Original Article

Global Health & Medicine. 2024; 6(4):225-235.
DOI: 10.35772/ghm.2024.01026

Employment status of older nursing staff aged 55 years and older incare facilities: A nationwide cross-sectional study in Japan
Ayako Furukawa1,*, Masayo Kashiwagi2, Noriko Morioka2
1

Department of Innovation in Fundamental and Scientific Nursing Care, Nursing Innovation Science, Graduate School of Health Care Sciences,
Tokyo Medical and Dental University, Tokyo, Japan;
2
Department of Nursing Health Services Research, Graduate School of Health Care Sciences, Tokyo Medical and Dental University, Tokyo, Japan.

Abstract: An aging nursing workforce requires addressing shortages due to retirement. This nationwide descriptive
cross-sectional study in Japan clarified the employment status of older nursing staff aged ≥ 55 years by facility type
during January–March 2022. Questionnaires were sent to 8,000 nursing directors, with 1,658 valid responses (response
rate: 20.7%). Descriptive statistics and Kruskal–Wallis or χ2 tests analyzed inter-facility differences. A violin plot
depicted the proportion of older nursing staff across facilities by age group, and generalized estimating equation(GEE) models examined associated factors at the facility level. Older nursing staff's distribution differed significantly
across age groups (p < 0.01), from 0% to 100% within the same facility type. Some facilities had high percentages ofstaff working beyond retirement age. GEE results showed higher percentages of full-time employees and nurses were
negatively associated with the percentage of older nursing staff across most facility types (p < 0.05). For those aged ≥65 years, the total population was positively associated with employment in bedded clinics (coef. = 0.07, 95% CI: 0.01
to 0.14, p = 0.03), but negatively associated with the total population (coef. = -0.06, 95% CI: -0.10 to -0.01, p = 0.02)
and percentage of the population aged ≥ 65 (coef. = -0.76, 95% CI: -1.43 to -0.08, p = 0.03) in long-term care insurance
facilities. Working conditions and environments should be improvement to potentially retain older nursing staff. Job
seekers should be matched with managers' needs in facilities with a higher proportion of older nursing staff to ensure asustainable workforce.
Keywords: aging, employment, nurse, retirement, workforce

Introduction
The aging nursing workforce poses significant
challenges for many countries. According to the Stateof Global Nursing 2020, a report by the World HealthOrganization (1), one in six (i.e., 17%) of the world's
nurses are aged ≥ 55 years and are expected to retire
within the next decade. Age-related retirement affects theentire nursing workforce and is a key global challenge(2). Consequently, retaining them in the workforce
necessitates attention to individual and organizational
factors, including attention to physical needs, flexible
work arrangements, role redesign, development ofeducational programs, financial incentives, and respect
for professional knowledge (3-7).
Japan, with the world's largest aging population, must
lead in addressing this challenge (8). The number ofnursing staff in Japan increased by 33.3% from 2007 to
2022. The proportion of employed nursing staff in their
40s slightly decreased from 25.8% in 2007 to 25.4% in
2022, while those in their 50s rose from 17.4% to 22.1%,

and those aged ≥ 60 years increased from 4.5% to 12.9%.
The proportion of older nursing staff aged ≥ 55 years also
grew from 9.4% in 2007 to 23.0% in 2022, reflecting theaging trend in Japan's nursing workforce (9).
Japan's mandatory retirement age system, prevalentacross industries, poses a concern for the nursing
profession. Typically, labor contracts terminate
when workers reach a predetermined age set by theirorganization, commonly 60 years for nurses (10,11).
According to the 2017 General Survey on Working
Conditions, 95.5% of industries have a mandatory
retirement age, with 79.3% of them setting this age at 60
years (12). These findings are consistent with the resultsof a 2019 hospital survey, which also reported that the
majority of hospitals set the retirement age for nurses at
60 years (13). There is growing apprehension regarding
the potential mass retirement of nurses and their exitfrom the nursing labor market.
In several countries, including the United States,
efforts to eliminate age discrimination, such as the Age
Discrimination in Employment Act of 1967, which(225)

