Use of health surveys in resource allocation

Matt Sutton

Senior Research Fellow

University of Glasgow

m.sutton@clinmed.gla.ac.uk

Health Survey's User Group Friday 23 January 2004

DoH, Skipton House, London

Colleagues on previous work

Scotland: “Derivation of an adjustment to the Arbuthnott formula for socioeconomic inequities in health care” with Alex McConnachie

England: “Allocation of Resources to English Areas

Report” with Hugh Gravelle, Stephen Morris, Alastair Leyland, Frank Windmeijer, Chris Dibben and Mike Muirhead

[http://www.isdscotland.org/isd/info3.jsp?pContentID=842&p_applic=CCC&p_service=Content.show&]

Outline

Overview of resource allocation formulae

Direct use of survey information

Indirect use of survey information experience in Scotland experience in England

Overview of resource allocation formulae

Purpose - Allocate national resources to health care organisations to:

Scotland: “promote equitable access to health care”

Wales: “ensure more equitable access to health services in accordance with health needs”

England: “contribute to the reduction of avoidable inequalities in health”

Structure of resource allocation formulae

Population size Adjusted for demography Adjusted for additional need factors Adjusted for additional cost factors

Market Forces Factor Additional costs of remoteness and rurality

Three basic approaches to allocation

1. Based on relationships between population characteristics and use of health care

2. Based on actual prevalence of ill-health

3. Based on relationships between population characteristics and prevalence of ill-health (predicted prevalence)

1. Adjustments based on average costs

% Rich % Poor Average NeedRegion A 75 25 0.75*£200 + 0.25*£400 = £250 per capitaRegion B 25 75 0.25*£200 + 0.75*£400 = £350 per capita

“Rich”£200

“Poor”£400

2. Adjustments based on actual prevalence

Average NeedRegion A £3000*0.05 = £150 per capitaRegion B £3000*0.07 = £210 per capita

Region A5%

Region B7%

Cost per prevalent case = £3000

3. Adjustments based on predicted prevalence

% Rich % Poor Average NeedRegion A 75 25 £3000*(0.75*0.04 + 0.25*0.08) = £150 per capitaRegion B 25 75 £3000*(0.25*0.04 + 0.75*0.08) = £210 per capita

“Rich”4%

“Poor”8%

Cost per prevalent case = £3000

Use of health surveys

Wales: Direct use of survey to make adjustments for demography and additional need

Scotland, England & NI: Indirect use of surveys to improve adjustments for additional need

Direct method

Obtain region-specific prevalence rate estimates for each age-group using a health survey

Apply region-specific prevalence rate estimates to age profile of resident population to obtain estimated numbers of cases

Calculate each region’s share of national cases Obtain national budget for the condition Derive regional budgets by multiplying national budget

by the regional shares of cases

Practical issues for direct method

Reliability of survey results at regional level

Representativeness of survey results

Updateability of survey results

Differences in reporting behaviour between regions

Conceptual issues for direct method

Choice of prevalence measure, e.g. circulatory disease Symptom-based measures – Rose Questionnaire Self-reported measures – longstanding illness Doctor-diagnosed measures Breadth of definition – IHD, CVD or CVC

Converting prevalence into need for health care resources Share of prevalent cases implies:

All “non-cases” have zero need for health care resources All “cases” have same need for health care resources

Alternative measures of circulatory disease

NHS Board Rose - Possible

MI

Doctor-diagnosed

CVC

Percentage change

Argyll & Clyde 9.0% 8.5% -5.6%Ayrshire & Arran 8.1% 7.5% -7.4%Fife 8.1% 7.1% -12.3%Forth Valley 3.1% 5.1% 64.5%Grampian 8.6% 9.4% 9.3%Greater Glasgow 22.6% 18.1% -19.9%Highland 2.9% 4.2% 44.8%Lanarkshire 11.4% 12.7% 11.4%Lothian 13.1% 13.7% 4.6%Tayside 6.8% 7.1% 4.4%Scotland 100.0% 100.0% 0.0%

Indirect methods

Model risk of being a prevalent case as a function of individual-level (age/gender) and area-level characteristics

Apply risk equation to small-area data to obtain prevalence rate estimates for each small area

Model relationship between use of services and prevalence rate estimate(s) to obtain relative needs index for each area

Approach in Scotland

Adjustment for additional need based on single composite needs variable:

“Arbuthnott Index”Standardised Mortality Ratio, 0-64 yearsProportion claiming income support, 65+ yearsStandardised rate of unemployment benefit

claimsProportion of households with multiple

deprivation

Indirect methods - Scotland

Original work assumed linear relationship between Arbuthnott Index and use of health care services

Work on adjustment involved: Non-linear modelling of relationship between Arbuthnott Index

and prevalence estimates from Scottish Health Survey Modelling of effect of fitted prevalence on use of care Simultaneously testing for unmet need

(whether high deprivation or low deprivation areas had levels of use that departed significantly from prevalence-use relationship)

Data - Scotland

1995 & 1998 Scottish Health Surveys 1995 = 7,932 individuals aged 16-64 years 1998 = 12,939 individuals aged 2-74 years

Respondents sampled from 451 of 717 areas

Standardised prevalence rates calculated for six longstanding illnesses

Circulatory disease prevalence and deprivation

Arbuthnott Index

Ob

serv

ed

/Exp

ect

ed

Pre

vale

nce

Ra

tio (

%)

-5 0 5 10 15

01

00

20

03

00

40

05

00

Model PrevalenceEffect

% AffluentCut

Effect % DeprivedCut

Effect R2

(iii)0.60

(0.53, 0.66)p<0.0001

47.3%

(iv)0.74

(0.61, 0.88)p<0.0001

27%2.85

(-0.33, 6.04)p=0.079

6%-4.08

(-7.13, -1.03)p=0.009

47.8%

Regression model results for acute care of circulatory disease with 95% confidenceintervals in parentheses and associated p-values.

