excess costs additional analysis presentation to nrac 26 february 2007 matt sutton heru david bailey...
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EXCESS COSTS ADDITIONAL ANALYSIS
Presentation to NRAC 26 February 2007
Matt Sutton HERU
David Bailey ISD
Keith MacKenzie ASD
Introduction
• First draft of Technical addendum• Two parts – Community and Hospital• Consider papers in conjunction with the
original HERU report• No HB level adjustment yet• Looking for NRAC’s views on options and
methodology/analysis• Decide what elements should be taken
forward to the final formula
Research ObjectivesThe contract with HERU specifies three elements
to this work1. Written responses to the peer review (done) and points raised in the consultation (pending/to be covered by technical addendum)2. Development of the Hospital adjustment3. Development of the Community adjustment
Today’s papers represent the first drafts on parts 2 and 3 of this work
The deadline for completion of this work is 28 March 2007
Format of presentation
• David Bailey will explain the Community work and his proposals for how the model should be changed
• Matt Sutton will explain the technical work on the Hospital adjustment
• Keith MacKenzie will set out the options for the Hospital adjustment with the pros and cons
• Discussion and questions • Agreement on the Community and Hospital
adjustments
COMMUNITY ADJUSTMENT
Explanation of the additional work by
David Bailey ISD
Questionnaire Response
• 35 – 40 Responses (30 included here)
• Five questions:1. Contact duration
2. Within settlement travel time
3. Island contacts
4. Base locations
5. Proportion of home-visits
Services Providing Feedback
• Community Nursing
• Physiotherapy
• Community Mental Health Teams
• Occupational Therapy
• Speech & Language Therapy
• Midwifery
• Arts Therapy• Dietetics• Community Learning
Disabilities Teams• Intensive Community
Support Services• Podiatry
General Comments
“It’s nice to see this finally being addressed!”
“…can you make allowance for the changing models of care delivery with older people and centralisation of specialist services which will require more care to be delivered locally. In the majority of workforce plans we see this as an area of growth in demand.”
A Central Theme
“…. grave concerns … the model … does not differentiate enough between rural and urban. It also does not differentiate between specific services. … This is a fundamental flaw and the assumptions are therefore grossly over or underestimated.”
“…more account needs to be taken of the higher proportion of home-visits in rural areas.”
“An island locality has different needs to urban/rural areas.”
“Physiotherapists shouldn't be lumped in with District Nurses.”
“Community Occupational Therapy is not clinic based … sessions are 1-4 hours duration The assumptions here don't reflect this.”
1. Average Contact Duration
• 16 thought our assumption reasonable
• 14 thought our assumption too short– Community Mental Health Teams– Art Therapy– Intensive Community Support Services– Occupational Therapy– Physiotherapy
Many Physios work with people with mental health difficulties. Contact duration depends on patient need and capability.
Average Contact Durations
Feedback from the ‘therapeutic’ services
Service Type Average Contact Duration (min)CMHT 45 - 60Art Therapy 45ICSS 45-60Physiotherapy 60Occupational Therapy 60
The model can be revised to allow inter-service variation
2. Within Settlement Travel Time
• 7 thought 5 minutes was reasonable
• 5 misinterpreted the question
• 18 thought 5 minutes too short
• Alternatives were 10 – 20 minutes
Within Settlement Travel Time
“…rarely managed less than 10 minutes”
“10 min would be fairer.”
“Even within the semi-urban area it can be 15-20 mins. 5 mins does not consider parking in busy areas.”
“No, Seems a significant underestimate.”
“Not at all. When I was working in a city I initially tried to use this figure when scheduling appointments and ended up with no lunch hour and usually finishing work well after 18.00! … When I actually recorded the time spent it averaged at around twenty minutes!”
