Comparisons of working conditions and health-related problems between older male and female crop farmers

Abstract

BACKGROUND:

Although the number of farmers in Korea is declining, the proportion of older farmers aged ≥60 increases.

OBJECTIVE:

This study aims to analyze gender differences in working conditions, exposure to risk factors, and health problems of older crop farmers aged ≥60.

METHODS:

This study used data from the 5th Korean Working Conditions Survey (KWCS) data of 818 male and 985 female crop farmers aged ≥60 years.

RESULTS:

This study showed that older female farmers had higher ratios of living alone (45.0% vs. 13.4%, p < 0.001), lower education levels (80.7% vs. 53.4%, p < 0.001), and lower average incomes (1.565 vs. 2.036 million KRW, p < 0.001) than that of males. Among subjective scores on the exposure of risk factors, only females’ repetitive motion (4.923) and awkward posture (4.415) scores were higher than that of males’ repetitive motion (4.601) (p < 0.001) and awkward posture (4.159) scores (p = 0.001). However, female’s health problem rates on upper limb pain (68.3%), lower extremity pain (67.6%), backache (48.4%), overall fatigue (44.8%), and headache and eyestrain (25.0%) were higher than that of male’s complaint rates on upper limb pain (48.5%) (p < 0.001), lower extremity pain (53.3%) (p < 0.001), backache (35.0%) (p < 0.001), overall fatigue (39.5%) (p = 0.024), and headache and eyestrain (19.4%) (p = 0.005). The rate of depression symptoms in females (54.6%), with a high proportion of single-person households, was higher than that of males (46.9%) (p = 0.001).

CONCLUSIONS:

The musculoskeletal pains and depression symptoms of older female farmers are prevalent, and efforts and support are required to improve working conditions.

Keywords

Farmworkers occupational health depression;musculoskeletal pain

1 Introduction

According to the Korean Standard Industrial Classification, growing crops refers to an industrial activity that cultivates and produces crops and seeds in the field or within a specific facility [1]. According to the Korea Standard Classification of Occupations, crop growers are classified into crop farmers, vegetable farmers, and fruits farmers [2]. A crop farmer refers to a person who sows seeds in arable land and cultivates them to harvest crops such as rice, barley, wheat, and beans [2].

The agricultural population in Korea is declining every year. The farm population decreased from 3.117 million persons (6.4% of the entire Korean population) in 2009 to 2.245 million persons (4.3% of the whole Korean population) in 2019 [3]. However, the proportion of older farmers, predominantly female older farmers, is increasing. The ratio of farmers aged ≥60 increased from 44.7% of all farmers in 2009 to 60.6% in 2019 [3]. In particular, the proportion of older female farmers risen from 46.5% of all female farmers in 2009 to 61.9% in 2019 [3]. In other words, the aging and feminization phenomenon is intensifying.

The aging and feminization of farmers lead to a workforce shortage, and working hours are increasing every year [4]. Agricultural work involves many risks [5, 6]. However, agricultural workers are mainly elderly and female workers. In addition, the use of farm machinery and pesticides causes occupational injuries and illnesses [4, 6]. Older workers are known to be more frequent in the injury-prone agricultural sector [7]. In Korea, the number of injured per 1,000 agriculture workers in 2019 was 8.08, higher than that of the entire industry (5.83) [8]. Also, the proportion of injured farmers aged ≥60 was 36.5% of all injured farmers, which was higher than that of the entire industry (29.0%) [8].

Agricultural work includes risk factors for musculoskeletal diseases due to lifting, pushing, and pulling heavy objects, as well as twisting or bending the waist, kneeling and squatting, and repeated use of arms [9 –12]. In Korea, agriculture is recognized as one of the most prevalent industries for work-related musculoskeletal disorders [13]. A cold or hot work environment can also be stressful. Contact with cold objects damages the sense of touch and reduces the alertness of the hand. If agricultural work is carried out in a hot, humid environment, it can entail excessive stress on the body. The high-temperature environment can cause heat-related disorders such as heat stress and heat stroke [5]. On the other hand, although agricultural work is exposed to various dangerous and harmful factors, it is known that elderly farmers have low awareness of the risk and lack of preventive activities [4].

