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. 2024 Mar 11;65(5):302–313. doi: 10.3349/ymj.2023.0135

Interrupting Effect of Social Distancing on Ischemic Heart Disease, Asthma, Stroke, and Suicide Attempt Patients by PM2.5 Exposure

Minseo Choi 1, Mia Son 1,✉, Sanghyuk Bae 2,✉, Whanhee Lee 3, Kyung-Nam Kim 4, Jung K Hyun 5
PMCID: PMC11045348  PMID: 38653569

Abstract

Purpose

This study aimed to examine the interrupting effect of social distancing (SD) on emergency department (ED) patients with ischemic heart disease (IHD), stroke, asthma, and suicide attempts by PM2.5 exposure in eight Korean megacities from 2017 to 2020.

Materials and Methods

The study used National Emergency Department Information System and AirKorea data. A total of 469014 patients visited EDs from 2017 to 2020. Interrupted time series analysis was employed to examine changes in the level and slope of the time series, relative risk, and confidence intervals (CIs) by PM2.5 exposure. The SD level was added to the sensitivity analysis.

Results

The interrupted time series analysis demonstrated a significant increase in the ratio of relative risk (RRR) of IHD patients in Seoul (RRR=1.004, 95% CI: 1.001, 1.006) and Busan (RRR=1.007, 95% CI: 1.002, 1.012) post-SD. Regarding stroke, only patients in Seoul exhibited a significant decrease post-SD (RRR=0.995, 95% CI: 0.991, 0.999). No significant changes were observed for asthma in any of the cities. In the case of suicide attempts, Ulsan demonstrated substantial pre-SD (RR=0.827, 95% CI: 0.732, 0.935) and post-SD (RRR=1.200, 95% CI: 1.057, 1.362) differences.

Conclusion

While the interrupting effect of SD was not as pronounced as anticipated, this study did validate the effectiveness of SD in modifying health behaviors and minimizing avoidable visits to EDs in addition to curtailing the occurrence of infectious diseases.

Keywords: Interrupted time series analysis, social distancing, particulate matter 2.5, emergency medicine

Graphical Abstract

graphic file with name ymj-65-302-abf001.jpg

INTRODUCTION

Fine particulate matter (PM2.5) is particulate matter with an aerodynamic diameter of <2.5 µm that can penetrate deep into the lungs.1 According to AirKorea, the national ambient air quality standard for PM2.5 in Korea is 35 µg/m3 or less in 24 hours and 15 µg/m3 or less in a year.2 Despite these standards, many cities in Korea continue to exceed safe PM2.5 exposure limits. Exposure to PM2.5 for a short period can cause respiratory symptoms and decreased lung function.3 Long-term exposure to PM2.5 has been associated with numerous diseases, including asthma, lung disease, and cardiovascular disease,4,5,6,7 as well as an increased risk of mortality,8,9,10 respiratory infection,11,12,13,14 stroke,7,15,16 and suicide attempts.12,15,16,17

Higher PM2.5 concentrations have led to increases in the number of emergency department (ED) visits, hospitalizations for severe disease, and deaths.13 According to the 2013 Korea Environmental Policy Evaluation, an increase in PM2.5 concentrations of 10 µg/m3 resulted in more hospitalizations due to respiratory system-related diseases among individuals aged 65 years or older than among those aged below 65 years.8,14 A previous study11 found that air pollution was associated with respiratory diseases, such as asthma, which affected hospital admissions.

The harmful effects of PM2.5 exposure are not limited to the respiratory and cardiovascular systems but extend to other systems, including the nervous system. Exposure to PM2.5 has been linked to an increased risk of suicide, as it may affect mental health and neurological function.12 Previous studies identified various personal factors, such as physical illness, mental disorders, economic difficulties, social isolation, disasters, and physical abuse, as causes of suicide.15,16 Recent research indicated that ambient air pollution, including PM2.5, was also associated with mental and neurological disorders, such as depression and headaches,17,18 and a study by the World Health Organization (WHO, 2021) confirmed a relationship between PM2.5 and suicide risk.12

Research19,20 confirmed that PM2.5 levels decreased and air quality improved after social distancing (SD) measures were implemented in Korea on March 22, 2020.21 Several studies reported a decrease in PM2.5 levels as a result of the lockdown and the implementation of SD measures in response to the COVID-19 epidemic.22,23,24 These measures represented a public health infection control strategy aimed at minimizing individual or group contact to reduce the transmission of infectious diseases. The measures are considered non-pharmaceutical interventions, meaning they do not involve the use of treatments or vaccines.25 They helped to prevent the spread of COVID-19 and other infectious diseases26 and contributed to reducing outdoor PM2.5 concentrations.19

Although previous studies examined the impact of SD on PM2.5 levels, research on the association between PM2.5 and ED visits across Korea during the COVID-19 pandemic is limited. Therefore, this study aimed to examine the interrupting effect of nationwide SD measures on ED visits by patients with ischemic heart disease (IHD), stroke, asthma, and suicide attempts. Specifically, we examined the effect of SD on the health outcomes of ED patients according to PM2.5 exposure from 2017 to 2020 in Korea.