Within Settlement Travel Time
• Traffic problems• Roadworks• Parking• Congestion• Time of Day• Single-track roads• Tourist traffic• Adverse weather
• Poor public transport• Patient-care decisions• Speed calming• Personal safety• Farm vehicles• One way systems• Lift Maintenance• Paperwork
Revisions to the Model
• Within settlement travel time increased to 20 minutes for ‘Primary Cities’ and ‘Urban Settlements’
• Increased to 15 minutes in other areas
3. Island Contact Time
• 12 responses
• 7 thought 120 minutes was reasonable
• 5 thought it unreasonable
• The Main Influences:– Dependence on ferries / Check-in time– Adverse weather / Tides
“For 8 consecutive weeks one respondent from Argyll & Clyde had 2-3 hours added to their journey by adverse weather and tides.”
Island Contact Time
• The feedback is island specific and shows a diversity of experience
• The responses appear to neglect the possibility of multiple visits in a single trip– A recipient who suggested that 120 minutes
was an underestimate also suggested 30-40 minutes for each patient contact
The original model assumption is retained unchanged.
4. Base Location
• 16 respondents disagreed• Some confused the settlement population
the assumption is based on with their own practice populations
• The group who disagree are similar to the ‘therapeutic’ group suggesting a longer contact duration
Those Who Disagreed
– ICSS– Physiotherapy– Dietetics– Midwifery– Community Learning Disabilities Team– Some CMHT staff– Some Speech & Language Therapists– Some Occupational Therapists
The Therapeutic Services
The model• A base location• Typically the base is a
settlement of 10,000• Staff work full-time
The Feedback• No base location• A much greater
population is served• Multiple clinics are
staffed for short periods
The feedback could indicate failure in communicating the model’s assumptions. In which case they can be retained.
5. Home-visit Proportion
• Responses varied with speciality
• Those who disagreed suggested new figures
Speciality Home-Visits (%) CommentsHealth Visitor 85CMHT 75-100 For elderly patientsICSS 95Community LearningDisabilities 70
Dietetics & Nutrition -Increasing local palliativecare
Are Home-visit Levels Constant?
• 9 Yes
• 21 No, rural areas have more home-visits
• Two main reasons:– Lack of adequate public transport– Scarcity of suitable clinic facilities
Revisions to the Model
• Lack of adequate public transport– Could be seen as a trend to increasing home-
visits with rurality
• Scarcity of suitable clinic facilities– Could be a seen as a step-increase in home-
visits with rurality
Proposals for the Revised Model
Parameter Value CommentsContact Duration (min) Service Specific Based on feedbackTravel Time (Within Settlement) (min)
2015
SEURC 1 & 2Other Areas
Total Island Contact Duration (min) 120 UnchangedSettlement Size 3,000/10,000 Unchanged
Proportion at home
Service SpecificCan step up to 90%
in rural areas
The rural increaseonly happens whenfeedback justifies
Comparison For Health Visitors
Original Model New ModelPrimary Cities 0.980 1.008Urban Settlements 0.980 1.008Accessible Small Towns 0.980 0.924Remote Small Towns 0.980 0.924Very Remote Small Towns - Mainland 0.980 0.924Very Remote Small Towns - Island 0.980 0.924Accessible Rural Areas 1.018 0.943Remote Rural Areas 1.097 0.997Very Remote Rural Areas - Mainland 1.445 1.429Very Remote Rural Areas - Island 1.482 1.486
Index
Next Steps
• Incorporate additional responses
• Reconcile service types to Cost Book
• Use adopted age-sex weights to estimate expected demand for each Output Area
• Aggregate to Board level
Hospital analysisMatt Sutton HERU
Extension to hospital analysis
• Avoid potential double-counting by considering rurality and MFF/location factor simultaneously
• Extend model to examine population characteristics (age, deprivation and ethnicity)
• Consider continuous specification of remoteness and rurality to avoid ‘cliff-edges’
Method development
• Same local to national average cost ratios• Population variables:
– % population aged 65+, 75+ and 85+– SIMD 2006 income domain score– % black and ethnic minority groups
• MFF and location factors attached to hospital locations by LA
• Significance testing allows for clustering by hospital
Continuous version of SEURC
• Settlement size (population)• Highly skewed so search for best-fitting power
function
• Drive time to nearest urban settlement (>10,000 population)
• Island location = travel over water to nearest urban settlement
Effects of input price indices
• Table 4.2 on page 10 of draft addendum
• MFF not significant (highly negative for LD)
• Location factors positive and significant for acute and outpatients (LD highly negative)
Original models
• Table 4.1 on page 9 of draft addendum
• Not significant for acute and LD
• Only significant at p=0.07 for outpatients
• Significant and consistent for maternity and inpatient mental health
