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JKM > Volume 42(2); 2021 > Article
Lee, Cha, Choi, Kim, Kim, Sung, Jeon, Kim, and Kim: Statistical Analysis of Patients Visiting Department of Acupuncture and Moxibustion in Korean Medicine Hospital Before and After COVID-19 - Focusing on a Korean Medicine Hospital in Daejeon -

Abstract

Objectives

The purpose of this study is to statistically analyze the demographic characteristics of patient who visited the department of Acupuncture & Moxibustion, korean medicine hospital(KMH) in Daejeon before and after COVID-19.

Methods

This study retrospectively analyzed the medical records of 63,185 patients who received treatment at KMH in Daejeon for 3 years from January 1, 2018 to August 31, 2020. Data were classified by year, month, new/old type, gender, age and Disease group. IBM-SPSS-Statistics ver25.0. was used for the data analysis.

Results

1. According to the analysis by gender, the rate of new patients after COVID-19 outbreak was similar to that of the previous years. The old patients showed an increase in the rate of visits by men and a decrease in the rate of visits by women. 2. According to the analysis by age group, the rate of new patients was similar to that of the previous year. the rate of patients in their 20s and 50s decreased, and the rate of patients over 60 increased. 3. According to the analysis by frequent disease, after the outbreak of COVID-19, the number of patients decreased in 14 disease groups excluding cervical sprains among the 15 frequent disease groups. 4. For inpatients, even after COVID-19 outbreak, the results of all variables were similar to the previous years.

Conclusions

After COVID-19, on outpatients, the percentage of female visits, the percentage of patients in their 20s to 50s and Number of patients with frequent disease decreased significantly. On Inpatients, the results of all variables were similar to those before COVID-19.

Fig. 1
Changes in the Total Number of Outpatients and Inpatients by Year
jkm-42-2-31f1.gif
Table 1
Correlation disease code for each Frequent disease
Disease Disease Code
Cervical sprain S134
Lumbar sprain S3350, S3351, S337
Vertebral fracture M4306, M4717B, M4849B, M4955B, S22030, S22050, S22090, S32020, S32030, S32040, S3220, S3270, S1220, S3210, S3220, T080
Other fracture S0220, S02180, S0230, S0290, S2220, S22330, S22430, S22440, S32890, S42160, S4230, S5260, S62331, S6280, S72090B, S72120, S7280, S8200, S82380, S8260B, S9230, S9290, S9250
Cervical HNP M4722, M500, M501, M503, M509, S130
Lumbar HNP M511, M511B, M512B, M519, S330
Vertebral stenosis M4800, M4802, M4806, M4807, M4809, M9951, M9952
Spondylosis M4712, M4722, M4796, M4780
Post neck pain M5421, M5422, M5423, M5429, M5483
Low back pain M5436, M5446, M5447, M5449, M5450, M5455, M5455B, M5456, M5456B, M5456C, M5457, M5457C, M5458, M5459, M5465, M5485, M5490B
Shoulder disease M2551E, M6521, M6641E, M6791, M750, M750B, M751, M751B, M753, M754, M758, M79110, M79118, S400, S434, S4600, S4608, S4678
Knee disease M170, M171, M171C, M179, M1996C, M224, M2321, M2329, M2331, M2369, M2385, M2391, M2556C, M79168C, S7618B, S8320, S8321, S8341, S8352, S836, S800, S7618B
Other musculoskeletal pain M5436, M5446, M5447, M5449, M5450, M5455, M5455B, M5456, M5456B, M5456C, M5457, M5457C, M5458, M5459, M5465, M5485, M5490B
Facial palsy G510, G510B, G518, G519
Table 2
Crossover Analysis of the Total Number of Outpatients and Inpatients by Year
Patient classification

Total number of patient Number of outpatient Number of inpatient Total X2
Year Previous years N 23167 22310 857 46334 16.415***
average % 50.0% 48.2% 1.8% 100.0%

