Health Effects of Air Pollution in Iceland

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 ISSN 0346-6612 Medicine ISBN 978-91-7601-082-2 Umeå university, 901 87 Umeå www.phmed.umu.se

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 PM10 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 Anatomical therapeutic classification system BC Black carbon BS Black smoke CO2 Carbon dioxide COPD Chronic obstructive pulmonary disease ER Emergency room FEV1 Forced expiratory volume in 1 second FVC Forced vital capacity GAM Generalized additive model GHQ General health questionnaire GLM Generalized linear model GP General practitioner ICD International classification of diseases H2S Hydrogen sulfide Lowess Locally weighted scatterplot smoothing NO2 Nitrogen dioxide O3 Ozone PAH Poly-aromatic hydrocarbons PEF Peak expiratory flow PM2.5 Particle matter with an aerodynamic diameter less than 2.5 µm PM10 Particle matter with an aerodynamic diameter less than 10 µm PTSD Post traumatic stress syndrome SO2 Sulfur dioxide UFP Particle matter with an aerodynamic diameter less than 1 µm VOC Volatile organic compounds WHO World health organization

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 PM10 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 Syd- Island 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 15th 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 18th 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 20th 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 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 20th century (Derwent et al., 2007; Solberg et al., 2005).

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).

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).

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

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).

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 O2 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,

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

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

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

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).

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.

Figure 6 The emissions from a geothermal power plant contain H2S (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; Partti- Pellinen 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

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, H2S 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 H2S 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 SO2. The adult residents (n=823) were surveyed in 2004 before they began to return

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 SO2 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

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

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 km2 (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 H2S, 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

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. 24-hour 1-hour Allowed Act no./Year µg/m3 µg/m3 exceedencesb

H2S 514/2010 50 5

NO2 251/2002 75 110 175

a a O3 745/2003 120 25

PM10 251/2002 50 7 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).

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.

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 one- fifth 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;

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).

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 anti- asthma medication in Reykjavík associated with changes in daily air pollution levels the previous 14 days during the period 2006- 2009? 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?

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).

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 PM10 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.

Figure 12 Ash dispersion around Eyjafjallajökull (top) and the exposure areas based on it (below) (Image: Thorsteinsson, from Carlsen et al., 2012a and b).

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

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, FEV1, 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

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.

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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).

Table 3 Ethics approval sought for each study.

Individ- level No Data Authority Ref. no. For data org/used a

Bioethics Committee, VSNb2008050023 Dispensing Medicines Data Protection /03-15, I of asthma dispensing No/no Agency, National 2008080569/ÞPJ medication data Directorate of Health

Hospital Bioethics Committee, VSNb2010120017/ II+ admissions Data Protection 03.7, Hospital Yes/no III and ER Agency, Hospital 2010121176AT/, contacts visits Executive Board Letter:2010/12/22 Medical exam, Public National Directorate IV spirometry, - Health Yes/yes of Health question- Survey naires Bioethics Committee, VSNb2010080002 Collecting Questionnai V 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

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 sine- cosine 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 multi- pollutant models can mean excluding a lot of data as there must be

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 Long- term effect studies.

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

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

)

in in

)

15 15 (3 (0 2, 7

- - -

0 day means 0 0 Lag

Not Not reported

as 1 as

agonist

- b

, pollen,

Sensitivityanalysis Exposure max, β drugs season Stratified by gender, age groups Main analysis repeated with healthy only

working working drugs.

) ) ).

/yr /yr

fast

(~5 df/yr)

sion. sion.

riod (1 df (1 riod

Timetrend adjustment Spline study for pe and season Spline study for df (1 period Spline study for period

)

test,

–

& &

category

GLM, DLNM GLM,

Stratified by by Stratified

(

exposed

test.

Statistic GAM GAM GAM, DLNM. Descriptive, t exposure Logistic Descriptive, (exposedregression vs non exposure

th Iceland

General General Health Questionnaire 12 item ver

binary binary

–

Exposure Daily pollution air Daily pollution air Daily pollution air (linear), variables indicator during Experiences eruption Iceland,:South 3 levels. exposure exposed No reference group.

n n

of of freedom. GHQ s s

Outcome forma Outcome Count Count Counts Lung function (%predicted). reporting GHQsymptoms, 12) Counts (n reporting general, mental and respiratory health symtoms.

degrees

df. appendix. Table 4 Schematic overview of methods used in each study of methods used in each Table overview Schematic 4 I II III I V N

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 2003-

2012 (figure 15) PM10 peaks tend to occur over the winter season, as do H2S 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 short- acting 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.

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.

Figure 15 Daily means of air pollutants (NO2, O3, PM10, H2S) measured in Reykjavík 2003-12.

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 H2S 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.

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 Grensásvegura % 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/m3 change in lags 0- 15 pollutant concentration. aWithin circled area in figure 16. b Eastern part of the circled area in figure 16.

3 Furthermore, the estimated effect of a 10 µg/m increase in H2S was higher when including only the postcodes in the direct line between the

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 th th hospital admissions and ER visits for a 5 to 95 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 3 the PM10 values exceeded 50µg/m . 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 PM10 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.

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

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.

Table 6 Reply rates for Eyjafjallajökull cohort study – web and online

Exposed Non-exposed

paper web paper web

Persons who received a 666 814 215 380 questionnaire Number of valied questionnaires 448 701 175 335 returned Reply rate by category 67.3% 86.1% 81.4% 88.2%

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

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 H2S 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 PM10 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 H2S 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 PM10 from volcanic ash and emergency hospital visits does not follow the same dose- response 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.

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.

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.

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 non- responders 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.

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 O3 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 PM10 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 H2S 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).

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 O3 levels at lags 0- 2. 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 PM10 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.

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.

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; Anna- Carin 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

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.

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