Air Pollution and Hospital Admission for Epilepsy in Kerman, Iran

authors:

avatar Mohammad Amin Farahmandfard 1 , avatar Hossein Ali Ebrahimi 1 , avatar Narges Khanjani 1 , 2 , * , avatar Moghaddameh Mirzaee 3

Neurology Research Center, Kerman University of Medical Sciences, Kerman, Iran
Environmental Health Engineering Research Center, Kerman University of Medical Sciences, Kerman, Iran
Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran

how to cite: Farahmandfard M A, Ebrahimi H A, Khanjani N, Mirzaee M. Air Pollution and Hospital Admission for Epilepsy in Kerman, Iran. Health Scope. 2022;11(3):e124245. doi: 10.5812/jhealthscope-124245.

Abstract

Background:

This study aimed to investigate the relation between air pollution and epilepsy admissions in Kerman, Iran.

Methods:

In this ecological study, the concentrations of ambient air pollutants and meteorological data were obtained from Kerman Environmental Protection Agency and Kerman Meteorology Organization, respectively. Additionally, epilepsy admission data were obtained from Kerman’s Shafa hospital epilepsy registry. Generalized additive models with lags up to 7 days were used to estimate rate ratios (RRs).

Results:

Within 2008 to 2020, 894 epilepsy admissions occurred in Kerman, 498 cases (55.7%) of whom were male. The strongest relations of epilepsy admission were observed in the over 59-year group for carbon monoxide (CO) in lag 0 (RR = 2.1455, 95% CI: 1.5823 - 2.9091), nitrogen dioxide (NO2) in lag 0 (RR = 1.0409, 95% CI: 1.0282 - 1.0537), and particulate matter under 2.5 microns (PM2.5) in lag 5 (RR = 1.0157, 95% CI: 1.0062 - 1.0252). There were also significant associations for particulate matter under 10 microns (PM10) in the under 18-year group in lag 2 (RR = 1.0064, 95% CI: 1.0029 - 1.0098), ozone in lag 0 (RR = 0.9671, 95% CI: 0.9581 - 0.9761), and sulfur dioxide in lag 5 (RR = 0.9937, 95% CI: 0.9891 - 0.9983).

Conclusions:

Exposure to CO, NO2, PM2.5, or PM10 air pollutants might be a risk factor for epilepsy admissions in Kerman. Epilepsy patients should better stay away from exposure to polluted air. Staying at home on polluted days or residing in areas with less air pollution might be an option.

1. Background

Epilepsy is one of the most common neurological diseases and affects individuals of all ages (1). Air pollution might cause central nervous system (CNS) damage and neurodegenerative disorders (2). The occurrence of seizures might be affected by the interaction of internal and pathologic factors and extrinsic factors, including medication and environmental factors (3, 4). Studies have shown that Parkinson’s disease, Alzheimer’s disease, multiple sclerosis, and stroke might be related to ambient air pollution (5, 6). Calderon et al. conducted a study in Mexico and reported that exposure to air pollutants could seriously affect children’s CNS (7). Another study in China also showed the possible relation between air pollution and neurodegenerative diseases (8). Fluegge and Fluegge reported hospitalization for epilepsy associated with changes in the concentrations of various pollutants, including nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3), and particulate matter (PM). Additionally, Fluegge and Fluegge showed that air pollutants might be a risk factor for epilepsy hospital admissions (9).

However, the effect of air pollution on the CNS has not been thoroughly studied. Kerman has a population of about 740,000 individuals according to the 2016 census and is located in Kerman province in southeastern Iran (10). In addition to human-made air pollution, Kerman faces sandstorms and increased ambient dust in specific seasons. The prevalence of epilepsy in Kerman was 7.87 per 1000 individuals in 2011 (1). A recent review estimated that there are about 840,000 individuals with active epilepsy living in Iran, and epilepsy prevalence in Iran is 1% (11).

2. Objectives

The present study aimed to evaluate hospital admissions in Kerman for epilepsy and its possible relation with air pollution.

3. Methods

In this ecological study, the concentrations of ambient air pollutants (i.e., CO, O3, NO2, SO2, and particulate matter under 10 and 2.5 microns [PM10 and PM2.5]) were obtained from Kerman Environmental Protection Agency within September 2008 to March 2020. The meteorological data, including temperature and relative humidity, were obtained from Kerman Meteorological Organization. The aforementioned variables were adjusted as confounders.