MATERIALS AND METHODS

Data collection

The National Emergency Department Information System (NEDIS) database is a national database in Korea that collects data of patients who visit EDs across the country.27 The data include patient age, gender, diagnosis codes, which are classified according to the International Classification of Diseases and Related Health Problems 10th Revision (ICD-10), and the daily number of patients with suicide attempts visiting EDs. For this study, we obtained data on three specific diagnoses (main and sub), IHD (ICD-10 codes I20–I25), stroke (I64), and asthma (J45) from eight megacities in Korea (Seoul, Busan, Incheon, Daegu, Daejeon, Gwangju, Suwon, and Ulsan) from 2017 to 2020. We also retrieved data on the number of patients with suicide attempts under the same conditions. We used the number of daily ED patients diagnosed with IHD (I20–I25), asthma (J45), stroke (I64), and suicide attempts before the implementation of SD (pre-SD) and after (post-SD) from January 1, 2017, to December 31, 2020. Daily PM2.5 data from 2017 to 2020, measured at air quality monitoring stations located in each city, were obtained from the Korea Environment Corporation (AirKorea).

Statistical analysis

Interrupted time series

Interrupted time series analysis was used to analyze the effect of SD (starting March 22, 202021) on health outcomes. Segmented regression analysis was used to model changes in the levels and trends in disease outcomes associated with daily PM2.5 levels (exposure) pre-SD and post-SD (intervention). The formula for the interrupted time series model with a quasi-Poisson distribution was as follows28:

Log (E[Yt])=β0+β1* XPM2.5+β2* XSD+β3*XPM2.5*XSD+β4*Xmt+β5*Xmh+seasonality by year,

where Yt is a daily count of events (IHD, stroke, asthma, and suicide attempt) at time t and E[Yt] is the expected event count at time t. XPM2.5 represents the lag PM2.5 concentration. XSD is a dummy variable indicating the intervention [pre-SD (coded 0), post-SD (coded 1)], with an indication date of March 22, 2020,21 which marked the implementation of a strong policy aimed at preventing COVID-19 on a national level in Korea. XPM2.5*XSD represents the interaction between the lag PM2.5 concentration and SD. Xmt represents the mean temperature, and Xmh represents the mean humidity. We used a natural cubic spline with 7 degrees of freedom (df) for the day of the year to estimate seasonality. β0 represents the baseline level at T=0. β1 represents effects on events following exposure (per 10 µg/m3 of PM2.5 concentration), as well as any changes in event count trends over time (slope change). β2 represents the effects of the intervention (pure SD) on events. β3 indicates the slope change for the interaction between exposure (per 10 µg/m3 of PM2.5 concentration) and intervention (pure SD). β4 and β5 estimate the effects of confounders (mean temperature and mean humidity) on event counts. Time-varying confounders that affected exposure (lag PM2.5 concentration), such as mean temperature and mean humidity, were considered in the analysis.

Descriptive analysis

Descriptive analysis, including number, percentage, mean, and standard deviation, were calculated. T-tests were used to compare the mean differences in IHD, stroke, asthma, and suicide attempts between pre-SD and post-SD periods in EDs by region. In this study, missing days with no ED patients with the study conditions were assigned a value of 0 (Supplementary Table 1, only online), and environmental variables, such as PM2.5, daily mean temperature, and daily mean humidity, were assigned the mean of the respective pre-SD and post-SD variables (Table 1, Supplementary Table 2, only online).

