Determining the likely place of HIV acquisition for migrants in Europe combining subject-specific information and biomarkers data. Statistical Methods in Medical Research
Pantazis et al. aimed to propose a method to derive subject-specific estimates of unknown HIV infection times among migrants in Europe using information from HIV biomarkers’ measurements, demographic, clinical, and behavioral data. For example, the method could determine if HIV-positive migrants participating in the Advancing Migrant Access to Health Services in Europe (aMASE) study, diagnosed in Europe, were infected pre- or post-migration.
The authors assumed that CD4 cell count (CD4) and HIV-RNA viral load trends after HIV infection follow a bivariate linear mixed model. Using post-diagnosis CD4 and viral load measurements and applying the Bayes’ rule, they derived the posterior distribution of the HIV infection time, whereas the prior distribution was informed by AIDS status at diagnosis and behavioral data. Parameters of the CD4–viral load and time-to-AIDS models were estimated using data from a large study of individuals with known HIV infection times (CASCADE).
Simulations showed substantial predictive ability (e.g. 84% of the infections were correctly classified as pre- or post-migration). Application to the aMASE study (n=2009) showed that 47% of African migrants and 67% to 72% of migrants from other regions were most likely infected post-migration.
In conclusion, applying a Bayesian method based on bivariate modeling of CD4 and viral load, and subject-specific information, the authors found that the majority of HIV-positive migrants in aMASE were most likely infected after their migration to Europe.