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Br J Gen Pract. 2017 January; 67(654): e20–e28.
Published online 2016 October 25. doi:  10.3399/bjgp16X687949
PMCID: PMC5198609

Opportunities for primary care to reduce hospital admissions: a cross-sectional study of geographical variation

John Busby, PhD, Senior research associate
National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care (NIHR CLAHRC) West, Bristol
Sarah Purdy, MD, FRCGP, Professor of primary care
Centre for Academic Primary Care; School of Social and Community Medicine, University of Bristol, Bristol
William Hollingworth, PhD, Professor of Health Economics

Abstract

Background

Reducing unplanned hospital admissions is a key priority within the UK. Substantial interpractice variation in admission rates for ambulatory care sensitive conditions (ACSC) suggests that decreases might be possible.

Aim

To identify the clinical areas and patient subgroups where the greatest opportunities exist for GPs to improve ACSC care.

Design and setting

Cross-sectional study using routine hospital data from patients registered at 8123 English GP practices during 2011 and 2012.

Method

The authors used random effects Poisson models to estimate interpractice variation after adjusting for several drivers of healthcare need and availability of local hospital services. Interpractice variation was contrasted across patient subgroups based on age.

Results

There were 1.8 million hospital admissions. Overall, high-utilisation practices had ACSC admission rates that were 55% (95% CI = 53 to 56) greater than low-utilisation practices. Differences of 67% (95% CI = 65 to 69) were found for chronic ACSCs, which was much larger than the 51% (95% CI = 49 to 52) difference exhibited by acute presentations. At least two-fold differences were found for 15 (54%) ACSCs, although large interpractice variations were not ubiquitous. Admission rates were consistently more variable among younger-than-average patients. The most variable conditions tended to disproportionately affect deprived patients.

Conclusion

Substantial interpractice variation suggests that current efforts to standardise primary care have had a limited effect on unplanned hospital admissions. GPs and healthcare commissioners should ensure they are offering best practice care for the most variable clinical areas and patient subgroups identified in the study, particularly in adults aged <70 years with chronic conditions.

Keywords: ambulatory care, general practice, geographical distribution, patient admission, primary health care

INTRODUCTION

Unplanned admissions place a tremendous strain on UK healthcare resources, accounting for 67% of hospital bed days, costing £12.5 billion annually,1 and disrupting elective care.2 In England, they have increased by 47% over the last 15 years,1 with some arguing that their continued rise threatens to bankrupt the NHS.3 Reducing the number of unplanned admissions is a key priority within the UK.4 Ambulatory care sensitive conditions (ACSCs) account for one in five unplanned admissions.5 ACSCs are conditions where GPs can potentially reduce admissions by ensuring that patients receive high-quality disease management, timely treatment, and appropriate referral.6

Concerns that some ACSC admissions are avoidable have been fuelled by wide interpractice variations.7,8 Part of this variation will be driven by factors beyond the control of GPs, such as patient characteristics (for example, age, deprivation, comorbidities), availability of community support (such as social services), and local hospital services (for example, A&E department proximity and bed availability).9,10 However, an unknown proportion is likely to result from interpractice differences in primary care quality.9,11,12 Improved understanding of the clinical areas where ACSC admission rates are most variable, and primary care might be most inconsistent, could lead to more targeted admission avoidance interventions and improved patient outcomes.

The authors used routine data from English hospitals to examine interpractice variation in unplanned ACSC admission rates that is not explained by markers of healthcare need or availability of hospital services. The study explored whether interpractice variation in admission rates is consistent across conditions, and whether it affects some patient age groups more than others.