Aging and loneliness are associated with farmers’ quality of life and depression [14]. According to Statistics Korea [3], the proportion of single-person households among all farmers increased from 14.9% in 2009 to 19.7% in 2019. WHO-5 index is a measure of older people’s mental well-being [14, 15] and has an adequate validity in screening for depression [16]. Depression is a mental state characterized by a pessimistic sense of inadequacy [14]. Also, depression is a major cause of suicide rates in the elderly, lowering the life quality of the elderly [17]. Thus, the study of depression is very meaningful in older farmers.

Despite the current status of agricultural work on the aging and feminization of farmers, there was a lack of studies on the risk factors and exposures in the working environments and health status of elderly farmers [18 –20]. Therefore, this study aims to analyze gender differences in the working environment and health status of older crop farmers aged ≥60.

2 Method

2.1 Data collection and subjects

This study was conducted using the 5th Korean Working Conditions Survey (KWCS) data hosted by the Occupational Safety and Health Research Institute [21]. The 5th KWCS undertaken in 2017 was benchmarked against the European Working Conditions Survey (EWCS) [22].

This study filtered 1,803 crop farmers aged ≥60 from 50,205 KWCS data by the Korean Standard Occupational Classification (code number 6111) [1]. The 1,803 crop farmers consisted of 818 men (45.4%) and 985 women (54.6%).

2.2 Research variables

Table 1 shows the research variables of this study. The variables of this study were selected from the items of the KWCS questionnaire [21] and the EWCS questionnaire [23]. Research variables are divided into worker characteristics, working environment, and health problems.

Table 1
Research variables of this study

Factor Variable Description Observed score

Respondents Gender 0 = female, 1 = male

No. of household Person

Education level 1 = elementary, 2 = middle, 3 = high school, 4 = college

Wage per month million KRW

Work experience Year

Working days/week Day

Working hours/week Hour

Working hazard exposure Physical hazards Vibration 1 = Never, 2 = Rarely, 3 = 1/4 times, 4 = 1/2 times, 5 = 3/4 times, 6 = Most of the time, 7 = Always

Noise

High temperature

Low temperature

Chemical and biological hazards Fumes, dust 1 = Never, 2 = Rarely, 3 = 1/4 times, 4 = 1/2 times, 5 = 3/4 times, 6 = Most of the time, 7 = Always

Vapor

Skin contact with chemical

Infection

Ergonomic hazards Awkward posture 1 = Never, 2 = Rarely, 3 = 1/4 times, 4 = 1/2 times, 5 = 3/4 times, 6 = Most of the time, 7 = Always

Handling of heavy objects

Standing posture

Repetitive motion

SHE information Safety &health information 1 = Never, 2 = Rarely, 3 = Good, 4 = Very good

Health problem Physical problem Hearing problems 1 = Yes, 0 = Other

Skin problems

Headache and eyestrain

Overall fatigue

MSDS problem Backache 1 = Yes, 0 = Other

Upper limb pain

Lower limb pain

Wellbeing score good spirit 0 = Never, 1 = Rarely, 2 = Under 1/2 times, 3 = Over 1/2 times, 4 = Most of the time, 5 = Always

relaxation

being active

vitality

general interest

Total wellbeing score 0∼25

Depression problem 1 = if total depression score < 13, 0 = Other

Factor	Variable	Description	Observed score
Respondents		Gender	0 = female, 1 = male
		No. of household	Person
		Education level	1 = elementary, 2 = middle, 3 = high school, 4 = college
		Wage per month	million KRW
		Work experience	Year
		Working days/week	Day
		Working hours/week	Hour
Working hazard exposure	Physical hazards	Vibration	1 = Never, 2 = Rarely, 3 = 1/4 times, 4 = 1/2 times, 5 = 3/4 times, 6 = Most of the time, 7 = Always
		Noise
		High temperature
		Low temperature
	Chemical and biological hazards	Fumes, dust	1 = Never, 2 = Rarely, 3 = 1/4 times, 4 = 1/2 times, 5 = 3/4 times, 6 = Most of the time, 7 = Always
		Vapor
		Skin contact with chemical
		Infection
	Ergonomic hazards	Awkward posture	1 = Never, 2 = Rarely, 3 = 1/4 times, 4 = 1/2 times, 5 = 3/4 times, 6 = Most of the time, 7 = Always
		Handling of heavy objects
		Standing posture
		Repetitive motion
	SHE information	Safety &health information	1 = Never, 2 = Rarely, 3 = Good, 4 = Very good
Health problem	Physical problem	Hearing problems	1 = Yes, 0 = Other
		Skin problems
		Headache and eyestrain
		Overall fatigue
	MSDS problem	Backache	1 = Yes, 0 = Other
		Upper limb pain
		Lower limb pain
	Wellbeing score	good spirit	0 = Never, 1 = Rarely, 2 = Under 1/2 times, 3 = Over 1/2 times, 4 = Most of the time, 5 = Always
		relaxation
		being active
		vitality
		general interest
		Total wellbeing score	0∼25
		Depression problem	1 = if total depression score < 13, 0 = Other

