any town health system: methodology pack

Any town health system: Methodology pack

January 2014

Contents

Introduction 3

Intervention research methodology 6

Measuring quality impacts 9

Measuring financial impacts: the financial model

16

Measuring financial impacts: input data and assumptions

31

2

Introduction: overview of Any town health system

4

What this pack contains

This pack has been created to provide an overview of the methodologies used to create the Any town health system report

It includes the following content:

1. An overview of how the interventions were researched and selected.

2. The national baseline we are working from (demographics, quality and finance), and the financial challenge this presents.

3. The methodology used to measure the quality impacts of the interventions.

4. The methodology used to measure the financial impact of the interventions.

5. Additional details of the input data and assumptions used for the financial modelling.

If you have additional questions on the process or methodology explained in this pack, please contact [email protected].

mailto:[email protected]

The genesis of Any town health system

Any town health system is a high-level tool that shows what a typical health system’s quality and financial baseline may look like in 2018/19 and how the application of high impact interventions may address this challenge.

Three modules reflect anonymised scenarios across England – an urban health system, a suburban health system and a rural health system.

The tool is not intended to consider acute reconfiguration, nor questions around whole system design.

✓

✗

A tool that you can use to guide local planning.

A collectively exhaustive list of interventions that will fill the financial gap.

What it is

A set of evidence-based interventions that could be applied in a local health economy.

✓

An indication of how quality and finance will be affected through the impact of the interventions.

What it isn’t

A bespoke tool that takes into account local demographic make-up.

✗

✗ A detailed implementation guide for health systems to follow.

✓

The Any town model is intended to present a series of evidence-based ideas to help health systems in their five-year strategic planning process

✗ A provider-side reconfiguration tool.

5

Intervention research and selection methodology

7

Research methodology for the evidence base

Incorporating Interventions from Existing Academic

Reviews

2

Assessing NHS Case Studies

1

• This process resulted in a ‘long-list’ of potential interventions, which were then screened to determine their suitability for inclusion in Any town health system.

• In addition a range of stakeholders were invited to review the list of interventions and offer any additional suggestions of content for inclusion.

• These internal case studies were supplemented with existing academic reviews of specific interventions (e.g. primary care referral management and patient self-help).

• These provided context on the state of the evidence base for each intervention, as well as providing some fully impact-assessed controlled studies of specific interventions.

• Furthermore, where available NICE assessments were consulted (e.g., for the cost-effectiveness of early diagnosis interventions).

Adding Specific Case Studies from Third

Sector Organisations

3 • Finally, specific examples of innovative interventions were drawn from publications produced by Third Sector organisations, such as the King’s Fund or the British Heart Foundation.

• While many of these case studies did not fully meet our impact assessment criteria, those that did were shortlisted for further study.

• Where these suggested the existence of impact assessment for interesting interventions we followed this up in the academic literature.

• We began with 270 self-reported case studies of healthcare interventions currently being implemented by health systems around the country, which provided an overview of the breadth of interventions already being trialled across the NHS.

• While many of these did not meet our inclusion criteria (see next slide), those that did were short-listed for further consideration.

We have performed a non-exhaustive literature review to collate the evidence base behind our selected high-impact interventions. This review was composed of three inter-connected phases:

8

The short-listing exercise

1

2

3

We have used four criteria to provisionally short-list from a long list of interventions. This short-list forms the provisional suite of interventions to be modelled.

The interventions are fully impact assessed both from a quality and finance perspective. Outcomes are clearly articulated, realised and easily measurable for modelling purposes.

The outcomes derived from the interventions would contribute to the quality and financial challenge indicated previously in this report – interventions where one benefits to the detriment of the other were excluded.

The narrative around the intervention is clearly articulated, so that an Any town health system could easily implement the interventions.

The intervention is easily scalable to a broad population group (i.e. no interventions targeting highly ‘niche’ population groups that are unlikely to exist in large numbers across many health systems) – this ensures the intervention produces a high impact.4

Measuring quality impacts

Increasing the number of people having a positive

experience of care outside

hospital

1.1 Potential years of life lost (PYLL) from causes considered amenable to healthcare.

2 Health-related quality of life for people with long-term conditions.

2.6 Unplanned hospitalisation for chronic ambulatory care sensitive conditions.

2B (OF) [Proxy] Proportion of older people who were still at home 91 days after hospital discharge into rehab services.

4.b Patient experience of hospital care.

4a.i Patient experience of GP services.

5c Hospital deaths attributable to problems in care.