METHOD

Data source and preparation

The authors used the Hospital Episode Statistics (HES) admitted patient care dataset to identify admissions between 1 April 2011 and 31 March 2012.13 HES includes demographic, clinical, and geographical information. The study included all unplanned admissions for 28 common ACSCs (more than 3000 admissions annually), which were identified using International Classification of Diseases (ICD)-10 diagnosis codes (Appendix 1).6 The authors classified ACSCs that generally require long-term management by GPs as chronic and the remainder as acute, and investigated differences in ACSC admission rates between 8123 primary care practices submitting data to the Quality and Outcomes Framework (QOF) in 2011–2012 (almost all English practices).14 The authors converted episodes into continuous inpatient spells (CIPS), meaning that care spanning multiple hospitals was counted only once.15 The authors included CIPS when the primary diagnoses from the admission episode indicated an ACSC. Patients with an invalid data entry for age or sex (<0.1%) were excluded.

How this fits in

Unplanned hospital admissions place a large and growing burden on healthcare resources. GPs play an important role in reducing these by ensuring that patients receive high-quality disease management, timely treatment or advice, and appropriate referral. This study used interpractice variation in unplanned admission rates to identify the clinical areas where primary care might be inconsistent. Targeted admission avoidance interventions could lead to improved patient outcomes. GPs and healthcare commissioners should ensure they are offering best-practice care for the most variable clinical areas and patient subgroups identified in this study.

Descriptive analysis and estimating interpractice variation

The authors described patient demographics, and calculated the number of admissions and bed days for each condition. They summed across conditions to calculate totals for acute, chronic, and all ACSCs combined. Before estimating interpractice variation a two-step process to adjust for differences in practice populations was used. The authors first calculated expected admission counts using indirect standardisation (utilising quinary age groups and sex) and national data16 to account for differences in the size and age–sex composition of practice populations (Appendix 2). The authors then used Poisson regression to further adjust for other key determinants of healthcare need. A proxy for the deprivation of the practice population was estimated based on the practice postcode.17 Data from QOF disease registers were used to adjust for the prevalence of atrial fibrillation, asthma, cancer, chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), dementia, epilepsy, heart failure, hypertension, learning disability, mental health problems, obesity, and stroke. The authors calculated the straight-line distance between the practice and the closest A&E department, and used this as a measure of emergency care accessibility. Other local non-primary-care factors (such as community healthcare supply and hospital admission policies) were adjusted for by including 151 dummy variables representing the primary care trust (PCT) in which the practice was located.

The authors used random effects Poisson models to quantify interpractice variation in admissions (Appendix 2). These models estimate the interpractice standard deviation (SD) in admission rates for each ACSC. A high SD indicates substantial unexplained variability. To improve interpretability, the authors calculated decile differences, defined as the percentage difference in admission rates between a high utilisation practice (at the 90th centile of the random effects distribution) and a low utilisation practice (at the 10th centile).

Contrasting interpractice variation across age subgroups

The authors calculated decile differences separately for five age subgroups (0–4, 5–19, 20–39, 40–69, and ≥70 years) using the methods described above. Subgroups containing fewer than 3000 admissions were excluded to ensure precise estimates, as were those containing less than 10% of admissions, as these represented atypical patients (for example, hypertension patients <20 years old). Four (14%) ACSCs had only one group remaining after these deletions and were excluded. The percentage difference between the decile difference in the youngest age group and those in older groups was calculated. The analyses were conducted in WinBUGS (version 1.4.3).

RESULTS

Descriptive statistics

There were 1.77 million admissions for ACSCs, accounting for 10.9 million bed days during 2011–2012 (Table 1). Many patients were older (mean age 56 years), from deprived communities (27% resided in the most deprived quintile of areas), had at least one comorbidity (58%), and were admitted through A&E (75%). These overall results concealed substantial variation between conditions (Table 2). Younger patients were more frequently admitted for a few ACSCs (for example, ENT infections) whereas others almost universally involved older patients (such as dementia). Some ACSCs exhibited a very steep socioeconomic gradient (for example, alcohol-related diseases and schizophrenia). There were wide disparities in the proportion of hospital admissions originating from primary care; 34% of ear, nose, and throat (ENT) infection admissions were GP referrals, compared with only 1% for fractured proximal femur.

