Elective hospital admissions

220 Pages·2017·7.67 MB·English

by  Chalkley M

HEALTH SERVICES AND DELIVERY RESEARCH
VOLUME 5 ISSUE 7 FEBRUARY 2017
ISSN 2050-4349
Elective hospital admissions: secondary data analysis
and modelling with an emphasis on policies to
moderate growth
Martin Chalkley, Barry McCormick, Robert Anderson, Maria Jose Aragon,
Nazma Nessa, Catia Nicodemo, Stuart Redding and Raphael Wittenberg
DOI 10.3310/hsdr05070

Elective hospital admissions: secondary
data analysis and modelling with an
emphasis on policies to moderate growth
Martin Chalkley,1 Barry McCormick,2*
Robert Anderson,2 Maria Jose Aragon,1
Nazma Nessa,2,3 Catia Nicodemo,2 Stuart Redding2
and Raphael Wittenberg2
1Centre for Health Economics, University of York, York, UK
2Centre for Health Service Economics and Organisation, Nuffield Department of
Primary Care, University of Oxford, Oxford, UK
3Department for Business, Innovation and Skills, London, UK
*Corresponding author
Declared competing interests of authors: Maria JoseAragon reports grants from theNational Institute
for Health Research (NIHR) during the conductof thestudy, grantsfrom theDepartment of Health, grants
from theDepartment of Health via Economics of Social and Health Care Research Unit (ESCHCRU), grants
from theWellcome Trust via theCentre for Chronic Diseases and Disorders(C2D2) and grants from NHS
England outsidethe submitted work. Martin Chalkley reports grantsfrom NIHR during theconduct of the
study, grants from theDepartment of Health, grants from ESCHCRU, grants from C2D2 and grants from
NHS England outside thesubmitted work.
Published February 2017
DOI: 10.3310/hsdr05070
This reportshould be referenced as follows:
ChalkleyM, McCormick B, Anderson R, Aragon MJ, NessaN, NicodemoC, et al.Elective hospital
admissions: secondary dataanalysis and modelling with anemphasis onpolicies to moderate
growth. Health Serv Deliv Res2017;5(7).