Worker characteristics consisted of respondent characteristics (number of households, education level, wage per month, work experience, working days per week, working hours per week).

The working environment is evaluated as the level of exposure to hazards and categorized into physical hazards (vibration, noise, high temperature, low temperature), chemical and biological hazards (fume and dust, vapor, skin contact, infection), and ergonomic hazards (awkward posture, handling of heavy objects, standing posture, and repetitive motion).

Health-related problems are expressed as complaints of physical health problems (hearing problems, skin problems, headache and eyestrain, overall fatigue), musculoskeletal pains (backache, upper limb pain, lower limb pain), and wellbeing and depression.

In the WHO’s 5 Well-Being Index [15], five items are measured with a total score of 0–25. If the total score is less than 13 points, or if even one question is scored 0 or 1, it is evaluated as having a risk of depression [24].

2.3 Data analysis

In this study, t-tests were used to investigate gender differences in terms of worker characteristics, exposure to hazards in the working environment, and subjective wellbeing score. Also, χ² tests were used to test for equality of distributions to determine whether there existed a significant difference between genders in complaints frequency. We used SPSS statistical package version 18 (SPSS Inc., Chicago, IL, USA) to conduct statistical tests, and the significance level was set to 0.05.

3 Results

3.1 Characteristics of respondents

3.1.1 Distributions of respondents by gender and education level

Table 2 shows the distribution of education level of respondents by gender. In Table 2, 68.3% of crop farmers were less than graduating from elementary school. There was a difference in the distribution of education level according to gender (χ² = 156.759, p < 0.001). The majority of females graduated from elementary school or less (80.7%), whereas males’ education was higher than that of the females.

Table 2
Distribution of respondents by gender and education level

Gender Elementary school Middle school Over high school Total

Female N 795 118 72 985

% 80.7% 12.0% 7.3% 100.0%

Male N 437 206 175 818

% 53.4% 25.2% 21.4% 100.0%

Total N 1232 324 247 1803

% 68.3% 18.0% 13.7% 100.0%

χ² test χ² = 156.759

p < 0.001

Gender	Elementary school	Middle school	Over high school	Total
Female	N	795	118	72	985
	%	80.7%	12.0%	7.3%	100.0%
Male	N	437	206	175	818
	%	53.4%	25.2%	21.4%	100.0%
Total	N	1232	324	247	1803
	%	68.3%	18.0%	13.7%	100.0%
χ² test	χ² = 156.759
p < 0.001

^*Significant difference at 0.05.

3.1.2 Distributions of respondents by gender and no. of household

Table 3 shows the distribution of the number of households according to gender. In Table 3, the proportion of respondents living alone was 30.7%, with two persons at 62.5% and three or more persons at 6.8%.

Table 3
Distributions of respondents by gender and households (persons)

Gender χ² test for distribution of households Mean test

1 2 3 or more Total

Female N 443 485 57 985 Mean 1.648

% 45.0% 49.2% 5.8% 100.0% SD 0.740

Male N 110 642 66 818 Mean 1.980

% 13.4% 78.5% 8.1% 100.0% SD 0.608

Total N 553 1127 123 1803 Mean 1.799

% 30.7% 62.5% 6.8% 100.0% SD 0.703

Statistical Testing χ² = 209.381 t = –10.484

p < 0.001 p < 0.001

Gender	χ² test for distribution of households	Mean test
Female	N	443	485	57	985	Mean	1.648
	%	45.0%	49.2%	5.8%	100.0%	SD	0.740
Male	N	110	642	66	818	Mean	1.980
	%	13.4%	78.5%	8.1%	100.0%	SD	0.608
Total	N	553	1127	123	1803	Mean	1.799
	%	30.7%	62.5%	6.8%	100.0%	SD	0.703
Statistical Testing		χ² = 209.381			t = –10.484
		p < 0.001			p < 0.001

Note: SD = Standard deviation. ^*Significant difference at 0.05.

