Ambient air pollution and emergency department visits and hospitalisation for cardiac arrest: a population-based case-crossover study

doi:10.21203/rs.3.rs-1734491/v1

Background

The association between ambient air pollution and cardiac arrhythmia has been explored in numerous studies; however, the small subgroup of cardiac arrest has not been closely studied thus far. The aim of the present study was to assess the association between traffic-related ambient air pollution and emergency hospital visits for cardiac arrest.

Methods

A bidirectional time-stratified case-crossover design was used. The setting was the Reykjavik capital area and the study population were the inhabitants 18 years and older identified by zip codes. Cases were those who had made emergency visits to Landspitali University Hospital during the period 2006 to 2017 and who were given the primary discharge diagnosis of cardiac arrest according to The International Classification of Diseases 10th edition (ICD-10), code I46. The pollutants were NO₂, PM₁₀, PM_2.5, and SO₂ with adjustment for H₂S, temperature and relative humidity.

Results

The 24-h mean NO₂ was 20.7 µg/m³, mean PM₁₀ was 20.5 µg/m³, mean PM_2.5 was 12.5 µg/m³, and mean SO₂ was 2.5 µg/m³. PM₁₀ level was positively associated with the number of emergency hospital visits (n = 453) for cardiac arrest. Each 10 µg/m³ increase in PM₁₀ was associated with increased risk of cardiac arrest (ICD-10: I46), odds ratio (OR) 1.093 (95%CI 1.033–1.162) on lag 2, and OR 1.118 (95%CI 1.031–1.212) on lag 0–2. Significant associations were shown between exposure to PM₁₀ on lag 2 and on lag 0–2 and increased risk of cardiac arrest in the age, gender, and season strata. Females and those 71 years and older were at higher risk for cardiac arrest, OR 1.193 (95%CI 1.059–1.344), and OR 1.186 (95%CI 1.066–1.320) on lag 2, per 10 µg/m³ increase in PM₁₀, respectively. Significant associations were sporadically found for NO₂ and SO₂ and emergency hospital visits for cardiac arrest, but these associations were weaker and did not show a discernable pattern.

Conclusions

A new endpoint was used for the first time in this study: cardiac arrest (ICD-10 code I46). Short-term increase in PM₁₀ concentrations was associated with cardiac arrest. The effect seemed higher among the elderly and females. Future ecological studies of this type and their related discussions should perhaps concentrate more on precisely defined endpoints.

Epidemiological studies have found increased risk of cardiovascular morbidity and mortality in association with particulate matter (PM) in air pollution (1, 2), and the overall evidence is considered to support the existence of a causal relationship between PM exposure and cardiovascular morbidity, primarily due to fine particles (2). Both short-term and long-term PM air pollution contribute to cardiovascular morbidity and mortality (1, 2). Urban ambient air pollution is a complex mixture of gases, particles and liquid, and in an attempt to monitor air quality, certain pollutants are traditionally measured. However, adverse cardiovascular health impacts of exposure to a combination of air pollutants are not completely understood at present. A recent multilocation analysis found that a short term increase in NO₂ on the previous day was associated with an increased risk of daily total, cardiovascular, and respiratory mortality (3). In a systemic review and meta-analysis of short-term exposure to NO₂ and ischemic heart diseases, the authors concluded that the relationship was likely causal (4), but uncertainties remained due to possible confounding in the epidemiological studies and lack of evidence from mechanistic studies. Several epidemiological studies have found that NO₂ and PM are associated with cardiovascular diseases (CVD), and the endpoints studied have included not only mortality from CVD (3), but also discharge diagnosis at hospitals and emergency departments for a wide range of CVD, ischemic heart disease, myocardial infraction, and different cardiac dysrhythmias (5–10). In a large US study (9), acute myocardial infarction, and multiple other cardiovascular outcomes, like cardiac dysrhythmia and heart failure were found to be associated with particulate air pollution; however, in that analysis it was only possible to consider potential confounding by ozone, not by other pollutants. In a case-crossover study in China, an association was found between particle matters, NO₂, and carbon monoxide, and number of hospital admissions for cardiac arrhythmia (10). In another case-crossover study in the UK (8), the risk of emergency hospital admissions for CVD, arrhythmias, atrial fibrillation, and heart failure was associated with an increased concentration of NO₂; however, cardiac arrest was not included as an outcome in the study. Cardiac arrest was mentioned in the aforementioned Chinese study (10) but was not analysed separately.

The comprehensive population and health registries in Iceland make this an optimal setting to study the association between relatively low daily exposure to air pollution, and different heart related conditions (7, 11, 12). The aim of the present study was to explore the association between traffic-related pollutants, NO₂, PM₁₀, PM_2.5, and SO₂ in the Reykjavik capital area and urgent hospital and emergency department visits for cardiac arrest as the primary discharge diagnosis, and to simultaneously adjust for meteorological variables and geothermal-originated pollutants.

Study base

The Reykjavik capital area is in the southwestern part of Iceland. Traffic emission is the main source of air pollution in the city. Other sources of air pollution include two geothermal power plants: Hellisheidi, located 26 km east-southeast of the city, and Nesjavellir, located 33 km east of the city. Ambient H₂S emission originates from the plants. Reykjavik’s capital area spreads over 247.5 km²and in 2017 the inhabitants numbered 217,000, equivalent to approximately two-thirds of the total Icelandic population (13). The study base included the residents of the Reykjavik capital area, which consists of seven municipalities (Gardabaer, Hafnarfjordur, Kjosarhreppur, Kopavogur, Mosfellsbaer, Reykjavik, and Seltjarnarnes) identified by 24 zip codes.

