Advanced Public Health Economic Evaluation Training Course

Course overview

Public health teams must often choose between programmes whose benefits occur at different times, affect different population groups, and depend on uncertain uptake, delivery costs and long-term outcomes. A screening expansion, vaccination campaign, smoking cessation service or community mental health intervention may appear promising, yet decision makers need defensible evidence on value for money, affordability, equity and uncertainty. This course equips experienced health and social care professionals to build, critique and communicate economic evaluations that can withstand review by commissioners, finance teams, clinical leaders and policy panels.

Participants work through advanced cost-effectiveness and cost-utility analysis, including decision trees, Markov models, probabilistic sensitivity analysis, discounting, modelling of treatment pathways, and interpretation of incremental cost-effectiveness ratios. The course addresses QALYs, DALYs, health-state utilities, resource-use measurement, costing perspectives, budget impact analysis and distributional cost-effectiveness analysis. Participants also learn to select model structures, identify credible parameter sources, manage uncertainty, test structural assumptions and apply CHEERS 2022 reporting requirements.

Instructor-led technical sessions are paired with worked public health cases and guided model-building exercises using Microsoft Excel, R and TreeAge Pro. Participants develop an economic evaluation plan and an auditable model specification for a chosen or supplied intervention, including a decision problem, parameter register, base-case analysis, sensitivity-analysis plan and decision-ready results narrative. This provides a practical document set that can be adapted for a live commissioning, service redesign or funding submission.

The course is suited to analysts and senior professionals who already contribute to public health evidence, commissioning or evaluation work and now need to lead or quality-assure more complex economic analyses.

Course objectives

By the end of this course, participants will be able to:

• Construct a decision analytic model using decision-tree and Markov state-transition structures for a public health intervention
• Calculate incremental costs, QALYs, DALYs and incremental cost-effectiveness ratios from model outputs
• Specify costing methods across health care, social care, patient and societal perspectives
• Conduct deterministic, scenario and probabilistic sensitivity analyses using parameter distributions and Monte Carlo simulation
• Produce cost-effectiveness planes, cost-effectiveness acceptability curves and net monetary benefit analyses
• Assess equity implications using distributional cost-effectiveness analysis and equity weighting assumptions
• Develop a budget impact model that distinguishes affordability from cost-effectiveness
• Prepare a CHEERS 2022-aligned economic evaluation plan and technical results summary

Benefits of attending

Target competencies

Who should attend

• Public Health Analysts — who need to model and defend the value for money of population-health interventions
• Health Economists — who want stronger capability in public health modelling, equity analysis and budget impact assessment
• Commissioning Managers — who must assess business cases and challenge economic assumptions before funding decisions
• Health Intelligence Leads — who translate epidemiological, service-use and outcome data into decision evidence
• Programme Evaluation Managers — who need to design evaluations that generate usable economic evidence alongside outcomes
• Policy Advisers and Strategy Managers — who prepare evidence for prevention, health improvement and resource-allocation decisions

Requirements and prerequisites

Participants should be comfortable interpreting basic health economic concepts, including costs, outcomes, comparators, ICERs and QALYs, and should have experience working with service, epidemiological or evaluation data. Familiarity with Excel formulas, tables and charts is assumed. Prior exposure to decision trees, Markov models or R is helpful but not essential; guided templates and code examples are provided. Participants should bring a laptop capable of running Excel and, where organisational policy permits, R/RStudio. Advanced mathematics, prior programming expertise and a completed economics degree are not required.

Training methodology

The five days combine instructor-led modelling demonstrations with structured exercises based on vaccination, screening and prevention-service cases. Participants build and test model components in Excel and TreeAge Pro, review selected R outputs, and work in groups to challenge parameter choices, costing perspectives and equity assumptions. Facilitated critique sessions use published-style evidence packs and decision papers rather than abstract calculations. Each participant finishes by applying the methods to a supplied or workplace-relevant intervention and producing a practical economic evaluation plan.

Course outline

Day 1: Framing public health economic decisions

• Decision problem formulation using population, intervention, comparator, outcomes and time horizon
• Economic evaluation perspectives: health system, public sector, societal and patient
• Comparator selection and treatment of usual care in public health programmes
• Cost-effectiveness, cost-utility, cost-benefit and cost-consequence analysis selection
• QALYs, DALYs and capability measures for population-health interventions
• Discounting costs and outcomes over long-term prevention horizons
• CHEERS 2022 reporting items and model transparency requirements

Workshop: Participants scope an economic evaluation for a targeted vaccination programme and produce a decision-problem protocol with comparators, perspective, outcome measures and time horizon.

Day 2: Building decision models and costing interventions

• Decision-tree construction for short-term intervention pathways
• Markov state-transition models for chronic disease progression
• Cycle length, half-cycle correction and absorbing health states
• Transition probability estimation from incidence, prevalence and survival data
• Micro-costing, gross costing and unit-cost source selection
• Resource-use measurement across delivery, treatment and downstream care
• Parameter registers, evidence hierarchies and source documentation

Workshop: Participants build a Markov model structure for a smoking cessation intervention and complete a parameter register covering states, transitions, costs and utilities.

Day 3: Analysing uncertainty and model validity

• Base-case analysis and incremental analysis rules
• One-way sensitivity analysis and tornado diagram interpretation
• Scenario analysis for implementation, uptake and adherence assumptions
• Probabilistic sensitivity analysis using beta, gamma, lognormal and Dirichlet distributions
• Monte Carlo simulation and convergence checks
• Cost-effectiveness planes and cost-effectiveness acceptability curves
• Structural uncertainty, external validation and calibration approaches

Workshop: Participants run deterministic and probabilistic sensitivity analyses on a screening model and produce a tornado diagram, cost-effectiveness plane and acceptability curve.

Day 4: Equity, affordability and public health implementation

• Equity-relevant subgroup analysis by deprivation, ethnicity and geography
• Distributional cost-effectiveness analysis principles
• Equity weights and opportunity-cost assumptions
• Budget impact analysis structure and annual cash-flow modelling
• Distinguishing affordability, cost-effectiveness and fiscal consequences
• Modelling reach, uptake, fidelity and intervention scale-up
• Value of information analysis for prioritising further evidence collection

Workshop: Participants assess a community prevention programme using subgroup outcomes and a three-year budget impact model, then draft a recommendation that states equity and affordability trade-offs.

Day 5: Communicating and quality-assuring economic evidence

• Critical appraisal of model structure, data inputs and assumptions
• Interpreting ICERs, net monetary benefit and decision uncertainty
• Creating technical appendices, model diagrams and parameter tables
• Writing decision-ready economic evaluation summaries
• Presenting uncertainty without overstating model precision
• Responding to commissioner, finance and stakeholder challenge
• Model governance, version control and reproducibility checks

Workshop: Participants complete and peer-review an economic evaluation plan and executive decision briefing for their selected intervention, producing an auditable action plan for workplace application.

Tools & standards covered

Microsoft Excel, R, TreeAge Pro, CHEERS 2022

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions