title: A general HIV incidence inference scheme based on likelihood of individual level data and a population renewal equation.
abstract: We derive a new method to estimate the age specific incidence of an infection with a differential mortality, using individual level infection status data from successive surveys. The method consists of a) an SI-type model to express the incidence rate in terms of the prevalence and its derivatives as well as the difference in mortality rate, and b) a maximum likelihood approach to estimate the prevalence and its derivatives. Estimates can in principle be obtained for any chosen age and time, and no particular assumptions are made about the epidemiological or demographic context. This is in contrast with earlier methods for estimating incidence from prevalence data, which work with aggregated data, and the aggregated effect of demographic and epidemiological rates over the time interval between prevalence surveys. Numerical simulation of HIV epidemics, under the presumption of known excess mortality due to infection, shows improved control of bias and variance, compared to previous methods. Our analysis motivates for a) effort to be applied to obtain accurate estimates of excess mortality rates as a function of age and time among HIV infected individuals and b) use of individual level rather than aggregated data in order to estimate HIV incidence rates at times between two prevalence surveys.
authors.affiliation: South African Centre for Epidemiological Modelling and Analysis, Stellenbosch University, Stellenbosch, South Africa. sgmahiane@sun.ac.za
authors.forename: Guy Severin
authors.initials: GS
authors.lastname: Mahiane
authors.forename: Rachid
authors.initials: R
authors.lastname: Ouifki
authors.forename: Hilmarie
authors.initials: H
authors.lastname: Brand
authors.forename: Wim
authors.initials: W
authors.lastname: Delva
authors.forename: Alex
authors.initials: A
authors.lastname: Welte
mesh_terms: D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D000367:Age Factors; D003198:Computer Simulation; D058872:Epidemics; D006678:HIV / Q000378:metabolism*; D015658:HIV Infections / Q000453:epidemiology* / Q000401:mortality; D006679:HIV Seropositivity; D015528:HIV Seroprevalence; D006801:Humans; D015994:Incidence; D016013:Likelihood Functions; D008875:Middle Aged; D015233:Models, Statistical; D015995:Prevalence; D011336:Probability; D013997:Time Factors