The epilepsy hospitalization data were obtained from Kerman’s Shafa hospital epilepsy registry and according to the International Classification of Diseases 10th Revision, which was code G40. The data were investigated regarding gender and age subgroups (under 18, within 18-59, and over 59 years). The data of all epilepsy patients that had stayed in the hospital for more than one day were included.

3.1. Statistical Analysis

Generalized additive models (GAMs) similar to the equation below were used to estimate the rate ratios (RRs) with lags up to 7 days. The time unit used was the day. The GAM has been used in numerous air pollution and health studies. This method can adjust for nonlinear confounding effects, such as seasonal changes and meteorological variables (12).

Yt~Poisson(μt)
logμt=α+βi(Xi)+Sj(Xj)

Where Yt denotes the daily number of epilepsy relapses in total, male, female, and age groups. βi is the coefficient for air pollutants (Xi) and denotes the log of the RR. Sj and (Xj) are the smoothing functions of meteorological variables (i.e., temperature and relative humidity).

Several studies have shown that meteorological factors, such as temperature, are associated with hospital admissions for epilepsy attacks (3, 13-16). Therefore, the models were adjusted for average daily temperature and relative humidity. The time unit used in the analysis was the day. Microsoft Office Excel software (version 2010) and SPSS software (version 22) were used for the primary analysis. Then, the ‘mgcv’ package in R i386 4.0.3 software was used for GAM analysis.

4. Results

In the less than 12-year period under study, 894 epilepsy admissions occurred in Kerman, 498 (55.7%) and 396 (44.3%) cases of whom were male and female, respectively. Table 1 shows the number of epilepsy admissions within 2008 to 2020 in different population subgroups. Table 2 shows descriptive statistics of daily air pollution and meteorological variables within 2008-2020. The findings showed that the mean daily concentrations of PM10, PM2.5, and SO2 were higher than the World Health Organization daily thresholds reported as 50, 25, and 20 μg/m3, respectively (17).

Table 1. Number of Epilepsy Admissions within 2008 to 2020 in Different Population Subgroups
YearMaleFemaleMale/Female Ratio< 18 years18 - 59 years> 59 years
200817101.75202
200961521.17216923
201053371.43205911
201139400.9727475
201237351.0511565
201354381.42186113
201452361.44146410
201531181.7215286
20164141116579
201733211.576399
201825211.1972712
201954471.14106724
2020 (only 3 months)10-001
Total4983961.25170594130
Table 2. Descriptive Statistics of Daily Air Pollution and Meteorological Variables
Mean ± SDMedianMinimumMaximum
O3 (ppb)30.21 ± 11.9129.721.9279
CO (ppm)1.07 ± 0.690.920.0312.27
NO2 (ppb)15.82 ± 7.9615.41078.45
SO2 (ppb)26.12 ± 20.64210128.46
PM10 (μg/m3)71.11 ± 47.4563.480382.76
PM2.5 (μg/m3)27.47 ± 16.1824.200112
Temperature (°C)16.75 ± 8.1017.2041
Humidity (%)31.15 ± 17.30273.5100

Table 3 shows the results of the adjusted GAM about the effect of air pollutants on overall epilepsy admissions in male, female, and age groups adjusted for average daily relative humidity and temperature. Increased epilepsy admissions in the total population were observed in various lags for CO, NO2, PM10, and PM2.5. Additionally, CO, NO2, PM10, and PM2.5 had a direct association with epilepsy admissions in male subjects. The strongest relations were observed in lag 4 for CO (RR = 1.2352, 95% CI: 1.0298 - 1.4815), lag 0 for NO2 (RR = 1.0409, 95% CI: 1.0282 - 1.0537), lag 2 for PM2.5 (RR = 1.0066, 95% CI: 1.0014 - 1.0118), and lag 0 for PM10 (RR = 1.0057, 95% CI: 1.0039 - 1.0075). PM10 and PM2.5 also had a direct association with epilepsy admissions in female subjects. The strongest relations were observed in lag 2 for PM2.5 (RR = 1.0071, 95% CI: 1.0015 - 1.0128) and lag 6 for PM10 (RR = 1.0031, 95% CI: 1.0011 - 1.0050).

