| Literature DB >> 32500177 |
Joseph A Weiner1, Peter R Swiatek1, Daniel J Johnson1, Philip K Louie2, Garrett K Harada3,4, Michael H McCarthy2, Niccole Germscheid5, Jason P Y Cheung6, Marko H Neva7, Mohammad El-Sharkawi8, Marcelo Valacco9, Daniel M Sciubba10, Norman B Chutken11, Howard S An3,4, Dino Samartzis12,13.
Abstract
PURPOSE: Spine surgeons around the world have been universally impacted by COVID-19. The current study addressed whether prior experience with disease epidemics among the spine surgeon community had an impact on preparedness and response toward COVID-19.Entities:
Keywords: COVID-19; Coronavirus; Global; Impact; Spine; Surgeons; Worldwide
Mesh:
Year: 2020 PMID: 32500177 PMCID: PMC7271833 DOI: 10.1007/s00586-020-06477-6
Source DB: PubMed Journal: Eur Spine J ISSN: 0940-6719 Impact factor: 2.721
Fig. 1Distribution of survey responses by country. World map depicting number of survey responses received internationally. Color-filled countries indicate that at least one survey was received. Green, under 10 surveys received; Blue, 11 to 25; Red, 26 to 50; Orange, 51 to 100; Purple, over 100; Grey, no surveys received
Survey respondent demographics
| All respondents ( | Previous epidemic experience (%) ( | No previous epidemic experience (%) ( | ||
|---|---|---|---|---|
| Age | 0.5 | |||
| 25–34 | 130 (14.5) | 31 (12.5) | 99 (15.3) | |
| 35–44 | 344 (38.4) | 97 (39) | 247 (38.2) | |
| 45–54 | 245 (27.4) | 77 (30.9) | 168 (26) | |
| 55–64 | 150 (16.8) | 38 (15.3) | 112 (17.3) | |
| 65 + | 26 (2.9) | 6 (2.4) | 20 (3.1) | |
| Sex | 0.61 | |||
| Female | 55 (6.2) | 17 (6.9) | 38 (6.0) | |
| Male | 826 (93.8) | 229 (93.1) | 597 (94.0) | |
| Estimated home city population | 0.0003 | |||
| < 100,000 | 46 (5.2) | 12 (4.8) | 34 (5.3) | |
| 100,000–500,000 | 185 (20.7) | 38 (15.3) | 147 (22.8) | |
| 500,000–1,000,000 | 136 (15.2) | 28 (11.3) | 108 (16.7) | |
| 1,000,000–2,000,000 | 144 (16.2) | 34 (13.7) | 110 (17.1) | |
| > 2,000,000 | 382 (42.8) | 136 (54.8) | 246 (38.1) | |
| Geographic region | ||||
| Africa | 44 (5.0) | < 0.0001 | ||
| Asia | 213 (24.2) | |||
| Australia | 8 (1.0) | |||
| Europe | 242 (27.5) | |||
| Middle East | 77 (8.7) | |||
| North America | 152 (17.3) | |||
| South America/Latin America | 145 (16.5) | |||
| Previous epidemic experience | ||||
| None | 647 (71.7) | 0 (0) | 647 (100) | < 0.0001 |
| SARS | 98 (10.9) | 97 (38.0) | 0 (0) | < 0.0001 |
| H1N1 swine flu | 128 (14.2) | 127 (49.8) | 0 (0) | < 0.0001 |
| MERS | 17 (1.9) | 17 (6.7) | 0 (0) | < 0.0001 |
| Ebola | 15 (1.7) | 15 (5.9) | 0 (0) | < 0.0001 |
| Specialty | 0.68 | |||
| Neurosurgery | 234 (26.4) | 65 (26.5) | 169 (26.4) | |
| Orthopedics | 637 (72.0) | 178 (72.7) | 459 (71.7) | |
| Pediatric surgery | 2 (0.2) | 0 (0) | 2 (0.3) | |
| Neurosurgery | 12 (1.4) | 2 (0.8) | 169 (26.4) | |
| Fellowship trained | 645 (71.5) | 192 (75.3) | 453 (70.0) | 0.11 |
| Years since training completion | 0.43 | |||
| Less than 5 years | 161 (25.3) | 49 (25.9) | 112 (25.1) | |
