decision-making for prevention and control under economic constraints
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Decision-making for prevention and control under economic constraints. John Edmunds London School of Hygiene & Tropical Medicine [email protected]. Overview. Economic decision making Example Wider practice Future research directions (personal and partial view) Conclude. - PowerPoint PPT PresentationTRANSCRIPT
Decision-making for prevention and control under economic constraints
John Edmunds
London School of Hygiene & Tropical [email protected]
Overview
• Economic decision making– Example– Wider practice
• Future research directions (personal and partial view)
• Conclude
Concluding remarks
.....Practical decision-making will in general require an integration of the epidemiologically based modelling with some form of socio-economic modelling. This means that the usual type of interdisciplinary OR team must be well acquainted with modern scenario analysis applied to public health activities. But it must be emphasised that such scenario analyses must be closely geared to epidemiological models that have been fitted to local data (i.e. not merely using plausible parameter values) as strongly urged in this review.
Norman Bailey
Concluding remarks
.....Practical decision-making will in general require an integration of the epidemiologically based modelling with some form of socio-economic modelling. This means that the usual type of interdisciplinary OR team must be well acquainted with modern scenario analysis applied to public health activities. But it must be emphasised that such scenario analyses must be closely geared to epidemiological models that have been fitted to local data (i.e. not merely using plausible parameter values) as strongly urged in this review.
Norman Bailey
Epidemic model integrated within economic evaluation
Sensitivity analyses
Multidisciplinary collaborations
Model fitting &statistical inferenceconfidence in predictions
Assessing seasonal flu options:Marc Baguelin et al.• Seasonal flu vaccine• Current strategy (since 2000) is to target high risk
individuals & everyone over the age of 65• Prior to 2000, strategy was to target those who were
high risk only
• Question: should we extend to low risk groups?– <5 years– 50-64 years– 5-16 years– <5 & 50-64 years– <5 & 5-16 years– <5 & 5-16 & 50-64 years– <64 years
Increasing cost £14.2m
£92.7m
Schematic of approachEpidemic parameters• Reproduction number• Incubation period• Infectious period• Susceptibility profile• Mixing patterns• ……
Data• RCGP• Swabbing• Serology
Outcomes• Risk and age:
• CFR• Hospit.• QALY loss• …
Epidemic projections
Vaccine assumptions• Coverage• By age & risk• By year & strain• Efficacy
Costs• Hospitalisation• Vaccine• Delivery• …
Projections in relevant units & CEA
Building a picture of what would have happened if....
• Epidemiology of flu has been disturbed by vaccination for many years
• Attempt to reconstruct epidemiology of flu– Detailed understanding of what happened (how many
cases, including how many prevented by vaccination)– Estimate what would have happened if we had followed
alternative policies
Burden of diseaseNeed: • Clinical cases by strain, age group & risk group• GP consultations by strain, age and risk group• Hospitalisaitons by strain, age and risk group• Deaths by strain, age and risk groupBUT• Data non-specific e.g. all-cause deaths• Use regression approaches to estimate
– Regress weekly laboratory confirmed cases of infection (RSV, flu, etc) against outcome of interest (e.g. hospitalisations for respiratory illness)
– Risk group specific data only routinely available through HES
Burden of disease: Cromer et al. (submitted)
• Regress weekly lab reports against GP consultation, hospitalisation & death data– Age & risk-group specific
• Estimate of cases of outcome attributable to influenza (& other causes)
Average weekly laboratory reports by pathogen
<5 years old
>65 years old
Estimating cost-effectivenessof alternative policies
• Sample set of reconstructed epidemics– Number of infections over time– For each epidemic, alternative vaccination scenarios are generated
• Link infections to outcomes via risk ratios, unit costs, etc.• Monte-Carlo simulations, sampling over distributions for:
– risk ratios– economic parameters– QALY parameters
• Calculate summary statistics, e.g. ICER or Net Benefit• Provides assessment of counterfactual
– But fitted to observed data (2000/1 to 2008/9)
Cost-effectiveness
Sensitivity & scenario analyses performed on:• Mortality rates• Coverage in low and high risk groups• Cost of vaccinating• Time frame of analysis• Discount rates
Validation of model against other serological data
Concluding remarks
.....Practical decision-making will in general require an integration of the epidemiologically based modelling with some form of socio-economic modelling. This means that the usual type of interdisciplinary OR team must be well acquainted with modern scenario analysis applied to public health activities. But it must be emphasised that such scenario analyses must be closely geared to epidemiological models that have been fitted to local data (i.e. not merely using plausible parameter values) as strongly urged in this review.
Norman Bailey
Epidemic model integrated within economic evaluation
Sensitivity analyses
Multidisciplinary collaborations
Model fitting &statistical inferenceconfidence in predictions
Types of models used in economic analyses
Thiry et al. 2003
Welte et al. 2005
Low et al. 2007
Newell et al. 2007
Systematic reviews of economic analyses
Vargas-Palacios(submitted)
A few problems• Queue fever
• Non-linearity in costs– Marginal costs of expansion– Timing of costs
• Seasonality
• Behaviour change– Declining incidence– Balancing epidemiological &
economic impact
Simple prophylactic interventions with limitless queue capacity (e.g., vaccination)
Queue inserted diverts susceptibles
λi δ*r*IS(t) I(t) D(t)
(1-δ)*r*I
λq Q(t) R(t)min(nμ,Q(t)μ)
Simple treatment interventions with limitless queue capacity and isolation of infecteds (e.g., quarantine + treatment)
Queue inserted diverts infecteds into treatment post-transmission
λi δ*r*IS(t) I(t) D(t)
λq (1-δ)*r*I
Q(t) R(t)min(nμ,Q(t)μ)
Concluding remarks
• Economic evaluations of ID control programmes would often fail “Bailey’s tests”, particularly:– Epidemiological model integrated within economic evaluation– Model fitting
• Worryingly, the bigger the problem the weaker the methods– Fit to what?– Insufficient data to develop an epidemic model?
• There is clearly a need to engage more with economists and public health officials– Multidisciplinary collaboration test
Concluding concluding remarks
“We need to think carefully about how to persuade them of the value of our work and how to understand what it is that constrains their world and decisions”
Brian Williams 2013
Acknowledgements
• Marc Baguelin, PHE/LSHTM• Stefan Flasche, LSHTM• Anton Camacho, LSHTM• Deborah Cromer, UNSW• Mark Jit, PHE/LSHTM• Liz Miller, PHE• Jonathan Weiss, LSHTM