take 6 months with publications submitted in
8-10
months
8-10 months.
after commencement of data processing.
Conference presentations will commence
at the
end of
2019-2020. Specific dates will be set once data has been released
2020 (subject
to
the applicant.
change and availability due to COVID-19).
[1 paragraph unchanged]
Expected measurable benefits
*Data has already been disseminated to the University of Hull. Upon receipt of the data, and before transfer into the Data Safe Haven - the University of Hull notified NHS Digital that the Data Safe Haven was not ready to receive the extracts. Therefore an interim solution is being proposed by the University of Hull - which has been subsequently approved by NHS Digital Security Consultant. Data will be transferred to encrypted storage devices, with a secure code, stored in a locked cupboard within the secure service of the Hull Health Trials Unit. Once the Data Safe Haven is ready, or an alternative processing centre has been sourced - then a further agreement will be issued by NHS Digital to the University of Hull that permits the processing of data. During this interim period - the only processing that is permitted is storing the data on the encrypted devices until the processing centre is ready to receive extracts.*
[3 paragraphs unchanged]
Objective for processing
With over 1 million alcohol-related hospital admissions the burden and unmet needs of excessive alcohol consumption and related conditions remain a priority under the NHS 10-year plan and for Public Health England (PHE, 2019).
In 20016/17 there were over 1 million alcohol-related admissions of which 300,000 hospital admissions were wholly attributable to alcohol in England, a rise of 29% over the last decade (PHE, 2018). With 65% of these admissions attributable to mental and behavioural disorders due to alcohol, the characteristics among those experiencing alcohol withdrawal and the relationship with readmission rates are not known.
Unplanned alcohol-related hospital admissions have been associated with physical multi-morbidity, coexisting mental health conditions and socioeconomic deprivation (Payne et al, 2013). Recent research in the US (Yedlapati and Stewart, 2018) has identified hospital readmission rates following alcohol withdrawal are linked to discharge against medical advice (AMA) and the complexity of the patients (i.e. co morbid mental health). Furthermore, being discharged against medical advice is associated with subsequent AMA events (Kraut et al, 2013). Whilst individual factors associated with re admissions rates for unplanned alcohol withdrawal can be identified, incomplete episodes of care for alcohol withdrawal may influence a return to excessive drinking on discharge and subsequent readmission.
The period required to complete a programme of alcohol withdrawal varies according to the needs of the individual although commonly require 5-7 days of clinical monitoring and treatment (NICE 2010,2011). HES-APC data contains primary and secondary diagnostic codes that allow for exploratory analysis of readmission rates amongst those with alcohol dependence including specifically alcohol withdrawal. Alcohol dependent patients (ICD-10 code: F10.2) who receive unplanned hospital care may experience alcohol withdrawal (F10.3/F10.4) due to the abrupt cessation or substantial reduction in alcohol consumption. Exploring the length of stay and characteristics for those experiencing unplanned alcohol withdrawal and the association with readmission will help to understand the impact of clinical practice and patient factors on their representation rates to A&E and re admissions. This study aims to examine routine hospital data to examine characteristics and predictors of alcohol withdrawal re admissions and ED attendances in England by linking Hospital Episode Statistics Admitted Patient Care (HES APC) data sets with HES Accident & Emergency (HES ED)
A previous study (Phillips et al., 2019) which was a PhD study considered the burden of alcohol disorders on ED and Inpatient Care and is published in Alcohol and Alcoholism (28 June, 2019). The study examined the characteristics of individuals with acute and chronic alcohol disorders using HES-APC and HES -ED data sets from 2009/10. This data that was used for this PhD is no longer accessible as it has been destroyed.
The study only used 2009/10 data. Since this time there have been changes in:
• quality of data reporting to HES
• increases in the recorded levels of alcohol withdrawal admissions from 21,590 in 2009/10 to 27,530 in 2017/18 despite no significant change in community prevalence of alcohol dependence
• significant changes in the commissioning of alcohol treatment which may have impacted on the care pathways for alcohol treatment
• Data published in the US indicating readmission rates for alcohol withdrawal are predicted by length of stay and discharges against medical advice. These areas have not previously been considered in UK populations.
The PhD study did not consider the entire inpatient dataset and differs in the following ways:
• The data requested in the PhD only included admission data following emergency presentations. This agreement includes admission data for those admitted via emergency presentations, booked and elective admissions. This is to include those cases where an elective admission is planned for and the patient experiences alcohol withdrawal.
• The data used in the PhD only used data where an individual case in the inpatient data could be matched to a coded ED presentation. As significant minority of ED presentations do not receive a coded ED ‘diagnosis’ (i.e. 36%). Hence, the PhD did not interrogate all possible cases if they meet the inclusion criteria set out in the protocol for this study.
• The previous study did not examine predictor of readmission or re-attendance, the impact of length of stay nor discharges against medical advice. The study referenced in this agreement will examine alcohol withdrawal admissions and consider the characteristics that predict readmission. Principally, the hypothesis will be drawn from the US study that indicates shorter lengths of stay will predict greater likelihood of readmission and ED re-attendance.
To summarise; this study plans to examine the impact of length of stay and discharges against medical advice in predicting readmission and ED re-attendances within 30 days following alcohol withdrawal admissions.
This agreement will therefore build on the original work completed by Phillips et al (2019) and inform future research aimed at reducing the nature and burden of unplanned alcohol treatment within non-specialist care settings.
The lawful basis for undertaking this research under the General Data Protection Regulation (GDPR) articles are:
• Article 6 (1)(e): processing is necessary for the performance of a task carried out in the public interest, improving the care for people with alcohol dependency and related conditions.
