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BMJ Open logoLink to BMJ Open
. 2013 Jan 3;3(1):e002007. doi: 10.1136/bmjopen-2012-002007

Is secondary preventive care improving? Observational study of 10-year trends in emergency admissions for conditions amenable to ambulatory care

Martin Bardsley 1, Ian Blunt 1, Sian Davies 2, Jennifer Dixon 1
PMCID: PMC3549201  PMID: 23288268

Abstract

Objective

To identify trends in emergency admissions for patients with clinical conditions classed as ‘ambulatory care sensitive’ (ACS) and assess if reductions might be due to improvements in preventive care.

Design

Observational study of routinely collected hospital admission data from March 2001 to April 2011. Admission rates were calculated at the population level using national population estimates for area of residence.

Participants

All emergency admissions to National Health Service (NHS) hospitals in England from April 2001 to March 2011 for people residents in England.

Main outcome measures

Age-standardised emergency admissions rates for each of 27 specific ACS conditions (ICD-10 codes recorded as primary or secondary diagnoses).

Results

Between April 2001 and March 2011 the number of admissions for ACS conditions increased by 40%. When ACS conditions were defined solely on primary diagnosis, the increase was less at 35% and similar to the increase in emergency admissions for non-ACS conditions. Age-standardised rates of emergency admission for ACS conditions had increased by 25%, and there were notable variations by age group and by individual condition. Overall, the greatest increases were for urinary tract infection, pyelonephritis, pneumonia, gastroenteritis and chronic obstructive pulmonary disease. There were significant reductions in emergency admission rates for angina, perforated ulcers and pelvic inflammatory diseases but the scale of these successes was relatively small.

Conclusions

Increases in rates of emergency admissions suggest that efforts to improve the preventive management of certain clinical conditions have failed to reduce the demand for emergency care. Tackling the demand for hospital care needs more radical approaches than those adopted hitherto if reductions in emergency admission rates for ACS conditions overall are to be seen as a positive outcome of for NHS.

Keywords: ambulatory care sensitive, emergency admission, trend


Article summary.

Article focus

  • ■ Unplanned hospital admissions for ambulatory care sensitive (ACS) conditions are an established marker of quality and access to primary care.

  • ■ Many policy initiatives have been targeted at reducing ASC admissions, particularly those for long-term conditions, yet little is known about the cumulative impact of these initiatives over time.

  • ■ The study presents trends in admissions for a range of ACS conditions in England over 10 years.

Key messages

  • ■ Trends are mixed by condition—some fell over time, many more rose.

  • ■ Increases in rates of emergency admissions suggest that efforts to improve the preventive management of certain clinical conditions have failed to reduce the demand for emergency care.

Strengths and limitations of this study

  • ■ The main strengths of this study are that it analysed every admission to a National Health Service hospital in England over a period of 10 years—nearly 140 million admissions.

  • ■ It applied a systematic framework to identify an ACS admission.

  • ■ Potential limitations are the assumption that emergency admissions for ACS conditions are a reasonable indicator of the performance of ambulatory care (it may also be linked to availability and quality of social care).

  • ■ Any study of changes over time is susceptible to artefacts caused by the way information is collected or recorded.

Introduction

Internationally, many health systems are facing the challenge of rising prevalence of chronic health problems and increasing numbers of frail older people needing care. Many countries are actively developing strategies of preventive care for affected population groups1 to improve heath and reduce avoidable costs particularly of hospital care.

The UK has been no exception and over the past decade there have been a plethora of policy initiatives. For example, national guidance was been developed on best practice for the treatment of common chronic conditions2 and financial incentives have helped to boost chronic disease management in primary care.3 Risk stratification has been encouraged4 5 6 to identify which individuals may be at high risk of emergency admission in future. A growing range of preventive initiatives have been designed to reduce that risk, including case management by community matrons,7 telephone coaching, telehealth, virtual wards8 or integrated care.9 At the same time there have been a range of developments in primary care with the increasing numbers of general physicians (GPs) practising with enhanced clinical skills in specific specialities (GPs with special interests) and changes in the arrangements for out-of-hours primary care.10

Some of these individual initiatives have been, and are being, evaluated. But there has been less work measuring the impact of the combination of these mostly community-based initiatives over time. One relatively simple approach is to use admissions for ‘ambulatory care sensitive’ (ACS) conditions—defined in the early 1990s11—as an overall indicator. ACS conditions are defined as clinical conditions for which the risk of emergency hospital admission can be reduced by timely and effective ambulatory care.12 Ambulatory care here mainly means primary care, community services and outpatient care. Higher rates of emergency admission could indicate suboptimal ambulatory care because the health of the individual had deteriorated to the extent that hospitalisation was necessary.

This approach has been developed and tested in a number of studies internationally including the UK.13 14 15 16 Perhaps unsurprisingly, strong relationships have been observed between ACS admissions and levels of deprivation16 17 18 or ethnicity.19 20 Recent analysis in England suggests that better management of ambulatory care could achieve savings of over £1.42 billion.21 Analyses of admissions for ACS conditions are currently being made available by commercial information vendors as a tool to improve local commissioning.22 Most recently, ACS conditions have been proposed as part of a national outcomes Framework for the National Health Service (NHS) in England23 which will be used by government to ensure the delivery of strategic goals for the service.

