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Use of health surveys in resource allocation
Matt Sutton
Senior Research Fellow
University of Glasgow
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