Health data

Hospital discharge data were obtained from January 1 2006 to December 31 2017 from computerised records in SAGA (Register of hospital-treated patients in Iceland) for certain heart diseases; the procedures have been described in a previous publication (7). The study included adult inhabitants (≥18 years) of the Reykjavik capital area, identified by zip code. We analysed data on urgent visits to the emergency department and urgent admissions to in-patient wards of Landspitali University Hospital (LUH). The study was confined to new admissions, meaning that no visits by appointment were included. LUH is operated by the Icelandic government and is the only acute care hospital serving the population of the Reykjavik capital area, making this study population-based. In Iceland, the national health insurance scheme is covered by taxes and is available to all residents. For ambulatory visits, patients pay a small fee that amounts to approximately 10 to 15 US dollars, but seniors are exempt from payment. Admission to the hospital ward is free of charge. Every inhabitant of Iceland receives a personal identification number at birth (or at immigration), and the identification numbers are widely used in Icelandic society and population registries, including the SAGA register. We received the identification numbers in encrypted form, which enabled us to identify repeated visits to LUH. Readmissions to LUH within 10 days with the same ICD-10 primary discharge diagnosis were excluded. From the SAGA register we received the following details: admission date, encrypted identification number, unique number of the admission, age, gender, primary discharge diagnosis for certain codes according to the International Classification of Diseases 10^th edition (ICD-10). In this study, both admission to the emergency department and formal admission to the hospital are included, so there is no requirement that a patient stayed overnight. The diagnoses are registered at discharge from the hospital, transfer to another hospital, and death in the hospital. In a previous study (7), the outcomes were heart diseases ICD-10 codes: I20-I25, I44-I50, ischemic heart diseases ICD-10 codes: I20-I25, cardiac arrhythmias and heart failure ICD-10 codes: I44-I50, and atrial fibrillation ICD-10 I48 (7). In the present study, the outcome analysed was cardiac arrest ICD-10 code I46. Emergency department visits and urgent hospital admissions were combined and are called emergency hospital visits.

Air pollutants and meteorological data

Information on pollution was obtained from Grensas monitor station (GRE), operated by the governmental institution Environment Agency of Iceland. GRE is in the centre of the Reykjavik capital area near one of the busiest road intersections in the city. Other stations in the city were not permanently located or were not continuously monitoring throughout the study period and were therefore not used in the study. However, to test whether GRE was reflective of the total capital area, Pearson’s correlation was calculated for GRE measurements and measurements from another station located in Dalsmari, Kopavogur municipality, for the period 2014-2017. Results of Pearson’s correlation coefficients between these two measurement stations were for PM₁₀ 0.44, for NO₂ 0.78, for SO₂ 0.98, and for H₂S 0.84. PM2.5 was not measured in Dalsmari.

Pollutants measured at GRE were NO₂, particulate matter with aerodynamic diameter less than 10 µm (PM₁₀), particulate matter with aerodynamic diameter less than 2.5 µm (PM_2.5), sulphur dioxide (SO₂), and hydrogen sulphide (H₂S), all measured in µg/m³. The meteorological data was obtained from the governmental institution Icelandic Meteorological Office and included temperature (°C) and relative humidity (RH). PM₁₀ and PM_2.5were measured with an Andersen EMS IR Thermo (model FH62 I-R), NO₂ with Horiba device (model APNA 360E), and SO₂ and H₂S with the Horiba model APOA 360E. Every 6-12 months the devices are calibrated. Exposure data included 12 years or 4,383 days. Daily averages (midnight to midnight the following day) were calculated from hourly concentrations if at least 75% of one-hour data existed. Missing daily averages for NO₂, PM₁₀, PM_2.5, SO₂, and H₂S were 383 days (8.7%), 165 days (3.8%), 923 days (21.1%), 200 days (4.6%), and 284 days (6.5%), respectively. Data gaps were seen and can be attributed to inactive measurement devices due to unknown causes, with the exception of 52 days of missing H₂S measurements at the beginning of the study period, as H₂S measurements at GRE started at the end of February 2006. For temperature and RH, 6 days (0.1%) and 6 days (0.1%) were missing, respectively. Minor gaps in the curves were fitted by linear interpolation.

Descriptive statistics were calculated and showed as daily concentration levels in µg/m³ of the pollutants, as well as Spearman’s correlation coefficient between pollutants and meteorological factors.

Analysis

Short-term associations between daily exposure to air pollutants and emergent hospital visits for cardiac arrest (ICD-10 code I46) were assessed using bidirectional time-stratified case-crossover design. The study period was divided into monthly strata, and exposure during case periods (24h) was compared to exposure during control periods, which were matched as the same weekdays within the same month (3-4 control periods per case period) (14,15). We did several calculations: single pollutant models were calculated in conditional logistic regression, multivariate models containing all traffic-related pollutants, H₂S, and the meteorological variables. Separate analyses were done for subgroups according to gender, age (≥71 and < 71 years), gender, and age combined, winter (November 1^st to April 30^th), and summer (May 1^st to October 31^st). It was possible to divide the diagnostic category I46 according to decimals into cardiac arrest with successful resuscitation (I46.0) and other categories without indication of successful resuscitation (I46, I46.1, and I46.9). These two subcategories were also analysed separately. The risk estimates were expressed as odds ratio (OR), and 95% confidence intervals (CI) were calculated for every 10 µg/m³ increase of pollutants (24h concentrations).

Different lag structures were applied. Single-day lag structure (lag 0 to lag 4), and multiple-day lag structure (lag 0-1, lag 0-2, lag 0-3, and lag 0-4, moving average of pollutants concentration) were employed in the analyses to explore the temporal association between pollutants and cardiac arrest. The results of the multivariate models with all lag structures are presented in the article, and other results are shown in the Appendix.

Although readmissions within 10 days with the same primary discharge diagnosis were excluded, it is still possible that some patients first went to the emergency department and were subsequently admitted that same day to in-hospital wards where they might have received a different diagnosis than they were given at the emergency department. To test whether this could distort the main result of the association between increased pollutant concentration and the emergency hospital visits, a sensitivity analysis was done, in which data was restricted to emergency department visits only.

Statistical analysis was done with R version 4.0.3 (https://www.r-project.org/). Statistical tests used in this study were all two-tailed and we considered results statistically significant for p < 0.05. The study was approved by the National Bioethics Committee (ref. no. VSNb2018120011/03.01), the Data Protection Authority (ref. no. 10-050), and the Scientific Committee of LUH.

The basic characteristics of cardiac arrest according to subcategories are shown in Table 1. The total number of visits with primary discharge diagnosis cardiac arrest (ICD-10 code I46) was 453, and repeated visits were extremely rare. The distribution of the 453 visits was even over the 4383-day study period. One visit per day was most common, but there were several days with up to two visits, which was the higest number of visits per day; thus, most days were without visits with cardiac arrest. The median age at the time of visits was 71 years. Descriptive statistics and Spearman’s correlation coefficients of traffic-related pollutants, H₂S, and meteorological variables are presented in Table 2. Missing daily average was highest for PM₁₀, but did not exceed 25% of the days of the study period. The concentrations of PM₁₀ and PM_2.5 were correlated, particle material did not correlated with the gaseous pollutants, and correlations among gaseous pollutants were moderate.

