3c. Hep AE Service Development Protocol

Exploring the socio-demographic characteristics associated with frequent
attendance at Accident and Emergency departments in Lambeth.
Rising levels of Accident & Emergency attendances is a national problem with up to 20%
of visits resulting in advice or guidance only, at an annual cost to the NHS of
approximately £136 million. Research has shown that socio-demographic factors such
as deprivation, ethnicity, and population morbidity and mortality are associated with
high use of A&E. This is a particular challenge in Lambeth, the 14th most deprived of
England’s 354 Boroughs, with a high proportion of residents from Black and Ethnic
Minority (BEM) groups, multiple languages, and a lower life expectancy than the
national average. However, many GPs in Lambeth feel that, in addition to the above
factors, there are groups of patients with particularly high use of A&E services.
Identification of such ‘clusters’ of characteristics requires patient-level data from both
primary care and hospital A&E. The local collaboration of GP practices, Public Health,
and researchers in Lambeth, the Lambeth Datanet (LDN), with it’s collection of detailed
socio-demographic characteristics (ethnicity, language, religion) embedded within the
GP clinical record, brings a unique opportunity to undertake this.
This project will link GP clinical data (LDN), GP consultation data, and Hospital Episode
Statistics (HES) A&E data at individual patient level in a secure NHS environment, the
Clinical Data Linkage Service (CDLS) at the South London and Maudsley (SLaM) NHS
Trust. After linkage the data, with all patient identifiers removed, will be made available
to analysts, who will undertake cluster analysis. As the presence of Long-term
conditions (LTCs) and practice characteristics will both influence the rate of A&E
attendance, these will be controlled for in the analysis. A health economic analysis will
also be performed.
This project will inform service commissioning and redesign to develop cost-effective
care tailored to patient need.
The role of Accident and Emergency Departments is to assess and treat patients with
serious injuries or illnesses (1). It has been estimated that up to 20% of A&E visits
(approximately 2 million annually) result in advice or guidance only (2). Each A&E
attendance costs the NHS an average of £111: an emergency ambulance call costs an
additional £455. In comparison, a GP attendance costs £32 (3). The annual cost of
‘unnecessary’ attendances in England has been estimated at £136 million (4).
Research has shown that several socio-demographic and clinical factors are associated
with high frequency of attendance at A&E departments i.e. socio-economic deprivation
(5-9), ethnicity (5, 7, 10, 11), population morbidity and mortality (5-10), population
rates of unhealthy behaviours e.g. smoking (6, 8, 10, 12) and urban / rural status (7, 8).
The age profile of the population is important. Several studies have found statistically
significant associations between A&E attendances and different age groups, although
findings have been conflicting; with some reports and studies showing higher rates in
children and young adults (9, 13-15) and others showing higher rates in older people
(5), although there is evidence that this high rate of attendance may simply reflect
higher need, with older people actually using emergency services more appropriately
than those in younger age groups (16).
Primary care factors seem to be important; quality of care as indicated by practice score
on the Quality Outcomes Framework (6, 10, 12, 17), access to primary care services
and/or GPs (5-8), and size of practice (9). A recent study has also shown that patient
understanding of how to access non-A&E sources of out of hours care is also significant
Lambeth is the 14th most deprived of England’s 354 Boroughs, with a high proportion of
residents from Black and Ethnic Minority (BEM) groups (18). Multiple languages are
spoken; with nearly 50% of the population speaking languages other than English, with
many being unable to converse in English (19). Lambeth has a lower life expectancy
than the national average, with the gap being explained by premature deaths in
Cardiovascular disease, Cancer and Respiratory Disorders, and infection (particularly
HIV). The combination of high prevalence of long-term conditions, socio-economic
deprivation and multiple languages provides challenges in providing care tailored to
patient need, and a high rate of attendance at A&E.
The studies described above have explored population and practice characteristics by
exploring associations with A&E attendance rates, with characteristics explored
separately (univariable analyses) and with multiple characteristics explored together
(multivariable analyses) using national or local data at general practice, trust, regional
or national level. Whilst such studies can help in allocation of health service resources to
match patient demand, they do not address the fact that individuals, families and
communities will have ‘clusters’ of characteristics, and that there may be some clusters
of characteristics that explain an additional proportion of the variation. If such clusters
of characteristics do explain an additional proportion of the variation in A&E
attendance, they could form the basis of new approaches to service redesign, exploring
with communities how the NHS could provide them with more cost-effective care
options for conditions not requiring A&E attendance on clinical grounds. Analysis to
identify such clusters, however, requires data to be available at individual patient level.