2.7 Unplanned hospitalisation for asthma, diabetes and epilepsy in under 19s.

4a.ii Patient experience of GP out of hours services.

3.1 Emergency admissions for acute conditions that should not usually require admission.

4a.iii Patient experience of dental services (national only).

3.4 Emergency admissions for children with lower respiratory tract infections.

Ambitions and quality indicators

Ambition 1 Ambition 2 Ambition 3 Ambition 4 Ambition 5 Ambition 7

Securing additional years of life for the people of England with

treatable conditions

Improving health related quality of

life of the 15 million+ people

with one or more long-term condition

Reducing the amount of time people spend avoidably in

hospital

Increasing the proportion of older

people living independently at home following discharge from

hospital

Increasing the number of people having a positive

experience of hospital care

Making significant progress towards

eliminating avoidable deaths in our hospitals

Indicators

Note: Indicator numbers refer to CCG, not NHS Outcomes Framework indicators, except where noted.

Ambition 6

Captured in Direct Commissioning (not Any town health system)

Quality Indicators are mapped to levels of ambition against which the impact of an intervention is measured:

10

Data collection methodology

Collect Indicator Data1

• Collect historical and projected data for indicators mapped to ambitions for CCGs, LAs and providers

Use NHS E Projection Methodology

aTeam Projects Trends

bNo Projection Possible

c

• Where provided by NHS England, use projection methodology for indicators

• Where trends not provided, project using exponential smoothing based on historic data

• As a last resort, where no trend or historical data provided, assume no change

Project Indicators to 2018/192

We have projected the seven ambitions using NHS England trends where possible, and exponential smoothing where not

11

Projection methodology

Exponential smoothing supports clearer future estimates while allowing for some historical trends in the data.

580

0

12/1311/1210/11

✓

✗

Available CCG data points are exponentially smoothed to produce forecast value

Short-term CCG trend is not extrapolated as quality will remain effectively constant overall; without intervention this will significantly widen the funding gap

CCG Data Projection

Exa

mp

le

Un

it

Smoothing constant

• With a large data set it is possible to optimise the constant in order to minimise absolute error in the smoothed values

• As CCG data is often limited, it is more appropriate to select a constant from within a reasonable range – approximately 0.60 to 0.80 in this context

• While a different constant could therefore be used, the resulting difference in projected value would be small

Defining population groups

• ‘Do Nothing’ projection is exponentially smoothed value of historic data

• Methodology similar to a moving average, but assigns exponentially decreasing weights over time

• Weighting mechanism is done through a smoothing constant: we have used 0.67

12

Normalisation methodology

Where the effect of an intervention could be expressed in quantitative terms, it was necessary to normalise the impact for the population of the Any town health system

Normalising by group size

• The specific population group affected by the intervention was identified and this allowed the quantitative impact to be normalised for group size

• This method also applies to conditions within a population group, e.g. relative proportion of dementia patients within the LTC population group

Defining population groups

• The subject population of the intervention was subdivided into population groups relevant to the case study: Elderly, Long-term Condition, Dementia etc.

• The size of these groups could then be compared with their equivalent in the health system

✓ Population groups identified and normalised by relative size

✗ Limited to quantitative impact only

Illustrative normalisation

Affected patients (intervention)

Affected patients (health system)

Effect of intervention

Effect applied to health system

20% of population

40% of population

10% improvement in admissions

20% improvement in admissions

✗ Age standardisation could only be taken in to account where calculation could be proxied by HES analysis

13

Normalisation methodology – worked example

The effect of the Dementia Pathways intervention on Ambition 2, Outcome 2

The intervention demonstrated an 8% improvement in QUALID score on the subject group of dementia patients. The outcome measures EQ-5D among LTC patients

• Establish reasonable equivalency between intervention metric and outcome metric – in this case QUALID to EQ-5D

• Determine how the intervention subject group and outcome population group are compared

• Normalise the intervention effect for the relative size of the affected group:

Demonstrated in The King’s Fund (2013), ‘Oxleas Advanced Dementia Service

Intervention metric: Dementia Outcome metric: LTCs(Dementia falls within LTCs)

Any town urban health system has 60,330 LTC patients, of which 1,678 have dementia = 2.78%

8% intervention effect applies to 2.78% of the outcome group

The normalised intervention effect is a 0.22% increase in EQ-5D

ILLUSTRATIVE

14

Assumptions

Where visibility of data was limited, or sources could not be directly aligned with outcomes, projections were based on reasonable assumptions made from the information available

