Phase 1: explaining variation in potentially avoidable admissions rates for geographically based systems

Alicia O’Cathain; Emma Knowles; Janette Turner; Ravi Maheswaran; Steve Goodacre; Enid Hirst; Jon Nicholl

Chapter 4Phase 1: explaining variation in potentially avoidable admissions rates for geographically based systems

Objective

In this chapter we present the methods and findings of a regression to explain variation in the SAAR for geographically based systems. This was published during the course of the study.³⁶

Methods

Identification of factors

Based on previous research on variation in emergency admission rates, and the focus here on the emergency and urgent care system, it was necessary to locate factors relating to the population, geography, health and range of emergency and urgent health services for the 150 geographically based systems. We searched national databases of routinely available data for relevant data on factors reported at PCT level. Where factors were available by financial year, we selected 2009–10, which was the mid-point year of the SAAR, or the calendar year 2009, or summed quarterly data for 2009. The source of data for factors is reported in Appendix 1.

Population factors

The SAAR was adjusted for age and sex, using 5-year age groups. We attempted to locate data on social deprivation, minority ethnic groups, density of elderly people and social isolation. The Index of Multiple Deprivation has often been identified as an explanatory factor for variation in emergency admissions. We used two domains of this index – employment and income deprivation – because the index itself includes standardised emergency admission rates.³⁷ The employment deprivation domain reports the proportion of the working age population unable to work because of unemployment, sickness or disability. The income deprivation domain reports the proportion of the population in families who are out of work or have low earnings. We included a factor on the percentage of the population aged over 75 years old even though the SAAR was adjusted for age in case the density of older people in an area created pressure on services.

Geography

We tested one geographical factor, which was a six-category urban–rural scale. We started the analysis with a three-category scale. A patient and public involvement (PPI) representative drew attention to the effect of geography on the SAAR when we presented preliminary findings. We then sought out a more detailed scale to represent geographical differences between systems.

Health

The standardised mortality ratio has been shown to explain variation in emergency admissions overall (see Table 1). We decided not to include it because it may be an indicator of both health status of a population and performance of services for that population. Instead we used indicators of morbidity: we found data on the prevalence of three conditions included in the 14 conditions in the SAAR.

Services in an emergency and urgent care system

We located factors about key services in the emergency and urgent care system: general practice, GP OOH, EDs, acute trusts and ambulance services. We wanted to locate data on the availability, accessibility and quality of all services but could locate data on only some aspects of these services.

The Atlas of Variation was a source of health service-related factors but the authors have expressed concerns that some variation may be accounted for by data management problems.³⁵ We replaced very high values with the median value for two factors: ED attendance rate (replaced two high values) and admissions from nursing homes rate (replaced five high values).

We calculated two factors using HES data. The first was the percentage of all emergency admissions staying less than 1 day because this had been identified as explaining the recent increase in emergency admission rates. We felt that this factor could indicate one of two things: either (1) coding differences between hospitals, for example, with patients waiting in the hospital for a few hours without using a bed coded as admissions in some hospitals and as ED discharges in another, or (2) different ways of managing patients within the hospital; for example, some hospitals may have been able to put community services in place to discharge ED attendances while others admitted patients to a hospital bed to arrange community services to allow discharge. Second, we calculated the percentage of all emergency admissions referred by GPs to identify the direct influence of general practice on emergency admissions.

Two factors about the emergency ambulance service – percentage of incidents not transported to hospital (non-conveyance) and the percentage of incidents meeting the 8-minute response target – were available only for ambulance services rather than PCTs. We allocated the ambulance service rate to each of the PCTs nested within that ambulance service, an underestimate of the variation at PCT level.

Services we could find no data for

We found no routine data for community/intermediate care, social care or system integration.

Analysis

We undertook general linear modelling in SPSS version 20 (IBM Corporation, Armonk, NY, USA) weighted for the size of the system population to account for larger uncertainty of estimates for smaller systems. The dependent variable was the SAAR for 150 systems. The independent variables were tested in a hierarchical multiple regression in two blocks, using forward stepwise regression within each block. Variables were included if the p-value for the t-test was < 0.05. The blocks were determined by the extent to which service providers could affect a factor. Block 1 was population-related factors. We used the residuals from block 1 as the dependent variable in block 2 and then tested the effect of service-related factors.