In all age groups, CO, NO2, PM10, and PM2.5 showed direct relations with epilepsy admissions. The strongest relation between CO and epilepsy admissions was observed in the over 59-year group in lag 0 (RR = 2.1455, 95% CI: 1.5823 - 2.9091), NO2 in the over 59-year group in lag 0 (RR = 1.0407, 95% CI: 1.0139 - 1.0681), PM2.5 in the over 59-year group in lag 5 (RR = 1.0157, 95% CI: 1.0062 - 1.0252), and PM10 in the under 18-year group in lag 2 (RR = 1.0064, 95% CI: 1.0029 - 1.0098).

Table 3. Results of Adjusted Generalized Additive Models about the Effect of Air Pollutants on Epilepsy Admissions in Total, Male, Female, and Age Groups Adjusted for Relative Humidity and Temperature
PollutantRR95% CIP-Value
Lag 0
Total
O30.98160.9756 - 0.98770.0001
CO0.90080.7906 - 1.02630.315
NO21.02661.0168 - 1.03640.001
SO20.99780.9945 - 1.00110.178
PM101.00371.0024 - 1.00500.0001
PM2.51.00170.9977 - 1.00570.402
Male
O30.99420.9854 - 1.00290.718
CO0.95430.8015 - 1.13600.598
NO21.04091.0282 - 1.05370.001
SO20.99670.9923 - 1.00120.234
PM101.00571.0039 - 1.00750.001
PM2.51.00010.9949 - 1.00540.581
Female
O30.96710.9581 - 0.97610.001
CO0.86650.7119 - 1.05470.240
NO21.00490.9894 - 1.02060.533
SO20.99880.9938 - 1.00380.560
PM101.00231.0002 - 1.00440.015
PM2.51.00420.9981 - 1.01040.215
< 18 years
O31.00620.9913 - 1.02120.089
CO0.58030.4138 - 0.81390.001
NO21.02131.0000 - 1.04350.004
SO20.99850.9906 - 1.00640.510
PM101.00511.0021 - 1.00810.0001
PM2.51.00070.9915 - 1.00990.510
18 - 59 years
O30.98120.9736 - 0.98880.0001
CO0.81880.6961 - 0.96320.015
NO21.02431.0123 - 1.03650.0001
SO20.99630.9922 - 1.00040.520
PM101.00391.0022 - 1.00560.0001
PM2.51.00050.9956 - 1.00530.540
> 59 years
O30.95450.9404 - 0.96890.0001
CO2.14551.5823 - 2.90910.0001
NO21.04071.0139 - 1.06810.002
SO21.00310.9948 - 1.01150.151
PM101.00220.9990 - 1.00540.216
PM2.51.01481.0038 - 1.02590.0001
Lag 1
Total
O30.99080.9844 - 0.99720.0002
CO1.02800.9148 - 1.15510.570
NO21.00831.0000 - 1.01780.001
SO20.99610.9928 - 0.99950.0201
PM101.00271.0010 - 1.00400.0001
PM2.51.00491.0010 - 1.00890.013
Male
O30.99500.9864 - 1.00370.141
CO1.00520.8471 - 1.19270.580
NO21.00800.9959 - 1.02030.713
SO20.99580.9913 - 1.00030.998
PM101.00331.0016 - 1.00500.026
PM2.51.00520.9999 - 1.01060.717
Female
O30.98540.9759 - 0.99510.001
CO1.01300.8486 - 1.20930.245
NO21.00680.9927 - 1.02110.107
SO20.99590.9909 - 1.00100.309
PM101.00221.0002 - 1.00420.020
PM2.51.00500.9990 - 1.01110.362
< 18 years
O30.99550.9807 - 1.01050.650