2020 N 19435 18565 870 38870
% 50.0% 47.8% 2.2% 100.0%

Total N 42602 40875 1727 85204
% 50.0% 48.0% 2.0% 100.0%

* p<.1,

** p<.05,

*** p<.001

Table 3
Crossover Analysis by Gender and Year According to Patient Classification by year
Patient classification Gender Total X2

Male Female
Old patients Year Previous years N 5099 10922 16021 45.079***
average % 31.8% 68.2% 100.0%

2020 N 4705 8530 13235
% 35.5% 64.5% 100.0%

Total N 9804 19452 29256
% 33.5% 66.5% 100.0%

* p<.1,

** p<.05,

*** p<.001

Table 4
Crossover Analysis Monthly Comparison of Gender According to Patient Classification by Year
Month Gender Total X2

Male Female
1 New patients (Old patients) Year Previous years N 190(659) 614(1325) 804(1984)
average % 23.6%(33.2%) 76.3%(66.8%) 100%(100%)

2020 N 155(605) 615(1039) 770(1644) 2.818*
% 20.1%(36.8%) 79.9%(63.2%) 100%(100%) (5.089)**

Total N 345(1264) 1229(1039) 1574(3628)
% 21.9%(34.8%) 78.1%(65.2%) 100%(100%)

5 Old patients Year Previous years N 612 1380 1992 9.740***
average % 30.7% 69.3% 100.0%

2020 N 618 1121 1739
% 35.5% 64.5% 100.0%

Total N 1230 2501 3731
% 33.0% 67.0% 100.0%

6 New patients (Old patients) Year Previous years N 165(678) 684(1406) 849(2084)
average % 19.4%(32.5%) 80.6%(67.5%) 100%(100%)

2020 N 173(669) 486(1130) 659(1799) 9.915**
% 26.3%(37.2%) 73.7%(62.8%) 100%(100%) (9.23)**

Total N 338(1347) 1170(2536) 1508(3883)
% 22.4%(34.7%) 77.6%(65.3%) 100%(100%)

7 New patients (Old patients) Year Previous years N 194(654) 757(1633) 951(2287)
average % 20.4%(28.6%) 79.6%(71.4%) 100%(100%)

2020 N 176(688) 515(1192) 691(1880) 5.895*
% 25.5%(36.6%) 74.5%(63.4%) 100%(100%) (30.240)***

Total N 370(1342) 1272(2825) 1642(4167)
% 22.5%(32.2%) 77.5%(67.8%) 100%(100%)

8 Old patients Year Previous years N 665 1526 2191 4.314**
average % 30.4% 69.6% 100.0%

2020 N 617 1230 1847
% 33.4% 66.6% 100.0%

Total N 1282 2756 4038
% 31.7% 68.3% 100.0%

* p<.1,

** p<.05,

*** p<.001

Table 5
Multiple Linear Regression Analysis of Number of Patients by Year, Month, Gender, and Patient Classification
Independent Variable Unstandardized Standardized t p F(p-value)
B SE beta
(Constant) 234314.214 55240.716 4.242 0.000 192.767***
Year −117.031 27.354 −0.149 −4.278 0.000
Month 19.119 5.969 0.111 3.203 0.002
Patient classification 551.125 27.354 0.699 20.148 0.000
Gender 501.813 27.354 0.637 18.345 0.000

Dependent variable: Number of patients; SE, Standard Error

* p<.1,

** p<.05,

*** p<.001

Table 6
Crossover Analysis Monthly Comparison by Year and Gender
Month Gender Total X2

Male Female
2 Year Previous years average N 39 56 95 3.116*
% 41.1% 58.9% 100.0%

2020 N 29 71 100
% 29.0% 71.0% 100.0%

Total N 68 127 195
% 34.9% 65.1% 100.0%

* p<.1,

** p<.05,

*** p<.001

Table 7
Multiple Linear Regression Analysis of Number of Patients by Year, Month and Gender
Independent Variable Unstandardized Standardized t p F(p-value)
B SE beta
(Constant) −1674.223 6952.409 −0.241 0.811 16.684***
Year 0.813 3.443 0.027 0.236 0.815
Month 0.598 0.751 0.090 0.796 0.433
Gender 24.188 3.443 0.795 7.026 0.000

