Survey population
We sent out 944 questionnaires, and 375 primary HCWs (response rate of 39.7%) responded; 59 were excluded because they did not meet the inclusion criteria (non-Shanghai area questionnaires); three participants were excluded because they answered fewer than two questions. A total of 313 qualified questionnaires were eligible for analysis.
The median age of our sample of 313 respondents was 37 years (interquartile range [IQR], 32–42), and the majority of the sample consisted of females (77.6%). The proportion of Shanghai-origin respondents was 62.3%. Over three-quarters of respondents (77.6%) were married, and 67.1% were the primary caretaker for at least one child. The sample mainly consisted of physicians (40.9%) and nurses (39.3%). Regarding job level, the majority of the sample (91.4%) had junior and mid-level titles. Table 1 lists the roles of the primary HCWs in our sample. Further, 262 (83.7%) of the survey respondents reported having two or more frontline workplaces during the pandemic. The top five job locations were enclosed communities (71.2%), outpatient clinics (55.0%), quarantine hotels (46.0%), inpatient wards (36.7%), and Fangcang shelter hospitals (8.0%, large scale temporary hospitals built in public venues).
Table 1
Demographics of Shanghai primary healthcare workers who responded to a survey on sleep health during the first wave of the COVID-19 pandemic in 2022 Data are presented as median (interquartile range) or n (%).
|
|
All participants (n = 313)
|
Age, median (IQR)
|
|
37 (32–42)
|
Sex, n (%)
|
|
304
|
|
Male
|
68 (22.4%)
|
|
Female
|
236 (77.6%)
|
Origin, n (%)
|
|
304
|
|
Origin Shanghai
|
195 (64.1%)
|
|
Origin Non-Shanghai
|
109 (35.9%)
|
Current level
|
|
|
|
Junior
|
121 (38.7%)
|
|
Intermediate
|
165 (52.7%)
|
|
Senior
|
27 (8.6%)
|
Relationship condition, n (%)
|
|
304
|
|
Single
|
43 (14.1%)
|
|
Unmarried but dating
|
20 (6.6%)
|
|
Married
|
236 (77.6%)
|
|
Divorced
|
5 (1.6%)
|
Number of family children, n (%)
|
|
313
|
|
0
|
103 (32.9%)
|
|
1
|
173 (55.3%)
|
|
2
|
37 (11.8%)
|
Age category of children, n (%)
|
|
|
|
Neonate/infant (< 1 year)
|
8 (3.9%)
|
|
Toddler/preschooler(1–4 yrs)
|
45 (21.8%)
|
|
School-age (5–12 yrs)
|
79 (38.3%)
|
|
Adolescents (12–18 yrs)
|
58 (28.2%)
|
|
Adults (> 18 yrs)
|
44 (21.4%)
|
Primary healthcare role, n (%)
|
|
313
|
|
Physician
|
128 (40.9%)
|
|
Nurse
|
123 (39.3%)
|
|
Hospital administrator
|
17 (5.4%)
|
|
Pharmacist
|
11 (3.5%)
|
|
Radiology Technologist
|
8 (2.6%)
|
|
Logistic staff
|
6 (1.9%)
|
|
Rehabilitation Technician
|
5 (1.6%)
|
|
Other: Maternal and child health worker (n = 3), Public Health Worker (n = 2), social worker (n = 1), unspecified (n = 9)
|
15 (4.8%)
|
Job locations, n (%)
|
|
|
|
Enclosed communities
|
223 (71.2%)
|
|
Outpatient clinics
|
172 (55.0%)
|
|
Inpatient wards
|
209 (36.7%)
|
|
Quarantine hotels
|
144 (46.0%)
|
|
Fangcang shelter hospitals
|
25 (8.0%)
|
|
Administration office
|
7 (2.2%)
|
|
Centers for Disease Control and Prevention (CDC)
|
6 (1.9%)
|
IQR, Interquartile range |
Sleep Patterns Before And During The Outbreak
Before the pandemic, most primary HCWs experienced good ("Often," "Usually," or "Always") sleep regularity (69.3%), quality (53.4%), timing (86.6%), efficiency (53.7%), and duration (73.3%). Half (48.9%) of the respondents reported "often" staying awake throughout the day. However, the proportion of respondents with regular healthy sleep patterns decreased significantly during the pandemic (p < 0.001) (Fig. 1).