Table 1

Descriptive statistics of emergency hospital visits for cardiac arrest (ICD-10: I46) to Landspitali University Hospital, according to subgroups, January 1st, 2006 to December 31st, 2017.
Discharge diagnosis (ICD-10)		No. of visits (%)	No. of patients
Cardiac arrest (I46)		453 (100)	447
	Females	125 (28)	123
	Males	328 (72)	324
	Older (≥ 71 year)	192 (42)	190
	Younger (< 71 year)	261 (58)	257
	Older females	57	56
	Younger females	68	67
	Older males	135	134
	Younger males	193	190
	Winter	236 (52)	236
	Summer	217 (48)	214
	Cardiac arrest (I46)	5	5
	Cardiac arrest with successful resuscitation (I46.0)	194	192
	Sudden cardiac death, so described (I46.1)	23	23
	Cardiac arrest, unspecified (I46.9)	231	229
	Emergency department visits, only	313	312
Winter: November 1st to April 30th .
Summer: May 1st to October 31st .

Table 2

Descriptive statistics of 24-hour concentration levels (µg/m³) of pollutants and meteorological data in the Reykjavík capital area during the study period, 20062017, and Spearman’s correlation between daily concentrations of pollutants
	PM₁₀	PM_2,5	NO₂	SO₂	H₂S	TEMP (°C)	RH (%)
24-h availability n (%)	4218 (96.2)	3460 (78.9)	4000 (91.3)	4183 (95.4)	4099 (93.5)	4377 (99.9)	4377 (99.9)
Mean (SD)	20.5 (19.7)	12.5 (21.8)	20.7 (15.0)	2.51 (13.8)	2.98 (5.2)	5.5 (4.9)	74.9 (10.6)
Summer* mean (SD)	17.4 (14.9)	10.8 (16.2)	16.2 (9.9)	2.48 (14.1)	2.08 (3.1)	9.1 (3.2)	74.6 (9.8)
Winter** mean (SD)	23.6 (23.2)	14.2 (26.1)	25.3 (17.6)	2.54 (13.5)	3.90 (6.6)	1.9 (3.4)	75.1 (11.3)
Range	2.4–381	0-423	0-119	0-409	0–96	-10.5-17.7	37–97
Median	15.1	7.0	16.6	1.1	1.2	5.6	77.0
Interquartile range	11.6	8.2	15.8	1.2	2.7	7.9	15.0
Spearman’s correlation
PM₁₀	1.00
PM_2,5	0.76	1.00
NO₂	0.09	0.00	1.00
SO₂	0.08	0.08	0.50	1.00
H₂S	-0.08	-0.11	0.31	0.39	1.00
TEMP (°C)	-0.16	-0.08	-0.44	-0.17	-0.23	1.00
RH (%)	-0.30	-0.56	0.09	-0.03	0.04	0.12	1.00
* May 1st to October 31st .
** November 1st to April 30th .
SD: standard deviation; H₂S: hydrogen sulphide; NO₂: nitrogen dioxide; PM₁₀: particulate matter ≤ 10µm in diameter; PM_2.5: particulate matter ≤ 2.5µm in diameter; RH: relative humidity; SO₂: sulphur dioxide; TEMP: temperature.

In the single pollutant analyses, positive associations were observed for exposure to PM₁₀ at lag 2 and lag 0–2, and unstratified emergency hospital visits for cardiac arrest (ICD-10 codes: I46); the increased risks of cardiac arrest were OR 1.096 (95% CI 1.033–1.162) and OR 1.118 (95% CI 1.031–1.212), respectively, per 10 µg/m³ increase of PM₁₀, as shown in Table A, Appendix. A positive association was observed between exposure to NO₂ at lag 4 and the increased risk of cardiac arrest; the increased risk was OR 1.081 (95% CI 1.002–1.166) per 10 µg/m³ increase of NO₂, as shown in Table A, Appendix.

In examining the daily lag exposure to PM₁₀ and unstratified emergency hospital visits for cardiac arrest, positive associations were observed in the multivariate model; the increased risks of cardiac arrest were OR 1.096 (95% CI 1.033–1.162) for lag 2, OR 1.118 (95% CI 1.031–1.212) for lag 0–2, OR 1.150 (95% CI 1.050–1.261) for lag 0–3, and OR 1.168 (95% CI 1.054–1.295) for lag 0–4 per 10 µg/m³ increase of PM₁₀ (Table 3). Significant associations were shown for exposure to NO₂ at lag 4 and for SO₂ at lag 0, and unstratified emergency hospital visits for cardiac arrest; the increased risks were OR 1.096 (95% CI 1.008–1.192) and OR 1.084 (95% CI 1.002–1.173) per 10 µg/m³ increase of NO₂ and SO₂, respectively (Table 3).

