Model parameter estimation and uncertainty analysis: a report of the ISPOR-SMDM modeling good research practices task force working group - 6

A model’s purpose is to inform medical decisions and health care resource allocation. Modelers employ quantitative methods to structure the clinical, epidemiological, and economic evidence base and gain qualitative insight to assist decision makers in making better decisions. From a policy perspective, the value of a model-based analysis lies not simply in its ability to generate a precise point estimate for a specific outcome but also in the systematic examination and responsible reporting of uncertainty surrounding this outcome and the ultimate decision being addressed. Different concepts relating to uncertainty in decision modeling are explored. Stochastic (first-order) uncertainty is distinguished from both parameter (second-order) uncertainty and from heterogeneity, with structural uncertainty relating to the model itself forming another level of uncertainty to consider. The article argues that the estimation of point estimates and uncertainty in parameters is part of a single process and explores the link between parameter uncertainty through to decision uncertainty and the relationship to value-of-information analysis. The article also makes extensive recommendations around the reporting of uncertainty, both in terms of deterministic sensitivity analysis techniques and probabilistic methods. Expected value of perfect information is argued to be the most appropriate presentational technique, alongside cost-effectiveness acceptability curves, for representing decision uncertainty from probabilistic analysis

Comparison of mass and targeted screening strategies for cardiovascular risk: simulation of the effectiveness, cost-effectiveness and coverage using a cross-sectional survey of 3921 people

Background: Cardiovascular primary prevention should be targeted at those with the highest global risk. However, it is unclear how best to identify such individuals from the general population. The aim of this study was to compare mass and targeted screening strategies in terms of effectiveness, cost effectiveness and coverage.\ud \ud Methods: The Scottish Health Survey provided cross-sectional data on 3921 asymptomatic members of the general population aged 40–74 years. We undertook simulation models of five screening strategies: mass screening, targeted screening of deprived communities, targeted screening of family members and combinations of the latter two.\ud \ud Results: To identify one individual at high risk of premature cardiovascular disease using mass screening required 16.0 people to be screened at a cost of £370. Screening deprived communities targeted 17% of the general population but identified 45% of those at high risk, and identified one high-risk individual for every 6.1 people screened at a cost of £141. Screening family members targeted 28% of the general population but identified 61% of those at high risk, and identified one high-risk individual for every 7.4 people screened at a cost of £170. Combining both approaches enabled 84% of high risk individuals to be identified by screening only 41% of the population. Extending targeted to mass screening identified only one additional high-risk person for every 58.8 screened at a cost of £1358.\ud \ud Conclusions: Targeted screening strategies are less costly than mass screening, and can identify up to 84% of high-risk individuals. The additional resources required for mass screening may not be justified

Investing in health: is social housing value for money? A cost-utility analysis

Background: There is a healthy public policy agenda investigating the health impacts of improving living conditions. However, there are few economic evaluations, to date, assessing value for money. We conducted the first cost-effectiveness analysis of a nationwide intervention transferring social and private tenants to new-build social housing, in Scotland.\ud \ud Methods: A quasi-experimental prospective study was undertaken involving 205 intervention households and 246 comparison households, over 2 years. A cost-utility analysis assessed the average cost per change in health utility (a single score summarising overall health-related quality of life), generated via the SF-6D algorithm. Construction costs for new builds were included. Analysis was conducted for all households, and by family, adult and elderly households; with estimates adjusted for baseline confounders. Outcomes were annuitised and discounted at 3.5%.\ud \ud Results: The average discounted cost was £18 708 per household, at a national programme cost of £28.4 million. The average change in health utility scores in the intervention group attributable to the intervention were +0.001 for all households, +0.001 for family households, −0.04 for adult households and −0.03 for elderly households. All estimates were statistically insignificant.\ud \ud Conclusions: At face value, the interventions were not value for money in health terms. However, because the policy rationale was the amenity provision of housing for disadvantaged groups, impacts extend beyond health and may be fully realised over the long term. Before making general value-for-money inferences, economic evaluation should attempt to estimate the full social value of interventions, model long-term impacts and explicitly incorporate equity considerations