After we had completed the analysis and moved on to phase 2 of the study, we felt that this regression did not capture enough of the conceptual framework of the study. Although our conceptual framework led us to include factors which other researchers had not – that is those from services other than primary care and hospitals – we had ignored the fact that factors might operate in sequence. That is, services outside the hospital would affect numbers who turned up at the hospital. For example, difficulty getting a GP appointment has been shown to explain higher rates of attendance at EDs.²⁶^,²⁷ We undertook a three-block analysis to reflect this:

block 1: population-related factors: population, geography, health
block 2: out of hospital service-related factors – GP access, GP quality, ambulance service performance, GP OOH service
block 3: in hospital service-related factors: ED attendance, conversion rate at ED, short length of stay in hospital.

Findings

Univariate linear regression

We display descriptions of the variables tested in the multiple regression (Table 4). It is interesting to note that two factors explained a very large amount of variation in the SAAR. Both were population factors related to deprivation: income deprivation and employment deprivation, explaining 60% and 72% of variation respectively. These are likely to be correlated. We explore correlation between factors in the next section.

TABLE 4

Description of all factors and explanatory power in the univariate linear regression (geographically based systems)

Correlations between factors

Some of the factors were correlated with others. It is important to describe these correlations to facilitate interpretation of the final multiple regression. Employment deprivation was positively correlated with income deprivation and the prevalence of some of the 14 conditions (Table 5). Urban/rural status was positively correlated with a number of factors including the proportion of elderly people in a system and some access to general practice factors.

TABLE 5

Correlations between factors (geographically based systems, r > 0.4)

Factors affecting standardised avoidable admissions rate in a multiple linear regression

We then undertook multiple linear regression to identify the combination of factors which predicted the SAAR (Table 6). We undertook the block 1 analysis – which tested population-related factors – and identified two factors which predicted the SAAR: employment deprivation and urban/rural status. The more deprived populations and more urban populations had higher SAARs. These two factors together predicted 75% of the variation. In the block 2 analysis – which tested service-related factors – those related to EDs, hospitals, emergency ambulance services and primary care explained further variation (r² = 85% in a non-hierarchical analysis). Systems with higher SAARs had higher attendance rates at EDs, higher rates of conversion of ED attendances to admissions, higher proportions of very short-stay patients, higher rates of ambulance calls transported to hospital and better perceived access to general practice (see Table 6). This last factor was in the opposite direction from the univariate linear regression and counterintuitive in the context of the conceptual framework of the study. We discuss this in Chapter 10.

TABLE 6

Multiple linear regression (geographically based systems)

Regression metrics are reported in Appendix 2, showing that the model was a good fit. There was no evidence of multicollinearity in block 2: variance inflation factors varied between 1.1 and 1.4. Some of the factors in the multiple regression were correlated with each other even though there was no multicollinearity: employment deprivation was negatively correlated with the proportion of ambulance callers not transported to hospital; urban/rural status was correlated with perceptions of access to general practice and the proportion of ambulance callers not transported to hospital (see Table 5). Because of the large amount of correlation between factors, it is likely that a number of regressions would fit the data equally well, so there is some uncertainty about the model presented here.

Test of a stronger system-based theoretical model

The first block was the same as block 1 in the primary analysis reported above. In block 2 – out-of-hospital factors – ambulance performance was the only factor adding to the regression. That is, factors about availability of, access to and quality of primary care did not explain further variation at this stage. In block 3 – in-hospital factors – the factors explaining further variation were those in the two-block analysis reported earlier. That is, this approach made little difference to our findings.

Conclusion

Variation in potentially avoidable emergency admissions was explained mainly by population factors. Health-care providers can reduce avoidable emergency admissions by investigating why some populations attend EDs more than others, why some EDs convert more attendances to admissions than others and why some ambulance services transport more of their calls to hospital than others. The greatest potential for reduction in avoidable emergency admissions lies with understanding more about how services can best provide care to deprived communities in ways that avoid emergency admission.

Link with phase 2: identifying residuals and potential case studies

A residual is the difference between the dependent variable for a case and its value predicted by the multiple regression. The description of qualitative residual analysis is brief²⁸^,²⁹ and there is no guidance on which type of residual to measure or the size of a large residual. The histogram of standardised residuals from our earlier regression shows that they were normally distributed (see Appendix 2). We identified large standardised residuals as > 1.5 or < 1.5. We noted that some systems with large residuals were in geographical clusters, in that neighbouring systems had similar SAARs and similar residuals. We changed our definition of a large residual to > 1.3 or < 1.3 to include systems within each of these geographical clusters. Twenty-seven systems had large residuals (Table 7).