Table 1. General Characteristics of IHD, Stroke, Asthma, and Suicide Attempt Visits to EDs and Environmental Variables in Eight Megacities in Korea Pre-SD and Post-SD.
Cities IHD (ICD-10, I20-I25) Stroke (ICD-10, I64) Asthma (ICD-10, J45)
Pre-SD Post-SD Total Mean daily patients Pre-SD Post-SD Total Mean daily patients Pre-SD Post-SD Total Mean daily patients
Pre-SD Post-SD Pre-SD Post-SD Pre-SD Post-SD
Seoul 54536(84.09) 10316(15.91) 64852 46.37 36.20 15773(80.73) 3765(19.27) 19538 13.41 13.21 17361(91.74) 1564(8.26) 18925 14.76 5.49
Busan 19489(83.27) 3916(16.73) 23405 16.57 13.74 1650(82.42) 352(17.58) 2002 1.40 1.24 5593(88.45) 730(11.55) 6323 4.76 2.56
Incheon 17405(82.19) 3771(17.81) 21176 14.80 13.23 2767(85.43) 472(14.57) 3239 2.35 1.66 7805(91.21) 752(8.79) 8557 6.64 2.64
Daegu 12602(82.73) 2630(17.27) 15232 10.72 9.23 2867(79.79) 726(20.21) 3593 2.44 2.55 3335(89.55) 389(10.45) 3724 2.84 1.36
Daejeon 12765(83.43) 2536(16.57) 15301 10.85 8.90 2068(82.36) 443(17.64) 2511 1.76 1.55 3172(92.08) 273(7.92) 3445 2.70 0.96
Gwangju 18059(82.02) 3958(17.98) 22017 15.36 13.89 4688(80.61) 1128(19.39) 5816 3.99 3.96 4352(90.61) 451(9.39) 4803 3.70 1.58
Suwon 17709(85.50) 3003(14.50) 20712 15.06 10.54 5077(81.73) 1135(18.27) 6212 4.32 3.98 3186(92.40) 262(7.60) 3448 2.71 0.92
Ulsan 3169(83.20) 640(16.80) 3809 2.69 2.25 589(68.57) 270(31.43) 859 0.50 0.95 2358(93.39) 167(6.61) 2525 2.01 0.59
Total 155748(83.41) 30988(16.59) 186736 132.44 108.73 35481(81.05) 8293(18.95) 43774 30.17 29.10 47251(91.09) 4621(8.91) 51872 40.00 16.21
Cities Suicide attempts PM2.5 (µg/m3) Mean temperature (℃) Mean humidity (%)
Pre-SD Post-SD Total Mean daily patients
Pre-SD Post-SD Pre-SD Post-SD Pre-SD Post-SD Pre-SD Post-SD
Seoul 21626(79.24) 5665(20.76) 27291 18.39 19.88 24.30 18.67 12.53 16.16 57.18 65.56
Busan 4788(77.94) 1355(22.06) 6143 4.07 4.75 23.13 16.11 14.83 17.40 61.57 66.12
Incheon 7755(77.94) 2267(22.62) 10022 6.59 7.95 23.46 16.15 12.14 15.64 65.24 67.53
Daegu 4890(78.79) 1316(21.21) 6206 4.16 4.62 22.61 18.61 13.87 17.16 59.25 64.50
Daejeon 4819(79.38) 1252(20.62) 6071 4.1 4.39 21.70 15.86 13.05 16.51 68.31 72.24
Gwangju 2524(79.75) 641(20.25) 3165 2.15 2.25 23.64 16.61 14.07 17.03 69.14 72.64
Suwon 4764(78.46) 1308(21.54) 6072 4.05 4.59 26.16 19.06 11.33 15.03 66.74 74.13
Ulsan 1924(80.60) 463(19.40) 2387 1.64 1.62 22.52 16.05 14.09 16.94 64.92 69.79
Total 53090(78.81) 14273(21.19) 67363 45.14 50.08 23.44 17.14 13.24 16.48 64.04 69.06
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IHD, ischemic heart disease; ED, emergency department; SD, social distancing; ICD-10, International Classification of Diseases and Related Health Problems 10th Revision.

Data are presented as n (%). Pre-SD refers to the period before the implementation of SD from January 1, 2017 to March 21, 2021 (1176 days). Post-SD refers to the period after the commencement of SD measures from March 22, 2021 to December 31, 2022 (285 days).

Distributed lag model

During the periods before and after SD implementation, unconstrained distributed lag modeling and constrained distributed lag modeling were conducted to assess the delayed effects of PM2.5 on patients visiting EDs. The constrained distributed lag model was attributed to a natural cubic spline with 2 df for each lag day. The correlation between delayed PM2.5 and SD for each disease in all eight megacities was also analyzed before and after SD implementation to determine the relationship between the variables.

Sensitivity analysis

A sensitivity analysis was conducted to compare the impact of varying SD levels in different cities. The primary goal was to assess how SD levels, categorized by city characteristics (metropolitan vs. rural), affected the study results. The analysis considered the potential correlation between SD, which was categorized by pre-SD and post-SD and SD levels, and address multicollinearity concerns through rigorous assessments, including variance inflation factor calculations, to ensure the reliability of the results. Second, we examined the residuals, autocorrelation function (ACF), and partial autocorrelation function (PACF) at different lag times (7, 8, 9, and 10 days) and df to assess model fitting.

Data management and statistical analyses were performed using SQL (IBM Corp., Armonk, NY, USA), SAS version 9.4 (SAS Institute Inc., Cary, NC, USA), and R version 4.4.2. Statistical significance was set at p<0.05.

This study was approved by the Kangwon National University Institutional Review Board (IRB No. KWNUIRB-2022-07-008-001). The requirement for informed consent was waived due to the retrospective nature of the study and the use of de-identified data from the NEDIS database.

RESULTS

General characteristics of patients with IHD, stroke, asthma, and suicide attempts in EDs and environmental variables in eight megacities pre-SD and post-SD

The general characteristics of the study patients and environmental variables are presented in Table 1. A total of 469014 patients visited EDs for IHD, stroke, asthma, and suicide attempts from 2017 to 2020 in eight megacities (Seoul, Busan, Incheon, Daegu, Daejeon, Gwangju, Suwon, and Ulsan) in Korea. Of these patients, 186736 were diagnosed with IHD, which was higher than the number of patients with suicide attempts (67363), asthma (51872), or stroke (43774).

By region, the average daily ED visits for IHD and asthma were higher before the implementation of SD measures than those after. The average daily ED visits for asthma patients in all regions decreased more than two-fold after the implementation of SD measures. For stroke patients, the average daily ED visits were higher before the implementation of SD measures in all regions except for Daegu (2.44, 2.55) and Ulsan (0.50, 0.95). As for suicide attempt patients, the average daily ED visits increased after the implementation of SD measures in seven megacities except for Ulsan (1.64, 1.62).

The mean PM2.5 concentration in the eight megacities was higher before the implementation of SD measures than that after, whereas an opposite trend was observed in the regional mean temperature and humidity, with higher values observed after the implementation of SD measures.