There was a gender difference in the distribution of household members (χ² = 209.381, p < 0.001). Whereas 13.4% of males live alone, 45.0% of females live alone, indicating a high proportion of older women living alone. Because women live longer than men, women have a high rate of living alone. The average of household members in males (1.980 persons) was higher than that of females (1.648 persons) (t = –10.484, p < 0.001).

3.1.3 Wage, work experience, and working days

Table 4 represents the results of mean tests between genders on monthly wage, work experience, and working days per week. The average monthly wage was higher for men (2.036 million won) than women (1.565 million won) (t = –9.330, p < 0.001). In terms of work experience, the average of women (40.584 years) was higher than that of men (37.895 years) (t = 3.676, p < 0.001). Also, the average number of working days per week (5.798 days) for males was higher than that of females (5.665 days) (t = –2.317, p = 0.021).

Table 4
Comparison of wage, work experience, and working days

Gender Wage (million KRW) Work experience (years) Working days (days)

Female Mean 1.565 40.584 5.665

SD (0.915) (14.895) (1.200)

Male Mean 2.036 37.895 5.798

SD (1.138) (15.583) (1.217)

Total Mean 1.791 39.364 5.725

SD (1.055) (15.266) (1.209)

Mean test^* t t = –9.330 t = 3.676 t = –2.317

p p < 0.001 p < 0.001 p = 0.021

Gender	Wage (million KRW)	Work experience (years)	Working days (days)
Female	Mean	1.565	40.584	5.665
	SD	(0.915)	(14.895)	(1.200)
Male	Mean	2.036	37.895	5.798
	SD	(1.138)	(15.583)	(1.217)
Total	Mean	1.791	39.364	5.725
	SD	(1.055)	(15.266)	(1.209)
Mean test^*	t	t = –9.330	t = 3.676	t = –2.317
	p	p < 0.001	p < 0.001	p = 0.021

Note: SD = Standard deviation. ^*Significant difference at 0.05.

3.1.4 Distributions of working hours/week

The gender distribution of working hours per week is shown in Table 5. The distribution of working hours per week was different according to gender (χ² = 63.682, p < 0.001). While 76.1% of women worked less than 40 hours per week, 58.6% of men worked less than 40 hours. In contrast, 15.8% of men worked more than 53 hours, indicating that men worked more than 9.1% of women.

Table 5
Distribution of respondents by gender and working hours

Gender χ² test for distribution of working hours Mean test

1 2 3 or more Total

Female N 750 145 90 985 Mean 33.740

% 76.1% 14.7% 9.1% 100.0% SD (14.625)

Male N 479 210 129 818 Mean 37.873

% 58.6% 25.7% 15.8% 100.0% SD (15.761)

Total N 1229 355 219 1803 Mean 35.615

% 68.2% 19.7% 12.1% 100.0% SD (15.286)

Statistical Testing χ² = 63.682 t = –5.727

p < 0.001 p < 0.001

Gender		χ² test for distribution of working hours	Mean test
Female	N	750	145	90	985	Mean	33.740
	%	76.1%	14.7%	9.1%	100.0%	SD	(14.625)
Male	N	479	210	129	818	Mean	37.873
	%	58.6%	25.7%	15.8%	100.0%	SD	(15.761)
Total	N	1229	355	219	1803	Mean	35.615
	%	68.2%	19.7%	12.1%	100.0%	SD	(15.286)
Statistical Testing			χ² = 63.682	t = –5.727
			p < 0.001	p < 0.001

Note: SD = Standard deviation. ^*Significant at significance 0.05.

In the case of working hours per week, the average working hours for men was 37.873 hours, which was higher than that of women (33.740 hours) (t = –5.727, p < 0.001).

