| Literature DB >> 34250865 |
Silje Mæland1,2, Ragnhild Bjørknes3, Stine Lehmann3, Gro Mjeldheim Sandal4, William Hazell5, Åsgeir Kjetland Rabben5, Øystein Vedaa6,7,8,9, Jens Christoffer Skogen6,10,11,12, Lars Thore Fadnes1,13.
Abstract
AIMS: The aim of this study was to examine how the Norwegian general adult population was affected by non-pharmaceutical interventions during the first six weeks of the COVID-19 lockdown. We assessed quarantine, symptoms, social distancing, home office/school, work status, social contact and health-care contact through digital access and knowledge.Entities:
Keywords: COVID-19; Non-pharmaceutical interventions; health-care seeking; health-care service utilisation; mitigation; pandemic; public health; social distancing; social isolation; suppression
Mesh:
Year: 2021 PMID: 34250865 PMCID: PMC8808225 DOI: 10.1177/14034948211027817
Source DB: PubMed Journal: Scand J Public Health ISSN: 1403-4948 Impact factor: 3.021
Demographic characteristics for the participants in each age group.
| Category/age (years) | 18–29 | 30–39 | 40–49 | 50–59 | 60–69 | 70+ | Total |
|---|---|---|---|---|---|---|---|
| 4448 (22%) | 5068 (31%) | 5461 (39%) | 5843 (46%) | 4798 (52%) | 3917 (42%) | 29,535 (36%) | |
| Females | 2758 (62%) | 2976 (59%) | 3097 (57%) | 3268 (56%) | 2447 (51%) | 1851 (47%) | 16,397 (56%) |
| Primary/secondary school | 1622 (49%) | 944 (23%) | 1141 (24%) | 1930 (36%) | 1866 (42%) | 1681 (46%) | 9184 (36%) |
| University/high school | 1715 (51%) | 3200 (77%) | 3579 (76%) | 2556 (63%) | 2632 (58%) | 1949 (54%) | 16,418 (64%) |
| Born in Norway | 4104 (92%) | 4383 (86%) | 5002 (92%) | 5581 (96%) | 4650 (97%) | 3840 (98%) | 27,560 (93%) |
| Household income in NOK (adjusted per person
| |||||||
| 0–250,000 | 1054 (36%) | 537 (13%) | 479 (11%) | 377 (8%) | 251 (7%) | 383 (13%) | 3081 (13%) |
| 250,000–500,000 | 1116 (38%) | 1977 (50%) | 2301 (51%) | 1842 (38%) | 1380 (36%) | 1441 (50%) | 10,057 (44%) |
| >500,000 | 740 (25%) | 1475 (37%) | 1700 (38%) | 2596 (54%) | 2230 (58%) | 1051 (37%) | 9792 (43%) |
| Number of people in household | |||||||
| 1 | 477 (14%) | 617 (15%) | 514 (11%) | 880 (17%) | 1236 (29%) | 1463 (43%) | 5187 (21%) |
| 2 | 1176 (36%) | 886 (22%) | 615 (13%) | 1620 (31%) | 2210 (51%) | 1561 (46%) | 8068 (32%) |
| 3–4 | 1225 (37%) | 1965 (48%) | 2316 (50%) | 2146 (42%) | 800 (18%) | 331 (10%) | 8783 (35%) |
| ⩾5 | 433 (13%) | 650 (16%) | 1211 (26%) | 510 (10%) | 84 (2%) | 51 (1%) | 2939 (12%) |
| Employed/in work | 2206 (50%) | 3598 (96%) | 4243 (78%) | 4654 (80%) | 2524 (53%) | 230 (6%) | 17,455 (59%) |
| Student | 1607 (36%) | 257 (5%) | 105 (2%) | 32 (1%) | 7 (0%) | 3 (0%) | 2011 (7%) |
| Lacking access to | |||||||
| Internet | 154 (3%) | 71 (1%) | 80 (1%) | 60 (1%) | 32 (1%) | 18 (0%) | 415 (1%) |
| Web camera | 87 (2%) | 117 (2%) | 116 (2%) | 152 (3%) | 181 (4%) | 232 (6%) | 885 (3%) |
| Laptop/computer/tablet | 39 (1%) | 65 (1%) | 51 (1%) | 57 (1%) | 25 (1%) | 23 (1%) | 260 (1%) |
| Smart phone | 11 (0%) | 10 (0%) | 10 (0%) | 26 (0%) | 19 (0%) | 66 (2%) | 142 (0%) |
| Necessary software | 87 (2%) | 96 (2%) | 122 (2%) | 127 (2%) | 121 (3%) | 134 (3%) | 687 (2%) |
| One or more of the above | 258 (6%) | 245 (5%) | 259 (5%) | 279 (5%) | 240 (5%) | 274 (7%) | 1555 (5%) |
Household income is divided with a person index calculated as 1 for first adult, 0.7 for other adults and 0.5 for a child.
