Any town health system: Methodology pack. January 2014. Contents. 2. Introduction: overview of Any town health system. This pack has been created to provide an overview of the methodologies used to create the Any town health system report. What this pack contains.
This pack has been created to provide an overview of the methodologies used to create the Any town health system reportWhat this pack contains
The Any town model is intended to present a series of evidence-based ideas to help health systems in their five-year strategic planning processThe genesis of Any town health system
What it is
A tool that you can use to guide local planning.
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 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 collectively exhaustive list of interventions that will fill the financial gap.
A bespoke tool that takes into account local demographic make-up.
A detailed implementation guide for health systems to follow.
A provider-side reconfiguration tool.
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:Research methodology for the evidence base
Assessing NHS Case Studies
Incorporating Interventions from Existing Academic Reviews
Adding Specific Case Studies from Third Sector Organisations
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 short-listing exercise
Quality Indicators are mapped to levels of ambition against which the impact of an intervention is measured:
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
Increasing the number of people having a positive experience of care outside hospital
Making significant progress towards eliminating avoidable deaths in our hospitals
Captured in Direct Commissioning (not Any town health system)
Note: Indicator numbers refer to CCG, not NHS Outcomes Framework indicators, except where noted.
We have projected the seven a long list of interventions. This short-list forms the provisional suite of interventions to be ambitions using NHS England trends where possible, and exponential smoothing where notData collection methodology
Collect Indicator Data
Collect historical and projected data for indicators mapped to ambitions for CCGs, LAs and providers
Project Indicators to 2018/19
Use NHS E Projection Methodology
Team Projects Trends
No Projection Possible
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
Exponential smoothing supports clearer future estimates while allowing for some historical trends in the data.Projection methodology
Defining population groups
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
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
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 systemNormalisation methodology
Defining population groups
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
Population groups identified and normalised by relative size
Limited to quantitative impact only
Age standardisation could only be taken in to account where calculation could be proxied by HES analysis
The intervention demonstrated an 8% improvement in QUALID score on the subject group of dementia patients. The outcome measures EQ-5D among LTC patients
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
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 availableAssumptions
Below we have described the high level architecture of our financial model.Overview of the financial model
The financial model comprises a demand, a supply and an affordability module.Financial model high level architecture
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 and supply module
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.
Affordability module setting of care.
Gap for the
How the interventions are defined and applied
Impact of the intervention on demand
Demand for POD5 by population subgroup 2 - Do Nothing
Demand for POD5 by population subgroup 2 - Phasing A
Demand for POD5 by population subgroup 2 - Phasing B
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.
We have used a combination of 2013/14 budget data and projection assumptions to model up to 2018/19.
The upward pressure on expenditure is based on a weighted average of a number of cost components, which vary by settings of care.
We have calculated the demographic and non-demographic growth by setting and population group .
We have normalised the Any town urban, suburban and rural health systems to the size of the average health system nationally.Input data: normalisation