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Repeat screening for HIV: when to test and why.

E H Kaplan1, G A Satten

  • 1Management Sciences and Public Health, Yale School of Management, New Haven, Connecticut, 06520-8200, USA. edward.kaplan@yale.edu

Journal of Acquired Immune Deficiency Syndromes (1999)
|June 3, 2000
PubMed
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This study introduces models for repeat human immunodeficiency virus (HIV) screening, linking undetected infections to testing intervals. It helps optimize screening frequency based on prevalence, costs, and testing technology selection.

Area of Science:

  • Epidemiology
  • Public Health
  • Biostatistics

Background:

  • Constant low human immunodeficiency virus (HIV) incidence presents unique challenges for screening programs.
  • Effective repeat HIV screening is crucial for managing public health and individual outcomes.

Purpose of the Study:

  • To develop and present models for optimizing repeat HIV screening intervals.
  • To establish methods for balancing screening costs against the costs of HIV infection.
  • To guide the selection of appropriate HIV testing technologies.

Main Methods:

  • Mathematical modeling of repeat HIV screening under constant low incidence.
  • Analysis of the relationship between undetected HIV prevalence and screening intervals.
  • Cost-benefit analysis to balance testing and infection costs.

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Main Results:

  • A direct correlation was found between the prevalence of undetected HIV infection and the frequency of screening.
  • The models provide a framework for selecting optimal screening intervals to achieve target prevalence levels.
  • The methodology can determine the cost of infection for which a given screening interval is optimal.

Conclusions:

  • The developed models offer a quantitative approach to optimize repeat HIV screening strategies.
  • These models can inform public health policy and resource allocation for HIV prevention and control.
  • The findings are applicable to diverse populations and existing screening programs, such as in the U.S. Army and Nevada commercial sex workers.