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 reablement 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, Accident Death Rate> Chronic +Chronic + deaths (Nature) + <Chronic Onset> Onset Rate Per PatientPatients Reducing Unplanned Care Bt Deaths due From Stage 3 Spend (DP)MANAGEMENT o complicati ons becoming Patients LONG TERM CONDITION chronic (Nature) + R STAGE 2 - Keep LTC patients independent <Dependenc Dependent More effective case - Effectiveness of y Onset> of chronic + managementPatients Chronic Onset From Stage 2 Available CP interventions patients Improved chronic <Injury, RAccident Death intervention health Constant 0 0Total 0CareCosts (AP) patient Reducing Unplanned Rate> outcomes capacity for CPs Investment in Resource + Delaying chronic new CPLONG TERM CONDITION Effectiveness of + (CP) Dependenc MANAGEMENT + onset resources + usage y interventions Onset <Chroni<Injury, DP 0of0Patient 0 Available STAGE 1 -Constant1 Delaying At Risk patients c Patient Total Costs (AR) Death Rate B Per B dependent Improved s>Accident Rate> intervention Constant 0 0 patient health becoming unhealthy + Average cost Spend (AP) Patients becoming Investment in outcomes Intervention Disease + capacity for DPs Investment in <PCT Commissionin per chronic + dependent Resource + resources for case + intervenions (CP) patient + new DP Onset Rate Per Patient Spend g Budget> B (Nature) LTC management usage Affected (DP) <Disease Onset>resources <Dependen Patients getting Constant1 0 0(AR) Patients Available money to Money spent on B t Patients> ill (Nature) Number of Dependenc invest on chronic From Stage 1 interventions used R + patients (CP) Investment in resources Average cost Intervention unplanned Patients <PCT Commissionin dependentperepisodes +case R at risk y Onset intervenions (DP) to delay chronic Effective + Netonset Inflow forpatients chronic patients Disease Onset g Budget> management reduces Keeping people Effectiveness of % Investment in unplanned attendence R + independent Available money to selfcare Money spent on Number of unplanned services for chronic invest on dependent interventions interventions used R Keeping people healthy patients Average A&E visits Available Unplanned care + patients (DP) Effectiveness of episodes for Improved at affected +LTC cost for chronic prevention intervention patient health fordependent chronicoutcomes patients patients Constant 0 interventions Effective interventions increase patients Improved at risk Available R capacity for AP Investment- in Resource + quality of care and reduce intervention patient health Length of Stay (CP) Constant outcomes Effective casein % Investment in unplanned attendence capacity usage (AP) Investment + <Affected + services for dependent new resources 0 Resource management reduces patients Average A&E visits Unplanned cost + newcare resources B LTC Patients><Patients at unplanned admissions usage Constant1 0 <Cost of stay> risk> + for dependent patients Investment in resources dependent patients B Averagefor cost Intervention Number of R Constant1 <PCT Commissionin affected per cost + Length +-Average to keep patients PCT Commissionin <Cost of of Stay unplanne unplanned intervenions (AP) Intervention -per + Investmentpatients in <Average g Budget> prevention g Budget independent admissions for d (DP) admission> Effective interventions at risk patients + prevention resources % at chronic ++patients intervenions Key chronic patients A&E costs> increase quality of care and Available money to Money spent onadmission admitted Number of Number of Available money toreduce unplanned invest on affected - invest interventions used <Costpatients of stay> in prevention Rspent on unplanned episodes - (AP) NumberMoney services ofintervention unplanned R unplanned episodes used Effective interventions increase <Cost of unplannefor affected Effective prevention patients for at risk patients <Average admissions forand reduce independence d admission> resources reduces unplanned % at dependent Investment in A&E%costs> dependent patients patients % Investment in unplanned attendence attendence prevention admitted services services for affected Average A&E patients Unplanned care + Average A&Evisits visits Unplanned care + for at risk cost of at risk cost for affected patients patients for affected patients R Lengthpatients of Stay REffective prevention (AR) Length of Stay (AP) + + LONG TERM CONDITION Per Patient MANAGEMENT B Total Costs (DP) STAGE 3 - Delaying chronic onset Spend (CP) LTC patient Popula*on by risk category Popula*o n by risk category and disease <Cost of stay> <Average A&E costs> Cost of stay Average A&E costs <Cost of unplanne d admission> Effective interventions resources reduces unplanned increase independence and +admission Number of unplanned reduce unplanned admission admissions for at risk Cost of unplanne patients Number of unplanned d admission admissions for affected patients % at risk patients admitted % at affected patients admitted 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 benefit from navigator clients given navigator access impact of navigator on lonelyness exit without navigator <expected trigger lonelyness 3> new potential day opps clients 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 access <newpeople3> initial clients could benefit day opps proportion benefitiong from day ops 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.
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