Table 3

Number of visits (n), odds ratios (OR) and 95% confidence intervals (CI) for the daily emergency hospital visits for cardiac arrest (ICD-10 code: I46) in Reykjavik capital area associated with 10 µg/m³ increase in NO₂, PM₁₀, PM_2.5, SO₂ and H₂S, adjusted for each pollutant, temperature and relative humidity, unstratified and stratified by gender, age, and season, at lag 0 to lag 4, lag 0–1, lag 0–2, lag 0–3, and lag 0–4.
		NO₂		PM₁₀		PM_2,5		SO₂		H₂S
Categories/Visits (n)	Lag	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI
All (453)	0	0.989	0.904–1.083	1.017	0.957–1.082	0.990	0.930–1.053	1.084	1.002–1.173	1.187	0.972–1.449
	1	1.003	0.916–1.099	1.041	0.987–1.097	0.994	0.936–1.055	0.983	0.873–1.107	1.056	0.829–1.344
	2	0.972	0.885–1.068	1.096	1.033–1.162	0.993	0.935–1.054	0.999	0.935–1.067	1.118	0.892–1.402
	3	1.052	0.963–1.150	1.038	0.988–1.090	1.022	0.968–1.079	0.991	0.916–1.074	1.191	0.980–1.449
	4	1.096	1.008–1.192	1.029	0.963–1.099	1.054	0.992–1.020	1.003	0.940–1.070	0.915	0.714–1.171
	0–1	0.982	0.876–1.101	1.049	0.978–1.124	0.988	0.922–1.059	1.084	0.971–1.211	1.214	0.923–1.597
	0–2	0.957	0.838–1.093	1.118	1.031–1.212	0.989	0.918–1.066	1.070	0.945–1.212	1.301	0.936–1.810
	0–3	0.981	0.846–1.137	1.150	1.050–1.261	0.998	0.921–1.081	1.061	0.922–1.221	1.423	1.007–2.011
	0–4	1.039	0.889–1.215	1.168	1.054–1.295	1.019	0.934–1.112	1.057	0.588–1.928	1.313	0.897–1.921
Females (125)	0	1.223	1.011–1.481	0.982	0.857–1.126	0.900	0.763–1.063	0.256	0.039–1.665	1.270	0.797–2.024
	1	1.029	0.860–1.231	1.006	0.928–1.091	0.895	0.764–1.049	0.740	0.237–2.316	1.036	0.688–1.561
	2	1.009	0.839–1.212	1.193	1.059–1.344	0.958	0.843–1.089	0.284	0.051–1.582	0.902	0.531–1.532
	3	1.029	0.860–1.232	1.040	0.959–1.129	0.983	0.857–1.127	0.286	0.064–1.287	1.156	0.759–1.760
	4	1.175	0.997–1.384	0.927	0.806–1.068	1.036	0.909–1.181	0.495	0.154–1.591	1.006	0.639–1.583
	0–1	1.214	0.948–1.555	1.016	0.893–1.157	0.867	0.724–1.038	0.404	0.076–2.132	1.184	0.698–2.008
	0–2	1.174	0.893–1.543	1.110	0.958–1.287	0.889	0.740–1.068	0.236	0.034–1.664	1.172	0.605–2.273
	0–3	1.177	0.867–1.597	1.162	0.982–1.374	0.901	0.748–1.085	0.134	0.015–1.166	1.350	0.665–2.740
	0–4	1.278	0.939–1.740	1.133	0.935–1.372	0.914	0.754–1.108	0.118	0.012–1.125	1.276	0.593–2.743
Males (328)	0	0.935	0.841–1.041	1.036	0.966–1.112	1.012	0.946–1.083	1.093	1.009–1.184	1.218	0.972–1.526
	1	0.989	0.888–1.101	1.067	0.993–1.147	1.013	0.951–1.079	0.992	0.882–1.116	1.094	0.805–1.486
	2	0.982	0.878–1.098	1.058	0.992–1.128	0.997	0.932–1.067	1.000	0.937–1.067	1.242	0.962–1.604
	3	1.087	0.978–1.208	1.028	0.966–1.094	1.031	0.972–1.094	0.996	0.921–1.077	1.238	0.988–1.550
	4	1.079	0.975–1.194	1.070	0.988–1.158	1.066	0.995–1.142	1.007	0.944–1.073	0.895	0.666–1.203
	0–1	0.924	0.808–1.057	1.077	0.989–1.174	1.015	0.943–1.092	1.102	0.984–1.233	1.302	0.936–1.810
	0–2	0.902	0.771–1.055	1.120	1.015–1.237	1.012	0.933–1.099	1.088	0.958–1.235	1.503	1.013–2.229
	0–3	0.942	0.793–1.120	1.139	1.020–1.273	1.022	0.935–1.117	1.086	0.940–1.255	1.628	1.078–2.457
	0–4	0.985	0.818–1.186	1.174	1.037–1.328	1.047	0.949–1.154	1.087	0.937–1.260	1.499	0.955–2.351
Older ≥ 71 (192)	0	1.035	0.901–1.188	0.995	0.898–1.102	1.038	0.940–1.147	1.089	0.967–1.226	1.096	0.815–1.475
	1	0.985	0.853–1.137	1.000	0.891–1.122	1.001	0.910–1.101	0.974	0.833–1.140	1.065	0.763–1.485
	2	1.099	0.953–1.267	1.186	1.066–1.320	1.034	0.942–1.135	0.989	0.901–1.085	1.210	0.883–1.659
	3	1.030	0.894–1.188	1.054	0.981–1.134	1.032	0.931–1.144	0.823	0.513–1.318	1.315	0.992–1.744
	4	1.071	0.943–1.216	1.057	0.969–1.152	1.024	0.938–1.117	1.000	0.895–1.117	0.887	0.618–1.272
	0–1	0.999	0.839–1.190	0.997	0.872–1.141	1.019	0.912–1.138	1.065	0.917–1.236	1.154	0.776–1.717
	0–2	1.055	0.862–1.291	1.151	1.005–1.319	1.036	0.921–1.164	1.045	0.891–1.226	1.266	0.796–2.014
	0–3	1.060	0.848–1.325	1.197	1.033–1.386	1.038	0.910–1.185	0.993	0.824–1.197	1.460	0.906–2.354
	0–4	1.087	0.860–1.374	1.243	1.056–1.463	1.056	0.915–1.218	0.998	0.824–1.208	1.371	0.808–2.324
Younger < 71 (261)	0	0.961	0.853–1.083	1.037	0.958–1.122	0.962	0.885–1.045	1.083	0.975–1.204	1.242	0.941–1.640
	1	1.019	0.905–1.148	1.055	0.993–1.122	0.989	0.916–1.068	0.998	0.833–1.195	1.031	0.724–1.469
	2	0.892	0.784–1.014	1.059	0.978–1.147	0.965	0.889–1.048	1.005	0.914–1.105	1.019	0.723–1.438
	3	1.076	0.957–1.209	1.025	0.958–1.097	1.020	0.956–1.088	1.069	0.941–1.214	1.094	0.814–1.470
	4	1.123	1.002–1.259	0.992	0.893–1.101	1.083	0.991–1.183	1.007	0.930–1.091	0.926	0.653–1.312
	0–1	0.973	0.837–1.132	1.075	0.990–1.166	0.970	0.887–1.060	1.108	0.941–1.306	1.236	0.840–1.818
	0–2	0.890	0.744–1.065	1.117	1.007–1.238	0.959	0.869–1.058	1.108	0.907–1.353	1.286	0.799–2.069
	0–3	0.930	0.761–1.136	1.136	1.009–1.279	0.975	0.881–1.080	1.149	0.915–1.445	1.349	0.808–2.253
	0–4	1.162	0.720–1.874	1.133	0.856–1.499	0.951	0.708–1.277	0.047	0.002–1.342	2.088	0.734–5.942

