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GMS Formula Analysis QRESEARCH 2005 09 Feb 2006 Julia Hippisley-Cox Jon Ford Ian Trimble.

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Presentation on theme: "GMS Formula Analysis QRESEARCH 2005 09 Feb 2006 Julia Hippisley-Cox Jon Ford Ian Trimble."— Presentation transcript:

1 GMS Formula Analysis QRESEARCH 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 characteristicsTo derive a regression model linking workload to patient and practice characteristics To inform revision of the funding formula for essential and additional servicesTo 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 analysisPatient level analysis Study period 01 April March 2004Study period 01 April March 2004 Included if registered at any point during study periodIncluded if registered at any point during study period Included temporary residents, new patients and patients who diedIncluded temporary residents, new patients and patients who died Person days denominator for ratesPerson days denominator for rates

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

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

8 Practice level variables List sizeList size Number of GP principalsNumber of GP principals Townsend scoreTownsend score RuralityRurality White/non whiteWhite/non white Mean prevalence of QOF diseasesMean prevalence of QOF diseases RegionRegion

9 Patient level analysis Variables included at patient or at practice levelVariables included at patient or at practice level Both person years and registered population were usedBoth 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 Wales454 practices in England and Wales 3.8 million patients registered at any point in study year3.8 million patients registered at any point in study year 33,727 deaths33,727 deaths 319,435 new patients319,435 new patients 97,239 temporary residents97,239 temporary residents

12 Summary of comparison QRESEARCH practices Slightly biggerSlightly bigger More in East Midlands and fewer in LondonMore in East Midlands and fewer in London Otherwise similar w.r.t. age-sex and disease prevalenceOtherwise 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 onesWe 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)‘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)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 periodPerson 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 patientspatients 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: % white = baseline % white = 0.5% lower % 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 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 A priori model: practice variables

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

22 Ecological fallacy Applying practice level variables to a patient population produces spurious and counter-intuitive resultsApplying practice level variables to a patient population produces spurious and counter-intuitive results Well described statistical phenomenonWell described statistical phenomenon Practice level models don’t workPractice 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)Requested additional patient level model WITHOUT prevalence (model 18) Key comparison then is patient level with and without prevalenceKey comparison then is patient level with and without prevalence

26 Explanatory power Akaike Information criterion AIC statistical test for explanatory powerAIC statistical test for explanatory power Lower values indicator better modelsLower values indicator better models Absolute value increases with sample sizeAbsolute value increases with sample size Relative difference more importantRelative 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/TRsPerson years adjustment give better fit for new registrations/TRs Patient level analyses produce intuitively acceptable resultsPatient level analyses produce intuitively acceptable results Practice level analyses counter- intuitive results likely to lead to controversy (ecological fallacy)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 considerationComparisons between patient level models with and with and without prevalence are presented for Plenary’s consideration


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