Investigating the relationship between quality of primary care and premature mortality in England

Investigating the relationship between quality ofprimary care and premature mortality in England

a spatial whole-population study

Evangelos Kontopantelis David Springate Mark AshworthRoger Webb Iain Buchan Tim Doran

Centre for Health Informatics, Institute of Population HealthFaculty of Medicine, University of Manchester

HSCIC public board meeting, 28th January 2015

Kontopantelis (University of Manchester) quality of primary care & mortality 28 Jan 2015 1 / 33

Outline

1 Background

2 Methods

3 Findings


Improving quality of careor quality of recorded care?

A pay-for-performance (p4p) program kicked off in April 2004 withthe introduction of a new GP contract

General practices are rewarded for achieving a set of quality targetsfor patients with chronic conditionsThe aim was to increase overall quality of care and to reducevariation in quality between practices

The incentive scheme for payment of GPs was named the Qualityand Outcomes Framework (QOF)Initial investment estimated at 1.8 bn for 3 years (increasing GPincome by up to 25%)QOF is reviewed at least every two years


Quality and Outcomes Frameworkdetails for years 1 (2004/5) and 7 (2010/11)

Domains and indicators in year 1 (year 7):Clinical care for 10 (19) chronic diseases, with 76 (80) indicatorsOrganisation of care, with 56 (36) indicatorsAdditional services, with 10 (8) indicatorsPatient experience, with 4 (5) indicators

Implemented simultaneously in all practices (a control group wasout of the question)Into the 11th year now (01Mar14/31Apr15); cost for the first 10years was above the estimate at 10 bn approximately


Investigated relentlesslyin Manchester and elsewhere

Main driver for complete computerisation in primary careAlthough a voluntary scheme, participation is almost complete andcomputerisation is a prerequisite

Led to improvement in quality more quickly, but the benefitsdiminish over timeReduced inequalities of careLed to some deterioration in unincentivised aspects of careContradictory evidence on its effect on hospital admissions


But what about harder outcomesnamely, mortality

Aimed to quantify the relationship between performance on theQuality and Outcomes Framework, and:

all cause premature mortalitycause-specific premature mortality linked closely with conditionsincluded in the scheme

No academic access to the practice mortality database


Design and setting

Design: Longitudinal spatial study, at the Lower Super OutputArea (LSOA) levelSetting: 32482 LSOAs (neighbourhoods of 1500 people onaverage), covering the whole population of England ( 53.5million), from 2007 to 2012Participants: 8647 English general practices participating in theQOF for at least one year of the study period, including over 99%of registered patientsIntervention: National pay-for-performance programmeincentivising performance on over 100 quality-of-care indicators


Main outcome measures

All-cause mortalityCause-specific mortality rates for six chronic conditions:

diabetesheart failurehypertensionischaemic heart diseasestrokechronic kidney disease


Generating the outcome variablesusing ONS data

Revised annual LSOA population estimates, 2005-2012:based on 2001 and 2011 census informationbroken down by age and sex

Got annual death counts at the LSOA level, 2005-2012:broken down by age and sex

Calculated annual and 2-year age and sex standardised mortalityrates at the LSOA level:

all-causecause-specific


Other dataand sources

LSOA levelIndex of Multiple Deprivation, 2007 and 2010 (ONS neighbourhoodstatistics)Rural vs urban (ONS neighbourhood statistics)Lots of collinear 2011 census variables (ONS census)

At the practice level (to be attributed to the LSOA level)QOF performance (HSCIC)QOF disease burden (HSCIC)practice list size (HSCIC)

Spatial shapefile data maps (ONS Geoportal)


Spatial estimationfirst approach: complete local attendance

32482 English LSOAs with complete census, rurality anddeprivation data 6500 practice-hub LSOAs (at least one practice)QOF achievement and morbidity burden calculated as sum of allnumerators over sum of all practice denominatorsGet longitude-latitude centroid coordinates for all LSOAsQOF achievement and morbidity scores estimated for the LSOAswith no practices as weighted means from the 5 closest hubs (oninverse distance listsize)



Bolton Bury

Manchester

Oldham

Rochdale

Salford

Stockport

Tameside

Trafford

Wigan

(87.0,91.2](84.6,87.0](82.1,84.6][66.2,82.1]No data



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(87.0,91.2](84.6,87.0](82.1,84.6][66.2,82.1]No data



007B

009C014B014E

020B020G

(87.0,91.2](84.6,87.0](82.1,84.6][66.2,82.1]No data


Spatial estimationsecond approach: attribution dataset

Complete local attendance assumption difficult to justify for allpatients in all areas, especially urbanHSCIC released information on the attribution of general practicepopulations to LSOAs and vice versaOnly covered 2014 but used it as a blueprint to generate annualattribution datasets from 2011/12 to 2006/7

