CT scans in childhood predict subsequent brain cancer: Finite mixture modelling can help separate reverse causation scans from those that may be causal

Abstract

BackgroundExcess brain cancers observed after computed tomography (CT) scans could be caused by ionizing radiation. However, as scans are often used to investigate symptoms of brain cancer, excess cancers could also be due to reverse causation bias. We used finite mixture models (FMM) to differentiate CT exposures that are plausibly causal from those due to reverse causation. MethodsPersons with at least one CT scan exposure and a subsequent diagnosis of brain cancer were selected from a cohort of 11 million young Australians. We fitted FMMs and used the posterior probability to inform the choice of exclusion periods. We validated our findings using a separate clinical dataset describing the time between first symptoms and brain cancer diagnosis (pre-diagnostic symptomatic interval; PSI). ResultsThe cohort included 1028 persons with a diagnosed brain tumor and exposed to a total of 1,450 CT scans. The best-fitting model was a generalized linear mixture model using the exponential distribution with three latent classes and two covariates (age at exposure and year of exposure). The 99th percentile classifier cutoff was 18.9 months. The sample-size weighted mean of the 99th percentile of the PSI, derived from clinical data, was 15.6 months. ConclusionsTo minimize reverse causation bias in studies of CT scan and brain cancer, the optimal exclusion period is one to two years (depending on the choice of classifier). This information will inform the interpretation of current and future studies.