Table 1.
Admission details for all Ambulatory Care Sensitive Conditions (ACSCs)
Table 2.
Characteristics of admitted patients by condition

Interpractice variation

Substantial differences in unplanned admission rates existed between English general practices (Table 3). For all ACSCs combined, high-utilisation practices (at the 90th centile) had admission rates 55% (95% CI = 53 to 56) higher than low-utilisation practices, after adjustment for age, sex, other markers of healthcare need, distance from A&E, and PCT-level effects. Differences of 67% (95% CI = 65 to 69) were found for chronic ACSCs, which was much larger than the corresponding figure of 51% (95% CI = 49 to 52) for acute presentations. Furthermore, the eight most variable conditions were chronic while the eight least variable were acute.

Table 3.
Magnitude of interpractice admission rate variation

The most variable condition was alcohol-related disease, where high-utilisation practices had admission rates 237% (95% CI = 224 to 252) greater than low-utilisation practices. However, large interpractice variations were commonplace. For example, differences in excess of 150% were found for diabetes complications, iron deficiency anaemia, hypertension, and COPD. In contrast, the differences for fractured proximal femur and stroke were only 33% (95% CI = 28 to 39) and 35% (95% CI = 30 to 39) respectively. The highest interpractice variations were found among conditions that disproportionately affect deprived patients. For example, 40%, 31%, and 45% of patients admitted for alcohol-related diseases, diabetes complications, and schizophrenia, the three highest variation conditions, resided in the most deprived quintile of areas (Tables 2 and and33).

Interpractice variation across age groups

There was a clear trend for higher interpractice variation in admissions among younger-than-average age groups (Table 4). When combining all ACSCs, decile differences for patients aged 40 to 69 years and ≥70 were 18% (95% CI = 14 to 22) and 32% (95% CI = 29 to 35) lower than those aged 20 to 39 years. This trend was even more stark for chronic conditions alone. Admission rates were 45% (95% CI = 42 to 48) less variable for patients aged >70 years compared with those aged 20 to 39 years. The youngest age group was also the most variable for 20 (83%) individual conditions, including dyspepsia/other stomach function, where interpractice variation for patients aged >40 years was at least 67% lower than those aged <5 years, and for congestive heart failure where admission rates for patients >70 years were 61% (95% CI = 55 to 66) less variable than for those aged 40 to 69 years.

Table 4.
Magnitude of interpractice admission rate variation across age subgroupsa

DISCUSSION

Summary

ACSCs accounted for 1.77 million admissions and 10.9 million bed days during 2011–2012. Overall, ACSC admission rates were 55% greater in high-utilisation practices than low-utilisation practices after adjustment for age, sex, other markers of healthcare need, accessibility of emergency hospital care, and PCT-level effects. Although the largest differences were observed in chronic conditions, substantial interpractice variation was found across a wide range of conditions. Large interpractice variation was not ubiquitous — differences of less than 35% were found for stroke and fractured proximal femur. Admission rates were consistently more variable among younger-than-average patients, while the most variable conditions tended to disproportionately affect deprived patients.

Strengths and limitations

This study was based on a large nationally representative dataset containing almost all unplanned admissions in England. Including a broad range of ACSCs provided a fuller description of interpractice variation than previous studies, which have focused on only a few conditions.18 This model-based approach to quantifying interpractice variation appropriately accounted for random chance, while the transformation to the decile difference aided interpretation. The study was based on observational evidence and hence open to confounding. The authors undertook extensive case-mix adjustment. However, it is possible that other unmeasured factors, which cannot be modified by GPs and vary within PCTs (for example, community care provision), could have affected the results. The moderate interpractice variation between practices for fractured proximal femur, where GPs probably have a relatively minor impact on the risk of admission (for example, osteoporosis detection and fall clinics), suggests that residual confounding could be responsible for some of the observed variation. Using the practice postcode to estimate deprivation could have impaired the authors’ ability to adjust for this factor, as practices may be located in areas that are unrepresentative of the population they serve. This may have introduced spurious variation into the analysis, particularly for younger and middle-aged patients where deprivation could be a particularly strong determinant of healthcare need.