Poisson and negative binomial regression modelsattributed population over time was adjusted for practice list size inthe respective year


Analyses

Three sets of multiple linear regressions used to investigate therelationship between QOF quality of care and all-cause andcondition specific mortality:

relationship between QOF scores and 2011-12 SMRsrelationship between changes in QOF scores over a 3 or 5-yearperiod and 2011-12 SMRssensitivity analysis, relationship between QOF quality of care andmortality over time

Following spatial weighted estimation data were complete for all32482 English 2001 LSOAsEach analysis set was applied to both spatial weighted estimationapproaches


Mortalityby region

North East North West Yorkshire & East MidlandWest MidlanEast EnglandLondon South East South Centr South West EnglandAll-cause death% (2011-12) 1.09 1.05 1.01 0.96 0.98 0.91 0.74 0.93 0.87 0.96 0.94Condition-specific death% (2011-12) 0.39 0.4 0.4 0.39 0.38 0.38 0.24 0.37 0.33 0.4 0.36

0 0.2 0.4 0.6 0.8 1 1.2

North East

North West

Yorkshire & Humber

East Midlands

West Midlands

East England

London

South East

South Central

South West Coast

England

Condition-specific death% (2011-12) All-cause death% (2011-12)


Standardised mortality ratesby region

North East North West Yorkshire & East MidlandWest MidlanEast EnglandLondon South East South Centr South West EnglandAll-cause SMR (2011-12) 574 580 541 508 528 466 563 456 482 448 513Condition-specific SMR (2011-12) 184 198 194 184 184 167 166 154 155 158 175

0 100 200 300 400 500 600 700

North East

North West

Yorkshire & Humber

East Midlands

West Midlands

East England

London

South East

South Central

South West Coast

England

Condition-specific SMR (2011-12) All-cause SMR (2011-12)


Overall health burdenGreater London

(1.8,3.9](1.6,1.8](1.4,1.6][0.4,1.4]


Overall quality of care (PA)Greater London

(83.8,90.9](82.5,83.8](81.1,82.5][68.7,81.1]


Overall health burdenGreater Manchester

(2.2,2.5](2.0,2.2](1.9,2.0][1.0,1.9]


Overall quality of care (PA)Greater Manchester

(84.9,89.8](83.6,84.9](82.1,83.6][73.6,82.1]


Overall health burdenWest Midlands

(2.2,2.7](2.1,2.2](1.9,2.1][0.7,1.9]


Overall quality of care (PA)West Midlands

(84.4,88.3](83.4,84.4](82.4,83.4][77.5,82.4]


Spatial analyseson all-cause SMRs

QOF Year 8 (2011/12)* QOF Year 7 (2010/11)* QOF Year 6 (2009/10)* QOF Year 5 (2008/9)* Outcome: all cause SMR; QOF predictors: overall population achievement, overall morbidity load Index of Multiple Deprivation 2010

7.44(7.24,7.65)

Spatial analyseson cause-specific SMRs

QOF Year 8 (2011/12)* QOF Year 7 (2010/11)* QOF Year 6 (2009/10)* QOF Year 5 (2008/9)* Outcome: condition specific SMR; QOF predictors: nine indicator outcome population achievement, five domains morbidity load Index of Multiple Deprivation 2010

2.41(2.27,2.55)

Spatial analysessummary of results

All-cause and cause-specific mortality rates declined over thestudy periodHigher mortality associated with:

greater area deprivationurban locationproportion of a non-white population

No relationship between practice performance on QOF qualityindicators and all-cause or cause-specific mortality rates in thepractice locality


Conclusions

Higher reported achievement of activities, incentivised under amajor, nationwide pay-for-performance programme for primarycare, did not appear to result in reduced incidence of prematuredeath in the population


Future workcomplex methods that need to be re-used to answer more questions

Spatial analysis linking pollution, smoking, BMI, IMD and othercensus variables to:

all deathscancer related deaths

Spatial analysis linking QOF, distance to practice, patientsatisfaction, IMD (except health sub-domain) to:

standardised all hospital admissionsstandardised emergency hospital admissions

Structural equation modelling (SEM) to investigate IMD subscaleson all-cause mortality at the population levelSEM to investigate obesity at the population level


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Kontopantelis E, Springate DA, Ashworth M, Webb RT, Buchan IE and Doran T.Investigating the relationship between quality of primary care and prematuremortality in England: a spatial whole-population study. BMJ, in print

Comments, suggestions: [email protected]


BackgroundMethodsFindingsAppendix

Science

Investigating the relationship between quality of primary care and premature mortality in England