Introduction to the training exercises

2020 ◽  
pp. 92-102
Author(s):  
Richard Cookson ◽  
James Love-Koh ◽  
Colin Angus ◽  
James Lomas
Keyword(s):  
Cost Effectiveness ◽  
Replacement Therapy ◽  
Nicotine Replacement Therapy ◽  
Health Inequality ◽  
Cost Effectiveness Analysis ◽  
Preventive Healthcare ◽  
Healthcare Programme ◽  
Reduce Health Inequality ◽  
Hands On ◽  
Training Exercises

This chapter introduces the handbook spreadsheet training exercises, which are designed to provide hands-on experience in using the methods of distributional cost-effectiveness analysis (DCEA). Seven of the exercises form a cumulative step-by-step sequence relating to nicotine replacement therapy (NRT) in England, which is a classic example of a preventive healthcare programme designed to improve health and reduce health inequality. This allows us to illustrate all the main steps involved in conducting a DCEA using a single common example. There are also two stand-alone exercises relating to other topics in other countries.

scholarly journals Impact of Socioeconomic Differences on Distributional Cost-effectiveness Analysis

2020 ◽  
Vol 40 (5) ◽  
pp. 606-618
Author(s):  
Fan Yang ◽  
Colin Angus ◽  
Ana Duarte ◽  
Duncan Gillespie ◽  
Simon Walker ◽  
...  
Keyword(s):  
Cost Effectiveness ◽  
Health Inequality ◽  
Local Level ◽  
National Level ◽  
Cost Effectiveness Analysis ◽  
Decision Makers ◽  
Socioeconomic Differences ◽  
Screening And Brief Intervention ◽  
Effectiveness Analysis ◽  
The Impact

Public health decision makers value interventions for their effects on overall health and health inequality. Distributional cost-effectiveness analysis (DCEA) incorporates health inequality concerns into economic evaluation by accounting for how parameters, such as effectiveness, differ across population groups. A good understanding of how and when accounting for socioeconomic differences between groups affects the assessment of intervention impacts on overall health and health inequality could inform decision makers where DCEA would add most value. We interrogated 2 DCEA models of smoking and alcohol policies using first national level and then local authority level information on various socioeconomic differences in health and intervention use. Through a series of scenario analyses, we explored the impact of altering these differences on the DCEA results. When all available evidence on socioeconomic differences was incorporated, provision of a smoking cessation service was estimated to increase overall health and increase health inequality, while the screening and brief intervention for alcohol misuse was estimated to increase overall health and reduce inequality. Ignoring all or some socioeconomic differences resulted in minimal change to the estimated impact on overall health in both models; however, there were larger effects on the estimated impact on health inequality. Across the models, there were no clear patterns in how the extent and direction of socioeconomic differences in the inputs translated into the estimated impact on health inequality. Modifying use or coverage of either intervention so that each population group matched the highest level improved the impacts to a greater degree than modifying intervention effectiveness. When local level socioeconomic differences were considered, the magnitude of the impacts was altered; in some cases, the direction of impact on inequality was also altered.

scholarly journals Cost-effectiveness analysis of renal replacement therapy in Austria

2011 ◽  
Vol 26 (9) ◽  
pp. 2988-2995 ◽  
Author(s):  
M. Haller ◽  
G. Gutjahr ◽  
R. Kramar ◽  
F. Harnoncourt ◽  
R. Oberbauer
Keyword(s):  
Cost Effectiveness ◽  
Renal Replacement Therapy ◽  
Replacement Therapy ◽  
Cost Effectiveness Analysis ◽  
Effectiveness Analysis

scholarly journals Cost-Effectiveness Analysis of the Spanish Renal Replacement Therapy Program

2012 ◽  
Vol 32 (2) ◽  
pp. 192-199 ◽  
Author(s):  
Guillermo Villa ◽  
Lucía Fernández–Ortiz ◽  
Jesús Cuervo ◽  
Pablo Rebollo ◽  
Rafael Selgas ◽  
...  
Keyword(s):  
Cost Effectiveness ◽  
Renal Replacement Therapy ◽  
Replacement Therapy ◽  
Markov Chain Model ◽  
Cost Savings ◽  
Quality Adjusted Life Year ◽  
Cost Effectiveness Analysis ◽  
Chain Model ◽  
Therapy Program ◽  
Effectiveness Analysis