• Article 9 (2)(j): processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) based on Union or Member State law which shall be proportionate to the aim pursued, respect the essence of the right to data protection and provide for suitable and specific measures to safeguard the fundamental rights and the interests of the data subject
The data requested will achieve the aim identified above by using encrypted HES ID to cases common to both the ED and APC data sets. No identifiable data will be shared to carry out this process. The ICD-10 codes will be searched to identify those who experienced alcohol diagnoses, including alcohol dependence, alcohol withdrawal and alcohol withdrawal with delirium. Previous research has identified an association between community prevalence and alcohol-related admissions (Brennan et al, 2019), furthermore clinical practice within hospital settings is also highly variable and therefore to obtain a robust understand of the impact of the length of stay for alcohol withdrawal data representing national coverage is required.
Socio-Demographic characteristics that influence clinical outcomes and patient engagement within these groups will be explored to characterise both cases (i.e. those who experience alcohol dependence and withdrawal) and controls (i.e. those without an in year history of alcohol dependence and withdrawal). Overall A&E attendances and admissions (i.e. hospital spells) will be identified. The length of hospital stay (LOS) will be identified for each spell where a patient experienced alcohol withdrawal (i.e. F10.3/4) statistical analysis will explore the association between LOS and subsequent re admissions and A&E attendances.
Common concomitant conditions will be examined and individuals with alcohol withdrawal and a common concomitant condition will be compared to controls without a recorded history of alcohol disorder (i.e. F10) but share the common concomitant condition. This analysis will help identify the relative burden of alcohol withdrawal accounting for the presence of co morbid health problems.
The hypothesis is that a shorter length of stay experienced by those with alcohol withdrawal during hospital admissions will be associated with greater ED re attendance and hospital readmission.
The research study requires demographic, and socioeconomic data to characterise cases and controls and to allow for matching cases using propensity score matching. This will allow the examination of the associations between individual characteristics and readmission. Additionally, data related to diagnosis will be essential in discriminating between cases and controls. Furthermore, being able to match cases and controls on age, gender and shared primary diagnosis (i.e. gastritis) will allow for the examination of the relative burden of alcohol on readmission. This is important, as gastritis may independently be associated with greater readmission rates. A series of analyses will be undertaken to examine characteristics between cases and controls (i.e. those without alcohol dependence/withdrawal). Demographic factors (age, gender, ethnicity, marital status, living circumstances, IMD, etc), clinical characteristics (length of stay, category of co-morbidity, A&E clinical diagnosis, emergency v elective admissions, previous discharges AMA, etc) and health service use (number of admissions, A&E re-attendances) will be explored against readmission.
Data Requested
i. Access to national data allows for examination of this hypothesis, which is unable to be achieved efficiently through prospective research. If an association is found between LOS and readmission and A&E attendance this will inform policy and future research applications through Research for Patient Benefit Grant or Programme Development grants applications. Therefore, the study request access to HES A&E data and APC data.
ii. The level of data: Pseudonymised is requested to allow for linkage between data sets at a ‘case’ level for the whole of England.
iii. One year of data would provide an adequate sample frame
iv. There is significant variation in the prevalence of alcohol disorders across the country and variation in admissions and practice – obtaining a countywide sample allows the study to establish national norms.
Recently published data for 2017/18 (PHE, 2019) identified that when both secondary and primary diagnosis were considered, alcohol withdrawal, and alcohol withdrawal with delirium accounted for over 28,000 alcohol-related hospital admissions. Previous research has identified an association between community prevalence of alcohol dependence and alcohol-related admissions (Brennan et al, 2019), furthermore, clinical practice within hospital settings is also highly variable and therefore to the most robust examination of the impact of LOS on hospital readmission and ED re-attendance a national spread of data is required.
v. This method of research is the least intrusive option to explore this question and is supported by the recently published Framework for Mental Health Research (DH, 2017), which supports the use of existing data sources to increase or understanding of mental health problems and how healthcare is provided. The fields requested have been kept to a minimum and do not include patient identifiable fields.
The University of Hull (UoH) is the Data Controller and the Data Processor. The Professor of Nursing (Addictions), University of Hull is the Chief Investigator for the study and is responsible for the overall design, and conduct of the study. The Epidemiologist, University of Hull will lead on the data management, and analysis. The Professor of Health Service Research, University of Kent is providing methodological support and is a collaborator on the study.
Data shared with the Collaborator (University of Kent) will be exclusively aggregate level summary table data with small numbers suppressed. No record level data will leave University of Hull - Hull Health Trials Unit. Only named UoH researchers will have access to the data. The Collaborator will be given opportunity to make comment on the summary data. Based on those comments further analysis may be considered but the decision as to what analyses to perform will be made exclusively by the research team at UoH. Importantly the comments will not drive further data requests, change the variables requested or the means of storage, processing or data flow. The decision as to what variables are included in the request to NHS Digital will be made exclusively by the research team at the UoH. Aggregate data will be shared using the Hull Health Trials Unit (HHTU) managed Box cloud storage. University of Kent (The Collaborator) will offer statistical support only and will have access to aggregate tables with small numbers suppressed in line with the HES analysis guide in read-only format only. In summary, the University of Kent has no role in determining the means by which the data are processed or influence over the outputs and dissemination.
Expected output
All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.
Outputs will be in the form of peer-review publications and conference presentations accessed by academics, commissioners and clinicians, with lay summaries made available for service users and the public. Summaries of findings will be used to inform patient and public involvement (PPI) and clinical staff focus groups supporting a programme grant application.