To date there has been little work in the UK examining trends in admissions for ACS conditions over time. This is important to do because new preventive care initiatives in the NHS are often grafted onto a range of old ones and develop over time, and evaluations of individual initiatives may not be long enough or take an account of synergies between different policies and initiatives. Given all the combined national and local policies outlined above designed to reduce avoidable ill health, plus other initiatives, what has been the overall impact over the last decade?

One huge benefit of having a single payer of healthcare in the UK—the NHS—with universal coverage of the population and comprehensive cover of healthcare is that several years of inpatient data are available for the whole population that can be used in time series analyses to answer this broad question.24 This study examines the pattern of admissions across England for people with ACS conditions over a decade.

Methods

The analysis was based on anonymised person-level records extracted from national hospital episode statistics for the period April 2001–March 2011. These records captured episodes of care for all NHS hospitals in England, totalling more than 150 million finished consultant episodes (FCEs) across the 10 years. Data were supplied by the Information Centre for Health and Social Care.25 Records from residents of Wales and Scotland were excluded, as were records with invalid age or sex fields.

This study focused on the first finished consultant episode (FCE) in each hospital spell (defined where field EPIORDER=1) for emergency inpatient admissions (ADMIMETH between 21 and 29). By taking only the first episodes of spells we aimed to focus on the reason for admission, rather than a condition that developed later in the spell.

There have been a number of different definitions used for ACS conditions (summarised in table 1). This analysis used a set identified by Victoria State Health Department,29 which was also the basis of common NHS subset of ACS conditions identified by Purdy.31 In addition, we included a condition based on tuberculosis that had been part of the original set by Billings (Billings J, fv personal communication, 2010) (detailed definitions are in appendix). The 27 ACS clinical conditions were split into three groups: acute; chronic; and ‘vaccine preventable’ categories as described by Billings11 and Ansari.32

Table 1.

Ambulatory care sensitive (ACS) conditions as defined by various key works and the NHS outcomes framework 2012/2013

Weissman et al 199226 Billings et al 199311 Bindman et al 199527 Sanderson et al 200012 AHRQ et al 200115 De Lia et al 200328 Victoria et al 200429 Caminal et al 200413 Bindman et al 200514 Dr Foster et al 200622 Ling et al 201030 NHS outcomes framework 2012/2013 (chronic)23 NHS outcomes framework 2012/2013 (acute)23
Asthma
Hypertension
Chronic obstructive pulmonary disease
Congestive heart failure
Diabetes
Diabetes complications
Convulsions and epilepsy
Ear, nose and throat infections
Severe ENT infections
Tuberculosis
Immunisation preventable conditions
Pneumonia
Influenza
Congenital syphilis
Angina
Cellulitis
Kidney/urinary infection
Pyelonephritis
Gastroenteritis
Dehydration
Iron deficiency anemia
Nutritional deficiency
Pelvic inflammatory disease
Dental Conditions
Appendicitis with complication
Perforated or bleeding ulcer
Hypokalaemia
Gangrene
Constipation
Dyspepsia and other stomach function disorders
Dementia
Atrial fibrillation and flutter

Conditions only included in one study: Roland 2010 (alcohol-related disease; Fractured proximal femur; Migraine/acute headache; Peripheral vascular disease);

SandsersonDixon 2000 (dysplasia, muccous polyp, erosion of cervix; Fracture of radius and ulna (lower, closed); In growing toenail; Lower limb ulcer except for decubitus; Sebaceous cyst; Viral infection unpsec); Caminal 2004 (disorders of hydro-electrolyte metabolism) and AHRQ 2000 (low birth weight).

Emergency admissions were linked to a specific ACS condition on the basis of primary diagnosis for most categories. In addition, four categories were also defined in terms of codes present as secondary diagnoses—these were ‘gangrene’, ‘diabetes complications’, ‘pneumonia’ and ‘other vaccine preventable conditions’.

Age-specific admission rates were calculated for England using national population estimates33 for the relevant year and aggregated across ages using the European Standard Population.34 This was to allow the rate of admission to be compared over time despite changing age structure of the population. Trends over time were analysed using the slope derived from a linear regression on quarterly observations.

As well as identifying emergency admissions for ACS conditions, we also identified emergency admissions for appendicitis (ICD-10 codes K35-K37)—a condition where admission rates are generally constant at a population level and relatively insensitive to the quality of ambulatory care. This category provided a check on whether trends could be linked with changes in the changes in the accuracy and completeness of diagnostic coding and recording.

The costs of emergency admissions for ACS conditions to commissioners were estimated for the year 2010/2011 from HES data using Payment by Results (PbR) tariffs.35 Activity not covered by the national tariffs was costed using the national reference costs (NRC)36 and adjusted to ensure they were directly comparable with 2010/2011 tariffs. If neither tariff nor NRC were available, the activity was costed as the average tariff for the specialty under which it was delivered, using a method developed for a national study of resource allocation.37 38

Results

In total, 138 million admissions to hospital were recorded as taking place in England between 1 April 2001 and 31 March 2011, of which 46 million were classified as emergency admissions. Less than 2% of emergency admissions (794 369) were excluded due to invalid age or gender codes, or were for people resident outside England. Of the remaining valid emergency admissions, 8.3 million (18.5%) were recorded as falling within 1 of the 27 conditions defined as ACS. The estimated cost to commissioners for these admissions in 2010/2011 was £1.9 billion. The mean age of patients admitted as an emergency with an ACS condition was 53 years and 49% were male.

