| Literature DB >> 35203013 |
Karolina Anuszkiewicz1, Piotr Stogowski2, Marta Zawadzka2, Przemysław Waszak3, Ewa Sokolewicz2, Natalia Aleksandra Dułak2, Kamil Dzwilewski2, Karolina Jażdżewska2, Kamila Karbowiak2, Daria Karlińska2, Anna Marczak2, Anna Niebrzydowska2, Bartosz Niebrzydowski2, Ewa Pasierbska2, Agnieszka Sadowska2, Małgorzata Szczęsna2, Piotr Stanisław Szczęsny2, Anna Szerszenowicz2, Kamil Sztramski2, Jakub Radziwon2, Magdalena Tkaczuk2, Kinga Ziołkowska2, Maria Mazurkiewicz-Bełdzińska2.
Abstract
INTRODUCTION: In 2020, Coronavirus Disease 2019 (COVID-19) was declared as a global pandemic. Self-reported stress, anxiety, and insomnia, which are believed to be common triggers for epilepsy, are more likely to occur. We aimed to establish the influence of COVID-19 pandemic itself on changes in the daily life routine related to pandemic on epilepsy course in pediatric patients. The unique form of clinical care which is telemedicine was also taken into consideration. We wanted to evaluate patients' satisfaction with telemedicine and if changing stationary visits into telemedicine influenced epilepsy course in our patients.Entities:
Keywords: COVID-19; Change; Children; Epilepsy; Satisfaction; Telemedicine
Mesh:
Year: 2022 PMID: 35203013 PMCID: PMC8784425 DOI: 10.1016/j.yebeh.2022.108581
Source DB: PubMed Journal: Epilepsy Behav ISSN: 1525-5050 Impact factor: 3.337
Fig. 1Study design.
Demographic data.
| Total, | |
|---|---|
| Females, | 167 (41.54%) |
| Males, | 235 (58.46%) |
| Age median, years (Q1-Q4) | 11 (6–14) |
| Duration of epilepsy, years (Q1-Q4) | 5 (3–8) |
| Village | 142 (35.32%) |
| city up to 50,000 habitants | 84 (20.9%) |
| city 50 000–100,000 habitants | 25 (6.22%) |
| city 100,000–500,000 habitants | 17 (4.23%) |
| city over 500,000 habitants | 134 (33.33%) |
| 0–50 km | 250 (62.19%) |
| 51–100 km | 91 (22.64%) |
| over 100 km | 61 (15.17%) |
| Yes | 353 (87.81%) |
| No | 49 (12.19%) |
| Focal | 156 (38.81%) |
| Generalized | 170 (42.29%) |
| Both | 76 (18.91%) |
Characteristic of groups.
| Variable | Improved | Stable | Worsened | |
|---|---|---|---|---|
| 12.94% | 70.65% | 16.42% | ||
| Age, median years (Q1-Q4) | 10 (4–14.5) | 11 (7–14) | 8 (5.5–14) | |
| Duration of epilepsy, years (Q1-Q4) | 4 (3–9) | 5 (3–8) | 6 (3–10) | |
| Males | 32 (61.54%) | 166 (58.45%) | 37 (56.06%) | |
| Females | 20 (38.46%) | 118 (41.54%) | 29 (43.94%) | |
| Village | 20 (38.46%) | 96 (33.80%) | 25 (37.88%) | |
| city up to 50,000 habitants | 9 (17.31%) | 63 (22.18%) | 12 (18.18%) | |
| city 50,000–100,000 habitants | 2 (3.85%) | 18 (6.34%) | 5 (7.58%) | |
| city 100,000–500,000 habitants | 3 (5.77%) | 13 (4.58%) | 1 (1.52%) | |
| city over 500,000 habitants | 18 (34.62%) | 94 (33.10%) | 23 (34.85%) | |
| focal | 16 (30.77%) | 115 (40.49%) | 26 (39.39%) | |