Peer-review publication: The University of Hull has identified the Journal Alcohol and Alcoholism as a key journal which is affiliated to the Medical Council on Alcohol, a national body which supports the translation of evidence to practice. All publications from the University of Hull are promoted through websites, blogs, and other social media (Twitter, LinkedIn)
The University of Hull aim to target National conferences (i.e. MCA Annual Conference, Society for the Study of Addiction (SSA) Annual Symposium) and international conferences (i.e. Research Society of Alcohol (RSA), USA). The latter publishes accepted abstracts in Alcohol: Clinical Experimental Research, a recognised peer-review journal.
The Lead Researcher is a member of a number of national expert groups, including the Alcohol Treatment Expert Group, Public Health England, which publishes guidance for providers and clinicians. The outcomes of this study will be reported to this group to support the recently published guidance by Public Health England, which considers the patient pathways between secondary and specialist care for alcohol dependent patients.
The Lead Researcher is also supporting the Humber, Coast and Vale Sustainability and Transformation Partnership (STP) in defining health priorities. The findings from this national study will also inform the work with a variety of NHS Trusts who are developing strategic responses to increasing alcohol-related admissions as part of the implementation of the NHS Long-Term Plan.
Analysis of data to inform publication is estimated to take 6 months with publications submitted in 8-10 months after commencement of data processing. Conference presentations will commence at the end of 2020 (subject to change and availability due to COVID-19).
Additionally, the data will support the development of postgraduate, postdoctoral and research grant applications aimed at addressing the burden and unmet needs of alcohol patients receiving unplanned care.
DARS-NIC-226185-B6C2J-v1.2 20 December 2019 to 1 May 2020
- Title
- Examining the characteristics and predictors of alcohol withdrawal readmissions and emergency department attendances
- Commercial
- No
- Sublicensing
- No
- Datasets
- 2
- Files released
- 0
Datasets: Hospital Episode Statistics Accident and Emergency (HES A and E); Hospital Episode Statistics Admitted Patient Care (HES APC)
What changed from DARS-NIC-226185-B6C2J-v0.11
Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.
| Field | Was | Became |
|---|---|---|
| Start date | 2019-12-20 | |
| End date | 2020-05-01 |
Objective for processing
*Data has already been disseminated to the University of Hull. Upon receipt of the data, and before transfer into the Data Safe Haven - the University of Hull notified NHS Digital that the Data Safe Haven was not ready to receive the extracts. Therefore an interim solution is being proposed by the University of Hull - which has been subsequently approved by NHS Digital Security Consultant. Data will be transferred to encrypted storage devices, with a secure code, stored in a locked cupboard within the secure service of the Hull Health Trials Unit. Once the Data Safe Haven is ready, or an alternative processing centre has been sourced - then a further agreement will be issued by NHS Digital to the University of Hull that permits the processing of data. During this interim period - the only processing that is permitted is storing the data on the encrypted devices until the processing centre is ready to receive extracts.*
[32 paragraphs unchanged]
Data shared with the Collaborator (University of Kent) will be exclusively aggregate level summary table data with small numbers suppressed. No record level data will leave
the UoH Data Safe Haven, within which only
University of Hull - Hull Health Trials Unit. Only
named UoH researchers will have access to
it.
the data.
The Collaborator will be given opportunity to make comment on the summary
[49 words unchanged]
data flow. The decision as to what variables are included in the
DARs
request to NHS Digital
will be made exclusively by the research team at the UoH. Aggregate
[61 words unchanged]
which the data are processed or influence over the outputs and dissemination.
Processing activities
Data extracts will be stored and processed within the Data Safe Haven of the Hull Health Trials Unit (HHTU) at the new Institute for Clinical and Applied Health Research (ICAHR), University of Hull. The HHTU is among a few university departments across the country that has a dedicated Data Safe Haven (DSH) meeting the NHS Toolkit Approvals. Lead investigators for this project are co-located and will conduct data management, cleansing, and processing within the DSH suite in accordance with, and overseen by the Information Systems Manager at the HHTU. All processing activities will be undertaken in Stata 15SE statistical software package. Alcohol-attributable diagnosis will be considered as primary and secondary diagnoses. Hospital Admission will be calculated using Information Centre Method 4 and re admissions calculated initially using the NHS Digital IAS Ref Code: IAP00333. The study will consider the impact of length of stay on those admitted with alcohol dependence (including alcohol withdrawal) and A&E re attendance and readmission.
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).
Data has already been disseminated to the University of Hull. Upon receipt of the data, and before transfer into the Data Safe Haven - the University of Hull notified NHS Digital that the Data Safe Haven was not ready to receive the extracts. Therefore an interim solution is being proposed by the University of Hull - which has been subsequently approved by NHS Digital Security Consultant. Data will be transferred to encrypted storage devices, with a secure code, stored in a locked cupboard within the secure service of the Hull Health Trials Unit. Once the Data Safe Haven is ready, or an alternative processing centre has been sourced - then a further agreement will be issued by NHS Digital to the University of Hull that permits the processing of data. During this interim period - the only processing that is permitted is storing the data on the encrypted devices until the processing centre is ready to receive extracts.
*Below is wording from the previous approved iteration of this agreement.:*
Data extracts will be stored and processed within The Hull Health Trials Unit (HHTU) at the new Institute for Clinical and Applied Health Research (ICAHR), University of Hull.
The HHTU is among a few university departments across the country that has a dedicated Data Safe Haven (DSH) meeting the NHS Toolkit Approvals. Lead investigators for this project are co-located and will conduct data management, cleansing, and processing within the DSH suite in accordance with, and overseen by the Information Systems Manager at the HHTU. All processing activities will be undertaken in Stata 15SE statistical software package. Alcohol-attributable diagnosis will be considered as primary and secondary diagnoses. Hospital Admission will be calculated using Information Centre Method 4 and re admissions calculated initially using the NHS Digital IAS Ref Code: IAP00333. The study will consider the impact of length of stay on those admitted with alcohol dependence (including alcohol withdrawal) and A&E re attendance and readmission.
