Air Pollution and Hospital Admission for Epilepsy in Kerman, Iran

Mohammad Amin Farahmandfard; Hossein Ali Ebrahimi; Narges Khanjani; Moghaddameh Mirzaee

doi:10.5812/jhealthscope-124245

Health Scope

Official Publication of Health Promotion Research Center

Outlines

Abstract
Acknowledgments
Footnotes
References
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https://doi.org/10.5812/jhealthscope-124245

Air Pollution and Hospital Admission for Epilepsy in Kerman, Iran

Author(s):

Mohammad Amin Farahmandfard

¹,

Hossein Ali Ebrahimi¹,

Narges Khanjani

^{1, 2,*},

Moghaddameh Mirzaee

1Neurology Research Center, Kerman University of Medical Sciences, Kerman, Iran

2Environmental Health Engineering Research Center, Kerman University of Medical Sciences, Kerman, Iran

3Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran

Health Scope:Vol. 11, issue 3; e124245

Published online:Aug 15, 2022

Article type:Research Article

Received:Apr 04, 2022

Accepted:Jun 29, 2022

How to Cite:Farahmandfard MA, Ebrahimi HA, Khanjani N, Mirzaee M. Air Pollution and Hospital Admission for Epilepsy in Kerman, Iran. Health Scope. 2022;11(3):e124245. doi: https://doi.org/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 (PM_2.5) 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, PM_2.5, 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.

Keywords

Air Pollution

Epilepsy

Kerman

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 PM_2.5]) 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).

Where

Y_{t}

denotes the daily number of epilepsy relapses in total, male, female, and age groups.

β_{i}

is the coefficient for air pollutants (

X_{i}

) and denotes the log of the RR.

S_{j}

and (

X_{j}

) 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, PM_2.5, 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

Year	Male	Female	Male/Female Ratio	< 18 years	18 - 59 years	> 59 years
2008	17	10	1.7	5	20	2
2009	61	52	1.17	21	69	23
2010	53	37	1.43	20	59	11
2011	39	40	0.97	27	47	5
2012	37	35	1.05	11	56	5
2013	54	38	1.42	18	61	13
2014	52	36	1.44	14	64	10
2015	31	18	1.72	15	28	6
2016	41	41	1	16	57	9
2017	33	21	1.57	6	39	9
2018	25	21	1.19	7	27	12
2019	54	47	1.14	10	67	24
2020 (only 3 months)	1	0	-	0	0	1
Total	498	396	1.25	170	594	130

Table 2.Descriptive Statistics of Daily Air Pollution and Meteorological Variables

	Mean ± SD	Median	Minimum	Maximum
O₃ (ppb)	30.21 ± 11.91	29.72	1.92	79
CO (ppm)	1.07 ± 0.69	0.92	0.03	12.27
NO₂ (ppb)	15.82 ± 7.96	15.41	0	78.45
SO₂ (ppb)	26.12 ± 20.64	21	0	128.46
PM₁₀ (μg/m³)	71.11 ± 47.45	63.48	0	382.76
PM_2.5 (μg/m³)	27.47 ± 16.18	24.20	0	112
Temperature (°C)	16.75 ± 8.10	17.2	0	41
Humidity (%)	31.15 ± 17.30	27	3.5	100

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 PM_2.5. Additionally, CO, NO₂, PM₁₀, and PM_2.5 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 PM_2.5 (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 PM_2.5 also had a direct association with epilepsy admissions in female subjects. The strongest relations were observed in lag 2 for PM_2.5 (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 PM_2.5 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), PM_2.5 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

