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Annals of Occupational and Environmental Medicine

Open Access

The relationship between working condition factors and well-being

  • Bum-Joon Lee1,
  • Shin-Goo Park1Email author,
  • Kyoung-Bok Min3,
  • Jin-Young Min4,
  • Sang-Hee Hwang2,
  • Jong-Han Leem1,
  • Hwan-Cheol Kim1,
  • Sung-Hwan Jeon1,
  • Yong-Seok Heo1 and
  • So-Hyun Moon1
Annals of Occupational and Environmental Medicine201426:34

https://doi.org/10.1186/s40557-014-0034-z

Received: 7 February 2014

Accepted: 29 September 2014

Published: 4 November 2014

Abstract

Objectives

Working conditions can exert influence on the physical, mental, and even social health of workers. Well-being is an appropriate index for the evaluation of a person’s overall health. This paper investigated the association between various working conditions and worker’s well-being.

Methods

Data from 10,019 interviews were collected from the second wave of the Korean Working Conditions Survey (2010) conducted by the Korea Occupational Safety and Health Agency between June and October 2010. The data from 5,995 employed workers were examined in this study. Well-being was measured through the WHO Five Well-Being Index (1998 version). Sociodemographic and working conditions were analyzed. Adjusted odds ratios for well-being were calculated with adjusted sociodemographic factors, working condition factors, or both.

Results

Workers’ well-being was significantly higher when they were satisfied with their working conditions (OR = 1.656, 95% CI = 1.454–1.885), when their actual working hours were the same as their anticipated working hours (OR = 1.366, 95% CI: 1.120–1.666) or exceeding less than 10 hours (OR = 1.245, 95% CI: 1.004-1.543), and when their employment was stable (OR = 1.269, 95% CI: 1.098–1.467).

Conclusions

This study supports the association between working condition factors and well-being in workers.

Keywords

Working condition surveyEmployed workerWorking conditionsWell-being

Introduction

Working conditions have significant influence on a worker’s quality of life. Workers spend more than a half of their waking hours on working [1]. Therefore, naturally, working conditions may exert influence on other areas of life, including eating, sleeping, housing, and developing interpersonal relationships, directly or indirectly. Moreover, Working conditions affect workers’ physical and mental health [2]. Due to industrial development, working conditions have become more diverse and complicated. Therefore, it is essential to closely measure and evaluate the influence of each working condition on workers’ health as society continues to evolve and change.

As industry has developed and the problems presented by poverty and illness have been resolved, society’s priority has transitioned from “survival” to “better life”. “The measurement for well-being (such as WHO well-being index)” is an appropriate measurement of a subject’s physical, mental, and social health because it encompasses the absence of both illness and negative emotions [3]. Well-being so closely related to mental health that subjects with a low WHO Five well-being index score are often recommended for depression screening tests [4]. Well-being has been linked with physical health by several studies that demonstrate that satisfied individuals have stronger immune systems and enjoy better physical health [5]-[7]. Additionally, there seems to be a positive feedback loop between a worker’s well-being and productivity. Individuals with high well-being are more productive in occupational activities than are individuals with low well-being, and productive individuals are happier than non-productive individuals [8].

Many previous studies on the influence of working conditions on health have focused on the occurrence of specific diseases like musculoskeletal illness [9],[10], occupational cancers [11], and sleep disorders [12],[13] in risky working conditions, or the influence of job stresses, job satisfaction on life habits like smoking and drinking [14],[15]. Although a few studies examined the influence of psychological occupational factors on well-being in specific occupations like teaching [16], there is a lack of research on the influence of overall working conditions on well-being. The present study examines how the working conditions are related to employed workers’ well-being. Data were gathered from the second wave of the Korean Working Conditions Survey.

Materials and methods

Working conditions survey

This study used a sample from the second wave of the Korean Working Conditions Survey (2010) conducted by the Korea Occupational Safety and Health Agency. The methodology and survey questionnaire used for the second Korean Working Conditions Survey is similar to those used in the European Working Conditions Survey, and the second survey built on investigations begun in the first survey (2006).