Between April 2001 and March 2011 the number of emergency admissions per year for ACS conditions increased by 40% rising from 701 995 to 982 482—an increase of 280 487 admissions per year (figure 1). However, throughout this period ACS conditions remained largely constant as a proportion of all emergency admission (range from 18.2% to 19.1%). Over the same time period, emergency admissions for all other (ie, non-ACS) conditions increased by a similar 34%.

Figure 1.

Figure 1

Number of emergency admissions by quarter 2001–2011.

Further analysis revealed that there was a threefold growth in the four ACS conditions defined by their secondary diagnoses and that this accounted for most of the additional growth in ACS over non-ACS admissions (ie, 40% vs 34%). This appears to be part of a general trend for more complete recording of diagnoses and comorbidities in hospital data during this decade.39 ACS emergency admissions defined by primary diagnosis increased by 35% between 2001/2002 and 2010/2011.

Emergency admissions for ACS conditions were more common among the oldest and youngest age groups (table 2) with children under 1 year and adults over 70 more than twice as likely to receive an emergency admission for an ACS condition as the general population. Likewise, the change in rates of ACS emergency admissions was not uniform across age bands. Under age 5 the rates of ACS emergency admissions changed much less than in other age groups over the decade. For the remaining age bands the rate of increase was lowest in ages from 60 to 79, rising by between 10% and 20%. Many age bands (including age 85+) increased by over 40%.

Table 2.

Numbers and rates of emergency admissions in 2001/2002 and 2010/2011 by age band

2001/2002
2010/2011
Change from 2001/2002
Age band (years) Observed number Rate per 100k Observed number Rate per 100k Change in rate per 100k—number (%)
0 31 739 5693 37 620 5570 −123 (−2.2)
01–04  74 701 3157 84 751 3270 113 (3.6)
05–09  24 381 781 29 214 1006 225 (28.8)
10–14  15 657 484 18 151 609 125 (25.9)
15–19  16 210 532 25 071 768 235 (44.2)
20–24  15 614 523 28 012 777 254 (48.6)
25–29  15 449 465 25 202 702 237 (50.9)
30–34  18 012 468 23 064 698 230 (49.2)
35–39  19 200 490 25 948 728 238 (48.6)
40–44  19 258 552 31 690 811 259 (47)
45–49  21 560 689 37 133 972 283 (41.1)
50–54  27 900 829 39 374 1190 361 (43.6)
55–59  32 748 1156 43 943 1479 323 (28)
60–64  40 535 1692 59 379 1891 199 (11.7)
65–69  50 921 2357 64 500 2649 292 (12.4)
70–74  64 906 3324 79 262 3862 539 (16.2)
75–79  73 833 4489 91 085 5460 970 (21.6)
80–84  64 710 5783 98 352 7848 2065 (35.7)
85+ 74 661 7787 140 731 11 749 3962 (50.9)

When the age-standardised rates of emergency admissions for ACS conditions were compared, the overall increase between 2001/2002 and 2010/2011 was 25% (when considering primary diagnoses only the age-standardised increase was 21%) indicating that some, but not all, of the change in crude admission rates could have to been due to the changing demographic structure of the population. On the basis of the change in age-standardised admission rate, we estimate the increase in ACS admissions above 2001/2002 levels would cost an additional £477 million per year in 2010/2011.

The change in age-standardised rates of emergency admissions varied between the three broad categories of ACS conditions (acute, chronic and vaccine preventable) as shown in figure 2. For comparison rates of admissions for the ACS-insensitive marker condition (acute appendicitis) are also shown. All the ACS rates show strong seasonal variations in year that are associated with higher admission in the winter months. Figure 2 shows that emergency admission rates for the acute group of ACS conditions increased by 44% over the decade (p<0.0001 based on quarterly trends) while rates for vaccine preventable ACS conditions increased by 136% (p<0.0001), although from a much lower baseline. In contrast, the rate of admissions for chronic ACS conditions decreased by 2%, but this was not statistically significant (p=0.5091). Admission rates for ACS-insensitive conditions showed a small but steady increase of 13% over the decade (p<0.0001). While the HES data do not distinguish between appendicitis and suspected appendicitis, they do record that 90% of appendicitis admissions had their appendixes removed. This proportion did not change notably over time (92% in 2001/2002 vs 89% in 2010/2011).

Figure 2.

Figure 2

Directly standardised rate of ambulatory care sensitive (ACS)-related emergency admissions in England 2001–2011 by quarter (with one ACS-insensitive marker condition).

Table 3 summarises changes in emergency admissions for individual ACS conditions. The total number of admissions and the directly age-standardised rate per 100 000 population in 2001/2002 and 2010/2011 are shown, as well as the percentage change in the standardised rate, statistical significance of the quarterly rate trend and absolute change in the number of admissions between the 2 years. The trend in rates over time was significantly different from zero in all but three conditions.