CO0.99330.7675 - 1.28560.591
NO21.01380.9953 - 1.03260.256
SO20.99400.9960 - 1.00210.250
PM101.00471.0013 - 1.00800.0002
PM2.51.00120.9915 - 1.01100.550
18 - 59 years
O30.99360.9857 - 1.00150.315
CO0.98940.8504 - 1.15120.510
NO21.00630.9944 - 1.01820.123
SO20.99540.9912 - 0.99960.013
PM101.00241.0008 - 1.00410.003
PM2.51.00460.9999 - 1.00950.050
> 59 years
O30.97070.9542 - 0.98750.0008
CO1.28341.0073 - 1.63510.006
NO21.10130.9868 - 1.04050.112
SO21.00150.9931 - 1.01000.051
PM101.00220.9992 - 1.00530.255
PM2.51.01311.0003 - 1.02310.0006
Lag 2
Total
O30.98820.9819 - 0.99470.0001
CO1.10440.9748 - 1.25110.195
NO21.00991.0008 - 1.01920.012
SO20.99640.9931 - 0.99980.009
PM101.00281.0014 - 1.00420.0001
PM2.51.00661.0028 - 1.01040.0001
Male
O30.99190.9833 - 1.00060.610
CO1.10100.9261 - 1.30880.133
NO21.01191.0001 - 1.02390.006
SO20.99600.9915 - 1.00510.112
PM101.00291.0011 - 1.00480.018
PM2.51.00661.0014 - 1.01180.009
Female
O30.98340.9739 - 0.99300.0001
CO1.13240.9459 - 1.35570.209
NO21.00660.9926 - 1.02070.103
SO20.99630.9913 - 1.00140.224
PM101.00271.0008 - 1.00470.0003
PM2.51.00711.0015 - 1.01280.009
< 18 years
O30.99960.9848 - 1.01460.580
CO0.91280.6928 - 1.20260.710
NO21.01440.9962 - 1.03290.308
SO20.99580.9880 - 1.00360.126
PM101.00641.0029 - 1.00980.003
PM2.51.00380.9945 - 1.01320.187
18 - 59 years
O30.98910.9813 - 0.99700.0001
CO1.10890.9513 - 1.29250.197
NO21.00830.9971 - 1.01970.253
SO20.99550.9913 - 0.99970.009
PM101.00201.0003 - 1.00360.005
PM2.51.00491.0003 - 1.00960.010
> 59 years
O30.96530.9489 - 0.98180.0001
CO1.07680.8739 - 1.32670.175
NO21.01000.9849 - 1.03580.284
SO21.00170.9932 - 1.01010.520
PM101.00300.9998 - 1.00620.30
PM2.51.01821.0088 - 1.02760.007
Lag 3
Total
O30.99000.9836 - 0.99650.003
CO1.11380.9875 - 1.25620.200
NO21.00831.0001 - 1.01760.002
SO20.99690.9935 - 1.00020.220
PM101.00301.0016 - 1.00450.0001
PM2.51.00421.0002 - 1.00820.0001
Male
O30.98990.9813 - 0.99860.025
CO1.15530.9787 - 1.36380.110
NO21.00950.9973 - 1.02200.291
SO20.99640.9919 - 1.00100.283
PM101.00381.0019 - 1.00570.0001
PM2.51.00480.9995 - 1.01020.220
Female
O30.98950.9800 - 0.99920.011
CO1.03740.8942 - 1.20360.580
NO21.00580.9924 - 1.01940.630
SO20.99700.9919 - 1.00200.149
PM101.00220.9999 - 1.00440.400
PM2.51.00360.9976 - 1.00970.155
< 18 years
O31.00390.9893 - 1.01880.610
CO0.91100.6910 - 1.20080.172
NO21.00680.9881 - 1.02590.177
SO20.99640.9887 - 1.00420.199
PM101.00601.0027 - 1.00930.0002
PM2.51.00100.9912 - 1.01080.343
18 - 59 years
