# GMS Formula Analysis QRESEARCH 2005 09 Feb 2006

- Slides: 28

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

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

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

Sampling: Practices Practice inclusion criteria for analysis ØEngland Wales only Ø At least 1000 patients ØAt least 2 consultations/person-year ØComplete data for period in question Ø Decided not to sample proportionately by region

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

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

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

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

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

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

Results: study practices • 454 practices in England Wales • 3. 8 million patients registered at any point in study year • 33, 727 deaths • 319, 435 new patients • 97, 239 temporary residents

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

Prevalence of diabetes in patients over 15

Consultation rates by age and sex

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)

Results: A priori Model 7 bi (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

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

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

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

A priori model: practice variables List size: 2. 2% lower rate for each additional thousand patients for a given number of GP principals (head count not wte) 1. 4% higher rates for each additional GP principal for a given list size

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)

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

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.

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

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

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

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

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

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