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raises the age at which pension benefits begin (14) andeliminates or raises the retirement age, seek to ensure
the continued employment of older nurses (15-17).
In 2004, the Japanese government enacted the "Law
Concerning Stabilization of Employment of Older
Persons" to alleviate restrictions on the employment ofolder individuals caused by the mandatory retirementage (18). Although the 2021 amendment to the same law,in addition to the obligation to ensure employment up
to the age of 65 years, attempted to raise the retirement
age to 70 years, introduce a continuous employment
system, or abolish the mandatory retirement age, limited
evidence has been collected on the employment statusof older nurses by facility (19-21). To ensure effective
employment continuity measures in Japan, facilities that
employ nurses aged ≥ 55 years should first be identified.
In Japan, as of 2020, more than 90% of nurses aged
≤ 29 years and 75-80% of nurses in their 30s were
employed in hospitals. However, as nurses age, theproportion working in hospitals decreases. Nurses in their
40s increasingly work in non-hospital settings, such as
clinics, long-term care insurance facilities (LTCIFs), andhome-visit nursing agencies (VNAs). By their 50s, morethan half are employed in these non-hospital settings (22).
However, existing surveys primarily focus on individual
nurses, leaving a gap in understanding the broader
employment landscape for older nurses. Therefore, thisstudy aimed to: 1) provide a comprehensive overview
of the proportion of older nursing staff aged ≥ 55 years
employed across various facilities in Japan and 2)
explore the factors associated with the percentage ofolder nursing staff at the facility level.
Materials and Methods
Study design and participantsThis nationwide descriptive cross-sectional study was
conducted between January and March 2022. The
focus was on four specific types of facilities in Japan:
hospitals, bedded clinics, LTCIFs, and VNAs. Stratified
random sampling was employed, considering a skewed
distribution based on the number of facilities perprefecture for each facility type; thus, there were 2,000
cases across a total of 8,000 facilities.
A list of addresses for each facility was obtained
from the Local Health and Welfare Bureau (as of
October 1, 2021) and the Ministry of Health, Labourand Welfare (MHLW) via their websites (as of June 30,2021). Excluding inactive facilities, the list included
8,164 hospitals, 6,202 bedded clinics, 14,362 VNAs, and12,812 LTCIFs. LTCIFs, in particular, included 7,895
welfare facilities, 4,184 healthcare facilities, and 733
sanatorium-type medical care facilities for older adults
requiring long-term care.
Data were collected using anonymous, selfadministered questionnaires sent to each facility's nursing

director. A cover letter explaining the study's aim andethical considerations was attached, along with a returnenvelope. Additionally, a reminder letter was sent three
weeks after the initial survey was mailed.
Types of target facilities
The four types of facilities investigated were hospitals,
bedded clinics, LTCIFs, and VNAs. At the time, morethan 1.2 million nurses were employed across Japan,
69% of whom were employed in hospitals, 13.2% inclinics, 7.9% in LTCIFs, and 4.9% in VNAs. Thesefacilities had the highest number of nurses employed,
as indicated by statistics published by the MHLW (12).
Given that nursing care is required in these facilities,
staffing standards were established by law for eachfacility.
Hospitals
Hospitals in Japan are classified into various types
according to their specialized services, sizes, andfunctions. This study included all hospital types.
Bedded clinics
Clinics are categorized into bedded and non-bedded
clinics. The former refers to small medical facilities with≤ 19 beds that provide outpatient and inpatient care andare staffed by nursing personnel. The latter focuses solely
on outpatient care and therefore does not follow nursing
staffing standards. To clarify the actual employment
status of nursing staff, this study only included bedded
clinics.
Long-term care insurance facilities (LTCIFs)
LTCIFs provide physical care, daily living assistance,and preventive care services required by older adults
to perform daily activities. This includes welfare,
healthcare, and convalescent care facilities and is basedon the long-term care insurance system. However,
medical insurance systems are only applied when
medical services are provided.
Home-visit nursing agencies (VNAs)
VNAs provide nursing care, monitor health status,
administer medications, impart health education, andcoordinate care with other healthcare providers within
the user's home. VNAs generally leverage benefits fromeither long-term care or health insurance depending on
the user's condition and needs. Approximately half of all
VNAs operate with fewer than five full-time nurses (23).
Instruments
The questionnaire comprised two main elements:organizational characteristics and number of nursing
staff. The questionnaire items were created by referring
to several previous studies and surveys (21,22,24–28). In(226)