[9 paragraphs unchanged]
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).
[2 paragraphs unchanged]
Expected output
*Data has already been disseminated to the University of Hull. Upon receipt of the data, and before transfer into the Data Safe Haven - the University of Hull notified NHS Digital that the Data Safe Haven was not ready to receive the extracts. Therefore an interim solution is being proposed by the University of Hull - which has been subsequently approved by NHS Digital Security Consultant. Data will be transferred to encrypted storage devices, with a secure code, stored in a locked cupboard within the secure service of the Hull Health Trials Unit. Once the Data Safe Haven is ready, or an alternative processing centre has been sourced - then a further agreement will be issued by NHS Digital to the University of Hull that permits the processing of data. During this interim period - the only processing that is permitted is storing the data on the encrypted devices until the processing centre is ready to receive extracts* [8 paragraphs unchanged]
Expected measurable benefits
*Data has already been disseminated to the University of Hull. Upon receipt of the data, and before transfer into the Data Safe Haven - the University of Hull notified NHS Digital that the Data Safe Haven was not ready to receive the extracts. Therefore an interim solution is being proposed by the University of Hull - which has been subsequently approved by NHS Digital Security Consultant. Data will be transferred to encrypted storage devices, with a secure code, stored in a locked cupboard within the secure service of the Hull Health Trials Unit. Once the Data Safe Haven is ready, or an alternative processing centre has been sourced - then a further agreement will be issued by NHS Digital to the University of Hull that permits the processing of data. During this interim period - the only processing that is permitted is storing the data on the encrypted devices until the processing centre is ready to receive extracts.* [3 paragraphs unchanged]
Benefits reported
Stated in the previous version and removed here.
Yielded Benefits is not a requirement for new applications.
Objective for processing
*Data has already been disseminated to the University of Hull. Upon receipt of the data, and before transfer into the Data Safe Haven - the University of Hull notified NHS Digital that the Data Safe Haven was not ready to receive the extracts. Therefore an interim solution is being proposed by the University of Hull - which has been subsequently approved by NHS Digital Security Consultant. Data will be transferred to encrypted storage devices, with a secure code, stored in a locked cupboard within the secure service of the Hull Health Trials Unit. Once the Data Safe Haven is ready, or an alternative processing centre has been sourced - then a further agreement will be issued by NHS Digital to the University of Hull that permits the processing of data. During this interim period - the only processing that is permitted is storing the data on the encrypted devices until the processing centre is ready to receive extracts.*
With over 1 million alcohol-related hospital admissions the burden and unmet needs of excessive alcohol consumption and related conditions remain a priority under the NHS 10-year plan and for Public Health England (PHE, 2019).
In 20016/17 there were over 1 million alcohol-related admissions of which 300,000 hospital admissions were wholly attributable to alcohol in England, a rise of 29% over the last decade (PHE, 2018). With 65% of these admissions attributable to mental and behavioural disorders due to alcohol, the characteristics among those experiencing alcohol withdrawal and the relationship with readmission rates are not known.
Unplanned alcohol-related hospital admissions have been associated with physical multi-morbidity, coexisting mental health conditions and socioeconomic deprivation (Payne et al, 2013). Recent research in the US (Yedlapati and Stewart, 2018) has identified hospital readmission rates following alcohol withdrawal are linked to discharge against medical advice (AMA) and the complexity of the patients (i.e. co morbid mental health). Furthermore, being discharged against medical advice is associated with subsequent AMA events (Kraut et al, 2013). Whilst individual factors associated with re admissions rates for unplanned alcohol withdrawal can be identified, incomplete episodes of care for alcohol withdrawal may influence a return to excessive drinking on discharge and subsequent readmission.
The period required to complete a programme of alcohol withdrawal varies according to the needs of the individual although commonly require 5-7 days of clinical monitoring and treatment (NICE 2010,2011). HES-APC data contains primary and secondary diagnostic codes that allow for exploratory analysis of readmission rates amongst those with alcohol dependence including specifically alcohol withdrawal. Alcohol dependent patients (ICD-10 code: F10.2) who receive unplanned hospital care may experience alcohol withdrawal (F10.3/F10.4) due to the abrupt cessation or substantial reduction in alcohol consumption. Exploring the length of stay and characteristics for those experiencing unplanned alcohol withdrawal and the association with readmission will help to understand the impact of clinical practice and patient factors on their representation rates to A&E and re admissions. This study aims to examine routine hospital data to examine characteristics and predictors of alcohol withdrawal re admissions and ED attendances in England by linking Hospital Episode Statistics Admitted Patient Care (HES APC) data sets with HES Accident & Emergency (HES ED)
A previous study (Phillips et al., 2019) which was a PhD study considered the burden of alcohol disorders on ED and Inpatient Care and is published in Alcohol and Alcoholism (28 June, 2019). The study examined the characteristics of individuals with acute and chronic alcohol disorders using HES-APC and HES -ED data sets from 2009/10. This data that was used for this PhD is no longer accessible as it has been destroyed.
The study only used 2009/10 data. Since this time there have been changes in:
• quality of data reporting to HES
• increases in the recorded levels of alcohol withdrawal admissions from 21,590 in 2009/10 to 27,530 in 2017/18 despite no significant change in community prevalence of alcohol dependence
• significant changes in the commissioning of alcohol treatment which may have impacted on the care pathways for alcohol treatment
• Data published in the US indicating readmission rates for alcohol withdrawal are predicted by length of stay and discharges against medical advice. These areas have not previously been considered in UK populations.
