Modelling the benefits of increased spending to promote

Modelling the benefits of
increased spending to
promote independence
Kevin Appleby & Lauren Willcox
25-Feb-2015
Introduc*on • 
• 
Extensive work has been undertaken to stra*fy the popula*on of Kent & Medway into 4 risk bands, and extensive data about use and cost of service exists by risk band and age Using current birth ad death rates (which are available by risk band) we can project the risk profile of the popula*on over 10 years and understand the rate of movement between bands • 
Trigger events are responsible for moving people between bands, and we can model the effect of avoiding or delaying these events on the healthcare cost of the popula*on • 
Our model demonstrates that a £1m investment in preventa*ve care can pay benefits of many *mes this amount over 10 years. Various processes help us… ...live in a range of se]ngs … ...from which we use different routes… ... to access healthcare… ... from which we go to… Families & friends Independent Self refer Voluntary ac*vi*es At home supported 111 GPs and clinics Reablement Aids & adapta*ons Special care housing Walk in centre Mental Health Residen*al reablement Social care Residen*al GP Acute -­‐ planned Discharge to assess Community Nursing Nursing Home 999 Acute -­‐ unplanned Con*nuing Health Care Primary care inc GPs Other Other primary care A&E End of Life Care Other social infrastructure Previous se]ng We know that care cost changes as the se]ng changes Trigger Events Severe condi*ons Has several condi*ons Has a condi*on Fit and well, • living in own home • Care cost minimal • Living in own home • Or may be in sheltered housing • Requires a carer • Some ongoing support needs • Care cost increasing Services provided principally to this cohort • High level of dependency • Probably in nursing home, or end of life care • Most likely to be in a care home • Increased level of nursing care The main cost to the health economy is in this cohort We can hypothesis that preven*ng or delaying the step changes could deliver significant benefit to the overall health economy The interven*ons that can be demonstrated in a simple model rate of new people
rate of onset of support
Death rate
At risk deaths
At risk
independent
population
new people
start needing support
<Reablement useage>
aida & adaptions
useage rates
Investment in aids
& adaptions
Available aids
& adaptions
Improved capability
Aids & adaptions
useage
<At risk
independent
population>
navigator useage rate
episodes requiring
navigator
Investment in
navigators
Investment in
reablement
Available care
navigator
capacity
Available
reablement
capacity1
at risk requiring GP
number of unplanned
episodes for at risk group
at risk episodes
requiring OOH/Walk in
at risk episodes
requiring 999
navigator useage
Reablement useage
number requiring
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at risk requiring A&E
bed cost per day
length of stay
cost of unplanned
admission
% admissions
needing reablement
We can demonstrate cause and effect, but the issue is being able to quan*fy the rela*onship and show the link between cost and benefit number of unplanned
admissions for at risk
% at risk admitted
3
4
Total
247,694
1,414,141
1,767,919
14%
80%
100%
4564
1607
14,708
1.8%
0.1%
0.8%
We can demonstrate cause and effect, but the issue is being able to quan*fy the rela*onship and show the link between cost and b5enefit Figure
Kaiser Permanente Pyramid Model of Care
Source: (Wennberg et al 2006)
ONS data for CCG area (popula*on by year of age) Impacts (CBA) by stakeholder Reducing Unplanned Care
LONG TERM CONDITION MANAGEMENT
STAGE 4 - Case Management of Chronic Patients
Total Costs (CP)
Reducing Unplanned Care
+
<Injury,
Accident Death
Rate>
Death Rate due
to complication
<Injury,
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Chronic
+Chronic + deaths (Nature) +
<Chronic Onset>
Onset Rate
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Reducing Unplanned
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Bt
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ons becoming
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LONG TERM CONDITION
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B
Total Costs (DP)
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Spend (CP)
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Popula*on by risk category Popula*o
n by risk category and disease <Cost of stay>
<Average
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Cost of stay
Average A&E costs
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Effective interventions
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increase independence
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Cost of unplanne
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Data on prevalence, efficacy and cost The Kent data is available in 5 year age bands and at CCG level Risk Band
0-44
45-49
50-54
55-59
60-64
65-69
Total populations by risk band 70-74
75-79
80-85
85-90
90-95
95+
Total
Episodes per 1000
gp consults per 1000
gp prescriptions per1000
OOH walk in visits per 1000
999 calls per 1000
A&E Visitper 1000
emergency admissionsper 1000
other hospital admissions per 1000
social care clients per 1000
CHC admissions per 1000
1
2
3
4
1368
289
295
325
495
585
616
949
1265
1208
1023
422
8840
20893
3636
3821
3833
6541
7412
6488
10312
12944
9857
8430
3080
97247
73638
12152
12204
10770
19741
20589
16919
31797
32252
20112
6637
1528
258339
861232
120288
106657
91211
79162
76259
49823
18175
1838
2
0
0
1404647
51.77514793
9544.378698
1.876172608
5595.684803
0.351203751
2116.266002
0.075756428
585.68052
2087.820513
1379.072398