Table 3.

Numbers and rates of emergency admissions in 2001/2002 and 2010/2011 by condition

2001/2002
2010/2011
Change over time
Observed number Directly standardised rate per 100k Observed number Directly standardised rate per 100k Estimated cost (£ million) Percentage of change DSR Qrtly trend p value* Absolute annual change
Acute ACS conditions
 Cellulitis 44 048 77.8 62 305 100.0 118 29 0.005 +18257
 Dehydration 5713 8.0 10 676 13.3 26 66 <0.001 +4963
 Dental conditions 5287 11.3 10 132 20.0 14 77 <0.001 +4845
 Ear, nose and throat infections 66 107 168.5 88 739 205.2 61 22 0.025 +22632
 Gangrene (primary diagnosis) 1665 2.6 1472 2.1 7 −19 <0.001  −193
 Gangrene (secondary diagnosis) 2321 3.5 6384 8.9 154 <0.001 +4063
 Gastroenteritis 43 181 89.1 73 066 127.4 109 43 <0.001 +29885
 Nutritional deficiencies 79 0.2 204 0.4 0.5 100 <0.001 +125
 Pelvic inflammatory disease 4839 10.1 4561 8.9 8 −12 <0.001 −278
 Perforated/bleeding ulcer 7773 12.0 5164 7.7 17 −36 <0.001 −2609
 UTI/Pyelonephritis 61 630 101.3 145 132 204.8 316 102 <0.001 +83502
Chronic ACS conditions
 Angina 91 867 149.8 61 125 87.8 97 −41 <0.001 −30742
 Asthma 57 234 125.5 61 151 124.9 58 0 0.762 +3917
 Chronic obstructive pulmonary disease 94 035 142.8 117 248 161.3 271 13 0.246 +23213
 Congestive heart failure 65 038 85.2 54 728 61.9 154 −27 <0.001 −10310
 Convulsions and epilepsy 59 936 128.5 77 165 148.2 91 15 <0.001 +17229
 Diabetes complications (primary diagnosis) 17 711 33.2 22 608 40.9 52 23 <0.001 +4897
 Diabetes complications (secondary diagnosis) 14 089 23.8 31 085 46.5 95 <0.001 +16996
 Hypertension 4970 8.5 6320 10.1 6 19 <0.001 +1350
 Iron deficiency anaemia 7543 11.0 11 425 15.5 21 41 <0.001 +3882
Vaccine preventable ACS conditions
 Influenza (primary diagnosis) 679 1.4 7422 14.7 13 950 0.002 +6743
 Influenza (secondary diagnosis) 203 0.4 1306 2.6 550 0.002 +1103
 Other vaccine preventable (primary diagnosis) 1363 3.5 884 1.9 1 −46 0.103 −479
 Other vaccine preventable (secondary diagnosis) 938 1.9 2278 4.2 121 <0.001 +1340
 Pneumonia (primary diagnosis) 33 994 58.6 90 252 127.6 235 118 <0.001 +56258
 Pneumonia (secondary diagnosis) 8071 12.1 28 045 37.3 208 <0.001 +19974
 Tuberculosis 1681 3.2 1605 3.0 6 −6 0.005 −76
ACS insensitive
 Appendicitis 31 896 67.2 37 667 76.1 1 13 <0.001 +5771

*p Value is on quarterly trend in DSR being significantly different from zero.

ACS, ambulatory care sensitive.

There were increased rates of admissions for the majority of the acute ACS conditions. The most extreme were for urinary tract infections (UTIs)/pyelonephritis and gastroenteritis groups, for which the age-standardised rates increased by 102% and 43%, respectively. These changes equated to an additional 113 387 observed admissions in 2010/2011, at an extra cost in that year of £369 million. Admissions for ENT infections and cellulitis had somewhat lower increases of 22% and 29%, but their high volumes meant that they contributed 40 889 extra-admissions.

In contrast, there were significant falls in the rates of admission for perforated/bleeding ulcer (−36%, p<0.001) and pelvic inflammatory disease (−12%, p<0.001). Gangrene as a primary diagnosis also fell (−19%), although gangrene as a secondary diagnosis increased significantly (+154%). These reductions were far less in scale than the increases of other acute conditions, representing just 3080 fewer admissions per year in 2010/2011 than in 2001/2002.

The trends for the chronic group of ACS conditions were more varied. The two conditions with the highest rise in the absolute numbers of admissions were chronic obstructive pulmonary disease (COPD), and convulsions and epilepsy but the rise in age-standardised rates of admission for these conditions was modest and not statistically significant for COPD (p=0.246). The number of admissions for diabetes as a secondary diagnosis grew by 95% and contributed an extra 16 996 admissions with a large and significant rise in age-standardised rates.

The rates of admissions for congestive heart failure and angina showed marked and significant reductions (−41% and −27%). Rates of admissions for asthma—another high-volume condition—remained largely unchanged.