Briefly, the goal of the survey was to gather comprehensive information on Korean working conditions to shed light the nature and types of changes affecting the workforce and the quality of work-life for employees. The specific objective of the survey was to develop social and occupational health indicators for the working environment. We used multi-stage, random sampling from the Enumeration Districts in the 2005 Population and Housing Census. The survey was carried out at a number of different sampling areas, determined using probability proportional to population size and to population density. The survey data were collected from a nationally representative sample of the economically active population 15 years and older, excluding retirees, unemployed, homemakers, and students [17].

In this study, we defined the subjects as only ‘employed workers’, so we exclude ‘a self-employed worker’ or ‘an unpaid worker for familial business. With these exclusion criteria, among 10,019 survey interviews conducted at workers' homes between June and October 2010, the data from only 5,995 employed workers were used in this study.

The Institutional Review Board of Inha University Hospital approved the study protocol.

WHO five well-being index

Well-being was evaluated through the WHO Five Well-Being Index (the 1998 version). Although the index was originally designed to measure well-being in diabetic patients [18], its effectiveness has been supported in diagnostic depression screening [4] and evaluation of emotional well-being in patients with chronic diseases including cardiovascular diseases [18] and Parkinson’s disease [19], and in young children [20], and elderly adults [21].

The index consists of five positively worded items, each of which reflects the presence or absence of well-being. Subjects respond to questions about their positive feelings within the last two weeks on 6-point scale (0–5). A raw score lower than 13 out of 30 or an individual item score of 0 or 1 on any of the five items implies poor well-being [22], and a raw point score considerably below 13 may necessitate screening for depression with the Major Depression Inventory (under ICD-10) [4]. This study has evaluated the states of well-being of the subjects by classifying subjects with a raw score below 13 or a score of 0 or 1 on one of more items into the “poor well-being” group. Meanwhile, those who responded to all the items with a score of 2 or higher and had a total score higher than 13 were assigned to the “fair well-being” group [22].

Definition of independent variables

The survey collected information on several sociodemographic factors. Age, educational levels and monthly income were classified. Based on survey responses, the degrees of difficulty balancing income and expenses with total monthly earnings were divided into “difficult,” “somewhat difficult,” “somewhat easy,” and “easy.” Overall subjective health status, smoking status and alcohol assumption scale were also remarked. Subjects were grouped into “non-drinkers,” “moderate drinkers,” and “excessive drinkers” (i.e., individuals who consumed more than 2 glasses of alcohol a day).

Information on working condition factors was also collected and organized. Occupations were divided into 9 categories based on the Korean employment classification of occupation. Length of employment was divided into “<1 year,” “1–5 years,” “5–10 years,” “10+ years.” Employment types were divided into “contingent” and “regular” employees, and employment stability (“stable” or “unstable”) was determined by researchers based on the subjects’ responses. The presence and the absence of shifts were noted, and total weekly work hours were categorized into “<40 hours,” “40 hours,” ”40–60 hours,” and “60+ hours.” Based on the calculated difference between the actual and desired working time, we had categories of individuals who “lacked work time,” worked “10+ hours longer” than desired, worked “<10 hours longer” than desired or experienced “no difference” between actual and desired work time. Monthly work on the weekends was divided into “4+ days a month,” “1–4 days,” and “none.” Work condition satisfaction was divided into “satisfied” and “unsatisfied.” Degree of work stress was divided into the categories of “mild,” “moderate,” and “severe.”

Statistical analysis

All data were analyzed with the SPSS (version 14.0) after encoding was completed. A descriptive analysis was carried out on sociodemographic and working condition factors. Pearson’s chi-squared analysis was carried out between each factor and well-being. Logistic regressions yielded crude and adjusted odds ratios of working condition factors and the well-being. For logistic regression analysis, three models were used for adjusting effect of other factors. The model 1 was adjusted for sociodemographic factors (gender, age, education, monthly income, balancing income and expenses, health status, smoking, and drinking); the model 2 was adjusted for working conditional factors (type of occupation, number of years employed, employment type, employment stability, weekly working time, difference between actual and desired working time, monthly weekend work, satisfaction with working conditions, and work stress); and the model 3 was adjusted for both sociodemographic and working conditional factors. Pearson correlation analysis was used to test for multicollinearity among individual factors. The significance threshold was 0.05.