Dependent variable: Number of patients; SE, Standard Error

* p<.1,

** p<.05,

*** p<.001

Table 8
Crossover Analysis by Age Group and Year According to Patient Classification by Year
Patient classification Age group Total X2

Under 10s 10s 20s 30s 40s 50s 60s 70s 80s Over 90s
Old patients Year Previous years N 22 429 1457 2074 3341 5423 4151 1822 475 21 19215 70.474***
average % 0.1% 2.2% 7.6% 10.8% 17.4% 28.2% 21.6% 9.5% 2.5% 0.1% 100.0%

2020 N 10 301 1224 1888 2647 4128 3637 1517 534 7 15893
% 0.1% 1.9% 7.7% 11.9% 16.7% 26.0% 22.9% 9.5% 3.4% 0.0% 100.0%

Total N 32 730 2681 3962 5988 9551 7788 3339 1009 28 35108
% 0.1% 2.1% 7.6% 11.3% 17.1% 27.2% 22.2% 9.5% 2.9% 0.1% 100.0%

* p<.1,

** p<.05,

*** p<.001

Table 9
Crossover Analysis Monthly Comparison of Age Group According to Patient Classification by Year
Month Age group Total X2

Under 10s 10s 20s 30s 40s 50s 60s 70s 80s Over 90s
1 Old patients Year Previous years N 1 79 207 242 409 676 504 202 43 2 2365 31.502***
average % 0.0% 3.3% 8.8% 10.2% 17.3% 28.6% 21.3% 8.5% 1.8% 0.1% 100.0%

2020 N 2 24 149 235 355 568 489 179 44 1 2046
% 0.1% 1.2% 7.3% 11.5% 17.4% 27.8% 23.9% 8.7% 2.2% 0.0% 100.0%

Total N 3 103 356 477 764 1244 993 381 87 3 4411
% 0.1% 2.3% 8.1% 10.8% 17.3% 28.2% 22.5% 8.6% 2.0% 0.1% 100.0%

2 Old patients Year Previous years N 6 63 155 189 313 571 405 171 35 4 1912 30.762***
average % 0.3% 3.3% 8.1% 9.9% 16.4% 29.9% 21.2% 8.9% 1.8% 0.2% 100.0%

2020 N 0 35 141 194 345 452 407 183 36 0 1793
% 0.0% 2.0% 7.9% 10.8% 19.2% 25.2% 22.7% 10.2% 2.0% 0.0% 100.0%

Total N 6 98 296 383 658 1023 812 354 71 4 3705
% 0.2% 2.6% 8.0% 10.3% 17.8% 27.6% 21.9% 9.6% 1.9% 0.1% 100.0%

3 Old patients Year Previous years N 2 48 153 270 408 687 447 213 56 6 2290 17.468**
average % 0.1% 2.1% 6.7% 11.8% 17.8% 30.0% 19.5% 9.3% 2.4% 0.3% 100.0%

2020 N 0 32 123 187 297 411 349 145 29 0 1573
% 0.0% 2.0% 7.8% 11.9% 18.9% 26.1% 22.2% 9.2% 1.8% 0.0% 100.0%

Total N 2 80 276 457 705 1098 796 358 85 6 3863
% 0.1% 2.1% 7.1% 11.8% 18.3% 28.4% 20.6% 9.3% 2.2% 0.2% 100.0%

5 Old patients Year Previous years N 2 40 171 262 459 653 505 232 61 0 2385 54.587***
average % 0.1% 1.7% 7.2% 11.0% 19.2% 27.4% 21.2% 9.7% 2.6% 0.0% 100.0%

2020 N 2 48 184 304 294 536 419 188 95 4 2074
% 0.1% 2.3% 8.9% 14.7% 14.2% 25.8% 20.2% 9.1% 4.6% 0.2% 100.0%

Total N 4 88 355 566 753 1189 924 420 156 4 4459
% 0.1% 2.0% 8.0% 12.7% 16.9% 26.7% 20.7% 9.4% 3.5% 0.1% 100.0%