Pollutant	RR	95% CI	P-Value
Lag 0
Total
O₃	0.9816	0.9756 - 0.9877	0.0001
CO	0.9008	0.7906 - 1.0263	0.315
NO₂	1.0266	1.0168 - 1.0364	0.001
SO₂	0.9978	0.9945 - 1.0011	0.178
PM₁₀	1.0037	1.0024 - 1.0050	0.0001
PM_2.5	1.0017	0.9977 - 1.0057	0.402
Male
O₃	0.9942	0.9854 - 1.0029	0.718
CO	0.9543	0.8015 - 1.1360	0.598
NO₂	1.0409	1.0282 - 1.0537	0.001
SO₂	0.9967	0.9923 - 1.0012	0.234
PM₁₀	1.0057	1.0039 - 1.0075	0.001
PM_2.5	1.0001	0.9949 - 1.0054	0.581
Female
O₃	0.9671	0.9581 - 0.9761	0.001
CO	0.8665	0.7119 - 1.0547	0.240
NO₂	1.0049	0.9894 - 1.0206	0.533
SO₂	0.9988	0.9938 - 1.0038	0.560
PM₁₀	1.0023	1.0002 - 1.0044	0.015
PM_2.5	1.0042	0.9981 - 1.0104	0.215
< 18 years
O₃	1.0062	0.9913 - 1.0212	0.089
CO	0.5803	0.4138 - 0.8139	0.001
NO₂	1.0213	1.0000 - 1.0435	0.004
SO₂	0.9985	0.9906 - 1.0064	0.510
PM₁₀	1.0051	1.0021 - 1.0081	0.0001
PM_2.5	1.0007	0.9915 - 1.0099	0.510
18 - 59 years
O₃	0.9812	0.9736 - 0.9888	0.0001
CO	0.8188	0.6961 - 0.9632	0.015
NO₂	1.0243	1.0123 - 1.0365	0.0001
SO₂	0.9963	0.9922 - 1.0004	0.520
PM₁₀	1.0039	1.0022 - 1.0056	0.0001
PM_2.5	1.0005	0.9956 - 1.0053	0.540
> 59 years
O₃	0.9545	0.9404 - 0.9689	0.0001
CO	2.1455	1.5823 - 2.9091	0.0001
NO₂	1.0407	1.0139 - 1.0681	0.002
SO₂	1.0031	0.9948 - 1.0115	0.151
PM₁₀	1.0022	0.9990 - 1.0054	0.216
PM_2.5	1.0148	1.0038 - 1.0259	0.0001
Lag 1
Total
O₃	0.9908	0.9844 - 0.9972	0.0002
CO	1.0280	0.9148 - 1.1551	0.570
NO₂	1.0083	1.0000 - 1.0178	0.001
SO₂	0.9961	0.9928 - 0.9995	0.0201
PM₁₀	1.0027	1.0010 - 1.0040	0.0001
PM_2.5	1.0049	1.0010 - 1.0089	0.013
Male
O₃	0.9950	0.9864 - 1.0037	0.141
CO	1.0052	0.8471 - 1.1927	0.580
NO₂	1.0080	0.9959 - 1.0203	0.713
SO₂	0.9958	0.9913 - 1.0003	0.998
PM₁₀	1.0033	1.0016 - 1.0050	0.026
PM_2.5	1.0052	0.9999 - 1.0106	0.717
Female
O₃	0.9854	0.9759 - 0.9951	0.001
CO	1.0130	0.8486 - 1.2093	0.245
NO₂	1.0068	0.9927 - 1.0211	0.107
SO₂	0.9959	0.9909 - 1.0010	0.309
PM₁₀	1.0022	1.0002 - 1.0042	0.020
PM_2.5	1.0050	0.9990 - 1.0111	0.362
< 18 years
O₃	0.9955	0.9807 - 1.0105	0.650
CO	0.9933	0.7675 - 1.2856	0.591
NO₂	1.0138	0.9953 - 1.0326	0.256
SO₂	0.9940	0.9960 - 1.0021	0.250
PM₁₀	1.0047	1.0013 - 1.0080	0.0002
PM_2.5	1.0012	0.9915 - 1.0110	0.550
18 - 59 years
O₃	0.9936	0.9857 - 1.0015	0.315
CO	0.9894	0.8504 - 1.1512	0.510
NO₂	1.0063	0.9944 - 1.0182	0.123
SO₂	0.9954	0.9912 - 0.9996	0.013
PM₁₀	1.0024	1.0008 - 1.0041	0.003
PM_2.5	1.0046	0.9999 - 1.0095	0.050
> 59 years
O₃	0.9707	0.9542 - 0.9875	0.0008
CO	1.2834	1.0073 - 1.6351	0.006
NO₂	1.1013	0.9868 - 1.0405	0.112
SO₂	1.0015	0.9931 - 1.0100	0.051
PM₁₀	1.0022	0.9992 - 1.0053	0.255