Results

Sociodemographic factors and well-being

The average raw score of the WHO Five Well-being Index in the 5,995 participants was 13.29 (SD: 5.53); 2,415 (40.3%) were in the poor well-being group, while 3,580 (59.7%) were in the fair well-being group.

Well-being did not significantly differ between genders. Subjects who were under 30 or 40–49 years old had higher well-being scores. Subjects with higher education levels had significantly higher scores of well-being. Subjects with income greater than 3 million KRW had significantly higher well-being than those with lower incomes. Subjects who experienced a balance between their income and expenses (income-expense balance) had significantly higher well-being than those who faced an imbalance between income and expenses. Workers who reported better health status had significantly higher scores of well-being. Smoking and drinking were both related to well-being (Table 1).
Table 1

Sociodemographic factors and well-being

   

Well-being

 
  

Total

Poor

Fair

 
  

N

N

%

N

%

p-value*

Total

 

5,995

2,415

40.3

3,580

59.7

 

Gender

Male

3,304

1,341

40.6

1,963

59.4

0.595

Female

2,691

1,074

39.9

1,617

60.1

Age

<29

901

328

36.4

573

63.6

<0.001

30–39

1,833

790

43.1

1,043

56.9

40–49

1,662

602

36.2

1,060

63.8

50–59

1,018

426

41.8

592

58.2

>60

581

269

46.3

312

53.7

Education

Middle school or lower

917

455

49.6

462

50.4

<0.001

High school

2,578

1,057

41.0

1,521

59.0

Junior college

755

325

43.0

430

57.0

College or higher

1,745

578

33.1

1,167

66.9

Monthly income (KRW)

<1 million

1,326

572

43.1

754

56.9

<0.001

1≤, <2 million

2,251

939

41.7

1,312

58.3

2≤, <3 million

1,382

547

39.6

835

60.4

≥3 million

1,036

357

34.5

679

65.5

Balancing income and expenses

Difficult

1,149

669

58.2

480

41.8

<0.001

Somewhat difficult

2,122

986

46.5

1,136

53.5

Somewhat easy

1,740

525

30.2

1,215

69.8

Easy

984

235

23.9

749

76.1

Health status

Bad

255

171

67.1

84

32.9

<0.001

Normal

1,396

831

59.5

565

40.5

Good

3,572

1,253

35.1

2,319

64.9

Very good

772

160

20.7

612

79.3

Smoking

Non smoker

3,420

1,328

38.8

2,092

61.2

0.025

Current smoker

1,999

850

42.5

1,149

57.5

Ex-smoker

576

237

41.1

339

58.9

Drinking

Non-drinker

1,715

688

40.1

1,027

59.9

<0.001

Moderate drinker

2,574

976

37.9

1,598

62.1

Excessive drinker

1,706

751

44.0

955

56.0

*p-value: Pearson’s chi-squared test.

Working condition factors and well-being

A univariate analysis revealed that of the 9 types of jobs, clerks had higher well-being, whereas subjects working in agriculture, forestry and fishing had lower well-being. Workers with 5–10 years’ experience showed somewhat higher well-being than workers with other lengths of work experience in univariate analysis, all the other working condition factors, employment types, employment stability, weekly working time, difference between the actual and desired working time, monthly weekend work, working condition satisfaction, work stress, had statistically significant relationship with workers' subjective well being except shift working (Table 2).
Table 2

Working condition factors and well-being

   

Well-being

 
  

Total

Poor

Fair

 
  