| 5–10 Years | 141 (22.2) | 41 (21.7) | 100 (22.4) | |
| 10–15 Years | 104 (16.4) | 38 (20.1) | 66 (14.8) | |
| 15–20 Years | 117 (18.4) | 29 (15.3) | 88 (19.7) | |
| Over 20 Years | 113 (17.8) | 32 (16.9) | 81 (18.1) | |
| Practice type | 0.33 | |||
| Academic | 405 (45.4) | 124 (50.4) | 281 (43.5) | |
| Academic/private combined | 204 (22.9) | 51 (20.7) | 153 (23.7) | |
| Private | 144 (16.1) | 36 (14.6) | 108 (16.7) | |
| Public/local hospital | 139 (15.6) | 35 (14.2) | 104 (16.1) | |
| Practice breakdown | ||||
| Percent research | 0.037 | |||
| 0–25% | 731 (81.9) | 192 (77.1) | 539 (83.7) | |
| 26–50% | 129 (14.4) | 49 (19.7) | 80 (12.4) | |
| 51–75% | 21 (2.4) | 4 (1.6) | 17 (2.6) | |
| 76–100% | 12 (1.3) | 4 (1.6) | 8 (1.2) | |
| Percent clinical | 0.22 | |||
| 0–25% | 22 (2.5) | 6 (2.4) | 16 (2.5) | |
| 26–50% | 87 (9.7) | 25 (10.1) | 62 (9.6) | |
| 51–75% | 194 (21.8) | 65 (26.2) | 129 (20.0) | |
| 76–100% | 590 (66.1) | 152 (61.3) | 438 (67.9) | |
| Percent teaching | ||||
| 0–25% | 668 (74.9) | 6 (2.4) | 16 (2.5) | |
| 26–50% | 152 (17.0) | 25 (10.1) | 62 (9.6) | |
| 51–75% | 50 (5.6) | 65 (26.2) | 129 (20.0) | |
| 76–100% | 22 (2.5) | 152 (61.3) | 438 (67.9) | |
COVID-19 perceptions stratified by previous epidemic experience
| All respondents ( | Previous epidemic experience (%) ( | No previous epidemic experience (%) ( | ||
|---|---|---|---|---|
| Mean worry (1—not worried to 5—very worried) | 3.7 ± 1.2 | 3.8 ± 1.1 | 3.7 ± 1.2 | 0.4 |
| 3 Greatest stressors | ||||
| Personal health | 358 (39.7) | 122 (47.8) | 236 (36.5) | 0.0017 |
| Family health | 640 (71.0) | 190 (74.5) | 450 (69.6) | 0.14 |
| Community health | 370 (41.0) | 95 (37.3) | 275 (42.5) | 0.15 |
| Hospital abilities | 332 (39.0) | 99 (38.8) | 253 (39.1) | 0.94 |
| Timeline to resume work | 378 (41.9) | 106 (41.6) | 272 (42.0) | 0.9 |
| Government/leadership | 154 (17.0) | 52 (20.4) | 102 (15.8) | 0.1 |
| Return to nonessential activities | 116 (12.9) | 34 (13.3) | 82 (12.7) | 0.79 |
| Economic issues | 385 (42.7) | 119 (46.7) | 266 (41.1) | 0.13 |
| Currently coping w/the stress | ||||
| Exercise | 463 (51.0) | 131 (51.4) | 332 (51.3) | 0.99 |
| Music | 330 (36.6) | 112 (43.9) | 218 (33.7) | 0.0041 |
| Meditation/mindfulness | 118 (13.0) | 42 (16.5) | 76 (11.8) | 0.058 |
| Tobacco | 29 (3.2) | 5 (2.0) | 24 (3.7) | 0.21 |
| Alcohol | 89 (9.9) | 23 (9.0) | 66 (10.2) | 0.59 |
| Research projects | 244 (27.5) | 76 (29.8) | 168 (36) | 0.24 |
| Spending time w/family | 578 (64.1) | 162 (63.5) | 416 (64.3) | 0.83 |
| Spiritual/religious activities | 116 (12.9) | 35 (13.7) | 81 (12.5) | 0.63 |
| Reading | 458 (50.8) | 125 (49.0) | 333 (51.5) | 0.51 |
| Television | 394 (43.7) | 101 (39.6) | 293 (45.3) | 0.12 |
| Telecommunication with friends | 322 (35.7) | 92 (36.1) | 230 (35.6) | 0.88 |
| Media coverage | 0.58 | |||
| Excessive and overblown | 298 (35.5) | 81 (34.5) | 217 (35.9) | |
| Accurate | 407 (48.5) | 120 (51.1) | 287 (47.4) | |
| Not serious enough | 135 (16.1) | 34 (14.5) | 101 (16.7) | |
| Media sources | 0.65 | |||