Mean difference in IHD, stroke, asthma, and suicide attempt patients in EDs pre-SD and post-SD

Mean differences were calculated by subtracting the post-SD mean from the pre-SD mean, where a positive mean difference indicated a decrease and a negative mean difference indicated an increase in the mean after SD (Table 2). The mean difference in asthma and suicide attempt patients was significant in EDs of all eight megacities (p<0.001). Among asthma patients, the mean decreased in all megacities. In contrast, the mean number of suicide attempts increased in all megacities. The mean difference in IHD patients was significant in four cities. Seoul (0.017, p<0.001) and Suwon (0.056, p<0.001) showed a decrease in the mean after SD, whereas Incheon (-0.045, p<0.001) and Gwangju (-0.043, p<0.001) showed a decrease in the mean after SD. For stroke patients, the mean difference was significant in five cities, and the mean of these cities increased after SD in Seoul (-0.022, p<0.001), Daegu (-0.018, p<0.001), Gwangju (-0.021, p<0.001), Suwon (-0.027, p<0.001), and Ulsan (-0.074, p<0.001), whereas the mean in Incheon (0.007, p=0.002) decreased after SD.

Table 2. Mean Difference in IHD, Stroke, Asthma, and Suicide Attempts in Each City Pre-SD and Post-SD.

Cities IHD (ICD-10, I20-I25) Stroke (ICD-10, I64)
Mean Difference (pre-SD-post-SD) 95% CI t(p) Mean Difference (pre-SD-post-SD) 95% CI t(p)
Pre-SD Post-SD Pre-SD Post-SD
Seoul 0.375 0.358 0.017 0.011, 0.023 5.61 (<0.001) 0.109 0.131 -0.022 -0.026, -0.018 -10.29 (<0.001)
Busan 0.394 0.388 0.006 -0.005, 0.016 1.05 (0.296) 0.033 0.035 -0.002 -0.005, 0.002 -0.78 (0.436)
Incheon 0.349 0.385 -0.035 -0.045, -0.025 -6.63 (<0.001) 0.056 0.048 0.007 0.003, 0.012 3.11 (0.002)
Daegu 0.363 0.363 0.000 -0.013, 0.012 -0.07 (0.963) 0.083 0.100 -0.018 -0.025, -0.010 -4.63 (<0.001)
Daejeon 0.428 0.423 0.005 -0.008, 0.019 0.77 (0.439) 0.069 0.074 -0.004 -0.012, 0.003 -1.22 (0.224)
Gwangju 0.496 0.526 -0.030 -0.043, -0.018 -4.75 (<0.001) 0.129 0.150 -0.021 -0.030, -0.012 -4.73 (<0.001)
Suwon 0.527 0.471 0.056 0.042, 0.069 8.16 (<0.001) 0.151 0.178 -0.027 -0.037, -0.017 -5.22 (<0.001)
Ulsan 0.280 0.299 -0.020 -0.040, 0.001 -1.84 (0.066) 0.052 0.126 -0.074 -0.089, -0.060 -9.93 (<0.001)
Cities Asthma (ICD-10, J45) Suicide attempts
Mean Difference (pre-SD-post-SD) 95% CI t(p) Mean Difference (pre-SD-post-SD) 95% CI t(p)
Pre-SD Post-SD Pre-SD Post-SD
Seoul 0.120 0.054 0.065 0.062, 0.068 41.2 (<0.001) 0.149 0.197 -0.048 -0.053, 0.043 -18.94 (<0.001)
Busan 0.113 0.072 0.041 0.035, 0.046 13.79 (<0.001) 0.097 0.134 -0.038 -0.045, -0.030 -10.31 (<0.001)
Incheon 0.157 0.077 0.080 0.074, 0.086 25.47 (<0.001) 0.156 0.231 -0.075 -0.084, -0.067 -16.56 (<0.001)
Daegu 0.096 0.054 0.042 0.036, 0.048 13.7 (<0.001) 0.141 0.182 -0.041 -0.051, -0.031 -8.36 (<0.001)
Daejeon 0.106 0.046 0.061 0.053, 0.069 18.85 (<0.001) 0.162 0.209 -0.047 -0.058, -0.036 -8.31 (<0.001)
Gwangju 0.119 0.060 0.060 0.053, 0.066 18.49 (<0.001) 0.069 0.085 -0.016 -0.023, -0.009 -4.56 (<0.001)
Suwon 0.095 0.041 0.054 0.048, 0.060 18.16 (<0.001) 0.142 0.206 -0.064 -0.075, -0.054 -11.75 (<0.001)
Ulsan 0.208 0.078 0.130 0.116, 0.144 18.72 (<0.001) 0.206 0.217 -0.047 -0.066, -0.028 -4.87 (<0.001)
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IHD, ischemic heart disease; SD, social distancing; ICD-10, International Classification of Diseases and Related Health Problems 10th Revision; CI, confidence interval.

Mean difference was calculated as Post-SD minus Pre-SD.

Interrupted time series

The relative risk (RR) of disease based on PM2.5 exposure varied across several cities pre-SD, post-SD (effect of interaction between PM2.5 and SD), and with SD (independent of SD intervention) during post-SD (Table 3, Figs. 1, 2, 3, 4). Specifically, there was a little increase in IHD visits in Daegu before the implementation of SD (pre-SD), with a RR of 1.003 [95% confidence interval (CI): 1.001, 1.005]. After SD implementation (post-SD), significant increases were observed in Seoul [ratio of relative risk (RRR)=1.004, 95% CI: 1.001, 1.006] and Busan (RRR=1.007, 95% CI: 1.002, 1.012) compared to the pre-SD period. The RR of IHD due to SD during post-SD was significant only in Suwon (RR=1.805, 95% CI: 1.246, 2.616), but no significant changes were observed in this city, either before or after SD.