Number and percentage in each age group experiencing COVID-19-related consequences (total estimates are weighted).
| 18–29 | 30–39 | 40–49 | 50–59 | 60–69 | ⩾70 | All | |
|---|---|---|---|---|---|---|---|
| Quarantined | 1251 (21%) | 887 (16%) | 715 (15%) | 705 (14%) | 587 (15%) | 736 (17%) | 16% (14–19) |
| COVID-19 symptoms | 448 (8%) | 448 (8%) | 361 (8%) | 304 (6%) | 140 (4%) | 92 (2%) | 6% (5–8) |
| Lived with people with COVID-19 symptoms | 374 (6%) | 274 (5%) | 232 (5%) | 215 (4%) | 86 (2%) | 52 (1%) | 4% (3–5) |
| Distancing from others | 4984 (85%) | 4655 (85%) | 3995 (83%) | 4214 (83%) | 3299 (84%) | 3614 (81%) | 84% (83–85) |
| Working from home/home schooling | 3654 (63%) | 3326 (61%) | 3040 (63%) | 2626 (52%) | 1221 (31%) | 185 (4%) | 51% (39–62) |
| Made redundant (temporarily/permanent) | 513 (12%) | 401 (8%) | 362 (7%) | 420 (7%) | 225 (5%) | 20 (1%) | 7% (5–9) |
| Reduced contact due to | |||||||
| Lack of digital equipment access | 339 (9%) | 326 (7%) | 383 (7%) | 424 (8%) | 359 (8%) | 349 (10%) | 8% (7–8) |
| Lack of knowledge in use of tools | 135 (4%) | 257 (6%) | 476 (9%) | 821 (15%) | 882 (19%) | 984 (27%) | 11% (6–16) |
| NPIs perceived acceptable | |||||||
| To large degree | 2843 (70%) | 3681 (76%) | 4356 (82%) | 4870 (85%) | 4068 (86%) | 3127 (82%) | 76% (71–82) |
| To some degree | 1146 (28%) | 1076 (22%) | 895 (17%) | 766 (13%) | 530 (11%) | 454 (12%) | 18% (14–23) |
| To limited degree | 77 (2%) | 76 (2%) | 62 (1%) | 103 (2%) | 134 (3%) | 241 (6%) | 2% (1–3) |
| NPIs perceived difficult | 750 (23%) | 698 (18%) | 456 (10%) | 361 (7%) | 209 (5%) | 116 (4%) | 11% (7–15) |
NPI: non-pharmaceutical interventions.
Figure 1.Change in use of health services from home psychologists or physiotherapy (left side) to during period of the COVID-19 pandemic (right side) among those reporting change in health or social services and using the respective health services before the pandemic. Weekly follow-up during the pandemic is labelled with deep blue, monthly follow-up during the pandemic is labelled with light blue, and less frequent follow-up during the pandemic is labelled with orange.
*p<0.001 for all changes.
