Health effects of air pollution in Iceland
Hanne Krage Carlsen
Department of Public Health and Clinical Medicine,
Occupational and Environmental Medicine
Umeå 2014
Department of Public Health and Clinical
Medicine
Umeå university, 901 87 Umeå
www.phmed.umu.se
ISSN 0346-6612
ISBN 978-91-7601-082-2
Statement of collaboration
This thesis and the work in it have been produced in collaboration between
University of Iceland and Umeå University.
The thesis was issued and defended at both institutions.
Responsible publisher under swedish law: the Dean of the Medical Faculty
This work is protected by the Swedish Copyright Legislation (Act 1960:729)
New Series No 1659
ISBN: 978-91-7601-082-2
ISSN: 0346-6612
Cover image: Hanne Krage Carlsen.
Elektronisk version tillgänglig på http://umu.diva-portal.org/
Tryck/Printed by: Print & Media
Umeå, Sweden 2014
For Steinn
Table of Contents
Table of Contents
i
Abstract
ii
Abbreviations
iv
Enkel sammanfattning på svenska
v
Luftföroreninger och hälsa
v
Försäljning av astmamediciner och höga föroreningshalter
v
Akutbesök och luftföroreninger från trafik
v
Akutbesök och föroreningar från naturliga källor
vi
Hög exponering under ett vulkanutbrott
vi
Vulkanaskaexponering –6 månaders uppföljning
vi
Sammanfattning av resultat
vii
1 Introduction
1
1.1 Air pollution
2
1.2 Air pollution effects on health
7
1.3 Respiratory health and air pollution in Iceland
17
2 Aims
23
3 Materials and methods
24
3.1 Material
24
3.2 Methods
31
4 Results
38
4.1 Paper I
38
4.2 Paper II
43
4.3 Paper III
43
4.4 Paper IV
43
4.5 Paper V
44
5 Discussion
47
5.1 Key results
47
5.2 Limitations and methodological considerations
47
5.3 Interpretation
52
5.4 External validity
52
6 Conclusions
53
Acknowledgements
55
References
57
i
Abstract
Background
Air pollution has adverse effects on human health. The respiratory system is
the most exposed and short-term changes in air pollution levels have been
associated with worsening of asthma symptoms and increased rates of heart
attacks and stroke. Air pollution in cities due to traffic is the major concern,
as many people are exposed. However, natural sources of air pollution such
as natural dust storms and ash from volcanic eruptions can also compromise
human health. Exposure to volcanic eruptions and other natural hazards can
also threaten mental health. Air pollution has not been extensively studied in
Iceland, in spite of the presence of several natural pollution sources and a
sizeable car fleet in the capital area.
The aim of this thesis was to determine if there was a measurable effect on
health which could be attributed to air pollution in Iceland. This aim was
pursued along two paths; time series studies using register data aimed to
determine the short-term association between daily variation in air pollution
and on one hand daily dispensing of anti-asthma medication or the daily
number of emergency room visits and emergency admissions for
cardiopulmonary causes and stroke. The other method was to investigate if
exposure to the Eyjafjallajökull volcanic eruption was associated with
adverse health outcomes, either at the end of the eruption, or 6 months later.
Original papers
In paper I time series regression was used to investigate the association
between the daily number of individuals who were dispensed anti-asthma
medication and levels of the air pollutants particle matter with an
aerodynamic diameter less than 10 μm (PM10), nitrogen dioxide (NO2),
ozone (O3), and hydrogen sulfide (H2S) during the preceding days. For the
study period 2006-9, there were significant associations between the daily
mean of PM10 and H2S and the sales of anti-asthma medication 3 to 5 days
later. Giving the exposure as the highest daily one-hour mean gave more
significant results. Air pollution negatively affected the respiratory health of
asthma medication users, prompting them to refill their prescriptions before
they had originally intended to.
In paper II the main outcome was the number of individuals seeking help
at Landspitali University Hospital emergency room for cardiopulmonary
disease or stroke. Time series regression was used to identify the lag that
gave the best predictive power, and models were run for data for 2003-9
pollutants PM10, NO2, and O3. O3 was significantly associated with the
number of emergency hospital visits the same day and two days later in all
ii
models, and both for men, women and the elderly. Only emergency hospital
visits of the elderly were associated with NO2, and there were no associations
with PM10.
In paper III the aim was to investigate if the health effects of PM 10 were
affected by the addition of volcanic ash from the 2010 eruption of
Eyjafjallajökull and 2011 eruption of Grímsvötn to PM10 in the capital area.
Time series regression of emergency hospital visits and PM10 before and after
the Eyjafjallajökull eruption showed that the effect tended to be higher after
the eruption, but the results were not significant. Analysis with a binary
indicator for high levels of PM10 from volcanic ash and other sources showed
that volcanic ash was associated with increased emergency hospital visits.
There were no associations with high levels of PM10 from other sources.
In paper IV, the health of the population exposed to the ongoing eruption
of Eyjafjallajökull in 2010 was investigated thoroughly. Lung function in
adults was better than in a reference group from the capital area, though
many reported sensory organ irritation symptoms and symptoms of stress
and mental unhealth, especially those with underlying diseases.
Paper V report the results from a questionnaire study which was carried
out six months after the Eyjafjallajökull eruption. The study population
comprised a cohort of south Icelanders exposed to the eruption to varying
degrees and a reference group from north Iceland. Respiratory and eye
symptoms were much more common in south Icelanders than in the
reference group, after adjusting for demographic characteristics. Mental
unhealth rates had declined considerably.
Conclusion
In the studies, we found that urban air pollution and natural particles have
short-term effects on anti-asthma medication dispensing and emergency
room visits and hospital admissions. Exposure to natural particles in the
form of volcanic dust was associated with increased respiratory symptoms in
a very exposed population. There were indications that volcanic ash particles
were associated with increased emergency hospital visits in the following
days.
iii
Abbreviations
ATC
BC
BS
CO2
COPD
ER
FEV1
FVC
GAM
GHQ
GLM
GP
ICD
H2S
Lowess
NO2
O3
PAH
PEF
PM2.5
PM10
PTSD
SO2
UFP
VOC
WHO
Anatomical therapeutic classification system
Black carbon
Black smoke
Carbon dioxide
Chronic obstructive pulmonary disease
Emergency room
Forced expiratory volume in 1 second
Forced vital capacity
Generalized additive model
General health questionnaire
Generalized linear model
General practitioner
International classification of diseases
Hydrogen sulfide
Locally weighted scatterplot smoothing
Nitrogen dioxide
Ozone
Poly-aromatic hydrocarbons
Peak expiratory flow
Particle matter with an aerodynamic diameter less than 2.5
µm
Particle matter with an aerodynamic diameter less than 10 µm
Post traumatic stress syndrome
Sulfur dioxide
Particle matter with an aerodynamic diameter less than 1 µm
Volatile organic compounds
World health organization
iv
Enkel sammanfattning på svenska
Luftföroreninger och hälsa
Luftföroreningar är skadligt för hälsan. Luftvägarna är mest exponerade och
luftföroreningar orsakar på kort sikt ökade symptom av KOL och astma,
samt ökad risk för hjärtinfarkt och stroke. Luftföroreningar i storstäder, från
till exempel trafik, är ett folkhälsoproblem eftersom många människor
exponeras. Även mer ”naturliga” föroreningskällor såsom damm,
sandstormar och aska från vulkanutbrott kan hota människors hälsa. På
Island på finns många naturliga föroreningskällor, och även ett betydande
bidrag från trafik, vilket gör Island lämpligt att studera hälsoeffekter av
luftföroreningar. Trots detta saknas tidigare studier om hälsoeffekter av
luftföroreningar på Island.
Syftet med denna avhandling var att undersöka om luftföroreningar har
en mätbar effekt på hälsan i Islands befolkning. Detta gjordes i två typer av
studier, å ena sidan med register-data från medicinförskrivnings- och
sjukhusregister som analyserades med tiddserieanalys för att se om
ändringar i föroreningshalt följdes av ändringar i antal personar som
hämtade receptbelagda astmamediciner på apotek, eller sökte akutvård för
hjärt- och lungsjukdom och stroke. Å andra sidan undersöktes hälsoeffekter
av vulkanutbrottet år 2010 i Eyjafjallajökull på befolkningen i vulkanens
omedelbara närhet.
Försäljning av astmamediciner och höga föroreningshalter
I delarbete I använde vi tidsserieanalys till att undersöka sambandet
mellan dagligt antal personer bosatta i huvudstadsområdet som hämtade ut
astmamedicin på apoteket och luftföroreningar; partiklar med diameter
under 10 μm (PM10), kvävedioxid (NO2), oson (O3), och svåvelväte (H2S) för
perioden 2006-2009. Vi fann ett statistiskt signifikant samband mellan
dygnsmedel av PM10 och H2S och hur många personer som hämtade
astmamediciner 3-5 dagar senare. Sambandet var starkare när exponeringen
räknades som det högsta en-timmes medelvärdet varje dag.
Akutbesök och luftföroreninger från trafik
I delarbete II studerade vi hur många individer bosatta i
huvudstadsområdet som sökte akutvård varje dag på grund av hjärt-lung
sjukdom eller stroke under perioden 2003-2009, och inkluderade både
personer som blev behandlade på akutmottagningen och som blev inlagda
(akutbesök). Återigen använde vi tidsserieanalys för att se vilka
föroreningstyper som var associerade med akutbesöken. Vi fann ett
v
signifikant samband mellan akutbesök och dygnsmedelvärdet av O3 på
samma dag, en och två dagar före. För personer äldre än 70 år fann vi även
ett samband med NO2. Vi fann inget samband med PM10, som var den enda
föroreningstypen som översteg hälsogränsvärdet.
Akutbesök och föroreningar från naturliga källor
I delarbete III var syftet att se om det fanns någon ändring i sambandet
mellan akutbesök och PM10-koncentration efter att vulkanaska från
utbrotten i Eyjafjallajökull och Grímsvötn bidrog till PM10-halten i Islands
huvudstadsområde. Vi studerade perdioden 2007-2012 och använde oss av
akutbesök som indikator för hälsan i befolkningen (samma som delarbete
II). Vi fann en tendens till att PM10 hade större effekt efter vulkanutbrotten,
men resultaten var inte statistiskt signifikanta. När vi använde en indikator
för att studera effekten av vissa källor av högre halter av PM10, såg vi att när
PM10 var högt på grund av vulkanaska så var det en nästan statistiskt
signifikant ökning i akutbesök i huvudstadsområdet. När PM 10 var högt på
grund av andra orsakar var det ingen ökning i antalet av akutbesök.
Hög exponering under ett vulkanutbrott
I delarbete IV undersökte vi hälsan hos de personer som bodde i närheten
av, och varit mycket exponerade för vulkanutbrottet i Eyjafjallajökull.
Spirometri användes för att undersöka påverkan på lungfunktion, deltagarna
blev undersökta av en läkare och svarade på frågar om deras symptom.
Lungfunktionen var inte påverkad i förhållande till en referenspopulation
från huvudstadsområdet, men det var svårt att jämföra eftersom det fanns
fler rökare i referenspopulationen. Ett flertal plågades av symptom från
ögon, näsa och hals, som klåda, torrhet och hosta, några var stressade och
mådde psykiskt dåligt. Individer med underliggande sjukdomar
rapporterade förhållandevis fler symptom i samband med utbrottet.
Vulkanaskaexponering –6 månaders uppföljning
I delarbete V rapporterade vi resultaten från en enkätundersökning från 6
månader efter att utbrottet i Eyjafjallajökull tog slut. Befolkningen på SydIsland som bor nära fjället och en kontrollgrupp från Nord-Island blev
inbjudna att delta. I svaren på enkäten kom det fram att symptom från
andningsorgan och ögon var dubbelt så vanliga hos Syd-Islänningar, efter
justering för ålder, kön, utbildning och rökning. Stress och psykiskt
illamående var ännu vanligare hos de som var mest exponerade i
befolkningen på Syd-Island, men var lägre än vad som rapporterades i
delarbete IV, 6 månader tidigare.
vi
Sammanfattning av resultat
I denna avhandling har det visats att exponering höga halter av
luftföroreningar leder till att mängden av uthämtad astmamedicin och
antalet som söker akutvård för hjärt-lung sjukdomar och stroke ökar.
Exponering för partiklar från vulkanaska var förknippade med ökade
symptomer från andningsorgan och ögon.
vii
viii
1 Introduction
Air pollution is defined as undesired gases and particles in ambient air,
commonly known as aerosols, because of smell, nuisance or adverse health
effects (WHO, 2000, pI).
