Care-intensive neighbourhoods

1. Care-intensive neighbourhoods A comparison of geodemographic systems for neighbourhood segmentation of hospital admission data Jakob Petersen Knowledge transfer partnership Southwark Primary Care Trust | Phil Atkinson Geography, UCL | Paul Longley & Pablo Mateos External funding: ESRC & DTI

2. Outline • NHS & the emerging healthcare market • Long-term diseases • Geodemographics • Segmentation metrics for comparison • Results • GP Hospital referral patterns • What next?

3. NHS & the emerging healthcare market 1911 Employer contributed health insurance 1948 NHS is founded. Universal, comprehensive, free at the point of delivery Publicly owned and funded by tax Integrated: no billing internally 1980 Transition to a free healthcare market. Privatisation of non-clinical tasks like cleaning • ‘Internal market’: hospital trusts to make own income by selling services to health authorities. From 1990-1994 245 hospitals had to close because they were not profitable. 1992 Private Finance Initiative (PFI). 2000-2005: 42% of hospitals built with private funds. 2000 The New NHS Plan Talbot-Smith & Pollock 2006, Pollock et al. 2007

4. NHS & the emerging healthcare market Talbot-Smith & Pollock 2006, Pollock et al. 2007

5. NHS & the emerging healthcare market The Health Care Market • Commodity: health care services (disease & accidents?) • Purchaser: PCT, GP • Seller: public or private providers • Regulator: independent MONITOR • Price ‘fixing’: the NHS tariff (set by Department of Health) • Market research • (profitable) local health care needs • Areas for cost reduction

6. Health care market Hospitals • 19% of contacts • 58% of costs Long-term diseases • 5-10% of patients use 55% of hospital bed days Source of figures: Talbot-Smith & Pollock 2006

7. Long-term diseases … affects 17 million in UK • Arthritis: 8.5 m • Asthma: 3.4 m; 1.5 m children • Back pain: 40% of adults; 6% chronically • Chronic Obstructive Pulmonary Disease:1 m • Coronary Heart Disease (CHD): 2.68 m • Diabetes Mellitus: 1.5 m • Epilepsy: 420,000 • Mental illness: 16.4% of adults DH 2004, 2005; Meldum et al. 2005; Petersen et al. 2004; Singleton et al. 2001

8. Long-term diseases • Locating services closer to home • Avoid hospitalisation • Improve patients’ experience • Community care services • Community matron to devise individual case management plans • Home visits from specialist nurses or health visitors • Specialist clinics • Primary care services • GPs can be paid for taking on patients with long-term care needs (QOF)

9. Long-term disease indicators

10. Pearson correlations: Log(chronic admission rates) * OAC variables Correlating variables Age 65+ Divorced Single person hh Single pensioner1 hh Rent public All flats Longterm ill SIR Provide unpaid care Unemployed

11. Geodemographics Hypotheses • Deprivation indices are more appropriate than geodemographics for explaining variation in disease patterns? • The finer the geographical scale, the better? postcode > output area > super output area > .. • Bespoke classifications are better than general classifications?

12. Disease counts and rates Ratei = diseasedxyz / at-risk populationxyz i : area xyz : sex, age group, ethnicity, occupation, …

13. Segmentation metrics

14. Segmentation metrics • Gini • half the relative mean difference between all pairs of observations • Quartile range ratio (p75/p25) • GE(2)

15. Contestants • Postcode • Mosaic UK Type • Acorn Type • Health Acorn Type • KRON50 rank based on total long-term admissions (HES) • Output area • OAC subgroup • LOAC group • Super output area • IMD 50 ranked segments • HESK bespoke classification (HES: Long-term disease groups)

19. GP hospital referral rates Requirements: • Adjusting for sex, age, (ethnicity) • New: adjusting for geodemographics • Denominators for robust risk estimates

20. Open Geodemographics

22. LOAC Group Segmentation

23. Risk maps

24. GP referrals • Age, Sex, and Geodemographic standardisation

25. GP referrals • Age, Sex • Age, Sex, Geodemographics

26. Co-morbidity • Transactional data • Apriori algorithm

27. MALE individual ANGINA 0.60 0.72 0.35 0.41 0.16 ACUTE MYO CHEST PAIN 0.23

28. FEMALE individual ANGINA 0.46 0.59 0.24 0.45 0.09 ACUTE MYO CHEST PAIN 0.21

29. Output Area (~300 pop.) ARTHROSES 0.92 0.93 BACK PAIN CHEST PAIN ANGINA 0.92 0.92 0.93 0.92 STROKE

30. Hypotheses • Deprivation indices are more appropriate than geodemographics for explaining variation in disease patterns? • No. Uni-directional order of IMD makes it less sensitive to neighbourhood differences in diseases • The finer the geographical scale, the better? postcode > output area > super output area > .. • No. OAC performs as well as postcode based systems • Yes. Super output area based performs less well than OAC • No. The better support with detailed and timely denominators at super output area level+ will allow more rigorous statistical analyses • Bespoke classifications are better than general classifications? • Not yet.. Transactional data (HES) has too many zeros for a classification at fine scale geographical level

31. What next? • Non-parametric clustering? • Cross-classification of geodemographic systems, e.g. in a scenario with targeting 10% ‘worst’ classified areas vs. density estimations • ML model for long-term diseases: sex, age, ethnicity, geodemographics, GP

33. IMD Segmentation