Modelling effect of prevalence on use of health care

Arbuthnott Index

-5 0 5 10 15

-50%

100%

150%

200%

Acute Care Need (i)(iii)(iv)

Relative need profiles under different models

Diagnostic group

10th (Most deprived) 35.1 40.9 52.7 63.1

9th 15.4 13.5 23.2 21.4

8th 9.1 9 13.7 11.4

7th 4.1 5.7 6.2 4.1

6th -0.5 2.1 -0.7 -2.2

5th -4.6 -2.2 -6.9 -8.2

4th -8.5 -7.3 -12.7 -14.6

3rd -11.9 -12.3 -17.9 -20.4

2nd -15.7 -18.6 -23.6 -25.4

1st (Most affluent) -22.6 -31.3 -34 -29.3

Depivation decile Orig NewOrig New

Circulatory Respiratory

Relative needs by deprivation decile

Approach in England

Additional needs modelled using a large number of potential indicators

Particular concerns raised about previous review’s ability to avoid ‘unmet need’

Use of surveys - England

Individual-level tests of unmet need

Unmet need tests in small area levels of health care use model risk of morbidity as function of area

characteristics augment set of potential need indicators with

predicted morbidity indices examine effects on other coefficients

Data - England

Health Survey for England, 1994-2000 Total respondents = 122,500 Binary measures of health care use since 1998

Individuals sampled from 5,893 of 8,414 electoral wards

Records linked to a range of population, utilisation and supply variables

Individual-level tests of unmet need

Population group Effect on general health Effect on utilisationLow income Have poorer general health Are less likely to have inpatient treatment

For GP consultations, outpatient and day case treatment the effect isinsignificant

Lower social classes Have poorer general health For all health service use the effect is insignificantUnemployed Have poorer general health Are less likely to have outpatient treatment

For GP consultations, and day case and inpatient treatment the effect isinsignificant

Low education attainment Have poorer general health Are less likely to have outpatient and day case and inpatient treatment

For GP consultations the effect is insignificantMinority ethnic groups Have poorer general health Some minority ethnic groups are more likely to have GP consultations

Some minority ethnic groups are less likely to have outpatient, day caseand inpatient treatment

Morbidity models

Diagnosis Psycho-social Nervous Circulatory Respiratory Musculoskeletal

Variable Coeff z Coeff z Coeff z Coeff z Coeff z

Ethnic minorities 0.256 2.8 0.242 2.4 -0.585 -5.2 -0.289 -3.6

IB/SDA claimants 0.002 9.0 0.001 5.6

University non-participation 0.008 3.7 0.005 3.1

SIR<75 0.003 4.3

Income domain 0.004 2.3

AA>60 1.722 3.2

CMF 0.001 2.4

Housing domain 0.029 1.9

SIR<65 0.003 5.9

Health domain 0.057 2.6

N 79356 121746 121746 121746 121746

Pseudo-R2 0.0176 0.0269 0.216 0.013 0.1341 All models additionally contain age, sex and year effects (results not shown)

Augmentation of model with morbidity indices

Model Basic model Morbidity model

Variable Coefficient t-ratio Coefficient t-ratioMean waiting time -0.094 -10.6 -0.104 -12.1Distance to general practice -0.049 -3.3 -0.044 -3.1Distance to hospital -0.032 -7.2 -0.033 -7.6Outpatients seen with 13 weeks 0.157 4.7 0.165 5.1Residential/Nursing home places -0.003 -2.2 -0.004 -2.7Access to private providers -0.026 -3.1 -0.023 -2.9Number of hospital beds 0.024 3.3 0.020 2.8

Ethnic minorities -0.012 -7.4 -0.016 -9.0Employment domain -0.065 -6.9 -0.133 -10.8 SIR<75 0.199 13.0 0.105 6.5Education domain 0.025 8.9 0.013 4.6Low birthweight 0.018 3.0 0.017 2.8AA/DLA claimants 0.072 8.2 0.051 5.6CMF<75 / SMR<75 0.121 10.5 0.087 7.8Aged 75+ living alone 0.034 2.7 0.027 2.3Child Poverty Domain 0.069 8.8 0.055 6.7Nervous system morbidity index 0.195 5.3Circulatory morbidity index 0.670 11.6 R2 0.7242 0.744 RESET 1.75p=0.155 1.76p=0.1533Observations 8414 8414

Care Programme Acute Acute

Model Basic Morbidity Index

Distribution across wards 5th percentile 0.763 0.71725th percentile 0.859 0.834Mean 1.000 1.00075th percentile 1.119 1.13995th percentile 1.319 1.397Standard Deviation 0.176 0.214

Impact on relative need indices

Summary - I

Health surveys are increasingly being used in resource allocation availability of data concerns about unmet need in activity-based

formulae

Direct methods practical issues conceptual issues

Summary - II

Indirect methods allow for non-linear relationships between

deprivation and need inform selection of need variables provide non-linear combinations of need variables

to augment data-set permit tests of unmet need