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

Yakov Ben-Haim, Clifford C. Dacso, Nicola M. Zetola

Research output: Contribution to journalArticle

5 Citations (Scopus)

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.

Original languageEnglish (US)
Article number1091
JournalBMC Public Health
Volume12
Issue number1
DOIs
StatePublished - 2012
Externally publishedYes

Fingerprint

Public Policy
Health Policy
Uncertainty
Public Health
HIV
Decision Theory
Recurrence
Population
HIV Infections
Decision Making
Tuberculosis
Theoretical Models
Guidelines

Keywords

  • Epidemiology
  • HIV-AIDS
  • Info-gap
  • Public health
  • Robustness
  • TB management
  • Uncertainty

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health

Cite this

Info-gap management of public health Policy for TB with HIV-prevalence and epidemiological uncertainty. / Ben-Haim, Yakov; Dacso, Clifford C.; Zetola, Nicola M.

In: BMC Public Health, Vol. 12, No. 1, 1091, 2012.

Research output: Contribution to journalArticle

Ben-Haim, Yakov ; Dacso, Clifford C. ; Zetola, Nicola M. / Info-gap management of public health Policy for TB with HIV-prevalence and epidemiological uncertainty. In: BMC Public Health. 2012 ; Vol. 12, No. 1.
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