LIFE Project Number
LIFE10 ENV/IT/327
Inception Report
Covering the project activities from 01/09/2011 to 31/05/2012
Reporting Date
30/06/2012
LIFE+ PROJECT NAME or Acronym
Particles size and composition in Mediterranean countries:
geographical variability and short-term health effects
ANNEX 12
Final protocol of health data collection
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 6.
Data collection on: daily cause-specific mortality and emergency hospitalizations
Summary: Protocol to collect in a systematic and comprehensive way data on health endpoints
(mortality and hospitalizations) in the Mediterranean cities involved in the project.
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Objective
To collect the relevant health data to estimate the effects of particles sizes and their components on
human health, also evaluating the potential for an effect modification of Saharan dust and forest fires
episodes. The health endpoints of interest in the MED-PARTICLES project will be total, cardiac,
cerebrovascular and respiratory mortality, as well as emergency hospitalizations for specific causes,
namely all cardiovascular and respiratory diseases as well as specific diseases entities like myocardial
infarction, heart failure, respiratory infections and chronic obstructive pulmonary diseases (COPD).
The outcomes at the individual level, where available, will be pooled for each day to get the daily counts
of deaths/hospitalizations, by age group, gender and cause/diagnosis, which will be put in relationship
with daily air pollution measurements to estimate short-term effects.
The data collection will be performed at the center-level and finally the city-specific data will be pooled
centrally (Action 7).
The anonymity of all the subjects included in the final databases is guaranteed, in compliance with
country-specific regulations.
Methods
Mortality data
For each city involved in the project, and for each day of the study period, data on daily mortality
counts will be collected with regards to the population resident of the city and died within the city.
Daily counts will be collected for specific groups of causes of death, for specific age groups (0-14, 1534, 35-64, 65-74, 75-84, 85+), and separately for men and women. The following causes of death will
be considered, with relative ICD-9 and ICD-10 codes:
Cause of death
ICD-9 code
ICD-10 code
Natural causes
001-799
A00 – R99
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
426
I44 – I45
Arrhythmias
427
I46 – I49
Heart failure
428
I50
430-437
I60 – I68
460-519
J00 – J99
466, 480-487
J09 – J18, J20 – J22
Acute bronchitis, bronchiolitis, unsp. LRTI
466
J20 – J22
Pneumonia
480-486
J12 – J18
Influenza
487
J09 – J11
Cerebrovascular diseases
Respiratory diseases
Low respiratory tract infections (LRTI)
Chronic obstructive pulmonary disease (COPD) 490-492,494,496
J40 - J44, J47,
In summary, the following dataset will be produced (named “city_mortality_YYbeg_YYend”), with
corresponding code book as detailed below (replace “city” with the name of the city, “YYbeg” with the
year of beginning of the time-series, and “YYend” with the year of end of the time-series):
Variable
Format
Description
city
Character
Name of the city
yy
Numeric
Year of death
mm
Numeric
Month of death
dd
Numeric
Day of death
m014nat
Numeric
Daily count of deaths for natural causes, age 0-14, males
f014nat
Numeric
Daily count of deaths for natural causes, age 0-14, females
m1534nat
Numeric
Daily count of deaths for natural causes, age 15-34, males
f1534nat
Numeric
Daily count of deaths for natural causes, age 15-34, females
m3564nat
Numeric
Daily count of deaths for natural causes, age 35-64, males
f3564nat
Numeric
Daily count of deaths for natural causes, age 35-64, females
m6574nat
Numeric
Daily count of deaths for natural causes, age 65-74, males
f6574nat
Numeric
Daily count of deaths for natural causes, age 65-74, females
m7584nat
Numeric
Daily count of deaths for natural causes, age 75-84, males
f7584nat
Numeric
Daily count of deaths for natural causes, age 75-84, females
m85nat
Numeric
Daily count of deaths for natural causes, age 85+, males
f85nat
Numeric
Daily count of deaths for natural causes, age 85+, females
 Replace “nat” with “diab” for diabetes
 Replace “nat” with “cvd” for cardiovascular diseases
 Replace “nat” with “card” for cardiac diseases
 Replace “nat” with “ace” for acute coronary events
 Replace “nat” with “cond” for conduction disorders
 Replace “nat” with “arrh” for arrhythmias
 Replace “nat” with “hf” for heart failure
 Replace “nat” with “cere” for cerebrovascular diseases
 Replace “nat” with “resp” for respiratory diseases
 Replace “nat” with “lrti” for LRTI
 Replace “nat” with “bron” for acute bronchitis, bronchiolitis, unspecified LRTI
 Replace “nat” with “pneu” for pneumonia
 Replace “nat” with “infl” for influenza
 Replace “nat” with “copd” for COPD
Hospitalization 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.
Daily counts will be collected for specific groups of primary diagnosis, for specific age groups (0-14,
15-34, 35-64, 65-74, 75-84, 85+), and separately for men and women.
Repeated events are allowed for each subject, 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
outcome, unless specified differently.