O30.98780.9800 - 0.99570.002
CO1.15810.9906 - 1.35390.302
NO21.00770.9964 - 1.01930.203
SO20.99630.9921 - 1.00040.100
PM101.00341.0015 - 1.00520.0001
PM2.51.00420.9994 - 1.00900.110
> 59 years
O30.98170.9653 - 0.99830.014
CO1.35000.9461 - 1.36150.213
NO21.00920.9862 - 1.03280.185
SO21.00260.9942 - 1.01100.168
PM101.00220.9989 - 1.00550.202
PM2.51.01111.0004 - 1.02180.008
Lag 4
Total
O30.99230.9859 - 0.99880.032
CO1.11140.9834 - 1.25610.110
NO21.00981.0008 - 1.01900.012
SO20.99650.9932 - 0.99990.006
PM101.00241.0010 - 1.00380.0001
PM2.51.00190.9978 - 1.00590.102
Male
O30.99290.9844 - 1.00160.325
CO1.23521.0298 - 1.48150.022
NO21.01110.9993 - 1.02310.300
SO20.99540.9909 - 1.00000.500
PM101.00271.0010 - 1.00450.001
PM2.51.00150.9959 - 1.00710.610
Female
O30.99090.9814 - 1.00050.151
CO1.05680.9836 - 1.24970.170
NO21.00680.9936 - 1.02020.118
SO20.99710.9921 - 1.00220.134
PM101.00301.0008 - 1.00520.0001
PM2.51.00260.9965 - 1.00870.193
< 18 years
O31.00080.9862 - 1.01560.155
CO1.04550.7878 - 1.38740.151
NO21.01210.9938 - 1.03090.188
SO20.99690.9895 - 1.00430.188
PM101.00471.0015 - 1.00800.0008
PM2.51.00070.9910 - 1.01040.141
18 - 59 years
O30.99170.9839 - 0.99960.007
CO1.08440.9349 - 1.25790.129
NO21.01031.0001 - 1.02190.002
SO20.99560.9914 - 0.99980.007
PM101.00211.0004 - 1.00380.009
PM2.51.00090.9959 - 1.00580.151
> 59 years
O30.98260.9664 - 0.99910.008
CO1.10310.8878 - 1.37050.197
NO21.00450.9829 - 1.02650.153
SO21.00150.9931 - 1.00990.151
PM101.00040.9999 - 1.00380.155
PM2.51.00900.9988 - 1.01930.200
Lag 5
Total
O30.99250.9861 - 0.99890.025
CO1.00900.8982 - 1.13360.878
NO21.01231.0023 - 1.02250.015
SO20.99530.9918 - 0.99870.007
PM101.00261.0011 - 1.00420.0001
PM2.51.00240.9983 - 1.00650.235
Male
O30.99560.9868 - 1.00440.349
CO1.00780.8475 - 1.19840.932
NO21.01551.0022 - 1.02900.027
SO20.99370.9891 - 0.99830.012
PM101.00291.0011 - 1.00470.001
PM2.51.00070.9949 - 1.00650.849
Female
O30.98850.9793 - 0.99780.015
CO1.11990.9404 - 1.33370.180
NO21.00370.9891 - 1.01840.616
SO20.99720.9480 - 1.04890.285
PM101.00211.0000 - 1.00420.045
PM2.51.00460.9986 - 1.01070.291
< 18 years
O31.01190.9972 - 1.02670.331
CO0.78560.5749 - 1.07370.282
NO21.00580.9860 - 1.02600.563
SO20.99270.9849 - 1.00000.072
PM101.00601.0028 - 1.00930.0002
PM2.50.99900.9890 - 1.00920.855
18 - 59 years
O30.99200.9841 - 0.99980.053
CO1.01890.8838 - 1.17470.795
NO21.01100.9988 - 1.02340.072
SO20.99550.9913 - 0.99970.036
PM101.00191.0002 - 1.00360.018
PM2.50.99930.9942 - 1.00450.808
> 59 years
