## Estimation and analysis of measures of disease for HIV infection in childbearing women using serial seroprevalence data.

##### Abstract

The prevalence and the incidence are two primary epidemiological parameters
in infectious disease modelling. The incidence is also closely related
to the force of infection or the hazard of infection in survival analysis
terms. The two measures carry the same information about a disease because
they measure the rate at which new infections occur. The disease
prevalence gives the proportion of infected individuals in the population at
a given time, while the incidence is the rate of new infections.
The thesis discusses methods for estimating HIV prevalence, incidence
rates and the force of infection, against age and time, using cross-sectional
seroprevalence data for pregnant women attending antenatal clinics. The
data was collected on women aged 12 to 47 in rural KwaZulu-Natal for each
of the years 2001 to 2006.
The generalized linear model for binomial response is used extensively.
First the logistic regression model is used to estimate annual HIV prevalence
by age. It was found that the estimated prevalence for each year
increases with age, to peaks of between 36% and 57% in the mid to late
twenties, before declining steadily toward the forties. Fitted prevalence for
2001 is lower than for the other years across all ages.
Several models for estimating the force of infection are discussed and applied.
The fitted force of infection rises with age to a peak of 0.074 at age
15, and then decreases toward higher ages. The force of infection measures
the potential risk of infection per individual per unit time. A proportional
hazards model of the age to infection is applied to the data, and shows that
additional variables such as partner’s age and the number of previous pregnancies
do have a significant effect on the infection hazard.
Studies for estimating incidence from multiple prevalence surveys are reviewed.
The relative inclusion rate (RIR), accounting for the fact that the
probability of inclusion in a prevalence sample depends on the individual’s
HIV status, and its role in incidence estimation is discussed as a possible
future approach of extending the current work.