The group of vaccine-preventable conditions showed relatively large increases in admission rates, but still accounted for only about one-eighth of the volume of all ACS admissions in 2010/2011. Of these, 90% were for pneumonia, the rate of which increased by 118% (primary diagnosis) and 208% (secondary) since 2001/2002 resulting in an extra 76 232 admissions. Admissions for pneumonia as a primary diagnosis cost £235 million in 2010/2011. Large increases in influenza (as primary or secondary diagnosis) appeared only from 2009/2010 onwards, and may be linked with the bird-flu pandemic.

Discussion

This analysis has shown that across England there was a 35% increase in the number of emergency admissions for 27 ACS conditions over a 10-year period—similar in magnitude to that seen for emergency admissions for all other (ie, non-ACS) conditions.

Most of the increase in age-standardised rates of admission occurred for the group of ‘acute’ ACS conditions (particularly urinary tract infections and gastroenteritis) and ‘vaccine preventable’ ACS conditions in particular pneumonia. For some specific conditions there were reductions in numbers and rates of emergency admissions, for example, for perforated/bleeding ulcer and pelvic inflammatory disease (−12%, p<0.001). These reductions were far less in scale than the increases of other acute conditions, representing just 3080 fewer admissions per year in 2010/2011 than in 2001/2002.

There were some clear differences in trends between ACS conditions. The reductions in rates of emergency admissions for angina and CHF could be linked with reductions in the prevalence of ischaemic heart disease40 which are in part due to changes in health-related behaviours and availability of effective preventive treatment, for example statins. This trend in admission rates has been identified in other countries.41

The significant reduction in admissions for perforated/bleeding ulcers may be due to the use of antibacterials and proton pump inhibitors in the preceding 20 years. Admissions for pelvic inflammatory disease have also fallen which is consistent with evidence of a falling prevalence of PID observed in GP records.42 The increase in admissions linked with complications of diabetes could in part be explained by increases in the prevalence of diabetes.43

The rise the number and rate of emergency admissions for pyelonephritis and urinary tract infection could be attributed to a number of factors. Diagnosis of symptomatic infection in older people is difficult44 and can be complicated by the presence of asymptomatic bacteriuria and non-specific symptoms. A rise in admissions for pneumonia in the older age groups has been observed internationally from at least the 1980s45while nationally GP consultations for pneumonia and pnuemonitis have been falling.46

For a range of chronic conditions, the position is ambiguous—taking into account changes in the age of the population leaves increases in rates of emergency admissions of 0% for asthma and 13% for COPD. These chronic respiratory conditions have been the particular focus for a range of national policy initiatives.

Potential limitations

This analysis is based on assumption that emergency admissions for ACS conditions are a reasonable indicator of the performance of ambulatory care. It could in fact be that, for frail elderly people this indicator also reflects the availability and quality of social care.

Any study of changes over time is susceptible to artefacts caused by the way information is collected or recorded. In this case the accuracy and completeness of hospital data has probably improved during this period especially since the introduction of case mix-based systems of reimbursement such as payment by results.47 The fact that ACS conditions recorded as secondary diagnoses in an admission increased more than those in primary suggests a shift in data recording during this time.

Policy implications

Using ACS indicators as an outcome measure in the Commissioning Outcomes Framework23 will be challenging for the NHS. There are clearly some aspects of measurement that will need to be considered in their presentation and interpretation (such as seasonal effects, the strong correlations with deprivation and the change in frequency of diagnostic recording). The behaviour of any metric based on ACS admissions will also be sensitive to the range of conditions included and their definition. These problems are not insurmountable.

There a range of differing explanations behind the observed increases in ACS admissions. For some ACS conditions changes could be due to differences in the underlying prevalence of disease; changes in health-related behaviours such as smoking or improvements in the effectiveness of treatments for acquired diseases for example, statins for angina.

In addition, there are possible explanations due to changes in the way health systems operate such as the admission threshold—is it that patients admitted now are less sick than they were 10 years ago or that decisions are risk averse and require admissions? The most significant increases in ACS conditions appear to be linked with short-term treatment of an acute problem in older people. It is quite probable that the increases are due to changing thresholds for admission rather than the severity of the problems of the presenting patient. Any changes in emergency admissions for ACS conditions have to be considered against the backdrop of increases associated with short stay emergency admissions and will form part of the general pattern of rising emergency admission.48 It may be that admission decisions are in part influenced by the perceived lack of alternatives to inpatient care, or the introduction of new forms of care to the inpatient setting.

The decade under observation saw record growth in NHS funding and a large range of initiatives to improve care. These include for example changes in the funding of secondary care through payment by results, the reorganisation of primary care in PCTs, the implementation of funding incentives in primary care and major changes to out-of-hours care.49 However, on this basis our results suggest that these changes have not meant improved care to the extent of reducing overall rates of avoidable admissions, although there has been some success for specific ACS conditions. Perhaps most significant though is that fact that some of the more common ACS conditions show trends that closely mirror the rise in rates of emergency admission for non-ACS conditions. This suggests that those factors linked with the organisation and financing of the health system itself are perhaps more important determinants than just the changing health needs of the population over the decade.

While many policy initiatives have been proposed as driving the increase in admissions, evidence connecting the trend to specific policies is weak.48 For example the introduction of the 4-hour A&E target and changes to the GP out-of-hours contract (both in 2004) are commonly cited as increasing the overall level of emergency admissions, yet figure 1 suggests that emergency admissions continued an established increasing trend at this point, with no obvious deviation in ACS or non-ACS admissions.