The following groups of primary diagnoses will be considered:
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
410-411*
I21 – I23
410*
I21, I23
Conduction disorders
426
I44 – I45
Arrhythmias
427
I46 – I49
Heart failure
428
I50
430-437
I60 – I68
Hemorrhagic stroke
430-431
I60, I61
Ischemic stroke
434, 436
I63, I65, I66
460-519
J00 – J99
466, 480-487
J09 – J18, J20 – J22
Acute bronchitis, bronchiolitis, unsp. LRTI
466
J20 – J22
Pneumonia
480-486
J12 – J18
Influenza
487
J09 – J11
Acute coronary events
Acute myocardial infarction
Cerebrovascular diseases
Respiratory diseases
Low respiratory tract infections (LRTI)
Chronic obstructive pulmonary disease (COPD) 490-492,494,496**
J40 - J44, J47
Asthma
J45-J46
493
* For these two specific outcomes, also secondary diagnoses will be used. Cases will be the hospitalizations with ICD-9:
410-411 (for acute coronary events) or 410 (for acute myocardial infarction only) as the primary diagnosis, or the
hospitalizations with ICD-9: 410-411 (or 410) as the secondary diagnosis and a complication of the coronary syndrome as
the primary one. Complications of the coronary syndrome (and of the myocardial infarction) include:
427.1
Paroxysmal ventricular tachycardia
I472
427.41
Ventricular fibrillation
I490
427.42
Ventricular flutter
I490
427.5
Cardiac arrest
I46
428.1
Left heart failure
I501
429.5
Rupture of chordae tendineae
I511
429.6
Rupture of papillary muscle
I512
429.71
Acquired cardiac septal defect
I510
429.79
Other sequelae of myocardial infarction, not
I258
elsewhere classified
429.81
Other disorders of papillary muscle
I512
518.4
Acute edema of lung, unspecified
J81
780.2
Syncope and collapse
R55, R579
785.51
Cardiogenic shock
R570
414.10
Aneurysm of heart (wall)
I253
423.0
Hemopericardium
I312
** For this specific outcome, also secondary diagnoses will be used. Cases will be the hospitalizations with ICD-9: 490492,494,496 as the primary diagnosis, or the hospitalizations with ICD-9: 490-492,494,496 as the secondary diagnosis and a
complication of the COPD as the primary one. Complications of the COPD include:
518.8
Other diseases of lung
J98
518.5
Pulmonary insufficiency following trauma and
J96, J80, J95
surgery
786.0
Dyspnea and respiratory abnormalities
R06
In summary, the following dataset will be produced (named “city_hosp_YYbeg_YYend”), with
corresponding code book as detailed below (replace “city” with the name of the city, “YYbeg” with the
year of beginning of the time-series, and “YYend” with the year of end of the time-series):
Variable
Format
Description
city
Character
Name of the city
yy
Numeric
Year of hospital admission
mm
Numeric
Month of hospital admission
dd
Numeric
Day of hospital admission
m014diab_h
Numeric
Daily counts of hospital admissions for diabetes, age 0-14, males
f014diab_h
Numeric
Daily counts of hospital admissions for diabetes, age 0-14, females
m1534diab_h
Numeric
Daily counts of hospital admissions for diabetes, age 15-34, males
f1534diab_h
Numeric
Daily counts of hospital admissions for diabetes, age 15-34, females
m3564diab_h
Numeric
Daily counts of hospital admissions for diabetes, age 35-64, males
f3564diab_h
Numeric
Daily counts of hospital admissions for diabetes, age 35-64, females
m6574diab_h
Numeric
Daily counts of hospital admissions for diabetes, age 65-74, males
f6574diab_h
Numeric
Daily counts of hospital admissions for diabetes, age 65-74, females
m7584diab_h
Numeric
Daily counts of hospital admissions for diabetes, age 75-84, males
f7584diab_h
Numeric
Daily counts of hospital admissions for diabetes, age 75-84, females
m85diab_h
Numeric
Daily counts of hospital admissions for diabetes, age 85+, males
f85diab_h
Numeric
Daily counts of hospital admissions for diabetes, age 85+, females
 Replace “diab” with “cvd” for cardiovascular diseases
 Replace “diab” with “card” for cardiac diseases
 Replace “diab” with “ace” for acute coronary events
 Replace “diab” with “ami” for acute myocardial infarction
 Replace “diab” with “cond” for conduction disorders
 Replace “diab” with “arrh” for arrhythmias
 Replace “diab” with “hf” for heart failure
 Replace “diab” with “cere” for cerebrovascular diseases
 Replace “diab” with “hems” for hemorrhagic stroke
 Replace “diab” with “iscs” for ischemic stroke
 Replace “diab” with “resp” for respiratory diseases
 Replace “diab” with “lrti” for LRTI
 Replace “diab” with “bron” for acute bronchitis, bronchiolitis, unspecified LRTI
 Replace “diab” with “pneu” for pneumonia
 Replace “diab” with “infl” for influenza
 Replace “diab” with “copd” for COPD
 Replace “diab” with “asth” for asthma
Conclusions: The health endpoints of interest in the MED-PARTICLES project are total, cardiac,
cerebrovascular and respiratory mortality, as well as emergency hospitalizations for specific causes,
namely all cardiovascular and respiratory diseases as well as specific diseases entities like myocardial
infarction, heart failure, respiratory infections and chronic obstructive pulmonary diseases (COPD).
For each city involved in the project, and for each day of the study period, data on daily mortality
counts are collected with regards to the population resident of the city and died within the city.
Data on daily hospitalization counts are collected with regards to the population resident of the city and
hospitalized within the city. Only ordinary (no day-hospital) and acute (no scheduled) hospitalizations
are considered. Daily counts are collected for specific groups of causes of diseases, for specific age
groups (0-14, 15-34, 35-64, 65-74, 75-84, 85+), and separately for men and women.
The outcomes at the individual level, where available, are pooled for each day to get the daily counts of
deaths/hospitalizations, by age group, gender and cause/diagnosis.
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geographical variability and short