O30.97260.9577 - 0.98770.001
CO1.28000.9305 - 1.36720.167
NO21.00730.9821 - 1.03320.569
SO20.99940.9908 - 1.00810.908
PM101.00070.9971 - 1.00440.671
PM2.51.01571.0062 - 1.02520.001
Lag 6
Total
O30.99360.9871 - 1.00000.054
CO1.04370.9369 - 1.16280.436
NO21.00960.9999 - 1.01950.051
SO20.99590.9925 - 0.99930.018
PM101.00271.0013 - 1.00400.0001
PM2.51.00220.9981 - 1.00640.278
Male
O30.99730.9886 - 1.00620.560
CO1.06080.8948 - 1.25750.496
NO21.01431.0011 - 1.02770.033
SO20.99550.9909 - 1.00010.053
PM101.00211.0003 - 1.00390.019
PM2.50.99950.9937 - 1.00530.807
Female
O30.98790.9787 - 0.99720.011
CO1.05550.9220 - 1.20830.433
NO21.00990.9956 - 1.02440.211
SO20.99660.9915 - 1.00170.186
PM101.00311.0011 - 1.00500.001
PM2.51.00550.9994 - 1.01160.072
< 18 years
O31.01000.9952 - 1.02500.201
CO0.92250.9090 - 0.93610.586
NO21.00050.9803 - 1.02110.958
SO20.99230.9842 - 1.00050.068
PM101.00531.0023 - 1.00820.0001
PM2.51.00360.9940 - 1.01320.461
18 - 59 years
O30.99390.9860 - 1.00190.137
CO1.04780.9168 - 1.19760.492
NO21.01231.0005 - 1.02420.039
SO20.99610.9920 - 1.00030.071
PM101.00181.0001 - 1.00360.037
PM2.50.99850.9932 - 1.00370.577
> 59 years
O30.97280.9560 - 0.98980.001
CO1.12160.9217 - 1.36480.251
NO21.00210.9770 - 1.02780.866
SO21.00110.9926 - 1.00980.784
PM101.00200.9988 - 1.00520.182
PM2.51.01481.0051 - 1.02450.003
Lag 7
Total
O30.99110.9848 - 0.99750.006
CO1.02720.9233 - 1.14270.621
NO21.01021.0002 - 1.02030.043
SO20.99470.9913 - 0.99820.001
PM101.00311.0017 - 1.00450.0001
PM2.51.00260.9638 - 1.04290.190
Male
O30.99670.9879 - 1.00560.494
CO1.10970.9176 - 1.34200.272
NO21.01481.0015 - 1.02830.016
SO20.99500.9905 - 0.99960.045
PM101.00311.0012 - 1.00500.001
PM2.50.99980.9943 - 1.00540.951
Female
O30.98740.9779 - 0.99710.011
CO1.06150.9289 - 1.21290.377
NO21.00410.9890 - 1.01930.838
SO20.99520.9901 - 1.00030.062
PM101.00301.0010 - 1.00490.002
PM2.51.00490.9990 - 1.01090.096
< 18 years
O31.00740.9925 - 1.02250.112
CO0.88330.6405 - 1.21790.449
NO20.99900.9786 - 1.01970.925
SO20.99250.9843 - 1.00070.075
PM101.00551.0023 - 1.00870.0001
PM2.51.01021.0017 - 1.01870.018
18 - 59 years
O30.99140.9836 - 0.99930.035
CO1.05650.9242 - 1.20790.420
NO21.01090.9998 - 1.02290.067
SO20.99420.9900 - 0.99840.007
PM101.00251.0009 - 1.00410.001
PM2.50.99800.9928 - 1.00320.453
> 59 years
O30.97070.9540 - 0.98770.0001
CO1.09640.8781 - 1.36900.416
NO21.00740.9792 - 1.03640.790
SO21.00250.9938 - 1.01140.560
PM101.00140.9981 - 1.00460.396
PM2.51.01101.0009 - 1.02120.030