| International news on television | 202 (26) | 53 (24.9) | 149 (26.4) | |
| National/local news on television | 72 (0.3) | 24 (11.3) | 48 (8.5) | |
| International news on Internet | 224 (28.8) | 57 (26.8) | 167 (29.6) | |
| National/local news on Internet | 177 (22.8) | 54 (23.4) | 123 (21.8) | |
| Newspapers | 28 (3.6) | 6 (2.8) | 22 (3.9) | |
| Social media | 75 (9.6) | 19 (8.9) | 56 (9.9) | |
Fig. 2Impact of previous pandemics on COVID-19 preparedness a Bar graph comparing access to COVID-19 testing stratified by previous epidemic experience. b Bar graph comparing access to adequate PPE stratified by previous epidemic experience
COVID-19 preparedness stratified by previous epidemic experience
| All respondents ( | Previous epidemic experience (%) ( | No previous epidemic experience (%) ( | ||
|---|---|---|---|---|
| Access COVID-19 testing | 0.44 | |||
| Yes | 701 (82.9) | 201 (84.5) | 500 (82.2) | |
| No | 145 (17.1) | 37 (15.6) | 108 (17.8) | |
| Personally tested for COVID-19 | 0.35 | |||
| Yes | 57 (6.7) | 13 (5.5) | 44 (7.2) | |
| No | 789 (93.3) | 225 (94.5) | 564 (92.8) | |
| Formal hospital guidelines | 0.19 | |||
| Yes | 452 (60.4) | 131 (64.2) | 321 (59.0) | |
| No | 296 (39.6) | 73 (35.8) | 223 (41.0) | |
| Adequate PPE for frontline workers | 0.4 | |||
| Yes | 415 (49.6) | 121 (51.9) | 294 (48.7) | |
| No | 422 (50.4) | 112 (48.1) | 310 (51.3) | |
| Forms of PPE provided | ||||
| N-95 mask | 451 (50.0) | 130 (51.0) | 321 (49.6) | 0.71 |
| Surgical mask | 738 (81.8) | 205 (80.4) | 533 (82.4) | 0.49 |
| Face shield | 415 (46.0) | 113 (44.3) | 302 (46.7) | 0.52 |
| Gown | 491 (54.4) | 131 (51.4) | 360 (55.6) | 0.25 |
| Full-face respirator | 95 (10.5) | 29 (11.4) | 66 (10.2) | 0.61 |
| Adequate ventilators | 0.53 | |||
| Yes | 343 (41.0) | 100 (42.7) | 243 (40.4) | |
| No | 493 (59.0) | 134 (57.3) | 359 (59.6) | |
COVID-19 response stratified by previous epidemic experience
| All respondents ( | Previous epidemic experience (%) ( | No previous epidemic experience (%) ( | ||
|---|---|---|---|---|
| COVID-19 diagnosis | ||||
| Know someone diagnosed | 392 (46.6) | 104 (44.3) | 288 (47.5) | 0.39 |
| Personally diagnosed | 9 (1.1) | 2 (0.8) | 7 (1.2) | 0.67 |
| Personally quarantined | 0.014 | |||
| Yes | 193 (22.9) | 68 (28.6) | 125 (20.7) | |
| No | 649 (77.1) | 170 (71.4) | 479 (79.3) | |
| Hospital restrictions | ||||
| Quarantine upon return from travel | 507 (56.2) | 144 (56.5) | 363 (56.1) | 0.94 |
| Limitations on domestic travel | 483 (53.6) | 130 (51) | 353 (54.6) | 0.33 |
| Cancellation of academic activities | 689 (76.4) | 192 (75.3) | 497 (76.8) | 0.63 |
| Nonessential staff to work from home | 558 (61.9) | 153 (60) | 405 (62.6) | 0.47 |
| Cancellation of hospital meetings | 674 (74.7) | 183 (71.8) | 491 (75.9) | 0.2 |
| Cancellation of elective surgeries | 714 (79.2) | 191 (74.9) | 523 (80.8) | 0.048 |
| Government restrictions | ||||
| Cancel elective surgery | 646 (71.6) | 171 (67.1) | 475 (73.4) | 0.057 |