Although there is much interest in how health services can safely reduce avoidable demand for hospital care, the evidence on the effectiveness of this is not always convincing.50 The trends reported in this paper clearly point to the need for much harder thinking, in particular how a combination of national and local policies are impacting on the need for admission for patients, either because of suboptimal preventive ambulatory care, or reducing the threshold for admission to hospital. The government is beginning to encourage integrated care9 as one solution, although the size of this challenge should not be underestimated.51 Similarly, the availability and quality of social care for frail older patients will be crucial to help maintain independent living and reduce the need for admission—there are some hard policy choices here too and it remains to be seen whether the forthcoming White Paper on social care will address them.

Supplementary Material

Author's manuscript
Reviewer comments

Appendix: Definition of ambulatory care sensitive (ACS) conditions

Condition ICD-10 codes Deviation from Victoria Deviation from Purdy (tab3, common)
Acute ACS conditions
 Cellulitis L03, L04, L08, L88, L980, L983
Principal diagnosis only
None Added L089, L983 from Victorian
 Dehydration E86
Principal diagnosis only
Split from gastro Split from gastro
 Dental conditions A690, K02-K06, K08, K098, K099, K12, K13
Principal diagnosis only
Addition of A690 (necrotising ulcerative stomatitis) None
 Ear, nose and throat infections H66, H67, J02, J03, J06, J312
Principal diagnosis only
None None
 Gangrene R02
Any diagnosis
None None
 Gastroentereritis K522, K528, K529
Principal diagnosis only
Split from dehydration Split from dehydration
 Nutritional deficiencies E40-E43, E55, E643
Principal diagnosis only
None None
 Pelvic inflammatory disease N70, N73, N74
Principal diagnosis only
None None
 Perforated/bleeding ulcer K250-K252, K254-K256, K260-K262, K264-K266, K270-K272, K274-K276, K280-K282, K284-K286
Principal diagnosis only
None None
 Urinary tract infection /Pyelonephritis N10, N11, N12, N136, N390
Principal diagnosis only
None > name change Added N390 from Victorian
Chronic ACS conditions
 Angina I20, I240, I248, I249
Principal diagnosis only
None None
 Asthma J45, J46
Principal diagnosis only
None None
 Chronic obstructive pulmonary disease J20, J41-J44, J47
Principal diagnosis only, J20 only with diag2 of J41 J42 J43 J44 J47
None None
 Congestive heart failure I110, I50, J81
Principal diagnosis only
None None
 Convulsions and epilepsy G40, G41, O15, R56
Principal diagnosis only
None None
 Diabetes complications E100-E108, E110-E118, E120-E128, E130-E138, E140-E148
In any diagnosis field
None None
 Hypertension I10, I119
Principal diagnosis only
None None
 Iron deficiency anaemia D501,D508,D509
Principal diagnosis only
None None
Vaccine preventable ACS conditions
 Influenza J10, J11
In any diagnosis field, excludes cases with secondary diagnosis of D57, and people under 2 months
Split from pneumonia Split from pneumonia
 Pneumonia J13, J14, J153, J154, J157, J159, J168, J181, J188
In any diagnosis field, excludes cases with secondary diagnosis of D57, and people under 2 months
Split from influenza Split from influenza
 Tuberculosis A15, A16, A19
Principal diagnosis only
Added Added
 Other vaccine preventable A35-A37, A80, B05, B06, B161, B169, B180, B181, B26, G000, M014
In any diagnosis field
None None

Other vaccine preventable conditions: A35 other tetanus; A36 diphtheria; A37 whooping cough; A80 acute poliomyelitis; B05 measles; B06 rubella (German measles); B16.1 acute hepatitis B with δ-agent (co-infection) without hepat coma; B16.9 acute hepatitis B without δ-agent and without hepat coma; B18.0 chronic viral hepatitis B with δ-agent; B18.1 chronic viral hepatitis B without δ-agent; B26 mumps; G00.0 haemophilus meningitis; M01.4 direct infections joint in infectious and parasitic dis EC.

Footnotes

Contributors: All authors were involved in the study design, analysis and interpretation of data and the writing of the article. MB and JD conceived the idea of the study, and MB was the lead writer. IB was the lead data analyst, and produced the tables and figures. SD assisted data analysis and reviewed the condition-specific literature. MB will act as guarantor for the work. The guarantor accepts full responsibility for the conduct of the study, had access to the data and controlled the decision to publish (http://bmj.com/cgi/content/full/323/7313/588).

Funding: The work was funded by the Nuffield Trust.

Competing interests: None.

Ethics approval: The study used fully anonymised secondary data only, use of which for this purpose was initially approved by The Database Monitoring sub-Group of the Ethics and Confidentiality Committee of the National Information Governance Board (July 2010). This sub-group was abolished in October 2010 and subsequent permissions were obtained through the Information Centre for Health and Social Care.

Provenance and peer review: Not commissioned; externally peer reviewed.

Data sharing statement: Information available from the corresponding author at martin.bardsley@nuffieldtrust.org.uk.

