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
Particles size and composition in Mediterranean countries:
geographical variability and short-term health effects
MED-PARTICLES
Minutes of the First teleconference of the Statistical Group, December 16 2012, 11.00-12.30
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Minutes for MED-PARTICLES Statistical Group Telephone Conference 16th December, 2011: 11.00-12.30
Participants: Massimo Stafoggia, Ester Alessandrini, Francesco Forastiere, Evi Samoli, Antonis Analitis,
Cecilia Scarinzi, Martina Gandini, Xavier Basagana, Christophe Declerque, Alain Le Tertre,
1. Main discussion point was about the statistical design to be adopted within MED-P for the study
of short-term association between PM and mortality/hospitalization: time-series or casecrossover?
It is agreed by all participants that there are no differences between the two approaches, the only
difference being time trend adjustment. If, on one side, time-stratified case-crossover is quite rigid
since it assumes constant underlying risk within stratum, on the other side traditional time-series
with smoothed terms for time trend can be very dependent on the investigator’s choices on dfs,
position and number of knots, natural VS penalized splines, etc. A mid-way approach, TSS casecrossover (Whitaker 2007, Atkinson 2010) can be a good compromise, but Evi points out that it
maybe excessively adjusts for time trend leaving short variability left for detecting PM effects. It is
concluded that both approaches will be carried on, within the Poisson time-series framework, and
that Evi and Massimo will provide an operative protocol for sensitivity analyses using the two
approaches by January 5th.
In addition and separately from the foretold protocol, a simulation study will be designed in order
to check the sensitivity of the results from the two approaches using simulated data with fixed
“true” effects. However, this design will be developed on a later stage of the project.
2. Afterward, a discussion started on the adjustment model, touching different points:
a. Temperature adjustment: it is first discussed whether apparent temperature is better than air
temperature plus humidity for adjustment confounding purposes, but then Alain and Antonis
explain that the metric does not count too much, what really counts is the lag structure for cold
and hot temperatures. On the issue of apparent VS air temperature, Massimo describes the
methodology used in EPIAIR (adjusting for air temperature, lag 1-6, for “cold days”, i.e. days
below the city-specific median, adjusting for apparent temperature, lag 0-1, for hot days). Evi
says that she prefers to keep air temperature and humidity separate so to allow different
functional forms in each city. Alain adds that humidity is not so important in confounding
adjustment and should be kept off the adjustment model. In the end, it is decided to adjust for
air temperature, at 0-1 lag for “hot” days and at 1-6 lag for “cold” days. Two issues remain open:
1) how to select cold and hot days? Based on city-specific median (as in EPIAIR) or somehow
else? 2) should we add a “heat wave” term? If so, and should it be defined? In the next few
weeks (maybe by mid January) Massimo will draft a proposal for a protocol based on EPIAIR but
incorporating all the decisions taken in the call, and it will be discussed by e-mail by all stat
group members.
b. Other confounders: a discussion is started by Alain on how to define the confounding
adjustment set of variables: all chosen a priori, or some tested in our data even without strong
prior beliefs (example, barometric pressure). Alain and Francesco suggest to choose them all a
priori, Massimo adds the possibility of testing some of them in specific sensitivity analyses. IN
the end, the set of confounders include: time trend (see above), temperature (see above), day
of the week, holidays, influenza epidemics, summer population decrease (only for
hospitalizations outcomes). The set does not include: barometric pressure, humidity, wind
speed/direction, , summer population decrease (for mortality outcomes).
3. In the middle of the previous point, also the issue of lag structure for air pollutants is touched.
All agree on the necessity of being careful in exploring different lags and allowing cumulative
effects. Massimo suggests to apply the EPIAIR protocol: according to it, both polynomial
distributed-lag models will be applied (up to lag 6), and some a priori cumulative lags chosen to
represent immediate effects (lag 0-1), delayed effects (lag 2-5) and prolonged effects (lag 0-5).
It is not clear whether, in case the TS case-crossover approach will be used with matching of day of
the week, we are allowed to explore latencies above 6 days (so overlapping contiguous time
windows of case/control and control/control). Massimo think we are not, Christophe disagrees.
However, Evi points out that it is not relevant in our project since PM effects are maximized in the
first days after exposure. Massimo will integrate this part in the protocol mentioned above.
4. Other issues.
Other issues, included in the agenda, were not discussed: co-pollutants and meta-analysis. Effect
modification was briefly addressed, but we decided not to go in depth because many cities will not
have individual data so it will be impossible to explore individual susceptibility factors.
Finally, it is suggested to circulate some papers on case-crossover and time-series methods, with
special regards to methodological issues and simulation applications.
A possible date for the next stat group telephone conference has not been defined, and will be
chosen via e-mail after Christmas.