OBJECTIVE: To develop methods for estimating the incidence of HIV-1 infection among adults from age-specific prevalence data derived in stable endemic conditions. METHODS: Two methods are proposed. The first method is the Cumulative Incidence and Survival Method which treats HIV-1 prevalence at any given age as the cumulative incidence of new infections at each preceding age, adjusted for mortality. A model for age-specific incidence is fitted to the data using maximum likelihood techniques. The other method is the Constant Prevalence Method whereby the incidence of new infections within a time interval (t-r, t) is calculated as the difference, after adjusting for mortality, between observed prevalence levels at two successive age intervals, whose mean ages are r years apart. The two methods were applied to data from Kampala, Uganda. RESULTS: Plausible estimates of age-specific and cumulative HIV-1 incidence were obtained from each of the methods. Estimates of HIV-1 incidence are sensitive to assumptions regarding the length of the survival period after infection and the stability of the epidemic. CONCLUSIONS: Reasonable estimates of HIV-1 incidence can be obtained from prevalence data derived in near-stable conditions. With the Constant Prevalence Method, these conditions may be relaxed if large sample sizes are available and age-reporting is good. The methods proposed could be used in the design and implementation of HIV-1 prevention trials. Cumulative incidence is a better indication of demographic impact than average age-specific incidence.
OBJECTIVE: To develop methods for estimating the incidence of HIV-1 infection among adults from age-specific prevalence data derived in stable endemic conditions. METHODS: Two methods are proposed. The first method is the Cumulative Incidence and Survival Method which treats HIV-1 prevalence at any given age as the cumulative incidence of new infections at each preceding age, adjusted for mortality. A model for age-specific incidence is fitted to the data using maximum likelihood techniques. The other method is the Constant Prevalence Method whereby the incidence of new infections within a time interval (t-r, t) is calculated as the difference, after adjusting for mortality, between observed prevalence levels at two successive age intervals, whose mean ages are r years apart. The two methods were applied to data from Kampala, Uganda. RESULTS: Plausible estimates of age-specific and cumulative HIV-1 incidence were obtained from each of the methods. Estimates of HIV-1 incidence are sensitive to assumptions regarding the length of the survival period after infection and the stability of the epidemic. CONCLUSIONS: Reasonable estimates of HIV-1 incidence can be obtained from prevalence data derived in near-stable conditions. With the Constant Prevalence Method, these conditions may be relaxed if large sample sizes are available and age-reporting is good. The methods proposed could be used in the design and implementation of HIV-1 prevention trials. Cumulative incidence is a better indication of demographic impact than average age-specific incidence.
Entities:
Keywords:
Adult; Africa; Africa South Of The Sahara; Age Factors; Demographic Factors; Developing Countries; Diseases; Eastern Africa; English Speaking Africa; Epidemics; Estimation Technics; Hiv Infections; Incidence; Measurement; Population; Population Characteristics; Prevalence; Research Methodology; Research Report; Uganda; Viral Diseases
Authors: R G White; E Vynnycky; J R Glynn; A C Crampin; A Jahn; F Mwaungulu; O Mwanyongo; H Jabu; H Phiri; N McGrath; B Zaba; P E M Fine Journal: Epidemiol Infect Date: 2007-01-12 Impact factor: 2.451
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Authors: Janet M McNicholl; J Steven McDougal; Punneeporn Wasinrapee; Bernard M Branson; Michael Martin; Jordan W Tappero; Philip A Mock; Timothy A Green; Dale J Hu; Bharat Parekh Journal: PLoS One Date: 2011-03-04 Impact factor: 3.240