| generalized | 23 (44.23%) | 123 (43.31%) | 22 (33.33%) | |
| both | 13 (25.00%) | 46 (16.20%) | 18 (27.27%) | |
| None | 5 (9.61%) | 147 (51.76%) | 17 (25.76%) | |
| 1–2 (once per year/half year) | 6 (11.53%) | 49 (17.25%) | 9 (13.63%) | |
| 3–5 | 9 (17.31%) | 20 (7.04%) | 9 (13.63%) | |
| 6–12 (once per month | 5 (9.61%) | 17 (5.99%) | 6 (9.52%) | |
| 13–20 | 5 (9.61%) | 15 (5.28%) | 7 (10.61%) | |
| 21–100 | 8 (15.38%) | 11 (3.87%) | 3 (4.56%) | |
| over 100 (daily) | 14 (26.92%) | 25 (8.80%) | 15 (22.73%) | |
| None | 12 (23.08%) | 173 (60.91%) | 0 (0.00%) | |
| 1–2 (once per year/half year) | 6 (11.54%) | 39 (13.73%) | 11 (16.67%) | (Bonferroni |
| 3–5 | 6 (11.54%) | 17 (5.98%) | 8 (12.12%) | correction |
| 6–12 (once per month) | 5 (9.62%) | 13 (4.58%) | 6 (9.10%) | |
| 13–20 | 4 (7.69%) | 10 (3.52%) | 10 (15.15%) | |
| 21–100 | 9 (17.31%) | 11 (3.87%) | 14 (21.21%) | |
| over 100 (daily) | 10 (19.23%) | 21 (7.39%) | 17 (25.76%) | |
Significant p value in post hoc analysis (after Bonferroni correction).
Factors associated with changes in epilepsy course.
| Variable | Improved | Stable | Worsened | |
|---|---|---|---|---|
| Yes | 22 (42.31%) | 68 (23.94%)* | 33 (50.00%)* | |
| No | 30 (57.69%) | 216 (76.06%)* | 33 (50.00%)* | (Bonferroni correction |
| Yes | 19 (36.54%) | 77 (27.11%)* | 37 (56.06%)* | |
| No | 33 (63.54%) | 207 (72.89%)* | 29 (43.94%)* | (Bonferroni correction |
| Yes | 21 (40.38%) | 85 (29.93%) | 22 (33.33%) | |
| No | 31 (59.62%) | 199 (70.07%) | 44 (66.67%) | |
| Yes | 22 (42.31%) | 118 (46.46%) | 26 (39.39%) | |
| No | 30 (57.69%) | 166 (58.45%) | 40 (60.61%) | |
| Yes | 41 (78.85%) | 219 (77.11%) | 40 (60.60%)* | |
| no - more frequently | 2 (3.85%) | 11 (3.87%)* | 14 (21.21%)* | (Bonferroni correction |
| no - less frequently | 9 (17.31%) | 54 (19.01%) | 12 (18.18%) | |
| Yes | 46 (88.46%) | 240 (84.50%) | 55 (83.33%) | |
| No | 6 (11.11%) | 44 (15.49%) | 11 (16.67%) | |
| Yes | 31 (67.39%) | 143 (59.58%) | 25 (45.45%) | |
| No | 15 (32.61%) | 97 (40.42%) | 30 (54.55%) | |
*Significant p value in post hoc analysis (after Bonferroni correction).
Telemedicine satisfaction.
| Satisfied with telemedicine | Not satisfied with telemedicine | ||
|---|---|---|---|
| yes, | 165 (82.91%) | 88 (61.97%) | |
| no, | 34 (17.09%) | 54 (38.03%) | |
| more frequent, | 16 (47.06% | 11 (20.37%) | |
| less frequent, | 18 (52.94%) | 43 (79.63%) | |
| up to 50 km | 126 (63.32%) | 91 (64.08%) | |
| 50–100 km | 47 (23.62%) | 30 (21.13%) | |
| more than 100 km | 26 (13.07%) | 21 (14.79%) | |
| yes, | 9 (4.52%) | 19 (13.48%) | |
| no, | 190 (95.48%) | 123 (86.61%) | |
| yes, | 48 (24.12%) | 40 (28.17%) | |
| no, | 151 (75.88%) | 102 (71.83%) | |
| yes, | 45 (97.83%) | 27 (67.50%) | |
| no, | 1 (2.17%) | 13 (32.50%) | |
Statistically significant results.