Chapter 18: Public health policy for cervical cancer prevention: The role of decision science, economic evaluation, and mathematical modeling

Abstract

Several factors are changing the landscape of cervical cancer control, including a better understanding of the natural history of human papillomavirus (HPV), reliable assays for detecting high-risk HPV infections, and a soon to be available HPV-16/18 vaccine. There are important differences in the relevant policy questions for different settings. By synthesizing and integrating the best available data, the use of modeling in a decision analytic framework can identify those factors most likely to influence outcomes, can guide the design of future clinical studies and operational research, can provide insight into the cost-effectiveness of different strategies, and can assist in early decision-making when considered with criteria such as equity, public preferences, and political and cultural constraints.

Introduction

When making decisions we can either base choices on evidence and the explicit comparison of alternative strategies or we can use the intuitive opinions of experts. Decision science provides a quantitative framework for evaluating available evidence and uses explicit models to synthesize data, acknowledge uncertainty, and extrapolate from specific studies [1].

Several innovations are changing the landscape for cervical cancer prevention and control, with important differences in the most relevant policy questions for different settings. In countries with existing screening programs, pressing questions center around the optimal use of HPV-DNA testing, reducing disparities, and defining synergies between screening and vaccination. Important questions in low-resource settings include how to implement and sustain screening strategies with fewer technical and infrastructure requirements than required by conventional cytology-based programs, how to target the appropriate age groups for screening, and how to overcome the logistical barriers associated with delivering a three-dose vaccine during early adolescence. Key questions include: what is the coverage that could realistically be achieved with screening between ages 35 and 40 versus vaccination at ages 10–12? Will both boys and girls need to be vaccinated? Is there a combined screening/vaccination strategy that could be cost-effective or will decision makers need to choose between the two?

No single empirical study can evaluate all possible strategies to inform these complex policy questions. By integrating the best biologic, epidemiologic, economic, and behavioral data, the use of modeling in a decision analytic framework can assist in early decision-making, can highlight where better data are needed and identify those factors most likely to influence outcomes, can inform clinical study design and guide the conduct of operational research, and can provide insight into the potential cost-effectiveness of different strategies. Here we review the elements of cost-effectiveness analysis and identify the methodological issues most relevant to upcoming policy questions in the cervical cancer field.