Comparison with existing literature

A previous international systematic review reported that interpractice and geographical variation in unplanned ACSC admission rates was almost ubiquitous across practices and other geographical units.18 Other studies have found substantial variation in admission rates for respiratory and cardiovascular ACSCs, even between English practices from similarly deprived areas.7,8 To the best of the authors’ knowledge, this is the first study to contrast interpractice variation in admission rates across age groups.

Implications for research and practice

Substantial interpractice variation in unplanned ACSC admission rates could be a symptom of inefficient care within the English primary care system. The results suggest that the current mechanisms to standardise primary care, such as the QOF and National Institute for Health and Care Excellence (NICE) guidelines, have had a limited effect on standardising hospital admission rates and that new strategies might be required. Contrasting interpractice variation across ACSCs helps to identify the clinical areas and patient subgroups (for example, childhood diabetes) where primary care might be most inconsistent, and further exploration is urgently required. National funders, such as the National Institute for Health Research, are well placed to commission new research to reduce key treatment uncertainties (such as optimal management strategies). GPs and healthcare commissioners should ensure they are currently offering best-practice care for the most variable clinical conditions and patient subgroups identified in the study.

These results suggest that the substantial variability in the way primary care is delivered across England could have important implications for patient outcomes. Interpractice differences have been reported in the quality of disease management,19 treatment of exacerbations,20 prescribing quality,21 and referral quality.11 Primary care access and continuity of care differ markedly, meaning that some patients might choose to directly access A&E and be admitted due to risk-averse hospital admission thresholds.12 Heterogeneity in decile differences across conditions suggests that these factors are particularly important for some ACSCs. Chronic conditions are actively managed by GPs and hence are more likely to be sensitive to the vagaries that exist in the availability and quality of primary care. For example, wide disparities have been identified in the quality of diabetes care (such as foot surveillance) and severe mental illness management (such as care planning),14,19 and some GPs have reported low levels of knowledge or motivation to deal with alcohol problems.22 Conditions where a high proportion of admissions originate from primary care (for example, iron deficiency anaemia) are likely to be more sensitive to variation in GP referral thresholds than those where patients typically go directly to A&E (for example, fractured proximal femur). Availability of clear referral guidelines and alternative treatment pathways could reduce admissions originating from primary care.

Initial investigations into the causes and implications of interpractice variation should focus on pathways for younger-than-average patients for several ACSCs. Consistently high variation among children could be explained by the challenges of minimising risk and making diagnoses (such as childhood diabetes23 and dyspepsia24), or pressure from anxious parents that acts to magnify the effect of variable GP referral thresholds. The strong gradient between age and prevalence for many ACSCs (such as coronary heart disease [CHD]25 and stroke26) — meaning that most middle-aged patients present as atypical or low risk — could amplify the effect of variable diagnostic quality among GPs. Furthermore, poorer patient compliance and delivery of disease management interventions among younger patients could lead to faster progression and earlier complications. For example, only 29% of patients with type 1 diabetes aged <40 years received eight of the nine recommended care processes, compared with 60% of those aged >80 years.19

The finding that conditions with the largest interpractice variations tended to be those most prevalent in deprived populations suggests that delivery of primary and community care might be most inconsistent for these ACSCs. In addition to the factors highlighted previously, avoiding admission for these conditions is likely to require substantial effort by GPs to case-find (for example, problem drinking), provide lifestyle interventions (such as smoking cessation), and engage with difficult-to-reach patients, such as the homeless and those with acute mental illness. These results could suggest this varies between practices, and efforts are required to standardise and improve care. This might be achieved through additional services or incentives — a UK-based study demonstrated that financial incentives can increase alcohol screening and intervention.27

