‘The Quality Outcomes Framework (QOF): Can it be used for more than just paying GPs?’

1. ‘The Quality Outcomes Framework (QOF): Can it be used for more than just paying GPs?’ Ananda AllanSenior Health Intelligence AnalystNHS Dumfries & Galloway

2. Today’s talk will cover… • What is the QOF? • What else can QOF be used for? • Our understanding of patient populations • Comparing disease registers • Geographical distribution of disease • Referral and Admission patterns

3. About the QOF • Started 2004 as part of new GP contract • “A voluntary system of financial incentives… rewarding contractors (GPs) for good practice through participation in an annual quality improvement cycle” • Pays GPs for: • looking after patients with specific chronic illnesses • qualitative practice improvement measures • 134 indicators overall in 2010/11 • 20 conditions across 80+ clinical indicators

4. 1. Patient Populations: accurate count of the full practice lists… • There are different ways of counting D&G patients: • NRS (was GROS) estimate June 2010: 148,190 • CHI residents May 2010: 154,184 • CHP (QOF) headcount July 2010: 155,381 • There may not be much difference between CHI residents and CHP (1,200) but these patients belong to only 3 practices!

5. 700 470 150 N.B. For those of you who are wondering why this doesn’t add up to 1,200… we have 200 patients living in D&G registered with an English GP in Longtown!

6. This is important because: • We can now calculate accurate GP practice activity rates using the CHP headcounts, thanks to the QOF • Prior to the QOF, GP populations were not regularly published • Publishing these figures nationally has forced transparency

7. 2. Disease Registers • Prior to the release of the QOF we had two sources for disease prevalence: • Individual disease registers/audits • Limited number of diseases and focus on acute activity: diabetes, stroke, renal failure, cancer audits • Continuous Morbidity Recording (CMR) • 70 ‘spotter’ practices producing age-specific rates (evolved into PTI) • Or… write out to every practice and ask!

8. Comparing Local Diabetes Register with CMR Estimates… Now SCI-DC Diabetes Register Co-ordinates with EMIS nightly

9. QOF disease prevalence figures are not without problems… The denominator is still ALL ages; overlap?

10. 3. Mapping the geographical burden of disease • Will QOF disease prevalence follow patterns of area deprivation? • Can we add value to existing GIS analysis?

11. Different in Urban areas?

12. 4. Correlating Disease Prevalence to Acute Activity • Some studies make an a priori assumption that disease prevalence correlates with emergency admissions • It has been shown that recorded prevalence of COPD accounts for 21.9% of admission variance (the APHO estimated prevalence was an even better predictor, accounting for 45.1%) (Calderón-Larrañaga et al, Thorax 2011) • However, local correlations have been disappointingly inconclusive

13. New Referral Rates to Cardiology and Diabetes & Endocrinology vs. QOF Prevalence New Referrals ≈ Incidence … ≠ Prevalence?

14. Emergency Admission Rates for All Heart Disease vs. QOF CHD Prevalence

15. Conclusions from the published papers…

16. So… • Too many other factors to use prevalence in isolation? • Small rural board = insufficient sample? • Under-diagnosis skewing figures (e.g. COPD)? • Despite the results… Examining outliers has led to new case-finding

17. In conclusion… • QOF has given added value to other health information • What we really need is: • Age/Sex breakdown of QOF prevalence • Knowledge of co-morbidity (overlap) • QOF Calculator not designed to extract this (and does not hold this) • We will continue to explore…

18. Acknowledgments • Dr Andrew Carnon, Consultant in Public Health Medicine • Carolyn Hunter-Rowe, Senior Health Intelligence Analyst