Figure 2A shows the proportion of respondents who reported the largest change in sleep patterns; nearly a third (32.3%) complained of sleeping later than the pre-pandemic time. Over four-fifths of primary HCWs (84.0%) showed a decline in at least one sleep dimension from the pre-pandemic levels, and over two-thirds (69.3%) showed a reduction in at least two dimensions; 16.3% of the individuals showed a decline in all six domains (Fig. 2B).
"Sleep quality" and "daytime sleepiness" showed the most significant deterioration from the pre-pandemic levels. As the pandemic continued, more than half of the primary HCWs (55.3%) experienced varying degrees of decline in "sleep quality". The proportion of HCWs who "often" had good sleep quality decreased from 53.4–18.2%, showing a reduction of 65.9% (p < 0.001). Before the pandemic, 51.1% of the survey respondents had already experienced chronic excessive daytime sleepiness, and this proportion reached a staggering 79.6% during the pandemic, showing an increment of 55.7% (p < 0.001). Additionally, 48.2% of the subjects reported an increase in daytime sleepiness frequency during the pandemic.
After excluding 145 (46.3%) HCWs with CSHS < 4 before the pandemic, we divided the remaining 168 HCWs into the intra-pandemic good sleep group (n = 58, 34.5%) and the poor sleep group (n = 110, 65.5%) for comparative analysis. Table 2 shows the basic information of the primary HCWs with good and poor intra-pandemic sleep behaviors. Of note, there were no differences in sex, medical roles, and levels between groups (P > 0.05). Participants with poor intra-pandemic sleep behaviors had significantly lower CSHS than those with good sleep habits (P < 0.001). Primary HCWs with high PSS scores (P < 0.001), especially scores > 28 (odds ratio [OR] 4.51; 95% confidence interval [CI], 2.09–9.72), had a higher likelihood of developing poor sleep patterns. Furthermore, those with burnout symptoms on a weekly basis had considerably increased odds of poor sleep during the pandemic (OR, 2.57; 95% CI, 1.32–5.03). (Table 3) After adjusting for significant differences between groups, a multivariable analysis was conducted. We found that higher "PSS score" was the only variable independently associated with increased risk of poor sleep during the pandemic (adjusted odds ratio [aOR], 1.12; 95% CI, 1.05–1.21).
Table 2
Comparison of basic information on primary healthcare workers with good and poor sleep behavior during the pandemic Data are presented as median (interquartile range) or n (%).
|
|
Poor intra-pandemic sleep behaviors (n = 110)
|
Good intra-pandemic sleep behaviors (n = 58)
|
P value
|
Median age, median (IQR)
|
|
38 (32–42)
|
39 (32–44)
|
0.953
|
sex, n (%)
|
|
|
|
0.734
|
|
male
|
24 (22%)
|
14 (25%)
|
|
|
female
|
84 (78%)
|
43 (75%)
|
|
Origin, n (%)
|
|
108
|
57
|
0.083
|
|
Origin Shanghai
|
67 (62%)
|
43 (75%)
|
|
|
Origin Non-Shanghai
|
41 (38%)
|
14 (25%)
|
|
Primary healthcare role, n (%)
|
|
110
|
58
|
0.924
|
|
Physician
|
40 (36%)
|
25 (43%)
|
|
|
Nurse
|
41 (37%)
|
20 (34%)
|
|
|
Hospital administrator
|
7 (6%)
|
3 (5%)
|
|
|
Pharmacist
|
4 (4%)
|
3 (5%)
|
|
|
Radiology Technologist
|
4 (4%)
|
2 (3%)
