Identifying needs, building interventions and busting myths: adventures in population health

At the recent Health Plus Care Show at London Excel I spoke in the Transforming Primary Care Theatre. The title of my talk was ‘Identifying needs, building interventions and busting myths: Adventures in Population Health.’

The insights I shared were harvested from a number of engagements that Sollis has had with Clinical Commissioning Groups (CCGs) over a five year period commencing in 2012. More recently this work has concerned itself with population health analytics, where we have worked with local commissioners, including clinicians, in order to help them gain a better understanding of the current and future health status/needs of their local populations.

The insights I shared then and which I summarise below are all driven from the data. Running the numbers has been critical to the planning operation. It is surely self-evident that potentially life changing decisions that involve the design and implementation of health and care services for ‘at need’ populations should be based on hard evidence not hearsay, anecdote and fairy-tale?

Multi-morbidity is the norm

When we run the numbers it is clear that it is more common for people to have multiple chronic conditions than to have just one chronic condition.

People with multi-morbidity are likely to have complex needs and account for a higher proportion of the workload and our analysis bears this out.

Knowing that multi-morbidity is the norm means that providers and commissioners can design intervention programmes — new care models — that are best suited to people with complex needs.

Multi-morbidity and frailty are not distributed evenly across a population

It is true that the likelihood of multi-morbidity and of frailty does increase with age. However, patients with multiple long-term conditions (LTCs) and patients who are frail can be found in almost every age band.

Multi-morbidity and frailty are not distributed evenly across a population.

It is multi-morbidity more than age that is the key driver of activity, cost and future risk

It is common to assume that multi-morbidity increases with age and therefore it is the most elderly in our populations that consume most resources.

Our analysis challenges this commonly held view. Multi-morbidity occurs across the whole adult range.

Thinking about cost for a moment, high cost individuals occur across all of the population particularly in in people aged 45+.

It is multi-morbidity more than age that is the key driver of activity, cost and future risk.

The top of the risk pyramid is not homogeneous

When thinking about different segments or cohorts within a population – like those most at risk of an emergency admission or those that are the frailest – it is often assumed that most of the people that appear in one cohort also appear in the other. Our analysis demonstrates that this is often not the case.

There is not as much overlap between different ‘at risk’ groups as you might think.

People in in the top of the so called risk pyramid are not homogeneous in their clinical make-up and therefore are likely to require different interventions. More work is needed to segment populations into homogeneous groups that have similar needs and who are impactable.

Case-mix can vary significantly between different GP populations

Primary care clinicians have long known that case-mix (the sum total of the diagnoses/diseases present in a population) varies across GP Practices.

Using the data we can now describe and quantify the differences in case-mix across GP Practices.

Some CCGs are now using this difference in case-mix to ensure resources are allocated in a fair and equitable manner, that is, directed at where there is greatest need. Case-mix analysis can now be taken into account when comparing resource use across populations, be that an Accountable Care System (ACS), a Sustainability and Transformation Partnership (STP) or a Primary Care Home (PCH).

Using case-mix and plotting variation across a health economy, we can identify outliers and benchmark provider performance justly and fairly.


All of these insights are generated from hard data and all have implications for new care model design.

Based on the data it is our contention that population health analysis should focus less on single disease conditions and focus more on the burden of multi-morbidity observable across a given population.

Our analytical work has been centred on helping to quantify and describe the burden of multi-morbidity that exists in populations.

We know that people with multi-morbidity are frequent users of primary care. It is our contention therefore that when designing intervention programmes, primary care needs to be front and centre.

The answers lie in the data.

For more information, download our white paper, Understanding Population Health.

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