A detailed understanding of the causes of the interpractice variation is crucial to guide the design of interventions to standardise care.28 Previous research suggests that strategies to improve the continuity of primary care could reduce secondary care demand for ACSCs.9 One English study found that unplanned ACSC admissions reduced by 0.5% for each percentage point increase in the proportion of patients able to book with a specific GP.29 There is little evidence to suggest that larger practices, or those with better appointment availability, have fewer unplanned ACSC admissions.9 Similarly, studies investigating the association between primary quality (measured by QOF scores) and unplanned admission rates have found little or no association except for a few diseases (such as COPD and CHD).30 Previous research suggests that the 10% of patients with the highest multimorbidity (four or more conditions) account for over half of all potentially preventable admissions. Interventions targeted at this patient group might prove most cost-effective.10

Further work is required to understand the causes for the widespread interpractice variations outlined in this study, and to design interventions to improve and standardise care. Qualitative methods could provide an in-depth understanding of why patients are admitted to hospital and the role GPs could play in averting this. Work should initially focus on the most variable ACSCs and patient subgroups as these are likely to offer the greatest gains.

Appendix 1. Included Ambulatory Care Sensitive Conditions (ACSCs) and the ICD-10 codes used to define them

ConditionICD-10 codes
Chronic conditions
  Alcohol-related diseasesF10
  AnginaI20,I240,I248,I249,I25,R072,R073,R074,Z034,Z035
  AsthmaJ45,J46
  Atrial fibrillation/flutterI471,I479,I495,I498,I499,R000,R002,R008
  Congestive heart failureI110,I130,I255,I50,J81
  COPDJ20,J40,J41,J42,J43,J44,J47
  Diabetes complicationsE100,E101,E102,E103,E104,E105,E106,E107,E108,E110,E111,E112,E113,E114,E115,E116,E117,E118,E120,E121,E122,E123,E124,E125,E126,E127,E128,E130,E131,E132,E133,E134,E135,E136,E137,E138,E139,E140,E141,E142,E143,E144,E145,E146,E147,E148,E149
  HypertensionI10,I119
  Iron deficiency anaemiaD460,D461,D463,D464,D501,D508,D509,D510,D511,D512,D513,D518,D520,D521,D528,D529,D531,D571,D580,D581,D590,D591,D592,D599,D601,D608,D609,D610,D611,D640,D641,D642,D643,D644,D648
  NeurosesF32,F40,F41,F42,F43,F44,F45,F46,F47,F48
  Peripheral vascular diseaseI73,I738,I739
  SchizophreniaF20,F21,F232,F25
  Senility/dementiaF00,F01,F02,F03,R54

Acute conditions
  CellulitisI891,L010,L011,L020,L021,L022,L023,L024,L028,L029,L03,L04,L080,L088,L089,L88,L980
  ConstipationK590
  Convulsions and epilepsyG253,G40,G41,O15,R56,R568
  Dehydration and gastroenteritisA020,A04,A059,A072,A080,A081,A083,A084,A085,A09,E86,K520,K521,K522,K528,K529
  Dental conditionA690,K02,K03,K04,K05,K06,K08,K098,K099,K12,K13
  Dyspepsia/other stomach functionK21,K30
  ENT infectionsH66,H67,J02,J03,J040,J06,J312
  Fractured proximal femurS720,S721,S722
  Influenza and pneumoniaA481,A70,J10,J11,J120,J121,J122,J128,J129,J13,J14,J153,J154,J157,J159,J160,J168,J18,J181,J189
  Migraine/acute headacheG43,G440,G441,G443,G444,G448,R51
  Pelvic inflammatory diseaseN70,N73,N74
  Perforated/bleeding ulcerK20,K210,K219,K221,K226,K250,K251,K252,K254,K255,K256,K260,K261,K262,K264,K265,K266,K270,K271,K272,K274,K275,K276,K280,K281,K282,K284,K285,K286,K920,K921,K922
  PyelonephritisN10,N11,N12,N136,N159,N300,N308,N309,N390
  Ruptured appendixK350,K351
  StrokeI61,I62,I63,I64,I66,I672,I698,R470

ACSC = ambulatory care sensitive condition. COPD = chronic obstructive pulmonary disease. ENT = ear, nose, and throat. ICD = International Classification of Diseases.