6 Old patients Year Previous years N 0 50 183 253 445 767 524 261 62 3 2548 68.279***
average % 0.0% 2.0% 7.2% 9.9% 17.5% 30.1% 20.6% 10.2% 2.4% 0.1% 100.0%

2020 N 2 34 133 276 280 542 503 209 113 1 2093
% 0.1% 1.6% 6.4% 13.2% 13.4% 25.9% 24.0% 10.0% 5.4% 0.0% 100.0%

Total N 2 84 316 529 725 1309 1027 470 175 4 4641
% 0.0% 1.8% 6.8% 11.4% 15.6% 28.2% 22.1% 10.1% 3.8% 0.1% 100.0%

8 Old patients Year Previous years N 6 58 200 289 440 712 620 247 75 2 2649 25.297**
average % 0.2% 2.2% 7.6% 10.9% 16.6% 26.9% 23.4% 9.3% 2.8% 0.1% 100.0%

2020 N 0 42 145 180 429 546 525 209 69 0 2145
% 0.0% 2.0% 6.8% 8.4% 20.0% 25.5% 24.5% 9.7% 3.2% 0.0% 100.0%

Total N 6 100 345 469 869 1258 1145 456 144 2 4794
% 0.1% 2.1% 7.2% 9.8% 18.1% 26.2% 23.9% 9.5% 3.0% 0.0% 100.0%

* p<.1,

** p<.05,

*** p<.001

Table 10
Multiple Linear Regression Analysis of Number of Patients by Year, Month and Gender
Independent Variable Unstandardized Standardized t p F(p-value)
B SE beta
(Constant) 46884.809 32848.102 1.427 0.154 33.075**
Year −23.306 16.265 −0.068 −1.433 0.153
Month 3.838 3.549 0.051 1.081 0.280
Patient classification 183.450 16.265 0.533 11.279 0.000
Age group 3.874 2.831 0.065 1.368 0.172

Dependent variable: Number of patients; SE, Standard Error

* p<.1,

** p<.05,

*** p<.001

Table 11
Crossover Analysis Monthly Comparison by Year and Age Group
Month Age group Total X2

Under 10s 10s 20s 30s 40s 50s 60s 70s 80s Over 90s
3 Year Previous years N 3 4 16 15 15 21 14 7 2 97 17.600**
average % 3.1% 4.1% 16.5% 15.5% 15.5% 21.6% 14.4% 7.2% 2.1% 100.0%

2020 N 0 0 7 12 24 18 12 8 9 90
% 0.0% 0.0% 7.8% 13.3% 26.7% 20.0% 13.3% 8.9% 10.0% 100.0%

Total N 3 4 23 27 39 39 26 15 11 187
% 1.6% 2.1% 12.3% 14.4% 20.9% 20.9% 13.9% 8.0% 5.9% 100.0%

5 Year Previous years N 1 1 8 8 16 32 14 10 10 0 100 15.778*
average % 1.0% 1.0% 8.0% 8.0% 16.0% 32.0% 14.0% 10.0% 10.0% 0.0% 100.0%

2020 N 2 4 20 22 11 35 20 13 4 1 132
% 1.5% 3.0% 15.2% 16.7% 8.3% 26.5% 15.2% 9.8% 3.0% 0.8% 100.0%

Total N 3 5 28 30 27 67 34 23 14 1 232
% 1.3% 2.2% 12.1% 12.9% 11.6% 28.9% 14.7% 9.9% 6.0% 0.4% 100.0%

* p<.1,

** p<.05,

*** p<.001

Table 12
Crossover Analysis According to Frequent Diseae by Year
Type Total X2

Cervical sprain Lumbar sprain Other sprain Vertebral Fracture Other fracture Cervical HNP Lumbar HNP Vertebral stenosis Spondylosis Post neck pain Low back pain Shoulder disease Knee disease Other musculoskeletal pain Facial palsy
Year Previous years N 870 847 398 41 28 317 870 241 181 725 368 336 297 369 719 6607 130.132***
average % 13.2% 12.8% 6.0% 0.6% 0.4% 4.8% 13.2% 3.6% 2.7% 11.0% 5.6% 5.1% 4.5% 5.6% 10.9% 100.0%

