Factors associated with health-related quality of life in Korean older workers

Subjects

Of the 25,534 participants, 6383 wage workers were selected. From these wage workers,
older workers ?55 years of age (n?=?1413) were selected based on the criterion presented in the Employment Promotion
for the Aged Act and the Employment Protection Act. Forty five individuals were excluded
for missing sociodemographic factor values (n?=?15), missing health-related factors (n?=?14), missing working condition-related values (n?=?12), and missing health status and HRQL values (n?=?4). The remaining 1368 persons were analyzed.

Methods

General characteristics

Sociodemographic factors included sex, age, education, household income, and marital
status (having a spouse). Age groups were divided into 55–64 years, 65–74 years, and
?75 years. Level of education was classified into elementary school graduate and below,
middle school graduate, high school graduate, and college graduate and above. Income
level according to household income quartile was classified into low, below middle,
above middle, and high. Drinking (none, 1–4 times a month, 2 times a week), smoking
(current smoker, ex-smoker, non-smoker), and obesity (body mass index 18.5, 18.5–22.9,
23.0–24.9, 25.0–29.9, ?30.0 kg/m
²
) were classified respectively.

Health status

Chronic diseases were examined because of their appreciable effects on older workers’
health status. Questions concerned cardiovascular diseases, musculoskeletal diseases,
and mental health. Cardiovascular diseases included the presence of hypertension,
hyperlipidemia, stroke, myocardial infarction, angina, and diabetes. The prevalence
of diseases like diabetes was determined based on the reported diagnosis. Musculoskeletal
diseases or symptoms included the presence of osteoarthritis and knee, hip and back
pain, with subjects queried whether they had experienced knee, hip and back pain for
30 days or more in the prior 3 months. Assessment of mental health status included
the presence of depression and stress recognition rate. The stress recognition rate
was classified based on responses to the occurrence of daily stress (high?=?very much
or much, low?=?little or rarely).

Working conditions

Types of occupation, types of employment by employment status and working time, weekly
average working hours, and shift work were examined. The types of occupation were
classified into office work (administrators, specialists and related workers, and
office workers), service work (service and sales employees), manufacturing work (skilled
agriculture workers, forestry and fishery workers, functional employees and related
functional employees, and equipment, machine operation, and assembly workers), and
elementary occupations. The subjects were classified according to their employment
status into regular employees, temporary employees, and daily employees, and whether
they worked part-time or full-time. The weekly average working hours was classified
as 40, 40–52, 52–60, and 60. Shift work, except for day duty workers, comprised
all workers who worked in the afternoon (2 p.m. to midnight), at night (9 p.m. to
8 a.m.), in regular rotation of shifts between the day and night shifts, in 24-h shifts,
in segmented shifts (working more than two shifts a day), and in irregular shifts.

HRQL

The World Health Organization (WHO) defines quality of life as the degree of realization
of a person’s interests, expectations, ideals, and hopes according to his or her current
value and cultural system, in his or her life 19], 20]. Quality of life is so comprehensive and extensive that it is sometimes classified
into non-health-related quality of life (NHRQL) and HRQL. HRQL represents general
well-being as well as elements that have direct effects on the individual’s physical,
psychological, and mental health 21]. There are many instruments for evaluating HRQL. They include the QWB scale 22], HUI-II 23] and –III 24], EQ-5D 25], and SF-6D 26]. EQ-5D, developed by the EuroQoL Group to measure general health status, has advantages
that include simple questions, applicability to diverse clinical situations, and easy
and quick preparation 25]. The fifth KNHANES also used EQ-5D to measure HRQL. The analysis in this study was
conducted based on the results of the survey. In the survey, the subjects were asked
to choose one of the following three responses, for each of the five given dimensions,
that best explained their current health status: “1?=?no problem,” “2?=?some problems”,
and “3 =?severe problems”. The five questions concerning health status expressed health
status between 1, which represents perfect health status, and ?1, which represents
a health status that is no better than death. In this research, the EQ-5D index, which
Nam et al. calculated using their estimated weighted quality value for Koreans 27], was used. The formula for the EQ-5D index is:

EQ-5D index?=?1 – (0.05?+?0.096 × M2?+?0.418 × M3?+ 0.046 × SC2?+?0.136 × SC3?+?0.051
× UA2?+?0.208 × UA3?+?0.037 × PD2?+?0.151 × PD3?+?0.043 × AD2?+ 0.158 × AD3?+?0.05
× N3).

where M2 – Mobility “level 2”?=?1; otherwise, 0; M3 – Mobility “level 3”?=?1; otherwise,
0; SC2 – Self-care “level 2”?=?1; otherwise, 0; SC3 – Self-care “level 3”?=?1; otherwise,
0; UA2 – Usual activities “level 2”?=?1; otherwise, 0 ; UA3 – Usual activities “level
3”?=?1; otherwise, 0; PD2 – Pain/discomfort “level 2”?=?1; otherwise, 0; PD3 – Pain/discomfort
“level 3”?=?1; otherwise, 0; AD2 – Anxiety/depression “level 2”?=?1; otherwise, 0;
AD3 – Anxiety/depression “level 3”?=?1; otherwise, 0; N3 – Only one “level 3”?=?1,
and the rest?=?0.

Data analysis

The subjects’ characteristics were presented using their frequency and percentage.
To compare the HRQL values according to the subjects’ sociodemographic characteristics,
working conditions, and health status, t-test and ANOVA were used with a statistical
significance level of ??=?0.05. To adjust for the effect of confounders and to understand
reciprocal action, logistic regressions were conducted. Because there is no standard
point that can be used as a standard dichotomy in EQ-5D index, and EQ-5D index score
is calculated by using their estimated weighted quality value of response to each
of the five dimensions, and the weighted quality values are different for each country,
we concluded that it is a more fundamental and concrete method to see response to
each dimension rather than EQ-5D index score in logistic regression analysis. We used
the response of EQ-5D dimensions as dependent variable. For all 5 dimensions level
2 and 3 on the EQ-5D dimensions were merged and thus dichotomized to “no problem”(0:
level 1) or “some or extreme problem” (1: level 2 and 3). All analyses were performed
in SPSS version 18.0 (SPSS, Chicago, IL, USA).