PM_2.5	1.0131	1.0003 - 1.0231	0.0006
Lag 2
Total
O₃	0.9882	0.9819 - 0.9947	0.0001
CO	1.1044	0.9748 - 1.2511	0.195
NO₂	1.0099	1.0008 - 1.0192	0.012
SO₂	0.9964	0.9931 - 0.9998	0.009
PM₁₀	1.0028	1.0014 - 1.0042	0.0001
PM_2.5	1.0066	1.0028 - 1.0104	0.0001
Male
O₃	0.9919	0.9833 - 1.0006	0.610
CO	1.1010	0.9261 - 1.3088	0.133
NO₂	1.0119	1.0001 - 1.0239	0.006
SO₂	0.9960	0.9915 - 1.0051	0.112
PM₁₀	1.0029	1.0011 - 1.0048	0.018
PM_2.5	1.0066	1.0014 - 1.0118	0.009
Female
O₃	0.9834	0.9739 - 0.9930	0.0001
CO	1.1324	0.9459 - 1.3557	0.209
NO₂	1.0066	0.9926 - 1.0207	0.103
SO₂	0.9963	0.9913 - 1.0014	0.224
PM₁₀	1.0027	1.0008 - 1.0047	0.0003
PM_2.5	1.0071	1.0015 - 1.0128	0.009
< 18 years
O₃	0.9996	0.9848 - 1.0146	0.580
CO	0.9128	0.6928 - 1.2026	0.710
NO₂	1.0144	0.9962 - 1.0329	0.308
SO₂	0.9958	0.9880 - 1.0036	0.126
PM₁₀	1.0064	1.0029 - 1.0098	0.003
PM_2.5	1.0038	0.9945 - 1.0132	0.187
18 - 59 years
O₃	0.9891	0.9813 - 0.9970	0.0001
CO	1.1089	0.9513 - 1.2925	0.197
NO₂	1.0083	0.9971 - 1.0197	0.253
SO₂	0.9955	0.9913 - 0.9997	0.009
PM₁₀	1.0020	1.0003 - 1.0036	0.005
PM_2.5	1.0049	1.0003 - 1.0096	0.010
> 59 years
O₃	0.9653	0.9489 - 0.9818	0.0001
CO	1.0768	0.8739 - 1.3267	0.175
NO₂	1.0100	0.9849 - 1.0358	0.284
SO₂	1.0017	0.9932 - 1.0101	0.520
PM₁₀	1.0030	0.9998 - 1.0062	0.30
PM_2.5	1.0182	1.0088 - 1.0276	0.007
Lag 3
Total
O₃	0.9900	0.9836 - 0.9965	0.003
CO	1.1138	0.9875 - 1.2562	0.200
NO₂	1.0083	1.0001 - 1.0176	0.002
SO₂	0.9969	0.9935 - 1.0002	0.220
PM₁₀	1.0030	1.0016 - 1.0045	0.0001
PM_2.5	1.0042	1.0002 - 1.0082	0.0001
Male
O₃	0.9899	0.9813 - 0.9986	0.025
CO	1.1553	0.9787 - 1.3638	0.110
NO₂	1.0095	0.9973 - 1.0220	0.291
SO₂	0.9964	0.9919 - 1.0010	0.283
PM₁₀	1.0038	1.0019 - 1.0057	0.0001
PM_2.5	1.0048	0.9995 - 1.0102	0.220
Female
O₃	0.9895	0.9800 - 0.9992	0.011
CO	1.0374	0.8942 - 1.2036	0.580
NO₂	1.0058	0.9924 - 1.0194	0.630
SO₂	0.9970	0.9919 - 1.0020	0.149
PM₁₀	1.0022	0.9999 - 1.0044	0.400
PM_2.5	1.0036	0.9976 - 1.0097	0.155
< 18 years
O₃	1.0039	0.9893 - 1.0188	0.610
CO	0.9110	0.6910 - 1.2008	0.172
NO₂	1.0068	0.9881 - 1.0259	0.177
SO₂	0.9964	0.9887 - 1.0042	0.199
PM₁₀	1.0060	1.0027 - 1.0093	0.0002
PM_2.5	1.0010	0.9912 - 1.0108	0.343
18 - 59 years
O₃	0.9878	0.9800 - 0.9957	0.002
CO	1.1581	0.9906 - 1.3539	0.302
NO₂	1.0077	0.9964 - 1.0193	0.203
SO₂	0.9963	0.9921 - 1.0004	0.100
PM₁₀	1.0034	1.0015 - 1.0052	0.0001
PM_2.5	1.0042	0.9994 - 1.0090	0.110
> 59 years
O₃	0.9817	0.9653 - 0.9983	0.014
CO	1.3500	0.9461 - 1.3615	0.213
NO₂	1.0092	0.9862 - 1.0328	0.185
SO₂	1.0026	0.9942 - 1.0110	0.168
PM₁₀	1.0022	0.9989 - 1.0055	0.202
