In the context of a couples cohort established to evaluate an optimised couples-focused behavioural intervention in rural South Africa, we examined: (1) Is couples’ relationship quality (RQ) associated with couples HIV testing and counselling (CHTC) uptake? (2) Does CHTC uptake or the intervention components uptake improve subsequent RQ? Enrolled couples, (n = 218), previously naïve to couples HIV testing, were invited to two group sessions and offered four couples counselling sessions (CS1-CS4), as part of the intervention and administered a questionnaire individually at baseline, four weeks, and four months, which included item-scales to measure RQ: satisfaction, intimacy, dyadic trust, conflict, and mutual constructive communication. Logistic models indicated that no baseline RQ measures were significantly associated with CHTC uptake. Linear regression models showed that CHTC uptake before four weeks assessment significantly improved couples’ satisfaction and trust at four weeks, and intimacy at four months. Attending at least one CS was associated with increased satisfaction, intimacy, and decreased conflict within couples at four weeks; the improvement in intimacy was sustained at four months. Consistent with the theoretical interdependence model, our findings suggest that CHTC and CS seemed to strengthen aspects of relationship quality, possibly leading to further collaboration in managing lifestyle changes and treatment adherence.

In this paper, we introduce two semiparametric single-index models for spatially and temporally correlated data. Our first model has spatially and temporally correlated random effects that are additive to the nonparametric function, which we refer to as the “semiparametric spatio-temporal single-index model (ST-SIM)”. The second model integrates the spatially correlated effects into the nonparametric function, and the time random effects are additive to the single-index function. We refer to our second model as the “semiparametric integrated spatio-temporal single-index model (IST-SIM)”. Two algorithms based on a Markov chain expectation maximization are introduced to simultaneously estimate the model parameters, spatial effects, and time effects of the two models. We compare the performance of our models using several simulation studies. The proposed models are then applied to mortality data from six major cities in South Korea. Our results suggest that IST-SIM (1) is more flexible than ST-SIM because the former can estimate various nonparametric functions for different locations, while ST-SIM enforces the mortality functions having the same shape over locations; (2) provides better estimation and prediction, and (3) does not need restrictions for the single-index coefficients to fix the identifiability problem.