• If no transformative action is taken, quality will be maintained while funding gap widens; the ‘Do Nothing’ scenario therefore uses a flat quality projection

• Population groups in Any town urban health system and in intervention subject populations are sufficiently comparable to allow normalisation directly proportional to their relative size, as detailed on slide 5

• Where several interventions are applied simultaneously, they ‘stack’ without interference from each other and full benefit is realised

• An applied intervention will fully affect 100% of the eligible population group

• Where the intervention case study demonstrated improvement in patients with a single condition, but the intervention could reasonably be extended to the entire population group (e.g. those with LTCs), the quantitative benefit was applied to the total population of that group

15

Technical Appendix: finance modelling

methodology

17

• The financial model contains a six-year (from 2013/14 to 2018/19) baseline scenario representing the financial and activity position of the representative Any town health system under the “do nothing” scenario, i.e. assuming that no intervention occurs.

• Where possible, the model considers financial and activity information disaggregated along a number of dimensions such as population subgroups and points of delivery (POD). This allows appropriate modelling of targeted, specific interventions:

• For secondary and mental health care, financial and activity data are disaggregated by population subgroup, setting of care, specialty and point of delivery on a population subgroup level;

• For primary care, financial and activity data are disaggregated by population subgroup and between prescribing versus non prescribing; and

• For community care, financial and activity data are disaggregated by population subgroup only.

• Interventions are then run through the model, changing activity demanded for targeted settings of care, point of delivery and population subgroups. The model estimates the resulting health system expenditure gross savings.

• The net savings from the interventions are estimated by applying net-to-gross savings ratios sourced from the literature.

• In summary, for the baseline and for the intervention scenario, the outputs of the model include:

• The health system expenditure, related funding gap and the relative variation due to the interventions.

• The level of demand for each activity and the relative variation due to the interventions.

• All outputs are disaggregated by setting of care, population subgroup and, for secondary and mental health, point of delivery.

Overview of the financial model

Below we have described the high level architecture of our financial model.

18

The modelling of the interventions’ financial and activity impacts required a number of assumptions, set out below.

• Definition of population subgroups for the rural and suburban health systems

• Detailed data obtained for the urban health system was used to construct the different population subgroup sizes. The direct data available for the rural and suburban health systems was less granular and therefore a combination of different data sources, mainly Census data, was used to construct the subgroups sizes. For a limited number of population subgroups, HES data has been used to estimate the subgroup size. Ultimately, the CCGs will need to develop detailed subgroups using available patient level datasets.

• Definition of service utilisation by each population subgroup in the rural and suburban health systems

• Detailed data on service utilisation by population subgroup was available for the urban health system. This allows the estimation of the probability of someone in a given subgroup to require a unit of activity of a certain specialty (e.g. paediatrics) within a given point of delivery (e.g. inpatients – elective). Such detailed information was not available for the rural and suburban health systems. Therefore, this had to be estimated by combining the service utilisation probabilities obtained for the urban health system with the population subgroup sizes estimated for the rural and suburban health systems.

• Interventions’ overlap and benchmarking/capping of activity reductions

• A capping has been applied to the maximum activity impacts generated by all HIIs’ at the same time. This capping varies across the urban, rural, and suburban health systems. For each health system, the capping does not allow activity per capita to drop to more than 5% below the lowest level of activity per capita amongst the urban, rural, and suburban CCGs, respectively.

• The capping is based on the distribution of secondary care activity per capita across CCGs. A tighter distribution of activity per capita amongst the rural CCGs compared to the urban CCGs, and the fact that the rural health system considered presents a comparatively lower level of activity per capita, implies a stricter capping for the rural health system. As a consequence, more limited activity reductions are modelled in this case.

• National initiatives and provider efficiencies

• It has been assumed that national initiatives flow through to CCG expenditure through prices. This is a conservative approach, as it will decrease the amount of the savings estimated by intervention.

Summary of modelling assumptions and limitations

19

• Interventions

• The HIIs are a set of evidence-based interventions that have been selected for their high impact and robust evidence of real-world impacts delivered. The financial impact of these interventions have been fully assessed in the literature.

• The EAIs are promising ideas which may offer commissioners further benefits. They have not yet been widely adopted or fully impact assessed and they have therefore been modelled at a higher level than the HIIs.