Data to be used in this project
Lambeth Datanet. Lambeth has the data required to such an analysis. Since 2000, a
broad profile of patient characteristics (ethnicity, language preference, religious
affiliation, country of birth) have been collected and entered into GP clinical records
throughout Lambeth practices, forming the Lambeth Datanet (LDN). This covers 49 of
the 50 General Medical Practices in Lambeth; the one excluded practice having an
incompatible clinical information system. LDN enables the linkage of patient profile and
clinical data, which are then extracted for analysis.
GP consultation data.
These data will be extraction directly from the GP clinical systems in Lambeth practices.
Quality and Outcomes Framework data
The Quality and Outcomes Framework (QOF) is a system of performance indicators for
primary care services. The system was introduced in 2004 as a way of measuring the
quality of provision of primary care services, and for providing a mechanism to
reimburse GPs, according to the quality of the services provided (20). The framework
has evolved since it’s inception and now consists of indicators across four domains:
clinical, public health, quality and productivity, and the patient experience (17),
National Accident and Emergency data. The national Hospital Episode Statistics (HES)
data are collected centrally by the NHS at individual patient level. Data include all
episodes of attendance at any A&E department in England since 2007.
In this project, these sets of data will be linked. LDN, GP consultation and HES data will
be linked using a pseudonym. The pseudonym is a 1-way encrypted NHS number
generated before transfer of data to the secure data linkage site, the Slam Clinical data
Linkage Service (CDLS); a national exemplar for secure storage and linkage of patient
data. QOF data will be added by CDLS using the General Practice identifier of the General
Practice with which the patient is registered.
Aims of this project. The primary aim of this project is to use an LDN-HES linked dataset
to explore weather there are ‘clusters’ of socio-demographic factors statistically
significantly associated with a higher rate of A&E attendance, controlling for patient
morbidity and General Practice characteristics. Secondary aims are (a) to explore any
differential impacts of mental health LTCs (SMI, dementia and depression) vs. physical
LTC on A&E attendance rate and (b) to explore the health economic implications of the
study findings.
Data flow and generation of dataset for analysis
The proposed data flow is shown in figure 1.
Figure 1: Project data flow12.
Step 1 and 2
Step 2
QMS: LDN data
extraction incl NHS
number (NHSN) &
practice ID
data extraction incl
NHS pseudonym
created for each
patient record
(NHSP) in LDN &
consult datasets
+ practice ID
Step 3 and 4
LDN data + NHSP,
Consult data + NHSP,
practice information
Step 5
Health and
Social Care
Centre (HSCIC)
A&E Data
Step 6
Step 6
LDN data
data +
Project data
For explanation of abbreviations used see page 11.
Exact details of data flow may be subject to change following CAG advice and approval
(through s251).
Details of Data Flow
Step 1: Lambeth Datanet (LDN) data for all Lambeth patients registered with 49 of the
50 Lambeth practices, including NHS number but no other Patient Identifiable
Information (PII) are extracted from GP practices by QMS Contract Focus (QMSCF), a
private IT company contracted by Lambeth CCG to extract LDN data for Public Health
and clinical care improvement audits. Annual GP consultation rates plus NHS number
are extracted by the CCG. QMSCF apply the same 1-way encryption algorithm to the
NHS number in both the LDN and GP consultation data creating a unique patient level
pseudonym (NHSP).
Step 2: LDN data and consultation data (including NHSP) are transferred to the South
London and Maudsley (SLaM) Clinical Data Linkage Service (CDLS). There is no transfer
of any PII other than the NHSP. Other data included at patient level in this transfer are
practice national identifier code, demographic data and clinical data. Practice national
identifier code is required as practice characteristics are a predictor of A&E usage, so
must be controlled for in the analysis. GP practice list size in Lambeth ranges from
3,060 to 18,698 (median 6,888), meaning the risk of patient identification brought by
inclusion of the practice identifier is low. Demographic data are ethnicity (16+1 format),
religion, language, and country of birth. Clinical data are whether or not patients are on
Quality and Outcome Framework (QOF) registers; Coronary Heart Disease, Stroke/TIA,
Diabetes, CKD, Hypertension, Atrial Fibrillation, Heart Failure, Peripheral arterial
disease, asthma, hypothyroidism, Chronic Obstructive Pulmonary Disease, Cancer,
epilepsy, osteoporosis, rheumatoid arthritis, palliative care, depression, Serious Mental
Illness (SMI), dementia, Learning Disability (LD), number of medications issued in
previous year and practice data e.g. list size, number of GPs, Practice Quality and
Outcome scores for clinical, public health, quality and productivity, patient experience
Step 3: CDLS will send NHSP (only) to HSCIC.
