Sickness absence among employees of healthcare organizations in the public sector in Mongolia: A cross-sectional study

概要

主論文の要旨

Sickness absence among employees of healthcare
organizations in the public sector in Mongolia:
A cross-sectional study
モンゴルの公立医療機関における従業員の病気欠勤について：
横断的研究

名古屋大学大学院医学系研究科
社会生命科学講座

総合医学専攻

医療行政学分野

（指導：山本 英子

教授）

SUKHEE Erdenetuya

【Introduction】
Sickness absence (SA) is one of the main concerns related to human resource management.
Health facilities are a high-risk environment for healthcare workers (HCWs) to have infections
and other diseases because of tough working conditions This study aimed to understand the
rate of SA among employees of public healthcare organizations in Mongolia, to identify factors
associated with long-term SA, and to estimate costs due to SA.
【Methods】
This cross-sectional study included employees of 282 facilities (193 healthcare organizations
in the public sector and 89 family health centers) who had certified SA from 2016 to 2018. SA
is based on the sick leave certificates issued by doctors and compensation for sick leave is 5075% of the wage, which is decided by years in employment. Socio-demographic factors and
occupational characteristics of absentees and the data of absences were collected. A logistic
regression analysis was performed to identify factors associated with long-term SA (≥15 days)
among employees who had SA. Absence parameters (Appendix) and the average costs due to
SA were calculated. The total cost due to SA at all public healthcare organizations was
estimated. This study was approved by the Ethics Committee of the Ministry of Health,
Mongolia (approval number 277).
【Results】
From 2016 to 2018, there were 13,653 absentees and 21,043 SA with a total 202,716 absence
days (Table 1). The number of absentees accounted for 19.7-21.5% of the total number of
employees. The AR was 0.9% and the absence FR was 31.8% during the three years. The
average length of absence was 9.63 days, the average absence length per absentee was 14.85
days, the average absence length per employee was 3.06 days, and the average frequency was
1.54. There were no differences of each absence parameter in the three years.
Of the total 21,043 absences, the most common diagnosis was diseases of the genitourinary
system (n=3,436), followed by disease of the digestive system (n=2,421), diseases of the
circulatory system (n=2,381), injury, poisoning and certain other consequences of external
causes (n=2,283), and diseases of the nervous system (n=1,991) (Table 2).
Most absentees were females (89.6%), 39 years old or younger (45.8%), married (82.7%),
having children (91.6%), and living at their own house or apartment (63.5%) (Table 3). In
terms of occupational factors, most absentees were nurse/midwife/feldsher (40.2%), worked
for the secondary level of healthcare organizations (52.6%), had nine years or less in
employment (42.8%), and had night shift (52.3%). Of all absentees, 4,453 absentees (32.6%)
repeated SA within a year. The characteristics of absentees in each year were almost the same.
Of 13,653 absentees, 3,809 absentees (27.9%) had 15 absence days or more in a year. In
multivariate analysis including all socio-demographic and work-related variables, the age

