Using independent cross-sectional survey data to predict post-migration health trajectories among refugees by estimating transition probabilities and their variances.

Health research is often concerned with the transition of health conditions and their relation with given exposures, therefore requiring longitudinal data. However, such data is not always available and resource-intensive to collect. Our aim is to use a pseudo-panel of independent cross-sectional data (e.g., data of T 0 $T_0$ and T 1 $T_1$ ) to extrapolate and approximate longitudinal health trajectories ( T 0 $T_0$ - T 1 $T_1$ ). Methods will be illustrated by examples of studying contextual effects on health among refugees by calculating transition probabilities with associated variances. The data consist of two cross-sectional health surveys among randomly selected refugee samples in reception ( T 0 $T_0$ ) and accommodation centers ( T 1 $T_1$ ) located in Germany's third-largest federal state. Self-reported measures of physical and mental health, health-related quality of life, health care access, and unmet medical needs of 560 refugees were collected. Missing data were imputed by multiple imputation. For each imputed data set, transition probabilities were calculated based on (i) probabilistic discrete event systems with Moore-Penrose generalized inverse matrix method (PDES-MP) and (ii) propensity score matching (PSM). By application of sampling approaches, exploiting the fact that status membership is multinomially distributed, results of both methods were pooled by Rubin's Rule, accounting for within and between-imputation variance. Most of the analyzed estimates of the transition probabilities and their variances are comparable between both methods. However, it seems that they handle sparse cells differently: either assigning an average value for the transition probability for all states with high certainty (i) or assigning a more extreme value for the transition probability with large variance estimate (ii).