N

N

%

N

%

p-value*

Type of occupation

Unskilled

1,143

531

46.5

612

53.5

<0.001

Semi-skilled

445

195

43.8

250

56.2

Skilled

685

300

43.8

385

56.2

Expertise

492

175

35.6

317

64.4

Sales

727

278

38.2

449

61.8

Service

922

402

43.6

520

56.4

Clerk

1,494

492

32.9

1,002

67.1

Senior management

53

18

34.0

35

66.0

Agriculture, forestry and fishing

34

24

70.6

10

29.4

Years employed

<1 yr

1,166

486

41.7

680

58.3

0.023

1≤, <5 yrs

2,847

1,179

41.4

1,668

58.6

5≤, ≤10 yrs

967

350

36.2

617

63.8

>10 yrs

1,015

400

39.4

615

60.6

Employment type

Contingent

1,288

601

46.7

687

53.3

<0.001

Regular

4,707

1,814

38.5

2,893

61.5

Employment stability

Unstable

1,871

830

44.4

1,041

55.6

<0.001

Stable

4,124

1,585

38.4

2,539

61.6

Shift working

Present

605

243

40.2

362

59.8

0.950

Absent

5,390

2,172

40.3

3,218

59.7

Weekly working time

<40 hrs

929

388

41.8

541

58.2

<0.001

40 hrs

1,893

648

34.2

1,245

65.8

40<, ≤60 hrs

2,687

1,142

42.5

1,545

57.5

>60 hrs

486

237

48.8

249

51.2

Difference between the actual and desired working time

≥10 hrs longer

774

395

51.0

379

49.0

<0.001

<10 hrs longer

1,128

461

40.9

667

59.1

No difference

3,390

1,240

36.6

2,150

63.4

Lack of working time

703

319

45.4

384

54.6

Monthly weekend work

More than 4 days

835

368

44.1

467

55.9

<0.001

1 ~ 4 days

2,611

1,166

44.7

1,445

55.3

none

2,549

881

34.6

1,668

65.4

Working condition satisfaction

Dissatisfied

1,795

1,004

55.9

791

44.1

<0.001

Satisfied

4,200

1,411

33.6

2,789

66.4

Work stress

Severe

1,855

789

42.5

1,066

57.5

0.018

Moderate

2,472

995

40.3

1,477

59.7

Mild

1,668

631

37.8

1,037

62.2

*p-value: Pearson’s chi-squared test.

In the analysis of the 9 types of jobs, only the model 2 showed a significant difference with an OR of 1.251 in office workers (95% CI: 1.042–1.502), and an OR of 0.433 in farmers and fishermen (95% CI: 0.200–0.937), but the difference was not significant in model 3. No relationship was observed between employment types and well-being in model 1, 2 and 3. A significant relationship between employment stability and well-being was present. Well-being was significantly higher when employment was stable, and this pattern remained constant in all the 3 models (model 1: OR = 1.204, 95% CI: 1.063–1.365; model 2: OR = 1.222, 95% CI: 1.065–1.402; model 3: OR = 1.269, 95% CI: 1.098–1.467).

A relationship was found between well-being and the difference between workers’ actual and desired working time. Subjects who reported that their actual working hours were as desired or exceeding less than 10 hours showed significantly higher well-being than subjects who worked ≥10 hours longer than desired. The OR was 1.533 (95% CI: 1.294–1.816), 1.297 (95% CI: 1.065-1.580) in model 1, 1.401 (95% CI: 1.159–1.693), 1.305 (95% CI: 1.064-1.602) in model 2, and 1.366 (95% CI: 1.120–1.666), 1.245 (95% CI: 1.004-1.543) in model 3. Additionally, subjects who did not work on weekends showed higher well-being than those who worked 4+ weekend days a month. These results were constant only in model 1.

Satisfaction or non-satisfaction with working conditions was related to well-being. In all the three models, subjects who were satisfied with their working conditions demonstrated significantly higher well-being. (Model 1: OR = 1.723, 95% CI: 1.519–1.955; Model 2: OR = 2.209, 95% CI: 1.957–2.492; Model 3: OR = 1.656, 95% CI: 1.454–1.885).

After adjusted for sociodemographic or working conditional factor, unremarkable relationship was found between degree of job stress and well-being (Table 3).
Table 3

Working condition factors and well being (Logistic regression analysis)

  

Crude OR

Model 1

Model 2

Model 3

  

