We displayed findings from each component in a matrix (Table 37). Then we looked for:
l convergence of findings from different components (where findings agreed)
l complementarity (usually where findings from the case studies explained or expanded on findings from
the regressions)
l discrepancy or disagreement (where findings from different components appeared to contradict
each other)
l silence (where findings from the case studies offered insights not available from the regression because
of a lack of data availability for inclusion in the regressions). PHASE 3 INTEGRATION OF REGRESSION AND CASE STUDIES
TABLE 37 Matrix of findings from each component
Factor Regressions phases 1 and 3 Case studies phase 2 Relationship
SAAR
14 conditions Based on 14 conditions. SAAR generally consistent for different conditions
Credible set of conditions for admission avoidance. Some successful pathways or services existed for specific conditions
Convergence. Not consistent– some pathways for conditions highly regarded but still had high SAAR
Variation Threefold variation in SAAR – Silence
Population
Deprivation Strongest predictor Big influence on avoidable admissions. Includes neediness for immediate access, lack of awareness of services, morbidity and some ethnic groups
Convergence and
complementarity (case studies explain why deprivation is important in regressions) Elderly SAAR adjusted for age but
% over 75 in population still explained 18% of variation in univariate regression. No longer explains variation when deprivation in regression. % elderly living alone does not explain variation in univariate regression. % nursing and residential home admissions explained 4% of variation in univariate regression. No longer explains variation when deprivation in regression
Both elderly living alone and in nursing homes increase avoidable admission rates
Convergence and
complementarity (regression shows that deprivation subsumes some of these age-related factors)
Ethnicity Explains variation in univariate regression (9%) but not once deprivation is in the regression
Can vary by ethnic group. Correlation between deprivation and ethnic groups. Effect of recent immigrants is on ED attendances, not admissions
Convergence and
complementarity (case studies show it is not as simple, as
‘ethnic groups have higher admissions’)
Morbidity Prevalence of individual diseases explains 20% of variation in univariate regression but not once deprivation is in the regression. Deprivation and morbidity correlated
Morbidity affects avoidable admission rates. COPD is a key condition for this
Convergence
Neediness – Modern consumerist society
expects instant access and this is more likely in deprived communities
Silence–not measured in regression but also
complementarity in that this may explain why deprivation explains so much variation in the regression
Awareness of services
Awareness of how to contact GP OOH services explains 10% of variation in univariate regression but not once deprivation is in the regression
People not aware of range of services available nationally. More likely in deprived communities
Convergence
TABLE 37 Matrix of findings from each component (continued)
Factor Regressions phases 1 and 3 Case studies phase 2 Relationship
Geography
Urban/rural Rural areas have lower SAARs Distance to hospital is an incentive to offer more services at home and in the community even though this is difficult because of distances to travel for service providers. GPs are better in rural areas. People may be more stoical in rural areas and more impatient for access in urban areas
Complementarity: case studies explain why urban and rural areas have different SAARs. Also highlights a relationship between perceived access to GP and rurality
Location of services
– Some MIUs/WICS/UCCs may be
in the wrong place
Silence (case studies identify factor for which there are no quantitative data)
Primary
population for an acute trust
– Some systems have an acute
trust that considers them to be their primary population so makes efforts to create integration and offer proactive services around admission avoidance for that population
Complementarity (case studies identify factor for which there are no quantitative data)
Services
Ambulance The more non-transport to ED undertaken by an ambulance service, the lower the SAAR
If ambulance services can contact support services such as social services, mental health, community nurses, WICs, GP, GP OOH, then they can keep people at home
Complementarity (case studies explain how ambulance can increase non-conveyance)
Acute trust
Supply High rate of acute beds per 1000 population means high SAARs
When beds are full, services work in integrated way to avoid admissions so bed numbers act as ceiling
Complementarity
Coding Short length of stay and conversion rate explained variation in the
multiple regression
ED attendances can be coded as admissions or not when in holding areas
Complementarity (explains what some variables in regression might represent: different approaches to coding may be measured by short length of stay and conversion rate)
Outpatients Short wait for outpatients explains higher SAARs, which is counterintuitive – Silence Proactive with senior review, rapid assessment team etc.