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groups of 40-49 years old (OR=1.17, 95% CI 1.04-1.31) and 50 years old or older (OR=1.30,
95% CI 1.12-1.49), working for the secondary level (OR=1.15, 95% CI 1.02-1.30) and the
tertiary level (OR=1.31, 95% CI 1.15-1.49) of healthcare organizations, 10-19 years in
employment (OR=1.14, 95% CI 1.02-1.27) and night shift (OR=1.13, 95% CI 1.04-1.23) were
significantly associated with having long-term SA.
Estimation of costs of absenteeism among HCWs at all the 282 healthcare organizations
from 2016 to 2018 showed that the average lost salary was 34.7 USD, the average lost
productivity was 129.8 USD, the average compensation was 130.9 USD, and the total average
cost was 295.5 USD (Table 5). The estimated total cost at all 653 healthcare organizations in
the public sector per year was 1,796,993 USD including total lost productivity (789,238 USD),
total lost salary (210,099 USD), and total compensation (795,767 USD) (Table 5).
【Discussion】
In this study, the AR of HCWs in the public sector in Mongolia was 0.9%, which was lower
compared to that in other countries. Previous studies demonstrated that the AR of HCWs was
different depending on countries and kinds of professionals. The reasons for the lower AR in
this study may be due to the regulations on medically certified SA and low compensation. A
shortage of nursing staff may also contribute to the low AR because nurses may feel pressure
to work and therefore may not take SA.
The top three causes of SA among HCWs were diseases of the genitourinary system, the
digestive system, and the circulatory system, which were common diseases in Mongolian
population. The fifth common cause of SA among HCWs was diseases of the nervous system,
which may be caused due to stress by heavy workload or anxiety related to their work.
Mongolian HCWs might have uncertified SA or work when they had minor respiratory diseases
such as acute respiratory illness or influenza-like illness.
Factors associated with taking a long-term absence among HCWs who had SA were the age
group ≥40 years old, 10-19 years in employment compared to 9 years or less, working at the
secondary and tertiary levels compared to the primary level, and having night shifts. Younger
workers have fewer chronic diseases, can recover from illness and injury earlier, and are more
likely to have absences due to minor health problems or motivational issues compared to older
workers. Shift work negatively affects an employee’s health and can increase the risk of
cardiovascular diseases, gastrointestinal disorders, and cancer. HCWs of organizations in the
secondary or tertiary level might be more exposed to physical and mental stress and have low
group cohesiveness. To reduce and manage long-term SA, especially at large healthcare
organizations, increasing the motivation of HCWs to return to their work by early intervention
by trained managers and robust implementation of SA policies are recommended.
The total cost due to SA in the public sector was estimated to be 1,796,993 USD per year,
which accounted for 0.8% of the average annual expenditure of all healthcare organizations in

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the public sector (222,615,514 USD). The results of this study showed that the absenteeism
cost at health organizations in the public sector are substantial, although the AR was low.
According to the Labor Law, the working hours should be reduced when employees are
diagnosed with occupational diseases or have an industrial accident but not general diseases
based on the decision of Occupational Health Center. Policy makers should evaluate the policy
and regulations related to SA to make sure healthy life of HCWs in Mongolia.
There are some limitations to this study. First, this study included only medically certified
SA but not non-certified SA or short-term SA for 1-3 days. Second, this study did not include
all healthcare organizations in the public sector, because the response rate was 43.2%. Third,
this study included only HCWs who had SA in 2016-2018 but did not include all HCWs.
【Conclusions】
The AR of HCWs in the public sector was 0.9% and the most common cause of certified SA
was diseases of the genitourinary system. Factors associated with taking a long-term SA among
HCWs who had SA were 40 years old or older, 10-19 years in employment compared to 9 years
or shorter, higher organizational level, and having night shifts. The average total cost per
absentee was estimated to be 295.5 USD and the total cost due to SA in the public sector was
estimated to be 1,796,993 USD per year. To reduce costs due to SA and promote the well-being
and health of HCWs, policy makers should review the policy and regulations related to SA and
develop national guidelines about SA for employers, healthcare managers, and absent HCWs
in Mongolia.

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Table 1 Parameters of sickness absences at 282 healthcare organizations from 2016 to 2018
Variables

2016

2017

2018

Total

Total number of employees

21,285

22,115

22,828

66,228

Available workdays (days)

247

249

252

748

5,257,395

5, 506,635

5,752,656

16,516,686

Number of absences

4,195
(19.7%)
6,320

4,745
(21.5%)
7,356

4,713
(20.6%)
7,367

13,653
(20.6%)
21,043

Total absence days

61,050

70,402

71,264

202,716

Absence rate (%)

0.8

0.9

0.9

0.9

Absence frequency rate (%)

29.7

33.3

32.3

31.8

9.66
[9.50-9.82]
14.55
[14.13-15.03]
2.87
[2.85-2.89]
1.51
[1.48-1.54]