Table 3. Relative Risk of SD for IHD, Stroke, Asthma, and Suicide Attempts Based on PM2.5 Exposure in Each City Pre-SD and Post-SD.

Pre-SD (PM2.5) Post-SD (SD) Post-SD (SD* PM2.5)
RR 95% CI p value RR 95% CI p value RRR 95% CI p value
IHD (ICD-10, I20-I25)
Seoul 1.000 0.999, 1.001 0.851 1.124 0.887, 1.424 0.334 1.004 1.001, 1.006 0.008
Busan 1.000 0.998, 1.001 0.846 1.186 1.030, 1.365 0.800 1.007 1.002, 1.012 0.010
Incheon 1.001 0.999, 1.002 0.296 1.025 0.741, 1.418 0.880 1.000 0.996, 1.005 0.870
Daegu 1.003 1.001, 1.005 0.001 1.198 0.777, 1.848 0.414 0.998 0.994, 1.003 0.416
Daejeon 1.001 1.000, 1.003 0.136 1.117 0.727, 1.716 0.614 1.000 0.994, 1.006 0.975
Gwangju 1.001 1.000, 1.002 0.126 0.986 0.715, 1.359 0.932 0.999 0.995, 1.004 0.789
Suwon 1.000 0.999, 1.001 0.878 1.805 1.246, 2.616 0.002 1.003 0.999, 1.007 0.191
Ulsan 1.000 0.996, 1.004 0.932 1.026 0.467, 2.254 0.950 1.002 0.986, 1.015 0.808
Stroke (ICD-10, I64)
Seoul 1.001 1.000, 1.002 0.196 1.173 0.820, 1.679 0.384 0.995 0.991, 0.999 0.013
Busan 1.001 0.997, 1.005 0.776 0.897 0.308, 2.613 0.842 1.001 0.989, 1.016 0.728
Incheon 1.002 0.999, 1.005 0.139 0.543 0.223, 1.321 0.178 1.001 0.992, 1.010 0.786
Daegu 1.000 0.996, 1.003 0.911 1.837 0.838, 4.028 0.129 0.998 0.989, 1.008 0.746
Daejeon 0.996 0.993, 1.000 0.041 1.239 0.471, 3.259 0.664 1.001 0.989, 1.014 0.875
Gwangju 1.003 1.001, 1.006 0.012 1.223 0.655, 2.284 0.528 0.999 0.991, 1.008 0.878
Suwon 1.001 0.999, 1.003 0.208 1.165 0.624, 2.175 0.633 1.002 0.995, 1.008 0.645
Ulsan 0.997 0.980, 1.005 0.448 0.172 0.048, 0.609 0.006 0.999 0.981, 1.017 0.909
Asthma (ICD-10, I64)
Seoul 1.002 1.000, 1.003 0.015 1.610 0.956, 2.713 0.074 0.998 0.992, 1.005 0.608
Busan 0.999 0.996, 1.002 0.459 2.424 1.110, 5.290 0.026 0.995 0.984, 1.007 0.446
Incheon 1.000 0.998, 1.002 0.778 1.187 0.555, 2.537 0.659 1.002 0.992, 1.012 0.751
Daegu 1.000 0.996, 1.003 0.876 1.218 0.397, 3.739 0.730 1.003 0.990, 1.017 0.609
Daejeon 0.999 0.996, 1.003 0.725 1.277 0.773, 2.572 0.340 1.014 0.998, 1.030 0.097
Gwangju 1.000 0.999 1.005 0.121 0.998 0.391, 2.551 0.997 1.000 0.986 1.014 0.991
Suwon 1.000 0.988, 1.013 0.767 0.829 0.267, 2.569 0.745 1.000 0.988, 1.013 0.940
Ulsan 1.001 0.998, 1.005 0.450 0.148 0.035, 0.632 0.010 0.995 0.972, 1.018 0.652
Suicide attempts
Seoul 0.999 0.998, 1.000 0.245 0.918 0.702, 1.199 0.529 0.999 0.996, 1.002 0.592
Busan 1.002 0.998, 1.016 0.767 1.109 0.657, 1.871 0.699 1.012 0.963, 1.063 0.644
Incheon 1.006 0.996, 1.016 0.263 1.178 0.480, 0.748 1.856 0.935 0.891, 0.982 0.405
Daegu 1.002 0.980, 1.025 0.864 1.983 1.079, 3.645 0.027 1.004 0.948, 1.063 0.886
Daejeon 1.014 0.999, 1.029 0.067 1.324 0.735, 2.386 0.350 0.977 0.917, 1.042 0.482
Gwangju 0.998 0.996, 1.000 0.023 0.908 0.411, 2.005 0.811 0.997 0.891, 0.976 0.278
Suwon 1.000 0.998, 1.002 0.723 1.208 0.681, 2.143 0.518 0.999 0.990, 1.007 0.683
Ulsan 0.827 0.732, 0.935 0.002 1.563 0.616 ,3.966 0.347 1.200 1.057, 1.362 0.005
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SD, social distancing; IHD, ischemic heart disease; RR, relative risk; RRR, ratio of relative risk; CI, confidence interval; ICD-10, International Classification of Diseases 10th Revision.