The PhD study did not consider the entire inpatient dataset and differs in the following ways:
• The data requested in the PhD only included admission data following emergency presentations. This agreement includes admission data for those admitted via emergency presentations, booked and elective admissions. This is to include those cases where an elective admission is planned for and the patient experiences alcohol withdrawal.
• The data used in the PhD only used data where an individual case in the inpatient data could be matched to a coded ED presentation. As significant minority of ED presentations do not receive a coded ED ‘diagnosis’ (i.e. 36%). Hence, the PhD did not interrogate all possible cases if they meet the inclusion criteria set out in the protocol fr this study.
• The previous study did not examine predictor of readmission or re-attendance, the impact of length of stay nor discharges against medical advice. The study referenced in this agreement will examine alcohol withdrawal admissions and consider the characteristics that predict readmission. Principally, the hypothesis will be drawn from the US study that indicates shorter lengths of stay will predict greater likelihood of readmission and ED re-attendance.
To summarise; this study plans to examine the impact of length of stay and discharges against medical advice in predicting readmission and ED re-attendances within 30 days following alcohol withdrawal admissions.
This agreement will therefore build on the original work completed by Phillps et al (2019) and inform future research aimed at reducing the nature and burden of unplanned alcohol treatment within non-specialist care settings.
The lawful basis for undertaking this research under the General Data Protection Regulation (GDPR) articles are:
• Article 6 (1)(e): processing is necessary for the performance of a task carried out in the public interest, improving the care for people with alcohol dependency and related conditions.
• Article 9 (2)(j): processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) based on Union or Member State law which shall be proportionate to the aim pursued, respect the essence of the right to data protection and provide for suitable and specific measures to safeguard the fundamental rights and the interests of the data subject
The data requested will achieve the aim identified above by using encrypted HES ID to cases common to both the ED and APC data sets. No identifiable data will be shared to carry out this process. The ICD-10 codes will be searched to identify those who experienced alcohol diagnoses, including alcohol dependence, alcohol withdrawal and alcohol withdrawal with delirium. Previous research has identified an association between community prevalence and alcohol-related admissions (Brennan et al, 2019), furthermore clinical practice within hospital settings is also highly variable and therefore to obtain a robust understand of the impact of the length of stay for alcohol withdrawal data representing national coverage is required.
Socio-Demographic characteristics that influence clinical outcomes and patient engagement within these groups will be explored to characterise both cases (i.e. those who experience alcohol dependence and withdrawal) and controls (i.e. those without an in year history of alcohol dependence and withdrawal). Overall A&E attendances and admissions (i.e. hospital spells) will be identified. The length of hospital stay (LOS) will be identified for each spell where a patient experienced alcohol withdrawal (i.e. F10.3/4) statistical analysis will explore the association between LOS and subsequent re admissions and A&E attendances.
Common concomitant conditions will be examined and individuals with alcohol withdrawal and a common concomitant condition will be compared to controls without a recorded history of alcohol disorder (i.e. F10) but share the common concomitant condition. This analysis will help identify the relative burden of alcohol withdrawal accounting for the presence of co morbid health problems.
The hypothesis is that a shorter length of stay experienced by those with alcohol withdrawal during hospital admissions will be associated with greater ED re attendance and hospital readmission.
The research study requires demographic, and socioeconomic data to characterise cases and controls and to allow for matching cases using propensity score matching. This will allow the examination of the associations between individual characteristics and readmission. Additionally, data related to diagnosis will be essential in discriminating between cases and controls. Furthermore, being able to match cases and controls on age, gender and shared primary diagnosis (i.e. gastritis) will allow for the examination of the relative burden of alcohol on readmission. This is important, as gastritis may independently be associated with greater readmission rates. A series of analyses will be undertaken to examine characteristics between cases and controls (i.e. those without alcohol dependence/withdrawal). Demographic factors (age, gender, ethnicity, marital status, living circumstances, IMD, etc), clinical characteristics (length of stay, category of co-morbidity, A&E clinical diagnosis, emergency v elective admissions, previous discharges AMA, etc) and health service use (number of admissions, A&E re-attendances) will be explored against readmission.
Data Requested
i. Access to national data allows for examination of this hypothesis, which is unable to be achieved efficiently through prospective research. If an association is found between LOS and readmission and A&E attendance this will inform policy and future research applications through Research for Patient Benefit Grant or Programme Development grants applications. Therefore, the study request access to HES A&E data and APC data.
ii. The level of data: Pseudonymised is requested to allow for linkage between data sets at a ‘case’ level for the whole of England.
iii. One year of data would provide an adequate sample frame
iv. There is significant variation in the prevalence of alcohol disorders across the country and variation in admissions and practice – obtaining a countywide sample allows the study to establish national norms.
Recently published data for 2017/18 (PHE, 2019) identified that when both secondary and primary diagnosis were considered, alcohol withdrawal, and alcohol withdrawal with delirium accounted for over 28,000 alcohol-related hospital admissions. Previous research has identified an association between community prevalence of alcohol dependence and alcohol-related admissions (Brennan et al, 2019), furthermore, clinical practice within hospital settings is also highly variable and therefore to the most robust examination of the impact of LOS on hospital readmission and ED re-attendance a national spread of data is required.
v. This method of research is the least intrusive option to explore this question and is supported by the recently published Framework for Mental Health Research (DH, 2017), which supports the use of existing data sources to increase or understanding of mental health problems and how healthcare is provided. The fields requested have been kept to a minimum and do not include patient identifiable fields.
The University of Hull (UoH) is the Data Controller and the Data Processor. The Professor of Nursing (Addictions), University of Hull is the Chief Investigator for the study and is responsible for the overall design, and conduct of the study. The Epidemiologist, University of Hull will lead on the data management, and analysis. The Professor of Health Service Research, University of Kent is providing methodological support and is a collaborator on the study.
