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When Did HIV Incidence Peak in Harare, Zimbabwe? Back-Calculation from Mortality Statistics

HIV prevalence has recently begun to decline in Zimbabwe, a result of both high levels of AIDS mortality and a reduction in incident infections. An important component in understanding the dynamics in HIV prevalence is knowledge of past trends in incidence, such as when incidence peaked and at what...

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Detalles Bibliográficos
Autores principales: Lopman, Ben, Gregson, Simon
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2248619/
https://www.ncbi.nlm.nih.gov/pubmed/18320032
http://dx.doi.org/10.1371/journal.pone.0001711
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author Lopman, Ben
Gregson, Simon
author_facet Lopman, Ben
Gregson, Simon
author_sort Lopman, Ben
collection PubMed
description HIV prevalence has recently begun to decline in Zimbabwe, a result of both high levels of AIDS mortality and a reduction in incident infections. An important component in understanding the dynamics in HIV prevalence is knowledge of past trends in incidence, such as when incidence peaked and at what level. However, empirical measurements of incidence over an extended time period are not available from Zimbabwe or elsewhere in sub-Saharan Africa. Using mortality data, we use a back-calculation technique to reconstruct historic trends in incidence. From AIDS mortality data, extracted from death registration in Harare, together with an estimate of survival post-infection, HIV incidence trends were reconstructed that would give rise to the observed patterns of AIDS mortality. Models were fitted assuming three parametric forms of the incidence curve and under nine different assumptions regarding combinations of trends in non-AIDS mortality and patterns of survival post-infection with HIV. HIV prevalence was forward-projected from the fitted incidence and mortality curves. Models that constrained the incidence pattern to a cubic spline function were flexible and produced well-fitting, realistic patterns of incidence. In models assuming constant levels of non-AIDS mortality, annual incidence peaked between 4 and 5% between 1988 and 1990. Under other assumptions the peak level ranged from 3 to 8% per annum. However, scenarios assuming increasing levels of non-AIDS mortality resulted in implausibly low estimates of peak prevalence (11%), whereas models with decreasing underlying crude mortality could be consistent with the prevalence and mortality data. HIV incidence is most likely to have peaked in Harare between 1988 and 1990, which may have preceded the peak elsewhere in Zimbabwe. This finding, considered alongside the timing and location of HIV prevention activities, will give insight into the decline of HIV prevalence in Zimbabwe.
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spelling pubmed-22486192008-03-05 When Did HIV Incidence Peak in Harare, Zimbabwe? Back-Calculation from Mortality Statistics Lopman, Ben Gregson, Simon PLoS One Research Article HIV prevalence has recently begun to decline in Zimbabwe, a result of both high levels of AIDS mortality and a reduction in incident infections. An important component in understanding the dynamics in HIV prevalence is knowledge of past trends in incidence, such as when incidence peaked and at what level. However, empirical measurements of incidence over an extended time period are not available from Zimbabwe or elsewhere in sub-Saharan Africa. Using mortality data, we use a back-calculation technique to reconstruct historic trends in incidence. From AIDS mortality data, extracted from death registration in Harare, together with an estimate of survival post-infection, HIV incidence trends were reconstructed that would give rise to the observed patterns of AIDS mortality. Models were fitted assuming three parametric forms of the incidence curve and under nine different assumptions regarding combinations of trends in non-AIDS mortality and patterns of survival post-infection with HIV. HIV prevalence was forward-projected from the fitted incidence and mortality curves. Models that constrained the incidence pattern to a cubic spline function were flexible and produced well-fitting, realistic patterns of incidence. In models assuming constant levels of non-AIDS mortality, annual incidence peaked between 4 and 5% between 1988 and 1990. Under other assumptions the peak level ranged from 3 to 8% per annum. However, scenarios assuming increasing levels of non-AIDS mortality resulted in implausibly low estimates of peak prevalence (11%), whereas models with decreasing underlying crude mortality could be consistent with the prevalence and mortality data. HIV incidence is most likely to have peaked in Harare between 1988 and 1990, which may have preceded the peak elsewhere in Zimbabwe. This finding, considered alongside the timing and location of HIV prevention activities, will give insight into the decline of HIV prevalence in Zimbabwe. Public Library of Science 2008-03-05 /pmc/articles/PMC2248619/ /pubmed/18320032 http://dx.doi.org/10.1371/journal.pone.0001711 Text en Lopman, Gregson. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Lopman, Ben
Gregson, Simon
When Did HIV Incidence Peak in Harare, Zimbabwe? Back-Calculation from Mortality Statistics
title When Did HIV Incidence Peak in Harare, Zimbabwe? Back-Calculation from Mortality Statistics
title_full When Did HIV Incidence Peak in Harare, Zimbabwe? Back-Calculation from Mortality Statistics
title_fullStr When Did HIV Incidence Peak in Harare, Zimbabwe? Back-Calculation from Mortality Statistics
title_full_unstemmed When Did HIV Incidence Peak in Harare, Zimbabwe? Back-Calculation from Mortality Statistics
title_short When Did HIV Incidence Peak in Harare, Zimbabwe? Back-Calculation from Mortality Statistics
title_sort when did hiv incidence peak in harare, zimbabwe? back-calculation from mortality statistics
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2248619/
https://www.ncbi.nlm.nih.gov/pubmed/18320032
http://dx.doi.org/10.1371/journal.pone.0001711
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