| Shelter protection/self-isolation | 570 (63.2) | 148 (58.0) | 422 (65.2) | 0.044 |
| No group gatherings > 50 | 365 (40.5) | 106 (41.6) | 259 (40.0) | 0.67 |
| No group gatherings > 100 | 488 (54.1) | 125 (49.0) | 363 (56.1) | 0.055 |
| Only gather with those in the same household | 371 (41.1) | 91 (35.7) | 280 (42.3) | 0.037 |
| Closure of nonessential businesses | 727 (80.6) | 522 (80.7) | 205 (80.4) | 0.92 |
| Closure of schools/universities | 795 (88.1) | 222 (87.1) | 573 (88.6) | 0.53 |
| Closure of all dine-in restaurant opportunities | 711 (78.8) | 528 (81.6) | 183 (71.8) | 0.0011 |
| Closure of public transportation | 239 (26.5) | 64 (25.1) | 175 (27.1) | 0.55 |
| Restrictions on elderly for leaving home | 426 (47.2) | 116 (45.5) | 310 (47.9) | 0.51 |
| Government stay-at-home order | 0.058 | |||
| Yes | 688 (88.2) | 182 (84.7) | 506 (89.6) | |
| No | 92 (11.8) | 33 (15.4) | 59 (10.4) | |
| Performing medical duties outside of specialty | 0.98 | |||
| Yes | 183 (22.8) | 51 (22.8) | 132 (22.8) | |
| No | 619 (77.2) | 173 (77.2) | 446 (77.2) | |
| Perception of government effectiveness | 0.97 | |||
| Appears in disarray/disorganized | 88 (11.3) | 23 (10.8) | 65 (11.5) | |
| Taken some action but not enough | 215 (27.6) | 61 (28.5) | 154 (27.3) | |
| Acceptable/appropriate | 456 (58.5) | 125 (58.4) | 331 (58.6) | |
| Actions are excessive and unnecessary | 20 (2.6) | 5 (2.3) | 15 (2.7) | |
| Perception of hospital effectiveness | 0.53 | |||
| Appears in disarray/disorganized | 68 (8.8) | 23 (10.8) | 45 (8.0) | |
| Taken some action but not enough | 215 (27.7) | 56 (26.3) | 159 (28.2) | |
| Acceptable/appropriate | 477 (61.4) | 128 (60.1) | 349 (61.9) | |
| Actions are excessive and unnecessary | 17 (2.2) | 6 (2.8) | 11 (2.0) | |
Fig. 3Radar chart depictions of current COVID-19 hospital and government policies by previous epidemic experience. Five-sided (pentagon) radar charts visually depicting cumulative percentage of responses verifying the enactment of a given COVID-19 a hospital and b government policy at the time of survey distribution. Queried policies are listed at the vertex of a given figure, whereby points falling on a vertex of the innermost pentagon correspond to a cumulative total of 0% of survey responses received. Each subsequent pentagon corresponds to a 20% increase in responses for a given category. *Indicates difference significant at the 95% confidence level (P < 0.05)
COVID-19 practice impact stratified by previous epidemic experience
| All respondents ( | Previous epidemic experience (%) ( | No previous epidemic experience (%) ( | ||
|---|---|---|---|---|
| Performing elective spine surgery | 0.0005 | |||
| Yes | 149 (18.5) | 59 (26.1) | 90 (15.6) | |
| No | 655 (81.5) | 167(73.9) | 488 (84.4) | |
| Performing essential spine surgery | 0.88 | |||
| Yes | 700 (87.3) | 197 (87.6) | 503 (87.2) | |
| No | 102 (12.7) | 28 (12.4) | 74 (12.8) | |
| Percent cases cancelled/postponed per week | 0.22 | |||
| < 25% | 72 (9.0) | 24 (10.7) | 48 (8.3) | |
| 25–50% | 69 (8.6) | 19 (8.4) | 50 (8.7) | |
