Impact of Morbidity in Populations of North London Clinical Commissioning Groups on Patient Admission Rates and GP Referrals

HS3 Impact of Morbidity in Populations of North London Clinical Commissioning Groups on Patient Admission Rates and GP Referrals Groom Z, Burgon J, Eddowes L, Wilson T, Kusel J Costello Medical Consulting Ltd. ISPOR 17 th Annual European Congress

Overview o Study Aims o The NHS England CCG Information Packs and Outcomes Tool o Key results of this analysis Baseline characteristics Non-elective admission rates Elective admission rates GP referral rates Prescribing spends o Potential applications and implications of these findings 2

Clinical Commissioning Groups (CCGs) o CCGs are NHS organisations set up by the Health and Social Care Act 2012, with the aim to organise the delivery of NHS Services in England, formally replacing primary care trusts. o They now hold a large proportion of the NHS budget, and make decisions as to which services they commission for the patients within their practices. For example, CCGs are involved in deciding upon the community services that they see as best for their patients and ensuring that these are provided. o The largest expenditure of CCGs is currently on secondary care (hospital admissions). 3

Aims o This research set out to ascertain whether disease prevalence alone can explain the following outcomes in 20 North London CCGs: 1. Non-elective patient admission rates 2. Elective patient admission rates 3. General practitioner (GP) referral rates 4. Prescribing spends 4

o Using information provided by NHS England CCG Information Packs and the NHS CCG Outcomes Tool, rates of the following indicators were extracted: 1. Age and sex standardised nonelective rates 2. Age and sex standardised elective rates 3. GP referral rates 4. Average prescribing spends o These rates were compared to national averages and to the prevalence of 19 diseases available through the Outcomes Tool (2011). 5

o The 19 diseases available for analysis included: Chronic heart disease Stroke or transient ischemic attack (TIA) Hypertension Chronic obstructive pulmonary disease (COPD) Hypothyroidism Cancer Mental health Asthma Heart failure Palliative care Dementia Atrial fibrillation Cardiovascular disease Diabetes Epilepsy Depression Chronic kidney disease Obesity Learning difficulties 6

: CCG Baseline Characteristics o CCG Population Size CCG Characteristics BD: Barking & Dagenham, BN: Barnet, BR: Brent, CH: City & Hackney, CM: Camden, EL: Ealing, EN: Enfield, HF: Hammersmith & Fulham, HG: Haringey, HL: Hillingdon, HR: Hounslow, HV: Havering, IL: Islington, KC: West London (K&C & QPP) NH: Newham, RB: Redbridge, TH: Tower Hamlets, WF: Waltham Forest, WM: Westminster 7

: CCG Baseline Characteristics o CCG Running Cost Allowance ( m) CCG Characteristics BD: Barking & Dagenham, BN: Barnet, BR: Brent, CH: City & Hackney, CM: Camden, EL: Ealing, EN: Enfield, HF: Hammersmith & Fulham, HG: Haringey, HL: Hillingdon, HR: Hounslow, HV: Havering, IL: Islington, KC: West London (K&C & QPP) NH: Newham, RB: Redbridge, TH: Tower Hamlets, WF: Waltham Forest, WM: Westminster 8

: CCG Baseline Characteristics o Age Characteristics: % of Population Aged 0-29 CCG Characteristics BD: Barking & Dagenham, BN: Barnet, BR: Brent, CH: City & Hackney, CM: Camden, EL: Ealing, EN: Enfield, HF: Hammersmith & Fulham, HG: Haringey, HL: Hillingdon, HR: Hounslow, HV: Havering, IL: Islington, KC: West London (K&C & QPP) NH: Newham, RB: Redbridge, TH: Tower Hamlets, WF: Waltham Forest, WM: Westminster 10

: CCG Baseline Characteristics o Age Characteristics: % of Population Aged 30-59 CCG Characteristics BD: Barking & Dagenham, BN: Barnet, BR: Brent, CH: City & Hackney, CM: Camden, EL: Ealing, EN: Enfield, HF: Hammersmith & Fulham, HG: Haringey, HL: Hillingdon, HR: Hounslow, HV: Havering, IL: Islington, KC: West London (K&C & QPP) NH: Newham, RB: Redbridge, TH: Tower Hamlets, WF: Waltham Forest, WM: Westminster 12

: CCG Baseline Characteristics o Age Characteristics: % of Population Aged 60-80+ CCG Characteristics BD: Barking & Dagenham, BN: Barnet, BR: Brent, CH: City & Hackney, CM: Camden, EL: Ealing, EN: Enfield, HF: Hammersmith & Fulham, HG: Haringey, HL: Hillingdon, HR: Hounslow, HV: Havering, IL: Islington, KC: West London (K&C & QPP) NH: Newham, RB: Redbridge, TH: Tower Hamlets, WF: Waltham Forest, WM: Westminster 13

: (1) Non-Elective Admission Rates o Four North London CCGs reported a higher than national average non-elective admission rate (per 1,000 of the population). o Of these, the disease prevalence was, on average, higher in only 6/19 diseases compared to those CCGs reporting lower than average non-elective rates. Non-Elective Admissions CCGs with higher than national average non-elective admissions Diseases with a higher than average prevalence across these 4 CCGs Barking & Dagenham City & Hackney Ealing Hypertension Cardiovascular Disease Diabetes Epilepsy Obesity Learning Difficulties Waltham Forest 14

