Indirect Effects in Health Economic Models of Vaccines: Should We Include Them, and How Do We Model Them?
Rachel J. Oidtman et al.
What the paper says
Indirect effects refer to the population-level effects of vaccination on vaccinated and unvaccinated individuals, including herd protection, serotype replacement, and age shifts, which can substantially impact the real-world effectiveness of vaccination programs. Despite their importance, most economic evaluations omit indirect effects, likely owing to the complexity of underlying mechanisms, data limitations, and difficulties in disentangling confounding factors. Static models and dynamic transmission models (DTMs) are two common approaches for incorporating indirect effects in economic evaluations. Static models rely on simplified assumptions and readily available data but often overlook the temporal and population-level dynamics of disease transmission. In contrast, DTMs account for complex factors (e.g., population mixing, serotype competition, and long-term epidemiological changes) but require more complex model structure and extensive data. In this article, we illustrate the applications, strengths, and limitations of these two modeling approaches through case studies. We demonstrate how choices of model structure and assumptions, data sources, and parameter estimation methodologies can influence projected epidemiological and economic outcomes, sometimes leading to divergent conclusions. While international guidelines increasingly recognize the importance of including indirect effects in economic evaluations, limited methodological guidance from National Immunization Technical Advisory Groups (NITAGs) and health technology assessment (HTA) bodies continues to hinder consistent implementation. This paper advocates for a collaborative effort among stakeholders to generate high-quality surveillance data and establish guidelines for methodological considerations to model indirect effects. Such efforts will improve the accuracy of epidemiological and economic assessments of vaccination programs and strengthen evidence-based decision-making in public health.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.