313.3826227
41.32730015
557.4842069
729.7568734
134.519595
12.76131912
127.2449835
308.6273428
37.77127871
2.963734409
20.36715749
72.45094319
4.011167286
0.326060569
total
957131
136365
122977
106139
105939
104845
73846
61233
48299
31179
16090
5030
1769073
Data is available that can show the total health and social care costs for the Kent popula*on With informa*on on birth and death rates it is possible to project popula*on changes over 10 years An ageing popula*on places more people in age bands that have a higher risk profile, indica*ng cost will increase dispropor*onately This informa*on allows us to understand the movement between risk bands by age From this informa*on it may be possible to more accurately target spend on preventa*ve and suppor*ve care We can hypothesize that significant propor*on of movement into higher risk bands is triggered by an episode •  In the frail and elderly popula*on there is good research to demonstrate that mul*ple condi*ons can be managed and kept in balance un*l an event e.g. a fall takes place •  We can build a model that simply looks at the over 65 cohort: –  We can age this popula*on over 10 years and see the change in demographic –  We can look at the risk profile typical of the new demographic –  We can infer a rate of decline through the risk bands –  We can see what happens if the rate of decline is slowed down We can use the model to show what happens to a cohort within the popula*on if trigger episodes can be delayed or avoided. There is evidence to support our hypothesis that movement into higher risk bands is triggered by an episode • 
• 
• 
A study by the Ins*tute of Public Care of 36 older people recently taken into care showed that 78% of them had decided to enter a care home ajer a cri*cal event (such as a fall or a hospital admission) Another study by Policy services ins*tute, Elderly People: Choice, Par*cipa*on and Sa*sfac*on, gives five main reasons why people enter care homes. 103 elderly were surveyed and the reasons are summarized below: Reason Number Fall/fracture 26 Deteriora*on in physical/mental health 26 Pressure on informal carer 20 Acute illness 14 Loneliness 14 An NAO report also showed that 19% of emergency admissions to hospital in 2012-­‐13 were readmissions. This allows us to model some causality to understand the movement between bands -­‐ par*cularly from band 3 to band 2 We know Reablement plays a part in helping slow down the movement, but is it possible to quan*fy? • 
• 
• 
• 
• 
There have been a number of studies around the costs and results of reablement services. Most (but not all) show a period of reablement reduces the care hours and individual requires at the end of the service period. For example, a study in Leicestershire showed that for a group of older people discharged from hospital comple*ng a reablement package: o  58% discon*nued the care package at first review compared with 5% of a control group o  17 % decreased the package compared with 13% of a control group. Another study showed that those who had completed a reablement package were 32% less likely to be readmined to hospital than those in home care According to a study by SPRU and PSSRU the unit cost of a typical reablement episode is £2,088 which is significantly higher than the cost of standard homecare. However, ajer reablement savings of up to 60% were seen in social care costs for the reablement groups/ VENSIM MODEL DEMONSTRATION Issues with a predic*ve model • 
Rapid risers • 
Regression to the mean -­‐ Offering preven*ve care to pa*ents who are currently experiencing mul*ple hospital admissions can be inefficient because, even without interven*on, such pa*ents will on average, have fewer unplanned hospital admissions in the future. • 
Demand increases to meet supply • 
Over emphasis on frequent flyers The next step is to model how other interven*ons impact trigger events <percent reabled 3>
proportion suitable
for reablement
new events for
reablement
<trigger events
per 1000>
events reabled
reablement
effectiveness
events
applicable to
reablement
<planned
admissions 3>
new potential aids
clients
trigger events avoided
reabled
initial clients that can
benefit from aids &
appliances
exit with aids &
appliances
exit without aids
trigger events per 1000
clients that can
clients with aids
benefit from
aids
clients given aids
impact of aids and
appliances on carer
issues
initial clients with
aids & appliances
<Population 3>
<initial level of
<new aids issued>
preventative measures>
level of preventitive
impact of navigator
impact of preventative
measures
on carer issues
measures on falls
improvement
through reablement
<unplanned
admissions 3>
initial level of
preventative measures
<newpeople3>
<expected trigger
carer issues>
missed opportunity
medication reviews
<expected
trigger
falls 3>
initial reablement level
<proportion with
navigator access>
initial events
applicable for
reablement
impact of day opps
on deteriration
trigger
falls
carer issues
unpreventable falls
trigger events baseline
<proportion with
navigator access>
<onset 3>
initial clients that could
benefit from navigator
initial clients with
navigator access
slowdown 3
impact of navigator
on deterioration
<initial navigator
proportion>
initial navigator
proportion
physical/mental
deteriation
impact of day ops
on lonelyness
<initial navigator
proportion>
spend on reablement
new aids issued
<expected trigger
<proportion<initial day opps
mental / physical deter
benefitiong from day
proportion>
3>
ops>
<events reabled>
per client spend on aids
acute illness
Funding
Available ECL
Funds
Spending
day opps unit cost
day opps capacity
LA Funding
unit cost of reablement
unit cost of navigator
new potential
navigator clients
<navigator
capacity slider>
trigger events 4
<day opps
capacity slider>
slowdown 4
clients that can
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navigator
clients given
navigator access
impact of navigator
on lonelyness
exit without navigator
<expected trigger
lonelyness 3>
new potential day
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exit with navigator
slowdown 2
clients that can
clients with day
benefit from
ops
day ops clients given day opps
exit without day opps
<newpeople3>
<navigator capacity>
trigger events 2
initial clients with
day opps
<day opps capacity>
clients with
navigator
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<newpeople3>
initial clients could
benefit day opps
proportion
benefitiong from day
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lonelyness
proportion with
navigator access
<expected trigger
acute illness 3>
navigator capacity
initial day opps
proportion
trigger events 3
<trigger events
per 1000>
This part of the model is very much work in progress – We need help to understand the rela*onships and quan*fy the cause and effect exit with day opps
Aids & Adap*ons – Preven*ng Falls A fall provides the trigger event for moving from band 3 to band 2 in 26% of all cases Research shows that where aids & adap*ons are in place falls are 32% less likely We have no good data on the current coverage of over 65 in band 3 with aids and adapta*ons; we need to build an es*mate of this amount. Once this es*mate is in place we can model the scope for increasing coverage and the likely long term impact this will have Care Navigators Savings through falls preven*on -­‐ effec*veness and costs of interven*ons to reduce falls •  The average cost to the State of a fractured hip is £28,665. This is 4.7 *mes the average cost of a major housing adapta*on (£6,000) and 100 *mes the cost of fi]ng hand and grab rails to prevent falls. •  Visual Impairment leads directly to 90,000 falls per year in England and Wales (almost half of all falls), mostly in people over 75, at a cost of £130 million. •  The current consensus on interven*ons to prevent the falls that lead to fractures is that individually-­‐tailored, mul*-­‐factorial approaches are the most effec*ve. The four key factors are individualised strength and balance training; home hazard assessment and interven*on; vision assessment and interven*on and a medica*on review with resultant modifica*on/withdrawal. A trial of such a mul*-­‐disciplinary interven*on across three PCTs from 2002 produced a 32% reduc*on in falls in 6 months. (hnp://www.wohnenimalter.ch/img/pdf/bener_outcomes_report.pdf) Savings through falls preven*on -­‐ effec*veness and costs of interven*ons to reduce falls •  Wanless, D (2004): The ac*ons they took included installa*on of grab-­‐rails and stair rails, improved ligh*ng and non-­‐slip mats as well as exercise classes, bener foot care and domiciliary eye-­‐tests. Evalua*on ajer 6 months demonstrated a 32 per cent reduc*on in falls in older people across the 3 PCT sites. •  Plautz, B, Beckm D, Selmar C. and Radetsky M (1996): focused en*rely on home modifica*ons and their effect in preven*ng falls and other accidents. Conclusion was that the modest home modifica*on interven*ons had significant effect in reducing accidents when all other factors were controlled for. 59 falls (25%) pre interven*on; 26 falls (9%) ajer interven*on. 16 burns/scalds pre-­‐interven*on; none ajerwards. (hnp://www.wohnenimalter.ch/img/pdf/bener_outcomes_report.pdf) Evidence for fall preven*on in frail elderly •  Help the aged – Exercise programme evidence •  Ajer a fall, an older person has a 50 per cent probability of having their mobility seriously impaired and a 10 per cent probability of dying within a year. •  Falls destroy confidence, increase isola*on and reduce independence, with around 1 in 10 older people who fall becoming afraid to leave their homes in case they fall again. •  A tailored exercise programme can reduce falls by as much as 54 per cent. •  Australia – Occupa*onal health •  31% improvement in fall rate ajer ini*al fall. BMJ Falls in care home & hospital • 
• 
Results 1207 references were iden*fied, including 115 systema*c reviews, expert reviews, or guidelines. Of the 92 full papers inspected, 43 were included. Meta-­‐analysis for mul*faceted interven*ons in hospital (13 studies) showed a rate ra*o of 0.82 (95% confidence interval 0.68 to 0.997) for falls but no significant effect on the number of fallers or fractures. For hip protectors in care homes (11 studies) the rate ra*o for hip fractures was 0.67 (0.46 to 0.98), but there was no significant effect on falls and not enough studies on fallers. For all other interven*ons (mul*faceted interven*ons in care homes; removal of physical restraints in either se]ng; fall alarm devices in either se]ng; exercise in care homes; calcium/vitamin D in care homes; changes in the physical environment in either se]ng; medica*on review in hospital) meta-­‐analysis was either unsuitable because of insufficient studies or showed no significant effect on falls, fallers, or fractures, despite strongly posi*ve results in some individual studies. Meta-­‐regression showed no significant associa*on between effect size and prevalence of demen*a or cogni*ve impairment. Conclusion There is some evidence that mul2faceted interven2ons in hospital reduce the number of falls and that use of hip protectors in care homes prevents hip fractures. There is insufficient evidence, however, for the effec*veness of other single interven*ons in hospitals or care homes or mul*faceted interven*ons in care homes.