Assessment of sexual mixing patterns

Sexual bridging socially and over time: a simulation model exploring the relative effects of mixing and concurrency on viral sexually transmitted infection transmission

BACKGROUND

Sexual partnerships between people at higher and lower risk for sexually transmitted infections (STIs) (i.e., bridging) occur through dissortative mixing and concurrent partnerships, yet the relative effects of these network patterns on population STI spread are poorly understood.

GOAL

Using a stochastic model, the authors investigated the impact of mixing and concurrency on the spread of a persistent viral STI.

STUDY DESIGN

A total of 1,050 populations were simulated of 1,000 subjects over 400 weeks with varied concurrency levels and mixing patterns. STI prevalence and the average number of secondary transmissions per subject were analyzed with regression.

RESULTS

Mixing had a greater impact on prevalence for all groups, whereas concurrency was significant for only the lowest activity group. Mixing patterns moderated the magnitude of concurrency's impact on secondary transmissions.

CONCLUSIONS

Through connecting subgroups of differential risk, sexual mixing facilitates dissemination of STIs throughout a population. Concurrency expedites transmission by shortening the time between sexual contacts among infected and susceptible persons, particularly during the highly infectious period.

Uncertainties in predicting the demographic impact of AIDS

A model giving the demographic impact of AIDS is analysed to examine the sensitivity of the projections when various complicating features are included. The model deals with age and sexual partner change rate as continuous variables and uses a device to specify arbitrary correlations between the ages of the people who form sexual partnerships. The device ensures consistency, in that the amount of partner formation is the same regardless of whether the partnerships are counted from the point of view of males or females. Arbitrary correlation between partner change rate and fertility is also permitted. The results show the uncertainty in model predictions that population growth will reduce over the next 20 years to approximately zero in parts of East Africa severely affected by the AIDS epidemic. The main sources of uncertainty in the model predictions are assumptions concerning the correlation between ages in a partnership, the correlation between partner change rate and fertility, the incubation period of AIDS, and the variability of the female partner change rate.

The impact of sexual mixing patterns on the spread of AIDS

It has previously been demonstrated that the distribution of sexual partner change rates may not be sufficient behavioral information to predict the spread of AIDS. A more adequate description of the sexual network through which AIDS may spread will require information on who mixes with whom. Previous models indicated that a highly "assortive" pattern of sexual mixing, in which people of high partner change rates nearly always partner similar people, would result in a relatively rapid epidemic but one that would ultimately be rather small as it would be largely confined to those with high partner change rates. However, in earlier models it was necessary to arbitrarily vary either the partnering patterns or the distribution of partner change rates as the epidemic progressed in order to ensure consistency in the partnering patterns. Consequently, the "pure" effects of assortiveness on the size of the epidemic could not be assessed. In this paper a new model is described in which consistency in partnering patterns is maintained as the epidemic selectively depletes those with higher partner change rates. However, this model maintains consistency without arbitrary adjustments to either the assortive nature of partnering or important features of the distribution of the partner change rate. It is then possible to test the effects of assortiveness on the outcome of the epidemic without important confounding effects from the mathematical device used to maintain consistency in partnering. A practical sensitivity test on the impact of assortiveness on model predictions shows that, contrary to expectations, increasing assortiveness can, under certain circumstances, lead to larger epidemics.