|
|
|
Others
|
14 (13%)
|
5 (9%)
|
|
The current level, n (%)
|
|
110
|
58
|
0.378
|
|
Junior
|
39 (35%)
|
22 (38%)
|
|
|
Intermediate
|
58 (53%)
|
33 (57%)
|
|
|
Senior
|
13 (12%)
|
3 (5%)
|
|
Relationship condition, n (%)
|
|
108
|
57
|
0.938
|
|
Single
|
12 (11%)
|
5 (9%)
|
|
|
Unmarried but dating
|
7 (6%)
|
4 (7%)
|
|
|
Married
|
86 (80%)
|
47 (82%)
|
|
|
Divorced
|
3 (3%)
|
1 (2%)
|
|
How the epidemic has altered one's occupation, n (%)
|
|
110
|
58
|
|
|
Increased work
|
99 (90%)
|
49 (84%)
|
0.294
|
|
Changed principal workplace
|
83 (75%)
|
38 (66%)
|
0.173
|
|
Changed job category
|
45 (41%)
|
23 (40%)
|
0.875
|
At least one child for which he or she is the primary carer, n (%)
|
|
77/110 (70%)
|
38/58 (66%)
|
0.552
|
Pandemic changed child care practice
|
|
63/110 (57%)
|
32/58 (55%)
|
0.794
|
Increased family burden
|
|
60/110 (55%)
|
27/58 (47%)
|
0.324
|
Daily COVID exposure
|
|
36/110 (33%)
|
17/58 (29%)
|
0.651
|
Respondents (or colleagues) COVID-positive, n (%)
|
|
80/110 (73%)
|
42/58 (72%)
|
0.965
|
IQR, interquartile range |
Table 3
Comparison of sleep and mental health between primary health care workers with good and poor sleep behavior during the pandemic Data are presented as n (%) or mean (standard deviation [SD]), unless otherwise specified.
|
Poor intra-pandemic sleep behaviors (n = 110)
|
Good intra-pandemic sleep behaviors (n = 58)
|
P value
|
The odds ratio of having poor pandemic sleep health
|
Pre-pandemic CSHS, mean ± SD
|
4.8 ± 0.6
|
4.9 ± 0.5
|
0.228
|
|
Intra-pandemic CSHS, mean ± SD
|
2.9 ± 0.6
|
4.7 ± 0.5
|
< 0.001
|
|
Pre-pandemic anxiety, n (%)
|
23 (21%)
|
9 (16%)
|
0.398
|
|
Pre-pandemic depression, n (%)
|
12 (11%)
|
6 (10%)
|
0.911
|
|
Pre-pandemic sleep apnea, n (%)
|
13 (12%)
|
3 (5%)
|
0.163
|
|
Pre-pandemic insomnia, n (%)
|
21 (19%)
|
5 (9%)
|
0.074
|
OR 2.50 (95% CI 0.89–7.03, P = 0.082)
|
Intra-pandemic depression screened positive, n (%)
|
31 (28%)
|
5 (9%)
|
< 0.001
|
OR 3.08 (95% CI 1.59–5.98, P = 0.001)
|
Burnout symptoms weekly, n (%)
|
59 (54%)
|
18 (31%)
|
0.005
|
OR 2.57 (95% CI 1.32–5.03, P = 0.006)
|
Average PSS score, mean ± SD
|
37.6 ± 9.8
|
27.5 ± 11.4
|
< 0.001
|
OR 1.10 (95% CI 1.06–1.15, P < 0.001)
|
PSS score > 28, n (%)
|
96 (87%)
|
35 (60%)
|
< 0.001
|
OR 4.51 (95% CI 2.09–9.72, P < 0.001)
|
Caffeinated beverage intake within 12 hours of bedtime (often-always)
|
27 (25%)
|
7 (12%)
|
0.056
|
OR 2.37 (95% CI 0.96–5.84, P = 0.061)
|
Prolonged viewing of electronic device screens at night (often-always)
|
64 (58%)
|
26 (45%)
|
0.099
|
OR 1.71 (95% CI 0.90–3.25, P = 0.100)
|
CSHS, Composite sleep health score; SD, Standard deviation; PSS, Perceived stress scale |
The respondents who reported pre-pandemic insomnia (OR, 2.50; 95% CI, 0.89–7.03), caffeinated beverage intake "often" within 12 hours of bedtime (OR, 2.37; 95% CI, 0.96–5.84) and prolonged viewing of electronic device screens "often" at night (OR, 1.71; 95% CI, 0.90–3.25) tended to sleep poorly, but this was not statistically significant. There were also no significant statistical differences in pre-pandemic CSHS, anxiety, depression, or sleep apnea between those with good and poor pandemic sleep (Table 3).
