J T Rowley1, R M Anderson. 1. Biology Department, Imperial College of Science, Technology and Medicine, London, UK.
Abstract
OBJECTIVES: This study explores how mathematical models can be used to examine the relationship between the effectiveness and costs of different prevention strategies. The analysis, based on a model designed to simulate the spread of HIV in sub-Saharan Africa, illustrates how the impact and relative cost-effectiveness of a prevention programme are sensitive to a number of factors including the rate of spread of HIV prior to the introduction of the programme, the measure used to evaluate the impact of the programme, and the point when the programme is introduced in the epidemic. RESULTS: Assessing the impact and cost-effectiveness of different HIV prevention strategies is problematic. Not only are there difficulties in collecting the relevant data, but the impact of a prevention programme may be highly non-linear in character because of the many factors that determine the net rate of viral transmission. The long incubation period of HIV further complicates the analysis as the benefits from preventing a case of infection extend over a number of years. CONCLUSIONS: The results highlight the need to collect more data on the impact and costs of prevention programmes if we are to ensure that the available resources for HIV prevention are to be used both efficiently and equitably.
OBJECTIVES: This study explores how mathematical models can be used to examine the relationship between the effectiveness and costs of different prevention strategies. The analysis, based on a model designed to simulate the spread of HIV in sub-Saharan Africa, illustrates how the impact and relative cost-effectiveness of a prevention programme are sensitive to a number of factors including the rate of spread of HIV prior to the introduction of the programme, the measure used to evaluate the impact of the programme, and the point when the programme is introduced in the epidemic. RESULTS: Assessing the impact and cost-effectiveness of different HIV prevention strategies is problematic. Not only are there difficulties in collecting the relevant data, but the impact of a prevention programme may be highly non-linear in character because of the many factors that determine the net rate of viral transmission. The long incubation period of HIV further complicates the analysis as the benefits from preventing a case of infection extend over a number of years. CONCLUSIONS: The results highlight the need to collect more data on the impact and costs of prevention programmes if we are to ensure that the available resources for HIV prevention are to be used both efficiently and equitably.
Entities:
Keywords:
Acquired Immunodeficiency Syndrome--prevention and control; Africa; Africa South Of The Sahara; Cost Effectiveness; Data Analysis; Demographic Factors; Demographic Impact; Developing Countries; Diseases; Evaluation; Evaluation Indexes; Hiv Infections--prevention and control; Mathematical Model; Models, Theoretical; Population; Population Dynamics; Quantitative Evaluation; Research Methodology; Viral Diseases