PT  - JOURNAL ARTICLE
AU  - L M Heaton
AU  - R Komatsu
AU  - D Low-Beer
AU  - T B Fowler
AU  - P O Way
TI  - Estimating the number of HIV infections averted: an approach and its issues
AID  - 10.1136/sti.2008.030247
DP  - 2008 Aug 01
TA  - Sexually Transmitted Infections
PG  - i92--i96
VI  - 84
IP  - Suppl 1
4099  - http://sti.bmj.com/content/84/Suppl_1/i92.short
4100  - http://sti.bmj.com/content/84/Suppl_1/i92.full
SO  - Sex Transm Infect2008 Aug 01; 84
AB  - Objective: To propose a methodology to estimate the number of new HIV infections averted. Knowledge of HIV infection has increased tremendously and modelling tools to project current epidemics into the future have greatly improved. Different types of models can be used to estimate HIV infections averted, although the number of new HIV infections averted cannot be measured directly.Method: Using cohort-component population projections, a disease modelling-based approach was used to compare the observed epidemiology of a disease after programme initiation with an expected epidemiology from past trends before programme initiation. The concept of modelling infections averted in a disease modelling-based approach involves a comparison between an “expected” or baseline epidemic with an “estimated” one. A hypothetical example was featured in order to demonstrate the proposed methodology. Using both the Estimation and Projection Package (EPP) and the Spectrum demographic modelling program, the underlying annual incidence levels implied by both the baseline and estimated epidemics were examined.Results: The difference between baseline and estimated incidence levels is interpreted as “infections averted”. Strengths and limitations of the approach are discussed.Conclusions: In this study an expected epidemiological approach was compared to one based on observation. Once sufficient data become available, the validation of various country data including HIV prevalence, mortality, and behaviour must be done. Additional information related to behaviour change may be critical to further support arguments for a change in disease trend. It is therefore important to use all available data, consequently strengthening findings from a disease modelling-based approach on HIV infections averted.