OR

95% CI

OR

95% CI

OR

95% CI

OR

95% CI

Type of occupation

Unskilled

1.000

-

1.000

-

1.000

-

1.000

-

Semi-skilled

1.112

0.892-1.387

1.183

0.924-1.515

1.194

0.946 -1.506

1.198

0.932 -1.540

Skilled

1.113

0.920-1.347

1.032

0.817-1.303

1.046

0.851 -1.286

0.996

0.785 -1.264

Expertise

1.572

1.264-1.955

0.974

0.731-1.298

1.192

0.942 -1.507

0.927

0.691 -1.243

Sales

1.401

1.160-1.694

1.008

0.804-1.262

1.206

0.982 -1.481

0.980

0.777 -1.236

Service

1.122

0.943-1.336

0.910

0.744-1.114

1.067

0.885 -1.286

0.933

0.758 -1.148

Clerk

1.767

1.508-2.071

1.203

0.963-1.503

1.251

1.042 -1.502

1.043

0.828 -1.314

Senior management

1.687

0.944-3.014

1.086

0.571-2.068

1.167

0.640 -2.128

0.956

0.499 -1.834

Agriculture, forestry and fishing

0.362

0.171-0.763

0.430

0.195-0.947

0.433

0.200 -0.937

0.468

0.209 -1.048

Years employed

<1 yr

1.000

-

1.000

-

1.000

-

1.000

-

1≤, <5 yrs

1.011

0.881-1.161

0.991

0.849-1.158

0.931

0.802 -1.081

0.970

0.827 -1.138

5≤, ≤10 yrs

1.260

1.057-1.501

1.271

1.038-1.557

1.079

0.892 -1.307

1.180

0.957 -1.454

>10 yrs

1.099

0.926-1.304

1.083

0.871-1.347

0.862

0.711 -1.046

0.972

0.775 -1.220

Employment type

Contingent

1.000

-

1.000

-

1.000

-

1.000

-

Regular

1.395

1.232-1.580

1.098

0.949-1.271

1.112

0.950 -1.303

0.982

0.828 -1.164

Employment stability

Unstable

1.000

-

1.000

-

1.000

-

1.000

-

Stable

1.277

1.143-1.427

1.204

1.063-1.365

1.222

1.065 -1.402

1.269

1.098 -1.467

Weekly working time

<40 hrs

1.000

-

1.000

-

1.000

-

1.000

-

40 hrs

1.378

1.173-1.619

1.067

0.871-1.306

0.975

0.795 -1.195

0.971

0.773 -1.219

40<, ≤60 hrs

0.970

0.834-1.129

0.861

0.709-1.046

0.922

0.749 -1.134

0.962

0.760 -1.218

>60 hrs

0.754

0.604-0.939

0.773

0.596-1.002

0.962

0.723 -1.279

1.040

0.758 -1.427

Difference between the actual and desired working time

≥10 hrs longer

1.000

-

1.000

-

1.000

-

1.000

-

<10 hrs longer

1.508

1.254-1.813

1.297

1.065-1.580

1.305

1.064 -1.602

1.245

1.004 -1.543

No difference

1.807

1.544-2.115

1.533

1.294-1.816

1.401

1.159 -1.693

1.366

1.120 -1.666

Lack of working time

1.255

1.022-1.540

1.444

1.148-1.817

1.142

0.879 -1.483

1.283

0.975 -1.688

Monthly weekend work

1–4 days

1.000

-

1.000

-

1.000

-

1.000

-

None

0.977

0.835-1.142

0.953

0.804-1.130

0.858

0.726 -1.013

0.887

0.744 -1.058

>4 days

1.492

1.272-1.749

1.328

1.115-1.583

1.118

0.928 -1.347

1.135

0.932 -1.383

Satisfaction with working conditions

Dissatisfied

1.000

-

1.000

-

1.000

-

1.000

-

Satisfied

2.509

2.241-2.809

1.723

1.519-1.955

2.209

1.957 -2.492

1.656

1.454 -1.885

Work stress

Severe

1.000

-

1.000

-

1.000

-

1.000

-

Moderate

1.099

0.972-1.242

1.051

0.921-1.199

1.005

0.885 -1.142

0.990

0.865 -1.133

Mild

1.216

1.063-1.392

1.144

0.987-1.327

1.112

0.963 -1.284

1.031

0.885 -1.201

Model 1: Adjusted for sociodemographic factors.

Model 2: Adjusted for working conditional factors.

Model 3: Adjusted for both sociodemographic and working conditional factors.

Discussion

We conducted this study to evaluate the association between general working condition factors and employed workers' well-being in a nationwide representative sample of Korea. After we adjusted sociodemographic and working condition factors, workers’ well-being was significantly higher when they were satisfied with their working conditions (OR = 1.656, 95% CI: 1.454–1.885), when their actual working hours were the same as their desired working hours (OR = 1.366, 95% CI: 1.120–1.666) or exceeding less than 10 hours (OR = 1.245, 95% CI: 1.004-1.543), and when their employment was stable (OR = 1.269, 95% CI: 1.098–1.467).