References

  • 1.Nolte E, McKee M, eds. Caring for people with chronic conditions: a health system perspective. Maidenhead: Open University Press, 2008 [Google Scholar]
  • 2. NICE About NICE guidance. http://guidance.nice.org.uk (accessed 25 Jul 2012)
  • 3. Information Centre Quality and Outcomes Framework. http://www.qof.ic.nhs.uk/ (accessed 25 Jul 2012)
  • 4.Billings J, Billings J, Dixon J, et al Case finding for patients at risk of readmission to hospital: development of algorithm to identify high risk patients BMJ 2006;333:327. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.SPARRA: Scottish patients at risk of readmission and admission. Edinburgh: NHS National Services Scotland, 2006. http://www.isdscotland.org/isd/files/SPARRA_Report.pdf (accessed 25 Jul 2012). [Google Scholar]
  • 6. NHS Wales Informatics Service GP practices trial tool to identify patients at risk. http://www.wales.nhs.uk/nwis/news/17929 Monday, 10 August 2009 (accessed 25 Jul 2012)
  • 7. Department of Health How a Community Matron can help you with your long term condition http://www.dh.gov.uk/en/Publicationsandstatistics/Publications/PublicationsPolicyAndGuidance/DH_4131689 1 February 2006 (accessed 25 Jul 2012)
  • 8.Lewis G, Wright L, Vaithianathan R. Multidisciplinary case management for patients at high risk of hospitalization: comparison of virtual ward models in the United kingdom, United States, and Canada Popul Health Manag 2012;15:315–21. doi: 10.1089/pop.2011.0086. Epub 2012 Jul 12 [DOI] [PubMed] [Google Scholar]
  • 9.Shaw S, Rosen R, Rumbold B. What is integrated care? London: Nuffield Trust, 2011 [Google Scholar]
  • 10.Grol R, Giesen P, van Uden C. After-hours care in the United Kingdom, Denmark, and the Netherlands: new models health affairs. Health Affairs (Millwood) 2006;25:1733–7 [DOI] [PubMed] [Google Scholar]
  • 11.Billings J, Zeitel L, Lukomnik J, et al. Datawatch: impact of socioeconomic status on hospital use in New York City. Health Affairs (Millwood) 1993;12:162–73 [DOI] [PubMed] [Google Scholar]
  • 12.Sanderson C, Dixon J. Conditions for which onset or hospital admission is potentially preventable by timely and effective ambulatory care. J Health Ser Res Policy 2000;5:222–30 [DOI] [PubMed] [Google Scholar]
  • 13.Caminal J, Starfield B, Sánchez E, et al. The role of primary care in preventing ambulatory care sensitive conditions. Eur J Public Health 2004;14:246–51 [DOI] [PubMed] [Google Scholar]
  • 14.Bindman AB, Chattopadhyay A, Osmond DH, et al. The impact of Medicaid managed care on hospitalizations for ambulatory care sensitive conditions. Health Ser Res 2005;40:19–38 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Agency for Healthcare Research and Quality Guide to prevention quality indicators: hospital admission for ambulatory care sensitive conditions. 2001. http://www.qualityindicators.ahrq.gov (accessed 25 Jul 2012)
  • 16.Purdy S, Griffin T. Reducing hospital admissions. BMJ 2008;336:4–5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Roos L, Walld R, Uhanova J, et al. Physician visits, hospitalizations, and socioeconomic status: ambulatory care sensitive conditions in a Canadian setting. Health Serv Res 2005;40:1167–85 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Shi L, Samuels ME, Pease M, et al. Patient characteristics associated with hospitalizations for ambulatory care sensitive conditions in South Carolina. South Med J 1999;92:989–98 [DOI] [PubMed] [Google Scholar]
  • 19.Billings J, Anderson GM, Newman LS. Race, poverty, and ACS admissions: the authors respond health affairs. 1997;16:225–225-a doi: 10.1377/hlthaff.16.1.225-a [Google Scholar]
  • 20.Howard DL, Hakeem FB, Njue C, et al. Racially disproportionate admission rates for ambulatory care sensitive conditions in North Carolina Public Health Reports (Washington, DC: 1974). 2007;122:362–72 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Tian Y, Dixon A, Gao H. Data briefing: emergency hospital admissions for ambulatory care-sensitive conditions. London: Kings Fund, 2012 [Google Scholar]
  • 22.Dr Foster Intelligence. Managing Long Term Conditions. 2009. http://www.drfosterintelligence.co.uk/managementInformation/longTermConditions.asp (accessed 25 Jul 2012).
  • 23.Department of Health NHS Outcomes Framework 2012–13 Department of Health. 2011. http://www.dh.gov.uk/en/Publicationsandstatistics/Publications/PublicationsPolicyAndGuidance/DH_131700 (accessed 25 Jul 2012).
  • 24.Lakhani A, Coles J, Eayres D, et al. Creative use of existing clinical and health outcomes data to assess NHS performance in England: art 1—performance indicators closely linked to clinical care. BMJ 2005;330:1426–31 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Information Centre for Health and Social Care Hospital Episode Statistics. Information Centre. http://www.hesonline.nhs.uk (accessed 25 Jul 2012).