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addition to the questionnaire, national public data wereobtained to examine the regional characteristics of the
facilities.
Organizational characteristics
Organizational characteristics included ownership,year of establishment, prefecture, and number of beds,
residents, and users. From the obtained questionnaire
data, the number of nursing staff per 100 beds wascalculated for each hospital, and the number of nursing
staff per 10 beds was calculated for bedded clinics and
LTCIFs. Regarding VNAs, the number of users per
nursing staff was calculated.
Number of nursing staff
The nursing staff were calculated according to job
category (registered nurses, associate nurses, and nursing
assistants) and job type (full-time or part-time). In thisstudy, the number of nursing staff members reflected the
combined number of registered and associate nurses.
Nursing staff aged 55 years and older were surveyed in
five-year age brackets (55-59, 60-64, 65-69, 70-74, 7579, and ≥ 80 years) to determine their numbers by age.
These numbers were then used to calculate the total
number of nursing staff aged ≥ 55, ≥ 60, ≥ 65, ≥ 70, ≥75, and ≥ 80 years. The employment percentage of older
nursing staff was calculated by dividing the number ofstaff aged ≥ 55, ≥ 60, and ≥ 65 years by the total numberof nursing staff.
Prefecture characteristics
The following variables were used as regional
characteristics: total population, percentage of population
aged ≥ 65 years, number of hospitals per 100,000
population, number of bedded clinics per 100,000
population, number of LTCIFs per 100,000 population
aged ≥ 65 years, and number of VNAs per 100,000
population aged ≥ 65 years. The total population andthe percentage of population aged ≥ 65 years wereobtained from the 2020 population census (29). The
number of hospitals and bedded clinics was obtained
from the Survey of Medical Institutions, and the numberof LTCIFs and VNAs was obtained from the Survey ofInstitutions and Establishments for Long-Term Care (30).
Statistical analysis
Descriptive statistics were calculated for each facility,
considering the organizational characteristics andemployment status of the nursing staff. Kruskal–Wallisor χ2 tests were conducted to evaluate the differences
between the facilities. A violin plot was prepared to
depict the percentage of older nursing staff by age group.
The violin plot depicted the volume of samples at eachpoint by width, with lines corresponding to the 25th,
median, and 75th percentiles.
Owing to the nature of data clustering within theregion (prefecture), generalized estimating equation(GEE) models were used to examine the factors
associated with the percentage of older nursing staff
at the facility level. Univariate and multivariate GEE
models were stratified by the type of facility, including
hospitals, bedded clinics, LTCIFs, and VNAs. The
following organizational and regional characteristics
were selected as independent variables: total population;
percentage of population aged ≥ 65 years; number ofhospitals per 100,000 population; number of bedded
clinics per 100,000 population; number of LTCIFs per
100,000 population aged ≥ 65 years; number of VNAs
per 100,000 population aged ≥ 65 years; ownership;year of establishment; number of beds, residents, andusers; number of nursing staff per 100 hospital beds, per
10 beds, and per 10 residents, and number of users per
nursing staff (VNA); percentage of full-time nursing staff
in all types of employment; and percentage of nurses
among nursing staff. In Japan, although the retirement
age system is being reconsidered and the age is gradually
being raised, the actual retirement age is often 60 years
old. Therefore, we used three dependent variables: cutoff
ages of 55, 60, and 65 years. Multicollinearity in themodels was assessed using calculated variance inflation
factors (< 7). The p-value of significance was set at p <0.05 (two-tailed). All statistical analyses were performed
using SPSS version 29.0 (IBM Corp., Armonk, NY,
USA), and R-4.3.0 was used to generate the figures.
Ethical considerations
This study was approved by the ethics review committeeof the Institute for Integrated Education of Tokyo
Medical and Dental University (no. C2021-005).
Participation was voluntary, and the protection of private
information and strict data handling were guaranteed.
The participants provided consent by checking the
research consent box on the questionnaire.
Results
In total, 2,438 questionnaires were collected from
8,000 facilities (response rate: 30.5%). After excluding
780 questionnaires due to lack of consent, missing
data, unanswered questions, or errors, completed
questionnaires from 1,658 facilities (valid response
rate: 20.7%) were included in the analysis, representing
431 hospitals, 363 clinics, 451 LTCIFs, and 413 VNAs
(Figure 1). The median (interquartile range [IQR])
number of beds, residents, and users was 134 (74-248)
in hospitals, 17 (10-19) in bedded clinics, 86 (63-100)
in LTCIFs, and 70 (50-110) in VNAs. The medians(IQR) of the number of nursing staff were 75 (42-156)
in hospitals, 11 (6-17) in bedded clinics, 7 (5-11) in
LTCIFs, and 6 (4-8) in VNAs. The proportion of nurses
varied significantly across facility types, with the highest
being 100% in VNAs and the lowest being 62.5% in
LTCIFs (Table 1).

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Figure 1. Selection flow of participating facilities.