Since the early days of industrialization, air pollution has been recognized
as a hazard to human health (WHO, 2000). The earliest reports of health
effects of airborne particles date back to the 15 th and 16th centuries; the
women of the Carpathian mountains (mostly in present-day Romania) went
through as many as seven husbands in their lifetime - the men who worked
in the granite mines died early from disease (silico-tuberculosis) caused by
exposure to the silica dust (Donaldson and Seaton, 2012). Falun, Sweden,
was home to the largest copper mine in Europe in the early 18 th century.
Visitors described that “Intense, black copper smoke lies dense over the
town. [The smoke] causes intense sneezing, and lung disease was more
common in this area” (Dunér, 2012, pp. 79-83).
Modern studies of air pollution and health began in the 20 th century
following the Meuse Valley disaster in Belgium 1930 (Nemery et al., 2001)
and the London fog (Logan, 1956), both events where short-term increases
in pollution levels caused increased morbidity and mortality. Results from
The Six Cities Study showed that chronic exposure to high levels of air
pollution were associated with increased morbidity and mortality rates
(Dockery and Pope, 1993).
Today, the studies of health effects of air pollution remain focused on
anthropogenic traffic pollution which affects many people who live in cities
and urban areas. In later years, increased focus has been pointed at air
pollution from natural sources such as desert dust (Morman and Plumlee,
2013), gas emitted from geothermal areas (Hansell and Oppenheimer, 2004)
or ash from volcanic eruptions (Horwell and Baxter, 2006). Air pollution is
responsible for a substantial part of respiratory mortality and morbidity
(Künzli et al., 2000).
In the introduction, I first describe types of pollution; secondly, the health
effects, and briefly review the epidemiological studies of health effects for
different types of pollution; and finally, describe the Icelandic situation and
studies of respiratory health and air pollution in Iceland.
1
1.1 Air pollution
Traffic pollution
In later years, abandoning of coal for space heating and the removal of lead
from gasoline has improved air quality in urban areas (Brunekreef and
Holgate, 2002). Motor traffic exhaust is the most prominent pollution
source, though local conditions, weather, building types, and topography
also affect the local pollution levels (WHO, 2013).
Gaseous pollutants from fuel combustion, mainly from traffic, contribute
greatly to air pollution in cities, for example the yellow smog clouds over
megacities like Athens and Los Angeles. These yellow clouds are acid
aerosols formed in heat-catalyzed reactions of automobile exhaust and sulfur
dioxide (SO2). SO2 is released from burning coal and other poor quality fossil
fuels. The burning of coal has been banned in many cities, other fuels are
cleaner, and SO2 has currently become less of a problem in the Western
world (Fenger, 2002; WHO, 2005).
Traffic exhaust contains a large number of gases and particles which have
known health effects (Fenger, 2002, WHO, 2005). Nitrogen dioxide (NO2) is
used as an indicator for traffic pollution as it correlates well with traffic
counts when measured at the roadside. In some studies other measures of
pollution such as black carbon (BC) or black smoke (BS) are used as proxies
for traffic proximity as they also correlate well with traffic counts (WHO,
2013).
NO2 is a precursor for ozone (O3) which forms when automobile exhaust
gases, including NO2, react with the atmosphere, consuming the NO2 to form
O3. This reaction is catalyzed by heat and sun radiation (Jenkins and
Clemitshaw, 2002). O3 levels are often lower in the presence of traffic during
the day (see figure 1). Volatile organic compounds (VOC) and polyaromatic
hydrocarbons (PAHs) are also formed when hydrocarbon fuels are
combusted. The availability of NOx and VOC determine how high the O3
concentration can become as described by Jenkins and Clemitshaw (2002).
In the presence of daylight (solar ultra-violet radiation, hv), the reactions
occur on a time scale of seconds according to
NO2 + hv → NO + O
Atmospheric oxygen, O2, will quickly react with the leftover O molecule
(Sillman, 2002, p. 359).
Even though O3 tends to decrease in the presence of traffic, background
levels of O3 have largely followed the trends in fossil fuel emissions since
systematic measuring began in the 20 th century (Derwent et al., 2007;
Solberg et al., 2005).
2
Figure 1 Concentrations of NO2 and O3 at an urban roadside measuring
station, Reykjavík over 4 days starting January 21, 2006.
Particulate matter
Particulate matter (PM) is in essence small particles that are suspended in
the air. Traffic-related PM contains a mix of materials from combustion
processes: carbon and sulfates, but also particles from soils, or mechanical
wear, mineral components and traces of heavy metals (WHO 2005, Bérubé
et al., 2006; Donaldson et al., 2006). PM is denoted by the aerodynamic
diameter of the particles; the coarse fraction is PM10-2.5 (PM with an
aerodynamic diameter between 2.5 and 10 µm), the fine fraction is PM0.1-2.5
(aerodynamic diameter between 0.1 and 2.5 µm), and the ultrafine fraction;
PM0.1 (aerodynamic diameter less than 0.1 µm) (Bérubé et al., 2006;
Skúladóttir et al., 2003; Akselsson et al., 1994 p 78).
In some studies, there is a distinction between primary particles or
aerosols directly emitted from the source, and secondary particles or
aerosols which are formed when primary pollutants reacts with the
atmosphere, or each other to form other components or larger particles. The
size fraction indicates the origin of the particle. The ultrafine fraction is
formed in anthropogenic processes, like engine combustion, whereas the fine
and coarse fraction particles are made up of primary natural aerosols or
formed in mechanical processes, for example from the wear of car tires on
asphalt paving (Akselsson et al., 1994 p77; WHO, 2013).
3
Figure 2 Reykjavík seen from the north on a day with clear skies (top) and on
a day with high levels of PM10 due to a dust storm (below) (image: Hanne
Krage Carlsen).
Cocktail effects
Most research has focused on identifying the health effects of single
pollutants, while adjusting for the effects of other pollutants. However,
cocktail effects are known from research in other areas of toxicology and are
beginning to appear within the field of air pollution studies. Recent research
methodology moves in the direction of a more holistic approach using
interaction models to appreciate these effects (Dominici et al., 2010), but
only a few studies have applied these methods so far, and with conflicting
results (Bobb et al., 2011; Yu et al., 2013; Anderson et al., 2012).
4
Dust and sand storms
Desert regions or areas with little vegetation are prone to erosion from both
wind and precipitation. Dust particles are lifted from the surface by the wind.
The size, weight and physical properties of the particles determine how easily
they are uplifted. Spores from fungi or bacteria have been found on the
surface of natural particles. These may affect ecosystems far from their home
regions, or work as allergens (Kellogg and Griffin, 2006). There are examples
of Asian dust storms that drift around the globe with the wind (Uno et al.,
2009).
Globally, the major dust contributors are the Sahara and Gobi Deserts
(Kellogg and Griffin, 2006). In recent years dust from Iceland´s glacial
outwash plains has been recognized as a significant contributor to dust in the
North Atlantic region (Prospero et al., 2012; Bullard, 2013).
Geothermal areas and hydrogen sulfide
Hydrogen sulfide (H2S) is a clear gas with the characteristic smell of rotten
eggs. H2S is abundant in geothermal areas. Geothermal areas with hot
springs, mud volcanoes and geysers are the surface manifestation of an
underlying cooling magma chamber. H2S is released from the harnessing of
geothermal energy (figure 6). H2S is also found as a biproduct of drilling and
refining of oil and gas and of some industrial processes like paper production
(Campagna et al., 2004).
Volcanic eruptions
Volcanoes are the surface manifestations of Earth’s inner thermal processes.
Solid, liquid or gaseous products, magma, from the molten part of the core
break through the earths crust and disperse as either lava, tephra, or ash.
The world's most active volcanoes are distributed along continental plate
margins (e.g. Japan, the Andes, and Indonesia in the Ring of Fire) or over
hot spots or mantle plumes that rise up from the underlying mantle (e.g.
Hawaii, Réunion). Iceland represents a mixture of the two forms of
volcanism as it is located on the oceanic plate boundary between the North
American and Eurasian plates above a mantle plume.
Volcanic ash refers to the small particles (<2mm in diameter; particles
larger than 2 mm are called tephra) emitted from volcanic eruptions.
Volcanic ash forms in eruptions of a certain magnitude as explosive energy is
necessary to produce and emit the small particles. In particular when water
is present, explosive energy is released from the molten rock to the water,
and the rock explodes into small fractions. Ash particles can have very
different characteristics depending on the chemical properties of the magma
5
and the mode of eruption (Francis and Oppenheimer, 2004, p. 126). During
a volcanic eruption, fine ash remains suspended in the air and is dispersed
via the eruption column. Volcanic ash can be characterized as PM (see figure
3).
Fresh, unweathered ash particles can carry other corrosive chemicals on the
surface and are likely to have a larger surface area than dust particles from
other sources (Gíslason et al., 2011).
Other sources
The study of indoor air pollution is a field in its own right. In the Western
world, the main indoor air quality concerns include smoking, mold and
spores, particles produced during cooking, and chemical exposures e.g. from
paint. Burning biomass fuel (coal, wood, peat and dried manure) inside for
heat or cooking has been largely abandoned in the Western world, but
remain a major concern in the developing world where indoor combustion
particles constitute the second largest environmental risk factor after
smoking (Lim et al., 2012). Wild fires in nature are also a significant source
of particles, both locally and regionally (Emmanuel, 2000; Naeher et al.,
2007; Künzli et al., 2006).
Figure 3 The ash plume of Eyjafjallajökull. The ash contained as much as
20% PM10. May 7 2010 (image: Hanne Krage Carlsen).
6
1.2 Air pollution effects on health
1.2.1 Pathways
The main effects of air pollution on the body are found in the
cardiopulmonary system. The respiratory system is most exposed as humans
breathe circa 20 m3 of air per day. The inhaled air enters the nose or mouth,
goes into the bronchi, the bronchioles and the alveoli, where inhaled oxygen
(O2) is exchanged through the epithelium of the alveolar wall. From there O 2
goes into the bloodstream, which delivers it to the cells in the body. Carbon
dioxide (CO2) from the cells is exchanged in the opposite direction to the
exhaled air (WHO, 2000 p 16).
Figure 4 Overview of the respiratory system and particle deposition (image:
Hanne Krage Carlsen/Creative Commons).
The nose works as a barrier for larger particles which impact inside the
nose or get caught by the nasal hairs (Eccles, 2006) but particles smaller
than 10 µm can enter the respiratory system along with air and gases.
Further into the airways, particle deposition depends on the respiration rate,
particle sizes and chemical and physical properties of the particles, but only
the smallest particles (PM<1 µm) can reach the alveoli and become deposited
there (figure 4) (Akselsson et al., 1994, p50). Once in the respiratory system,
7
the air pollution affects first the lungs, and in turn, the cardiovascular system
(see figure 5). Research in animals indicates that the smallest particles also
reach other organs (Kreyling et al., 2010).
Adverse effects from exposure to air pollution can be more severe in
susceptible individuals. These are often considered to be the very old and
very young, or individuals with a pre-existing condition which makes them
more sensitive to exposure to air pollution, such as obstructive lung disease,
which is described below.
Obstructive lung disease
Asthma affects a substantial proportion of the world’s population; the
rates are highest in the Western world, especially among Europeans (To et
al., 2012). Asthma is an inflammatory disorder where the airway walls react
with bronchial hyperresponsivity (BHR), restricting the diameter of the
airways, and patients experience difficulty exhaling, called obstruction
(Holgate et al., 2010). Wheezing, dyspnea, chest tightness and cough are
common symptoms (D’Amato et al., 2005), but asthma has a number of
clinical expressions (Janson et al., 2008). In allergic asthma, BHR occurs as
a reaction to stimuli such as allergens. Asthma management revolves around
quick-relief medications such as short acting β-agonists and longer acting
medications like corticosteroids. Asthma is a heterogeneous disease which
can be exacerbated by infectious disease epidemics, climate, and exercise,
but more importantly, exposure to allergens and air pollution (von Mutius
and Braun-Fahrländer, 2002). In asthma, some air pollution types can act
directly as irritants to the airways, but another possible mechanism suggests
that air pollution damages the cells in the epithelial wall of the alveoli,
inducing an inflammatory response, which increases the penetrability so that
PM and harmful gases can pass through into the blood serum (von Mutius
and Braun-Fahrländer, 2002; Holgate et al., 2010).Asthma and chronic
obstructive pulmonary disease (COPD) share a number of risk factors such
as a family history of asthma, but COPD primarily develops in adults with a
history of smoking (de Marco et al., 2007). COPD can have a wide range of
clinical expressions, but in general the alveolar tissue breaks down, and in
patients with COPD increased levels of several inflammation markers were
associated with worse lung function after adjusting for other risk factors
(Thorleifsson et al., 2009); other studies in healthy individuals found
stronger effects in men than in women (Ólafsdóttir et al., 2007; Ólafsdóttir
et al., 2011).
