Table 1 shows the descriptive statistics of the study population (individuals with at least one sick leave episode throughout the study period, N = 2,850). The mean number of sick leave episodes was 10.6 occurrences over a four-year period. 52.6% of sample participants were females, and the mean age of the sample was 38 years old. Most of the HCWs were registered nurses (RN) (30.6%), followed by clerks and clinical assistants (CA), (26.5%), and orderly, practical nurses (PA), and nursing assistants (NA) (15.9%). More than half of the sample participants (54.5%) were involved in direct patient care, and the remaining were either in administrative positions (19.6%), indirect patient care (15.6%), and physical labor (10.3%). The most prevalent medical conditions among the participants were respiratory problems (62.3%) and gastrointestinal problems (42.8%). The prevalence of smoking in the sample was high, reported for 51.5% of the employees.
Table 1: Descriptive statistics of healthcare workers with sick leave episodes (n= 2,850).
Variable
|
(n= 2850)
|
Mean ±SD
|
Sick Leave Episodes
|
10.6 ± 10.6
|
Sick leave duration
|
2.60 ± 4.5
|
|
Frequency (%)
|
Gender
Male
Female
|
1351 (47.4%)
1499 (52.6%)
|
Marital Status
Married
Not married
|
1686 (59.2%)
1164 (40.8%)
|
Division
Physical labor
Direct patient care
Administrator
Indirect patient care
|
294 (10.3%)
1552 (54.5%)
558 (19.6%)
446 (15.6%)
|
Grade
G1 to G8
G9 to G13+
|
1309 (45.9%)
1541 (54.1%)
|
Position
Clerk- CA
RN
Orderly- PN-NA
Housekeeping
Dietary
Laboratory technician
Physical plant, laundry, Central Sterilization Department (CSD), motorpool
Physical therapist
Radiology technician
Respiratory therapist
Pharmacist
Other
|
755 (26.5%)
871 (30.6%)
454 (15.9%)
124 (4.4%)
75 (2.6%)
164 (5.8%)
205 (7.2%)
27 (0.9%)
73 (2.6%)
22 (0.8%)
73 (2.6%)
7 (0.2%)
|
Medical History
Respiratory problems (cough, bronchitis, other respiratory problems)
Yes
Gastrointestinal problems (diarrhea, irritable bowel syndrome, abdominal pain)
Yes
Back pain
Yes
Bronchitis
Yes
Headache
Yes
Dizziness
Yes
Anemia
Yes
Anxiety
Yes
Hypertension
Yes
Chest pain
Yes
Diabetes
Yes
Allergy
Yes
Depression
Yes
Kidney Disease
Yes
|
1772 (62.2%)
1198 (42.8%)
816 (28.6%)
316 (11.1%)
293 (10.3%)
230 (8.1%)
220 (7.7%)
144 (5.1%)
127 (4.5%)
74 (2.6%)
34 (1.2%)
27 (0.9%)
12 (0.4%)
4 (0.1%)
|
Lifestyle Factors
|
|
Smoking
Yes (current and ex-smoker)
|
1445 (51.5%)
|
Exercise
Yes
|
1351 (55.2%)
|
Alcohol
Yes
|
492 (17.3%)
|
SD: standard deviation
Note: this table should be placed right after line 149.
When looking at the distribution of sick leave diagnoses, it appears that infectious diseases and musculoskeletal disorders were responsible for the highest number of sick leave episodes: 6,598 (21,7%) and 6,097 (20.0%) respectively (table 2). Mean duration of sick leave ranged between 2.39 days (Genitourinary, gynecological and obstetrics diagnosis) and 2.87 days (respiratory illness diagnosis). All diagnoses had a short sick leave duration.
Table 2: Distribution of sick leave episodes and duration of sick leave by diagnosis.
Sick leave diagnosis
|
Number of episodes* N (%)
|
Sick leave duration (in days)
Mean ± SD
|
Infectious diseases
|
6,598 (21.7%)
|
2.6 ± 5.0
|
Musculoskeletal disorders
|
6,097 (20.0%)
|
2.6 ± 5.1
|
Surgeries/ injuries
|
4,792 (15.7%)
|
2.5 ± 4.5
|
Gastrointestinal problems
|
3,714 (12.2%)
|
2.6 ± 4.5
|
Other diagnoses&
|
3,175 (10.4%)
|
2.5 ± 4.4
|
Genitourinary, gynecology obstetrics
|
1,612 (5.3%)
|
2.4 ± 3.9
|
Neurological, psychological, and behavioral problems
|
1,270 (4.2%)
|
2.6 ± 6.2
|
Dental problems
|
931 (3.0%)
|
2.7 ± 5.1
|
Respiratory illness
|
629 (2.0%)
|
2.9 ± 6.6
|
Ophthalmology
|
624 (2.0%)
|
2.8 ± 4.6
|
Cardiovascular diseases
|
395 (1.3%)
|
2.3 ± 3.2
|
Ear, nose, and throat conditions (ENT)
|
331 (1.0%
|
2.6 ± 4.8
|
Dermatology
|
250 (0.8%)
|
2.3 ± 3.5
|
Total
|
30,418
|
|
*the number of sick leave episodes taken in the 4-year period.
&other diagnoses include metabolic, congenital, hematology/oncology, health maintenance, general symptoms, and non-specified diagnoses
SD: standard deviation
Note: this table should appear right after line 154.
Bivariate analysis was run between the number of sick leave times and each predictor. Predictors with a p-value < 0.2 were chosen to be entered in the multiple linear regression model, and these include age, grade, marital status, position, certain medical history conditions (gastrointestinal problems, back pain, anxiety, chest pain, diabetes, headache, respiratory problems, kidney disease, and allergy), smoking, and exercise (Table 3). Gender was also included due to its importance.
