Challenges in using mathematical modelling for public health decision-making

John Edmunds, Jess Metcalf, Justin Lessler

john.edmunds@lshtm.ac.uk

Why use models?• Observational studies of impact are retrospective,

and difficult to interpret• Intervention studies (trials) relatively small & key

endpoints rarely observed, therefore models used for extrapolation – Other endpoints (e.g. deaths)– Other population groups (perhaps excluded from study)– Over time– To others in the population

• Synthesise the results of different studies in a unified framework– Improve our understanding of key drivers

• Identify and quantify knowledge gaps

Why use models?• Observational studies of impact are retrospective,

and difficult to interpret• Intervention studies (trials) relatively small & key

endpoints rarely observed, therefore models used for extrapolation – Other endpoints (e.g. deaths)– Other population groups (perhaps excluded from study)– Over time– To others in the population

• Synthesise the results of different studies in a unified framework– Improve our understanding of key drivers

• Identify and quantify knowledge gaps

Synthesis & prediction

Understanding, research and surveillance

Main role of mathematical modelling in UK infectious disease policy

Epidemics• Contingency planning• Risk assessment• Epidemic forecasting (& “now-casting”)• Impact of interventions

“Endemic” diseases• Vaccine policy• Other control policy (e.g. Screening and treatment)• Burden of disease assessment (priority setting)

Epidemics• Contingency planning• Risk assessment• Epidemic forecasting (& “now-casting”)• Impact of interventions

“Endemic” diseases• Vaccine policy• Other control policy (e.g. Screening and treatment)• Burden of disease assessment (priority setting)

Impact of alternative policies (scenarios)

Transmission models

Transmission models

Main role of mathematical modelling in UK infectious disease policy

Challenges

• Nature of the problem:– “It is difficult to make predictions, particularly of

the future” (Niels Bohr, Yogi Berra, Somebody..)• Multiple agencies:

– Communication, co-ordination, “translation”• Responding to the needs of policy-makers

Nature of the problem: dynamics & uncertainty

Years after introduction

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Vaccinating only elderlyChildhood vaccination - 1 doseChildhood vaccination - 2 doseChildhood vaccination - 2 dose + vaccinating elderly

Varicella incidence

Zoster incidence

Cost-effectiveness of a combined scheduleover time

Van Hoek et al. Vaccine (2011)

Nature of the problem: unexpected events• Pathogen emergence very difficult to

predict– Drivers (mechanisms) poorly understood

• Low probability high impact events– E.g. Characteristics of next flu pandemic– What is the probability of an epidemic

worse than 1918?– Other infections?

• The next HIV or vCJD?• Remain vigilant but surveillance is

retrospective

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Epidemic Deaths1918 200,000

1957 18,000

1968 46,000

2009 600

Nature of the problem: unexpected events• Models rapidly employed during an

outbreak– Characterise novel pathogen

• Reproduction numbers, case fatality, etc– Inference: can “make sense” of limited data– Though early analyses must be treated with

caution• Delays and censoring of data• Bias• Chance

• Can be employed (later) to forecast the epidemic progress and evaluate interventions

Fraser et al. Science (2009)

Baguelin et al. Vaccine 2010

September predictions• Peak height second wave estimated

to be similar to first• Peak early Nov., end Jan.• Vaccination has limited impact on

casesOctober• Further data reduced uncertainty

but did not alter central estimate

Nature of the problem: unexpected events

Multi-agency: communication & co-ordination

• Modelling is a component of the advice

• Advisory body (e.g. JCVI, NICE) made up of individuals from diverse backgrounds– Lack of practical modelling

experience

• Advice is distilled• Serves different purposes

– E.g. different outcomes may be critical for different “audiences”

– Presented in different ways

DH advises Ministers

JCVI

PHE AcademiaMHRA NIBSC

Communicating the limits of modelling• Public health officials might want clear quantitative statements about the

impact of an intervention– “Single version of the truth”

• Precise quantitative predictions highly likely to be misleading• Uncertainty

– Parametric– Structural– “Rules”

• Representing uncertainty properly, presenting it to decision-makers.– Can clear policy advice still be made?

• Sensitivity analysis– What happens if it can’t?

• Clear guidance of further research• Sensitivity analysis• Value of information

• Assessing the quality of other research

• Predictive power comes from appropriate model structure (mechanism)– As well as parameter

values• If key drivers are omitted

then results will be unreliable

• Review and assess other work – Second opinion

initially infected

R0 Cases at 30 days

Cases at 365 days

10 1.5 31 224,000

10 3 64 774 billion

1,000 1.5 3,094 22 million

1,000 3 6,387 77 trillion

Communicating the limits of modelling: structural uncertainty

Meltzer et al. 2001 EID 7(6)

Mathematical models

Limits of modelling:predictions vs scenarios

Ong et al. PLoS One 2010

Scenarios“things that could happen”

Predictions“things we think will happen”

Khazeni et al. Annal Intern Med 2009

Limits of public health:Optimal vaccine allocation

• Vaccine given before outbreak

• Optimised coverage by age class

• For different outcomes• Compare with CDC

recommended strategy

Medlock & Galvani Science (2009)

Limits of public health:Optimal vaccine allocation

• Vaccine given before outbreak• Optimised coverage by age

class• For different outcomes• Compare with CDC

recommended strategy

• Vaccine coverage cannot be optimised– Not under the control of central

decision-maker

• Many other examples of modelling interventions that are not likely to be considered