Step 4: QMSCF will send the NHS number encryption algorithm (including SALT) to
Step 5: HSCIC transform the NHS numbers of patients within the A&E data set, using the
encryption algorithm provided by QMSCF. This creates a patient level pseudonym
(NHSP) in an identical format to that created for the LDN NHS numbers. All A&E records
containing the same pseudonym (NHSP) provided by CDLS are then extracted.
Step 6: HSCIC send A&E data with NHSP to CDLS. A&E data are: Arrival mode
(ambulance, other), Attendance category (first, follow-up (planned, unplanned)),
Attendance disposal (discharged home, admitted, died), department type (A and E,
walk-in centre), Source of referral (e.g. self-referral, GP), Arrival date, Arrival time,
Clinical diagnosis (A&E diagnosis, includes not classifiable and nothing abnormal
detected), Clinical investigation (e.g. blood tests, X rays), A&E treatment (e.g. dressing,
sutures, none), Dominant procedure (the procedure with the greatest impact on
resources), Country of residence, CCG of residence, Lower Super Output Area (LSOA),
Provider code (3 & 5 digit) (identifies the Trust or PCT ), PCT of responsibility, GP
Practice, Age on arrival. No Patent Identifiable Information other than NHSP will be
transferred to CDLS by HSCIC.
Step 7: CDLS will host A&E data and LDN clinical data, the records of which are linked
using the NHSP (pseudonymised NHS number) as a common unique identifier. CDLS
will add Index of Multiple Deprivation (IMD) scores using patient LSOA.
Step 8: CDLS will control and manage access to linked data in accordance with a security
protocol agreed with Lambeth CCG.
Step 9: CDLS will replace all NHSP with a project specific anonym before transferring
any data extract outside of CDLS.
Method of analysis
Stage 1. Multilevel modelling will be undertaken to explore which variables predict high
rates of attendance at A&E. The variables included will be (a) practice characteristics (b)
presence of one or more Long-term Conditions (LTC) and (c) patient socio-demographic
Stage 2: Cluster analyses will be used to explore whether there are any clusters of sociodemographic variables that are associated with high rates of attendance at A&E.
Stage 3: It can be hypothesised that physical, mental health LTC and LD might have
differing associations with the rate of A&E attendance. A sub-analysis will explore this.
The physical LTCs to be explored are Coronary Heart Disease, Stroke/TIA, Diabetes,
CKD, Hypertension, Atrial Fibrillation, Heart Failure, Peripheral arterial disease, asthma,
hypothyroidism, Chronic Obstructive Pulmonary Disease, Cancer, epilepsy, osteoporosis,
rheumatoid arthritis, palliative care. The mental health LTCs to be explored are
depression, Serious Mental Illness (SMI), and dementia.
Stage 4: Health service cost data will be calculated using the national tariff, and the
health economic associations of any associations found will be described.
Analysis team
Dr Gill Rowlands, Clinical Senior Lecturer, King’s College London
Mr David Whitney, Lambeth Datanet data manager and analyst, King’s College London
Prof Paul McCrone, Health Economist, Institute of Psychiatry, King’s College London.
Information Governance and Ethics
The research team will apply to the Lambeth CCG Information Governance Committee
for approval for the project. In addition, s251 approval will be requested from the
HSCIC. Lambeth CCG, CDLS and the research team will develop an agreement on data
storage and access3. A written contract will be developed between CDLS and the
Lambeth CCG Data Controller, making explicit the legal Terms and Conditions.
As the data released to the research team will be anonymised and this is a service
development project, ethics approval is not required. The research team will get written
confirmation of this from the National Research Ethics Committee.
Project outcome(s)
1. A detailed understanding of the socio-demographic factors associated with high
and very high rates of attendance at A&E departments by Lambeth patients.
2. Identification of any ‘clusters’ of socio-demographic factors that are statistically
significantly associated with a higher rate of A&E attendance.
Upon request, SLaM ICT will also provide a work space within the SLaM firewall for
research users to hold data extracts and work on data analysis. This would ensure
overarching SLAM ICT Security and IG framework is applied to secondary data (linked
datasets compiled by and extract from CDLS) as well as dataset stored within CDLS. In
this case, all users will require SLAM network accounts and ICT Service Desk support
(e.g. disk space, software licences, remote access capability etc.)
Future work arising from this project
If this project does show clusters of socio-demographic characteristics associated with
high use of A&E services, this could form the basis for a social marketing approach to
gain a better understanding of the drivers of high frequency of attendance at A&E
and/or low frequency of using alternative, more cost-effective, options for emergency
care for those not requiring A&E attendance on clinical grounds. This approach has been
successfully used to optimise health promotion campaigns by tailoring messages to the
needs and characteristics of segments of the population with identifiable characteristics
(21), and could be applied to inform service redesign for emergency services in
Full name
Accident and
g Groups
Clinical Data
statistics data
Lower Super
Output Area
Quality and
South London
and Maudsley
GP Practice
Official groups including GPs and managers who
purchase hospital services on behalf of the patients
Data linkage service based in SLaM with experience
in secure clinical data management and linkage
Data on every contact every patient has with English
The record of every patient registered with a GP in
Lambeth. Includes personal details (age, sex,
ethnicity, language, religion) and clinical data.