Table 3

Continued
Older females (57)	0	1.394	1.036–1.876	0.926	0.750–1.142	0.772	0.523–1.140	0.420	0.024–7.404	0.847	0.418–1.713
	1	0.970	0.717–1.312	1.020	0.840–1.238	0.927	0.767–1.119	0.454	0.045–4.562	1.168	0.676–2.019
	2	1.228	0.926–1.628	1.177	1.010–1.371	0.983	0.840–1.151	0.150	0.005–4.657	0.534	0.182–1.565
	3	1.002	0.778–1.291	1.039	0.945–1.144	0.966	0.781–1.194	0.630	0.101–3.918	1.074	0.633–1.823
	4	1.128	0.908–1.401	0.925	0.772–1.108	0.935	0.761–1.148	3.684	0.414–32.751	0.736	0.351–1.545
	0–1	1.330	0.907–1.950	0.967	0.753–1.242	0.861	0.653–1.136	0.470	0.028–7.870	0.994	0.472–2.093
	0–2	1.435	0.941–2.189	1.105	0.869–1.404	0.933	0.732–1.189	0.195	0.006–6.790	0.777	0.270–2.242
	0–3	1.364	0.879–2.115	1.167	0.916–1.486	0.931	0.726–1.194	0.174	0.006–5.284	0.951	0.338–2.669
	0–4	1.401	0.924–2.124	1.116	0.847–1.469	0.918	0.701–1.201	0.421	0.017–10.137	0.727	0.217–2.434
Younger females (68)	0	1.131	0.873–1.465	1.052	0.864–1.281	0.930	0.776–1.113	0.184	0.017–2.013	2.148	1.038–4.446
	1	1.074	0.853–1.350	1.013	0.926–1.109	0.857	0.649–1.130	0.896	0.250–3.212	0.958	0.509-1.800
	2	0.853	0.661-1.100	1.186	0.976–1.440	0.901	0.714–1.137	0.481	0.072–3.209	1.194	0.638–2.232
	3	1.062	0.815–1.384	1.037	0.879–1.224	0.999	0.834–1.197	0.097	0.007–1.338	1.248	0.608–2.560
	4	1.186	0.910–1.546	0.880	0.693–1.118	1.197	0.964–1.486	0.187	0.023–1.544	1.137	0.603–2.146
	0–1	1.149	0.828–1.595	1.044	0.900-1.211	0.892	0.696–1.142	0.363	0.045–2.893	1.624	0.711–3.711
	0–2	1.001	0.687–1.459	1.124	0.921–1.371	0.866	0.653–1.150	0.277	0.027–2.883	1.763	0.701–4.434
	0–3	1.013	0.652–1.573	1.167	0.910–1.496	0.896	0.674–1.192	0.105	0.006–1.872	2.165	0.764–6.133
	0–4	1.162	0.720–1.874	1.133	0.856–1.499	0.951	0.708–1.277	0.047	0.002–1.342	2.088	0.734–5.942
Older males (135)	0	0.940	0.795–1.111	1.021	0.907–1.150	1.079	0.969–1.202	1.097	0.971–1.240	1.222	0.877–1.703
	1	1.000	0.846–1.183	0.985	0.846–1.145	1.032	0.925–1.151	0.996	0.851–1.164	1.116	0.722–1.725
	2	1.095	0.919–1.305	1.167	1.001–1.362	1.039	0.920–1.172	0.984	0.897–1.079	1.483	1.019–2.159
	3	1.053	0.881–1.258	1.058	0.947–1.182	1.059	0.940–1.192	0.831	0.518–1.334	1.457	1.000-2.121
	4	1.006	0.850–1.191	1.126	1.005–1.262	1.051	0.952–1.161	1.003	0.895–1.123	0.938	0.910–1.440
	0–1	0.920	0.748–1.132	1.010	0.856–1.191	1.067	0.943–1.207	1.090	0.938–1.268	1.381	0.848–2.248
	0–2	0.948	0.745–1.208	1.138	0.959–1.350	1.078	0.937–1.240	1.062	0.906–1.245	1.738	1.000-3.021
	0–3	0.966	0.739–1.263	1.177	0.972–1.424	1.092	0.924–1.289	1.020	0.846–1.230	1.963	1.062–3.628
	0–4	0.949	0.709–1.272	1.280	1.037–1.579	1.125	0.942–1.344	1.033	0.851–1.254	1.907	0.990–3.673
Younger males (193)	0	0.941	0.818–1.081	1.050	0.960–1.147	0.971	0.883–1.067	1.093	0.981–1.217	1.194	0.871–1.637
	1	0.984	0.855–1.134	1.097	1.009–1.193	1.005	0.929–1.087	1.001	0.834–1.203	1.076	0.694–1.668
	2	0.922	0.792–1.074	1.018	0.926–1.118	0.976	0.894–1.066	1.011	0.919–1.113	0.974	0.640–1.484
	3	1.111	0.970–1.272	1.017	0.942–1.099	1.025	0.956–1.098	1.082	0.945–1.238	1.108	0.798–1.538
	4	1.134	0.997–1.291	1.017	0.905–1.143	1.076	0.975–1.188	1.013	0.936–1.096	0.850	0.559–1.292
	0–1	0.932	0.781–1.113	1.110	1.003–1.229	0.989	0.899–1.087	1.123	0.948–1.329	1.208	0.767–1.902
	0–2	0.868	0.704–1.071	1.127	0.995–1.276	0.979	0.882–1.086	1.134	0.919-1.400	1.244	0.697–2.222
	0–3	0.929	0.738–1.169	1.125	0.979–1.293	0.993	0.891–1.107	1.186	0.924–1.522	1.318	0.716–2.426
	0–4	1.020	0.801-1.300	1.123	0.961–1.312	1.017	0.902–1.146	1.185	0.903–1.556	1.173	0.596–2.306
Winter (236)	0	1.003	0.906–1.111	1.064	0.992–1.141	0.958	0.884–1.039	1.119	0.990–1.265	1.224	0.980–1.529
	1	1.030	0.927–1.145	1.047	0.984–1.115	0.982	0.905–1.065	0.957	0.781–1.172	0.988	0.741–1.319
	2	1.021	0.919–1.133	1.071	1.003–1.143	0.988	0.908–1.076	0.972	0.886–1.067	1.129	0.878–1.451
	3	1.082	0.979–1.195	1.038	0.980–1.100	0.995	0.926–1.069	0.990	0.912–1.074	1.207	0.976–1.493
	4	1.087	0.988–1.195	1.085	0.993–1.187	1.089	0.995–1.191	0.979	0.900-1.064	0.951	0.722–1.252
	0–1	1.013	0.889–1.154	1.089	1.003–1.182	0.961	0.877–1.054	1.104	0.953–1.279	1.200	0.876–1.645
	0–2	1.016	0.873–1.183	1.148	1.041–1.266	0.965	0.872–1.068	1.037	0.874–1.230	1.301	0.889–1.904
	0–3	1.056	0.892–1.249	1.187	1.061–1.328	0.965	0.866–1.075	1.016	0.848–1.218	1.451	0.980–2.149
	0–4	1.109	0.927–1.326	1.240	1.089–1.412	0.989	0.881–1.111	0.996	0.833–1.191	1.362	0.882–2.104
Summer (217)	0	0.914	0.743–1.126	0.864	0.736–1.014	1.036	0.934–1.148	1.083	0.967–1.214	1.043	0.633–1.717
	1	0.993	0.814–1.211	0.997	0.889–1.118	1.008	0.920–1.105	0.984	0.852–1.136	1.441	0.888–2.340
	2	0.821	0.658–1.023	1.175	1.046–1.320	0.992	0.912–1.079	1.175	0.915–1.509	1.009	0.583–1.744
	3	0.927	0.753–1.142	1.047	0.951–1.152	1.073	0.982–1.172	1.041	0.723–1.499	0.964	0.540–1.720
	4	1.036	0.841–1.275	0.951	0.844–1.072	1.022	0.938–1.114	1.373	0.863–2.187	0.759	0.416–1.384
	0–1	0.903	0.703–1.160	0.910	0.773–1.071	1.027	0.920–1.147	1.073	0.917–1.257	1.363	0.763–2.435
	0–2	0.800	0.594–1.076	1.033	0.878–1.216	1.014	0.907–1.135	1.132	0.920–1.393	1.360	0.684–2.703
	0–3	0.764	0.546–1.070	1.061	0.891–1.263	1.039	0.921–1.172	1.176	0.902–1.534	1.354	0.617–2.970
	0–4	0.802	0.556–1.156	1.021	0.843–1.236	1.056	0.924–1.207	1.257	0.925–1.708	1.133	0.479–2.679

