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PubMed · 10209129

HIV.

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A McKnight. 1999-04-08. HIV.. https://doi.org/10.1016/s0960-9822(99)80150-x

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Using AIDS mortality data to reconstruct HIV/AIDS epidemics.

The back-calculation methods used to reconstruct HIV epidemics and to predict future AIDS cases use data on the number of AIDS reported cases and information about the time from HIV infection to AIDS diagnosis. In this paper we modify a back-calculation method, presented in Verdecchia and Mariotto, to estimate HIV incidence from AIDS mortality data rather than the AIDS registered cases. In this application the AIDS incubation time is replaced by the time from HIV infection to AIDS death. Since AIDS is an irreversible condition leading to death within a few years, mortality data is expected to be approximately as informative as AIDS data. The main application of this method would be in countries where an AIDS registry is not available or where the registration of AIDS cases is not consistent, for example, due to changes in the definition of AIDS. Application to the Italian AIDS mortality data shows that the number of AIDS cases reconstructed from this method is very close to the registered number of cases. Also, the two estimates of HIV incidence and prevalence obtained from back-calculation on both AIDS mortality data and the number of AIDS reported cases were very similar, although there was more variability in the estimates obtained from mortality data.

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Uncertainty of AIDS incubation time and its effects on back-calculation estimates.

Incubation time is the period from the onset of HIV infection to AIDS. The distribution of the incubation time is one of the main parameters of the back-calculation method for the estimation of incidence of HIV infection. Because of the long and variable incubation time, the assessment of its distribution is uncertain and this uncertainty spreads through the back-calculation method and affects the estimation of the precision of incidence of HIV infection. We propose a method to investigate the sensitivity of the estimates to variations of the incubation times, with particular regard to the covariate AGE in the modelling of the incubation period, making use of the parametric bootstrap. An application to the HIV epidemic in Italy is presented. The amplification of the uncertainty of the HIV incidence estimates resulting from the implementation of our proposed method tends to concentrate around the earlier periods of the epidemic, corresponding to the right tail of the incubation time distribution, which is very sensitive to small perturbations.

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