Comparing the employment status of older nursing staff
by facility types
The differences in the distribution of older nursing
staff aged ≥ 55 years employed by facility type were
statistically significant across all age groups (p <0.01) (Figure 2). The percentage of older nursing staff
employed in LTCIFs exceeded that of other facility
types, followed by bedded clinics, VNAs, and hospitals.
In the ≥ 55 years age group, clinics and VNAs had a
median employment percentage of approximately 25%,
whereas LTCIFs had a median employment percentage
of approximately 45%. However, the distribution
ranged from 0% to 100% and was slightly skewed. The
proportion of older nursing staff employed in hospitals
was distributed across a range of 0%-75%, with amonomodal right-skewed distribution of approximately
13%. When the cutoff age changed from ≥ 55 to ≥ 60
and ≥ 65 years, the percentage of older nursing staff
decreased among all facility types. Although the mode
for the percentage of nursing staff aged ≥ 65 years was
0%, 9.3% of the facilities had a percentage of 25% ormore.
Factors associated with the percentage of older nurses
Univariate analysis results using GEE models showedthat the factors associated with the percentage of older
nursing staff aged ≥ 55 years differed across the four
facility types: hospitals, bedded clinics, LTCIFs, andVNAs (Supplemental Tables S1-S4, https://www.
globalhealthmedicine.com/site/supplementaldata.
html?ID=86). In the case of hospitals, a higher
proportion of older staff was associated with a lower
number of beds (coef. = -0.02 to -0.01, 95% CI: -0.03
to 0.00, p < 0.01), a lower number of staff per 100
beds (coef. = -0.17 to -0.06, 95% CI: -0.22 to -0.04, p< 0.01), a lower percentage of full-time nursing staff(coef. = -0.31 to -0.12, 95% CI: -0.43 to -0.03, p <0.01), a lower percentage of nurses among nursing
staff (coef. = -0.58 to -0.22, 95% CI: -0.65 to -0.18, p< 0.01), and a lower total population (coef. = -0.08 to-0.05, 95% CI: -0.12 to -0.02, p < 0.01); these negative
associations were consistently observed across all
age categories. (Supplemental Table S1, https://www.
globalhealthmedicine.com/site/supplementaldata.
html?ID=86). In bedded clinics, a positive association
with the number of beds was found only in the ≥ 65
years age group (coef. = 0.17, 95% CI: 0.01 to 0.33, p= 0.03), while the association with prefecture variables
was minimal (Supplemental Table S2, https://www.
globalhealthmedicine.com/site/supplementaldata.
html?ID=86). In the case of LTCFs, an association with
the number of nursing staff per 10 residents was foundonly in the ≥ 65 years age group (coef. = 1.13, 95%
CI: -2.07 to -0.18, p = 0.02) (Supplemental S3, https://www.globalhealthmedicine.com/site/supplementaldata.
html?ID=86). Finally, in VNAs, an association withthe percentage of full-time nursing staff in all typesof employment was found only in the ≥ 65 years age
group (coef. = -0.07, 95% CI: -0.12 to -0.02, p < 0.01)(Supplemental S4, https://www.globalhealthmedicine.
com/site/supplementaldata.html?ID=86).
Multivariate analysis using GEE models stratified byfacility types revealed that the higher percentage of older
nursing staff aged 55 years and older was negatively
associated with ownership by a non-profit medical
corporation (coef. = -3.96, 95% CI: -6.50 to -1.42, p <0.01), number of beds (coef. = -0.01, 95% CI: -0.01 to-0.00, p = 0.03), number of nursing staff per 100 beds
(coef. = -0.08, 95% CI: -0.12 to -0.03, p < 0.01), fulltime nursing staff in all types of employment (%) (coef.
= -0.20, 95% CI: -0.30 to -0.10, p < 0.01), and nurses
among nursing staff (%) (coef. = -0.49, 95% CI: -0.59
to -0.39, p < 0.01) in hospitals. No association withprefecture variables was found for all four facility types
for the percentage of nursing staff aged ≥ 55 years (Table
2).
When the age category was set to ≥ 60 years,
the same trend was observed in the association withthe percentage of older nursing staff. However, anassociation with the total population, which is aprefectural characteristic, was observed only in bedded
clinics (coef. = 0.18, 95% CI: 0.05 to 0.31, p < 0.01)
and VNAs (coef. = 0.08, 95% CI: 0.01 to 0.16, p = 0.04)(Table 3).
Regarding the percentage of nursing staff aged ≥(228)

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Table 1. Descriptive statistics of organizational characteristics according to total and facility type (n = 1,658)
By category
Variable

Ownership, n (%)
National
Public medical institution
Social insurance group
Non-profit medical corporation
Social welfare corporationFor-profit corporation
PrivateOther
Year of establishment, Median (IQR)
Area, n (%)
Hokkaido
Tohoku
Kanto
Chubu
Kinki
Chugoku/Shikoku
KyushuNumber of beds/residents/users, Median(IQR)
Number of nursing staff, Median (IQR)
Percentage of nurses among nursing staff ,Median (IQR)
Percentage of full-time nursing staff in all
types of employment, Median (IQR)
Number of nursing staff aged ≥ 55 years,
Median (IQR)
Number of nursing staff aged ≥ 60 years,
Median (IQR)
Number of nursing staff aged ≥ 65 years,
Median (IQR)
Number of nursing staff aged ≥ 70 years,
Median (IQR)

p-value†

Hospital(n = 431)

Bedded clinic(n = 363)

Long-term careinsurance facility(n = 451)

12 (2.8)
79 (18.3)
6 (1.4)268 (62.2)
13 (3.0)0 (0.0)5 (1.2)48 (11.1)
1976 (1955–1991)

0 (0.0)6 (1.7)2 (0.6)273 (75.2)
1 (0.3)0 (0.0)71 (19.6)
10 (2.8)
1994 (1981–2003)

0 (0.0)18 (4.0)
1 (0.2)124 (27.5)
301 (66.7)
0 (0.0)0 (0.0)7 (1.6)
1998 (1991–2006)

1 (0.2)18 (4.4)
2 (0.5)91 (22.0)
21 (5.1)
202 (48.9)
24 ( 5.8)
54 (13.1)
2016 (2007–2019)< 0.00134 (7.9)39 ( 9.0)
103 (23.9)
69 (16.0)
70 (16.2)
53 (12.3)
63 (14.6)
134 (74-248)

21 (5.8)41 (11.3)
57 (15.7)
54 (14.9)28 ( 7.7)
66 (18.2)
96 (26.4)
17 (10-19)

18 (4.0)56 (12.4)
123 (27.3)
68 (15.1)
76 (16.9)
49 (10.9)61 (13.5)
86 (63-100)

25 (6.1)
30 (7.3)
114 (27.6)
57 (13.8)
86 (20.8)
32 (7.7)
69 (16.7)
70 (50-110)< 0.00175 (42-156)
90.0 (74.4-97.6)

11 (6-17)
66.7 (40.0- 83.3)

7 (5-11)62.5 (45.5-78.8)

6 (4-8)100 (90.0-100.0)< 0.001< 0.001

89.8 (82.4-95.2)

80.0 (60.0-100.0)

75.0 (57.1-100.0)

71.4 (50.0-100.0)< 0.001

Home-visit nursing
agency(n = 413)< 0.001< 0.00112 (7-22)

3 (1-5)3 (2-5)

1 (0-2)< 0.001

6 (3-10)

1 (0-2)2 (1-3)0 (0-1)< 0.001

1 (0-3)0 (0-1)0 (0-1)0 (0-0)< 0.0010 (0-1)0 (0-0)0 (0-0)0 (0-0)< 0.001†Kruskal–Wallis test was conducted for quantitative variables, and the χ2 test was conducted for categorical variables. Abbreviations: IQR,
interquartile range.

