  During the last two decades, a number of countries or cities established
heat-health warning systems in order to alert public health authorities when
some heat indicator exceeds a predetermined threshold. Different methods were
considered to establish thresholds all over the world, each with its own
strengths and weaknesses. The common ground is that current methods are based
on exposure-response function estimates that can fail in many situations. The
present paper aims at proposing several data-driven methods to establish
thresholds using historical data of health issues and environmental indicators.
The proposed methods are model-based regression trees (MOB), multivariate
adaptive regression splines (MARS), the patient rule-induction method (PRIM)
and adaptive index models (AIM). These methods focus on finding relevant splits
in the association between indicators and the health outcome but do it in
different fashions. A simulation study and a real-world case study hereby
compare the discussed methods. Results show that proposed methods are better at
predicting adverse days than current thresholds and benchmark methods. The
results nonetheless suggest that PRIM is overall the more reliable method with
low variability of results according to the scenario or case.
