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Ananda Allan Senior Health Intelligence Analyst ‘The Quality Outcomes Framework (QOF): Can it be used for more than just paying GPs?’ Ananda Allan Senior.

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Presentation on theme: "Ananda Allan Senior Health Intelligence Analyst ‘The Quality Outcomes Framework (QOF): Can it be used for more than just paying GPs?’ Ananda Allan Senior."— Presentation transcript:

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

2 Ananda Allan Senior Health Intelligence Analyst 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 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 Ananda Allan Senior Health Intelligence Analyst 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 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 Ananda Allan Senior Health Intelligence Analyst 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! 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 Ananda Allan Senior Health Intelligence Analyst 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 Ananda Allan Senior Health Intelligence Analyst 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 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 Ananda Allan Senior Health Intelligence Analyst 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! 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 Ananda Allan Senior Health Intelligence Analyst Comparing Local Diabetes Register with CMR Estimates… Now SCI-DC Diabetes Register Co-ordinates with EMIS nightly

9 Ananda Allan Senior Health Intelligence Analyst QOF disease prevalence figures are not without problems… ConditionPrevalenceConditionPrevalence Stroke2.3%Dementia0.9% Palliative Care0.2%CVD Risk1.0% New Depression %COPD2.3% Mental Health0.8%Chr Kidney Dis % Learning Disability %CHD5.1% Hypothyroidism3.5%Cancer1.9% High Blood Pressure14.8%Atrial Fibrillation1.7% Heart Failure0.9%Asthma5.8% Epilepsy %Obesity % Diabetes % ConditionPrevalenceConditionPrevalence Stroke2.3%Dementia0.9% Palliative Care0.2%CVD Risk1.0% New Depression %COPD2.3% Mental Health0.8%Chr Kidney Dis % Learning Disability %CHD5.1% Hypothyroidism3.5%Cancer1.9% High Blood Pressure14.8%Atrial Fibrillation1.7% Heart Failure0.9%Asthma5.8% Epilepsy %Obesity % Diabetes % The denominator is still ALL ages; overlap?

10 Ananda Allan Senior Health Intelligence Analyst 3. Mapping the geographical burden of disease Will QOF disease prevalence follow patterns of area deprivation? Can we add value to existing GIS analysis? Will QOF disease prevalence follow patterns of area deprivation? Can we add value to existing GIS analysis?

11 Ananda Allan Senior Health Intelligence Analyst Different in Urban areas?

12 Ananda Allan Senior Health Intelligence Analyst 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 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 Ananda Allan Senior Health Intelligence Analyst New Referral Rates to Cardiology and Diabetes & Endocrinology vs. QOF Prevalence New Referrals ≈ Incidence … ≠ Prevalence?

14 Ananda Allan Senior Health Intelligence Analyst Emergency Admission Rates for All Heart Disease vs. QOF CHD Prevalence

15 Ananda Allan Senior Health Intelligence Analyst Conclusions from the published papers… 1. Bankart et al, Emerg Med J (2011) [2 PCT England] High Emg Adm = closer to hospital, small list size, older (removed CHD prevalence), white ethnicity, female,  deprivation, not seeing own GP 2. Purdy et al, Public Health (2011) [All England] High Emg Adm =  deprivation, CHD prevalence, smoking but not QOF quality of care factors for CHD 3. Purdy et al, J Health Serv Res Policy (2011) [All England] High Emg Adm =  deprivation, Asthma and COPD prevalence, smoking, urban, closer to hospital, bed availability 4. Calderón- Larrañaga et al, Thorax (2011) [All England] High Adm =  deprivation, COPD QOF and undiagnosed prevalence, smoking, lower flu jabs, worse GP access/staffing

16 Ananda Allan Senior Health Intelligence Analyst 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 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 Ananda Allan Senior Health Intelligence Analyst 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… 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 Ananda Allan Senior Health Intelligence Analyst Acknowledgments Dr Andrew Carnon, Consultant in Public Health Medicine Carolyn Hunter-Rowe, Senior Health Intelligence Analyst Dr Andrew Carnon, Consultant in Public Health Medicine Carolyn Hunter-Rowe, Senior Health Intelligence Analyst


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