65 years, higher percentages of full-time employees
and nurses in nursing staff were negatively associatedwith most facility types, while the number of beds was
not associated with any facility type, except VNAs.
Additionally, regarding prefectural characteristics, thetotal population was positively associated with thepercentage of older nursing staff in bedded clinics (coef.
= 0.07, 95% CI: 0.01 to 0.14, p = 0.03), whereas in
LTCIFs, negative associations were found with the total
population (coef. = -0.06, 95% CI: -0.10 to -0.01, p =0.02) and the percentage of the population aged ≥ 65
years (coef. = -0.76, 95% CI: -1.43 to -0.08, p = 0.03).
Furthermore, in bedded clinics, the number of VNAs
per 100,000 population aged ≥65 years was negatively
associated (coef. = -0.12, 95% CI: -0.22 to -0.01, p =0.03), while it was positively associated in VNAs (coef.
= 0.10, 95% CI: 0.00 to 0.20, p = 0.04) (Table 4).
Discussion
This nationwide survey depicted the distribution offacilities in Japan employing older nursing staff. The
results revealed that LTCIFs employed the highest
median percentage of older nursing staff in all considered
age groups (≥ 55 years, ≥ 60 years, and ≥ 65 years),followed by clinics, VNAs, and hospitals. Additionally,
the percentage of nurses among nursing staff in thefacilities and the number of full-time employees in
the nursing staff were negatively associated with thepercentage of older nursing staff in the facilities.
Although the biennial survey conducted by the
MHLW reported the number of employed nurses by
age group, this study was significant in that it showed
employment trends for nursing staff in a facility. In
a previous study, 21.8% of the total number of older
nursing staff were aged ≥ 55 years, and 11.8% were aged
≥ 60 years (22). Conversely, this study found that somefacilities, such as LTCIFs, VNAs, bedded clinics, andhospitals (particularly, smaller ones), rely on a workforce
aged 55 to 60 years and older. Owing to the overall agingof the nursing workforce, this group may experience
workforce shortages in the near future because of(229)

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Figure 2. Violin plots showing the percentage of older nursing staff employed by facility type and age group. These plotsshow the sample size at each point in terms of width, with lines corresponding to the 25th percentile, median, and 75th percentileand dots corresponding to the mean. Kruskal–Wallis test results showed statistically significant differences between facilities
concerning the percentage of older nursing staff employed based on all age groups (p < 0.001).

retirement and age-related departures. Based on this,strategies should be implemented to ensure that older
nurses can work beyond the retirement age. Furthermore,in facilities with a high proportion of older nursing staff,
the specific skills and working conditions required bymanagers must be further explored and clarified. By

considering ways to match job seekers with managers'
needs, stable nursing care can be provided.
Furthermore, this study also found that facilities withhigher percentages of nurses among the nursing staff andthose with a higher percentage of full-time employees
among the nursing staff were less likely to employ older(230)

(231)

95% CI

0.44
0.16
0.21
0.51
0.38

-0.85 - 0.37

-0.20 - 1.21

-0.57 - 0.13

-0.19 - 0.39

-0.16 - 0.06

0.31

0.36

0.18

0.15

0.06

< 0.01*-0.59 - -0.39

0.05
0.67

< 0.01*-0.30 - -0.10

0.05-0.06 - 0.04

0.51
0.03*
< 0.01*-0.04 - 0.02
-0.01 - 0.00
-0.12 - -0.03

0.02
0.00
0.02

0.02

< 0.01*-6.50 - -1.42< 0.01*p

1.30

32.48 45.67 - 173.00

SESE

95% CI

540.50

0.19

0.06

0.28

-0.55

1.19

0.12

-0.29

0.17

0.38

0.62

1.07

0.91

0.10

0.05

0.07

0.08

-0.10
-0.11

0.09
0.31

3.00

-0.03
-0.32

-5.33

-0.14 - 0.52

-0.68 - 0.81

-0.94 - 1.49

-2.65 - 1.55

-0.59 - 2.96

-0.07 - 0.31

-0.39 - -0.18-0.24 - 0.03

-0.26 - 0.06

-0.20 - 0.14
-0.93 - 0.28

-11.22 - 0.56

79.90 167.60 -248.58 - 408.38

coef.