• The savings from the HIIs and from the EAIs have been estimated separately in the modelling. Therefore, while overlaps within HIIs and within EAIs have been accounted for, the overlap between the two sets of interventions has not been fully captured. For instance, the capping applied to the activity impacts of the HIIs was not applied to the activity reductions generated by the joint effect of the HIIs and EAIs. However, whenever the capping applies to the impact of the HIIs, no incremental activity reductions are assumed to be generated by the EAIs.

• A number of minor impacts from the interventions are calculated offline (e.g. interventions’ impacts on length of stay). Whenever savings are calculated offline, the overlap between these and the other interventions’ impacts has not been accounted for. These, however, represent a small share of the total savings.

• The savings associated with each intervention reflect the savings if the intervention were implemented separately. Any overlap between interventions (including adjustments in relation to capping) is stated separately, and is not implicitly included in the savings for each individual intervention.

• Costs of the interventions

• For most HIIs, the model estimates the related gross savings. These are then translated into net benefits by using an indication of net-to-gross benefits ratios from the relevant literature.

• No detailed indication of net-to-gross savings was available for the EAIs in the literature. The costs of the EAIs have therefore been modelled by benchmarking the EAIs against similar HIIs and assuming the assuming the same net-to-gross savings ratios.

• No consideration has been given to capital costs aside from capital outlined and identifiable as part of the interventions.

Summary of modelling assumptions and limitations (cont.)

Financial model high level architecture

20

The financial model comprises a demand, a supply and an affordability module.

• The total patient population has been split into mutually exclusive subgroups based on pre-defined rules.

• Each subgroup is characterised by a different level of utilisation of each setting of care.

• A number of high level interventions generate reductions of activity demand for targeted population groups and settings of care.

• Cost data by activity and overall activity levels pre- and post- interventions allow the estimation of the total health system expenditure before and after the interventions.

• A comparison of overall health system expenditure data and health system resources provides an estimate of the funding gap.

LTCs – Adults

End of life care – Adults and children

Frailty/dementia – Older people

Complex needs – Adults

Telecare/ Telehealth

Early Diagnosis

Palliative care

Primary care(Prescribing, Non-prescribing)

Secondary care(e.g. A&E, Outpatients – elective)

Interventions

Mental health(e.g. Inpatient, Community)

Community care(Not disaggregated)

1

CCG budget by setting of care

LTCs – Children

Good health – Older people

Early years (0-4)

Maternity

Demand module Supply module Affordability module

Good health – Children

Good health – Adults

Costs and expenditure by

population subgroup and setting of care

…

Population subgroups

Settings of care

CCG resources

By population subgroup

By setting of care

CCG expenditureActivity demand by setting of

care

2 3

Funding gap

Demographic growth assumptions

Inflation assumptions

21

Model dimensions: population subgroups

# Population subgroup name Population subgroup description

1 End of life care - Adults and children 1. Age: 0 years +2. On Palliative care register

2 Long term conditions - Adults 1. Age: 19 years +2. On one or more of the LTC register

3 Frailty/dementia – Older people 1. Age: 65 years +2. Secondary care activity including: dementia, broken bones in the upper body, falls

4 Complex needs - Adults 1. Age: 19 years +2. Presents two or more of: drug abuse, alcohol abuse, mental health, homeless

5 Long term conditions - Children 1. Age: 0 - 18 years 2. On one or more of the LTC register

6 Good health – Older people 1. Age: 65 years +2. Included in no other group

7 Early years (0-4) 1. Age: 0 to 42. Included in no other group

8 Maternity 1. Women who have given birth 2. Women who have received antenatal services

9 Good health – Children 1. Age: 5 - 18 years 2. Included in no other group

10 Good health - Adults 1. Age: 19 - 64 years 2. Included in no other group

Financial and activity data are disaggregated by the following population subgroups.

22

Model dimensions

Points of delivery for secondary care

A&E

Outpatients - 1st appointment

Outpatients - Follow-up

Inpatients - Emergency

Inpatients - Elective

Inpatients - Maternity

Points of delivery for mental health

Community

Inpatient

Outpatient

Ward Attender

Primary care

Prescribing

Non-prescribing

Community care

Not disaggregated

Expenditure and activity data are disaggregated by setting of care and, for secondary and mental health care, by point of delivery.

Expenditure and activity data for secondary care and mental health are also disaggregated by specialty.