Logistic regression of odds for reduction in frequency of follow-up based on various socio-demographic indicators.
| General practitioner | Psychologist/physiotherapist | Hospital follow-up | Nursing care | Other health care | Any | |
|---|---|---|---|---|---|---|
| Age | ||||||
| 18–30 | 1 | 1 | 1 | 1 | 1 | 1 |
| 30–39 | 0.8 (0.5–1.4) | 1.2 (0.7–2.1) | 0.9 (0.3–2.6) | 0.7 (0.2–3.0) | 1.1 (0.5–2.3) | 1.0 (0.6–1.5) |
| 40–49 | 0.7 (0.4–1.2) | 1.5 (0.9–2.7) | 2.3 (0.7–7.6) | 0.4 (0.1–1.9) | 1.5 (0.6–3.4) | 1.1 (0.7–1.8) |
| 50–59 | 0.9 (0.5–1.6) | 1.3 (0.7–2.3) | 0.6 (0.2–1.9) | 0.5 (0.1–2.0) | 1.7 (0.7–4.5) | 1.2 (0.7–1.9) |
| 60–69 | 0.9 (0.4–1.8) | 1.5 (0.7–3.2) | 1.0 (0.3–3.5) | 0.2 (0.1–1.0) | 2.1 (0.6–7.3) | 1.1 (0.6–1.9) |
| ⩾70 | 0.6 (0.3–1.2) | 2.1 (0.9–5.3) | 1.1 (0.3–4.5) | 0.3 (0.1–1.2) | 0.8 (0.2–2.7) | 0.9 (0.5–1.5) |
| Sex (female) | 1 | 1 | 1 | 1 | 1 | 1 |
| Male | 1.1 (0.7–1.6) | 0.9 (0.6–1.4) | 0.5 (0.2–0.9) | 1.5 (0.6–3.4) | 1.3 (0.6–2.5) | 0.8 (0.6–1.0) |
| Access to digital tools | 1 | 1 | 1 | 1 | 1 | 1 |
| Lack of access to digital tools | 1.0 (0.5–1.8) | 2.0 (0.9–4.5) | 1.0 (0.3–3.3) | 0.4 (0.1–1.6) | 0.6 (0.2–1.8) | 1.2 (0.7–2.0) |
| Norwegian | 1 | 1 | – | 1 | 1 | 1 |
| Migrant | 0.8 (0.4–1.6) | 4.3 (1.3–14) | 7.8 (0.9–69) | 1.1 (0.3–4.3) | 1.9 (1.0–3.7) | |
| Household income | ||||||
| <250,000 | 1 | 1 | 1 | 1 | 1 | 1 |
| 250,000–500,000 | 0.7 (0.5–1.1) | 1.2 (0.7–1.8) | 0.8 (0.3–1.8) | 1.3 (0.5–3.1) | 0.9 (0.5–1.9) | 0.9 (0.7–1.3) |
| >500,000 | 0.8 (0.5–1.4) | 1.5 (0.9–2.6) | 0.6 (0.3–1.6) | 1.9 (0.7–5.7) | 1.2 (0.5–2.5) | 1.2 (0.8–1.8) |
| Number of people in household | ||||||
| 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 2 | 0.9 (0.5–1.4) | 2.0 (1.2–3.5) | 0.8 (0.3–2.0) | 1.8 (0.7–4.6) | 1.6 (0.7–3.8) | 1.4 (0.9–2.1) |
| 3–4 | 1.0 (0.6–1.6) | 1.5 (0.9–2.3) | 0.6 (0.2–1.4) | 2.0 (0.7–5.3) | 1.0 (0.5–2.1) | 1.1 (0.8–1.6) |
| ⩾5 | 0.6 (0.3–1.1) | 0.8 (0.4–1.5) | 0.6 (0.2–1.8) | 1.2 (0.3–4.2) | 2.1 (0.8–5.5) | 0.8 (0.5–1.2) |
Data are presented for health care from general practitioner, psychologist and physiotherapist, hospital follow-up, nursing care, other health care and any type of health care as odds ratios with 95% confidence intervals.