: (1) Non-Elective Admission Rates Non-Elective Admissions Disease Prevalence (%) 16 14 12 10 o Test for correlations between disease prevalence and nonelective admission rates: 8 6 4 2 0 National Average: 111 70 80 90 100 110 120 Non-Elective Admission Rate (per 100,000 of the population) Chronic Heart Disease Stroke or TIA Hypertension COPD Hypothyroidism Cancer Mental Health Asthma Heart Failure Palliative Care Dementia Atrial Fibrillation Cardiovascular Disease Diabetes Epilepsy Depression Chronic Kidney Disease Obesity Learning Difficulties 15

: (1) Non-Elective Admission Rates Non-Elective Admissions Disease Prevalence (%) o Of the 6 diseases identified as having a higher than average prevalence, 3 were significantly correlated with non-elective admission rates (p<0.05). o Alternative explanations for high rates of non-elective admissions include ineffective management in primary care, poor NHS community provision, a low admission threshold and/or that rate could be influenced by proximity of patients to accident & emergency (A&E) departments. 16 14 12 10 8 6 4 2 National Average: 111 Obesity Cardiovascular disease Learning difficulties (p=0.011) (p=0.0014) (p=0.04) 0 70 80 90 100 110 120 Non-Elective Admission Rate (per 100,000 of the population) 16

: (2) Elective Admission Rates o Four CCGs reported higher than national average elective admission rates (per 1,000 of the population). o Of these, the disease prevalence was, on average, higher in 13/19 diseases than CCGs reporting lower than average elective rates. Elective Admissions CCGs with higher than national average elective admissions Ealing Enfield Haringey Islington Diseases with a higher average prevalence across the 4 CCGs Stroke or TIA Hypertension Cancer Mental Health Asthma Heart Failure Dementia Cardiovascular Disease Diabetes Depression Epilepsy Obesity Learning Difficulties 17

: (2) Elective Admission Rates o Of the 13 diseases identified as having a higher than average prevalence, 4 were significantly correlated with elective admission rates (p<0.05). o Alternative suggestions for high rates of elective admission rates include ineffective management in primary care, a high availability of specialist services or a low level of patients receiving private treatment. Elective Admissions 6 National Average: 123 Disease Prevalence (%) 5 4 3 2 1 Asthma Cancer Epilepsy Dementia (p=0.005) (p=0.05) (p=0.003) (p=0.01) 0 70 90 110 130 150 Elective Admission Rate (per 100,000 of the population) 18

: (3) GP Referral Rates o Sixteen CCGs reported higher than national average GP referral rates, and in these CCGs 10/19 diseases had a higher prevalence compared to those CCGs reporting lower than average rates. GP Referrals CCGs with higher than national average GP referral rates Barking & Dagenham Barnett Brent Camden Ealing Enfield Hounslow Hammersmith & Fulham Havering Hillingdon Islington Newham Redbridge Tower Hamlets West London (K&C & QPP) Westminster Diseases with a higher average prevalence across the 16 CCGs Chronic Heart Disease Stroke or TIA COPD Hypothyroidism Cancer Palliative Care Dementia Atrial Fibrillation Epilepsy Chronic Kidney Disease 19

: (3) GP Referral Rates o Of the 10 diseases identified as having a higher than average prevalence, 1 was significantly correlated with GP referral rates (P<0.05). o Alternative suggestions for high GP referral rates include ineffective management in primary care or a high level of inappropriate referrals. GP Referrals Disease Prevalence (%) 16 14 12 10 8 6 4 2 National Average: 188 COPD (p=0.03) 0 160 180 200 220 240 260 GP Referral Rates (per 100,000 of the population) 20

: (4) Average Prescribing Spends o No CCGs reported a higher than national average prescribing spend (, per person, per 1000). Prescribing Spends 21

Summary of Key Points 1 Morbidity in North London CCGs may best explain rates of elective admissions compared to non-elective admission rates. 2 Non-elective admissions commonly occur in emergency situations, thus making the prediction of these events challenging for primary care management and NHS community provision. 3 Inadequacies in primary care management may result in increased hospital admissions, irrespective of morbidity in the population. The discrepancy may also be influenced by patient proximity to A&E departments. 22

Potential Implications 1 As CCGs are faced with the challenges of austerity within the NHS, finding methods to prioritise areas for savings are of interest to CCGs, particularly as they will have to contend with population growth alongside increasingly constrained budgets. 2 The largest expenditure of CCGs is on secondary care (hospital admissions) and therefore reducing spend on this is of great interest to CCGs. 3 As patient admissions to hospitals (elective and non-elective) and visits to A&E continue to be recorded, CCGs become better equipped to monitor secondary care use within their population. 4 Factors contributing to higher rates of admissions, such as disease morbidity, management in primary care and NHS community provision, should be evaluated in order to identify priority areas for CCG investment. 23

Questions Thank you for your attention Costello Medical Consulting Ltd. www.costellomedical.com zoe.groom@costellomedical.com +44 (0) 1223 913 037 24