Specialties for secondary care

General Medicine

Paediatrics

Obstetrics

General Surgery

Gastroenterology

Accident & Emergency

Urology

Gynaecology

Well Babies

Ophthalmology

Haematology (Clin)

Oral Surgery

Cardiology

Geriatric Medicine

ENT

Pain Management

Respiratory Medicine

Plastic Surgery

Paediatric Dentistry

Specialties for mental health

A&E Liaison

Adult: Acute Care

Adult: Drugs and Alcohol

Adult: Other

Adult: Rehabilitation

Assertive Outreach

Clinical Psychology (Non-Consultant)

Crisis Resolution Home Treatment

Day Care: Benchmark

Day Care: Bridges Day Unit

Day Care: Young Onset DementiaEating Disorders Service (Non-Consultant)

Homeless Team

Later Life

Later Life: Acute Care

Psychotherapy

Psychotherapy (Non-Consultant)

Treatment Suite: Adults

Treatment Suite: Older Age

23

Demand and supply module

Setting of Care Secondary care Mental health PrimaryCommunity

Service A&E visitOutpatients –

follow up… Inpatient Outpatient … Prescribing

Non prescribing

Probability of requiring the service

P1 P2 … P3 P4 … P5 P6 P7

Population subgroup size X X .. X X … X X X

Total (probability weighted) demand per service

X ∙ P1 X ∙ P2 … X ∙ P3 X ∙ P4 … X ∙ P5 X ∙ P6 X ∙ P7

Population sub-group: End of life care

• The following steps are taken to populate the matrix:

• Probability of requiring the service. The model determines the likelihood of services being required by each of the population sub-groups.

• Population subgroup sizes. Growth rates vary by population subgroups. This results in a different population mix year on year.

• Total (probability weighted) demand. Based on subgroup population and the probability of requiring the different services, total demand is estimated.

• Given data constraints, detailed activity data is only available for secondary and mental health care. For primary and community care, the model assumes that the utilisation levels by population subgroup are the same as for secondary care.

• The total units of service demanded are an input to the supply module. They constitute the base for the calculation of total health system expenditure by population subgroup, setting of care, point of delivery and activity.

The supply module estimates total commissioner expenditure based on the outputs of the demand module

• The total commissioner cost of providing these services is a product of the calculated unit prices for each service and of the relevant activity demanded for the service, by population subgroup.

• In the model, demand for services by each population sub-group is constructed based on available data. This generates a matrix of service required across each setting of care, point of delivery and, depending on data availability, by activity.

Demand in the model is segmented by population group and setting of care.

24

• The funding gap for commissioners is calculated as the difference between projected CCG expenditure and the projected resources.

• The 2013/14 available resources (split by setting of care) are based on 2013/14 budget data provided by health systems to NHS England. Projected resources are calculated by:

• Applying a number of assumed inflation growth rates to the 2013/14 overall commissioning budget; and

• Apportioning the overall commissioning budget to the Any town health system, based on an assumed allocation of commissioning resources.

• Commissioners’ expenditure in the base year (2013/14) is based on 2013/14 budgets, as provided by the Any town health system to NHS England.

• Projected health system costs are calculated by applying the assumed inflation growth rates to the 2013/14 prices and the estimated demographic and non demographic growth rates to the activity numbers.

Gap for the commissioner

Affordability module

The approach to the estimation of the funding gap is described below

Projected Resources

Projected Expenditure

FY 18/19FY 17/18FY 16/17FY 15/16FY 14/15FY 13/14

£m

ILLUSTRATIVE

Modelling the effect of the High Impact Interventions

25

Calculation of the savings from the interventions

• Interventions are assumed to start during 2014/15 at the earliest.

• The model computes the funding gap under two scenarios: Do nothing and Post-intervention. The difference in the funding gap across the two scenarios is the gross savings generated by the interventions.

• An overlap occurs when two or more interventions impact the same point of delivery and population subgroup. This is accounted for by compounding the interventions that affect similar subgroups and points of delivery (more details in the reminder of this Appendix).

• The overlap in savings is quantified as follows:

1. Perform eight separate model runs (one for each intervention) and then sum the eight resulting gross savings figures

2. Perform a joint run of the model, i.e. including all eight interventions together

3. The overlap is the difference between the total gross savings calculated in point (1) and the total gross savings estimated in point (2)

• In certain cases, even after accounting for the overlap, the interventions reduce activity levels for certain points of delivery substantially. To ensure that the aggregate impact of all interventions is conservative, a cap on the reduction of activity has been set for HIIs.

• The caps are applied when, as a result of all interventions being implemented together, the reductions in activity in a certain point of delivery are very large.

