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Background. Published cost-effectiveness estimates can vary considerably, both within and between countries. Despite extensive discussion, little is known empirically about factors relating to these variations. Objectives. To use multilevel statistical modeling to integrate cost-effectiveness estimates from published economic evaluations to investigate potential causes of variation. Methods. Cost-effectiveness studies of statins for cardiovascular disease prevention were identified by systematic review. Estimates of incremental costs and effects were extracted from reported base case, sensitivity, and subgroup analyses, with estimates grouped in studies and in countries. Three bivariate models were developed: a cross-classified model to accommodate data from multinational studies, a hierarchical model with multinational data allocated to a single category at country level, and a hierarchical model excluding multinational data. Covariates at different levels were drawn from a long list of factors suggested in the literature. Results. We found 67 studies reporting 2094 cost-effectiveness estimates relating to 23 countries (6 studies reporting for more than 1 country). Data and study-level covariates included patient characteristics, intervention and comparator cost, and some study methods (e.g., discount rates and time horizon). After adjusting for these factors, the proportion of variation attributable to countries was negligible in the cross-classified model but moderate in the hierarchical models (14%−19% of total variance). Country-level variables that improved the fit of the hierarchical models included measures of income and health care finance, health care resources, and population risks. Conclusions. Our analysis suggested that variability in published cost-effectiveness estimates is related more to differences in study methods than to differences in national context. Multinational studies were associated with much lower country-level variation than single-country studies. These findings are for a single clinical question and may be atypical. [ABSTRACT FROM AUTHOR]

Background: The 'Physical Activity Care Pathway' (a Pilot for the 'Let's Get Moving' policy) is a systematic approach to integrating physical activity promotion into the primary care setting. It combines several methods reported to support behavioural change, including brief interventions, motivational interviewing, goal setting, providing written resources, and follow-up support. This paper compares costs falling on the UK National Health Service (NHS) of implementing the care pathway using two different recruitment strategies and provides initial insights into the cost of changing physical activity behaviour. Methods: A combination of a time driven variant of activity based costing, audit data through EMIS and a survey of practice managers provided patient-level cost data for 411 screened individuals. Self reported physical activity data of 70 people completing the care pathway at three month was compared with baseline using a regression based 'difference in differences' approach. Deterministic and probabilistic sensitivity analyses in combination with hypothesis testing were used to judge how robust findings are to key assumptions and to assess the uncertainty around estimates of the cost of changing physical activity behaviour. Results: It cost £53 (SD 7.8) per patient completing the PACP in opportunistic centres and £191 (SD 39) at disease register sites. The completer rate was higher in disease register centres (27.3% vs. 16.2%) and the difference in differences in time spent on physical activity was 81.32 (SE 17.16) minutes/week in patients completing the PACP; so that the incremental cost of converting one sedentary adult to an 'active state' of 150 minutes of moderate intensity physical activity per week amounts to £ 886.50 in disease register practices, compared to opportunistic screening. Conclusions: Disease register screening is more costly than opportunistic patient recruitment. However, additional costs come with a higher completion rate and better outcomes in terms of behavioural change in patients completing the care pathway. Further research is needed to rigorously evaluate intervention efficiency and to assess the link between behavioural change and changes in quality adjusted life years (QALYs). [ABSTRACT FROM AUTHOR]

Background: The European Innovation Partnership on Active and Healthy Ageing (EIP on AHA) is a European Commission led policy initiative to address the challenges of demographic change in Europe. For monitoring the health and economic impact of the social and technological innovations carried out by more than 500 stakeholder's groups ('commitments') participating in the EIP on AHA, a generic and flexible web-based monitoring and assessment tool is currently being developed.Aim: This paper describes the approach for developing and implementing this web-based tool, its main characteristics and capability to provide specific outcomes that are of value to the developers of an intervention, as well as a series of case studies planned before wider rollout.Methods: The tool builds up from a variety of surrogate endpoints commonly used across the diverse set of EIP on AHA commitments in order to estimate health and economic outcomes in terms of incremental changes in quality adjusted life years (QALYs) as well as health and social care utilisation. A highly adaptable Markov model with initially three mutually exclusive health states ('baseline health', 'deteriorated health' and 'death') provides the basis for the tool which draws from an extensive database of epidemiological, economic and effectiveness data; and also allows further customisation through remote data entry enabling more accurate and context specific estimation of intervention impact. Both probabilistic sensitivity analysis and deterministic scenario analysis allow assessing the impact of parameter uncertainty on intervention outcomes. A set of case studies, ranging from the pre-market assessment of early healthcare technologies to the retrospective analysis of established care pathways, will be carried out before public rollout, which is envisaged end 2015.Conclusion: Monitoring the activities carried out within the EIP on AHA requires an approach that is both flexible and consistent in the way health and economic impact is estimated across interventions and commitments. The added value for users of the MAFEIP-tool is its ability to provide an early assessment of the likelihood that interventions in their current design will achieve the anticipated impact, and also to identify what drives interventions' effectiveness or efficiency to guide further design, development or evaluation. [ABSTRACT FROM AUTHOR]