♦BackgroundWe undertook a cost-effectiveness analysis of the Spanish Renal Replacement Therapy (RRT) program for end-stage renal disease patients from a societal perspective. The current Spanish situation was compared with several hypothetical scenarios.♦MethodsA Markov chain model was used as a foundation for simulations of the Spanish RRT program in three temporal horizons (5, 10, and 15 years). The current situation (scenario 1) was compared with three different scenarios: increased proportion of overall scheduled (planned) incident patients (scenario 2); constant proportion of overall scheduled incident patients, but increased proportion of scheduled incident patients on peritoneal dialysis (PD), resulting in a lower proportion of scheduled incident patients on hemodialysis (HD) (scenario 3); and increased overall proportion of scheduled incident patients together with increased scheduled incidence of patients on PD (scenario 4).♦ResultsThe incremental cost-effectiveness ratios (ICERs) of scenarios 2, 3, and 4, when compared with scenario 1, were estimated to be, respectively, -€83 150, -€354 977, and -€235 886 per incremental quality-adjusted life year (ΔQALY), evidencing both moderate cost savings and slight effectiveness gains. The net health benefits that would accrue to society were estimated to be, respectively, 0.0045, 0.0211, and 0.0219 ΔQALYs considering a willingness-to-pay threshold of €35 000/ΔQALY.♦ConclusionsScenario 1, the current Spanish situation, was dominated by all the proposed scenarios. Interestingly, scenarios 3 and 4 showed the best results in terms of cost-effectiveness. From a cost-effectiveness perspective, an increase in the overall scheduled incidence of RRT, and particularly that of PD, should be promoted.

scholarly journals Uncertainty Analysis in Intervention Impact on Health Inequality for Resource Allocation Decisions

2021 ◽  
pp. 0272989X2110098
Author(s):  
Fan Yang ◽  
Ana Duarte ◽  
Simon Walker ◽  
Susan Griffin
Keyword(s):  
Smoking Cessation ◽  
Cost Effectiveness ◽  
Uncertainty Analysis ◽  
Health Inequality ◽  
Cost Effectiveness Analysis ◽  
Analysis Of Covariance ◽  
Model Parameters ◽  
Socioeconomic Differences ◽  
Decision Uncertainty ◽  
Effectiveness Analysis

Cost-effectiveness analysis, routinely used in health care to inform funding decisions, can be extended to consider impact on health inequality. Distributional cost-effectiveness analysis (DCEA) incorporates socioeconomic differences in model parameters to capture how an intervention would affect both overall population health and differences in health between population groups. In DCEA, uncertainty analysis can consider the decision uncertainty around on both impacts (i.e., the probability that an intervention will increase overall health and the probability that it will reduce inequality). Using an illustrative example assessing smoking cessation interventions (2 active interventions and a “no-intervention” arm), we demonstrate how the uncertainty analysis could be conducted in DCEA to inform policy recommendations. We perform value of information (VOI) analysis and analysis of covariance (ANCOVA) to identify what additional evidence would add most value to the level of confidence in the DCEA results. The analyses were conducted for both national and local authority-level decisions to explore whether the conclusions about decision uncertainty based on the national-level estimates could inform local policy. For the comparisons between active interventions and “no intervention,” there was no uncertainty that providing the smoking cessation intervention would increase overall health but increase inequality. However, there was uncertainty in the direction of both impacts when comparing between the 2 active interventions. VOI and ANCOVA show that uncertainty in socioeconomic differences in intervention effectiveness and uptake contributes most to the uncertainty in the DCEA results. This suggests potential value of collecting additional evidence on intervention-related inequalities for this evaluation. We also found different levels of decision uncertainty between settings, implying that different types and levels of additional evidence are required for decisions in different localities.

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