TABLE 7

Systems with large residuals from regression reported in Table 6

We used purposive sampling to identify six systems for our in-depth case studies. We wanted to select cases with high, medium and low SAARs in case the way in which systems operated differed by size of SAAR. The median SAAR was 2258 with an IQR of 1808–2662. We labelled any system in the highest quartile ‘high SAAR’, any system in the lowest quartile ‘low SAAR’ and all others ‘medium SAAR’. We also wanted to select systems where the residual was positive and negative, indicating over- and underprediction of the SAAR. Some systems had predicted SAARs that were higher than the actual SAAR (overpredicted) and some had predicted SAARs that were lower than the actual SAAR (underpredicted). We categorised the 27 cases by size of SAAR and direction of residual (see Table 7). We then grouped geographical neighbours together with the intention of selecting only one case from a cluster in order to maximise variation of selected cases.

Our intention had been to select one case from each ‘type’ (see column 1 in Table 7). However, ‘low SAAR underpredicted’ did not have any cases and ‘high SAAR overpredicted’ was very similar to the ‘medium SAAR overpredicted’. Instead we selected cases that could represent a cluster of systems distributed across the types. We labelled selected cases using H for high SAAR, M for medium SAAR, L for low SAAR, U and O for under- and overpredicted, and 1–6 to indicate case number. Selected cases HU1, LO2, MO3, MU4, LO5 and MO6 are indicated in bold in Table 7.

Publication Details

Copyright

Copyright © Queen’s Printer and Controller of HMSO 2014. This work was produced by O’Cathain et al. under the terms of a commissioning contract issued by the Secretary of State for Health. This issue may be freely reproduced for the purposes of private research and study and extracts (or indeed, the full report) may be included in professional journals provided that suitable acknowledgement is made and the reproduction is not associated with any form of advertising. Applications for commercial reproduction should be addressed to: NIHR Journals Library, National Institute for Health Research, Evaluation, Trials and Studies Coordinating Centre, Alpha House, University of Southampton Science Park, Southampton SO16 7NS, UK.

Included under terms of UK Non-commercial Government License.

Publisher

NIHR Journals Library, Southampton (UK)

NLM Citation

O’Cathain A, Knowles E, Turner J, et al. Explaining variation in emergency admissions: a mixed-methods study of emergency and urgent care systems. Southampton (UK): NIHR Journals Library; 2014 Dec. (Health Services and Delivery Research, No. 2.48.) Chapter 4, Phase 1: explaining variation in potentially avoidable admissions rates for geographically based systems.

Factor	Source^a	Median (IQR)	Range	Pearson’s r² (%)	p-value	Direction of correlation
Population
INCOME: % population in an area that live in income-deprived families	IMD 2010	15 (11–19)	7–33	60	< 0.001	+
EMPLOYMENT: % population with employment deprivation	IMD 2010	6 (5–8)	3–12	72	< 0.001	+
ETHNICITY: % population minority ethnic	ONS	9 (5–20)	3–63	9	< 0.001	+
OVER75: % population aged 75 or over	ONS	7 (7–9)	3–13	18	< 0.001	–
ALONE: % pensioners living alone	ONS	6 (5–6)	3–8	< 1	0.547	+
Geography
URBAN/RURAL	ONS	No PCTs (%)		29	< 0.001	–
Major urban		58 (39%)
Large urban		19 (13%)
Other urban		23 (15%)
Significant rural		26 (17%)
Rural 50%		22 (15%)
Rural 80%		2 (1%)
Health^b
CHD: prevalence of coronary heart disease	QOF	4 (3–4)	1–5	3	0.032	+
COPD: prevalence of COPD	QOF	2 (1–2)	1–3	20	< 0.001	+
DIABETES: prevalence of diabetes	QOF	5 (5–6)	3–9	18	< 0.001	+
Hospital
MANAGEMENT/CODING: % emergency admissions with length of stay < 1 day	HES	28 (24–30)	14–41	16	< 0.001	+
MANAGEMENT: % population waiting 4 or more weeks for elective admission	Department of Health	62 (53–68)	35–98	1	0.198	–
EDs
EDUSE: directly age-, sex- and deprivation-standardised rate of ED attendances per 100,000 population	NHS Atlas of Variation 2011	275 (241–324)	149–909	15	< 0.001	+
EDCONVERSION: directly age-, sex- and deprivation-standardised rate of conversion from ED attendance to emergency admissions	NHS Atlas of Variation 2011	100 (88–112)	70–148	7	0.001	+
Community
CAREHOME: admission rate for people aged >74 from nursing home or residential care home settings per 10,000	NHS Atlas of Variation 2011	12 (6–29)	1–193	4	0.013	+
General practice
GPSUPPLY: GPs per 100,000 population	London Health Observatory	69 (64–75)	54–99	0	0.855	+
GPACCESS1: % GP single handed	Information Centre	13 (7–23)	0–48	17	< 0.001	+
GPACCESS2: % practices offering extended opening hours	Department of Health	75 (69–86)	39–100	0	0.901	–
GPACCESS3: % able to see GP in 48 hours	GP survey	80 (77–83)	71–89	25	< 0.001	–
GPADMISSION: % admissions from general practice	HES	18 (7–26)	0–44	11	< 0.001	–
GP QUALITY: QOF indicators	QOF	Six indicators tested		0–9	< 0.001–0.570	–^c
General practice OOH
GPOOH1: % know how to contact GP OOH	GP survey	66 (62–69)	48–79	10	< 0.001	–
GPOOH2: % of GP OOH users reporting ‘very easy’ to contact by phone	GP survey	38 (34–42)	20–55	1	0.231	–
Ambulance service
AMBRESPONSE: % category A response within 8 minutes^d	Information Centre	75 (73–75)	71–78	13	< 0.001	–
AMBTRANSPORT: % not transported to hospital^d	Information Centre	21 (16–30)	8–34	35	< 0.001	–