Impact Of The Pandemic On Primary Hcws’ Lives And Careers
The respondents' opinions of the impact of COVID-19 are summarized in “Table 4”. Self-reported pandemic adjustments in job requirements were almost universal (98.4% of respondents) and challenging situations were also frequently reported (97.8%); 89.4% were assigned SARS-CoV-2 nucleic acid testing duties and night attendance. Over two-thirds (70.3%) were concerned about the closure-induced difficulties in the case of unexpected sickness and lack of access to medical care for self/family.
Table 4
A description of how COVID-19 affected the lives and careers of primary healthcare workers during the first wave of Shanghai in 2022. Reporting of data is in the form of mean ± SD or n (%).
|
|
All respondents (n = 313)
|
What's changed in one's job in the first wave of the 2022 Shanghai COVID-19 pandemic? (multiple-choice questions)
|
|
n = 313
|
|
Increased working hours
|
271 (86.6%)
|
|
Decreased working hours
|
1 (0.3%)
|
|
Changed the primary workplace
|
239 (76.4%)
|
|
Changed the nature of primary work
|
133 (42.5%)
|
|
No change
|
5 (1.6%)
|
|
Others
|
6 (1.9%)
|
What difficulties have the first wave of the 2022 Shanghai COVID-19 pandemic caused you? (multiple-choice questions)
|
|
n = 313
|
|
more domestic responsibilities
|
174 (55.6%)
|
|
Frequent SARS-CoV-2 nucleic acid testing duties and occasional night attendance
|
281 (89.8%)
|
|
Concern about unexpected sickness and difficulties in accessing medical care for self/family
|
220 (70.3%)
|
|
Financial challenges
|
108 (34.5%)
|
|
Increased emotional stress
|
114 (36.4%)
|
|
No personally difficulty
|
7 (2.2%)
|
|
Others
|
9 (2.9%)
|
Respondents who care for at least one child at home
|
|
n = 210
|
|
Changed the child care management plan
|
180 (85.7%)
|
|
Additional adult to assist with child care in the family
|
129 (61.4%)
|
|
Spending more than 30 minutes daily assisting your children with educational responsibilities
|
80 (31.8%)
|
|
Change of residence because of concern about children or older adults being infected
|
116 (55.2%)
|
Intra-pandemic mental health issues
|
|
|
|
Burnout symptoms weekly
|
146 (46.6%)
|
|
Depression Screened positive
|
173 (55.3%)
|
|
The average score on the 14-items PSS
|
35.4 ± 11.1
|
|
PSS > 28 (moderate to severe stress)
|
255 (81.5%)
|
|
Frequently having nightmares
|
43 (13.7%)
|
Daytime habits reported as "often" or "usually", or "always", which may impact sleep
|
|
|
|
Caffeinated beverage intake within 12 hours of bedtime
|
65 (20.8%)
|
|
Drinking alcohol within 6 hours before bedtime
|
11 (3.5%)
|
|
Smoking within 6 hours before bedtime
|
16 (5.1%)
|
|
Prolonged viewing of electronic device screens at night
|
179 (57.2%)
|
PSS, Perceived stress scale; SD, Standard deviation |
Over a fifth (23.3%) had positive results on depression screening. The average PSS score was 35.4 ± 11.1, and 81.5% of respondents scored at least 29 points, which indicates moderate-to-severe stress. Additionally, about one-fifth (20.8%) "often" took caffeinated beverages 12 hours before bedtime, and more than half (57.2%) of the participants would frequently view electronic devices from prolonged periods at night.
Further, 85.7% of the respondents with children (n = 210) cited a loss of resources for childcare due to the epidemic. Over half (55.2%) changed their residence because of concerns about children or older adults being infected by COVID.
Contact With Patients Or Colleagues With Confirmed Covid-19 Status
Over half (58.8%) of the respondents reported weekly or daily exposure to COVID-19 patients; 229 (73.2%) stated that their colleagues had been diagnosed with COVID-19. A significantly higher proportion of nurses (95/123,77.2%) than physicians (84/128, 65.6%) (P < 0.001) were diagnosed with COVID-19.