This current study is the first one that studied the relationship between general working conditional factors and workers’ well-being. Therefore, few comparable outcomes exist. Previous findings [23] merely compared a few working conditions such as employees to the unemployed, and professionals to inexperienced workers. Previous studies have not addressed the relationship between other working conditional factors and well-being.

The sociodemographic factors were drawn from a nationwide survey on working conditions and included gender, age, educational level, monthly income, income-expense balance, health status, smoking, and drinking. The workers’ well-being has no differences between the genders. This finding is consistent with a previous study that gender accounts little for the variance in individual differences in well-being [24]. Older subjects tended to have lower well-being. This result contradicts the finding that older individuals are happier with their lives than younger individuals [25]. However, another study [26] proposed that older individuals tended to have lower well-being than younger individuals because the former experience more health problems and less satisfaction with the future than the latter. That study and the current one both found a positive correlation between education levels and well-being. Another research [27] has suggested a correlation between income and well-being at lower income levels but not at higher income levels. In the current study, the subjects with a monthly income of 3 million or more KRW had significantly higher well-being than subjects with a lower income. However, income-expense balance had a stronger correlation with well-being than absolute monthly income did. Subjects who reported it was either somewhat difficult or easy to balance their income and expenses experienced higher well-being than those who reported it was difficult.

Subjects with normal, good, or very good self-reported health status had higher well-being than those with poor self-reported health status. This finding accords with the results of a previous study [23]. Interestingly, only 264 subjects (4.2%) had bad self-reported health status, whereas 4,571 (72.6%) reported their health status was “good” and “very good.” We can hypothesize that there is a “healthy worker effect” that may occur in surveys that are distributed only to workers who are currently employed.

Current smokers had somewhat lower scores of well-being than non-smokers, respectively. Moderate drinkers showed higher well-being status than non-drinkers. Excessive drinkers showed the lowest well-being status. This result is consistent with previous findings [28]-[30] that suggest that smoking and excessive drinking are detrimental to physical as well as mental health, as measured by life satisfaction and well-being.

Fifth EWCS (2010) evaluated mental well-being of the workers by using WHO-Five well-being index. According to the fifth EWCS Overview report (2012), the result of the index regarded as a good predictor of mortality and functional ability. 20% (male: 18%, female: 22%) of the workers of the EU 27 countries were classified into 'mental health at risk'. Percentage of the elder workers (above 50 year-old) was 7 percent point more than the younger's (below 35 year-old). In this study, the percentage of low mental well-being group in Korean employed workers was 40.3%. Comparing between this study and fifth EWCS Overview report, well-being of the Korean employed workers were lower than average of the 27 EU counties. The relations between ages and well-being or job and well-being were similar. There are little information about the relation between well-being and working conditional factors in EWCS report [31].

This study, however, has several limitations. First, although it demonstrates a relationship between working condition factors and well-being, we cannot make conclusions regarding causality. Second, we did not take into account the “healthy worker effect” during our analysis, which could influence our results. Third, we did not examine variations in individual personality traits that could influence workers’ well-being. Although an existing study explored the hypothesis that individuals’ positive personalities are closely related to their well-being [32], we were not able to investigate personality traits because the working conditions survey did not contain the necessary items. Fourth, we didn’t assessed the relationship between well-being and physically or chemically hazardous working conditional factors which could be exist on specific job such as noise, heavy metals, organic chemicals. However, some of these factors could be included in type of occupation.

Conclusions

This is the first study that analyzed association between general working conditions and workers’ well-being. We expect further research to figure out causal relationships between general or specific working conditions and workers’ well-being.

Declarations

Acknowledgements

This work was supported by Korea occupational safety and health agency.

Authors’ Affiliations

(1)
Department of Occupational and Environmental Medicine, Inha University
(2)
Department of Dentistry, Keimyung University School of Medicine
(3)
Department of Occupational & Environmental Medicine, Ajou University School of Medicine
(4)
Institute of Health and Environment, Seoul National University

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© Lee et al.; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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