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 13.
Exploratory analysis of the role of PM components on mortality and morbidity
Summary: Protocol providing a detailed statistical methodology for the analysis of the differential
toxicity of the PM constituents on human health
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Objective
The objective of this chapter of the statistical protocol is to provide details for the statistical analysis
applied for the investigation of the relationship between PM constituents and mortality and hospital
admissions in five cities in the Mediterranean area.
Methods
Study population
The analysis will include five cities, three in Spain (Barcelona, Madrid and Huelva) and two in Italy
(Rome and Bologna). The overall period of study is from January 2003 to April 2013, although the
different cities contribute to different periods according to air pollution data availability.
Mortality data
Daily mortality counts for all non-external causes [International Classification of Diseases, 9th
Revision (ICD-9) codes 001–799; 10th revision (ICD-10) codes A00–R99] (ref: WHO 1999),
cardiovascular causes (ICD-9 codes 390–459, ICD-10 codes I00–I99) and respiratory causes
(ICD-9 codes 460–519, ICD10 codes J00–J99) will beanalyzed for all cities.
Hospital admissions data
Daily counts of emergency hospital admissions have been collected from national or regional health
information systems. Hospitalizations only of residents ≥ 15 years of age have been considered.
Two study outcomes will be defined on the basis of the primary discharge diagnosis: cardiovascular
hospitalizations [International Classification of Diseases, 9th Revision (ICD-9) codes 390–459; and
10th Revision (ICD-10) codes I00–I99] (WHO 1999), and respiratory hospitalizations (ICD-9 codes
460–519; ICD-10 codes J00–J99).
Exposure data
PM10 and PM2.5 data for speciation analysis are available from a single station in each city.
Madrid’ station is urban while all other are urban background stations. PM2.5 data were not
available for Rome while PM10 data were not available for Bologna. The period and frequency of
sampling was: Barcelona, 2003-2010, approximately every third day; Madrid, 2007-2008, two
measurements per week; Huelva, 2003-2010, one measurement per week; Rome, 2005-2008, daily
data; Bologna, 2012-2013, daily data. PM data collected during intense African dust outbreaks,
identified using the methodology of Pey et al. (2013), will be excluded from the analysis.
In the three Spanish cities, daily samples of PM2.5 and PM10 were collected by high-volume
samplers (MCV, S.A., Barcelona, Spain) with a flow rate of 30 m3/hr using quartz filters. PM10
and PM2.5 concentrations were determined gravimetrically. Subsequently, the quartz filters were
analyzed for water-soluble ions (NH4+, Cl-, SO42-, NO3-), major elements (Al, Ca, K, Mg, Fe, Na)
and 46 trace elements by ion chromatography, inductively coupled plasma atomic emission, ICPAES, and inductively coupled plasma mass spectrometry, ICP-MS, respectively (Querol et al.
2001). In Rome, PM10 sampling was conducted by a beta attenuation monitor SM200 (Opsis AB
Furulund-S) operating at flow rate of 1 m3 h-1 and teflon filters used as a substrate while a
MICRODUST sequential sampler (AQUARIA, Lacchiarella, MI-I) operating at a flow rate of 2 l
min-1 was used for PM sampling on quartz filters. The Teflon filters were subjected to energydispersion X-ray fluorescence analysis (X-Lab2000, Spectro Analytical Instruments, Kleve-D) for
the determination of major and minor elements: Al, Si, Fe, K, Ca, As, Cr, Cu, Mn, Ni, Pb, Ti, V,
Zn. Then, the filters were extracted in deionized water and analyzed by ion chromatography (ICS90
Dionex Co) for the determination of Cl-, NO3-, SO4=, Na+, NH4+, K+, Mg++ and Ca++.
Elemental EC, and organic carbon OC were analyzed in the quartz filters by thermo-optical analysis
(OCEC Sunset Analyser, Sunset Laboratory) using the NIOSH temperature protocol. In Bologna,
two Dual Channel Monitors (SWAM, Fai Instrument) were used to sample PM on quartz filters. EC
and OC were determined by thermo-optical analysis (OCEC Sunset Analyser, Sunset Laboratory)
using the EUSAAR_2 protocol. Ion chromatography (Dionex) was used for the determination of Cl, NO3-, SO4=, Na+, NH4+, K+, Mg++ and Ca++. Major and trace metals were determined.
Only 17 pre-specified PM constituents were included in the mortality analyses: silicon (Si)
(calculated as 3 times the aluminum concentration in the Spanish cities), calcium (Ca), aluminum
(Al), iron (Fe), potassium (K), magnesium (Mg), zinc (Zn), copper (Cu), titanium (Ti), manganese
(Mn), vanadium (V), nickel (Ni), nitrate (NO3–), sulfate (SO42-), elemental carbon (EC), organic
carbon (OC) and total carbon (TC), calculated as EC+OC. The list of 17 species was based on their
detectability in the included cities and on a review of the previous literature linking PM constituents
and health. If one of the above constituents had more than 20% of values missing or below the limit
of detection or quantification in a certain city, the city was excluded from the analysis of that
particular constituent. Otherwise, non-detectable values were replaced by half the limit of detection.
Daily average temperature and relative humidity were collected from each city, and they were
combined to calculate apparent temperature (O'Neill et al. 2003). Information on influenza
epidemics was obtained from hospital admissions records. Information on bank holidays was also
collected for each city.
Study design and data analysis
A time-stratified case-crossover study design will be used (Levy et al. 2001). In cities with daily
data, all days from the same year, month and day of the week will be grouped in the same stratum.
In cities without daily data, strata will be composed of days from the same year and month. Since
the case-crossover analysis conducts comparisons within strata, seasonal and long-term effects are
eliminated and they do not confound the results. Analyses will be performed using conditional
Poisson regression (Whitaker et al. 2006) and will be further adjusted for holidays, influenza
epidemics, day of the week (in cities without daily data), and apparent temperature. To account for
non-linearity and for the different lag structure of cold and hot temperatures, two separate spline
terms for apparent temperature will be included, with 3 degrees of freedom each, one for the cold
period (temperatures below the annual median) using average temperature for lags 0-5, and the
other for the warm period using average temperature for lags 0-1.
All analyses will be conducted separately for each city, and results will be combined using randomeffects meta-analysis. Lags 0, 1 and 2 of the effect of each pollutant will be examined in different
models, as models accounting for multiple lags cannot be built in cities without daily data.
In order to account for potential confounding for total PM levels, while accounting for the fact that
PM constituents are components of PM, the recently proposed constituent residual method
(Mostofsky et al. 2012) will be applied. First, a linear regression model for the constituent as a
function of total PM levels will be fitted. Then, the residuals for this model, which represent the
variation in constituent levels independent of PM, will be included as an explanatory variable in the
case-crossover model, along with total PM levels. The coefficient for the residuals represents the
effect of the constituent while holding PM constant, i.e. the independent contribution of the
constituent; and the PM coefficient represents the independent effect of total PM.
Conclusions: The analysis includes five cities, three in Spain (Barcelona, Madrid and Huelva) and two in
Italy (Rome and Bologna). Data on PM10 and PM2.5 components are available from a single station in
each city and PM data collected during intense African dust outbreaks are excluded from the analysis. A
time-stratified case-crossover study design is used, by means of conditional Poisson regression adjusted
for holidays, influenza epidemics, day of the week, and apparent temperature.
All analyses will be conducted separately for each city, and results will be combined using randomeffects meta-analysis.