• For each point of delivery, when a cap applies, the effect of the intervention is reduced for all population subgroups, proportionally to the original pre-cap impact of the intervention.

• For most interventions, the model estimates the gross savings generated by the different interventions. These are then translated into net benefits by using the estimated net to gross benefits ratios from the relevant literature . The exceptions are Tele-care (for which the intervention costs are calculated directly based on the figures provided in the literature), Early diagnosis (for which the literature only provides an indication of the net savings), and Palliative care (for which the ongoing intervention costs are accounted for with an increase in community care activity).

• There is no consideration of capital aside from capital outlined and identifiable as part of the interventions.

Modelling the effect of the High Impact Interventions

26

Estimation of the interventions’ overlap

• An overlap of the interventions’ impacts can occur when two or more interventions impact the same population subgroup and point of delivery.

• For example, this can happen if two interventions are both targeted at reducing the number of A&E attendances by adults with Long Term Conditions. Intervention A might reduce the number of unique patient attendances for adults with LTCs, while Intervention B might reduce the number of attendances per capita across the population.

• When these two interventions are implemented together, the resulting savings are estimated as less than the sum of the savings resulting from each intervention implemented in isolation.

Accounting for intervention costs: from gross savings to net savings

• The model estimates gross savings generated by each of the interventions. The cost of each intervention is then estimated by applying net-to-gross savings ratios to the gross savings estimated from the model:

• Whenever an overlap is identified on the benefits side, this overlap is quantified and the aggregate gross savings from the interventions are appropriately scaled down. At the same time, a proportional reduction is also applied to the costs of the interventions, in order to account for potential overlap of costs. This will need to be considered on the ground by CCGs.

• The overall net benefits from the interventions are estimated as the total gross benefits (net of the overlap) minus the interventions’ total costs (net of the overlap):

where are generated from the model by applying all interventions together, and

Modelling the impact of the interventions

27

How the interventions are defined and applied

Definition• Interventions are specified by point

of delivery (POD) and by population subgroup.

• An intervention is defined as a percentage shift in activity:

• For example, intervention X here is expected to lower utilisation by 15% in the Inpatients – elective POD, for the LTC-adults population subgroup.

• The impact of the interventions can be profiled through time via phasing assumptions:

• For example, under phasing A, the Inpatients – elective POD will only experience 50% of the full impact in 15/16, only from 17/18 would it experience the full impact.

Impact of intervention X on activity within secondary carePoints of Delivery

Subgroups A&EOutpatients – 1st

appointmentOutpatients follow-up

Inpatients emergency

Inpatients elective

Inpatients maternity

End of life care – adults and children Long term conditions – adults -15% Frailty/dementia – older people Complex needs – adults Long term conditions – children Good health – older people Early years (0-4) Maternity Good health – children Good health – adults

13/14 14/15 15/16 16/17 17/18 18/19Phasing A - 25% 50% 75% 100% 100%

13/14 14/15 15/16 16/17 17/18 18/19Phasing B - 100% 100% 100% 100% 100%

120

170

160

150

140

130

110

100

0

2018/192016/17 2017/182014/15 2015/162013/14

Demand for POD5 by population subgroup 2 - Phasing B

Demand for POD5 by population subgroup 2 - Phasing A

Demand for POD5 by population subgroup 2 - Do Nothing

Impact of the intervention on demand

ILLUSTRATIVE

ILLUSTRATIVE

Modelling the impact of the interventions – Phasing

28

• In 2014/15, it is expected that 25% of intervention 3’s full impact is released.

• By 2017/18, the full impact of intervention 3 is released.

• The inputs include:

• Which population subgroups and point of delivery are impacted by the intervention (LTC – adults and POD2, 3, and 5 here).

• The magnitude of the effect of the intervention on activity demand.

• The phasing of the intervention’s impact.

Intervention 3: Self-Help: Patient-Carer Community

POD1 POD2 POD3 POD4 POD5 POD6EOL LTC-A -6.20% -6.20% -49.70% F/D CN-A LTC-C GH-OP EYR MAT GH-C GH-A

13/14 14/15 15/16 16/17 17/18 18/19Phasing - 25% 50% 75% 100% 100%

Inp

uts

Intervention 3 – Effect in 2014/15

POD1 POD2 POD3 POD4 POD5 POD6LTC-A - -1.55% -1.55% - -12.43% -



Ou

tpu

ts

13/14 14/15 15/16 16/17 17/18 18/19Phasing 25% 50% 75% 100% 100%

ILLUSTRATIVE

Modelling the intervention impacts – Impact aggregation

29

• To illustrate, the aggregate impact of intervention 3 and 5 on LTC-adults’ demand for POD 5 in 2016/17 is calculated as follows:

• Intervention 3 decreases demand by 37.28% and intervention 5 by 10.5%, after taking into account the phasing structure.