The COPD diagnosis is based on results from lung function tests,
specifically the ratio between the Forced Expiratory Volume in 1 second
(FEV1) and Forced Vital Capacity, FVC (FEV1/FVC). Staging of COPD is
based on how low the FEV1 is. COPD is a rather common disease in older
smokers. A wide variation in prevalence was found in an international study
8
by Buist and colleagues (2007) and poverty and socioeconomic status also
play an important role (Burney et al., 2013).
Many COPD patients experience exacerbation with irritation symptoms
when exposed to irritants, such as high air pollution levels (Yang et al., 2005;
Halonen et al., 2008).
Figure 5 Suggested biological pathways for health effects of air pollution to
differet parts of the circulatory system (image: Hanne Krage
Carlsen/Creative commons).
Cardiovascular disease
Both long-term and short-term exposure to air pollution have been
associated with cardiovascular health effects of air pollution (Pope and
Dockery, 2006).
The processes leading to a cardiovascular event like a cardiac infarction,
such as building up of plaque in the blood vessels, can take decades.
However, the mechanisms that trigger cardiovascular events in a susceptible
population may work in a matter of days or hours. The risk factors for the
two processes may not be the same (Künzli and Tager, 2005; Künzli et al.,
2011). Several pathways and mechanisms have been suggested for these
9
health effects (Sun et al., 2010). First, the immune system responds to the air
pollutants in the respiratory system. Inflammation biomarkers are increased
after exposure to air pollution (Delfino et al., 2009). Inflammation is
associated with accelerated formation of atherosclerotic plaque (Künzli et al.,
2011), and increased blood viscosity, causing thrombosis (Pope and Dockery,
2006; Gold and Mittleman, 2013). A destabilized atherosclerotic plaque can
disrupt in the cardiac artery wall and start the formation of a clot which
obstructs the blood flow to the heart’s muscles and cause a cardiac
infarction.
Other suggested pathways for effects of air pollution include disturbance
in the cardiac autonomic function which controls the heart rate, and in the
vasomotor function that controls the widening and narrowing of blood
vessels (Sun et al., 2010).
Nearly all cardiovascular risk factors also increase the long-term risk of
developing stroke. On a shorter time scale, increases in blood viscosity and
clotting factors, along with disturbances in the heart’s autonomic function,
also increase the risk of ischemic stroke (Gold and Mittleman, 2013).
Disturbances in vasomotor function and damage to the blood vessels may
increase the risk of hemorrhagic stroke after short-term increases in air
pollution exposure levels (Yorifuji et al., 2011).
Other health outcomes
Exposure to natural hazards like volcanic eruptions is associated with
increased risk of stress, depression and fatigue (Shore et al., 1986), some
factors probably related to the loss or damage to property or livestock, crop
failure and general insecurity imposed by living near an active volcano
(Tobin et al., 2012). However, exposure to natural disasters is generally less
likely to result in post traumatic stress disorder (PTSD) than exposure to acts
of terrorism or war (Bracha, 2006).
In recent years more associations between chronic exposure to air
pollution and health effects outside the cardiovascular system have been
found, such as the development of dementia (Forsberg et al., 2013), and
giving birth to a child with autism spectrum disorders (Volk et al., 2014).
However, these findings are novel and it is uncertain whether they represent
correlation rather than causation.
1.2.2 Evidence from epidemiological studies
Traffic-related pollution
Short-term changes in levels of traffic-related air pollutants like PM10 are
associated with increased mortality rates (Meister et al., 2012) and
10
respiratory disease hospital admissions (Peng et al., 2008). Short-term
exposure to increased levels of fine particles, PM2.5, is associated with
increased hospital admissions for cardiovascular events (Pope et al., 2006;
Pope et al., 2008).
Long-term exposure, or living in an area with higher levels of total PM and
fine PM, is associated with higher mortality rates and reduced life
expectancy (Dockery et al., 1993; Pope et al., 2009), and higher risk of a
cardiac infarction attack in women (Miller et al., 2007). Exposure to air
pollution from traffic is associated with stunted lung growth in children
(Gaudermann et al., 2007).
Exposure to short-term increases in traffic pollution, PM10, O3, SO2, NO2,
exacerbates already existing asthma in adults and children (Antó et al., 2012;
Samoli et al., 2011) and is associated with increased emergency room visits
and hospital admissions for asthma (Villeneuve et al., 2007; D´Amato et al.,
2005), and COPD (Sunyer et al., 2000; Yang et al.; 2005). Long-term
exposure to traffic pollution in urban areas is associated with increased risks
of developing asthma in children (Gaudermann et al., 2007; Bråbäck and
Forsberg, 2009; McConnell et al., 2010), but whether adults are also at
increased risk is debated (Anderson et al., 2013; Jacquemin et al., 2012).
Short-term increases in air pollution levels are associated with increased
mortality risk in COPD patients (Sunyer et al., 2000) and an increased risk
of being admitted to hospital (Yang et al., 2005). Long-term exposure to
traffic-related air pollution is associated with increased risk of
hospitalization for COPD (Andersen et al., 2011) and development of COPD
in women (Schikowski et al., 2014).
A short-term increase in PM2.5 exposure is associated with increased risk
of cardiovascular events and cardiovascular mortality (Brook et al., 2010;
Sun et al., 2010; Gold and Mittleman, 2013). Short-term exposure to gaseous
pollutants is associated with adverse changes in heart rate, arrhythmia
indicators (Devlin et al., 2003) and inflammatory markers (Sun et al., 2010;
Gold and Mittleman, 2013; Chuang et al., 2007). Long-term exposure to
higher PM2.5 levels at the residence is associated with increased risk of
developing hypertension which is associated with other adverse cardiac
outcomes (Chen et al., 2013).
Short-term exposure to PM2.5 and NO2 is associated with increased risk of
stroke (Yorifuji et al., 2011; Gold and Mittleman, 2013). Studies of exposure
to short-term changes in O3 show only associations with ischemic stroke in
men (Henrotin et al., 2007; Henrotin et al., 2010). Long-term exposure to
PM10 also increases the risk of stroke (Oudin et al., 2010), whereas results
regarding exposure to background NO2 are not in agreement (Oudin et al.,
2009; Andersen et al., 2012).
11
Mineral dust and sand storms
Dust storms are considered a natural source of pollution, though erosion, the
main cause of dust storms, is facilitated by human activities (Morman and
Plumlee, 2013). As the health effects of the particles are highly dependent on
the origin, these studies are presented by region.
In Anchorage, Alaska, US, high levels of PM from geological sources, some
from a volcanic eruption, have been associated with increases in bronchitis,
upper respiratory infection and asthma medical visits (Choudhury et al.,
1997; Gordian et al., 1996). People under 20 had increased risk of outpatient
asthma visits and anti-asthma medication (beta-agonists) and same day, and
weekly median exposure to PM pollution (Chimonas and Gessner, 2007).
School nurse anti-asthma medication dispensing was associated with PM
levels in the preceding days (Gordian and Choudhury, 2003). Studies of dust
storms in the desert regions of the US in the 1930s have shown that there
was an association between dust events and hospitalizations for respiratory
illness (Morman and Plumlee, 2013; Hefflin and Jalaludin, 1994). Dust
storms in Sydney, Australia were associated with a 15% increase in mortality
(Johnston et al., 2011).
Asian dust storms have their origin in the desert regions of China and
Mongolia and contribute to pollution in the Asian mega-cities (Chan and
Yao, 2008); the dust is carried across the Pacific Ocean and has been
measured in the northern US (Duce et al., 1980). Associations have been
found between Asian dust storms and increased mortality in Taipei and
Korean cities (Chan and Ng, 2011; Lee et al., 2013) and increased risk of
emergency hospital admissions for COPD (Tam et al., 2012).
Saharan dust particles have been associated with increased risk of
pediatric asthma admissions, hospital admissions for meningococcal
meningitis, adverse birth outcomes, and cardiopulmonary disease mortality
in large cities in southern Europe (Karanasiou et al., 2012). Most notably,
Pérez and colleagues (2012) found that the increase in mortality was higher
when Saharan dust was present in the PM pollution, than when the PM had
a traffic-related origin (Pérez et al., 2012).
Hydrogen sulphide
The toxic effects of H2S in occupational settings are well known (WHO,
2000). H2S works as a signalling molecule in the human body and is
involved in several organ systems, notably the respiratory system (Olson.,
2011), but the mechanisms are not yet known.
12
Figure 6 The emissions from a geothermal power plant contain H 2S (image:
Hanne Krage Carlsen).
Industrial sources
A high H2S level from a wastewater treatment facility was associated with
increased risk of asthma hospital visits the following day (Campagna et al.,
2004). Long-term exposure to H2S emitted from mostly industrial sources
(oil refinery, cheese plant, natural oil and gas wells, and a sewage plant) was
associated with adverse neuropsychological effects in residents around the
plants (Kilburn and Warshaw, 1995; Kilburn et al., 2010). Living in areas
exposed to H2S from pulp- and sawmills is associated with increased sensory
organ irritation symptoms and headaches (Jaakkola et al., 1996; ParttiPellinen et al., 1996; McGavran, 2001). A 2003 report (Roth and Goodwin,
2003) called for better reporting of exposure and more studies of chronic,
low-level exposure and studies on asthmatics.
Geothermal sources
The epidemiological studies of respiratory effects from exposure to H2S from
geothermal sources have conflicting results (McGavran, 2001).
Exposure to volcanic gases, H2S among them, from geothermal areas and
volcanoes that are not erupting is associated with incidences of respiratory
cancers and respiratory illness, and respiratory mortality but not childhood
asthma or decreased lung function. Furthermore, most studies have a
moderate or high risk of bias because of the study design or methods of
surveying; e.g. self-reporting can be very biased by beliefs about the health
13
risks of exposure. Also, many studies lack information about important
confounders (Hansell and Oppenheimer, 2004)
Short-term exposure to increased H2S levels is not associated with
dispensing of medication for angina pectoris the following 7 days
(Finnbjornsdottir et al., 2013). In a trial where healthy volunteers were
exposed to H2S, only minor sensory and cognitive effects were found (Fiedler
et al., 2008).
Studies of long-term effects of exposure to background sulphur gases from
geothermal sources in Hawaii, New Zealand and the Azores, H 2S among
them, indicated that long-term exposure is associated with adverse
cardiopulmonary effects, both measured and self-reported (Durand and
Wilson, 2006; Longo et al., 2008; Longo and Yang, 2008; and Longo, 2009)
and increased rates of bronchitis (Amaral and Rodrigues, 2007; Longo et al.,
2008). No association was found between chronic exposure to H 2S in
residents in a geothermal field and self-reported asthma symptoms (Bates et
al., 2013). In the same cohort, no neuropsychological effects were found
(Bates, 2013).
Volcanic eruptions
Human dwellings are clustered around volcanoes in many parts of the world,
since volcanic soils can be very fertile and humans also utilize the warm
water and geothermal heat. Worldwide, 500 million people live within 100
km of an active volcano (Small and Naumann, 2001). In Iceland, the
corresponding number makes up 95% of the population (figure 9). During
the 20th century nearly 100,000 people were killed directly by volcanic
eruptions and more than 4.5 million people were affected or displaced
(Doocy et al., 2013). Volcanic eruptions pose immediate dangers to human
health when pyroclastic flows and lahars (mud flows) reach inhabitated
areas (Hansell et al., 2006; Heggie, 2009). Active volcanoes emit various
sulfur compounds like SO2 and H2S, CO2, and acid aerosols. Exposure to
volcanic aerosols from African, and West Indian volcanoes was associated
with asthma and COPD exacerbations in studies from before 2004 (Hansell
and Oppenheimer, 2004).
Gases from volcanic eruptions
The residents of the village Miyake, Japan, were forced to evacuate from
their homes when Mt. Oyama erupted in 2000 because of the dangerous or
lethal levels of gases (Iwasawa et al., 2009). The residents began to return in
2005, and groups of volunteers came with them to help them re-establish the
community even though the volcano still emitted large amounts of SO 2. The
adult residents (n=823) were surveyed in 2004 before they began to return
14
to their homes and again after their return to the island in 2006. After
returning, the adults had no deterioration in lung function, but the
prevalence of cough and phlegm was higher. Those in the areas with higher
levels of SO2 had more symptoms (Iwasawa et al., 2009). The self-reported
incidence of cough, other throat symptoms and breathlessness showed a
dose-response association with SO2 exposure in healthy volunteers working
in the areas. Women were generally more susceptible (Ishigami et al., 2008).