2020 N 978 803 250 12 24 280 776 203 177 551 200 255 267 200 573 5549
% 17.6% 14.5% 4.5% 0.2% 0.4% 5.0% 14.0% 3.7% 3.2% 9.9% 3.6% 4.6% 4.8% 3.6% 10.3% 100.0%

Total N 1848 1650 648 53 52 597 1646 444 358 1276 568 591 564 569 1292 12156
% 15.2% 13.6% 5.3% 0.4% 0.4% 4.9% 13.5% 3.7% 2.9% 10.5% 4.7% 4.9% 4.6% 4.7% 10.6% 100.0%

* p<.1,

** p<.05,

*** p<.001

Table 13
Crossover Analysis Monthly Comparison by Year and Frequent Diseae
Month Type

Cervical sprain Lumbar sprain Other sprain Vertebral Fracture Other fracture Cervical HNP Lumbar HNP Vertebral stenosis Spondylosis Post neck pain Low back pain Shoulder disease Knee disease Other musculoskeletal pain Facial palsy Total X2
1 Year Previous years N 97 111 41 5 4 32 106 26 29 82 41 41 36 42 94 787 22.738*
average % 12.3% 14.1% 5.2% 0.6% 0.5% 4.1% 13.5% 3.3% 3.7% 10.4% 5.2% 5.2% 4.6% 5.3% 11.9% 100.0%

2020 N 127 108 29 2 5 48 95 38 19 68 35 26 37 35 74 746
% 17.0% 14.5% 3.9% 0.3% 0.7% 6.4% 12.7% 5.1% 2.5% 9.1% 4.7% 3.5% 5.0% 4.7% 9.9% 100.0%

Total N 224 219 70 7 9 80 201 64 48 150 76 67 73 77 168 1533
% 14.6% 14.3% 4.6% 0.5% 0.6% 5.2% 13.1% 4.2% 3.1% 9.8% 5.0% 4.4% 4.8% 5.0% 11.0% 100.0%

3 Year Previous years N 102 93 47 4 4 37 101 25 20 94 40 39 42 40 89 777 22.092*
average % 13.1% 12.0% 6.0% 0.5% 0.5% 4.8% 13.0% 3.2% 2.6% 12.1% 5.1% 5.0% 5.4% 5.1% 11.5% 100.0%

2020 N 106 93 25 1 2 20 76 16 10 52 22 31 26 22 71 573
% 18.5% 16.2% 4.4% 0.2% 0.3% 3.5% 13.3% 2.8% 1.7% 9.1% 3.8% 5.4% 4.5% 3.8% 12.4% 100.0%

Total N 208 186 72 5 6 57 177 41 30 146 62 70 68 62 160 1350
% 15.4% 13.8% 5.3% 0.4% 0.4% 4.2% 13.1% 3.0% 2.2% 10.8% 4.6% 5.2% 5.0% 4.6% 11.9% 100.0%

5 Year Previous years N 103 102 53 4 5 39 102 26 24 83 44 42 33 44 88 792 29.745**
average % 13.0% 12.9% 6.7% 0.5% 0.6% 4.9% 12.9% 3.3% 3.0% 10.5% 5.6% 5.3% 4.2% 5.6% 11.1% 100.0%

2020 N 142 99 30 2 2 33 107 28 30 71 24 30 34 24 71 727
% 19.5% 13.6% 4.1% 0.3% 0.3% 4.5% 14.7% 3.9% 4.1% 9.8% 3.3% 4.1% 4.7% 3.3% 9.8% 100.0%

Total N 245 201 83 6 7 72 209 54 54 154 68 72 67 68 159 1519
% 16.1% 13.2% 5.5% 0.4% 0.5% 4.7% 13.8% 3.6% 3.6% 10.1% 4.5% 4.7% 4.4% 4.5% 10.5% 100.0%