• If the original demand is 100 attendances, the post-intervention demand will be: attendances.

• Therefore the total impact is: .

• Aggregation of interventions is accomplished through compounding individual interventions’ impacts.

• This accounts for the fact that there will be overlap in the benefits generated from different interventions impacting the same population subgroup and point of delivery. A simple additive aggregation would not allow accounting for such overlap.

Aggregate effect of interventions 3 and 5

POD1 POD2 POD3 POD4 POD5 POD6EOL LTC-A -1.50% -15.14% -15.14% 4.50% -43.87% F/D -1.50% -11.00% -11.00% 4.50% -10.50% CN-A LTC-C GH-OP EYR MAT GH-C GH-A

13/14 14/15 15/16 16/17 17/18 18/19Phasing - 25% 50% 75% 100% 100%

Intervention 3: Self help - Patient-Carer Community

POD1 POD2 POD3 POD4 POD5 POD6EOL LTC-A -6.20% -6.20% -49.70% F/D CN-A LTC-C GH-OP EYR MAT GH-C GH-A

Intervention 5: Case Management and Coordinated Care

POD1 POD2 POD3 POD4 POD5 POD6EOL LTC-A -3.00% -22.00% -22.00% 9.00% -21.00% F/D -3.00% -22.00% -22.00% 9.00% -21.00% CN-A LTC-C GH-OP EYR MAT GH-C GH-A


POD1 POD2 POD3 POD4 POD5 POD6LTC-A -1.50% -11.00% -11.00% 4.50% -10.50% -F/D -1.50% -11.00% -11.00% 4.50% -10.50% -

13/14 14/15 15/16 16/17 17/18 18/19Phasing - - 25% 50% 75% 100%



ILLUSTRATIVE

Modelling the impact of the interventions - capping

30

• In certain cases, multiple interventions affect the same point of delivery. When taken together, the interventions reduce activity levels for certain points of delivery substantially.

• To ensure that the aggregate impact of all interventions does not produce unreasonable activity reductions within a certain point of delivery, a cap on the reduction of activity has been set for the urban, rural and suburban health systems.

• The caps are activated when, as a result of all interventions being implemented together, the reductions in activity in a certain point of delivery imply an unreasonable post-intervention level of activity per population.

• For each point of delivery, the capping does not allow activity per capita in the urban (rural/suburban) health system to drop more than to a level that is 5% lower than the lowest level of activity per capita amongst all urban (rural/suburban) CCGs.

Technical Appendix: finance modelling input

data and assumptions

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Input data: baseline

The model’s baseline year (2013/14) uses a combination of data sourced from the NHS England earlier study (A Call to Action), and secondary care and disease data on the Any town health system.

Patient population data

• Subgroup population sizes for 2013/14 are based on secondary care and disease data on the Any town health system.

Budget data

• The Any town health system budget data for 2013/14 (split by setting of care) is based on 2013/14 budget data provided by health systems to NHS England.

• Projected health system expenditure is based on an assumption that health system budget is flat in real terms and hence, in nominal terms, the base year health system expenditure is set to grow at the global inflation assumption.

Health system expenditure data

• Health systems’ expenditures across the different settings of care are based on 2013/14 budgets, as provided by the Any town health system to NHS England.

• Health system expenditure is split across population subgroups and points of delivery based on secondary care and disease data on the Any town health system.

• Projected health system costs are calculated by applying the assumed inflation growth rates to the 2013/14 prices and the assumed demographic and non demographic growth rates to activity numbers (more details on this are contained in the remainder of this Appendix).

Activity data

• Activity data for the different population subgroups and points of delivery is based on secondary care and disease data on the Any town health system (more details on this are contained in the remainder of this Appendix) .

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Input data: budget upward pressure

• The Any town health system budget data for 2013/14 (split by setting of care) is based on 2013/14 budget data provided by health systems to NHS England.

• Projected health system expenditure is based on an assumption that health system budget is flat in real terms and hence , in nominal terms, the base year health system expenditure is set to grow at the global inflation assumption. This means that budget for the Any town urban, suburban and rural health system will be subject to the same growth rate assumptions.