| 51–75% | 123 (15.3) | 42 (18.7) | 81 (14.0) | |
| > 75% | 539 (67.1) | 140 (62.2) | 399 (69.0) | |
| Top allocation of time | 0.95 | |||
| Spending time with family | 312 (49.5) | 90 (51.4) | 222 (48.8) | |
| Personal wellness | 59 (9.4) | 18 (10.3) | 41 (9.0) | |
| Resting | 38 (6.0) | 10 (5.7) | 28 (6.2) | |
| Planning for future | 19 (3.0) | 6 (3.4) | 13 (2.9) | |
| Engaging in hobbies | 17 (2.7) | 5 (2.9) | 12 (2.6) | |
| Academic projects/research | 32 (5.1) | 6 (3.4) | 26 (5.7) | |
| Community outreach programs | 13 (2.1) | 3 (1.7) | 10 (2.2) | |
| Spine practice/medical center-related work | 140 (22.2) | 37 (21.1) | 103 (22.6) | |
| Impact on income | 0.004 | |||
| Planned reduction, on salary | 138 (18.1) | 51 (24.5) | 87 (15.7) | |
| No impact, on salary | 244 (32.1) | 62 (29.8) | 182 (32.9) | |
| Planned reduction, compensation-based income | 64 (8.4) | 22 (10.6) | 42 (7.6) | |
| No impact, compensation-based income | 7 (0.9) | 4 (1.9) | 3 (0.5) | |
| Losing income | 308 (40.5) | 69 (33.2) | 239 (43.2) | |
| Percent of personal revenue lost | 0.042 | |||
| < 25% | 219 (28.9) | 57 (27.5) | 162 (29.5) | |
| 25–50% | 226 (29.9) | 77 (37.2) | 149 (27.1) | |
| 51–75% | 142 (18.8) | 36 (17.4) | 106 (19.3) | |
| > 75% | 170 (22.5) | 37 (17.9) | 133 (24.2) | |
| Percent of hospital revenue lost | 0.36 | |||
| < 25% | 169 (22.3) | 47 (22.5) | 122 (22.3) | |
| 25–50% | 199 (26.3) | 64 (30.6) | 135 (24.6) | |
| 51–75% | 207 (27.3) | 53 (25.4) | 154 (28.1) | |
| > 75% | 182 (24.0) | 45 (21.5) | 137 (25.0) | |
| Staff furlough | 0.57 | |||
| Yes | 307 (40.5) | 91 (43.5) | 216 (39.3) | |
| No | 286 (37.7) | 74 (35.4) | 212 (38.6) | |
| Potentially | 165 (21.8) | 44 (21.1) | 121 (22.0) | |
| Hospital layoffs | 0.41 | |||
| Yes | 67 (8.8) | 15 (7.2) | 52 (9.4) | |
| No | 586 (77.0) | 160 (76.6) | 426 (77.2) | |
| No, but have plans to | 108 (14.2) | 34 (16.3) | 74 (13.4) | |
| Personally laid off staff | 0.38 | |||
| Yes | 39 (5.1) | 10 (4.8) | 29 (5.2) | |
| No | 683 (89.8) | 191 (91.8) | 492 (89.0) | |
| No, but have plans to | 39 (5.1) | 7 (3.4) | 32 (5.8) | |
| Time frame to resume elective surgeries | 0.052 | |||
| No current stoppage | 85 (10.6) | 29 (13) | 56 (9.7) | |
| < 2 weeks | 31 (3.9) | 12 (5.4) | 19 (3.3) | |
| 2–4 weeks | 136 (16.9) | 44 (19.6) | 92 (15.9) | |
| 1–2 months | 127 (15.8) | 40 (17.9) | 87 (15.0) | |
| > 2 months | 33 (4.1) | 10 (4.5) | 23 (4.0) | |
| Unknown time frame | 392 (48.76) | 89 (39.7) | 303 (52.2) | |
| Timeline to resume “baseline operation” | 0.38 | |||
| < 2 weeks | 96 (12.7) | 23 (11.0) | 73 (13.3) | |
| 2–4 weeks | 177 (23.3) | 59 (28.2) | 118 (21.5) | |
| 4–6 weeks | 177 (23.3) | 44 (21.1) | 133 (24.2) | |
| 6–8 weeks | 108 (14.2) | 29 (13.9) | 79 (14.4) | |
| 8 + weeks | 201 (26.5) | 54 (25.8) | 147 (26.7) | |
| Impact on how you treat patients in 1 year | ||||
| No change | 133 (14.8) | 36 (14.1) | 97 (15.0) | 0.74 |
| Heightened awareness of hygiene | 435 (48.2) | 119 (46.7) | 316 (48.8) | 0.56 |