Fig. 1. Interrupted time series of IHD in eight megacities in Korea. IHD, ischemic heart disease.

Fig. 1

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Fig. 2. Interrupted time series of stroke in eight megacities in Korea.

Fig. 2

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Fig. 3. Interrupted time series of asthma in eight megacities in Korea.

Fig. 3

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Fig. 4. Interrupted time series of suicide attempt in eight megacities in Korea.

Fig. 4

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Regarding stroke, significant decreases were observed in Daejeon before SD (RR=0.996, 95% CI: 0.993, 1.000), whereas increases were noted in Gwangju (RR=1.003, 95% CI: 1.001, 1.006). However, the results in the two cities were not significant after the implementation of SD. In contrast, a significant decrease was observed in Seoul post-SD (RRR=0.995, 95% CI: 0.991, 0.999) compared to pre-SD (RR=1.001, 95% CI: 1.000, 1.002). The RR of stroke due to SD during post-SD was significant only in Ulsan (RR=0.172, 95% CI: 0.048, 0.609), with no significant differences before or after SD in the city.

Asthma demonstrated a significant increase only in Seoul before SD, with an RR of 1.002 (95% CI: 1.000, 1.003), but no significant results were seen in any city after SD. However, the RR of asthma due to SD during post-SD was significant in Busan (RR=2.424, 95% CI: 1.110, 5.290) and Ulsan (RR=0.148, 95% CI=0.035, 0.632).

Suicide attempts significantly decreased in Gwangju (RR=0.998, 95% CI: 0.996, 1.000) pre-SD compared to post-SD. Especially, Ulsan showed significant increases (0.373) in suicide attempts, with the RR changing from 0.827 (95% CI: 0.732, 0.935) before SD to an RRR of 1.200 (95% CI: 1.057, 1.362) after SD. In contrast, the RR of suicide attempts during SD was only significant in Daegu (RR=1.983, 95% CI: 1.079, 3.645).

Distributed lag model

The results of both the unconstrained and constrained distributed lag models are shown in Supplementary Figs. 1, 2, 3, 4 (only online). The delayed effects of PM2.5 were observed in the constrained distributed lag models. For all cities, the single lag model showed the same RR for 0 to 4 lag days. In contrast, the RR of the distributed lag model exhibited different patterns in the eight megacities. The RR of IHD in Busan, Daegu, and Gwangju decreased according to PM2.5 lag days, whereas RR increased in Seoul, Incheon, Daejeon, Suwon, and Ulsan with lag days. For stroke, the RR in Seoul, Incheon, Daegu, Gwangju, Suwon, and Ulsan decreased with PM2.5 lag days, and the RR of Busan and Daejeon increased. The RR for asthma increased in Seoul and Suwon, whereas it decreased in Busan, Daegu, Gwangju, and Ulsan with increases in PM2.5 lag days. However, RR in Incheon and Daejeon did not differ with lag days. For suicide attempts, the RR increased in Seoul, Incheon, Daejeon, Gwangju, Suwon, and Ulsan, whereas it decreased in Busan and Daegu with longer PM2.5 lag days.

Sensitivity analysis

The results of the sensitivity analysis, which was conducted to compare the means of two models [the original model and a modified model that included SD levels (Supplementary Table 3, only online), are shown in Supplementary Table 4 (only online)]. Significant differences in IHD based on SD level were observed in Busan (F=2.931, p=0.08) and Gwangju (F=7.756, p=0.01). Differences in stroke were identified based on the SD level in Daejeon (F=3.932, p=0.05), Gwangju (F=7.616, p=0.01), and Suwon (F=3.825, p=0.05). However, significant differences in asthma and suicide attempts were found solely in Ulsan (asthma: F=3.762, p=0.05; suicide attempts: F=6.185, p=0.01), indicating that these variables were impacted by the stage of SD.

Various diagnostic measures were employed to assess the accuracy of the models used in this study. These measures encompassed residual analysis and ACF and PACF assessments at different lag times (7, 8, 9, and 10 days). The results of these diagnostic measures showed stable ACF and PACF patterns across different lag times (Supplementary Fig. 5, only online).

DISCUSSION

This study assessed the impact of SD measures on ED visits related to IHD, stroke, asthma, and suicide attempts, compared to before their implementation in eight major cities, based on PM2.5 exposure. The results revealed marginal fluctuations in ED visits across various regions and diseases.