9.57
[9.44-9.71]
14.84
[14.39-15.28]
3.18
[3.16-3.21]
1.55
[1.52-1.58)

9.67
[9.54-9.82]
15.12
[14.65-15.59]
3.12
[3.10-3.14]
1.56
[1.53-1.59]

9.63
[9.55-9.71]
14.85
[14.60-15.10]
3.06
[3.05-3.07]
1.54
[1.52-1.56]

Total available workdays of all
employees (days)
Number of absentees (%)

Average absence length (days)
[95% CI]
Average absence length per
absentee (days) [95% CI]
Average absence length per
employee (days) [95% CI]
Average frequency of absences
per absentee [95% CI]
CI, confidence interval.

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Table 2 The absence frequency and the average length of absence according to top 10 ICD-10 diagnostic
categories from 2016 to 2018 (n=21,043)

Diseases of the genitourinary system

Absence
n (%)
3,436 (16.3%)

Average absence length (days)
[95% CI]
8.71 [8.58-8.86]

Diseases of the digestive system

2,421 (11.5%)

8.53 [8.34-8.73]

Diseases of the circulatory system

2,381 (11.3%)

9.07 [8.88-9.28]

Injury, poisoning and certain other consequences
of external causes
Diseases of the nervous system

2,283 (10.8%)

13.55 [13.19-13.93]

1,991 (9.5%)

8.97 [8.77-9.17]

Diseases of the respiratory system

1,726 (8.2%)

7.58 [7.39-7.78]

Diseases of the musculoskeletal system and
connective tissue
Neoplasms

1,232 (5.9%)

9.31 [9.04-9.59]

937 (4.5%)

14.05 [13.53-14.56]

Certain infectious and parasitic disease

720 (3.4%)

12.89 [12.21-13.70]

Diseases of the skin and subcutaneous tissue

609 (2.9%)

8.72 [8.38-9.09]

ICD-10 diagnostic category

ICD-10, International Classification of Diseases 10th Revision; CI, confidence interval.

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Table 3 Socio-demographic and occupational characteristics of absentees (n=13,653)
Variables
Gender
Male
Female
Age group (years old)
≤39
40-49
50≤
Marital status
Single/divorced
Married
Number of children
0
1-2
3≤
Housing type
Own house/apartment
Rental/dormitory
Traditional ger
Occupation
Nurse/midwife/feldsher
Other medical
Non-medical/director/
manager
Level of health organization
Primary
Secondary
Tertiary
Health department
Years in employment (years)
≤9
10-19
20≤
Night shift
No
Yes
Repeat of sickness absence
No
Yes

2016
(n=4,195)
n (%)

2017
(n=4,745)
n (%)

2018
(n=4,713)
n (%)

Total
(n=13,653)
n (%)

453 (10.8)
3,742 (89.2)

488 (10.3)
4,257 (89.7)

475 (10.1)
4,238 (89.9)

1,416 (10.4)
12,237 (89.6)

1,929 (46.0)
1,401 (33.4)
865 (20.6)

2,118 (44.6)
1,545 (32.6)
1,082 (22.8)

2,211 (46.9)
1,436 (30.5)
1,066 (22.6)

6,258 (45.8)
4,382 (32.1)
3,013 (22.1)

777 (18.5)
3,418 (81.5)

782 (16.5)
3,963 (83.5)

804 (17.1)
3,909 (82.9)

2,363 (17.3)
11,290 (82.7)

304 (7.2)
2,708 (64.6)
1,183 (28.2)

403 (8.5)
2,968 (62.2)
1,374 (29.0)

444 (9.4)
2,993 (63.5)
1,276 (27.1)

1,151 (8.4)
8,669 (63.5)
3,833 (28.1)

2,688 (64.1)
234 (5.6)
1,273 (30.3)

3,038 (64.0)
251 (5.3)
1,456 (30.7)

2,948 (62.6)
247 (5.2)
1,518 (32.2)

8,674 (63.5)
732 (5.4)
4,247 (31.1)