In a follow-up 6 years later, those living in the areas with higher levels of SO 2
were associated with lower than expected lung function in adults (Kochi et
al., 2013) and irritation symptoms in children (Iwasawa et al., 2013).
There are few studies of short-term exposure. A 2009 increase in volcanic
activity lasting 14 weeks in Hawaii’s Kilauea volcano provided the frame for a
natural experiment, following the increase in emissions, which were as high
as three-fold; there was an increase in respiratory complaints and
headaches, especially in the elderly and native Hawaiians (Longo et al.,
2010). In an overview article summarizing results from previous studies,
Longo et al. (2009) also report that some 35% of 335 people surveyed in a
questionnaire study found their health to be affected by the eruption.
Current and former smokers and people with chronic respiratory disease
were most affected. In a follow-up in 2012, exposed individuals reported a
more than 15-fold increase in irritation symptoms while being outdoors
(Longo et al., 2013).
Volcanic ash
Volcanic ash (figure 7) can damage vegetation and cause buildings to
collapse and injure or kill people (e.g. due to bad visibility, figure 7).
Inhalation of ash, skin abrasion and eye irritation are the main long-term
concerns to human health. Though fine volcanic ash can be considered a
form of PM, it is not certain if the dose-response functions which have been
developed from traffic-related PM10 apply to volcanic ash (Oudin et al.,
2013). Both the physical and chemical characteristics of volcanic ash vary
greatly between volcanoes and eruptions (Horwell and Baxter, 2006).
The short-term health effects of exposure to volcanic ash range from none
to region-wide epidemics of asthma and respiratory symptoms. A similar
range was found in the clinical and toxicological studies. The major effects
seem to be short-lived and depend very much on the properties of the ash
(Horwell and Baxter, 2006).
After a 28-day long eruption of Guagur Pichincha in Ecuador in 2000,
Naumova et al. (2007) studied the pediatric hospital emergency room (ER)
visits in the city of Quito. In the weeks following the April eruption there was
a significant increase in diagnosed primary respiratory conditions. The risk
of lower respiratory infection was doubled and for upper respiratory
15
infection, the risk was increased 70%. The effect was larger in small children
and the rate of asthma diagnoses doubled.
Figure 7 Ash from the Eyjafjallajökull eruption causes bad visibility mid-day
May 7, 2010 (image: Hanne Krage Carlsen).
Following a large eruption of Mt. Ruapehu in New Zealand in 1996, ash
was detected in the air near large cities 200-300 km from the volcano.
Hospital records were later examined and researchers found the highest
rates of respiratory mortality for the 1990's for these cities during the month
following the eruption. Researchers concluded that the ash was responsible
for the excess respiratory mortality (Newnham et al., 2010).
Shimizu et al. (2007) surveyed 236 adult asthma patients from an area
exposed to ash from an eruption of Mt. Asama in Japan. In the most exposed
region, where more than 100g/m2 ash fell, about half of the patients with
asthma reported worsening, their spirometry (lung function) measurements
were lower and they used more medication.
Mental health
The natural forces unleashed during a volcanic eruption mean that
experiencing a volcanic eruption can be a traumatic event as individuals fear
for their own lives and the lives of their neighbors.
The mental health effect following the eruption of Mount St. Helens
included increased ER visit rates, increased use of mental health services and
domestic violence, also increased self-reported stress and mental health in
those who had lost a friend or relative in the disaster; and up to ten-fold
16
increases in levels of anxiety, stress and major depression in people who had
experienced damage to property (Shore et al., 1986) Studies of survivors of a
deadly eruption of Mt. Unzen, Japan, had very high rates of adverse mental
health which subsided with time (Ohta et al., 2003). Societies near volcanoes
with catastrophic eruptions who are exposed over many generations
incorporate stories of the disasters into their myths and cosmology which
help future generations understand and recover in case of disaster (Cashman
and Cronin, 2008).
1.3 Respiratory health and air pollution in Iceland
Historically, asthma and allergic disease prevalence have been low in Iceland
(Gislason et al., 2002), but both asthma symptom prevalence and use of
asthma medication increased in adult Icelanders between 1990 and 2007.
However, it is unclear if this increase has been due to changes in diet,
smoking, higher diagnosis rates, or other factors (Sigurkarlsson et al., 2011).
The prevalence of COPD in Iceland was found to be 18% in individuals over
40 years of age (Benediktsdóttir et al., 2007),
Only a few studies have addressed the association between air pollution
and human health in Iceland. First, The European Community Respiratory
Health Study (ECRHS, Burney et al., 1994), which Iceland has participated
in for a number of years (Gíslason et al., 2002), showed that chronic
bronchitis and phlegm increased in participants exposed to high levels of
NO2 at their residence and who lived close to major roads (Sunyer et al.,
2006). A study using pharmaco-epidemiological methods found associations
between dispensing of angina–pectoris relief medication and levels of O3 and
NO2 (Finnbjornsdottir et al., 2013).
Iceland's capital area has a reputation for being among the cleanest
capitals in the world. There is little industrial pollution and geothermal
energy has replaced the use of fossil fuels for space heating. There is
considerable traffic-related air pollution (Jóhansson, 2007; UHR, 2007).
The capital area of Iceland covers around 700 km 2 (National Land Survey
of Iceland, 2012) and most of the city is characterized by low houses usually
two to three stories high. As of 2011, there were nearly 200 000 inhabitants
of which 150,000 were old enough to have a driving license (18 years of age,
Statistics Iceland, 2013a).
The air pollution regulation is based on health limits in the EU (Table 1;
Althingi, 2002; Altingi, 2003), except for H2S. H2S Health Limit Regulations
were implemented in Iceland in 2010 (Althingi, 2010) and were based on
regulation from other locations with occurrence of H 2S, such as Hawaii
(Department of Health, Hawaii, 2001).
In the capital area, there were 134,000 passenger cars in 2006 (newest
available, Statistics Iceland, 2013b), which makes 9 cars per 10 adults, one of
17
the highest car to people ratios in the world (Laych et al., 2009). Car use is
an ingrained part of Icelandic culture (Collin-Lange, 2013), and the
infrastructure of the capital area is very car-oriented.
The levels of NO2, a traffic indicator pollutant, are significant around
major roads (Sunyer et al., 2006). O3 levels in Iceland tend to be higher in
early spring, which is believed to be related to atmospheric conditions in the
Arctic (Solberg et al., 2005). PM10 levels in Iceland’s capital area are mainly
traffic-related, so particles are resuspended during drier periods (UHR,
2007; Johansson, 2007). Another contributor to PM is vehicles which are
driven with studded tires during winter. The tire studs wear the asphalt into
small particles which mix with the salt and sand which is spread on roads
and pavements during winter to prevent skidding.
Table 1 Air pollution health limits in Iceland µg/m3.
Act no./Year
24-hour
µg/m3
1-hour
µg/m3
Allowed
exceedencesb
H2S
514/2010
50
NO2
251/2002
75
O3a
745/2003
120a
25
PM10
251/2002
50
7
5
110
175
aMaximum
8 hour mean. b Allowed exceedences is a target for the number of
times the health limit may be violated, 2010 is given as an example.
1.3.1 Mineral dust sources
During the warmer season, dust storms originating in south Iceland areas
with scarce vegetation are a more common source of PM10. Twenty-two % of
Iceland has little or no vegetation and is characterized as an arctic desert.
Dust storms are frequent in the highlands (Dagsson-Waldhauserowa et al.,
2013). Dust storms in Iceland originate in the sand plains of the south and
affect the air quality of the capital area to the west (see figure 2;
Thorsteinsson et al., 2011). The dust is mainly basaltic glass (Arnalds, 2010)
and dust storms from the east/south-east now include a substantial
contribution of ash from the Eyjafjallajökull eruption (Arnalds et al., 2011;
Arnalds et al., 2013; Thorsteinsson et al., 2011).
18
1.3.2 Hydrogen sulfide
H2S is emitted naturally, but with the opening of the geothermal harnessing
sites at Hellisheiði and Nesjavellir, 25 and 35 kilometres from the capital, the
levels have risen substantially (Ólafsdóttir and Garðarsson, 2013; Ólafsdóttir
et al., 2014).
H2S levels measured in the city have not been found in levels which are
known to be toxic from studies of occupational exposure, but they routinely
surpass the WHO nuisance guideline value of 7µg/m3 (WHO, 2000) and
Iceland’s own H2S regulation.
Figure 8 Hellisheidi geothermal powerplant seen from the north (image:
Hanne Krage Carlsen).
1.3.3 Icelandic volcanoes
Iceland experiences a volcanic eruption every 5 years on average. Most are
small and have little impact beyond the very local environment (Thordarson
and Höskuldsson, 2002). Iceland is thankfully rather sparsely populated, so
in most cases, there is no danger to human life or health, but there are a few
exceptions which will be discussed here.
19
Laki/Skaftáreldar 1783-1784
The 1783-4 eruption of the Laki fissure was the most fatal in historical times
in Europe and produced the largest lave flow on Earth in historical times.
First, the acid gas and aerosol fog from the eruption and subsequently
degassing lava led to an atmospheric haze that persisted into 1786 (Lacy,
1998) and caused an increase in respiratory mortality, first and foremost in
Iceland and northern Europe. The sulfur haze lay over Europe and even Asia
and North America as well, where the gases reduced the sun-light and
caused crop failure. In Iceland, at least half of the livestock died off, and onefifth of the population died (Hansell and Oppenheimer, 2004; Tweed, 2012).
In Europe and Alaska famine disasters were described, caused by colder
climate and acid rain (Jacoby et al., 1999; Durand and Grattan, 2001;
Hansell and Oppenheimer, 2004). In England, 20,000 were estimated to
have died from the indirect effects of the Laki eruption (Witham and
Oppenheimer, 2004). If a similar eruption happened today, it is estimated
that it would cause hundreds of thousands of deaths (Schmidt et al., 2011).
The 2010 Eyjafjallajökull eruption
The Eyjafjallajökull stratovolcano in south Iceland had been dormant since
an eruption in 1821-23 (Figure 10). After years of increasing seismic unrest,
the Eyfjafjallajökull volcano erupted on March 20, 2010 in a flank eruption
with basaltic effusive lava fountains. A magma intrusion triggered the April
14 explosive eruption of the summit crater which produced large amounts of
fine ash (Sigmundsson et al., 2010). In the first hours, the main concern was
flood risks, as the glacier melted quickly and the water came down the
volcano into the Markarfljót River. Inhabitants of low-laying areas were
evacuated.
The ash dispersed over a wide area with prevailing westerly winds. The
ash was fine during the first eruption phase, with about 20% 10 µm or less in
aerodynamic diameter (Institute of Earth Science 2010; Thorsteinsson et al.,
2011; Stohl et al, 2011; Gislason et al., 2011; Gudmundsson et al., 2012).
The ash particles from Eyjafjallajökull had a large surface area, and had a
potential to increase inflammation markers (Horwell et al., 2013). The ash
had a high iron (Fe) content, and was found in cytotoxicological studies to
have effects on alveolar macrophage function which led to increased bacteria
growth in vitro (Monick et al., 2013). Ash from other Icelandic volcanoes also
has a high iron content (Dagsson-Waldhauserowa et al., 2013).
In the period after the eruption some of the most extreme wind erosion
events on earth were recorded in south Iceland (Arnalds et al., 2013). PM
dust levels were higher in the area surrounding the volcano and in the capital
area more than 100 km from the volcano (Thorsteinsson et al., 2011;
20
Thorsteinsson et al., 2012). Volcanic ash is now a significant contributor to
PM10 in the capital area (Vegagerðin 2013, Figure 11).
Figure 9 The Eyjafjallajökull volcano 2008 (left) and 2010 (right) (image:
Hanne Krage Carlsen).
As we have seen, the population of Iceland is exposed to significant air
pollution both from traffic-related air pollution and pollution from natural
sources such as geothermal harnessing and volcanic eruptions. The health
effects of these remain un-investigated.
Figure 10 Composition of ambient PM10 in Reykjavík 2003 and 2013 (Image:
Hanne Krage Carlsen based on Skúladóttir et al., 2003 & Vegagerðin, 2013).
21
2 Aims
The aim of this project was to determine if, and to what extent, residents
in Iceland experience adverse health effects from air pollution. The target
populations are the residents of the Icelandic capital area, who are mainly
exposed to traffic-related air pollution, and the residents of south Iceland,
who were exposed to volcanic ash from the 2010 Eyjafjallajökull eruption.