Data shared with the Collaborator (University of Kent) will be exclusively aggregate level summary table data with small numbers suppressed. No record level data will leave University of Hull - Hull Health Trials Unit. Only named UoH researchers will have access to the data. The Collaborator will be given opportunity to make comment on the summary data. Based on those comments further analysis may be considered but the decision as to what analyses to perform will be made exclusively by the research team at UoH. Importantly the comments will not drive further data requests, change the variables requested or the means of storage, processing or data flow. The decision as to what variables are included in the request to NHS Digital will be made exclusively by the research team at the UoH. Aggregate data will be shared using the Hull Health Trials Unit (HHTU) managed Box cloud storage. University of Kent (The Collaborator) will offer statistical support only and will have access to aggregate tables with small numbers suppressed in line with the HES analysis guide in read-only format only. In summary, the University of Kent has no role in determining the means by which the data are processed or influence over the outputs and dissemination.
Expected output
*Data has already been disseminated to the University of Hull. Upon receipt of the data, and before transfer into the Data Safe Haven - the University of Hull notified NHS Digital that the Data Safe Haven was not ready to receive the extracts. Therefore an interim solution is being proposed by the University of Hull - which has been subsequently approved by NHS Digital Security Consultant. Data will be transferred to encrypted storage devices, with a secure code, stored in a locked cupboard within the secure service of the Hull Health Trials Unit. Once the Data Safe Haven is ready, or an alternative processing centre has been sourced - then a further agreement will be issued by NHS Digital to the University of Hull that permits the processing of data. During this interim period - the only processing that is permitted is storing the data on the encrypted devices until the processing centre is ready to receive extracts*
All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.
Outputs will be in the form of peer-review publications and conference presentations accessed by academics, commissioners and clinicians, with lay summaries made available for service users and the public. Summaries of findings will be used to inform patient and public involvement (PPI) and clinical staff focus groups supporting a programme grant application.
Peer-review publication: The University of Hull has identified the Journal Alcohol and Alcoholism as a key journal which is affiliated to the Medical Council on Alcohol, a national body which supports the translation of evidence to practice. All publications from the University of Hull are promoted through websites, blogs, and other social media (Twitter, LinkedIn)
The University of Hull aim to target National conferences (i.e. MCA Annual Conference, Society for the Study of Addiction (SSA) Annual Symposium) and international conferences (i.e. Research Society of Alcohol (RSA), USA). The latter publishes accepted abstracts in Alcohol: Clinical Experimental Research, a recognised peer-review journal.
The Lead Researcher is a member of a number of national expert groups, including the Alcohol Treatment Expert Group, Public Health England, which publishes guidance for providers and clinicians. The outcomes of this study will be reported to this group to support the recently published guidance by Public Health England, which considers the patient pathways between secondary and specialist care for alcohol dependent patients.
The Lead Researcher is also supporting the Humber, Coast and Vale Sustainability and Transformation Partnership (STP) in defining health priorities. The findings from this national study will also inform the work with a variety of NHS Trusts who are developing strategic responses to increasing alcohol-related admissions as part of the implementation of the NHS Long-Term Plan.
Analysis of data to inform publication is estimated to take 6 months with publications submitted in months 8-10 months. Conference presentations will commence end of 2019-2020. Specific dates will be set once data has been released to the applicant.
Additionally, the data will support the development of postgraduate, postdoctoral and research grant applications aimed at addressing the burden and unmet needs of alcohol patients receiving unplanned care.
DARS-NIC-226185-B6C2J-v0.11 1 September 2019 to 1 December 2020
- Title
- Examining the characteristics and predictors of alcohol withdrawal readmissions and emergency department attendances
- Commercial
- No
- Sublicensing
- No
- Datasets
- 2
- Files released
- 2
Datasets: Hospital Episode Statistics Accident and Emergency (HES A and E); Hospital Episode Statistics Admitted Patient Care (HES APC)
Objective for processing
With over 1 million alcohol-related hospital admissions the burden and unmet needs of excessive alcohol consumption and related conditions remain a priority under the NHS 10-year plan and for Public Health England (PHE, 2019).
In 20016/17 there were over 1 million alcohol-related admissions of which 300,000 hospital admissions were wholly attributable to alcohol in England, a rise of 29% over the last decade (PHE, 2018). With 65% of these admissions attributable to mental and behavioural disorders due to alcohol, the characteristics among those experiencing alcohol withdrawal and the relationship with readmission rates are not known.
Unplanned alcohol-related hospital admissions have been associated with physical multi-morbidity, coexisting mental health conditions and socioeconomic deprivation (Payne et al, 2013). Recent research in the US (Yedlapati and Stewart, 2018) has identified hospital readmission rates following alcohol withdrawal are linked to discharge against medical advice (AMA) and the complexity of the patients (i.e. co morbid mental health). Furthermore, being discharged against medical advice is associated with subsequent AMA events (Kraut et al, 2013). Whilst individual factors associated with re admissions rates for unplanned alcohol withdrawal can be identified, incomplete episodes of care for alcohol withdrawal may influence a return to excessive drinking on discharge and subsequent readmission.