3.2 Comparison of working conditions

3.2.1 Physical hazard exposures

Table 6 shows the degree of exposure to physical risk factors on a 7-point scale. Overall, the degree of exposure to physical risk factors was highest in high temperature (3.416), followed by low temperature (2.604), vibration (2.349), and noise (1.998). Table 6 shows differences between men and women in the level of exposure to noise and vibration. The degree of exposure to vibration was higher in men (2.471) than in women (2.249) (t = –3.828, p < 0.001). Also, the exposure level of noise showed higher in males (2.054) than that of females (1.952) (t = –2.177, p = 0.029).

Table 6
Means of subjective scores on vibration, noise, high temperature, and low temperature

Gender Vibration Noise High temperature Low temperature

Female Mean 2.249 1.952 3.464 2.661

SD 1.199 0.949 1.791 1.440

Male Mean 2.471 2.054 3.358 2.535

SD 1.248 1.015 1.766 1.388

Total Mean 2.349 1.998 3.416 2.604

SD 1.226 0.981 1.780 1.418

Mean test t = –3.828 t = –2.177 t = 1.258 t = 1.872

p < 0.001^* p = 0.029^* p = 0.209 p = 0.061

Gender		Vibration	Noise	High temperature	Low temperature
Female	Mean	2.249	1.952	3.464	2.661
	SD	1.199	0.949	1.791	1.440
Male	Mean	2.471	2.054	3.358	2.535
	SD	1.248	1.015	1.766	1.388
Total	Mean	2.349	1.998	3.416	2.604
	SD	1.226	0.981	1.780	1.418
Mean test		t = –3.828	t = –2.177	t = 1.258	t = 1.872
		p < 0.001^*	p = 0.029^*	p = 0.209	p = 0.061

Note: SD = Standard deviation. ^*significant at 0.05, Subjective score (1 = Never, 2 = Rarely, 3 = 1/4 times, 4 = 1/2 times, 5 = 3/4 times, 6 = Most of the time, 7 = Always).

3.2.2 Ergonomic hazard exposures

Table 7 shows the exposure level of ergonomic risk factors on a 7-point scale. The subjective score for exposure of ergonomic risk factors was highest in repetitive motion (4.777), followed by awkward posture (4.299), standing posture (3.793), and heavy material handling (3.027). There were gender differences in exposure levels of awkward posture (t = 3.391, p = 0.001), heavy material handling (t = –3.006, p = 0.003), standing posture (t = –5.352, p < 0.001), and repetitive motion (t = 4.038, p < 0.001). The females’ exposure to awkward postures or repetitive motion was higher than that of males. On the other hand, males’ exposure scores were higher than that of heavy material handling or standing posture.

Table 7
Means of subjective scores on ergonomic hazard exposures

Gender Awkward posture Material handling Standing posture Repetitive motion

Female Mean 4.415 2.957 3.646 4.923

SD 1.608 1.041 1.225 1.624

Male Mean 4.159 3.110 3.969 4.601

SD 1.589 1.100 1.341 1.753

Total Mean 4.299 3.027 3.793 4.777

SD 1.604 1.070 1.289 1.691

Mean test t = 3.391 t = –3.006 t = –5.352 t = 4.038

p = 0.001^* p = 0.003^* p < 0.001^* p < 0.001^*

Gender		Awkward posture	Material handling	Standing posture	Repetitive motion
Female	Mean	4.415	2.957	3.646	4.923
	SD	1.608	1.041	1.225	1.624
Male	Mean	4.159	3.110	3.969	4.601
	SD	1.589	1.100	1.341	1.753
Total	Mean	4.299	3.027	3.793	4.777
	SD	1.604	1.070	1.289	1.691
Mean test		t = 3.391	t = –3.006	t = –5.352	t = 4.038
		p = 0.001^*	p = 0.003^*	p < 0.001^*	p < 0.001^*

Note: SD = Standard deviation. ^*significant at 0.05, Subjective score (1 = Never, 2 = Rarely, 3 = 1/4 times, 4 = 1/2 times, 5 = 3/4 times, 6 = Most of the time, 7 = Always).