The overarching research question of the articles is: do the residents of the
capital area or south Iceland experience adverse health effects due to air
pollution from traffic or urban sources, or the volcanic sources of ash or
H2S?
Specific aims for each paper:
I.
Were the daily number of individuals obtaining prescribed antiasthma medication in Reykjavík associated with changes in daily
air pollution levels the previous 14 days during the period 20062009?
II.
Were 2003-2009 emergency room visits and acute hospital
admissions (emergency hospital visits) for cardiopulmonary
disease and stroke, associated with daily air pollution levels in
Iceland's capital area. Were any subgroups (elderly, women) more
vulnerable than others to the exposure?
III.
Were the association between emergency hospital visits and PM10
modified by the source of PM10 pollution (natural dust, volcanic
ash or other), or the introduction of volcanic ash from
Eyjafjallajökull or Grímsvötn? Also, was there an association with
H2S levels?
IV.
Was the health of 207 individuals exposed to the Eyjafjallajökull
volcanic eruption surveyed with lung function measurements,
questionnaires and physician's evaluation associated with adverse
health effects?
V.
Was exposure to the eruption of Eyjafjallajökull associated with
increased rates of respiratory- and mental health symptoms six
months after the eruption in the population of south Iceland
compared to a demographically matched reference group in north
Iceland? Did the reported symptom rate correspond to the level of
exposure within south Iceland?
23
3 Materials and methods
The studies in this thesis fall into two categories, on one hand studies of
short-term effects where time series methods and register-data are used to
study associations between pollution levels and a health outcome. On the
other hand, there are studies of chronic exposure in South Icelanders
exposed to the Eyjafjallajökull volcanic eruption; these use survey or
questionnaire data and employ logistic regression and descriptive statistics
to analyze the data.
The following section first lists the data sources used and types (see table
2), followed by presentation of the methods used in the two types of studies
(see table 4).
3.1 Material
3.1.1 Exposure
Air pollution in Reykjavík
The air pollution exposure variables used in paper I, II, and III were
routinely measured air pollution levels in Reykjavík. The data were
measured at one site in the geographical centre of the town (Figure 12). Data
from other measuring stations were not of sufficient quality and were not
used.
Figure 11 Reykjavík and surrounding area with main pollution sources
and measuring stations (Adapted from Carlsen et al., 2013).
24
The data were collected, validated and corrected and provided as 30
minute or 60 minute means by the Environmental Branch of the City of
Reykjavík (data for 2003-2008) and the Environmental Agency of Iceland
(data from 2009 and later).
We chose four pollutants which have known health effects and which are
indicative of the pollution mix in Reykjavík; PM10, NO2, O3 and H2S. H2S
measurements only began in February 2006 and ozone measurements were
incorrect from 2010 and onwards (see figure 15). As covariates, we included
weather and pollen counts.
In addition, we obtained information about sources of PM 10 from the
Reykjavík Municipality Environmental Branch and Dr. Thorsteinsson and
Rebekka Kienle, both University of Iceland (personal communication,
Thröstur Thorsteinsson, 29 oktober, 2012).
Eyjafjallajökull
Exposure to the Eyjafjallajökull volcanic eruption (paper IV and V) was
multi-faceted as the population was exposed first to hazards in the form of
flood risk, and after the immediate flood risk subsided the ash fall became
the major concern. At the same time noise from the explosion of the eruption
column could be heard hundreds of kilometers away in some directions.
Earthquakes were frequent during the eruption, as is to be expected. The
degree of exposure to the ash was estimated from knowledge of the ash
plume thickness and wind direction during the eruption (figure 12).
3.1.2 Health outcomes
To measure health in a population it is most useful to use an outcome
measure which is both a good indicator of the outcome of interest and which
is sensitive, meaning that it has considerable variation and correlation with
the exposure. It is necessary to have both a reliable end point and a
meaningful number of events so that the study will have sufficient statistical
power.
Paper I, II, and III are register-based studies meaning that they make use
of data which are collected routinely in registers (Wallgren and Wallgren,
2007). This method is useful in studies when large amounts of data are
needed as the data are already collected, so that the data can be gathered
quickly and cheaply. A caveat is that the data were not originally intended for
research purposes; usually there are only limited possibilities of adding
variables of interest to research. An overview of the data used in each study
can be found in table 2.
25
Figure 12 Ash dispersion around Eyjafjallajökull (top) and the exposure
areas based on it (below) (Image: Thorsteinsson, from Carlsen et al.,
2012a and b).
26
Hospital admissions
Hospital admissions are rather rare events, and this outcome is also
governed by factors which are not related to the actual morbidity, such as the
availability of hospital beds, socio-economic and geographic factors like age,
or whether the patient lives far from the hospital or has relatives living
nearby. Emergency room visits are less influenced by availability, but are
rather unspecific. The differences between hospital admissions and ER visits
for asthma and other respiratory causes and in different age groups have
been investigated by Winquist et al. (2012).
The data on ER visits and admissions used in paper I and II were collected
in the Landspitali University Hospital patient data base. The data are
registered to each individual using the personal identification number in the
database. When the data were extracted from the database, the identification
number was coded into a number in such a way that an individual could be
recognized only within the data. The data for acute ER visits and admissions
for the relevant diagnoses were extracted along with information about
gender, age, day of admission/visit, duration of admission (if admitted), and
the primary and secondary diagnoses. The address was indicated by a postal
code. The ICD codes (letter and first two digits) that were extracted were:
pulmonary outcomes: J20-J22, J40-J46 and J96; cardiac: I20-I27, I46, I48,
I50; stroke: G45-G46, I61-I69.
Pharmaceutical dispensing
Pharmaceutical dispensing used in paper I of relief medication for asthma
(R03 in the anatomical therapeutic classification (ATC) is considered an
indirect indicator for respiratory health in a population. In an American
study emergency room visits for asthma were found to be well-correlated
with dispensing of asthma medication (Naureckas et al., 2005), but this may
not be true for other populations with better access to primary care. Other
papers (Hallas, 2005) and a recent review (Menichini and Mudu, 2010) have
treated the subject in detail.
A caveat of using anti-asthma medication dispensings as an indicator is
that they are given as relief for a wide range of respiratory illnesses, so there
is little specificity to asthma. Also, these medications are often dispensed in
volumes deemed to last approximately 3 months (Gíslason et al., 1997). In a
time series study looking at short-term effects, at any given day, only a small
part of the population will actually be at risk of having to refill their
prescriptions.
Databases of prescription medicine dispensing are kept in many countries,
so data are available retrospectively. As these register the sale of the drugs in
the pharmacy, rather than when the medication is actually taken, they are
27
less reliable than other outcomes. The Icelandic prescription drug database
goes back to 2003 and was established for surveillance purposes (Icelandic
Directorate of Health, 2012). It has since then been used in several research
papers. From these data, a time line of the daily number of individuals who
received anti-asthma drugs was created.
Spirometry
Spirometry is a measure of lung health, where the flow and volume of
exhaled air is measured. Some of the important measures are forced vital
capacity (FVC), which is the maximum amount of air exhaled after filling the
lungs as much as possible. Forced expiratory volume in 1 second, FEV 1, is the
amount of air exhaled in the first second (GOLD, 2010), which is a measure
of airway obstruction. Spirometry values depend highly on gender, age, and
height, so most often the results are reported as percent of a predicted value
(usually the average found in a general population sample, e.g. NHANES,
Hankinson et al., 1999). Low spirometry values are indicative of lung
disease, but can also be due to respiratory infections. Spirometry was used in
paper IV.
Self-reported health
Self-reported health outcomes were used in paper IV and V. In
questionnaires, study participants were asked to rate their own health. Most
often these include questions if the subject has experienced easily
recognizable symptoms and to what degree.
In the studies in this thesis we have used questionnaires or questionnaire
items which have already been used in other studies, which facilitates
comparisons of the results with the results from other studies. Questions
about respiratory health were drawn from the European Community Health
Survey (ECRHS, Burney et a., 1994). The General Health Questionnaire-12
question version (GHQ-12) measures psychological morbidity which has
been validated in several populations and has been used in other studies
(Goldberg et al., 1988; Goldberg et al., 1997). GHQ had previously been used
in Iceland (Tómasson and Gudmundsson, 2009), so a translated version was
available.
Other outcomes
Mortality from any cause is an indicator of health in a population, if most
deaths are due to natural causes; this outcome is useful as an indicator for
health in a population (Logan, 1956) whereas in countries with a high
mortality due to causes other than natural, it is less useful. It is preferable to
28
use non-accidental mortality (that is, excluding deaths due to external
causes) or cause-specific mortality where researchers typically select
cardiopulmonary causes, which is used in many studies (Dockery et al., 1993;
Halonen et al., 2010), but as this is a rare outcome it requires a population of
a certain size to be useful, so these outcomes were not used in these studies.
3.1.3 Covariates
Weather
In studies of short-term effects, seasonal weather changes are of importance,
as temperature and humidity influences the symptoms of asthma (Vernon et
al., 2012), but also influences the levels of air pollutants (WHO, 2013). In the
studies of long-term health effects, the change of season must be considered,
not least because respiratory infections are more common in winter.
Pollen
Pollen levels can gravely affect symptoms in individuals with allergic asthma,
so in time series analyses, it is important to adjust for daily pollen counts.
These are measured from April to September by the Natural History
Institute of Iceland (Icelandic Institute of Natural History 2014, personal
communication Margrét Hallsdóttir November 16, 2010 and Lára
Guðmundsdóttir May 21, 2013). The samples were collected at the
Meteorological Institute of Iceland (see Figure 12). In paper I, II and III, the
analyses were adjusted for pollen counts in the initial analysis, though the
results were not significant.
Influenza
When studying respiratory health in a population, information about
epidemics of infectious diseases is of importance as the susceptible
population is most at risk of experiencing worsening of their disease when
getting sick from influenza. Information about influenza epidemics is
reported to the Directorate of Health which reports it in a newsletter and on
their website (Chief Epidemiologists of Iceland, 2012a and b). Influenza
epidemic periods were defined as more than 300 cases reported to the
Directorate of Health per month. In the time series analyses (paper I, II and
III), we adjusted for infectious disease epidemics.
29
Table 2 Schematic overview of the data used in each study.
Outcome data
Exposure
Covariates
Complet
Source
Time
Weather, influenza
season, pollen
eness
PM10, NO2, O3, H2S
period
Daily number of anti-asthma
(R03) drug dispensing,
adults in the capital area
PM10, NO2, O3d
No
The Icelandic
prescription drug
database
Daily number of ER visits
and admissions for
cardiopulmonary disease
and strokec, adults in the
capital area.
PM10,
Age category, gender,
Exposure to volcanic
phenomenae.
44% (620
/1394)b
Landspítali
University
Hospital
Admissions
database
Daily number of ER visits
and acute admissons for
cardio-respiratory outcomes
Having lived or
worked near the
volcano.
Age, gender,
education, smoking
status.
2006-9a
2% (2355
/2557)
Same as paper II
Lung function, physicians
report, self-reported
symptoms of general, mental
and respiratory health
Residence near the
volcano or in
unexposed region
III
II
I
28-54%e
(10321961
/3653)
Medical
examination,
spirometry,
questionnaires
Self-reported general,
mental and respiratory
health
lag 1 of the outcome
influenza season,
Weather,
lag 1 of the outcome
influenza season,
Weather,
2007-12
93%
(207/223)
Questionnaires
2003-9
31 May7 June
2010
72% (1656
/2315)
d
IV
Novemb
er 2010March
2011
H 2S
V
period: 8 March 2006. bOnly multi-pollutant analysis. c ICD codes for pulmonary outcomes: J20-J22, J40-J46 and J96; cardiac:
NO2,
aStudy
I20-I27, I46, I48 ,I50, stroke: G45-G46, I61-I69. d O3 only available until 2009 and H2S only available from February 2006, so excluded
from analysis in paper 3 and 2 respectively. eLow number for multi-pollutant analysis, high number for single-pollutant analysis.
30
3.1.4 Ethics approval
For papers 1, 2, 3 and 5, ethics approval was sought. As the data used were
collected either for surveillance purposes (paper IV) or for use within health
care institutions (paper II and III) it is possible to argue that the data were
used beyond their originally intended purpose (table 3). Thus, extra care
must be taken that individual level data are not distributed beyond the
source and that the use of the data does not compromise the privacy of the
patients.
3.2 Methods
In all papers, data were presented with descriptive statistics; other methods
were applied to comply with the type of data and the aim of the study.