Health Services and Delivery Research
ISSN2050-4349(Print)
ISSN2050-4357(Online)
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DOI:10.3310/hsdr05070 HEALTHSERVICESANDDELIVERYRESEARCH2017 VOL.5 NO.7
Abstract
Elective hospital admissions: secondary data analysis and
modelling with an emphasis on policies to moderate growth
Martin Chalkley,1 Barry McCormick,2* Robert Anderson,2
Maria Jose Aragon,1 Nazma Nessa,2,3 Catia Nicodemo,2
Stuart Redding2 and Raphael Wittenberg2
1Centre forHealth Economics, University ofYork, York, UK
2Centre forHealth Service Economics and Organisation, Nuffield Department ofPrimary Care,
University ofOxford, Oxford, UK
3Department for Business, Innovation and Skills, London, UK
*Corresponding author [email protected]
Background: The English NHS faces financial pressures that may render thegrowth rates of elective
admissions seenbetween 2001/2and 2011/12 unsustainable. A better understanding of admissions
growth, and theinfluence of policy, areneeded to minimise theimpact onhealth gainfor patients.
Objectives: This project had several objectives: (1) to better understand the determinants of elective
activity and policy to moderate growthat minimum healthloss for patients; (2) to build arich data set
integrating health, practice and local areadata to study general practitioner (GP) referrals andresulting
admissions; (3) to predict patients whosetreatment is unlikely to be cost-effective using patient-reported
outcomes and to examine variation in provider performance; and (4) to study how policies that aim
to reduce elective admissions may changedemand for emergency care. The main drivers of elective
admissions growth haveincreased either supplyof or demand for care, and could include, for example,
technical innovations or increased awareness of treatment benefits. Ofthe factors studied, neither system
reform norpopulation ageing appears to be akeydriver. The introduction of theprospective payment
tariff ‘Payment byResults’appears to have led to primary care trusts (PCTs) having increasingly similar
lengths of stay. In deprived areas, increasing GP supply appears to moderate elective admissions. Reducing
the incidence of single-handed practices tends to reduce referrals and admissions. Policies to reduce
referrals arelikely to reduce admissions buttreatments maybe particularly reduced inthe lowest referring
practices, in which resulting health loss maybegreatest. In this model, perfull-time equivalent, female and
highly experiencedGPs identify more patients admitted by specialists.
Results: It appears from ourstudies that some patient characteristics areassociated with notachieving
sufficient patient gainto warrant cost-effective treatment. The introduction of independent sector
treatment centres isestimated to have caused an increase in emergency activity rates at local PCTs.The
explanations offered for increasing elective admissions indicate that theyare manageableby healthpolicy.
Conclusions: Further work isrequired to understand some of the results identified, suchas whether or not
high-volume Clinical Commissioning Groupsare fulfilling unmet need;why somepractices refer at low
rates relative to admissions; whythe periodeffect, which results from factors that equally affect all in the
study at apoint in time, dominates in the age–period–cohort analysis; andexactly howthe emergency and
©Queen’sPrinterandControllerofHMSO2017.ThisworkwasproducedbyChalkleyetal.underthetermsofacommissioningcontractissuedbytheSecretaryofStatefor
Health.Thisissuemaybefreelyreproducedforthepurposesofprivateresearchandstudyandextracts(orindeed,thefullreport)maybeincludedinprofessionaljournals v
providedthatsuitableacknowledgementismadeandthereproductionisnotassociatedwithanyformofadvertising.Applicationsforcommercialreproductionshouldbe
addressedto:NIHRJournalsLibrary,NationalInstituteforHealthResearch,Evaluation,TrialsandStudiesCoordinatingCentre,AlphaHouse,UniversityofSouthamptonScience
Park,SouthamptonSO167NS,UK.
ABSTRACT
elective sections of hospital treatment interact. This project relies on the analysis of secondary data. This
type of research does not easilyfacilitate theimportant inputof clinical experts or service users. It would
be beneficial ifother methods, including surveys and consultation with keystakeholders, could be
incorporated into future research now that we have uncovered important questions.
Funding: TheNational Institute for Health Research HealthServices and Delivery Research programme.
vi
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DOI:10.3310/hsdr05070 HEALTHSERVICESANDDELIVERYRESEARCH2017 VOL.5 NO.7
Contents
List oftables xi
List offigures xv
List ofboxes xix
List ofabbreviations xxi
PlainEnglish summary xxiii
Scientific summary xxv
Chapter 1Background and research objectives 1
Chapter 2Demand management for elective care:system reform andother
driversof growth –anexamination of thefactors affecting the growth of
electivehospital activity in Englandfrom 1998to 2012and the implications of
these formanaging demand forelective activity 3
Summary 3
Introduction 3
Background 3
Modelling growth in elective activity: therole of another jurisdiction 5
Different perspectives on activity 8
The importance of differential trends 10
A framework for understanding elective activity and its growth 10
The management challenge 11
Summary of findings 11
Dataand empirical methods 14
Data 14
Empirical methods 15
Modelling system reform 18
Variables 19
Results 22
Growth incontinuous inpatient stays 23
Decline in average length of stay 24
Estimating the impact of system reform 24
Interpreting growth in elective activity inEngland 26
Subgroup analysis: high-expenditure Healthcare Resource Groups 26
Examining the effects acrossprimary caretrust clusters 29
Checks onthe sensitivity of the main results 30
Information for commissioning 36
A genericprimary caretrust cluster report 36
A bespokeClinical Commissioning Group report 37
Chapter 3Declining variation in English hospital bed-use andPayment by Results 39
Introduction 39
Thechanges in variation of hospital bed-useacross primary caretrusts, 2002/3–2008/9 40
Thetariff and variation in hospital bed-use 44
©Queen’sPrinterandControllerofHMSO2017.ThisworkwasproducedbyChalkleyetal.underthetermsofacommissioningcontractissuedbytheSecretaryofStatefor
Health.Thisissuemaybefreelyreproducedforthepurposesofprivateresearchandstudyandextracts(orindeed,thefullreport)maybeincludedinprofessionaljournals vii
providedthatsuitableacknowledgementismadeandthereproductionisnotassociatedwithanyformofadvertising.Applicationsforcommercialreproductionshouldbe
addressedto:NIHRJournalsLibrary,NationalInstituteforHealthResearch,Evaluation,TrialsandStudiesCoordinatingCentre,AlphaHouse,UniversityofSouthamptonScience
Park,SouthamptonSO167NS,UK.
CONTENTS
Analternative explanation: thechanging geography of resource allocation and bed-use 48
Therelationship between efficiency gain and initial mean length of stay 50
Conclusions 52
Chapter 4Trends in elective admissions: an age–period–cohort analysis 53
Introduction 53
Diagnostic procedures admissions 54
Hipand knee replacement procedures 59
Coronary circulation admissions 62
Menorrhagia procedure admissions 66
Conclusions 69
Chapter 5Will increasing the supplyof gatekeeper general practitioners reduce
referralsand hospital admissions? 71
Introduction 71
Modelof general practitoner gatekeeping, diagnosis and referrals 74
Illness, treatment, utility and registration 74
Expected nethealth gainand the referral threshold 76
Proposition 1 77
Proposition 2 79
Comparative statics and thewelfare economics of generalpractitioner employment 80
Discussion 80
Why does amore elastic demand for registration haveno effect on s*and patient
welfare? 81
General practitioner practice density 81
Econometric strategy 81
Identification and instrumental variables 82
Data 83
Clinical data 83
Lower-layer super output area anddeprivation controls 84
The supply of general practitioners, the sizeof practices and hospital admissions 84
Summary statistics 85
Results 87
Outpatient and elective admissions 87
Emergency admissions 90
Robustness checks 92
Conclusions 98
Chapter 6The determinants of general practitioner referralsand elective
hospital admissions: apractice-level study 101
Introduction 101
Data 103
Three stylised facts 104
Empirical strategy and hypotheses 108
Hypotheses and model 110
Results 112
The model of referrals 112
The model of hospital admissions following first referral 113
Explaining thevariation in hospital admission rates between practices 116
Robustness checks 117
Quantile regression 117
Conclusions 120
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