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
ACTION 11.
Health effects of PM10, coarse particles (PM2.5-10) and fine particles (PM2.5) on daily
cause-specific hospitalizations: city-specific results and meta-analysis
Summary: Protocol to define the standardized methodological steps to investigate the association
between emergency hospitalizations and PM concentrations, with regards to different causes, age
groups and different PM fractions.
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BACKGROUND
The knowledge on the effects of air pollution on human health have been growing in last decades as
the result of the enormous scientific effort to design and conduct large epidemiological studies.
At present, we rely on a growing consolidated knowledge that airborne particles with a diameter < 10
μm is the most important airborne pollutant associated with the short-term effects on human health,
while fine particles (PM2.5) is the fraction responsible for the most severe health effect. There are
several additional aspects that we understand in a more comprehensive way. We know that coarse
fraction (PM 2.5-10μm) has a predominantly natural origin while the fine fraction is produced by
combustion (vehicles, industries and electric power plants). We know that traffic and heating are the
most relevant sources in determining the increases of mortality and morbidity due to respiratory and
heart diseases. We understand the shape of the concentration-response function and the existence of
no-effects threshold between concentration of airborne particles and health effects. We know that
particles are able to increase not only exacerbations of diseases, but also mortality as well as the onset
of respiratory and cardiovascular diseases. We understand that the oxidative stress is the most
important mechanism of damage at cellular level, but inflammation is not the only consequence, since
increasing blood coagulability, heart autonomic functions, and the atherosclerosis processes are also
influenced by air pollution.
Notwithstanding the growing of epidemiological research, scanty data are available in the European
Mediterranean countries, especially on PM2.5 and PM2.5-10, so that multi-center studies on the shortterm health effects of fine and coarse fractions on mortality and morbidity are virtually non-existent.
Objective
The objective of this protocol is to define the methodological steps to investigate the association
between unscheduled hospital admissions and PM concentrations, with regards to: 1) different causes
of hospital admissions; 2) different PM fractions; 3) different modeling choices of confounding
adjustment. The analyses will be conducted within each city participating the Med-Particles project, and
then pooled estimates will be derived with random-effects meta-analytical procedures.
Study population and outcomes
Data on daily hospital admissions will be collected for at least 10 cities from Italy, Spain and France.
No Greek cities will participate to this Action because of lack of data. For each city and for each day of
the study period, data on daily hospitalization counts will be collected with regards to the population
resident of the city and hospitalized within the city. Repeated events are allowed for each subject,
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however repeated hospitalizations within 28 days since the previous one and with the same primary
diagnosis will be eliminated, under the assumption that the two events represent the same “episode”
(Example: if a subject is discharged from a hospital with a primary diagnosis of acute myocardial
infarction, ICD-9: 410, and s/he is re-admitted with the same diagnosis within 28 days since the
discharge of the first hospitalization, the second hospital admission is not included among the study
outcomes). Only ordinary (no day-hospital) and acute (no scheduled) hospitalizations will be
considered, since the aim of the study is to investigate the association between daily pollutants
concentrations and acute health outcomes. Finally, only the primary diagnosis will be considered for the
identification of the outcomes. For each city, daily counts of hospital admissions will be available, for
the following diseases. We distinguish two groups of outcomes: primary outcomes, on which all
analyses will be conducted, and secondary outcomes, in italics, on which only selected analyses will be
carried out:
Diagnosis of hospital discharge
ICD-9 code
ICD-10 code
Diabetes
250
E10 – E14
Cardiovascular diseases
390-459
I00 – I99
Cardiac diseases
390-429
I00 – I52
Acute coronary events
410-411
I21 – I23
Conduction disorders or arrhythmias
426-427
I44 – I49
Heart failure
428
I50
Cerebrovascular diseases
430-437
I60 – I68
Hemorrhagic stroke
430-431
I60, I61
Ischemic stroke
434, 436
I63, I65, I66
Respiratory diseases
460-519
J00 – J99
466, 480-487
J09 – J18, J20 – J22
480-486
J12 – J18
Low respiratory tract infections (LRTI)
Pneumonia
Chronic obstructive pulmonary disease (COPD) 490-492,494,496
J40 – J44, J47
Asthma
J45 – J46
493
Further details are reported in the protocol for data collection on health endpoints, Action 6.
Environmental variables
For each city, daily average exposures to PM2.5, PM10 and PM2.5-10 will be derived from monitor-specific
data, as detailed in the protocol for data pooling, Action 7. In addition, data are available for each city
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and day on other air pollutants (NO2, O3, CO, O3) and meteorological parameters (temperature,
humidity, barometric pressure).
Other confounders
Data have been collected on other daily variables, useful for confounding adjustment in the PMmorbidity association. They include influenza epidemics and indicator variables relating to population
dynamics during holidays and summer.
Further details on environmental variables and other confounders are reported in the protocol for
environmental data collection, Action 3.
Methods
a) Methodological framework
The PM-morbidity association will be investigated using Poisson regression models allowing for
overdispersion (using quasi(poisson) in R). The model is of the form:
log E [Yt]=β0 + b * PMt + confounders
where E[Yt] is the expected value of the Poisson distributed variable Yt indicating the daily count of
unscheduled hospital admissions for a specific disease on day t, with Var(Yt)=φE[Yt], φ being the overdispersion parameter, PMt is the concentration of particles (for a specific fraction) on day t, and
“confounders” include an extensive list of confounding factors, as described below. b, the parameter of
interest, is the adjusted log(relative risk) of hospitalization for a unit increase in PM.
b) Confounder list
The confounders are chosen a priori based on past knowledge and preliminary exploratory analyses.