  • 26.Weissman JS, Gatsonis C, Epstein AM. Rates of avoidable hospitalization by insurance status in Massachusetts and Maryland. JAMA 1992;268:2388–94. [PubMed] [Google Scholar]
  • 27.Bindman AB, Grumbach K, Osmond D, et al. Preventable hospitalizations and access to health care. JAMA 1995;274:305–11 [PubMed] [Google Scholar]
  • 28.DeLia D. Distributional Issues in the Analysis of Preventable Hospitalizations Health Services Research 38:6, Part II (December 2003) [DOI] [PMC free article] [PubMed]
  • 29.State Government of Victoria, Australia, Department of Human Services Victorian ambulatory care sensitive conditions. Melbourne: Public Health Division, Victorian Government Department of Human Services, 2001. http://www.health.vic.gov.au/healthstatus/admin/acsc/index.htm (accessed 25 Jul 2012) [Google Scholar]
  • 30.Ling T, Bardsely M, Adams J, et al. Evaluation of UK Integrated Care Pilots: research protocol Int J Integr Care 2010;10:e056. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Purdy S, Griffin T, Salisbury C, et al. Ambulatory care sensitive conditions: terminology and disease coding need to be more specific to aid policy makers and clinicians. Public Health 2009;123:169–73 [DOI] [PubMed] [Google Scholar]
  • 32.Ansari Z, Laditka JN, Laditka SB. Access to health care and hospitalization for ambulatory care sensitive conditions. Med Care Res Rev 2006;63:719–41 [DOI] [PubMed] [Google Scholar]
  • 33. Office for National Statistics. Mid-year population estimates. http://www.ons.gov.uk/ons/search/index.html?newquery=1.%09Mid-year+population+estimates+ (accessed 25 Jul 2012)
  • 34.NHs Public Health Network. Directly age standardised rates. http://www.avon.nhs.uk/phnet/Methods/directly_age_standardised_rates.htm (accessed 25 Jul 2012)
  • 35.Department of Health Payment by results guidance for 2010–11. London: Department of Health, 2010 [Google Scholar]
  • 36.Department of Health NHS reference costs 2007–08. London: Department of Health, 2009 [Google Scholar]
  • 37.Dixon J, Smith P, Gravelle H, et al. A person based formula for allocating commissioning funds to general practices in England: development of a statistical model. BMJ 2011;343:d6608. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Dixon, et al. Updating and enhancing a resource allocation formula at general practice level based on individual level characteristics (person-based resource allocation) (Forthcoming)
  • 39.Robinson P. Data briefing: cash incentives improve coding. Health Ser J 2007;117:21. [PubMed] [Google Scholar]
  • 40.Scarborough P, Bhatnagar P, Wickramasinghe K, et al. Coronary heart disease statistics. London: British Heart Foundation, 2010. edn [Google Scholar]
  • 41.Commonwealth Fund Why Not the Best? Results from the National Scorecard on U.S. Health System Performance, 2011 The Commonwealth Fund Commission on a High Performance Health System 2011 http://www.commonwealthfund.org/~/media/Files/Publications/Fund%20Report/2011/Oct/1500_WNTB_Natl_Scorecard_2011_web.pdf (accessed 25 Jul 2012)
  • 42.French CE, Hughes G, Nicholson A, et al. Estimation of the rate of pelvic inflammatory disease diagnoses: trends in England, 2000–2008. Sex Transm Dis 2011;38:158–62. [DOI] [PubMed] [Google Scholar]
  • 43. Information Centre Prescribing for Diabetes in England: 2005/06 to 2010/11 Information Centre. 2011. http://www.ic.nhs.uk/webfiles/publications/prescribing%20diabetes%20200506%20to%20201011/Prescribing_for_Diabetes_in_England_20056_to_201011.pdf (accessed 25 Jul 2012)
  • 44.Beveridge L, Davey PG, Phillips G, et al. Optimal management of urinary tract infections in older people. Clin Interv Aging 2011;6:173–80 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Hebert PL, McBean AM, Kane RL. Explaining trends in hospitalizations for pneumonia and influenza in the elderly. Med Care Res Rev 2005;62:1077–5587 [DOI] [PubMed] [Google Scholar]
  • 46.Fry AM, Sahy DK, Curns AT, et al. Trends in hospitalizations for pneumonia among persons aged 65 years or older in the United States, 1988–2002. JAMA 2005;294:2712–9. [DOI] [PubMed] [Google Scholar]
  • 47.2010. Audit Commission improving data quality in the NHS Audit Commission.
  • 48.Blunt I, Bardsley M, Dixon J. Trends in emergency admissions in England 2004–2009: is greater efficiency breeding inefficiency? London: Nuffield Trust, 2010 [Google Scholar]
  • 49.Thompson C, Hayhurst C, Boyle A. How have changes to out-of-hours primary care services since 2004 affected emergency department attendances at a UK District General Hospital? A longitudinal study. Emerg Med J 2010;27:22–5 [DOI] [PubMed] [Google Scholar]
  • 50.Purdy S. Avoiding hospital admissions: what does the research evidence say? London: King's Fund, 2010 [Google Scholar]
  • 51.Goodwin N, Smith J, Davies A, et al. Integrated care for patients and populations: Improving outcomes by working together. London: Nuffield Trust, 2012 [Google Scholar]

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