  The age dynamics of sexual partnership formation determine patterns of
sexually transmitted disease transmission and have long been a focus of
researchers studying human immunodeficiency virus. Data on self-reported sexual
partner age distributions are available from a variety of sources. We sought to
explore statistical models that accurately predict the distribution of sexual
partner ages over age and sex. We identified which probability distributions
and outcome specifications best captured variation in partner age and
quantified the benefits of modelling these data using distributional
regression. We found that distributional regression with a sinh-arcsinh
distribution replicated observed partner age distributions most accurately
across three geographically diverse data sets. This framework can be extended
with well-known hierarchical modelling tools and can help improve estimates of
sexual age-mixing dynamics.