| Will increase use of PPE | 344 (38.1) | 94 (36.9) | 250 (38.6) | 0.62 |
| Ask patient to reschedule if they feel sick | 285 (31.6) | 86 (3.7) | 199 (30.8) | 0.39 |
| Pursue increased non-operative measures prior to surgery | 150 (16.6) | 43 (16.9) | 107 (16.5) | 0.96 |
| Growth in digital options for communication | 314 (34.8) | 77 (30.2) | 237 (36.6) | 0.068 |
Multivariable analysis of impact on prior epidemic exposure
| Variable | Prior epidemic exposure odds ratio (95% CI) | |
|---|---|---|
| Greatest stressors | ||
| Personal health | 1.66 (1.21–2.27) | 0.0015 |
| Coping mechanisms for stress | ||
| Music | 1.67 (1.21–2.3) | 0.0016 |
| Has been personally quarantined | 1.24 (0.83–1.83) | 0.29 |
| Hospital Restrictions | ||
| Cancellation of elective surgeries | 0.99 (0.67–1.46) | 0.96 |
| Government restrictions | ||
| Shelter protection/self-isolation | 0.84 (0.61–1.16) | 0.29 |
| Only gather with those in the same household | 0.94 (0.68–1.3) | 0.71 |
| Closure of all dine-in restaurant opportunities | 0.76 (0.52–1.11) | 0.76 |
| Performing elective spine surgery | 1.55 (1.01–2.38) | 0.045 |
Multivariate logistic regression analysis of effect of previous epidemic exposure on current response and preparedness controlling for differences in baseline demographics (home city population, region, fellowship training, and percent research). Variables with p > 0.2 from univariate analysis were tested. Odds ratios with 95% confidence interval reported for dichotomous categorical variables; likelihood ratios reported for variables with multiple responses
Fig. 4Global health security index scores by country. World map depicting Global Health Security Index scores by country. Color of countries indicate relative preparedness for global pandemic as ranked by Johns Hopkins Center for Health Security. Red, least prepared; Orange, more prepared; Yellow, most prepared; average overall GHSI score is 40.2. Data
source: Nuclear Threat Initiative and Johns Hopkins Center for Health Security
Fig. 5COVID-19 preparedness perceptions and global health security index scores. Scatter plot of COVID-19 preparedness perceptions and Global Health Security Index scores. All countries with > 10 respondents were included in the analysis (n = 687). A total of 21 countries were included. Mean responses to questions on the presence of formal guidelines, adequate PPE, N95 masks, and ventilators were plotted against the GHSI score of respondents’ countries. Linear regression analysis revealed poor correlations with R2 < 0.3
Fig. 6Impact of COVID-19 by GHSI score. Bar graph comparing the impact of COVID-19 stratified by country/GHSI score. All countries with > 10 respondents were included in the analysis (n = 687). A total of 21 countries were included
Fig. 7Assessing the need for formal international guidelines. Pie chart reporting the overwhelming support for international formal guidelines to mitigate the impact of future pandemics. 95% of respondents from all regions of the world were in favor of formal guidelines