Report

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 Concluding remarks .....Practical decision-making will in general require an integration of the epidemiologically based modelling with some form of socioeconomic 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 socioeconomic 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 Multidisciplinary collaborations Sensitivity analyses 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 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 Elements Epidemic model Burden of disease Economic analysis Schematic of approach Epidemic parameters Data • Reproduction number • Incubation period • Infectious period • Susceptibility profile • Mixing patterns • …… • RCGP • Swabbing • Serology Epidemic projections Vaccine assumptions • Coverage • By age & risk • By year & strain • Efficacy Outcomes Costs • Risk and age: • CFR • Hospit. • QALY loss •… • 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) Average weekly laboratory reports by pathogen <5 years old • Regress weekly lab reports against GP consultation, hospitalisation & death data – Age & risk-group specific • Estimate of cases of outcome attributable to influenza (& other causes) >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) What might have happened H3N2-LR H3N2-HR H1N1-LR H3N2-LR Each box= 1000 epidemics H1N1-HR B-LR B-HR 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 socioeconomic 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 Multidisciplinary collaborations Sensitivity analyses Model fitting & statistical inference confidence in predictions Case study: flu Types of models used in economic analyses Systematic reviews of economic analyses Thiry et al. 2003 Welte et al. 2005 Low et al. 2007 Newell et al. 2007 Vargas-Palacios (submitted) A few problems • Queue fever Simple prophylactic interventions with limitless queue capacity (e.g., vaccination) λi δ*r*I S(t) I(t) D(t) (1-δ)*r*I λq Q(t) R(t) min(nμ,Q(t)μ) • Non-linearity in costs – Marginal costs of expansion – Timing of costs • Seasonality Queue inserted diverts susceptibles Simple treatment interventions with limitless queue capacity and isolation of infecteds (e.g., quarantine + treatment) λi δ*r*I S(t) I(t) D(t) λq (1-δ)*r*I Q(t) R(t) min(nμ,Q(t)μ) • Behaviour change – Declining incidence – Balancing epidemiological & economic impact 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