1,690 (40.3)
1,275 (30.4)
1,230 (29.3)

1,887 (39.8)
1,463 (30.8)
1,395 (29.4)

1,910 (40.5)
1,431 (30.4)
1,372 (29.1)

5,487 (40.2)
4,169 (30.5)
3,997 (29.3)

564 (13.4)
2,179 (52.0)
1,363 (32.5)
89 (2.1)

660 (13.9)
2,466 (52.0)
1,525 (32.1)
94 (2.0)

646 (13.7)
2,540 (53.9)
1,435 (30.4)
92 (2.0)

1,870 (13.7)
7,185 (52.6)
4,323 (31.7)
275 (2.0)

1,757 (41.9)
1,028 (24.5)
1,410 (33.6)

1,979 (41.7)
1,155 (24.3)
1,611 (34.0)

2,107(44.7)
1,168 (24.8)
1,438 (30.5)

5,843 (42.8)
3,351 (24.5)
4,459 (32.7)

1,964 (46.8)
2,231 (53.2)

2,316 (48.8)
2,429 (51.2)

2,237 (47.5)
2,476 (52.5)

6,517 (47.7)
7,136 (52.3)

2,883 (68.7)
1,312 (31.3)

3,214 (67.7)
1,531 (32.3)

3,103 (65.8)
1,610 (34.2)

9,200 (67.4)
4,453 (32.6)

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Table 4 Factors associated with having long-term sickness absences (n=13,653)
Variables

Short-term
n (%)

Long-term
n (%)

OR
(95% CI)

Gender
Male
1,029 (72.7)
387 (27.3)
1 (Reference)
Female
8,815 (72.0)
3,422 (28.0)
1.03 (0.91-1.17)
Age group (years old)
≤39
4,690 (74.9)
1,568 (25.1)
1 (Reference)
40-49
3,090 (70.5)
1,292 (29.5)
1.25 (1.15-1.36)***
50≤
2,064 (68.5)
949 (31.5)
1.37 (1.25-1.51)***
Marital status
Single/divorced
1,713 (72.5)
650 (27.5)
1 (Reference)
Married
8,131 (72.0)
3,159 (28.0)
1.02 (0.93-1.13)
Number of children
0
857 (74.5)
294 (25.5)
1 (Reference)
1-2
6,231 (71.9)
2,438 (28.1)
1.14 (0.99-1.31)
3≤
2,756 (71.9)
1,077 (28.1)
1.14 (0.98-1.32)
Housing type
Own house/apartment
6,233 (71.9)
2,441 (28.1)
1 (Reference)
Rental/dormitory
546 (74.6)
186 (25.4)
0.87 (0.73-1.03)
3,065 (72.2)
1,182 (27.8)
0.98 (0.91-1.07)
Traditional ger
Occupation
Nurse/midwife/feldsher
3,932 (71.7)
1,555 (28.3)
1 (Reference)
Other medical
3,091 (74.1)
1,078 (25.9)
0.88 (0.80-0.97)**
Non-medical/director/
2,821 (70.6)
1,176 (29.4)
1.05 (0.96-1.15)
manager
Level of health organization
Primary
1,413 (75.6)
457 (24.4)
1 (Reference)
Secondary
5,214 (72.6)
1,971 (27.4)
1.17 (1.04-1.31)**
Tertiary
3,013 (69.7)
1,310 (30.3)
1.34 (1.19-1.52)***
Health department
204 (74.2)
71 (25.8)
1.08 (0.80-1.44)
Years in employment (years)
≤9
4,383 (75.0)
1,460 (25.0)
1 (Reference)
10-19
2,373 (70.8)
978 (29.2)
1.24 (1.12-1.36)***
20≤
3,088 (69.3)
1,371 (30.7)
1.33 (1.22-1.45)***
Night shift
No
4,788 (73.5)
1,729 (26.5)
1 (Reference)
Yes
5,056 (70.9)
2,080 (29.1)
1.14 (1.06-1.23)**
OR, odds ratio; AOR, adjusted odds ratio; CI, confidence interval.
*
P<0.05, **P<0.01, ***P<0.001.
†
Adjusted for all variables listed in the table.

