Decision-making for prevention and control under economic constraints John Edmunds London School of Hygiene & Tropical Medicine

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 inference confidence 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 – years – 5-16 years – <5 & years – <5 & 5-16 years – <5 & 5-16 & years – <64 years Increasing cost £14.2m £92.7m

Elements Epidemic model Economic analysis Burden of disease

Schematic of approach Epidemic 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

Building a picture of what would have happened if....

Burden of disease Need: 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 group BUT 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-effectiveness of 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)

H3N2-LR H1N1-LR B-LR H3N2-HR H1N1-HR B-HR What might have happened H3N2-LR Each box= 1000 epidemics

Results: extending vaccination

Net benefit of extending vaccination to different low-risk groups by coverage

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 inference confidence in predictions

Case study: flu

Types of models used in economic analyses Thiry et al Welte et al Low et al Newell et al 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 Simple treatment interventions with limitless queue capacity and isolation of infecteds (e.g., quarantine + treatment) Queue inserted diverts infecteds into treatment post-transmission

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

The end