PM_2.5	1.0111	1.0004 - 1.0218	0.008
Lag 4
Total
O₃	0.9923	0.9859 - 0.9988	0.032
CO	1.1114	0.9834 - 1.2561	0.110
NO₂	1.0098	1.0008 - 1.0190	0.012
SO₂	0.9965	0.9932 - 0.9999	0.006
PM₁₀	1.0024	1.0010 - 1.0038	0.0001
PM_2.5	1.0019	0.9978 - 1.0059	0.102
Male
O₃	0.9929	0.9844 - 1.0016	0.325
CO	1.2352	1.0298 - 1.4815	0.022
NO₂	1.0111	0.9993 - 1.0231	0.300
SO₂	0.9954	0.9909 - 1.0000	0.500
PM₁₀	1.0027	1.0010 - 1.0045	0.001
PM_2.5	1.0015	0.9959 - 1.0071	0.610
Female
O₃	0.9909	0.9814 - 1.0005	0.151
CO	1.0568	0.9836 - 1.2497	0.170
NO₂	1.0068	0.9936 - 1.0202	0.118
SO₂	0.9971	0.9921 - 1.0022	0.134
PM₁₀	1.0030	1.0008 - 1.0052	0.0001
PM_2.5	1.0026	0.9965 - 1.0087	0.193
< 18 years
O₃	1.0008	0.9862 - 1.0156	0.155
CO	1.0455	0.7878 - 1.3874	0.151
NO₂	1.0121	0.9938 - 1.0309	0.188
SO₂	0.9969	0.9895 - 1.0043	0.188
PM₁₀	1.0047	1.0015 - 1.0080	0.0008
PM_2.5	1.0007	0.9910 - 1.0104	0.141
18 - 59 years
O₃	0.9917	0.9839 - 0.9996	0.007
CO	1.0844	0.9349 - 1.2579	0.129
NO₂	1.0103	1.0001 - 1.0219	0.002
SO₂	0.9956	0.9914 - 0.9998	0.007
PM₁₀	1.0021	1.0004 - 1.0038	0.009
PM_2.5	1.0009	0.9959 - 1.0058	0.151
> 59 years
O₃	0.9826	0.9664 - 0.9991	0.008
CO	1.1031	0.8878 - 1.3705	0.197
NO₂	1.0045	0.9829 - 1.0265	0.153
SO₂	1.0015	0.9931 - 1.0099	0.151
PM₁₀	1.0004	0.9999 - 1.0038	0.155
PM_2.5	1.0090	0.9988 - 1.0193	0.200
Lag 5
Total
O₃	0.9925	0.9861 - 0.9989	0.025
CO	1.0090	0.8982 - 1.1336	0.878
NO₂	1.0123	1.0023 - 1.0225	0.015
SO₂	0.9953	0.9918 - 0.9987	0.007
PM₁₀	1.0026	1.0011 - 1.0042	0.0001
PM_2.5	1.0024	0.9983 - 1.0065	0.235
Male
O₃	0.9956	0.9868 - 1.0044	0.349
CO	1.0078	0.8475 - 1.1984	0.932
NO₂	1.0155	1.0022 - 1.0290	0.027
SO₂	0.9937	0.9891 - 0.9983	0.012
PM₁₀	1.0029	1.0011 - 1.0047	0.001
PM_2.5	1.0007	0.9949 - 1.0065	0.849
Female
O₃	0.9885	0.9793 - 0.9978	0.015
CO	1.1199	0.9404 - 1.3337	0.180
NO₂	1.0037	0.9891 - 1.0184	0.616
SO₂	0.9972	0.9480 - 1.0489	0.285
PM₁₀	1.0021	1.0000 - 1.0042	0.045
PM_2.5	1.0046	0.9986 - 1.0107	0.291
< 18 years
O₃	1.0119	0.9972 - 1.0267	0.331
CO	0.7856	0.5749 - 1.0737	0.282
NO₂	1.0058	0.9860 - 1.0260	0.563
SO₂	0.9927	0.9849 - 1.0000	0.072
PM₁₀	1.0060	1.0028 - 1.0093	0.0002
PM_2.5	0.9990	0.9890 - 1.0092	0.855
18 - 59 years
O₃	0.9920	0.9841 - 0.9998	0.053
CO	1.0189	0.8838 - 1.1747	0.795
NO₂	1.0110	0.9988 - 1.0234	0.072
SO₂	0.9955	0.9913 - 0.9997	0.036
PM₁₀	1.0019	1.0002 - 1.0036	0.018
PM_2.5	0.9993	0.9942 - 1.0045	0.808
> 59 years
O₃	0.9726	0.9577 - 0.9877	0.001
CO	1.2800	0.9305 - 1.3672	0.167
NO₂	1.0073	0.9821 - 1.0332	0.569
SO₂	0.9994	0.9908 - 1.0081	0.908
PM₁₀	1.0007	0.9971 - 1.0044	0.671
PM_2.5	1.0157	1.0062 - 1.0252	0.001