When applying the multivariate model in the stratified analyses of daily pollutants exposure and the association of emergency hospital visits for cardiac arrest (ICD-10 code I46), a discernible pattern emerges. Significant associations were shown between exposure to PM₁₀ at lag 2 and/or exposure to PM₁₀ at lag 0–2, lag 0–3, and lag 0–4, and increased risks of cardiac arrest, in the age, gender, age and gender combined, and season strata, i.e., in all the strata except in the stratum of young females (Table 3). We found higher effect estimates for the association between PM₁₀ exposure and cardiac arrest: for females the increased risk was OR 1.193 (95% CI 1.059–1.344) at lag 2 per 10 µg/m³ increase of PM₁₀; for males the increased risks were OR 1.120 (95% CI 1.015–1.237) at lag 0–2, OR 1.139 (95% CI 1.020–1.273) at lag 0–3, and OR 1.174 (95% CI 1.037–1.328) at lag 0–4, per 10 µg/m³ increase of PM₁₀; for older people (≥ 71 years), the increased risks were OR 1.186 (95% CI 1.066–1.320) at lag 2, OR 1.151 (95% CI 1.005–1.319) at lag 0–2, OR 1.197 (1.033–1.386) at lag 0–3, and OR 1.243 (1.056–1.463) at lag 0–4, per 10 µg/m³ increase of PM₁₀; for younger people (< 71 years) the increased risk was OR 1.117 (95% CI 1.007–1.238) at lag 0–2, and OR 1.136 (1.009–1.279) at lag 0–3, per 10 µg/m³ increase of PM₁₀; for older females the increased risk was OR 1.177 (95% CI 1.010–1.371) at lag 2 per 10 µg/m³ increase of PM₁₀; for older males the increased risks were OR 1.167 (95% CI 1.001–1.362) at lag 2, OR 1.126 (95% CI 1.005–1.262) at lag 4, and OR 1.280 (95% CI 1.037–1.579) at lag 0–4, per 10 µg/m³ increase of PM₁₀; for younger males the increased risks were OR 1.097 (95% CI 1.009–1.193) at lag 1, and OR 1.110 (95% CI 1.003–1.229) at lag 0–1, per 10 µg/m³ increase of PM₁₀; for winter (November 1st to April 30th ) the increased risks were OR 1.071 (95% CI 1.003–1.143) at lag 2, OR 1.089 (95% CI 1.003–1.182) at lag 0–1, OR 1.148 (95% CI 1.041–1.266) at lag 0–2, OR 1.187 (95% CI 1.061–1.328) at lag 0–3, and OR 1.240 (95% CI 1.089–1.412) at lag 0–4, per 10 µg/m³ increase of PM₁₀; and for summer (May 1st to October 31st ) the increased risk was OR 1.175 (95% CI 1.046–1.320) at lag 2, per 10 µg/m³ increase of PM₁₀. In the stratified multivariate analyses, a positive association was observed among females and older females, where the increased risks of cardiac arrest were OR 1.223 (95% CI 1.011–1.481), and OR 1.394 (95% CI 1.036–1.876) at lag 0, per 10 µg/m³ increase of NO₂, respectively; and among younger females (< 71 years) the increased risks of cardiac arrest was OR 1.123 (95% CI 1.002–1.259) at lag 4 per 10 µg/m³ increase of NO₂. Among males the increased risk of cardiac arrest was OR 1.093 (95% CI 1.009–1.184) at lag 0 per 10 µg/m³ increase of SO₂. In these analyses, a positive significant association was observed among males, younger females and older males between emergency hospital visits for cardiac arrest per 10 µg/m³ increase of H₂S, but these OR did not show any particular pattern and were not seen in the unstratified analysis. In Fig. 1, OR and 95% CI of cardiac arrest per 10 µg/m³ increase of PM₁₀ concentrations in multi-pollutant models are shown at lag 0 to lag 4 for different strata and unstratified.

In the single pollutant analyses, positive associations were observed for exposure to PM₁₀ at lag 0–3, and lag 0–4, and emergency hospital visits for cardiac arrest with successful resuscitation (ICD-10 codes: I46.0); the increased risks of cardiac arrest were OR 1.161 (95% CI 1.014–1.329), and OR 1.177 (95% CI 1.013–1.368), respectively, per 10 µg/m³ increase of PM₁₀, as shown in Table B, Appendix. A positive association was observed for exposure to PM_2.5 at lag 3, and lag 4, and the increased risk of cardiac arrest with successful resuscitation; the increased risk was OR 1.090 (95% CI 1.008–1.178), and OR 1.101 (95% CI 1.006–1.204), per 10 µg/m³ increase of PM_2.5, respectively, as shown in Table B, Appendix. A positive association was observed for exposure to PM₁₀ at lag 2, and the increased risk of cardiac arrest without indication of successful resuscitation (ICD-10 codes: I46, I46.1, and I46.9); the increased risk was OR 1.079 (95% CI 1.008–1.155), as shown in Table B, Appendix.

