1. 1 Quantifying inequities for a commissioning strategy: the example of Luton tPCT Paul Brotherton, for Luton tPCT June 2007

2. 2 Overview  Context  Background  Luton’s strategic approach  Forecasting  Example – CVD  Comments

3. 3 Context  Population 190,000  33% from black and minority ethnic groups  50% of school children from BME groups  Largest BME groups: Pakistani, Irish, Black Caribbean, Bangladeshi, Indian, Black African.  SMR (all ages all causes): 111.  High activity levels for emergency admissions and low level for electives.

4. 4 Background to the strategy  Various strategies and partnership plans in place, but not an overarching plan  Annual planning cycle  Needed to communicate an overall vision  NHS generally poor at modelling and forecasting activity

5. 5 Luton’s strategic approach  Vision & values and strategic objectives  Priorities for action (phased)  Population, activity, forecasts and costs over three years  Detailed delivery plan for each priority topic

6. 6 Strategic approach - impact of equalities data  Choice of strategic objectives  Choice of priority topics (eg CVD, diabetes, HIV, perinatal mortality)  Activity and cost forecasts  Detailed delivery plans

7. 7 Forecasting demand for health care Some key factors: Key determinants - economic - environment - education - lifestyle etc Demography - age - sex - ethnicity Incidence and prevalence Primary care Pharmacy NHS Direct 999 A&E Health seeking behaviour, expectations, etc Supply, information & advice, accessibility, cost, eligibility Care at home Propensity to refer; admission thresholds Capacity; targets; tertiary referrals; Hospital and community- based secondary care ACTION: Joint action on wider determinants of health; public health interventions; health protection measures; demand management; control of supply/market management; practice based commissioning; national guidelines eg NICE & DH imperatives; incentives; local guidelines & pathways; redesign; efficiency; new technology; NHS reforms; reconfiguration;

8. 8 Forecasting – assumptions made for:  Population change (size, age, ethnicity)  Shift from hospital to primary care  Increased emergency admissions  Increases related to specific local health needs (CVD, diabetes, HIV, low birthweight and PMR)  Elective and non-elective inpatients, new outpatients, total outpatients

9. 9 Example - CVD  SMR for all circulatory diseases (age under 75): 113  Higher prevalence in some key local groups (Irish, Pakistani, Indian males; Irish and Black Caribbean females)  Forecast 12.5% increase in number with CVD in Luton 2006-2010  Assumption: 1.75% pa activity rise

10. 10 Stop smoking service – equity audit  40% of Bangladeshi males are smokers (2004 Health Survey)  4.1% of Luton population are Bangladeshi  Only 1.1% of level 2 stop smoking service users are Bangladeshi.  Targeted action to reduce inequity

11. 11 Benefits of using ethnic group data  Breaking activity and cost into smaller populations allows more detailed assumptions and forecasts  Can measure equity of service use  Can tailor interventions to specific groups  Can measure the success of those interventions.

12. 12 Comment (1)  Can make more use of data available Eg could benchmark activity by ethnic group (eg care and resource utilisation)  Forecasting needn’t be sophisticated – make assumptions explicit and review regularly

13. 13 Comment (2)  Ethnicity data/segmenting the population should improve planning and thus help meet overall objectives.  Ethnic monitoring needs to keep up with the times (eg Eastern Europe)  Primary care data is the missing link.

14. 14 Acknowledgements/ contacts  Luton tPCT information analysts: Kanan Kannan Louise Choo 01582 528840  or: paul@paulbrothertonconsulting.org