Analysis of Workload Factors Affecting Patient Care Funding Formula Revision

1. GMS Formula AnalysisQRESEARCH 2005 • 09 Feb 2006 • Julia Hippisley-Cox • Jon Ford • Ian Trimble

2. Aims of presentation • Brief overview of methods • Present key results from analysis • Comparison of models • Hand over to Jon Ford

3. Overall aim of the analysis • To derive a regression model linking workload to patient and practice characteristics • To inform revision of the funding formula for essential and additional services

4. Sampling: Practices • Practice inclusion criteria for analysis • England and Wales only • At least 1000 patients • At least 2 consultations/person-year • Complete data for period in question • Decided not to sample proportionately by region

5. Patient inclusion criteria • Patient level analysis • Study period 01 April 2003- 31 March 2004 • Included if registered at any point during study period • Included temporary residents, new patients and patients who died • Person days denominator for rates

6. Principal outcome • Number of consultations (GP + nurse) in study year • Regardless of setting • Excluding community/district nurses

7. Patient level variables • Sex • Ageband: standard as in Carr Hill • Registration period (6+ months; <6 or new) • Temporary patients (yes/no) • New GMS diseases (yes/no for each) • Townsend score/IMDS • % white/non white

8. Practice level variables • List size • Number of GP principals • Townsend score • Rurality • White/non white • Mean prevalence of QOF diseases • Region

9. Patient level analysis • Variables included at patient or at practice level • Both person years and registered population were used

10. QRESEARCH practices • Compared with UK average • Similar size • Similar distribution • Similar prevalence • Similar age-sex • Comparable consultation rate • LARGE Representative sample • Results generalisable

11. Results: study practices • 454 practices in England and Wales • 3.8 million patients registered at any point in study year • 33,727 deaths • 319,435 new patients • 97,239 temporary residents

12. Summary of comparison • QRESEARCH practices • Slightly bigger • More in East Midlands and fewer in London • Otherwise similar w.r.t. age-sex and disease prevalence

13. Prevalence of diabetes in patients over 15

14. Consultation rates by age and sex

15. Models • We fitted a series of ‘a priori’ statistical models specified in our protocol and then were asked to fit additional ones • ‘a priori’ models included patient level assigned data where available (eg QOF diseases, Townsend score) • Supplementary models included practice level data (QOF disease prevalence, mean Townsend score)

16. Results: A priori Model 7bi(person years denominator) • Consultation rates: • Registered for 6+ months = baseline • Registered for < 6 months = 72% higher rate • Temporary residents = 86% higher rate • Person years controls for length of registration period • patients registered within 6 months before start of study year or during study year have a 72% higher consultation rate compared to long standing patients

17. A priori model: Townsend score • Fairly flat gradient with deprivation • (Quintile 5 is deprived) • Quintile 1 = baseline • Quintile 2 = 0.4% higher • Quintile 3 = 1.4% higher • Quintile 4 = 4.1% higher • Quintile 5 = 6.1% higher

18. A priori model:Rurality and ethnicity • Urban areas = baseline • Rural areas = 1.7% higher • Ethnicity: • 99-100% white = baseline • 97-98.9% white = 0.5% lower • 90-96.9% white = 4.1% lower • < 90% white = 11.6% lower

19. A priori model: QOF diseases • For all diseases, people with the disease • had higher consultation rates compared • to those without the disease • e.g. • CHD = 38% higher • Diabetes = 54% higher • Asthma = 63% higher

20. A priori model: practice variables • List size: • 2.2% lower rate for each additional • thousand patients for a given number • of GP principals • GP principals (head count not wte) • 1.4% higher rates for each additional • GP principal for a given list size

21. Process • Undertook patient level modelling • Then asked to do practice level modelling for implementation • Concerns about how well practice level models can be applied at patient level • Results were counter-intuitive (Ecological fallacy)

22. Ecological fallacy • Applying practice level variables to a patient population produces spurious and counter-intuitive results • Well described statistical phenomenon • Practice level models don’t work

23. Additional model : (practice level data) • Inclusion of all QOF disease prevalence • values together in models showed some • negative associations: • e.g. CHD = 4.7% lower rate • Thyroid disease = 1.1% lower rate • both for a 1% increase in practice • prevalence.

24. Additional model: Townsend score • Inclusion of mean practice Townsend • score showed a negative association: • Consultation rates were 2.9% lower for • a 1 unit increase in mean practice • Townsend score

25. FRG review • Requested additional patient level model WITHOUT prevalence (model 18) • Key comparison then is patient level with and without prevalence

26. Explanatory powerAkaike Information criterion • AIC statistical test for explanatory power • Lower values indicator better models • Absolute value increases with sample size • Relative difference more important

27. AIC results • Both models patient level, person years denominator, age-sex, rurality, ethnicity • Model 7b AIC = 16,415,351 • Townsend quintile • Prevalence • No region • Model 18 AIC = 16,763,190 • Townsend continuous • No prevalence • Region

28. Summary • Person years adjustment give better fit for new registrations/TRs • Patient level analyses produce intuitively acceptable results • Practice level analyses counter-intuitive results likely to lead to controversy (ecological fallacy) • Comparisons between patient level models with and with and without prevalence are presented for Plenary’s consideration