When we applied the multivariate model to the stratification of whether the cardiac arrests were indicated with successful resuscitation (ICD-10 code I46.0) or not (ICD-10 code I46, I46.1, and I46.9) and the association with daily pollutant exposure, a similar pattern emerges to that of the single pollutants analyses, shown in Table 4; however, the OR were somewhat higher and were observed at more lags and pollutants. A positive association was observed between exposure to PM₁₀ and emergency hospital visits for cardiac arrest with successful resuscitation (ICD-10 codes: I46.0); the increased risks of cardiac arrest were OR 1.104 (95% CI 1.002–1.216) at lag 2, OR 1.153 (95% CI 1.021–1.301) at lag 0–2, OR 1.202 (95% CI 1.039–1.392) at lag 0–3, and OR 1.209 (95% CI 1.027–1.422) at lag 0–4, per 10 µg/m³ increase of PM₁₀, as shown in Table 4. A positive association was observed between exposure to PM_2.5 and the increased risk of cardiac arrest with successful resuscitation; the increased risk was OR 1.088 (95% CI 1.001–1.182) at lag 3, and OR 1.104 (95% CI 1.006–1.21) at lag 4, per 10 µg/m³ increase of PM_2.5, as shown in Table 4. A positive association was observed for exposure to NO₂ and the increased risk of cardiac arrest with successful resuscitation; the increased risk was OR 1.194 (95% CI 1.023–1.393) at lag 4, as shown in Table 4. A positive association was observed between exposure to PM₁₀ and the increased risk of cardiac arrest without indication of successful resuscitation (ICD-10 codes: I46, I46.1, and I46.9); the increased risk was OR 1.090 (95% CI 1.013–1.173) at lag 2, as shown in Table 4. In Fig. 2, OR and 95% CI of cardiac arrest per 10 µg/m³ increase of PM₁₀ concentrations in multi-pollutant models are shown at lag 0 to lag 4 when stratified on season and whether there is an indication of successful resuscitation or not.

Table 4

Number of visits (n), odds ratios (OR) and 95% confidence intervals (CI) for the daily emergency hospital visits for cardiac arrest with successful resuscitation(ICD-10 code: I46.0) and other cardiac arrest categories grouped together (ICD-10 codes I46, I46.1 and I46.9) in Reykjavik capital area associated with 10 µg/m³ increase in NO₂, PM₁₀, PM_2.5, SO_2, and H₂S, adjusted for each pollutant, temperature and relative humidity, at lag 0 to lag 4, lag 0–1, lag 0–2, lag 0–3, and lag 0–4.
		NO₂		PM₁₀		PM_2,5		SO₂		H₂S
Categories/Visits (n)	Lag	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI
I46.0 (194)	0	0.988	0.856–1.139	1.057	0.967–1.156	0.983	0.875–1.105	1.386	0.758–2.534	1.008	0.735–1.383
	1	1.012	0.881–1.163	1.053	0.982–1.129	0.999	0.894–1.115	0.845	0.471–1.515	1.055	0.707–1.577
	2	0.953	0.826-1.100	1.104	1.002–1.216	1.037	0.957–1.125	1.078	0.893–1.301	1.177	0.797–1.737
	3	1.072	0.937–1.227	1.035	0.941–1.138	1.088	1.001–1.182	0.995	0.910–1.089	1.141	0.794–1.642
	4	1.194	1.023–1.393	1.040	0.936–1.157	1.104	1.006–1.211	0.292	0.084–1.010	1.313	0.847–2.036
	0–1	0.992	0.832–1.183	1.083	0.985–1.190	0.987	0.870–1.121	1.167	0.932–1.462	1.050	0.663–1.664
	0–2	0.952	0.774–1.171	1.153	1.021–1.301	1.017	0.899–1.150	1.196	0.941–1.520	1.203	0.680–2.129
	0–3	0.986	0.781–1.246	1.202	1.039–1.392	1.058	0.936–1.197	1.120	0.879–1.426	1.323	0.693–2.527
	0–4	1.047	0.818–1.340	1.209	1.027–1.422	1.093	0.957–1.248	1.015	0.785–1.314	1.421	0.702–2.877
I46, I46.1, I46.9 (259)	0	0.983	0.875–1.105	0.991	0.909–1.081	0.992	0.921–1.068	1.055	0.964–1.155	1.327	1.012–1.739
	1	0.998	0.882–1.130	1.024	0.940–1.116	0.991	0.923–1.065	0.996	0.876–1.132	1.055	0.778–1.430
	2	0.985	0.869–1.116	1.090	1.013–1.173	0.945	0.861–1.036	0.987	0.914–1.067	1.123	0.843–1.497
	3	1.041	0.923–1.176	1.027	0.968–1.089	0.960	0.876–1.052	0.993	0.829–1.190	1.191	0.943–1.505
	4	1.082	0.975–1.201	1.030	0.945–1.121	1.012	0.929–1.102	1.056	0.968–1.152	0.781	0.564–1.082
	0–1	0.972	0.835–1.130	1.011	0.908–1.126	0.987	0.908–1.072	1.051	0.927–1.190	1.312	0.928–1.856
	0–2	0.958	0.803–1.142	1.093	0.976–1.223	0.968	0.879–1.066	1.030	0.895–1.185	1.386	0.922–2.084
	0–3	0.968	0.796–1.177	1.108	0.982–1.250	0.951	0.852–1.062	1.033	0.869–1.227	1.485	0.984–2.240
	0–4	1.019	0.830–1.252	1.126	0.983–1.290	0.963	0.852–1.087	1.079	0.905–1.286	1.283	0.813–2.027

In the sensitive analysis of the association between daily exposure to PM₁₀ and emergency hospital visits for cardiac arrest (ICD-10 code I46), when restricting the calculation to emergency department visits only (353 visits), the results did not change substantially: in the unstratified analysis, the increased risk of cardiac arrest was OR 1.099 (95% CI 1.028–1.175) at lag 2 per 10 µg/m³ increase of PM₁₀.

Our study examined the association between ambient air pollution and emergency hospitalization and emergency department visits where the primary discharge diagnosis was cardiac arrest (ICD-10 code I46). To our knowledge, that single outcome has not been used in previous studies of a similar type. The main results of this study were the association between increased PM₁₀ and cardiac arrest at lag 2, lag 0–2, lag 0–3, and lag 0–4. The effects seemed high in most subcategories, in both seasons, and among those who were successfully resuscitated.