Appendix 2. Further details on estimating interpractice variation

Calculation of age–sex specific GP population

As age–sex specific practice populations were not available for 2011–2012, these were estimated using data from 2013–2014. The authors calculated the proportion of the practice population in each of 18 age groups (grouped by 5 years up to the age of 85, with all patients over 85 joined together) and two sex groups. These proportions were multiplied by the 2011–2012 practice list size. Due to practice closures and mergers, data were not available for a small number of practices (n = 183, 2.3%). For these practices, the authors estimated the age–sex composition using the populations of the five geographically closest practices where data were available.

Estimation of interpractice variation

It was assumed that the number of admissions within each practice i, for condition j (Observedij), was drawn from a Poisson distribution with mean μij. The authors calculated the number of admissions that would be expected given the size and age–sex composition of the practice (Expectedij), using indirect standardisation. Other differences in practice populations (for example, the prevalence of chronic disease) were accounted for by including k regression coefficients, βjk, which estimate the effect of each covariate, X, on the admission rate. Crucially, the linear predictor includes a normally distributed random effect, termed the practice effect (Pij), which allows for differences in the linear predictor for each practice. The main parameter of interest is σj (the standard deviation of the practice effects), which the authors transformed to a decile difference (DDj) for ease of interpretation. The full model is:

Observedij~Poisson(μij)log(μij)=Expected_Age_Sexij+βjkΧjk+PijPij~Normal(θj,σj2)

DDj=100×(exp(1.282(σj))exp(-1.282(σj))-1)=100×(exp(2.564×σj)-1)

Notes

Funding

John Busby is funded by the National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care (NIHR CLAHRC) West.

Ethical approval

Not applicable.

Provenance

Freely submitted; externally peer reviewed.

Competing interests

The authors have declared no competing interests.