Not measured in regression but may have been measured by length of stay<1 and conversion rate
Some acute trusts very proactive with admission avoidance by offering senior review by medical staff and within ED, GPs in EDs and proactive multidisciplinary rapid assessment teams
Complementarity
Quality of acute trust
Foundation trust status explained
variation in SAAR –
Silence PHASE 3 INTEGRATION OF REGRESSION AND CASE STUDIES
TABLE 37 Matrix of findings from each component (continued)
Factor Regressions phases 1 and 3 Case studies phase 2 Relationship
ED
Shortage of consultants
Not measured in phase 1 but variable about senior cover in phase 3 did not explain variation
Shortage of ED consultants can affect functioning of department in relation to hospital and community. Junior staff request more diagnostics, which breaches 4-hour wait and increases likelihood of admission
Disagreement
Busyness Not measured Very busy departments feel chaotic and may admit to deal with this
Silence
GP OOH
Supply Not measured No doctors to run some sessions Silence Quality Ease of contact with GP OOH
did not explain variation in the univariate regression
Use of locums produces low-quality service in which general public loses faith. We would expect to see ease of contact variable explain variation
Disagreement. On reflection, people may stop using GP OOH due to loss of faith so do not complete questions about ease of access
General practice
Supply Does not explain variation in univariate regression
– Silence
Access Perceived access to GP explained 25% of variation in univariate regression. Remains in the multiple regression but in counterintuitive direction. Demand for EDs explains variation in univariate and multiple regression
Belief that poor access to GPs causes people to attend EDs. Once people get to the ED they are seen by junior doctors who run tests and admit
Convergence and disagreement. The relationship between GP access and ED attendance may explain counterintuitive finding. Complementarity
WICs Not measured People can be taken to WICs by
ambulance instead of EDs. People use these instead of GP in general practices
Silence
Community services
Not measured Community matrons, district nurses and multidisciplinary teams can offer admission avoidance support
Silence
Social services Not measured Social services under-resourced and hard to access for admission avoidance support
Silence
Mental health Prevalence of mental health problems explains variation in SAAR in univariate regression but not in multiple regression
Mental health services under-resourced and hard to access in order to avoid admission
Complementarity
Nursing homes Admission rate from nursing homes explained 4% of variation in SAAR in univariate
regression only
Nursing homes send patients to hospital unnecessarily
Convergence
We expected to see more complementarity than convergence because we had selected the cases for the qualitative component that were not well predicted by the regression in phase 1. Findings from the
different components of the study mainly agreed or offered complementary information (seeTable 37).
We considered where there appeared to be disagreement. The comparisons drew attention to the number of issues raised in the case studies on which we did not have routine data in our regressions. This matrix
helped us to draw conclusions from the whole study, which are reported inChapter 10.
TABLE 37 Matrix of findings from each component (continued)
Factor Regressions phases 1 and 3 Case studies phase 2 Relationship
System Availability of OOH services
Explained 4% of variation in SAAR in univariate regression but not in multiple regression. Tested in phase 3 by proxy variable and did not add to the regression
Support services not available at weekends and evenings
Disagreement. This may have been due to use of a proxy quantitative variable in phase 3 or because a variable already in the regression accounted for this e.g. length of stay<1
Integration Not measured Integration could occur between the acute and community services, or between health and social services. Aspects of integration were colocation, joint posts, communication and condition-specific pathways
Silence
Hospital-centric Not measured Some systems had a lot of community beds and services to keep people at home
Silence
Proactive avoidance
Not measured Some systems had initiatives proactively focused on admission avoidance
Silence
Resources available
Surplus/deficit status explained 14% of variation in SAAR in the univariate regression but not in the multiple regression
Some services were described as under-resourced and others as being cut because of financial constraints
Convergence but correlation with deprivation in the regression
MIU, minor injuries unit; WIC, walk-in centre.
Chapter 10
Discussion
Summary of findings
There were 3,273,395 potentially avoidable admissions in 2008–11, accounting for 22% of all emergency
admissions. The mean age- and sex-adjusted admission rate was 2258 per year per 100,000 population, with a 3.4-fold variation between systems (1268 to 4359). Characteristics of the population explained the majority of variation: employment deprivation rates explained 72% of variation, with urban/rural status explaining 3% more. Systems serving populations with high levels of deprivation and in urban areas had high rates of potentially avoidable admissions. Interviewees highlighted the complexity of deprivation, which may include high levels of morbidity, low awareness of alternative services to EDs and a desire for immediate access to urgent care. Factors related to a range of services in the emergency and urgent care system explained a further 10% of variation: EDs, hospitals, emergency ambulance services and general practice. Systems with high potentially avoidable admission rates had high rates of acute beds (suggesting supply-induced demand), high rates of attendance at EDs, high rates of conversion from ED attendances to admissions, and low rates of non-conveyance by emergency ambulances. The six case studies revealed further possible explanations of variation. There was variation in how hospitals coded admissions; some systems focused proactively on admission avoidance (in particular using multidisciplinary rapid assessment teams and senior review in acute trusts) whereas others were more interested in hospital discharge; there appeared to be different levels of integration between different services such as health and social care, and acute and community trusts; and some systems faced more challenging problems around geographical boundaries than others. The system with the highest SAAR in our six case studies appeared to have a combination of these issues: it focused more on discharge than admission avoidance, coded people who did not use hospital beds as admissions, described shortages of ED consultants, was not the primary catchment area for any acute trust and appeared to have lower levels of integration between services.