The mean percentage of patients with different diseases visiting EDs varied across cities in this study. Specifically, the mean percentage of patients with suicide attempts significantly increased in all cities after the implementation of SD measures. The mean percentage of asthma patients showed the opposite trend in all the megacities studied. For stroke, most cities (Seoul, Daegu, Gwangju, Suwon, and Ulsan) except for Incheon showed an increase. The mean percentage of IHD patients increased significantly in Incheon and Gwangju, whereas it decreased in Seoul and Suwon after SD. This decline in patient visits after SD can be attributed to the government’s implementation of quarantine measures to prevent COVID-19 infections, which was more strictly enforced during this period than before SD to further reduce air pollution19,20,21,22,23 and curb the spread of infectious diseases.26 The WHO reported that “each suicide in a population is accompanied by more than 20 suicide attempts.”29 Therefore, we can estimate that the same patients visited EDs several times for treatment after suicide attempts. In Korea, the COVID-19 National Mental Health Survey30 showed that the number of suicide attempt patients increased post-SD, consistent with the 2021 quarterly results announced by the Ministry of Health and Welfare in Korea. Another study31 also demonstrated a link between mental health and SD, consistent with the results of this study. Therefore, it is reasonable to conclude that the change in ED patient visits was primarily due to the implementation of these policies during the period.

In this study, the gender distribution among IHD and stroke patients was consistent, with more males with IHD and stroke than females before and after SD implementation (Supplementary Table 5, only online). Even though asthma patients showed the same trend as IHD and stroke patients before SD implementation, a contrasting trend was observed in the case of asthma and suicide attempts after SD measures were enacted. Patient classification by age showed that among IHD and stroke cases, 50.4% and 62% of the patients, respectively, in the 65 years or older age group visited EDs during the study period. Conversely, for asthma and suicide attempts, most patients (66% and 89%, respectively) were aged under 65 years. Notably, the 20 to 29-year-old age group constituted the largest portion of suicide attempt cases, with 18209 instances (27%).

It is important to acknowledge the possibility of multiple visits to ED by the same patient, making it uncertain how many unique individuals were involved in these IHD, stroke, asthma, and suicide attempt events. Thus, a limitation of the NEDIS data lies in that it only provides results based on total visit numbers, without the ability to determine the exact visit count per individual due to the absence of patient-specific identifiers.27

While the influence of regional variations in SD measures was not pronounced enough to fully elucidate the observed differences, certain regions did exhibit variations in relation to disease-specific SD levels. These discrepancies were most prominent in their alignment with the extent of SD adopted within specific provincial areas. Interestingly, notable distinctions in SD levels emerged across various provinces. For instance, the incidence of IHD was significantly different between Busan and Gwangju, whereas the instances of stroke displayed marked variations in Daejeon, Gwangju, and Suwon. Additionally, conditions like asthma and suicide attempts demonstrated differences in prevalence within Ulsan. These disparate outcomes could be interpreted as the consequences of the varying impacts of distinct SD levels. Importantly, these variations might be attributed to different factors, including variations in population density, healthcare infrastructure, and administrative frameworks between metropolitan and provincial areas.32

Kang33 reported a decrease in ED patients during the implementation of SD measures during the global COVID-19 pandemic. Due to the lack of specialists and an increased risk of infection in EDs during the pandemic, specialists met with patients during specific times, and many patients avoided ED visits due to concerns about infection.33,34,35,36 The decrease in ED visits could also be attributed to the government’s policy of quarantining people at home to reduce the risk of COVID-19 transmission. In contrast, the number of suicide attempt patients increased post-SD, consistent with previous studies30,31 that found an association between SD measures and mental health issues.

Interestingly, in this study, there was a noticeable decrease in the number of ED visits in some cities before the implementation of SD measures (March 22, 202221). The period is similar to that officially announced by the Korean Center for Disease Control and Prevention on January 3, 2020.26 Media coverage and the public perception of COVID-19 during this time may have influenced people’s health-related behaviors. Other non-therapeutic effects of SD and wearing a mask29 could also be reasons for the reductions in infectious and other diseases.

The present study had certain limitations that need to be considered. First, it was difficult to separate the main effect of SD from other conditions, such as wearing a mask, hand washing, and less traffic, compared to before the COVID-19 pandemic. Second, we only observed four diseases that were triggered by PM2.5 exposure. Third, the study relied on NEDIS data, which lacked accurate patient information. Fourth, the post-SD period of 285 days (19.5%) from March 22, 2020 to December 31, 2020 was relatively short to fully evaluate the long-term effects of PM2.5 exposure on disease, particularly during the SD period. Fifth, stratified interrupted time series sensitivity analysis by sex and age could not be conducted as the number of patients of each sex and gender in each city with each condition was insufficient to analyze differences between the two periods.

Therefore, future studies should be conducted to address these limitations with more comprehensive data to evaluate the impact of various health-related factors, including COVID-19, on a wider range of diseases. This will provide a more accurate understanding of the effects of PM2.5 exposure on health and help with making policy decisions aimed at reducing exposure to harmful air pollutants.

The findings of this study suggest that the implementation of government-mandated SD measures during the COVID-19 pandemic may have influenced other health conditions and the overall environment. Recognizing the pivotal role of media in shaping COVID-19 perceptions and driving health-related behaviors is important. In this study, the conspicuous decrease (Fig. 1) in ED visits before the enforcement of SD measures can be attributed to the media’s impact on the citizens’ actions, such as adhering to practices like mask-wearing, handwashing, and refraining from unnecessary ED visits.37

This phenomenon could also represent an additional reason for the reduction in patient visits, as citizens complying with government policies altered the traditional role of EDs to mitigate the risk of novel infectious diseases. Throughout the pandemic, EDs played a pivotal role in delivering essential medical care to patients. Given these factors, it is imperative to investigate the impact of SD on patients seeking care at EDs during the COVID-19 pandemic. The insights gleaned from such investigations can inform the development of novel policies for effectively addressing similar challenges in future pandemics.