Medlock & Galvani Science (2009)

Responding to the needs of policy-makers

• Often want information on whether an intervention is worth implementing when compared to alternatives– Economic analysis

• Explicitly weigh costs and benefits• A number of countries have recommended

methodologies that should be followed– Also journals and societies (e.g. ISPOR/SMDM)– However, many of these “rules of the game” reflect ethical

stances that are not universally shared• E.g. Discounting of health benefits

– May be very influential

Responding to the needs of policy-makers: Vaccines in UK

• Epidemic models and associated economic models used heavily by JCVI in formulating their advice and recommendations

• NHS Constitution– Minister (England) bound to accept recommendation of JCVI if

the programme is cost-effective– CEA is therefore critical

• Transmission model is not required (but usually done)– Critical to be done correctly

• Large scale programmes• Difficult to terminate

Responding to the needs of policy-makers:• Many layers of approval

– Require different outputs, or results presented in different forms

– Can be time consuming• “Mission creep”

• Multiple revisions– Comments from committees,

referees etc.

• Short deadlines• Academics rarely incentivised

to participate in these processes

Date Milestone Activity

Sept ‘06

JCVI subgroup

Review existing models

Refine questions (e.g age at vaccination and delivery route)

Feb ‘07

JCVI subgroup

Review model assumptions

Preliminary results

July ‘07

Papers submitted to JCVI secretariat

Sent for peer review

Sept ‘07

Peer review comments received

Revised analysis

Oct ‘07

JCVI recommendation

Responding to the needs of policy-makers:• Many layers of approval

– Require different outputs, or results presented in different forms

– Can be time consuming• “Mission creep”

• Multiple revisions– Comments from committees,

referees etc.

• Short deadlines• Academics rarely incentivised

to participate in these processes

Choi et al. Vaccine (2010)

Responding to the needs of policy-makers:• Many layers of approval

– Require different outputs, or results presented in different forms

– Can be time consuming• “Mission creep”

• Multiple revisions– Comments from committees,

referees etc.

• Short deadlines• Academics rarely incentivised

to participate in these processes

Jit et al. BMJ (2008)

Tackling challenges: UK vaccines and flu • Relatively simple decision-making framework

– Centralised, clear, limited number of actors

• Relatively healthy (multiple) modelling groups– Mechanisms in place for these to feed into decisions

• E.g. Modelling representative on JCVI• Peer review and scrutiny

• Strong modelling team within the public health agency– Ensure right questions are addressed– Outputs are appropriate– Standing resource (core funding leads to flexibility and rapid response)– Optimises use of all relevant data – adding value– Inputs into design of surveillance system

• Long-term relationships & working patterns built-up• Sophisticated “interpreters” and users of modelling within decision-making

framework• Clear role for economic analyses and clear (?) rules for conducting these• Limited role of industry and lobby groups

Tackling challenges: UK vaccines and flu • Relatively simple decision-making framework

– Top down

• Relatively healthy (multiple) modelling groups– Wasteful

• Strong modelling team within the public health agency– Research best done by academics– No role for un-publishable quick and dirty analyses

• Long-term relationships & working patterns built-up– Club

• Sophisticated “interpreters” and users of modelling within decision-making framework– Results “digested” into summary statements where qualifiers & context lost

• Clear role for economic analyses and clear (?) rules for conducting these– Economists know the price of everything and the value of nothing

• Limited role of industry and lobby groups– Have a legitimate role to play in a healthy democracy

Integrating modelling with public health decision-making• There are technical challenges that will always

remain– Highly non-linear systems– Imperfectly understood natural history– Uncertainty in parameter values– Imperfectly observed process, bias in data sets– unethical to measure, too expensive to design the

experiment, etc

• Other challenges that will always remain due to:– Complex decision-making environment– May be subject to press or political scrutiny– Compromises & time pressures that will inevitably

occur– Inadequate resources

• Enormously rewarding to play a part in improving the health of the nation

Integrating modelling with public health decision-making• There are technical challenges that will always

remain– Highly non-linear systems– Imperfectly understood natural history– Uncertainty in parameter values– Imperfectly observed process, bias in data sets– unethical to measure, too expensive to design the

experiment, etc

• Other challenges that will always remain due to:– Complex decision-making environment– May be subject to press or political scrutiny– Compromises & time pressures that will inevitably

occur– Inadequate resources

• Enormously rewarding to play a part in improving the health of the nation

Integrating modelling with public health decision-making• There are technical challenges that will always

remain– Highly non-linear systems– Imperfectly understood natural history– Uncertainty in parameter values– Imperfectly observed process, bias in data sets– unethical to measure, too expensive to design the

experiment, etc

• Other challenges that will always remain due to:– Complex decision-making environment– May be subject to press or political scrutiny– Compromises & time pressures that will inevitably

occur– Inadequate resources

• Enormously rewarding to play a part in improving the health of the nation

Integrating modelling with public health decision-making• There are technical challenges that will always

remain– Highly non-linear systems– Imperfectly understood natural history– Uncertainty in parameter values– Imperfectly observed process, bias in data sets– unethical to measure, too expensive to design the

experiment, etc

• Other challenges that will always remain due to:– Complex decision-making environment– May be subject to press or political scrutiny– Compromises & time pressures that will inevitably

occur– Inadequate resources

• Enormously rewarding to play a part in improving the health of the nation

IPD incidence E&W, HPASerotypes in PCV7, <2 yrs