• Significant for GLS but remoteness inconsistent
Full multivariate models
• Each model contains several insignificant factors
• Population characteristics insignificant and inconsistent
• MFFs not significant
Alternative model - acute
Variable Coefficient p-value 95% lower 95% upper Location factor 0.921 0.024 0.124 1.718 Constant term 6.535 0.870 -72.265 85.335 Adjusted R2 4.7% Observations 79689 Hospitals 135
• Only significant predictor of acute care costs
Alternative model - outpatients
Variable Coefficient p-value 95% lower 95% upper Location factor 3.040 0.025 0.382 5.699 Mainland very remote 16.636 0.017 2.957 30.314 Constant term -209.501 0.119 -473.460 54.458 Adjusted R2 8.9% Observations 81151 Hospitals 153
• Influence of location factor is large
• Inclusion of ‘mainland very remote’ avoids under-prediction
Alternative models - maternity
Continuous settlement size measure Categorical settlement size measure Variable Coeff. p-value 95%
lower 95% upper
Coeff. p-value 95% lower
95% upper
Settlement size1
0.000 0.478 -0.001 0.000
Urban settlement
6.272 0.305 -5.890 18.435
Small Town 1.971 0.732 -9.537 13.480 Rural Area 3.040 0.667 -11.100 17.181 Island 30.711 0.295 -27.719 89.142 28.898 0.337 -31.101 88.897 Drive time 0.194 0.331 -0.204 0.592 0.242 0.294 -0.218 0.703 Constant term
99.961 0.000 89.111 110.812 94.828 0.000 81.063 108.594
Adjusted R2 9.56% 9.63%
• Little to choose between all models in explanatory power
• Individual variables not significant in these models
Alternative models – mental health
Continuous settlement size measure Categorical settlement size measure Variable Coeff. p-value 95%
lower 95% upper
Coeff. p-value 95% lower
95% upper
Settlement size
-0.000 0.003 -0.000 -0.000
Urban settlement
7.040 0.140 -2.348 16.428
Small Town 10.085 0.039 0.511 19.659 Rural Area 6.595 0.214 -3.867 17.057 Island 31.338 0.029 3.252 59.425 30.702 0.031 2.916 58.488 Drive time 0.129 0.026 0.016 0.242 0.149 0.047 0.002 0.296 Constant term
103.342 0.000 97.575 109.110 94.269 0.000 85.932 102.605
Adjusted R2 17.44% 12.98%
• Continuous specification offers best explanatory power
Alternative models – GLS
Continuous settlement size measure Categorical settlement size measure Variable Coeff. p-value 95%
lower 95%
upper Coeff. p-value 95%
lower 95%
upper Settlement size1
-7.852 0.000 -12.144 -3.560
Small Town 17.735 0.003 6.310 29.159 Rural Area 18.262 0.000 9.936 26.588 Constant term
122.425 0.000 109.873 134.976 93.995 0.000 87.877 100.114
Adjusted R2
3.07% 3.89%
• Categorical specification offers best explanatory power
• Not as good as original model but more consistent
Alternative model – LD
Variable Coefficient p-value 95% lower 95% upper Settlement size -1.1E-05 0.018 -2E-05 -2.02E-06 Constant term 104.536 0.000 92.57329 116.4993 Adjusted R2 5.24%
• Only significant predictor of LD costs
Options for the Hospital adjustment
• HERU examined a number of models• Some were rejected as the variables did not
significantly affect unit costs (e.g. staff MFF)• The Options set out in the paper are those which
HERU felt offered a combination of– Goodness of fit– Face validity– Statistical significance
The OptionsThere are three options for the Hospital adjustment:-
1. Use the SEURC categories – effectively this is the approach set out in the original report
2. Use the best model for the each type of service (care programme?) – this would mean using the model which gives us the best statistical fit
3. Use the continuous measure of remoteness
Arguably there is a ‘fourth’ option which would be to use a combination of all the approaches based on a judgement about what is appropriate for each care programme
Option 1 – SEURC categories• The approach used in the original HERU
report.• Questions raised about it in consultation
which led to this research being commissioned:-– Categories are too wide to reflect the differing
needs of small groups of population– Unexpected Primary Cities result– Doesn’t take account of variation in input
prices– Doesn’t take account of other population
characteristics (e.g. deprivation
Pros of Option 1Pros
• Practicality – in terms of running the formula this is the most straightforward, and would be the easiest for ISD to update each year
• Transparency – the SEURC categories are published and (relatively) easy to explain
Cons of Option 1• Relevance/Face validity - The further
analysis highlights that the costs in Acute and Outpatients are not significantly related to urban-rural category
• Face validity – By reverting to the original proposals, we would need to explain why we had apparently ignored the concerns raised about SEURC in consultation. Though we can now say that we have given other options a thorough examination
Option 2 – Best ‘fitting’ model
• This approach would involve selecting the approach that gave us the best statistical fit for each care programme.
