Epidemiology & Forecasting

The changing face of

epidemiology & forecasting

Outline

• Traditional uses of epidemiology /

patient data in pharma forecasting

• Emerging markets

• Changing face of the pharma industry

• Future for epidemiology in forecasting

Pharma models – the traditional role

of patients in market forecasting

• Because of the chronic nature of the majority of pharmaceutical products, any patient driven forecast tends to be based on prevalence. – Straight forward measure informing total number of patients alive with

a disease at a point in time

– Patients are diagnosed, then allocated to drug therapy to produce patient shares (from here the step to revenue is evident)

– Patient-based forecasts also come into their own in markets where there is either no market data, or the data that exist are extremely difficult to match up to patients (e.g. markets with insufficient level of homogeneity).

• Including patients as the foundation to a market model, as well as any data on existing treatments, can provide a more informed forecast

Prevalence-based Forecasting

Total Prevalent Population

Diagnosed/Treated

Forecasting Emerging Markets

• Markets with little or no data.

• Can be applied to new territories or new tech /

disease areas.

• Regardless, there is a wealth of population, health

& disease data available for any country (WHO,

CDC etc).

• Even for seemingly new diseases, data can be

found 99% of the time (with a little digging, applied

intelligence & understanding ).

The changing face of Healthcare,

Disease & the World Economy

• Chronic disease burden higher than ever.

• Healthcare costs forecast to increase drastically over next decade (predominantly western societies).

• Increased pressure on cost-containment.

• Overall movement towards prevention & “early health”, coupled with remote patient monitoring/self-monitoring.

• And all this was moving into place long before we hit the global recession…..

Impact for the Pharma Industry

• Overall movement towards prevention & “early health”. – Companies acquiring /collaborating or expanding into

vaccines & diagnostics

– Development of therapeutics targeted earlier in the disease pathway

• Maximisation of current assets – Deeper understanding of market dynamics to drive more

accurate forecasts & strategy

• Increased focus on health economics & market access – Pressure from regulators & payers to demonstrate cost-

benefit

What this means for the

Forecast……

And the Analyst

“Early Health” - Vaccines &

Diagnostics • No longer forecasting ‘disease’ population per se

• Requires an understanding of risk, incidence or both

• More in-depth disease knowledge will be required – Diagnostics, more individuals screened than those in the

‘prevalent’ population

• Greater investigative research skills required by analyst

• More support & insight for senior management when viewing the revenue forecasts

Revenue Profile of a New Vaccine

/ Diagnostic

Assumes a ‘one-shot’ dose/diagnostic. Acute vs chronic use

Increased Market Knowledge -

Market dynamics

• Understanding & forecasting using patient segments – New/naïve, switch, restarts etc

• Links product uptake & utilisation to: – Rate of churn within the market

– Patient status (esp. important for HIV & oncology) – naïve/drug resistant, previous line of therapy etc

– Marketing strategy

• Gain more accurate/defendable forecasts if known where patients can be accessed & at what rate

• More support & insight for senior management when reviewing forecasts & assumptions (esp. uptake rates)

Market Dynamic Product Share

Forecast Accuracy & Market

Dynamics • One contributory cause for inaccurate forecasts, especially

for new products, is an uptake rate into the market that is unrealistic – Often driven by finance or marketing

– Need to achieve “X%” market share within first year of launch

• Market dynamics may not support patient share capture if there are not enough: – New patients for which new product can be used

– Patients on existing therapies that can be switched to new product

• ‘Dynamic’ patients within the market will dictate rate of uptake

• Understanding these dynamics (and what can happen during the launch phase) can not only provide a more accurate forecast but can illuminate areas of focus for marketing

Dynamic Patient Modelling in

Practice • Of course, if it was that easy we’d all be doing it

already

• By moving towards a more dynamic view of patients (be it from an incidence/prevalence perspective or new & switch patients), the level of model complexity & data required to drive it is increased.

• It also increases the ‘black-box’ nature of any model as although the maths can still stay relatively straight-forward, the understanding of the mechanics of the model becomes more abstract

• Markets where it is important to understand this: – HIV (chronic markets where drug resistance is an issue),

– Oncology (where line of therapy is an issue, high mortality)

Static vs. Dynamic

Patient Based Models

Used for markets where treatment paradigms and patient populations are not changing over time

– Static, cross-sectional

– Isolated variables

– Capture equilibrium

– Concrete

– Transparent

– Spreadsheet software

– Data-Driven

Patient Flow Models

Used for markets where treatment paradigms and patients are in transition or evolving

– Dynamic, transitional

– Interactive, relational variables

– Capture continuous state changes

– Abstract, conceptual

– Can sometimes be ‘black-box’

– Complex system software

By severity

%diagnosed

%treated

By severity

%diagnosed

%treated

treateduntreated

OI recurrence

withdrawfail

rebound death

death maintain

treateduntreated

OI recurrence

withdrawfail

rebound death

death maintain

Reproduced with kind permission from G. Heaney

Step-change in Perspective

• Dynamic forecast modelling is quite different to the traditional ‘static’ approach where many variables were ‘implicit’

• Relationships need to be well understood (such as incidence, prevalence & mortality, start of therapy, duration, point of switch)

• Can find some unexpected results when testing the logical limits of the model..

Future for Epidemiology in

Forecasting

Move towards Dynamic Forecasts

• Early health

– Move from prevalence to incidence/

recurrence & risk evaluation

• Market dynamics

– From static snapshot to patient flow

• Market access & health economics

– Dynamic patient modelling

Market Access & Dynamic Patient

Modelling • With increasingly limited health care resources,

regulators & payers are demanding more information regarding cost-benefit / savings to budgets etc before awarding price & reimbursement

• Dynamic disease models are on the rise as they allow companies to demonstrate effectiveness of their compound throughout a disease pathway to show: – Anticipated reductions in downstream events

– Improved outcomes & health status/utility of a given population

– Expected savings to health care budgets & resources

• Models of this type are invaluable not only as tools to investigate price & reimbursement, but throughout the drug pipeline to find thresholds for drug candidates (in terms of efficacy) & assess potential acquisitions.

Questions?