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REFERENCES

1. National Audit Office . Emergency admissions to hospital: managing the demand. London: NAO; 2013.
2. Caesar U, Karlsson J, Olsson LE, et al. Incidence and root causes of cancellations for elective orthopaedic procedures: a single centre experience of 17,625 consecutive cases. Patient Saf Surg. 2014;8:24. [PMC free article] [PubMed]
3. Robinson P. Are hospital admissions out of control? Birmingham: CHKS; 2010.
4. NHS England Quality premium 2014/15. Available from: http://www.england.nhs.uk/ccg-ois/qual-prem/ (accessed 15 Sep 2016).
5. Blunt I. Focus on preventable admissions. London: Nuffield Trust; 2013.
6. Purdy S, Griffin T, Salisbury C, Sharp D. Ambulatory care sensitive conditions: terminology and disease coding need to be more specific to aid policy makers and clinicians. Public Health. 2009;123(2):169–173. [PubMed]
7. Purdy S, Griffin T, Salisbury C, Sharp D. Emergency respiratory admissions: influence of practice, population and hospital factors. J Health Serv Res Policy. 2011;16(3):133–140. [PubMed]
8. 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. [PubMed]
9. Huntley A, Lasserson D, Wye L, et al. Which features of primary care affect unscheduled secondary care use? A systematic review. BMJ Open. 2014;4:e004746. [PMC free article] [PubMed]
10. Payne RA, Abel GA, Guthrie B, Mercer SW. The effect of physical multimorbidity, mental health conditions and socioeconomic deprivation on unplanned admissions to hospital: a retrospective cohort study. CMAJ. 2013;185(5):E221–E228. [PMC free article] [PubMed]
11. King‘s Fund. The quality of GP diagnosis and referral. London: King‘s Fund; 2010.
12. NHS England GP Patient Survey. 2014. https://gp-patient.co.uk/surveys-and-reports#july-2014 (accessed 15 Sep 2016).
13. NHS Digital Hospital Episode Statistics, Admitted Patient Care — England, 2011–12 [NS]. 2012. NHS Digital. http://content.digital.nhs.uk/catalogue/PUB08288 (accessed 12 Oct 2016).
14. NHS Digital Quality and Outcomes Framework — 2011–12. Publication date: October 30, 2012. http://content.digital.nhs.uk/catalogue/PUB08135 (accessed 12 Oct 2016).
15. Health and Social Care Information Centre . Methodology to create provider and CIP spells from HES APC data. Leeds: HSCIC; 2014.
16. Health and Social Care Information Centre. Numbers of patients registered at a GP practice — April 2013. https://data.england.nhs.uk/dataset/numbers-of-patients-registered-at-a-gp-practice-april-2013 (accessed 22 Sep 2016).
17. UK Data Service Census Support GeoConvert. http://geoconvert.mimas.ac.uk/ (accessed 15 Sep 2016).
18. Busby J, Purdy S, Hollingworth W. A systematic review of the magnitude and cause of geographic variation in unplanned hospital admission rates and length of stay for ambulatory care sensitive conditions. BMC Health Serv Res. 2015;15(1):324. [PMC free article] [PubMed]
19. Health and Social Care Information Centre. National Diabetes Audit 2012–2013. Report 1: Care processes and treatment targets. Leeds: HSCIC; 2014.
20. Boggon R, Hubbard R, Smeeth L, et al. Variability of antibiotic prescribing in patients with chronic obstructive pulmonary disease exacerbations: a cohort study. BMC Pulm Med. 2013;13:32. [PMC free article] [PubMed]
21. King‘s Fund. The quality of GP prescribing. London: King‘s Fund; 2011.
22. Wilson GB, Lock CA, Heather N, et al. Intervention against excessive alcohol consumption in primary health care: a survey of GPs‘ attitudes and practices in England 10 years on. Alcohol Alcohol. 2011;46(5):570–577. [PMC free article] [PubMed]
23. Usher-Smith JA, Thompson MJ, Zhu H, et al. The pathway to diagnosis of type 1 diabetes in children: a questionnaire study. BMJ Open. 2015;5(3):e006470. [PMC free article] [PubMed]
24. National Institute for Health and Care Excellence . Gastro-oesophageal reflux disease in children and young people: diagnosis and management NG1. London: NICE; 2015. https://www.nice.org.uk/guidance/ng1?unlid=149969625201672210456 (accessed 15 Sep 2016).
25. Bhatnagar P, Wickramasinghe K, Williams J, et al. The epidemiology of cardiovascular disease in the UK 2014. Heart. 2015 doi: 10.1136/heartjnl-2015-307516. [PMC free article] [PubMed] [Cross Ref]
26. Lee S, Shafe AC, Cowie MR. UK stroke incidence, mortality and cardiovascular risk management 1999–2008: time-trend analysis from the General Practice Research Database. BMJ Open. 2011;1(2):e000269. [PMC free article] [PubMed]
27. Hamilton FL, Laverty AA, Gluvajic D, et al. Effect of financial incentives on delivery of alcohol screening and brief intervention (ASBI) in primary care: longitudinal study. J Public Health. 2014;36(3):450–459. [PubMed]
28. King‘s Fund. Variations in health care. The good, the bad and the inexplicable. London: King‘s Fund; 2011.
29. Bankart MJ, Baker R, Rashid A, et al. Characteristics of general practices associated with emergency admission rates to hospital: a cross-sectional study. Emerg Med J. 2011;28(7):558–563. [PubMed]
30. Gillam SJ, Siriwardena AN, Steel N. Pay-for-performance in the United Kingdom: impact of the quality and outcomes framework: a systematic review. Ann Fam Med. 2012;10(5):461–468. [PubMed]

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