Lag 6
Total
O₃	0.9936	0.9871 - 1.0000	0.054
CO	1.0437	0.9369 - 1.1628	0.436
NO₂	1.0096	0.9999 - 1.0195	0.051
SO₂	0.9959	0.9925 - 0.9993	0.018
PM₁₀	1.0027	1.0013 - 1.0040	0.0001
PM_2.5	1.0022	0.9981 - 1.0064	0.278
Male
O₃	0.9973	0.9886 - 1.0062	0.560
CO	1.0608	0.8948 - 1.2575	0.496
NO₂	1.0143	1.0011 - 1.0277	0.033
SO₂	0.9955	0.9909 - 1.0001	0.053
PM₁₀	1.0021	1.0003 - 1.0039	0.019
PM_2.5	0.9995	0.9937 - 1.0053	0.807
Female
O₃	0.9879	0.9787 - 0.9972	0.011
CO	1.0555	0.9220 - 1.2083	0.433
NO₂	1.0099	0.9956 - 1.0244	0.211
SO₂	0.9966	0.9915 - 1.0017	0.186
PM₁₀	1.0031	1.0011 - 1.0050	0.001
PM_2.5	1.0055	0.9994 - 1.0116	0.072
< 18 years
O₃	1.0100	0.9952 - 1.0250	0.201
CO	0.9225	0.9090 - 0.9361	0.586
NO₂	1.0005	0.9803 - 1.0211	0.958
SO₂	0.9923	0.9842 - 1.0005	0.068
PM₁₀	1.0053	1.0023 - 1.0082	0.0001
PM_2.5	1.0036	0.9940 - 1.0132	0.461
18 - 59 years
O₃	0.9939	0.9860 - 1.0019	0.137
CO	1.0478	0.9168 - 1.1976	0.492
NO₂	1.0123	1.0005 - 1.0242	0.039
SO₂	0.9961	0.9920 - 1.0003	0.071
PM₁₀	1.0018	1.0001 - 1.0036	0.037
PM_2.5	0.9985	0.9932 - 1.0037	0.577
> 59 years
O₃	0.9728	0.9560 - 0.9898	0.001
CO	1.1216	0.9217 - 1.3648	0.251
NO₂	1.0021	0.9770 - 1.0278	0.866
SO₂	1.0011	0.9926 - 1.0098	0.784
PM₁₀	1.0020	0.9988 - 1.0052	0.182
PM_2.5	1.0148	1.0051 - 1.0245	0.003
Lag 7
Total
O₃	0.9911	0.9848 - 0.9975	0.006
CO	1.0272	0.9233 - 1.1427	0.621
NO₂	1.0102	1.0002 - 1.0203	0.043
SO₂	0.9947	0.9913 - 0.9982	0.001
PM₁₀	1.0031	1.0017 - 1.0045	0.0001
PM_2.5	1.0026	0.9638 - 1.0429	0.190
Male
O₃	0.9967	0.9879 - 1.0056	0.494
CO	1.1097	0.9176 - 1.3420	0.272
NO₂	1.0148	1.0015 - 1.0283	0.016
SO₂	0.9950	0.9905 - 0.9996	0.045
PM₁₀	1.0031	1.0012 - 1.0050	0.001
PM_2.5	0.9998	0.9943 - 1.0054	0.951
Female
O₃	0.9874	0.9779 - 0.9971	0.011
CO	1.0615	0.9289 - 1.2129	0.377
NO₂	1.0041	0.9890 - 1.0193	0.838
SO₂	0.9952	0.9901 - 1.0003	0.062
PM₁₀	1.0030	1.0010 - 1.0049	0.002
PM_2.5	1.0049	0.9990 - 1.0109	0.096
< 18 years
O₃	1.0074	0.9925 - 1.0225	0.112
CO	0.8833	0.6405 - 1.2179	0.449
NO₂	0.9990	0.9786 - 1.0197	0.925
SO₂	0.9925	0.9843 - 1.0007	0.075
PM₁₀	1.0055	1.0023 - 1.0087	0.0001
PM_2.5	1.0102	1.0017 - 1.0187	0.018
18 - 59 years
O₃	0.9914	0.9836 - 0.9993	0.035
CO	1.0565	0.9242 - 1.2079	0.420
NO₂	1.0109	0.9998 - 1.0229	0.067
SO₂	0.9942	0.9900 - 0.9984	0.007
PM₁₀	1.0025	1.0009 - 1.0041	0.001
PM_2.5	0.9980	0.9928 - 1.0032	0.453
> 59 years
O₃	0.9707	0.9540 - 0.9877	0.0001
CO	1.0964	0.8781 - 1.3690	0.416
NO₂	1.0074	0.9792 - 1.0364	0.790
SO₂	1.0025	0.9938 - 1.0114	0.560
PM₁₀	1.0014	0.9981 - 1.0046	0.396
PM_2.5	1.0110	1.0009 - 1.0212	0.030