Table 3
Bivariate analysis of number of sick leaves with all potential predictors.
|
Number of sick leaves
Mean (± SD)
|
p-value
|
Gender
Male
Female
|
10.3 ± 10.6
10.8 ± 10.6
|
0.268
|
Age
< 35
≥ 35
|
9.1 ± 9.2
12.1 ± 11.8
|
< 0.001*
|
Marital status
Married
Not married
|
11.6 ± 11.1
9.1 ± 9.7
|
< 0.001*
|
Grade
G1 to G8
G9 to G13
|
13.5 ± 12.3
8.1 ± 8.2
|
< 0.001***
|
Position
Clerk-CA
RN
Orderly- PN-NA
Housekeeping
Dietary
Lab technician
Physical plant, laundry, CSD, motorpool
Physical therapist
Radiology technician
Respiratory therapist
Pharmacist
Other
|
8.5 ± 9.0
10.3 ± 9.5
13.8 ± 12.3
14.3 ± 14.5
14.4 ± 14.2
8.2 ± 8.6
14.2 ± 12.9
6.9 ± 5.5
6.9 ± 7.4
9.3 ± 6.4
6.1 ± 5.9
5.3 ± 6.5
|
< 0.001*
|
Medical History
Respiratory Problems
Yes
No
Gastrointestinal problems
Yes
No
Back pain
Yes
No
Headache
Yes
No
Dizziness
Yes
No
Anemia
Yes
No
Anxiety
Yes
No
Hypertension
Yes
No
Bronchitis
Yes
No
Chest pain
Yes
No
Diabetes
Yes
No
Allergy
Yes
No
Depression
Yes
No
Kidney Disease
Yes
No
|
11.5 ± 10.9
9.0 ± 9.8
11.9 ± 11.2
9.6 ± 10.1
13.3 ± 11.5
9.5 ± 10.1
12.2 ± 10.8
10.4 ± 10.6
11.1 ± 9.7
10.5 ± 10.7
7.0 ± 5.3
10.6 ± 10.7
13.9 ± 13.9
10.4 ± 10.4
10.5 ± 11.8
10.6 ± 10.6
12.3 ± 12.3
10.3 ± 10.4
13.2 ± 13.5
10.5 ± 10.5
8.2 ± 8.5
10.6 ± 10.6
9.7 ± 9.9
10.6 ± 10.7
12.3 ± 10.4
10.6 ± 10.6
19.8 ± 9.7
10.5 ± 10.6
|
< 0.001*
< 0.001*
< 0.001*
0.006***
0.409
0.224
0.003***
0.942
0.007***
0.087
0.195
0.002***
0.561
0.083
|
Lifestyle Factors
Alcohol
Yes
No
Exercise
Yes
No
Smoking
Yes (current and ex-smoker)
No
|
10.5 ± 10.6
10.9 ± 10.7
10.5 ± 10.5
11.2 ± 10.8
12.2 ± 11.5
8.9 ± 9.4
|
0.403
0.119
< 0.001*
|
*p < 0.05; **p < 0.01; ***p < 0.001 |
In the multivariate analysis, the following variables remained significant predictors of taking a higher number of sick leave episodes: the female gender, older age (35 years or above), G1-G8 grade and marital status. Working in the following positions also showed to be significant predictors: dietary, RN, orderly-PN-NA, housekeeping, and physical plant, laundry, motor pool, CSD staff. As for medical history, the following medical conditions and symptoms remained significant in the model: gastrointestinal problems, back pain, anxiety, headache, respiratory problems, and kidney disease. Finally, being a smoker remained a significant predictor (Table 4).
Table 4
Linear regression of the predictors of number of sick-leave episodes.
|
|
95% CI
|
|
B
|
Lower Bound
|
Upper Bound
|
p-value
|
Gender- ref male
Female
|
2.415
|
1.581
|
3.249
|
< 0.001***
|
Age – ref less than 35 years
(≥ 35)
|
2.096
|
1.227
|
2.965
|
< 0.001***
|
Grade – ref grade G9 to G13
G1 to G8
|
5.859
|
4.776
|
6.941
|
< 0.001***
|
Marital status- ref not married
Married
|
1.725
|
0.882
|
2.568
|
< 0.001***
|
Position- ref clerk
|
Orderly-PN-NA
|
2.527
|
1.223
|
3.831
|
< 0.001***
|
Housekeeping
|
3.319
|
1.277
|
5.361
|
0.001**
|
Dietary
|
3.593
|
1.097
|
6.089
|
0.005*
|
Physical plant, laundry, CSD, motor pool
|
3.921
|
2.282
|
5.561
|
< 0.001***
|
RN
|
5.302
|
4.138
|
6.466
|
< 0.001***
|
Medical History- ref no
|
Gastrointestinal problem
|
2.220
|
1.436
|
3.004
|
< 0.001***
|
Anxiety
|
3.061
|
1.352
|
4.770
|
< 0.001***
|
Back pain
|
2.947
|
2.081
|
3.813
|
< 0.001***
|
Headache
|
2.184
|
0.922
|
3.447
|
0.001**
|
Kidney
|
11.277
|
1.763
|
20.791
|
0.020*
|
Respiratory problems
|
2.213
|
1.414
|
3.012
|
< 0.001***
|
Lifestyle factors
|
Smoking- ref non-smoker
|
|
Smoker (current and ex-smoker)
|
1.671
|
0.849
|
2.493
|
< 0.001***
|
Employees with a kidney diagnosis seemed to take the highest number of sick leave times, with an average of 11 more times as compared to those with no kidney diagnosis.