Bedded clinic

-0.50
-0.34
-0.19
0.13

0.61
0.65
0.87
0.26

598.95

0.60

-0.19

< 0.01*

0.19

-0.18

0.12

-0.07

-2.10

0.24

0.21

-0.19
-0.07

0.72
0.30

1.85

455.62

0.63
0.08

coef.

p

95% CI
p

0.13

0.24

0.61

0.87

0.73

0.04

0.05

0.05

0.92

0.09
0.03

2.02

-0.13 - 0.38

-0.65 - 0.27

-1.55 - 0.86

-2.19 - 1.20

-0.84 - 2.04

-0.15 - 0.01

-0.29 - -0.08-0.29 - -0.08-3.90 - -0.29-0.36 - -0.03-0.13 - -0.01-2.10 - 5.80

0.33

0.41

0.57

0.57

0.41

0.10

< 0.01*< 0.01*

0.02*

0.02*
0.02*

0.36

166.53 129.23 - 782.01 < 0.01*

SE

Long-term care insurance facility

524.53

0.00

0.38

0.68

-0.58

0.95

0.07

-0.32

0.12
-0.02

1.53
-0.39
-0.07

790.08

coef.

0.15

0.43

0.76

1.06

0.82

0.06

0.06

0.13
0.05

2.24
0.15
0.02

317.89SE-0.29 - 0.30

-0.46 - 1.23

-0.82 - 2.17

-2.65 - 1.50

-0.67 - 2.56

-0.04 - 0.19

-0.45 - -0.20-0.13 - 0.36
-0.11 - 0.07

-2.86 - 5.92
-0.69 - -0.08-0.10 - -0.04

167.03 - 1413.12

95% CI

Home-visit nursing agency

0.99

0.38

0.38

0.59

0.25

0.20

< 0.01*

0.35
0.69

0.49
0.01*
< 0.01*0.01*

p

Dependent variable: Percentage of nursing staff aged 55 years and older among all staff. Results were based on generalized estimating equations. Each independent variable's regression coefficient (coef.), standard error(SE), and p-value (p) were reported. *p < 0.05 (statistical significance level). Additionally, 95% confidence intervals (CIs) were provided. Adjustments for clustering by prefecture and robust estimation were applied.
Abbreviations: LTCF, Long-term care insurance facility; VNA, Home-visit nursing agency.

109.34(Intercept)
Facility characteristics
Ownership (ref. Other)-3.96
Non-profit medical corporation
Social welfare corporationFor-profit corporation-0.01
Year of establishment-0.01
Number of beds/residents/users-0.08
Number of nursing staff per 100
beds
Number of nursing staff per 10
beds/residents
Number of users per nursing staff-0.20
Percentage of full-time nursing
staff in all types of employment-0.49
Percentage of nurses among
nursing staff
Prefectural characteristics-0.01
To t a l p o p u l a t i o n ( 1 0 0 , 0 0 0
persons)-0.24
Percentage of population aged ≥65
0.50
Number of hospitals per 100,000
population-0.22
Number of bedded clinics per
100,000 population
0.10
Number of LTCFs per 100,000
population aged ≥ 65
-0.05
Number of VNAs per 100,000
population aged ≥ 65
107.55(Scale)coef.

Hospital

Table 2. Generalized estimating equations results for the percentage of nursing staff aged 55 years and older by facility type

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(232)

95% CI

coef.

0.22
0.31
0.47
0.23
0.14

-0.53 - 0.12

-0.22 - 0.70

-0.39 - 0.18

-0.08 - 0.33

-0.12 - 0.02

0.17

0.23

0.15

0.10

0.04

< 0.01*-0.48 - -0.31

0.04
0.77

0.02*

-0.21 - -0.02

0.05-0.02 - 0.03

0.28
0.77
< 0.01*-0.03 - 0.01
0.00 - 0.00
-0.07 - -0.02

0.01
0.00
0.01

0.01

0.34

-3.20 - 1.10

273.88

-0.24

0.26

0.54

-0.36

0.21

0.18

-0.17

0.13

0.27

0.39

0.71

0.63

0.06

0.04

0.05

0.05

-0.12

2.08
0.05
0.20

-0.13

95% CI-0.49 - 0.02

-0.27 - 0.79

-0.22 - 1.29

-1.74 - 1.02

-1.03 - 1.46

0.05 - 0.31

-0.24 - -0.10-0.24 - -0.03-0.22 - -0.03-0.17 - 0.04
-0.68 - 0.11

-5.73 - 2.43

107.21 -57.60 - 362.67

SE

-0.06
-0.29

-1.65

< 0.01* 152.54

p

1.10

22.93 36.47 - 126.36

SE

Bedded clinic

0.52
-0.30
-0.18
0.01

0.61
0.16
0.33
0.07

501.12

-0.07

-0.18

< 0.01*

0.74

-0.22

0.01*

-0.05

-1.61

0.01*

< 0.01*

-0.06
-0.06

0.23
0.15

1.69

194.18

0.16
0.43

coef.

p

95% CI

0.13

0.22

0.45

0.67

0.59

0.05

0.05

0.06

1.03

0.08
0.03

1.83

0.44
0.50
0.42
0.91

-0.80 - 1.84
-1.18 - 0.57
-0.60 - 0.25
-0.24 - 0.27

< 0.01*-0.27 - -0.08

0.91< 0.01*-0.33 - -0.11-1.22 - 1.08

0.12

-3.62 - 0.40

0.32

0.46
0.03*

-0.23 - 0.10
-0.11 - -0.01-0.16 - 0.05

0.35

0.25

p

-1.89 - 5.27

167.01 -133.16 - 521.52

SE

Long-term care insurance facility

295.07

0.00

0.41

0.05

-0.63

0.94

0.08

-0.19

0.02
-0.06

2.06
-0.06
-0.04

122.43

coef.