They include the following (see the protocol of statistical analysis, Action 9, for further details):
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Time-trend. In order to take into account long-term as well as seasonal time-trend, a penalized
regression spline of day of the event will be fitted. Natural cubic splines will be used as basis
functions for the penalized regression splines. We will choose 8 effective degrees of freedom
(edfs) per calendar year of available data to control for seasonality.
Two sensitivity analyses will be performed: 1) one model will use the time series approach with
penalised splines for seasonality control (as the main model) but with the final edfs for time
trend based on the choice of the smoothing parameter for time that minimizes the absolute
value of the sum of the partial autocorrelations (PACF) of the residuals from lags 1 to 30; 2) a
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three-way interaction between year, month and day of the week will be fitted as alternative to
the spline term + day of the week adjustment, since this approach has been shown to be equal
to the case-crossover design, with “time-stratified” strategy for controls selection;
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Air temperature. It will be adjusted for with two natural splines, in order to control for both high
and low temperatures at different reference lags: lag 0 for high summer temperatures, lag 1 or
lag 1-6 for low winter temperatures. The choice between lag 1 and lag 1-6 for cold temperatures
adjustment will be based at the city level on the lag which minimizes AIC. For both natural
splines, 3 degrees of freedom will be fixed a priori at quartiles of the city-specific distribution.
One sensitivity analysis will be performed on temperature adjustment, consistent with the
EPIAIR2 study protocol: cold temperatures are modeled with a quadratic polynomial term for
lag 1-6 air temperature, limited to days below the city-specific median value; high temperatures
are modeled with a natural spline term for lag 0-1 air temperature, on days above the cityspecific median value, and knots located at the 75th and 90th percentiles of the city-specific
distribution of lagged 0-1 air temperature.
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Relative humidity. It will be adjusted for with a linear term at lag 0.
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Influenza epidemics. The data on influenza should preferably be daily counts i.e. number of cases.
When there are weekly or biweekly data on number of cases then daily values should be
calculated by division. If the only available information is the existence of an epidemic, then a
variable taking the value 1 for epidemic days and 0 otherwise should be used. In case there are
no influenza data available we will use the APHEA-2 method for influenza control, including a
dummy variable taking the value of 1 when the 7-day moving average of the respiratory
mortality was greater than the 90-th percentile of its city-specific distribution. In this case, since
influenza definition is based on the distribution of respiratory mortality, we will include the
influenza dummy variable only when analyzing non respiratory-related hospital admissions. In
all other influenza definitions, instead, it will be included for all study outcomes, including
respiratory ones.
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Population dynamics during holidays and summer. This phenomenon will be adjusted for with
indicator variables for holidays and summer population decreases, as described in the protocol
for environmental data collection, Action 3.
c) Lag structure of exposure
According to the EPIAIR protocol, for each primary outcome the following three steps will be
implemented: a) cubic polynomial distributed lag models and single-lag models from lag 0 to lag 6 to
visually examine the lag structure of the association between PM exposures and health outcomes; b)
three cumulative lags chosen a priori to represent immediate effects (lag 0-1), delayed effects (lag 2-5)
and prolonged effects (lag 0-5); c) for each combination of exposure/outcome, choice of one of these
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three lags as the reference lag, based on the meta-analytic polynomial distributed lag shape, the metaanalytic estimates of the a priori cumulative lags, and the heterogeneity among city-specific estimates.
This choice is relevant to identify the lagged exposure to be used for additional analyses as bi-pollutant
models, sensitivity analyses on time-trend and temperature adjustment, and analysis of the secondary
outcomes. In particular, for each secondary outcome the referent lag from the primary outcome of the
same group will be applied (for example, if the relevant lag for PM2.5 and cardiac mortality is 0-1, the
same lag is applied to the secondary outcomes “Acute coronary events”, “Conduction disorders or
arrhythmias” and “Heart failure”).
d) Two-pollutant models
For each primary outcome, and for PM2.5 and PM25-10, we will run two-pollutant models, including
NO2 and ozone in turn. Special care will be given to the inclusion of NO2 in PM2.5 models if there is
high correlation (r>0.7). In addition, two pollutant models will be run with PM2.5 and PM2.5-10 as
well. Concerning the lag chosen for two-pollutant models, the two pollutants will be put in the model
at the same lag, equal to the relevant lag for the PM exposure. For example, if from the analysis of the
lag structure there is evidence that the most relevant lag for PM2.5 and cardiac mortality is 0-1, the twopollutant models for PM2.5 will be run by adding both PM2.5 and the other pollutant (NO2, O3 or
PM2.5-10) in the model, both at lag 0-1.
e) Effect modification for age and gender
For each primary outcome, and for all PM fractions, models will be run with interaction terms between
PM exposure and age class (0-14, 15-34, 35-64, 65-74, 75-84, 85+) and gender, and effect modification
will be tested and reported.
e) Meta-analytical techniques
Once city-specific effect estimates are obtained, they will be meta-analyzed with random-effects
models, as detailed in the statistical protocol, Action 9. For each meta-analytical estimate, a test for
heterogeneity will be performed and corresponding p-value reported, together with the I2 statistics,
which represents the proportion of total variation in effect estimates that is due to between-cities
heterogeneity.
Conclusions: The protocol defines the methodological steps to investigate the association between
hospitalization outcomes and PM concentrations, with regards to different causes of disease, different
PM fractions and different modeling choices of confounding adjustment. Data on daily hospital
admissions of people resident of the city and hospitalized within the city will be collected for at least 10
cities from Italy, Spain and France. Only ordinary (no day-hospital) and acute (no scheduled)
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hospitalizations will be considered. The city-specific estimates obtained will be meta-analyzed with
random-effects models.
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