The period required to complete a programme of alcohol withdrawal varies according to the needs of the individual although commonly require 5-7 days of clinical monitoring and treatment (NICE 2010,2011). HES-APC data contains primary and secondary diagnostic codes that allow for exploratory analysis of readmission rates amongst those with alcohol dependence including specifically alcohol withdrawal. Alcohol dependent patients (ICD-10 code: F10.2) who receive unplanned hospital care may experience alcohol withdrawal (F10.3/F10.4) due to the abrupt cessation or substantial reduction in alcohol consumption. Exploring the length of stay and characteristics for those experiencing unplanned alcohol withdrawal and the association with readmission will help to understand the impact of clinical practice and patient factors on their representation rates to A&E and re admissions. This study aims to examine routine hospital data to examine characteristics and predictors of alcohol withdrawal re admissions and ED attendances in England by linking Hospital Episode Statistics Admitted Patient Care (HES APC) data sets with HES Accident & Emergency (HES ED)
A previous study (Phillips et al., 2019) which was a PhD study considered the burden of alcohol disorders on ED and Inpatient Care and is published in Alcohol and Alcoholism (28 June, 2019). The study examined the characteristics of individuals with acute and chronic alcohol disorders using HES-APC and HES -ED data sets from 2009/10. This data that was used for this PhD is no longer accessible as it has been destroyed.
The study only used 2009/10 data. Since this time there have been changes in:
• quality of data reporting to HES
• increases in the recorded levels of alcohol withdrawal admissions from 21,590 in 2009/10 to 27,530 in 2017/18 despite no significant change in community prevalence of alcohol dependence
• significant changes in the commissioning of alcohol treatment which may have impacted on the care pathways for alcohol treatment
• Data published in the US indicating readmission rates for alcohol withdrawal are predicted by length of stay and discharges against medical advice. These areas have not previously been considered in UK populations.
The PhD study did not consider the entire inpatient dataset and differs in the following ways:
• The data requested in the PhD only included admission data following emergency presentations. This agreement includes admission data for those admitted via emergency presentations, booked and elective admissions. This is to include those cases where an elective admission is planned for and the patient experiences alcohol withdrawal.
• The data used in the PhD only used data where an individual case in the inpatient data could be matched to a coded ED presentation. As significant minority of ED presentations do not receive a coded ED ‘diagnosis’ (i.e. 36%). Hence, the PhD did not interrogate all possible cases if they meet the inclusion criteria set out in the protocol fr this study.
• The previous study did not examine predictor of readmission or re-attendance, the impact of length of stay nor discharges against medical advice. The study referenced in this agreement will examine alcohol withdrawal admissions and consider the characteristics that predict readmission. Principally, the hypothesis will be drawn from the US study that indicates shorter lengths of stay will predict greater likelihood of readmission and ED re-attendance.
To summarise; this study plans to examine the impact of length of stay and discharges against medical advice in predicting readmission and ED re-attendances within 30 days following alcohol withdrawal admissions.
This agreement will therefore build on the original work completed by Phillps et al (2019) and inform future research aimed at reducing the nature and burden of unplanned alcohol treatment within non-specialist care settings.
The lawful basis for undertaking this research under the General Data Protection Regulation (GDPR) articles are:
• Article 6 (1)(e): processing is necessary for the performance of a task carried out in the public interest, improving the care for people with alcohol dependency and related conditions.
• Article 9 (2)(j): processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) based on Union or Member State law which shall be proportionate to the aim pursued, respect the essence of the right to data protection and provide for suitable and specific measures to safeguard the fundamental rights and the interests of the data subject
The data requested will achieve the aim identified above by using encrypted HES ID to cases common to both the ED and APC data sets. No identifiable data will be shared to carry out this process. The ICD-10 codes will be searched to identify those who experienced alcohol diagnoses, including alcohol dependence, alcohol withdrawal and alcohol withdrawal with delirium. Previous research has identified an association between community prevalence and alcohol-related admissions (Brennan et al, 2019), furthermore clinical practice within hospital settings is also highly variable and therefore to obtain a robust understand of the impact of the length of stay for alcohol withdrawal data representing national coverage is required.
Socio-Demographic characteristics that influence clinical outcomes and patient engagement within these groups will be explored to characterise both cases (i.e. those who experience alcohol dependence and withdrawal) and controls (i.e. those without an in year history of alcohol dependence and withdrawal). Overall A&E attendances and admissions (i.e. hospital spells) will be identified. The length of hospital stay (LOS) will be identified for each spell where a patient experienced alcohol withdrawal (i.e. F10.3/4) statistical analysis will explore the association between LOS and subsequent re admissions and A&E attendances.
Common concomitant conditions will be examined and individuals with alcohol withdrawal and a common concomitant condition will be compared to controls without a recorded history of alcohol disorder (i.e. F10) but share the common concomitant condition. This analysis will help identify the relative burden of alcohol withdrawal accounting for the presence of co morbid health problems.
The hypothesis is that a shorter length of stay experienced by those with alcohol withdrawal during hospital admissions will be associated with greater ED re attendance and hospital readmission.
The research study requires demographic, and socioeconomic data to characterise cases and controls and to allow for matching cases using propensity score matching. This will allow the examination of the associations between individual characteristics and readmission. Additionally, data related to diagnosis will be essential in discriminating between cases and controls. Furthermore, being able to match cases and controls on age, gender and shared primary diagnosis (i.e. gastritis) will allow for the examination of the relative burden of alcohol on readmission. This is important, as gastritis may independently be associated with greater readmission rates. A series of analyses will be undertaken to examine characteristics between cases and controls (i.e. those without alcohol dependence/withdrawal). Demographic factors (age, gender, ethnicity, marital status, living circumstances, IMD, etc), clinical characteristics (length of stay, category of co-morbidity, A&E clinical diagnosis, emergency v elective admissions, previous discharges AMA, etc) and health service use (number of admissions, A&E re-attendances) will be explored against readmission.
Data Requested
i. Access to national data allows for examination of this hypothesis, which is unable to be achieved efficiently through prospective research. If an association is found between LOS and readmission and A&E attendance this will inform policy and future research applications through Research for Patient Benefit Grant or Programme Development grants applications. Therefore, the study request access to HES A&E data and APC data.
ii. The level of data: Pseudonymised is requested to allow for linkage between data sets at a ‘case’ level for the whole of England.
iii. One year of data would provide an adequate sample frame
iv. There is significant variation in the prevalence of alcohol disorders across the country and variation in admissions and practice – obtaining a countywide sample allows the study to establish national norms.