6 Year Previous years N 116 117 54 5 4 49 116 37 27 102 52 47 37 52 91 906 23.940**
average % 12.8% 12.9% 6.0% 0.6% 0.4% 5.4% 12.8% 4.1% 3.0% 11.3% 5.7% 5.2% 4.1% 5.7% 10.0% 100.0%

2020 N 127 94 27 1 4 35 111 32 26 72 27 36 38 27 64 721
% 17.6% 13.0% 3.7% 0.1% 0.6% 4.9% 15.4% 4.4% 3.6% 10.0% 3.7% 5.0% 5.3% 3.7% 8.9% 100.0%

Total N 243 211 81 6 8 84 227 69 53 174 79 83 75 79 155 1627
% 14.9% 13.0% 5.0% 0.4% 0.5% 5.2% 14.0% 4.2% 3.3% 10.7% 4.9% 5.1% 4.6% 4.9% 9.5% 100.0%

7 Year Previous years N 118 111 53 7 2 42 126 33 24 102 64 46 36 64 96 924 35.738***
average % 12.8% 12.0% 5.7% 0.8% 0.2% 4.5% 13.6% 3.6% 2.6% 11.0% 6.9% 5.0% 3.9% 6.9% 10.4% 100.0%

2020 N 134 104 38 2 3 36 109 21 31 81 26 39 31 26 61 742
% 18.1% 14.0% 5.1% 0.3% 0.4% 4.9% 14.7% 2.8% 4.2% 10.9% 3.5% 5.3% 4.2% 3.5% 8.2% 100.0%

Total N 252 215 91 9 5 78 235 54 55 183 90 85 67 90 157 1666
% 15.1% 12.9% 5.5% 0.5% 0.3% 4.7% 14.1% 3.2% 3.3% 11.0% 5.4% 5.1% 4.0% 5.4% 9.4% 100.0%

8 Year Previous years N 125 114 55 6 4 49 120 41 22 100 57 38 34 57 91 913 36.727***
average % 13.7% 12.5% 6.0% 0.7% 0.4% 5.4% 13.1% 4.5% 2.4% 11.0% 6.2% 4.2% 3.7% 6.2% 10.0% 100.0%

2020 N 125 110 42 1 3 44 107 23 26 82 19 28 34 19 70 733
% 17.1% 15.0% 5.7% 0.1% 0.4% 6.0% 14.6% 3.1% 3.5% 11.2% 2.6% 3.8% 4.6% 2.6% 9.5% 100.0%

Total N 250 224 97 7 7 93 227 64 48 182 76 66 68 76 161 1646
% 15.2% 13.6% 5.9% 0.4% 0.4% 5.7% 13.8% 3.9% 2.9% 11.1% 4.6% 4.0% 4.1% 4.6% 9.8% 100.0%

* p<.1,

** p<.05,

*** p<.001

참고문헌

1. Zhu N, Zhang D, Wang W, et al. A novel coronavirus from patients with pneumonia in China, 2019. New England Journal of Medicine. 2020.
crossref

2. Wang F-S, Zhang C. What to do next to control the 2019-nCoV epidemic? The Lancet. 2020; 395:391–393.
crossref

3. WHO Director-General’s remarks at the media briefing on 2019-nCoV on 11 February 2020 [Internet]. [cited 2020 Oct 30]. Available from: https://www.who.int/director-general/speeches/detail/who-director-general-s-remarks-at-the-media-briefing-on-2019-ncov-on-11-february-2020


4. 질병관리청보건복지부. 코로나바이러스감염증-19 (COVID-19) [Internet]. 코로나바이러스감염증-19(COVID-19). [cited 2020 Oct 29]. Available from: http://ncov.mohw.go.kr/


5. 손창우. 코로나 19 (COVID-19) 대응을 통해 본 서울시 신종감염병 관리의 현재와 미래. 정책리 포트. 2020; 1–36.