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AOR†
(95% CI)
1 (Reference)
1.02 (0.89-1.15)
1 (Reference)
1.17 (1.04-1.31)*
1.30 (1.12-1.49)***
1 (Reference)
1.02 (0.91-1.13)
1 (Reference)
1.00 (0.85-1.16)
0.93 (0.78-1.10)
1 (Reference)
0.91 (0.77-1.09)
0.99 (0.91-1.08)
1 (Reference)
0.91 (0.83-1.00)
1.06 (0.97-1.17)
1 (Reference)
1.15 (1.02-1.30)*
1.31 (1.15-1.49)***
1.16 (0.86-1.56)
1 (Reference)
1.14 (1.02-1.27)*
1.14 (0.99-1.30)
1 (Reference)
1.13 (1.04-1.23)**

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Absentee
(n)

Lost productivity
(USD)
Total
Average‡

795,767

-

-

112.0
107.0
123.5

149.4

125.4
124.5
126.5

112.3
115.8
102.1

130.9
121.8
137.5

1USD = 2,489 Mongolian Tugriks (MNT) in 2016, 2,427 MNT in 2017, and 2,644 MNT in 2018.
†
All healthcare organizations of the tertiary level located in Ulaanbaatar City.
‡
Average per absentee.
§
The total number of healthcare organizations including family health centers was 654 in 2016, 652 in 2017, and 653 in 2018.
¶
The estimated average number of absentees per year.

30,802
20,430
10,372

32.1
30.4
36.0

900,720
518,997
381,723

32.6
31.6
34.1
645,883

210,064
161,342
48,722

30.5
29.5
33.6

40.2

1,787,470
700,769
1,086,701

Compensation
(USD)
Total
Average‡

34.7
31.0
37.4

Lost salary
(USD)
Total
Average‡

Total
13,653
1,772,805
129.8
473,951
Rural
5,752
682,704
118.7
178,508
Urban
7,901
1,090,101
138.0
295,443
Primary level
Total
1,870
204,020
109.1
57,096
Rural
1,393
150,182
107.8
41,089
Urban
477
53,838
112.9
16,007
Secondary level
Total
7,185
893,613
124.4
234,431
Rural
4,168
512,407
122.9
131,610
Urban
3,017
381,206
126.4
102,821
Tertiary level
Total (urban)†
4,323
644,165
149.0
173,595
Health department
Total
275
31,007
112.8
8,829
Rural
191
20,114
105.3
5,809
Urban
84
10,893
129.7
3,020
Estimation at all healthcare organizations in the public sector (per year)§
Total
6,078.2¶
789,238
210, 099

Level of
organizations

1,796,993

70,638
46,353
24,285

1,463,643

2,028,764
1,163,014
865,750

471,180
352,613
118,567

-

256.9
242.7
289.1

338.6

282.4
279.0
287.0

252.0
253.1
248.6

295.5
271.6
312.9

Total cost
(USD)
Total
Average‡
4,034,226
1,561,981
2,472,245

Table 5 Absenteeism costs of healthcare workers from 2016 to 2018 according to the levels of healthcare organizations and areas

Appendix Formulas for parameters of sickness absences
Parameter

Formula

(A)

Total number of employees

(B)

Available workdays (days)

(C)

Total available workdays of all employees (days)

(D)

Number of absentees

(E)

Number of absences

(F)

Total absence days

(G)

Absence rate (%)

(H)

Absence frequency rate (%)

(I)

Average absence length (days)

(F) / (E)

(J)

Average absence length per absentee (days)

(F) / (D)

(K)

Average absence length per employee (days)

(F) / (A)

(L)

Average frequency of absences per absentee

(E) / (D)

(A) x (B)

((F) x 5 / 7) / (C) x 100
(E) / (A) x 100

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