Distributed Lag Models: An Analysis of Milan Mortality Data

The aim of this paper is to demonstrate a methodological development for distributed lag models using a case-crossover (clustering) approach to control for long timescale variation. As a demonstrative example, we perform an analysis of the Milan, Italy mortality data set using our newstatistical approach.

As scientific progress on the methodology and techniques related to air pollution exposure and associated health conditions is continuing, it is interesting to consider new techniques for estimation of associations that are widely known and published in the scientific literature. This study uses distributed lag non-linear models (DLNMs) to effectively represent and quantify associations showing non-linear and delayed effects in time-series data. In particular, we apply DLNMs to quantify mortality displacement as in. This is a methodological paper using real data as a demonstrative example.  Read more>>>>>>>>>>>