Particles size and composition in Mediterranean countries: geographical variability and short-term health effects
MED-PARTICLES Project 2011-2013
Under the Grant Agreement EU LIFE+ ENV/IT/327
ACTION 12.
Effect modification of Saharan dust advection days and of forest fires days on the association between particles and health endpoints: city-specific results and meta-analysis.
Summary: Detailed statistical protocol for the analysis of the effect modification of Saharan dust and forest fires days on the association between particles and health endpoints.
--------------------------------------------------------

Objective
The objective of this chapter of the statistical protocol is to provide detailed steps for the statistical analysis applied for the investigation of the effect modification of the Saharan dust and forest fires episodes in the association between the PM concentrations and health outcomes. The R statistical package (version 2.13.0, package mgcv) will be used for the statistical analysis.
Data
Exposure
In addition to the data already described in the previous chapter, other data will be collected and used in the statistical modelling, as described below:
DUST : indicator variable reporting, for each day, whether it was affected by Saharan dust
(yes=1), or not (no=0);
PM10dust : daily concentrations of PM10 from desert. These are equal to zero when no advection is identified, equal to actual concentrations from desert otherwise. Furthermore, since for the desert contribution a PM10 from rural monitoring station is needed, the variable assumes “missing” when there is dust advection, but the monitoring reading is missing at the rural site for that day;
PM10nodust : daily concentrations of PM10 from anthropogenic sources. These are equal to the difference between total PM10 and PM10dust.
FIRE : indicator variable reporting, for each day, whether it was affected by a forest fires episode, and its intensity: (yes, severe=3; yes, moderate=2, yes, mild=1; no=0);
FIRE01 : 2-level indicator variable reporting, for each day, whether it was affected by a forest fires episode (yes=1), or not (no=0).
Outcomes
Daily counts of cause-specific mortality and emergency hospitalizations will be collected under
Action 6 and used also in this epidemiological analysis.
Methods a) General Strategy and Time-trend control

The particles’ indices-health outcomes associations for each health outcome will be investigated using Poisson regression models allowing for overdispersion (using quasi(poisson) in R). Details on the models are already described in the previous chapter. b) Evaluation of effect of PM10 from desert
The main focus of this analysis will be the estimation of the relative contribution of desert dust and anthropogenic sources to the PM10-health associations. This will be approached in two complementary ways:
1.
Interaction analysis: an interaction term between PM10 and DUST yes/no indicator will be added to the main model. The results will represent the effect of PM10 during no-dust days, and the increment in the effect of PM10 from no-dust to dust days;
2.
Two-pollutant models: the two sources of PM10, from desert and from anthropogenic sources, will be added simultaneously to the main model. The results will represent the independent effects of the two sources of air pollution, to check whether PM10 originating from desertic areas is harmful for human health, and to what extent. c) Evaluation of effect of PM10 during forest fires episodes
The main focus of this analysis will be the estimation of the effect modification of forest fires episodes in the association between PM10 and health endpoints. This is relevant to understand whether PM10 during forest fires is more toxic for human health than PM10 during other days.
Since we have no data on the amount of PM10 originating from forest fires, the statistical analysis will be carried out in a simple way, by adding an interaction term between PM10 and FIRES yes/no indicator to the main model. The results will represent the effect of PM10 during no-fires days, and the increment in the effect of PM10 from no-fires to fires days.

Conclusions: The particles’ indices-health outcomes associations for each health outcome are investigated using Poisson regression models allowing for overdispersion.
We estimate:
- the relative contribution of desert dust and anthropogenic sources to the PM10-health associations by means of an interaction analysis and two-pollutant models
-the effect modification of forest fires episodes in the association between PM10 and health endpoints by adding an interaction term between PM10 and FIRES yes/no indicator to the main model