• Global inflation assumption is extracted from the Office for Budget Responsibility forecast of GDP deflator as at December 2013.

We have used a combination of 2013/14 budget data and projection assumptions to model up to 2018/19.

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Input data: Health system expenditure upward pressure

• Health systems’ expenditures across the different settings of care are based on 2013/14 budgets, as provided by the Any town health system to NHS England. Expenditures by setting of care are also split across population subgroups based on secondary care and disease data on the Any town health system.

• For each setting of care, the upward pressure on expenditure is based on a weighted average of a number cost components (e.g. staff, drug, and other inflation pressures), as outlined below.

• The weightings vary by setting of care and are defined based on the base year (2013/14) health cost composition.

The upward pressure on expenditure is based on a weighted average of a number of cost components, which vary by settings of care.

Source/Comments

Pay Historical cost weighted activity growth of Hospital and Community Health Services (HCHS) + inflation assumption

Drugs Trust and Foundation Trust (2000-2011) historical trend stripping out high cost drugs and activity growth

High cost drugs Historical cost weighted growth for specific component parts including non-pay and capital costs

Non pay, Non drugs Historical cost weighted growth for specific component parts including non-pay, CNST and capital costs

Other GDP deflator as at December 2013

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Input data: activity upward pressure

• This upward pressure is made of two components, demographic growth and non-demographic growth

• Demographic growth represents the expected population expansion and demographic change in the age structure. As a population grows the need for healthcare will rise; in addition, an increasing number of elder people will create further upward demand pressure for healthcare.

• Non-demographic growth captures the part of the growth exceeding demographic expansion. This includes: Increasing expectation and demand for healthcare services; Improving access to care; Changes in healthcare technology; Changes in medical practice; Changes in disease profile; Government policy e.g. waiting list targets.

• Activity upward pressure in the model varies by population subgroups.

• The starting point are demographic and non-demographic growth rate assumptions by setting of care, as detailed in the table below.

• Different demographic and non-demographic growth assumptions are then attributed to the different population subgroups as explained in the next slide.

Source/Comments

Demographic growth

Secondary Care Hospital Episode Statistics

Mental health Mental Health Minimum Data Set, excluding care by GPs or Patients with both Mental and Physical Problems

Primary care NHS Information Centre – Prescribing Measures Page

Community services NHS internal data

Non - demographic growth

Secondary CareHistorical cost from Hospital and Community Health Services (HCHS), discounting for demographic growth and specialised services

Mental health As above

Primary Care Historical cost weighted volume growth of HCHS, discounting for demographic growth across the sector

Community services NHS internal data

Demographic and non-demographic growth drive the upward pressure.

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• The weighted average demographic (and non-demographic) growth rate across all settings of care have been calculated, using the total spend for each setting of care as the weights. This gives average demographic (and non-demographic) growth rates across the population and across settings of care.

• By using a number secondary care and disease data sources on the Any town health system, differential demographic (and non-demographic) growth rates have been assumed for different by population subgroups. These tell which population subgroups are expected to grow more/less than the average across the population. This information has been used to attribute differential demographic (and non-demographic) growth rate assumptions to the population subgroups.

• These demographic and non-demographic growth assumptions have been applied to the base year activity levels of each population subgroup in order to obtain the projected activity levels for each subgroup.

Input data: activity upward pressure (cont.)

We have calculated the demographic and non-demographic growth by setting and population group .

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Input data: normalisation

• Three representative health systems belonging to different geographic areas (urban, suburban, rural) have been modelled. An England wide average health system has not been modelled.

• For each of the three cases considered (urban, suburban, rural), the analysis focuses on one representative health system. Hence, the relating population, activity and financial data is divided by the number of CCGs that compose the health system. For instance, if the urban health system considered comprises two CCGs, the relating population, activity and financial data would be divided by two.

• The health system data resulting from the above adjustment is then normalised to the size of the average health system nationally. This is based on the ratio of the representative health system budget and the average budget across all health systems nationally.

• For example, if the representative Any town health system budget is £250m and the average national health system budget is £200m, then all data relating to the representative Any town health system would be multiplied by a scaling factor equal to 0.8 (£200m/£250m).

• The average national health system budget for the baseline year (2013/14) is based on a mandate (the “Mandate”) from the Government to the NHS Board: April 2013 to March 2015 by the Department of Health (2012).

• Any monetary amount in the model is expressed in nominal terms.

We have normalised the Any town urban, suburban and rural health systems to the size of the average health system nationally.

any town health system: methodology pack

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