• This would mean choosing the approach with the greatest explanatory power (see figures in ‘red’ in the following table)
EXCESS COSTS ADDITIONAL WORK
Hospital Services adjustment - alternative models by care programme
Degree of explanatory power (%)
Care ProgrammeOriginal model
Alternative (1)
Alternative (2) Explanation of alternative model
Acute 1 4.7 - (1) building location factors
Outpatient 1 8.9 - (1) building location factors plus mainland very remote
Maternity 9.5 9.56 9.63 (1)continuous settlement size plus island plus drive time; (2)urban plus small town plus rural area plus island plus drive time
Inpatient mental health
13.2 17.44 12.98 (1)continuous settlement size plus island plus drive time; (2)urban plus small town plus rural area plus island plus drive time
Geriatric long stay 5.3 3.07 3.89 (1)continuous settlement size; (2)small town plus rural
Learning disabilities 1.3 5.24 - (1)continuous settlement size
Pros of Option 2• Objectivity – The resulting adjustment
would offer the best statistical fit available with the data used
• Face validity – The model has been changed to reflect concerns raised in consultation, so now where the SEURC categories do not appear appropriate, alternative approaches have been used that better reflect the position for each care programme
Cons of Option 2
• Practicality – This would be more complex for ISD to update – it effectively uses a mixture of the original approach and the alternatives approaches
• Transparency – By moving away from one categorisation, the adjustment will be harder to explain to Health Boards, the Minister, etc
Option 3 – Continuous measure of remoteness
This approach aims to resolve the issue of the SEURC categories not being an ideal ‘fit’ for health services. It uses the variables that underlie the index to give a continuous classification
Pros of Option 3• Practicality – By using the variables that underlie
the SEURC, the data required should be relatively easy for ISD to obtain each year
• Objectivity – It provides a better fit for three of the care programmes (maternity, inpatient mental health, learning disabilities) than the original SEURC categories would do
• Equity – By using a continuous classification, this model contains fewer ‘cliff edges’
Cons of Option 3• Relevance/Face validity – As in Option 1, the
further analysis highlights that the costs in Acute and Outpatients are not related to urban-rural category
• Objectivity – This approach would not give the best fit for three of the care programmes (acute, outpatients and geriatric long stay). It also contains some variables that are not statistically significant
• Transparency – Although we would be able to say we were using the SEURC variables, this is not as readily explainable as using the categories themselves
The fourth Option?• In theory there could be a further option which
would be a variation on Option 2.• Instead of choosing the approach with the best
statistical fit for each care programme, we could choose the one that in the Committee’s opinion was the ‘best choice’ for each care programme.
• Looking at the table this might suggest the following approach
- Acute and outpatients Building location factors
- Maternity, inpatient mental health
learning disabilities, GLS Continuous SEURC variables
Other points to note
• Important to remember that all of the options here are an improvement on what we currently have in the Arbuthnott formula. No one has suggested that we revert to the road kms per 100 people measure
• This project has been looking at “excess costs” not just remoteness