A main distinction in environmental epidemiology is between studies of
short-term effects, such as whether there was an increase in pollution levels
on Monday associated with more cases of a certain disease on Tuesday? and
long-term effects, where areas are usually compared. In these studies a
typical research question would be: does exposure to higher levels of
pollution (e.g. living in an area with higher background pollution levels, for
example, or close to a large road) increase the risk of a disease or symptom
compared to an area with lower pollution levels. The danger when
comparing different areas is that they may differ with respect to other factors
than the pollution.
In this section, methods used in the studies are presented; the methods
are presented schematically for all studies in table 4 after a general
discussion.
Short-term effect studies
Studies of short-term effects seek to find an association between temporal
changes in exposure and changes in the outcome shortly after. A temporal
association like this does not prove causality. The time from exposure to
outcome is called a lag.
The primary analytical method used is regression, with lagged exposures,
sometimes called time series regression. In brief, the health outcome,
dependent variable, is regressed on the exposure, the independent
variable(s), usually with a lag (delay) so that exposure during the previous
days is taken into account. The selection of the lag time to use in the model
must be built on knowledge of the physiological mechanisms and other
factors that occur between the time of exposure and registration of the
outcome of interest (Peng et al., 2008; Peng and Dominici, 2008; Dominici
et al., 2003).
31
Table 3 Ethics approval sought for each study.
No Data
Authority
Ref. no.
For
Individlevel
data
org/used
a
I
Bioethics Committee, VSNb2008050023
Dispensing
Medicines
Data Protection
/03-15,
of asthma
dispensing
Agency, National
2008080569/ÞPJ
medication
data
Directorate of Health
Hospital
Bioethics Committee,
II+ admissions Data Protection
III and ER
Agency, Hospital
visits
Executive Board
Medical
exam,
National Directorate
IV spirometry,
of Health
questionnaires
V
VSNb2010120017/
03.7,
Hospital
2010121176AT/,
contacts
Letter:2010/12/22
Public
Health
Survey
-
No/no
Yes/no
Yes/yes
Bioethics Committee, VSNb2010080002 Collecting
Questionnai
Data Protection
/03.7, S4878/2010 and keeping Yes/yes
re
Agency
(announced)
data
awere
the original data provided as individual level data?/ were individual-level data
used in the study analysis?
It is necessary to adjust for covariates such as weather, day of week, and
season indicators. Both exposures and outcomes have seasonal trends, air
pollution levels are affected by meteorological variables; respiratory health
outcome severity can be affected by weather, infectious disease epidemics, or
pollen counts which all have seasonal patterns (Peng and Dominici 2008;
Tolbert et al., 2007).
Generalized linear models (GLM) and generalized additive models (GAM)
are both frequently used in time series analysis for air pollution
epidemiology. For a count outcome such as the number of patients admitted
to hospital at time t (for example a certain day; Yt) at a certain exposure
vector Xt., GAM or GLM take the following form:
E(Yt) = exp(β0 + β*Xt + S(t) + S(meteot) + ϒ*DOWt)
where meteo denotes a measure of weather conditions; DOW is an
indicator of day of week. S denotes a smooth function, which is applied in
32
order to account for fluctuations in the outcome that are due to other causes
than the exposure (e.g. seasonal variation).
Seasonal variation may confound the effect estimates of the exposure
(Dominici et al., 2003). A smoother can be constructed using different
methods; lowess (locally weighted scatterplot smoothing) smoothers
(Vittinghoff et al., 2004 p.159), sine-cosine terms or splines (parametric in
GLM, non-parametric in GAM) (Dominici et al., 2003). Visual inspection of
the spline and the placing of knots can be used to estimate the non-linear
effects of an exposure. In paper I, II and III, splines were used to adjust for
time trends. Initial analyses of the data in paper I were done using sinecosine terms.
The distributed lag non-linear model (DNLM) is a newer method in
testing regression of time series data (Gasparrini et al., 2010). The model
allows for non-linear modelling (threshold, knots, splines) both across lags
and exposure values.
Exposure assignment
In air pollution studies, exposure is often assessed based on a geographical
measure of the subject’s residence. Other factors like occupational
exposures, the quality and indoor climate of the residence or the exposure at
the workplace, or school in the case of children, can modify the total
exposure. It is also possible to assign study subjects to carry personal
measurement devices. Personal exposure methods give a better exposure
assignment (Jerrett et al., 2004), but are impractical and expensive to use.
Inadequate exposure assignment can lead to faulty results, but in most cases
they will underestimate the effect of the exposure (Zeger et al., 2000).
Multi- and single pollutant models
It has been a goal of the air pollution studies to determine which pollutants
are most harmful to a specific outcome, but some pollutants have high
correlation as the same factors (traffic, weather) influence their levels.
Inserting all pollutants into a model can adjust for this, but may also disguise
important associations, e.g. if two pollutants are highly correlated and are
associated with increases in health outcome measures. Intercorrelated
pollutants are at risk of violating the assumption of independence (Dominici
et al., 2003; Tolbert et al., 2007).
Missing data
In the case where time series data have gaps of missing data, using multipollutant models can mean excluding a lot of data as there must be
33
information about pollution levels for all pollutants for each day to be
included in the analysis (figure 13). Using long lag periods increases the
severity of the problem, as one day of missing data means that a whole lag
period must be excluded. Single-pollutants models with short lags are less
sensitive to this problem. Results from single-pollutant models are often
presented alongside results from multi-pollutant models.
Figure 13 Example of missing data in a multi-pollutant time series. The
shaded areas will be excluded from regression analysis as one or more
pollutants are missing.
Auto-correlation
Many outcomes are correlated with themselves, so that the outcome on a
given day is highly correlated with the same outcome they day before or after
(Madsen, 2007). The autocorrelation function can give an indication of
whether there are signs of autocorrelations, and this can be often be resolved
by including the lag 1 of the outcome as a predictor variable in the regression
model. Serial autocorrelation due to seasonal trends can be adjusted with
splines (Schwartz, 2000).
Adjusting - and overadjusting
While most epidemiological studies adjust for various confounders and time
trend it is possible to go too far. Using splines to adjust for temporal trends,
it is possible to over-adjust, as the spline tries to adapt to the temporal
changes in the outcome. A spline with too many degrees of freedom can
over-adjust and is in danger of eradicating the signal caused by the outcome
of interest. A model showing signs of overadjustment will have no lags
outside the 95% confidence interval in the autocorrelation function. 2 Longterm effect studies.
34
In studies of long-term effects, the aim is to find associations between
certain exposures and occurrence of certain diseases. There is a hierarchy in
the level of evidence inferred from each study type, and the outcomes that
can be determined from them.
An important measure in epidemiology is prevalence, i.e. how many
individuals have a given disease at a certain time. Another is incidence, the
proportion of new cases (new occurrences of the outcome) in a population
per time unit.
Cross sectional study
A cross-sectional study describes a moment in time; how many of the people
in the study had a certain outcome at this given moment. Prevalence is the
only measure that can be estimated from cross-sectional studies. Other
caveats of these methods are spatial confounding, and the difficulty in
distinguishing between whether an observed outcome is associated with
either a chronic (background) exposure or a recent (acute) exposure. For
these reasons, causality cannot be inferred from observational studies like
these (Rothman, 2002).
Cohort studies
In cohort studies, a group of people are selected based on a common
characteristic (most often residences within a geographic area, but other
versions are known, e.g. occupation, year of birth). Characteristics of interest
with respect to the outcome are measured at baseline (Rothman, 2002;
Dominici et al., 2003). Then the group is followed and at certain time points
the number of new occurrences of selected outcomes is measured and the
incidence of the outcome of interest can be calculated. Cohort studies are
both expensive and time-consuming, and it is hard to distinguish whether
chronic or acute exposures were the cause of a particular outcome at a given
time. Also, adjustment for residual confounding is always a concern, as both
exposure and outcome can be associated with confounders with a geographic
distribution (Dominici et al., 2003).
Methods used in studies of long-term effects
In paper IV and V several methods are used to determine if the occurrence of
a certain characteristic is significantly different between two groups (exposed
and unexposed); Χ2–tests were used for binary outcomes (Vittinghoff et al.,
2004). T-tests were used to compare means of numerical outcomes between
two groups. In paper V, we compared the occurrence of a binary outcome
between two or more groups adjusting for several predictors or covariates
35
using logistic regression (Daniel, 2005; Vittinghoff et al., 2004). See Table 4
for an overview of the methods used.
A logistic regression with one binary predictor is identical to a Χ2-test. The
logistic regression model takes the form;
P(x) = E [ y | x ] = β0+β1x
36
37
Counts
Counts
II
III
Descriptive, Logistic
regression (exposed vs
non-exposed &
exposure category)
South Iceland,: 3
exposure levels. Non-
exposed North Iceland
reference group.
and respiratory
health symtoms.
exposure
t-test. Stratified by
Descriptive, χ2 – test,
general, mental
eruption
Experiences during
indicator variables
(linear), binary
Counts (n reporting
12)
symptoms, GHQ-
reporting
(%predicted). n
GAM
Daily air pollution
GAM, DLNM.
GAM (GLM, DLNM)b
Daily air pollution
Daily air pollution
Statistic
Exposure
a
healthy onlyb
repeated with
Main analysis
groups
gender, age
seasonb
Stratified by
drugsa, pollen
max, β-agonist
Exposure as 1-hr
0-7
0-2, (0-5)c
(30-15
day
means)
Sensitivity analysis Lag
fast-working drugs. b Not reported cin
period (~5 df/yr)
Spline for study
period (1 df/yr).
Spline for study
and season.
period (1 df/yr)
Spline for study
Time trend
adjustment
df. degrees of freedom. GHQ-12 – General Health Questionnaire 12 item version.
appendix.
V
IV
Counts
I
Lung function
Outcome format
No
Table 4 Schematic overview of methods used in each study
4 Results
The three time series studies used data from registers of drug dispensing
(paper I) and ER visits and acute admissions for cardiovascular and stroke
diagnoses (paper II and 3). In the data from 2003-2012 there were both
seasonal trends and a decline in the number of events during the study
period (figure 14). However, upon expection of the hospital contacts data
stratified for when the subjects were admitted to stay at least one night in
hospital (blue triangles) or were sent home from the ER, it was clear that
most of the seasonal variation and the decline over time was due to the ER
visits. There were also seasonal trends in the levels of air pollutants 20032012 (figure 15) PM10 peaks tend to occur over the winter season, as do H 2S
levels, which peak during cold weather with no precipitation; NO2 levels have
much less variation. The highest levels occured during winter, and O3 levels
were highest during spring, in April.
In the studies of the health effects form the Eyjafjallajökull eruptions, we
surveyed 207 individuals thoroughly, and received questionnaires from 1656
individuals. We found strong indications that there was a good contrast in
exposure, both between the exposed and non-exposed area, and the three
exposure regions of south Iceland.
In the following, the full time series for pollution data from Reykjavík, and
some of the hospital admissions are presented, as are results for each paper.
4.1 Paper I
Only 40% of the time series 2006-2009 had complete data for all four
pollutants. Levels of all pollutants were higher in the winter season. On
average, 76 people bought a prescription drug from the pharmacy per day.
NO2 levels were significantly correlated with temperature and O3.
In the regression analysis, exposure was given first at 24-hour means
(peak) and 1 hour daily maximum (peak) pollution. The outcomes were given
first as all anti-asthma prescription medication (R03) and secondly as shortacting adrenergic inhalants (R03A) only. Three-day moving averages of the
exposure up to lag 14 were used.
Mean H2S was associated with increased dispensing of all asthma
medication and adrenergic inhalants at lags 3-5.
38
Figure 14 Time series of acute, daily admission and ER (All
contacts, upper line) and af those who are admitted to stay for at
least one day (lower line) 2003-12.
39
Figure 15 Daily means of air pollutants (NO2, O3, PM10, H2S) measured in
Reykjavík 2003-12.
40
Both mean and peak PM10 were associated with increased dispensing of all
asthma medication and adrenergic inhalants at lags 3-5. Peak, but not mean
NO2, was associated with increased dispensing of adrenergic inhalant drugs
at lag 3-8. For O3, only peak values at lags 3-5 and 9-11 were associated with
increased dispensing of all anti-asthma drugs and adrenergic inhalants.
Figure 16 Overview of Reykjavík postcodes and measuring stations. Circled
postcodes are re-analysed (see figure 17 and Table 5).
H2S comes from point sources and distributes in a different way than the
traffic-related pollution. Later research has shown that the weather
conditions that cause the highest levels of H 2S in Reykjavik are a slow breeze
from the east, during cold weather, and that the resulting exposure “cloud” is
only some 3 km wide (Ólafsdóttir et al., 2013). As the study area is some 20
km from north to south, the measured value at the station was not entirely
representative of the exposure of the whole study population.