Factors in multiple regression	Factors correlated with	Pearson’s r
EMPLOYMENT	INCOME	+0.87
	COPD	+0.55
	DIABETES	+0.42
	AMBTRANSPORT	–0.44
URBAN/RURAL	ETHNICITY	–0.60
	OVER75	+0.62
	CHD	+0.42
	GPACCESS3	+0.66
	GPOOH1	+0.61
	GPADMISSION	+0.59
	AMBTRANSPORT	+0.45
GPACESS3	INCOME	–0.54
	ETHNICITY	–0.63
	OVER75	+0.71
	ALONE	+0.43
	URBAN/RURAL	+0.66
	CHD	+0.44
	GPACCESS1	–0.46
	GPOOH1	+0.62
	GPADMISSION	+0.52

Block	Predictors^a	Unstandardised coefficient	95% CI	Standardised coefficient	p-value
1: population, geography, health	EMPLOYMENT	203	179 to 227	0.78	0.001
	URBAN/RURAL			–0.17	0.001
	Major urban	536	138 to 934
	Large urban	437	23 to 850
	Other urban	452	38 to 867
	Significant rural	386	–14 to 787
	Rural 50%	322	–81 to 725
2: services	EDUSE	0.9	0.4 to 1.3	0.30	0.001
	EDCONVERSION	7.3	5.1 to 9.4	0.46	0.001
	MANAGEMENT/CODING	18.4	10.1 to 26.6	0.29	0.001
	AMBTRANSPORT	–5.6	–10.2 to –0.9	–0.18	0.02
	GPACCESS3	16.6	6.5 to 26.8	0.24	0.001

Type	System grouped by cluster	Residual	SAAR	Predicted SAAR	Rank	Predicted rank
High SAAR (overpredicted)	HO	–2.51	2669	3238	115	147
High SAAR (underpredicted)	HU1	1.55	3493	3172	147	144
	HU	2.41	4359	3637	150	148
	HU	1.31	3295	3029	143	138
	HU	1.72	3383	3016	146	136
	HU	2.16	3272	2767	142	127
	HU	3.07	2861	2363	130	87
	HU	1.85	2808	2580	124	107
	HU	1.43	2860	2534	129	103
Medium SAAR (overpredicted)	MO3	–1.47	2481	2710	100	118
	MO	–1.50	2432	2731	96	121
	MO	–1.74	2427	2742	94	122
	MO	–1.34	1961	2299	53	81
	MO	–2.03	2116	2572	65	106
	MO6	–1.45	2421	2748	93	123
	MO	–1.32	2108	2430	63	92
Medium SAAR (underpredicted)	MU	2.10	1821	1441	39	2
	MU4	1.39	1905	1690	48	22
	MU	1.57	2270	1949	77	42
	MU	1.70	2661	2312	113	84
	MU	1.43	2530	2204	104	66
Low SAAR (overpredicted)	LO5	–2.67	1776	2300	32	82
	LO	–2.23	1754	2232	31	72
	LO2	–2.98	1572	1950	9	43
	LO	–2.02	1607	2113	14	55
	LO	–1.66	1656	2128	20	59
	LO	–1.78	1268	1553	1	8
Low SAAR (underpredicted)	None	–	–	–	–	–