Identification of seasonal variation in the diagnosis of acute myeloid leukaemia: a population-based study

Abstract

Until now, the role that seasonal factors play in the aetiology of acute myeloid leukaemia (AML) has been unclear. Demonstration of seasonality in AML diagnosis would provide supportive evidence of an underlying seasonal aetiology. To investigate the potential seasonal and long-term trends in AML diagnosis in an overall population and in subgroups according to sex and age, we used population-based data from a Spanish hospital discharge registry. We conducted a larger study than any to date of 26 472 cases of AML diagnosed in Spain between 2004 and 2015. Using multivariable Poisson generalized linear autoregressive moving average modelling, we found an upward long-term trend, with monthly incidence rates of AML annually increasing by 0.4% [95% confidence interval (CI), 0.2%-0.6%; p = 0.0011]. January displayed the highest incidence rate of AML, with a minimum average difference of 7% when compared to February (95% CI, 2%-12%; p = 0.0143) and a maximum average difference of 16% compared to November (95% CI, 11%-21%; p < 0.0001) and August (95% CI, 10%-21%; p < 0.0001). Such seasonal effect was consistent among subgroups according to sex and age. Our finding that AML diagnosis is seasonal strongly implies that seasonal factors, such as infectious agents or environmental triggers, influence the development and/or proliferation of disease, pointing to prevention opportunities.

Keywords: acute myeloid leukaemia; diagnosis; infection; leukaemias; seasonality.