41
Figure 17 The association between anti-asthma drug dispensing in east
Reykjavik and H2S.
In a further analysis, an attempt was made to improve the exposure
classification, and the analysis was repeated counting only subjects who lived
in the postcodes in line with the measuring station (Figure 16). The results
for those who lived in the post codes areas between the exposure source and
the measuring station (east Reykjavík) are shown in table 5 and Figure 17.
Table 5 H2S and anti-asthma medication dispensing in areas within
Reykjavík lags 0-15. Adjusted for other pollutants, weather and time
trend.
In line with
Between Hellisheiði
All area
Hellisheiði –
– Grensásvegurb
a
Grensásvegur
%
95% CI
%
95% CI
%
95% CI
H2S
0.66
0.10, 1.21
0.99
0.27, 1.72
1.11
0.20, 2.05
PM10
-0.00
-0.37, 0.32
-0.29
-0.61, 0.16
-0.14
-0.73, 0.43
NO2
0.49
-0.08, 1.05
0.46
-0.25, 1.19
0.23
-0.68, 1.26
O3
-0.42
-0.83, -0.01
-0.21
-0.73, 0.31
-0.58
-1.24, 0.09
The results are given as percent increase per 10µg/m 3 change in lags 015 pollutant concentration. aWithin circled area in figure 16. b Eastern
part of the circled area in figure 16.
Furthermore, the estimated effect of a 10 µg/m3 increase in H2S was
higher when including only the postcodes in the direct line between the
42
source and the measuring station. We found that the effect size increased for
H2S, whereas the results for the other pollutants did not change in any
significant way, Analysis stratified by gender revealed that the association
was stronger in women.
4.2 Paper II
The pollution data from 2003-9 were 59-92% complete. There were 9.6 ER
vists and hospital admissions per day, most were cardiac, and more than
60% were for people 60 years or older.
Only O3 was consistently associated with increased admissions to hospital
for cardiopulmonary disease or stroke, and these results were very robust
when the analysis was repeated with different lag times and covariates.
We excluded H2S from the final analysis of this paper and did not report
the results in the paper, but we found a 1% (95% CI: 0, 2%) increase in
hospital admissions and ER visits for a 5 th to 95th percentile increase in H2S,
but the increase was not statistically significant.
4.3 Paper III
The study period was 2007-12. There were 121 days in the time series when
the PM10 values exceeded 50µg/m3. Volcanic ash contributed to the high
levels 20 times, and dust storms were a contributor 16 times. During the
study period the average of number daily emergency hospital visits was 10.5.
In a basic model, we found no association between PM 10 and emergency
hospital visits; the effect estimate was 0.8% (95% CI -0.5, 2.2). To answer the
question if the effect of PM10 had changed after the volcanic eruption, we
analyzed the period before and after the eruption separately. We found that
the effect estimate for the latter period had increased to 1.6%, (95%CI -1.1,
4.3), but remained statistically insignificant.
In the indicator analysis, high PM10 levels due to any cause were not
associated with emergency hospital visits. High PM10 levels due to volcanic
ash were near-significantly associated with emergency hospital visits when
adjusting for other pollutants by 9.2% (95% CI -0.0, 19.6%). High PM10
levels due to dust storms were not associated with emergency hospital visits,
the effect estimate was 4.5% (95% CI -1.6, 11.0%).
4.4 Paper IV
At the end of the eruption of Eyjafjallajökull in 2010 a total of 167 adults and
40 children (under 18) were investigated with questionnaires, spirometry
and a medical examination. Lung function was no worse than in a reference
group from the capital area.
43
In the questionnaires, the respondents reported high levels of irritation
symptoms; more than half reported symptoms from the sensory organs that
were severe enough to disturb their daily life. A high percentage reported
mental unhealth (39% fulfilled GHQ-12 criteria), whereas only few had
symptoms of post-traumatic stress disorder. All children with asthma had
reported using more medication than normal during the eruption.
In the medical interview, a number of people reported stress; though only
a few fulfilled the criterion for posttraumatic stress disorder. In the
questionnaire that was handed out, others voiced their concerns.
During the eruption while the ash was falling, my appetite was
bad, I slept badly, was nauseous and anxious. Chronic fatigue
the first week, afraid of more disasters and uncertainty about
the future.
Man, early 30s
Was surprised how large the effects were on the lungs – felt in
my lungs the minute ash began to fall here. Day or night –
would have known if I was blindfolded.
Woman, Late 30s
I seem to have phlegm in the lungs and the dust (ash) must
have gotten stuck there.
Male, 50s
I found it hard to experience, unreal. Was stressed because the
roads were closed for a while and also because of the ash fall.
Many friends and acquaintances called, empathy, yet people
did not really understand.
Female, 60s
Being exposed to the volcanic eruption, worrying about the safety and
health of oneself, family and neighbors, was a disturbing event, but some
also reported positive outcomes, such as having made new friends.
4.5 Paper V
Six months after the eruption of Eyjafjallajökull, questionnaires were sent to
a cohort of south Icelanders who lived between 10 and 100 km from
Eyfjafjallajökull and a reference group from north Iceland. The exposed
44
south Icelanders had two to three times increased risk of most respiratory
and eye symptoms, after adjusting for demographic factors. About half of the
south Icelanders had symptoms from eyes, nose or throat, whereas one third
of the reference group had symptoms. The sensory organ symptoms
clustered in the exposed population, so individuals with one symptom were
more likely to report other symptoms.
The proportion reporting mental unhealth had declined from 39% at the
end of the eruption to 24% in the most exposed population, while it was 20%
in the unexposed reference group. Bird and Gísladóttir (2012) polled people
exposed to the eruption and showed that the eruption from Eyjafjallajökull
gave a more realistic expectation of what to expect from future eruptions.
Figure 18 Proportion of Eyjafjallajökull cohort reporting “some” or “a lot” of
exposure to volcanic phenomena at their residence.
While there was a dose-response association between exposure and
symptoms, we found that most individuals in the cohort had experienced ash
fall and heard rumbling from the volcano. Only a minority had felt
earthquakes. Generally, those with “medium exposure” had experienced
more volcanic phenomena (figure 18).
Participants were given the option of replying online, and while the overall
reply rate for the study was 72% (number of replies/number of people who
the researchers were able to contact) there were differences between the
reply categories.
In stratifying the reply modes by exposure category (table 6), we found
higher reply rates from people who agreed to answer online. This could be in
part because they online repliers were younger, but that cannot entirely
explain this difference. The numbers in Table 6 do not correspond to the
actual reply rates reported in the paper as these only include people with
accepted participation and who were sent a questionnaire.
45
Table 6 Reply rates for Eyjafjallajökull cohort study – web and online
Exposed
Persons who received a
questionnaire
Number of valied
questionnaires
returned
Reply rate by category
Non-exposed
paper
web
paper
web
666
814
215
380
448
701
175
335
67.3%
86.1%
81.4%
88.2%
46
5 Discussion
In the studies of short-term effects we found associations between increased
pollution levels and increases in adverse health indicators. There was an
association between PM10 and H2S levels and anti-asthma drug dispensing to
adults in the capital area.
5.1 Key results
Only O3 was consistently associated with emergency hospital visits due to
cardiopulmonary causes in adult males, females and the elderly over 70
years of age in 2003 to 2009. NO2 levels were only associated with increased
emergency hospital visits in those over 70 years of age, and there were no
associations between PM10.
High PM10 levels due to volcanic ash from Eyjafjallajökull were associated
with increased emergency hospital visits for cardiopulmonary causes in
adults 2007-2012, but the association was not significant. The association
between PM10 and emergency hospital visits was stronger after the eruption
of Eyjafjallajökull than before, but neither was significant.
In the studies of the South Iceland population exposed to ash from the
Eyjafjallajökull eruption, we found that at the end of the eruption more than
half of those exposed reported having experienced symptoms that were
severe enough to disturb their daily life. Many experienced stress and
psychological morbidity. Compared to a reference population from the
capital area, lung function was not lower in the exposed population;
however, the reference population included many heavy smokers.
Six months later, the exposed population had two to three times increased
risk of most respiratory and eye symptoms, and nearly half had symptoms
from the eyes, nose or throat, compared to one third of the reference
population.
5.2 Limitations and methodological considerations
5.2.1 Exposure
In paper I and II, on the effects of pollution from urban sources, NO2 and O3
were found on ER visits and hospital admissions, but not for dispensing of
anti-asthma medication in paper I. Effects from PM10 were only found in the
dispensing of anti-asthma drugs, but not on ER visits and hospital
admissions in 2003-2009. Data on PM2.5 were not available, which is
unfortunate as many of the known health effects of PM are for this fraction.
With PM2.5 data, it would be possible at least to distinguish between mainly
47
traffic related particles from fossil fuel combustion and particles from
mechanical wear and dust storms, which tend to be larger.
In paper III, we found mainly effects with H 2S and high levels of PM10
from volcanic ash, but O3 was not available. A lack of adjustment for O3 is
likely to affect the effect estimates for NO2, so this is unfortunate.
The Icelandic capital area is surrounded by sea on three sides, and PM 10
has a significant salt content, though this has. Salt is considered to relieve
some asthma symptoms, as has been suggested by other researchers
(Andersen et al., 2008). The salt content could be a contributing factor to
our null result for PM10 in paper II and partially in paper III.
In paper I, PM10 was associated with the dispensing of anti-asthma drugs,
which is similar to the findings of other studies using anti-asthma drug
dispensing as outcome (Zeghnoun et al., 1999; Pitard et al., 2004; Laurent et
al., 2008), and also for PM10 from natural sources and asthma morbidity in
children and adolescents (Chimonas and Gessner, 2007). No associations
were found with NO2 and O3 levels, but the concentrations of both were
somewhat lower in Reykjavík than in the other studies, whereas levels of
PM10 were similar to those in Laurent et al., (2008). Other studies used
another metric of combustion particles, Black Smoke (BS).
There are relatively few studies of pharmaceutical dispensing, and none
except this one considers H2S. Some studies suggest that interactions
between geothermal emissions and traffic pollution may further increase the
aerosol level in urban areas (Lin et al., 2010). A study from New Zealand
found that residential exposure to background H 2S was associated with
lower risk of asthma (Bates et al., 2013).
In paper II, hospital emergency admissions 2003-2009 were only
associated with O3, and NO2 in the elderly. The null result for PM10 was
surprising, and possible causes for this are discussed in below. No-effect
levels for NO2 have been found in experimental studies, but those levels were
much lower than in the current study (Hesterberg et al., 2009). Influenza
epidemics were not a significant predictor for increased hospitalizations or
ER visits. It is possible that efforts to inoculate the susceptible population
have been sufficient to remove this risk factor.
The results of paper III indicate that the association between PM 10 from
volcanic ash and emergency hospital visits does not follow the same doseresponse functions as traffic-related PM10. These finding are similar to those
of Oudin et al., (2013) who found a bigger-than expected, but non-significant
increase in mortality the week after the ash from the Grimsvötn volcanic
eruption in 2011 passed over Sweden.
O3 was missing or suspected faulty since 2010 due to a fault in the
measuring equipment. Therefore O3 had to be excluded from the analysis of
the data before and after the eruption. This means that the results are not
immediately comparable to those from the 2003-2009 periods.
48
In paper I, II and III, the exposure measured at one station was used as a
proxy for the exposure for the entire population. This is not an unusual
method, but as the recalculation of the results from paper I illustrate (table
5, Figure 17), the exposure assessment for H2S was probably improved when
only including the post codes areas near the measuring station. In these
studies, we used microgram per cubic meter air as it was readily available.
Other exposure metrics such as particle number concentration, size
distribution and surface area are used in other studies. Newer results
indicate that certain particle characteristics may be relevant to particular
disease outcomes (Andersen et al., 2008; Peng et al., 2009; Atkinson et al.,
2010). In paper IV all participants were exposed at their residence or when
they were working in the area. Some were probably more exposed that
others, but the study size or the information about the ash fall did not permit
us to distinguish between degrees of exposure.
A high level of variation (contrast) in the exposure variable is vital to the
robustness of time series methods (Dominici et al., 2003; Künzli and
Schindler, 2005). In our data, PM10 and H2S exhibited the highest degree of
variability, and we found the most robust results for exactly those two
pollutants in paper I, with only peak O3 and NO2 significantly associated with
the outcome. In paper II, we found significant results for O3, but not other
pollutants. In paper III, the contrast in PM10 increased after the
Eyfjafjallajökull volcanic eruption, which may have been a contributing
factor to the higher effect of PM after the eruption.