Abbreviations: RR, rate ratio; CI, confidence interval.

5. Discussion

This study showed significant relations between short-term exposure to air pollutants CO, NO₂, PM₁₀, and PM_2.5 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, PM_2.5 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 PM_2.5 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 PM_2.5 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.

Acknowledgments

Footnotes

Authors' Contribution: NK conceptualized the idea and helped in writing and editing the manuscript. MAF acquired, cleaned, and analyzed the data and wrote the initial manuscript. HAE reviewed the manuscript and provided scientific advice for neurological diseases. MM supervised data analysis and provided statistical consultation. All the authors read and approved the final manuscript.
Conflict of Interests: The authors declare that there is no competing interest.
Data Reproducibility: The data are available from the first author upon reasonable request.
Ethical Approval: The Ethics Research Committee of Kerman University of Medical Sciences approved the study protocol (IR.KMU.REC.1398.582) (ethics.research.ac.ir/EthicsProposalView.php?id=110570).
Funding/Support: This study was financially supported by Kerman University of Medical Sciences through grant number 98000854.
Informed Consent: Individual consent was not needed and was waived by the Ethics Committee because the information inquired was only in counts and not identifiable.

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Air Pollution and Hospital Admission for Epilepsy in Kerman, Iran

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1. Background

2. Objectives

3. Methods

3.1. Statistical Analysis

4. Results

5. Discussion

5.1. Conclusions

Acknowledgments

Footnotes

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