5. Discussion

This study showed significant relations between short-term exposure to air pollutants CO, NO2, PM10, and PM2.5 with epilepsy admissions in Kerman, Iran. In this study, CO increased epilepsy admissions. Consistent with the results of this study, Bao et al.’s study in China showed an association between CO and increased epilepsy hospitalization (1.1%, 95% CI: 0.1 - 2.1%) (18). In addition, in Mexico, there was an association between ambient CO and epilepsy admissions (RR = 1.098, 95% CI: 1.045 - 1.155) (19).

In this study, NO2 had a significant relation with epilepsy admissions in total and several different age groups, and the strongest relation was observed in male subjects. Automobile exhaust is one of the most important sources of NO2. Wang et al.’s study in China demonstrated a significant association between the NO2 of automobile exhaust and neurobehavioral function in school-age children. In the aforementioned study, two primary schools were chosen. One school was located in a clear area and the other in a traffic dense and polluted area. NO2 had been monitored for the effect of traffic-related air pollution on the school campuses and classrooms. Children participated in assisted neurobehavioral testing to assess neurobehavioral performance (20).

A systematic review in 2017 concluded that high concentrations of NO2 in polluted air significantly affect the CNS in children and adults and represent a significant risk factor for human health (21). A study in China showed a significant relation between the increasing concentration of NO2 with epilepsy attacks (2%, 95% CI: 0.5 - 3.6%) (18). Furthermore, in Mexico, an ecological study showed a significant relation between NO2 and epilepsy attacks (RR = 1.083, 95% CI: 1.038 - 1.13) (19). In a cohort study in Denmark, residential exposure to road traffic and air pollution was associated with a higher risk for febrile seizures (IRR = 1.05, 95% CI: 1.02 - 1.07) (22). A study in southern Spain showed that even low levels of NO2 exposure and traffic-related air pollution had adverse effects on children’s neurodevelopment (23). Xu et al.’s study in China showed that the RR for epilepsy attacks was 3.17 (95% CI: 1.41 - 4.93) per 10 μg/m3 increase of NO2 (13). However, a study in the USA showed a protective effect for N2O on epilepsy (IRR = 0.85, 95% CI: 0.74 - 0.97) (9). N2O is a different compound and is derived mainly from agricultural fertilizers and natural sources; nonetheless, NO2 is mainly produced by vehicles.

Another pollutant evaluated in this study was PM10, which had a significant relation with epilepsy admissions in total and several subgroups, with the strongest relation observed in the under 18-year subgroup. Several studies have shown relations between ambient PM10 and epilepsy attacks (19, 24, 25). Consistent with the results of this study, Cakmak et al. in Mexico showed an association between PM10 and hospital admissions for epilepsy (RR = 1.083, 95% CI: 1.038 - 1.13) (19). A study in six cities in Italy demonstrated positive associations between PM10 exposure and total emergency calls within 2002 to 2006 (26). Additionally, increased emergency calls for epilepsy attacks were observed with exposure to PM10 in China (RR = 1.5, 95% CI: 1.1 - 2.0) (24). Radmanesh et al.’s study in Iran showed that patients with different types of headaches and epilepsy increased on dusty days, compared to clean days, and there were significant associations between increased concentrations of ambient PM10 and hospital admissions for these problems (25). Another study from Iran showed that exposure to PM10 increased oxidative stress and the expression of inducible nitric oxide synthase messenger ribonucleic acid levels and reduced the concentrations of antioxidant enzymes (27).

In this study, PM2.5 had a significant relation with epilepsy admissions in total, and several subgroups, with the strongest relation, observed in the over 59-year subgroup. Consistent with the results of this study, a significant association was observed between PM2.5 and epilepsy attacks in Mexico (RR = 1.065, 95% CI: 1.002 - 1.132) (19). Other studies have shown that oxidative stress, neuroinflammation, glial activation, and cerebrovascular damage are the primary pathways for inducing brain pathology by air pollution (28). Oxidative stress, changes in autonomous function, and progression of atherosclerosis can be exacerbated by exposure to ambient PM (29).

In this study, O3 and SO2 were inversely related to epilepsy admissions. In an ecological study in China, Xu et al. also reported negative associations between ambient O3 and epilepsy attacks (-0.84%, 95% Cl: -1.58 - 0.09%) (13). An interventional study showed that O3 could be protective against pentylenetetrazole-induced epilepsy attacks (30). However, in another study conducted in Mexico, a significant direct association was observed between O3 and hospital admissions for epilepsy attacks (RR = 1.100, 95% CI: 1.025 - 1.181) (19). In Fluegge and Fluegge’s study in the USA, no significant relation was observed between O3 and epilepsy attacks (9). In the current study the average concentration of O3 was 30.21±11.19 ppb, which is less that Xu et al.’s study (mean = 100 ± 63 ppb) (13) which showed a negative association, and Cakmak et al.’s study (mean = 93.26 ppb) (19). Further research is needed to clarify the effect of O3 exposure on epilepsy.

Some of the strengths of this study included about 12-year data on air pollution and epilepsy admissions and the use of GAMs to adjust for nonlinear confounder variables. However, given the ecological nature of this study, the results cannot be easily inferred at the individual level.

5.1. Conclusions

Exposure to ambient CO, NO2, PM10, and PM2.5 might be related to epilepsy admissions in Kerman. This study further emphasizes the necessity to control and reduce ambient air pollutants. Additionally, epilepsy patients should better stay away from exposure to polluted air. Staying at home on polluted days or residing in areas with less air pollution might be an option.

Acknowledgements

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