6. Hatchett RJ, Mecher CE, Lipsitch M. Public health interventions and epidemic intensity during the 1918 influenza pandemic. Proceedings of the National Academy of Sciences. 2007; 104:7582–7587.
crossref

7. CDC. Coronavirus Disease 2019 (COVID-19) [Internet]. Centers for Disease Control and Prevention;2020. [cited 2020 Nov 3]. Available from: https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/social-distancing.html


8. Baldwin R, Weder di Mauro B. Economics in the Time of COVID-19. CEPR Press;2020.


9. 코로나19로 인한 국민의 의료이용행태 변화 [Internet]. 국민건강보험. 2020. [cited 2020 Nov 3]. Available from: https://www.nhis.or.kr/nhis/together/wbhaea01600m01.do?mode=view&articleNo=138736&article.offset=0&articleLimit=10


10. Middle East Respiratory Syndrome Coronavirus Outbreak in the Republic of Korea, 2015. Osong Public Health Res Perspect. 2015; 6:269–278.
pmid pmc

11. WHO. WHO statement on the ninth meeting of the IHR Emergency Committee regarding MERS-CoV [Internet]. WHO World Health Organization;[cited 2020 Nov 4]. Available from: https://www.who.int/mediacentre/news/statements/2015/ihr-ec-mers/en/


12. Lee W-C. Post-MERS: the strategies to minimize the risks from new epidemics. J Korean Med Assoc. 2015; 58:689–691.
crossref

13. 코로나19에 대한 국민 인식 및 경험조사 개요 [Internet]. 국립중앙의료원. 2020. [cited 2020 Nov 3]. Available from: https://www.nmc.or.kr/nmc/bbs/B0000008/view.do?nttId=7458&menuNo=200394&pageIndex=2


14. 전병율. 전염병 추이와 전망. KOREA TOURISM POLICY. 2009; 34–41.


15. Gralinski LE, Menachery VD. Return of the Coronavirus: 2019-nCoV. Viruses. 2020; 12:135
crossref pmc

16. Lee CJ. A Review on the Biomedical Aspects of COVID-19. THEOLOGY AND OTHER DISCIPLINES. 2020; 29:12–27.


17. Choi S, Ki M. Analyzing the effects of social distancing on the COVID-19 pandemic in Korea using mathematical modeling. Epidemiology and Health. 2020; 42:e2020064
crossref pmid pmc

18. Kim TH. Institutional preparedness for infectious diseases and improving care. J Korean Med Assoc. 2015; 58:606–610.
crossref

19. Case A, Paxson C. Sex differences in morbidity and mortality. Demography. 2005; 42:189–214.
crossref pmid

20. Cho KS. The differences in behaviors of utilization on western and oriental medical care in Korea [MSc Thesis]. Yonsei University;2001.


21. Sung KJ. Statical study of the patient of Acupuncture and Moxibustion Medicine at Daejeon Korean Medicine Hospital [MSc Thesis]. Dajeon University;2020.


22. Kim CD. Study on managing the medical expenses caused by population aging [Ph.D. Thesis]. Dong-A University;2019.


23. e-national indicators [Internet]. Statistics Korea. [cited 2020 Nov 4]. Available from: https://www.index.go.kr/unify/idx-info.do?idxCd=4235


24. Jang H-K, Jung IC, Park YC, et al. Analysis of Choice factors of Korean Medicine and Pain Assessment - Using a Korean Longitudinal Study of Ageing(KLoSA) -. kjopp. 2018; 32:411–417.


25. Choi B, Son C, Lim B. The relationship between the Use of Korean and Western medicine in treating musculoskeletal disease. J Korean Med. 2014; 35:22–31.
crossref

26. Jo EJ. Patients’ Preference of Oriental Medical Clinics to Western Medical Clinics [MSc Thesis]. Graduate School of Public Health, Kosin University Department of Hospital Management;2000.


27. National Police Agency. Statistical report of traffic accident in 2016.


28. Lee CS, Lee HJ, Chae JM. A Study on the Analysis of Factors for the Increase of Oriental Medicine Expenditure in the Automobile Insurance. 2019; 20:1. 121–130.


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