These findings serve as valuable reference points when formulating policies to address potential future crises, including pandemics. Concurrently, these findings underscore the critical importance of addressing the issue of air pollution and its associated health risks, particularly during these unprecedented times.

ACKNOWLEDGEMENTS

This research was supported by the Government-wide R&D Fund Project for Infectious Disease Research, Republic of Korea (grant number: HG18C0025) and the Catholic University of Korea Environmental Health Center, Ministry of Environment, Republic of Korea. Minseo Choi was supported as a trainee in the environmental health training program, which was provided by the Environmental Health Centre of the Catholic University of Korea and funded by the Ministry of Environment, Republic of Korea (2022). The authors would like to thank ChatGPT for providing grammatical corrections, which allowed them to improve the quality of their writing before seeking professional English proofreading.

Footnotes

Co-coresponder Sanghyuk Bae serves on Catholic University of Korea Environmental Health Center of a Director of Environmental Health Center. Co-author Kyung-Nam Kim and Whanhee Lee was lectures for environmental health training program. The other authors have no potential conflicts of interest to disclose.

AUTHOR CONTRIBUTIONS:
  • Conceptualization: Minseo Choi.
  • Data curation: Minseo Choi and Jung K Hyun.
  • Formal analysis: Minseo Choi, Kyung-Nam Kim, and Whanhee Lee.
  • Funding acquisition: Minseo Choi.
  • Investigation: Minseo Choi.
  • Methodology: Minseo Choi and Sanghyuk Bae.
  • Project administration: Minseo Choi and Sanghyuk Bae.
  • Resources: Minseo Choi and Sanghyuk Bae.
  • Software: Minseo Choi.
  • Supervision: Mia Son and Sanghyuk Bae.
  • Validation: Minseo Choi, Sanghyuk Bae, and Whanhee Lee.
  • Visualization: Minseo Choi.
  • Writing—original draft: Minseo Choi.
  • Writing—review & editing: Minseo Choi, Sanghyuk Bae, and Mia Son.
  • Approval of final manuscript: all authors.

SUPPLEMENTARY MATERIALS

Supplementary Table 1

Days with no Study Patients on Emergency Department

ymj-65-302-s001.pdf (28.3KB, pdf)
Supplementary Table 2

Correlation between LagPM2.5 in the Eight Megacities

ymj-65-302-s002.pdf (30.4KB, pdf)
Supplementary Table 3

Level of SD by Eight Megacities

ymj-65-302-s003.pdf (31.6KB, pdf)
Supplementary Table 4

Sensitivity Analysis of IHD, Stroke, Asthma, Suicide Attempt on Each City by Level of SD

ymj-65-302-s004.pdf (30.2KB, pdf)
Supplementary Table 5

General Characteristics of ED Visit Patients by IHD, Stroke, Suicide Attempt by Pre-SD and Post-SD

ymj-65-302-s005.pdf (32.5KB, pdf)
Supplementary Fig. 1

Distributed model of IHD and PM2.5 by regions

ymj-65-302-s006.pdf (149.7KB, pdf)
Supplementary Fig. 2

Distributed model of stroke and PM2.5 by regions

ymj-65-302-s007.pdf (161.1KB, pdf)
Supplementary Fig. 3

Distributed model of asthma and PM2.5 by regions

ymj-65-302-s008.pdf (154KB, pdf)
Supplementary Fig. 4

Distributed model of suicide attempt and PM2.5 by regions

ymj-65-302-s009.pdf (157.7KB, pdf)
Supplementary Fig. 5

ACF and PACF plots

ymj-65-302-s010.pdf (305.6KB, pdf)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Table 1

Days with no Study Patients on Emergency Department

ymj-65-302-s001.pdf (28.3KB, pdf)
Supplementary Table 2

Correlation between LagPM2.5 in the Eight Megacities

ymj-65-302-s002.pdf (30.4KB, pdf)
Supplementary Table 3

Level of SD by Eight Megacities

ymj-65-302-s003.pdf (31.6KB, pdf)
Supplementary Table 4

Sensitivity Analysis of IHD, Stroke, Asthma, Suicide Attempt on Each City by Level of SD

ymj-65-302-s004.pdf (30.2KB, pdf)
Supplementary Table 5

General Characteristics of ED Visit Patients by IHD, Stroke, Suicide Attempt by Pre-SD and Post-SD

ymj-65-302-s005.pdf (32.5KB, pdf)
Supplementary Fig. 1

Distributed model of IHD and PM2.5 by regions

ymj-65-302-s006.pdf (149.7KB, pdf)
Supplementary Fig. 2

Distributed model of stroke and PM2.5 by regions

ymj-65-302-s007.pdf (161.1KB, pdf)
Supplementary Fig. 3

Distributed model of asthma and PM2.5 by regions

ymj-65-302-s008.pdf (154KB, pdf)
Supplementary Fig. 4

Distributed model of suicide attempt and PM2.5 by regions

ymj-65-302-s009.pdf (157.7KB, pdf)
Supplementary Fig. 5

ACF and PACF plots

ymj-65-302-s010.pdf (305.6KB, pdf)

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