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Open Access Research article

Info-gap management of public health Policy for TB with HIV-prevalence and epidemiological uncertainty

Yakov Ben-Haim1, Clifford C Dacso2 and Nicola M Zetola3*

Author Affiliations

1 Yitzhak Moda’i Chair in Technology and Economics, Technion—Israel Institute of Technology, Haifa 32000, Israel

2 Molecular & Cell Biology and Medicine, Baylor College of Medicine, Houston, Texas

3 Division of Infectious Diseases, Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania

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BMC Public Health 2012, 12:1091  doi:10.1186/1471-2458-12-1091

Published: 19 December 2012

Abstract

Background

Formulation and evaluation of public health policy commonly employs science-based mathematical models. For instance, epidemiological dynamics of TB is dominated, in general, by flow between actively and latently infected populations. Thus modelling is central in planning public health intervention. However, models are highly uncertain because they are based on observations that are geographically and temporally distinct from the population to which they are applied.

Aims

We aim to demonstrate the advantages of info-gap theory, a non-probabilistic approach to severe uncertainty when worst cases cannot be reliably identified and probability distributions are unreliable or unavailable. Info-gap is applied here to mathematical modelling of epidemics and analysis of public health decision-making.

Methods

Applying info-gap robustness analysis to tuberculosis/HIV (TB/HIV) epidemics, we illustrate the critical role of incorporating uncertainty in formulating recommendations for interventions. Robustness is assessed as the magnitude of uncertainty that can be tolerated by a given intervention. We illustrate the methodology by exploring interventions that alter the rates of diagnosis, cure, relapse and HIV infection.

Results

We demonstrate several policy implications. Equivalence among alternative rates of diagnosis and relapse are identified. The impact of initial TB and HIV prevalence on the robustness to uncertainty is quantified. In some configurations, increased aggressiveness of intervention improves the predicted outcome but also reduces the robustness to uncertainty. Similarly, predicted outcomes may be better at larger target times, but may also be more vulnerable to model error.

Conclusions

The info-gap framework is useful for managing model uncertainty and is attractive when uncertainties on model parameters are extreme. When a public health model underlies guidelines, info-gap decision theory provides valuable insight into the confidence of achieving agreed-upon goals.

Keywords:
TB management; HIV-AIDS; Public health; Epidemiology; Uncertainty; Robustness; Info-gap