Area in which patient lives, at the level of 15,000 per
details such as name, address, date of birth, NHS
number that enables identification of individuals
current provider for Lambeth CCG for clinical data
Payment by results scheme for GPs
This is a process to allow linkage of two or more
sources of patient data. Approval is obtained by
submission of a request to the CAG. S251 approval is
not automatically required for data linkage for
service development work; however the project team
has been advised that, given the nature of the
proposed project, the HSCIC will require s251
approval before releasing the HES data
Specialist Mental Health NHS trust
Data held by CCG on practice: list size, number of GPs,
sex of GPs, languages spoken by GPs
Emergency and urgent care services: A&E: NHS Choices; [8 August 2013].
Available from:
Emergency admissions to hospital: managing the demand. London: National
Audit Office, 2013 31 October. Report No.
Williams N. Delivering value for the NHS: NHS Direct; 2009 [8 August 2013].
Available from: http://www.northwest.nhs.uk/document_uploads/Emergency Care
Review September Event/Working together - delivering value.pdf.
Reducing needless A&E visits could save NHS millions: The Co-operative group;
[8 August 2013]. Available from: http://www.cooperative.coop/corporate/Press/Press-releases/Pharmacy/Reducing-needless-AEvisits-could-save-NHS-millions/.
Bankart MJG, Baker R, Rashid A, Habiba M, Banerjee J, Hsu R, et al. Characteristics
of general practices associated with emergency admission rates to hospital: a crosssectional study. Emerg Med J 2011;28(7):558–63.
Calderon-Larranaga A, Carney L, Soljak M, Bottle A, Partridge M, Bell D, et al.
Association of population and primary healthcare factors with hospital admission rates
for chronic obstructive pulmonary disease in England: national cross-sectional study.
Thorax. 2011;66:191-6.
Purdy S. Avoiding hospital admissions. What does the research evidence say?
London: King's Fund, 2010.
Purdy S, Griffin T, Salisbury C, Sharp D. Emergency admissions for coronary heart
disease: a cross sectional study of general practice, population and hospital factors in
England. Public Health. 2011;125(1):46-54.
Scantlebury R. Socio-Economic Deprivation and Accident and Emergency
Attendance Rates - Characteristics of Primary Care which can Mitigate this Relationship:
King’s College London; 2013.
Dusheiko M, Doran T, Gravelle H, Fullwood C, Roland M. Does higher quality of
diabetes management in family practice reduce unplanned hospital admissions? Health
Serv Res. 2011;46(1 ):27–46.
Saxena S, George J, Barber J, Fitzpatrick J, Majeed A. Association of population and
practice factors with potentially avoidable admission rates for chronic diseases in
London: cross sectional analysis. J R Soc Med. 2006;99:81–9.
Soljak M, Calderon-Larrañaga A, Sharma P, Cecil E, Bell D, Abi-Aad G, et al. Does
higher quality primary health care reduce stroke admissions? A national cross-sectional
study. Br J Gen Pract. 2011;61(593):e801–7.
Shah SM, Cook DG. Socio-economic determinants of casualty and NHS Direct use.
Journal of Public Health. 2008;30:75-81.
Hospital Episode Statistics: Accident and Emergency Attendances in England
(Experimental Statistics) 2011-12 2013.
Hendry SJ, Beattie TF, Heaney D. Minor illness and injury: factors influencing
attendance at a paediatric accident and emergency department. Archives of Disease in
Childhood. 2005;90:629-33.
Gruneir A, Silver MJ, Rochon PA. Emergency department use by older adults: a
literature review on trends, appropriateness, and consequences of unmet health care
needs. Medical Care Research and Review. 2011;68:131-55.
Quality and Outcomes Framework guidance for GMS contract 2013/14. Mar
State of the Borough. London: Lambeth First, 2011.
Annual Public Health Report 2008 - 2009. Health inequalities in Lambeth Review
of progress. London: NHS Lambeth, 2009.
Ashworth M, Armstrong D. The relationship between general practice
characteristics and quality of care: a national survey of quality indicators used in the UK
Quality and Outcomes Framework, 2004-5. BMC Family Practice. 2006;7:68.
Smith A, Humphreys S, Heslington L, La Placa V, McVey D, MacGregor E. The
Healthy Foundations Lifestyle segmentation. Research report no. 2: The qualitative
analysis of the motivation segments. . Department of Health (England): 2011.