0.08

0.26

0.51

0.86

0.70

0.04

0.06

0.06
0.03

1.53
0.07
0.01

146.66

SE-0.15 - 0.15

-0.09 - 0.91

-0.95 - 1.06

-2.31 - 1.05

-0.43 - 2.30

0.01 - 0.16

-0.31 - -0.07-0.11 - 0.14
-0.11 - 0.00

-0.95 - 5.06
-0.21 - 0.08
-0.06 - -0.02-165.02 - 409.88

95% CI

Home-visit nursing agency

0.98

0.11

0.92

0.46

0.18

0.04*

< 0.01*

0.80
0.04

0.18
0.38
< 0.01*0.40

pDependent variable: Percentage of nursing staff aged 60 years and older among all staff. Results were based on generalized estimating equations. Each independent variable's regression coefficient (coef.), standard error(SE), and p-value (p) were reported. *p < 0.05 (statistical significance level). Additionally, 95% confidence intervals (CIs) were provided. Adjustments for clustering by prefecture and robust estimation were applied.
Abbreviations: LTCF, Long-term care insurance facility; VNA, Home-visit nursing agency.

81.41(Intercept)
Facility characteristics
Ownership (ref. Other)
Non-profit medical corporation -1.05
Social welfare corporationFor-profit corporation-0.01
Year of establishment
0.00
Number of beds/residents/users
Number of nursing staff per 100 -0.05
beds
Number of nursing staff per 10
beds
Number of users per nursing staff
Percentage of full-time nursing -0.11
staff in all types of employment
Percentage of nurses among -0.40
nursing staff
Prefectural characteristics
To t a l p o p u l a t i o n ( 1 0 0 , 0 0 0 0.00
persons)
Percentage of population aged ≥ -0.20
65
Number of hospitals per 100,000 0.24
population
Number of bedded clinics per -0.10
100,000 population
Number of LTCFs per 100,000 0.13
population aged ≥ 65
Number of VNAs per 100,000 -0.05
population aged ≥ 65
56.74
(Scale)coef.

Hospital

Table 3. Generalized estimating equations results for the percentage of nursing staff aged 60 years and older by facility type

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(233)

0.16

0.08

0.06

0.02

0.16

-0.14

0.08

-0.04

25.30

0.10

0.03

-0.19

-0.13

0.04

-0.08

0.01

0.01
0.00
0.01

-0.01
0.00
-0.02

0.01

-2.06 - 1.33

0.86

-0.37

-0.08 - 0.01

-0.05 - 0.20

-0.30 - 0.03

-0.15 - 0.47

-0.31 - 0.06

-0.01 - 0.03

-0.25 - -0.14-0.16 - 0.00

-0.02 - 0.01
0.00 - 0.00
-0.03 - 0.00

16.33 12.95 - 76.97

95% CI

44.96

SE

coef.

0.12

0.24

0.10

0.31

0.19

0.24

< 0.01*

0.05

0.26
0.33
0.05

0.67

104.70

-0.12

0.08

0.46

-0.35

0.05

0.07

-0.09

0.05

0.14

0.24

0.44

0.40

0.03

0.02

0.03

0.03

0.02
-0.10

0.03
0.08

1.69

56.29

SE

-0.05
0.20

-3.89

< 0.01* 117.28

p-0.22 - -0.01-0.19 - 0.35

-0.01 - 0.92

-1.22 - 0.52

-0.73 - 0.84

0.01 - 0.14

-0.13 - -0.04-0.16 - -0.03-0.05 - 0.08

-0.11 - 0.00
0.04 - 0.36

-7.21 - -0.57

6.94 - 227.61

95% CI

Bedded clinic
coef.

0.09
-0.17
0.13
-0.01

0.43
0.05
0.56
0.03*

205.21

-0.76

-0.10

< 0.01*

0.89

-0.19

< 0.01*

-0.06

-1.21

0.61

0.03*

-0.07
-0.01

1.26

0.07
0.02*

0.02*

0.04* 193.22

p

0.08

0.14

0.28

0.58

0.34

0.02

0.04

0.04

0.52

0.05
0.01

1.09

98.59

SE

0.88
0.54
0.35
0.95

-1.05 - 1.23
-0.71 - 0.37
-0.15 - 0.41
-0.16 - 0.15

< 0.01*-0.18 - -0.03

0.03*< 0.01*-0.26 - -0.11-1.43 - -0.08

0.02*-2.23 - -0.20

0.02*

0.15
0.61

-0.17 - 0.03
-0.04 - 0.02

-0.10 - -0.01

0.24

0.05

0.01 - 386.45-0.86 - 3.39

p

95% CI

Long-term care insurance facility

120.82

0.10

0.26

-0.04

-0.11

-0.21

0.01

-0.15

0.02
-0.06

1.45
-0.04
-0.01

101.68

coef.