Recently published data for 2017/18 (PHE, 2019) identified that when both secondary and primary diagnosis were considered, alcohol withdrawal, and alcohol withdrawal with delirium accounted for over 28,000 alcohol-related hospital admissions. Previous research has identified an association between community prevalence of alcohol dependence and alcohol-related admissions (Brennan et al, 2019), furthermore, clinical practice within hospital settings is also highly variable and therefore to the most robust examination of the impact of LOS on hospital readmission and ED re-attendance a national spread of data is required.
v. This method of research is the least intrusive option to explore this question and is supported by the recently published Framework for Mental Health Research (DH, 2017), which supports the use of existing data sources to increase or understanding of mental health problems and how healthcare is provided. The fields requested have been kept to a minimum and do not include patient identifiable fields.
The University of Hull (UoH) is the Data Controller and the Data Processor. The Professor of Nursing (Addictions), University of Hull is the Chief Investigator for the study and is responsible for the overall design, and conduct of the study. The Epidemiologist, University of Hull will lead on the data management, and analysis. The Professor of Health Service Research, University of Kent is providing methodological support and is a collaborator on the study.
Data shared with the Collaborator (University of Kent) will be exclusively aggregate level summary table data with small numbers suppressed. No record level data will leave the UoH Data Safe Haven, within which only named UoH researchers will have access to it. The Collaborator will be given opportunity to make comment on the summary data. Based on those comments further analysis may be considered but the decision as to what analyses to perform will be made exclusively by the research team at UoH. Importantly the comments will not drive further data requests, change the variables requested or the means of storage, processing or data flow. The decision as to what variables are included in the DARs will be made exclusively by the research team at the UoH. Aggregate data will be shared using the Hull Health Trials Unit (HHTU) managed Box cloud storage. University of Kent (The Collaborator) will offer statistical support only and will have access to aggregate tables with small numbers suppressed in line with the HES analysis guide in read-only format only. In summary, the University of Kent has no role in determining the means by which the data are processed or influence over the outputs and dissemination.
Expected output
All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.
Outputs will be in the form of peer-review publications and conference presentations accessed by academics, commissioners and clinicians, with lay summaries made available for service users and the public. Summaries of findings will be used to inform patient and public involvement (PPI) and clinical staff focus groups supporting a programme grant application.
Peer-review publication: The University of Hull has identified the Journal Alcohol and Alcoholism as a key journal which is affiliated to the Medical Council on Alcohol, a national body which supports the translation of evidence to practice. All publications from the University of Hull are promoted through websites, blogs, and other social media (Twitter, LinkedIn)
The University of Hull aim to target National conferences (i.e. MCA Annual Conference, Society for the Study of Addiction (SSA) Annual Symposium) and international conferences (i.e. Research Society of Alcohol (RSA), USA). The latter publishes accepted abstracts in Alcohol: Clinical Experimental Research, a recognised peer-review journal.
The Lead Researcher is a member of a number of national expert groups, including the Alcohol Treatment Expert Group, Public Health England, which publishes guidance for providers and clinicians. The outcomes of this study will be reported to this group to support the recently published guidance by Public Health England, which considers the patient pathways between secondary and specialist care for alcohol dependent patients.
The Lead Researcher is also supporting the Humber, Coast and Vale Sustainability and Transformation Partnership (STP) in defining health priorities. The findings from this national study will also inform the work with a variety of NHS Trusts who are developing strategic responses to increasing alcohol-related admissions as part of the implementation of the NHS Long-Term Plan.
Analysis of data to inform publication is estimated to take 6 months with publications submitted in months 8-10 months. Conference presentations will commence end of 2019-2020. Specific dates will be set once data has been released to the applicant.
Additionally, the data will support the development of postgraduate, postdoctoral and research grant applications aimed at addressing the burden and unmet needs of alcohol patients receiving unplanned care.
Benefits reported
Yielded Benefits is not a requirement for new applications.
Register history
When this agreement appeared in, or was edited in, each monthly edition of the register. Built by comparing every edition this site holds, the earliest of which is July 2021.
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July 2021 —
already listed in the earliest edition this site holds, so it may be older. 3 versions: DARS-NIC-226185-B6C2J-v0.11, DARS-NIC-226185-B6C2J-v1.2, DARS-NIC-226185-B6C2J-v2.2
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August 2021
1 version added: DARS-NIC-226185-B6C2J-v3.4
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December 2022
Register-wide edit DARS-NIC-226185-B6C2J-v0.11, DARS-NIC-226185-B6C2J-v1.2, DARS-NIC-226185-B6C2J-v2.2 — Datasets: legal basis: “
s261(1) and” taken out. Made to 639 agreements in this edition, so it is reported once, on the changes page, and not counted as an amendment of this agreement.
Cite this page
NHS England (2026) Data Uses Register, September 2026 edition, agreement DARS-NIC-226185-B6C2J, “Examining the characteristics and predictors of alcohol withdrawal readmissions and emergency department attendances”. Read via NHS Data Access Explorer (unofficial), https://healthdatauses.uk/agreements/dars-nic-226185-b6c2j/ (accessed [date]).
This address stays the same, but the page is rebuilt with each monthly edition, so the citation names the edition it shows. Every edition's data is kept in the facts store.
Source: datausesregister_september2026.xlsx, September 2026 edition of the NHS England Data Uses Register. Search that workbook for DARS-NIC-226185-B6C2J to see the original rows.