Cardiac arrest is a life-threatening event. The decimal following the code I46 indicates whether the patients were successfully resuscitated or not. Patients categorised as I46 comprise those who have survived and those who have died, and in the present study these are in nearly equal proportion. The associations between pollutants and mortality and hospital admission are commonly analysed separately (8). The primary discharge diagnosis cardiac arrest (ICD-10 code I46) has in previous studies been included within all CVD, or large subgroups such as cardiac dysrhythmias, and the endpoint closest to this entity may be out-of-hospital cardiac arrest (OHCA). OHCA has often been the subject of studies on association with air pollutants. In a review of 67 studies on OHCA (16), OHCA was associated with high mortality, with a global average survival rate of 7%. The definition of OHCA is: 1) the cardiovascular collapse has occurred outside a hospital, and 2) the event has elicited a resuscitation attempt. Neither of these conditions is required for cardiac arrest according to ICD-10 code I46, when this diagnosis is used at hospitals or emergency departments. Some studies have shown that the risks of OHCA were associated with a short-term increase in exposure to particulate matter, sometimes PM_2.5, or ultrafine particulate matter (17–20), and sometimes PM₁₀ (21), with various associations with O₃, other caseous pollutants, and high temperature. In the previously mentioned UK study (8) on the association between air pollution and hospital admission for different cardiovascular events, exposure to NO₂ was significant, while in the same publication some of the mortality outcomes were associated with exposure to PM_2.5 but not to NO₂ (8), indicating the possibility of different pathogenetic pathways for the outcomes, as has been discussed briefly in the case of NO₂ (4), and in the comprehensive review of the causal role of particulate material (2). The category cardiac arrest in the ICD-10 coding system represents a small group compared to other CVD diagnoses and seems to be concealed within the larger summary group of cardiac arrhythmias (10) or omitted from the analysis (8). In this respect, our recent study on the same dataset is not an exception: increased risk of cardiac arrhythmias (ICD-10 codes I44 to I50) was associated with an increase in NO₂ exposure (7), a finding that hides the association between cardiac arrest (ICD-10 code I46) and exposure to PM₁₀ as demonstrated in the present study.

Among the strengths of this study is that it is population-based, as the hospital and emergency department data were obtained from the only emergency health care institution, the LUH, serving the population in the catchment area of the Reykjavik capital. The design of the study is also a strength, as the bidirectional time-stratified case-crossover approach virtually excludes confounding of individual characteristics and the matching adjusted for weekly pattern and time trends. Another strength of the study is its use of the encrypted identification number of each patient in the Register of hospital-treated patients, which ensures the correct counting and identification of the cases and their admissions. Furthermore, it is noteworthy that visits of the cases receiving the primary discharge diagnosis cardiac arrest (453 cases) were evenly distributed over the study period (4383 days), so the distribution diminished the risk of overlapping the sets of case and control days.

That said, there are few limitations. First, the concentration of the pollutants derived from one monitoring station in the Reykjavik capital area, and not from individual exposure measurements. The results from these measurements did, however, correlate well with measurements from another monitoring station located in the capital area during three years of the study period. Another limitation is that only the primary discharge diagnosis was included in the study, meaning that the cases may have underlying diseases that could modify the result.

Furthermore, the quality of the routine discharge diagnosis at the LUH has not been investigated in a separate study for accuracy or reliability, which is a weakness our study shares with the many studies based on hospital records. The primary discharge diagnosis of cardiac arrest (ICD-10 code I46) set at emergency admission to the hospital and emergency department does not indicate whether the causes were cardiac- or trauma related, and there may be doubt as to where the patient developed the cardiac arrest, i.e., whether the event initially occurred outside or inside the hospital. The study population consisted of patients aged 18 years and older, which limits the generalisability of the results to those under 18.

The present study concentrates on traffic related pollutants; however, emissions from the volcanic eruptions occurring in Iceland during the study period may have confounded the results. The Eyjafjallajökull eruption in 2010 was found to have minor health effect on the local population, but not the population in the Reykjavik capital area (22). The Holuhraun eruption in 2014 to 2015 emitted a massive amount of SO₂ and mature volcanic plume, and the exposure to these was associated with an increase in the dispensing of asthma medication and an increase in health care utilisation for respiratory diseases in the Reykjavik capital area during four months in the year 2014 (23, 24). The present study was not designed to catch the possible effect of these emissions on the cardiovascular health of the population of the Reykjavik capital area, and its role remains unknown with that respect.

We made several stratifications to explore the possible association between air pollutants and emergency hospital visits for cardiac arrest in this study. Because of this, some concerns may emerge about multiple comparisons; however, this has been dealt with in the literature (25).

This study was, to our knowledge, the first to utilise the new endpoint of cardiac arrest (ICD-10 code I46). This outcome has in previous epidemiological studies been included in larger groups of CVDs, and its special status may have been overshadowed by the more common diagnosis of CVDs. Our results indicate a positive association between short-term increase in PM₁₀ and emergency hospital visits for cardiac arrest in the Reykjavik capital area, known for having low levels of traffic-related pollution. The effects were found in most subgroups, and were highest among the elderly, and in the winter season, and among those who were successfully resuscitated. Future ecological studies of this type should perhaps concentrate more on precisely defined endpoints; however, doing so will not replace the obvious lack of exact individual exposure measurements for each of the cases.

µm Micrometre

AF Atrial fibrillation

CI Confidence interval

CVD Cardiovascular diseases

ED Emergency department

GRE Air quality measurement station located at

Grensasvegur-Miklabraut intersection in Reykjavik

H₂S Hydrogen sulphide

ICD-10 International Classification of Diseases 10th edition

IHD Ischemic heart disease

km Kilometre

LUH Landspitali University Hospital

NO₂ Nitrogen dioxide

OR Odds ratio

PM₁₀ Particulate matter less than 10 µm in aerodynamic diameter

PM_2.5Particulate matter less than 2.5 µm in aerodynamic diameter

RH Relative humidity

SAGA Register of Hospital-treated Patients in Iceland

SO₂ Sulphur dioxide

yr Years Declarations

Ethical approval and consent to participate

The study was approved by the National Bioethics Committee (ref. no. VSNb2018120011/03.01), the Data Protection Authority (ref. no. 10-050), and the Scientific Committee of LUH.

Availability of data and materials

The hospital data contain sensitive individual-level information which is not publicly available. It can be made available to researchers after obtaining approval of a formal application to the National Bioethics Committee and the Scientific Committee of LUH. The dataset of air pollution used and analysed during the current study are available from the corresponding author on reasonable request.

Competing interest

The authors declare that they have no competing interests.

Funding

The study had no external funding.

Authors’ contribution

SH, RGF, VR designed the study; SH, RGF, BTE, VR planned the analysis; SH, GG, VR collected the data; SH, RGF, BTE analysed the data; VR wrote the first draft; SH, RGF, BTE, OSG, GG, VR read the manuscript, interpreted the conclusion, and agreed on the final version.

Acknowledgements

Not applicable.

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No competing interests reported.

Ambient air pollution and emergency department visits and hospitalisation for cardiac arrest: a population-based case-crossover study

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Introduction

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