0.05

0.17

0.26

0.41

0.42

0.03

0.05

0.02
0.02

0.90
0.04
0.01

82.96

SE

0.00 - 0.20

-0.07 - 0.58

-0.55 - 0.48

-0.91 - 0.70

-1.04 - 0.62

-0.05 - 0.07

-0.25 - -0.04-0.02 - 0.07
-0.10 - -0.02-0.32 - 3.22
-0.12 - 0.03
-0.02 - 0.00

-60.92 - 264.27

95% CI

Home-visit nursing agency

0.04*

0.12

0.89

0.80

0.62

0.76

0.01*

0.34
< 0.01*

0.11
0.27
0.01*

0.22

p

Dependent variable: Percentage of nursing staff aged 65 years and older among all staff. Results were based on generalized estimating equations. Each independent variable's regression coefficient (coef.), standard error(SE), and p-value (p) were reported. *p <0.05 (statistical significance level). Additionally, 95% confidence intervals (CIs) were provided. Adjustments for clustering by prefecture and robust estimation were applied.
Abbreviations: LTCF, Long-term care insurance facility; VNA, Home-visit nursing agency.

(Intercept)
Facility characteristics
Ownership (ref. Other)
Non-profit medical corporation
Social welfare corporationFor-profit corporation
Year of establishment
Number of beds/residents/users
Number of nursing staff per 100
beds
Number of nursing staff per 10
beds/residents
Number of users per nursing staff
Percentage of full-time nursing
staff in all types of employment
Percentage of nurses among
nursing staff
Prefectural characteristics
To t a l p o p u l a t i o n ( 1 0 0 , 0 0 0
persons)
Percentage of population aged ≥65
Number of hospitals per 100,000
population
Number of bedded clinics per
100,000 population
Number of LTCFs per 100,000
population aged ≥ 65
Number of VNAs per 100,000
population aged ≥ 65
(Scale)coef.

Hospital

Table 4. Generalized estimating equations results for the percentage of nursing staff aged 65 years and older by facility type

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nursing staff, at all cut-offs of ≥ 55 years, ≥ 60 years, and≥ 65 years. This finding was consistent with the resultsof previous studies (22,31). The number of associatenurses in training is declining, and the workforce isaging. Additionally, they are more often employed ona part-time basis compared to nurses. Consequently,
facilities that employ a large number of assistantnurses may be concerned about an aging workforce
and the challenges of securing nursing staffs. Negative
associations with the number of beds and staffing levels
may stem from training programs in large hospitals andVNAs, which attract younger nursing staff who tend toprefer these settings. The negative correlation with years
of establishment and the number of beds in LTCIFs andVNAs may be due to the small number of nursing staff
who continue to work. On the contrary, the association
between regional variables and the percentage of
employees aged ≥ 55 years and those ≥ 60 years was
minimal. Based on this, future research should consider
the supply and demand of nursing staff and the age ofworking nursing staff at the secondary medical care level,
which serves as a general unit for healthcare provision,
rather than at the prefectural level.
Notably, some facilities employed a certain
percentage of nursing staff aged > 65 years. Currently,
policies are being implemented to promote homehealthcare and long-term care services to meet thegrowing demand for nursing and long-term care,prioritizing the need to secure human resources (19,3234). However, an association was observed between thepercentage of nursing staff aged ≥ 65 years in bedded
clinics and LTCIFs, and regional variables such as
total population warrant consideration. Delaying theretirement of older nursing staff affects the overall
supply of the nursing workforce (35). Policies to address
the concurrent retirement of an aging nursing workforce
should consider the impact of facilities that employ ahigh proportion of older nurses.
This study had several limitations. First, owing to
the stratified random sampling of facilities nationwide,
it did not constitute a complete survey. However, thedistribution of the number of beds across the surveyedfacilities was similar to that of the national data and may
be somewhat representative. Second, the employment
percentage of older nursing staff was used as the primary
outcome variable. Therefore, the results should beinterpreted with caution, particularly for facilities witha small overall number of employees such as VNAs,
owing to the high per capita rate. Third, this study did
not examine the reasons for hiring older nursing staff.
Nursing staff well beyond retirement age may continue
to be employed not only to secure human resources but
also because they are expected to be highly proficient intheir jobs. Further studies are required to examine thisphenomenon.
Future studies should clarify the characteristics offacilities with a higher percentage of older nurses, as

revealed in this study. The results of this study can serveas important material for policies that consider the impactof employing older nurses to secure human resources inJapan, where increased demand for medical and longterm care services is expected.
Acknowledgements
We thank all the participants for their cooperation.
Funding: This study was supported by a research grantfrom the Mitsubishi Foundation (no. 202130014).
Conflict of Interest: The authors have no conflicts ofinterest to disclose.
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---Received March 25, 2024; Revised June 30, 2024; Accepted
July 9, 2024.
Released online in J-STAGE as advance publication August 3,
2024.
*Address correspondence to:
Ayako Furukawa, Department of Innovation in Fundamental
and Scientific Nursing Care, Nursing Innovation Science,
Graduate School of Health Care Sciences, Tokyo Medical and
Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 1138510, Japan.
E-mail: ns200006@tmd.ac.jp(235)