Some missing data meant that especially the analysis with long lags (15
days in paper I) had a lot of missing data. As we had a long time series, the
statistical power was sufficient. But stratifying further (gender, age groups)
we found no significant results.
In paper V, we estimated exposure from the participants area of residence
address and from satellite data estimates of the eruption plume, but the
actual exposure would have been highly sensitive to the activities of the
participants (working indoors or outdoors). Some left the exposed area for
some periods during and after the eruption to “get a break”, but such
behavior would mainly decrease the actual exposure and bias the results
towards the null hypothesis.
5.2.2 Outcomes
In paper I, we used pharmaceutical dispensing as the outcome. Use of this
outcome is increasing as several countries now register dispensing in
databases. This outcome is considered more sensitive as it reflects increased
symptoms or disease worsening which is less severe than one which causes
people to seek treatment in the emergency room or be admitted to hospital.
49
There are also more events, as many people take medication continuously,
which gives the results more statistical power.
Drawbacks with pharmaceutical dispensing as outcome include significant
uncertainty of the time the medication is actually used relative to the time it
is dispensed (which is the date registered in the data), or if the medication
was used at all. For anti-asthma medication a further caveat is that there is
little specificity to asthma, as the medications are used to treat symptoms of
a number of diseases.
In paper II and 3, we combined ER visits and hospital admission for
cardiopulmonary causes and stroke. These two outcomes have quite different
associations with air pollution (Winquist et al., 2012). In our data, ER visits
displayed a more temporal trend (figure 18), but we refrained from analyzing
the ER visits and hospital admissions separately as the statistical power
would have been considerably diminished.
In paper IV, standardized spirometry methods (BOLD) were used to
assess respiratory health, which facilitated comparisons with other studies
using the same methods. Unfortunately, we had no reference group for the
people under 40, and spirometry cannot be used on children under the age
of 6, so the respiratory health of these groups was not compared to a
reference group.
The self-reported health and symptom prevalence used in papers IV and V
are sensitive to the respondents’ mental state at the time of answering the
questionnaire, and there are concerns that the replies were influenced by
gender and age. The questionnaires used to assess mental health in papers
IV and V were rather robust (Goldberg et al., 1997).
In paper IV, the free text replies and reported adverse mental health
results have to be seen in light of the considerable level of uncertainty at the
time of the study, May 31 – June 11, 2010. The eruption had subsided, but
geoscientists suspected that it could start again. Eyjafjallajökull has a history
of year-long eruptions with intermittent quiescence. An even greater concern
was that nearby volcano Katla would erupt soon after an eruption of
Eyjafjallajökull as had happened several times previously (Sturkell et al.,
2010). A limitation of paper IV is the limited sample size, the lack of
knowledge about the health status before the eruption and the lack of
reference population and exposure contrast. Unfortunately, some
participants did not answer the questionnaire, which further decreased the
power of the study.
In paper V, which was based on questionnaire data, the demographically
matched reference group and the adjustment for demographic factors in the
regression analysis should minimize the risk of over- or underestimation of
symptoms in the exposed group. Still, it is impossible to eliminate a risk of
over-reporting in the exposed population.
50
We identified large differences in the occurrence of reported symptoms
and other complaints; and adjusting for personal characteristics should have
excluded most residual confounding.
It is a limitation of the study that the health of the populations before the
eruption is unknown, and we have little information about the nonresponders and non-participants. Both the exposed and non-exposed
populations are relatively small and tended to by rather few health
professionals. Differences in professional preference and diagnostic criteria
could mean a relatively high risk of diagnosis bias. This could possibly
explain the higher asthma prevalence reported in the non-exposed
population. Our exposure categories were based on ash fall estimates and
knowledge of the area, but local conditions can increase or decrease both
perceived and actual exposure. This could bias the results in either direction,
but most likely towards the null. The non-exposed population was surveyed
later in winter (January-March) when there are more respiratory infections,
which would bias the results towards the null.
5.2.3 Statistical methods
In the time series studies (paper I, II and III) we adjusted for temporal
trends using splines. Using splines with too many degrees of freedom can
disguise the true correlations. Inspection of the splines and autocorrelation
plots for signs of overfitting and a moderate selection of degrees of freedom
ensured that the splines were suitable. Remaining signs of autocorrelation at
lag 1 were removed when the lag 1 of the outcome was introduced as a
predictor. Case crossover methods adjust for temporal trends by design; the
exposure on a day with an event in an individual (index) is compared to the
exposure during a day with no event (control) (Maclure, 1991; Maclure and
Mittleman, 2000), but these methods have their own set of drawbacks, (see
e.g. Whitaker et al., 2007), and were not used in the current study.
The DLNM method which was used in paper III is a rather new method. It
appears highly sensitive to the selection of parameters, and while the method
has gained popularity since its introduction by Gasparrini and Armstrong
(2010), there is no best practice and little consensus as to how to best fit and
optimize these models.
In paper V, we presented the percentage reporting symptoms in each
exposure category. We used logistic regression adjusted for age, gender,
education and smoking status to obtain the risk estimates for the different
exposure categories. Other information could have been included in the
model.
51
5.3 Interpretation
The results from paper I indicate that dispensing of anti-asthma medication,
a proxy for population respiratory health, was affected by even moderate
levels of air pollution as found in Reykjavík 2006-9. The results with regard
to H2S are of particular interest as this was a rare exposure.
The results from paper II showed an association between O 3 and
emergency hospital visits for cardiopulmonary and stroke. O3 levels never
surpassed the health limits in Iceland during the study period.
The results from paper III indicated that the pathogenic effects of PM 10
increased after volcanic ash from Eyjafjallajökull was added to the urban
PM10. PM10 from volcanic ash and dust storms have substantial health
effects, and are an ignored factor in air pollution studies.
In paper IV, we found a high prevalence of self-reported irritation
symptoms to the sensory organs in the population exposed to volcanic ash
during the Eyjafjallajökull eruption. After 6 weeks of exposure, the
participants did not have lower lung function than a matched reference
group.
In paper V, the results of this large cohort study suggest that exposure to a
volcanic eruption is associated with increased rates of irritation symptoms of
the sensory organs and psychological morbidity 6 months later, in spite of a
concerted disaster response and mitigation efforts.
5.4 External validity
The results from paper I, II and III are based on data from high quality
registers in a health care system with universal coverage, making the studies
in effect population based. Population-based studies are not prone to
selection bias and have a high generalizability. The results from the studies
add to the body of knowledge about exposure to moderate levels of air
pollution in near-Arctic conditions and particularly exposure to H 2S and dust
from volcanic ash, which are rare exposures that have not been extensively
studied previously.
The results from paper IV and V provide useful information about
symptom prevalence and resilience in a well-defined population exposed to a
volcanic eruption and volcanic ash. Pathogenic properties of volcanic ash
vary greatly between volcanoes and eruptions, but methods to determine
pathogenic properties of ash are constantly improving (Monick et al., 2013;
Horwell et al., 2013).
52
6 Conclusions
From the research presented in this thesis, we found that both
anthropogenic and natural sources contribute to air pollution which has
significant effects on respiratory health in Iceland.
Paper I The daily number of dispensed anti-asthma drugs was associated
with the levels of PM10 and H2S 3-5 days previously, and more so for the
daily maximum than the 24-hour mean. This association was more
significant when restricting the analysis to short-acting medications,
indicating that they are a better proxy for short-term changes in respiratory
health in a population.
Paper II Daily emergency hospital admissions and ER visits for
cardiopulmonary disease and stroke were associated with O 3 levels at lags 02. Women and the elderly were more susceptible. NO2 was also associated
with increased admissions in the elderly.
Paper III In this study of the association between emergency hospital
contacts and particle sources during days with high PM 10 levels, we found
higher effect estimates when the source was volcanic, and smaller effect
estimates when PM10 levels were high because of natural dust storms. The
effect estimates of PM10 tended to be higher after the eruption of
Eyjafjallajökull, though not significantly.
Paper IV The population which lived within 40 km of Eyjafjallajökull
reported high levels of irritation symptoms in the sensory organs as a
consequence of being exposed to fresh volcanic ash for six weeks. Lung
function was not affected, and mitigation efforts had been effective.
Individuals with underlying obstructive lung disease, elderly and children
were more vulnerable.
Paper V Exposure to the Eyjafjallajökull volcanic eruption was associated
with increased risk of respiratory symptoms and psychological morbidity,
and the association followed a dose-response pattern. Psychological
morbidity was more common among those who reported more than one
physical symptom; mitigation efforts could be aimed at such subgroups.
Take home message
Both anthropogenic pollution and natural particles have adverse
consequences for respiratory health in Iceland. Mitigation measures in the
capital area are already installed (dust binding) to reduce PM, but the
natural source pollution has if anything been increasing and is a concern in
the future.
53
Limitations of the current study and future perspectives
The current studies of short-term exposure did not take the effects of
personal risk factors or underlying diseases in to account; this could be the
subject of a future study, as several large cohorts with long follow-up times
are in place in Iceland..
In the studies of exposure to Eyjafjallajökull, we found that children had
been severely affected by exposure to the ash from Eyjafjallajökull, and all
children with asthma had experienced worsening of their symptoms, even
though they were not in the area during the whole eruption.
Ideas for future studies:
 Short-term health effects of traffic-related air pollution in
children

Short-term health effects of traffic-related susceptible groups,
such as those with underlying diseases and the elderly.

Exposure models for the capital area are being developed and can
be used to estimate the effects of long-term exposure to traffic
pollution.

In studies of other outcomes, effect modification from chronic
exposures to air pollution can be investigated using information
from the models.

Follow-up of the Eyjafjallajökulll cohort in medicines and chronic
disease registers.
54
Acknowledgements
Many people have contributed to this work, and I would like to thank them
all. First of all the people of South Iceland and Skagafjörður who took the
time to answer the questionnaires and participate in the studies.
I want to thank my main supervisor Þórarinn Gislason for his
encouragement and scientific mentorship for this thesis and tireless effort in
finding support for it.
I thank my advisor Anna Oudin for her support and the effort she has put
into this thesis.
Thank you also to Unnur Valdimarsdóttir who always thought I would
make a good PhD candidate.
I am very grateful to my advisors and collaborators in Sweden; AnnaCarin Olin, Kadri Meister and Bertil Forsberg for expert advise and much
support.
A big thank you to my co-authors, Arna Hauksdóttir, Bryndís
Benediktsdóttir, Þórir Björn Kolbeinsson, Þröstur Þorsteinsson, Gunnlaug
Einarsdóttir, Halldór Runólfsson, Sigurður Guðmundsson & Þorsteinn
Jóhansson. Paper I would not have been publishable without the tireless
efforts of Helga Zoëga and Birgir Hrafnkelsson, who helped guided me
through decades of evolution in time trend adjustment methods. The
collaboration around the Eyjafjallajökull studies taught me a lot, especially
Gudrún Pétursdóttir and Haraldur Briem deserve much thanks for their help
with publishing the results.
Dóra Ólafsdóttir at the Centre of Public Health at University of Iceland
has been most helpful with my long-distance navigation of University of
Iceland administration.
My fellow PhD student Ragnhildur Finnbjörnsdóttir has been a good
collaborator and friend during this period, and I thank her, as well as my
other colleagues at the Centre of Public Health at the University of Iceland,
Umeå University, and AMM in Gothenburg.
The following have kindly helped me gather the data used in this thesis:
Ingibjörg Richter, Anna Rósa Böðvarsdóttir, Hildur Friðriksdóttir, Kristinn
Jónsson, Margrét Hallsdóttir and Lára Guðmundsdóttir.
The following institutions supported this project:
The University of Iceland Centre of Public Health and Umeå University
Department of Occupational and Environmental Medicine, Occupational
and Environmental Medicine at Sahlgrenska School of Public Health and
Community Medicine, the latter has been my work place for the last two
years, both the colleagues and the professional environment has been a big
55
encouragement and inspiration. Also the Sæmundur Fróði Institute for
Sustainability Studies and The Swedish Environmental Research Institute in
Gothenburg (IVL).
I thank my family for their support and encouragement, and my husband,
Steinn, my rock. Finally, I want to thank my friends; especially Aysha
Hussain, Gunnar Thorarensen & Virgile Collin-Lange who all contributed to
this thesis with valuable input.
This thesis was funded through Rannis - The Icelandic Centre for
Research PhD, the Department for Occupational and Environmental
Medicine, Umeå University, and the Icelandic Ministry of Welfare
(Eyjafjallajökull health studies). Additional funding have been provided by
the University of Iceland travel grant, The Swedish Society of Medicine
Section for Occupational and Environmental health travel grant, and the
Icelandic Association of Academics re-education grant.
56
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