3.2.3 Chemical and biologic hazard exposures

Table 8 shows the exposure to chemical and biological risk factors on a 7-point scale. Overall, fume and dust (2.186) were the highest for subjective scores, followed by skin contact (1.882), vapor (1.618), and infection (1.509). The subjective exposure score for chemical and biological risk factors was higher only in skin contact for males (1.934) than for females (1.839) (t = –2.153, p = 0.031).

Table 8
Means of subjective scores on chemical and biologic hazard exposures

Gender Fumes, dust Vapor Skin contact Infection

Female Mean 2.197 1.608 1.839 1.497

SD 1.425 0.843 0.906 0.757

Male Mean 2.172 1.630 1.934 1.522

SD 1.413 0.859 0.972 0.793

Total Mean 2.186 1.618 1.882 1.509

SD 1.419 0.850 0.938 0.774

Mean test t = 0.366 t = –0.534 t = –2.153 t = –0.671

p = 0.714 p = 0.594 p = 0.031^* p = 0.503

Gender		Fumes, dust	Vapor	Skin contact	Infection
Female	Mean	2.197	1.608	1.839	1.497
	SD	1.425	0.843	0.906	0.757
Male	Mean	2.172	1.630	1.934	1.522
	SD	1.413	0.859	0.972	0.793
Total	Mean	2.186	1.618	1.882	1.509
	SD	1.419	0.850	0.938	0.774
Mean test		t = 0.366	t = –0.534	t = –2.153	t = –0.671
		p = 0.714	p = 0.594	p = 0.031^*	p = 0.503

Note: SD = Standard deviation. ^*significant at 0.05, Subjective score (1 = Never, 2 = Rarely, 3 = 1/4 times, 4 = 1/2 times, 5 = 3/4 times, 6 = Most of the time, 7 = Always).

3.2.4 Safety and health information

Table 9 shows the distribution of respondents’ responses to whether or not to provide safety and health information. 64.0% of respondents answered no information about work-related safety and health. It was found that there was no gender difference in the provision of safety and health information (χ² = 5.951, p = 0.114).

Table 9
Providing safety and health information

Gender Never Rarely Good Very good

Female N = 985 262 388 255 80

% 26.6% 39.4% 25.9% 8.1%

Male N = 818 199 304 254 61

% 24.3% 37.2% 31.1% 7.5%

Total N = 1,803 461 692 509 141

% 25.6% 38.4% 28.2% 7.8%

χ² test χ² = 5.951

p = 0.114

Gender		Never	Rarely	Good	Very good
Female	N = 985	262	388	255	80
	%	26.6%	39.4%	25.9%	8.1%
Male	N = 818	199	304	254	61
	%	24.3%	37.2%	31.1%	7.5%
Total	N = 1,803	461	692	509	141
	%	25.6%	38.4%	28.2%	7.8%
χ² test			χ² = 5.951
			p = 0.114

^*significant at 0.05.

3.3 Comparison of health problems

3.3.1 Self-reported physical health problems

Table 10 shows the percentage of respondents who complained of health problems in the question of whether they had health problems in the last 12 months. The rate of complaining of overall fatigue was the highest at 42.4%, followed by headache and eyestrain (22.5%), hearing problems (6.9%), and skin problems (2.6%).

Table 10
Ratios of self-reported physical health problems

Gender Total Respondents (%) with physical health problems

(%) Hearing problem Skin problem Headache and eyestrain Overall fatigue

Female 985 65 27 246 441

100.0% 6.6% 2.7% 25.0% 44.8%

Male 818 59 19 159 323

100.0% 7.2% 2.3% 19.4% 39.5%

Total 1,803 124 46 405 764

100.0% 6.9% 2.6% 22.5% 42.4%

χ² test χ² = 0.263 χ² = 0.315 χ² = 7.866 χ² = 5.112

p = 0.608 p = 0.575 p = 0.005^* p = 0.024^*

Gender	Total	Respondents (%) with physical health problems
Female	985	65	27	246	441
	100.0%	6.6%	2.7%	25.0%	44.8%
Male	818	59	19	159	323
	100.0%	7.2%	2.3%	19.4%	39.5%
Total	1,803	124	46	405	764
	100.0%	6.9%	2.6%	22.5%	42.4%
χ² test		χ² = 0.263	χ² = 0.315	χ² = 7.866	χ² = 5.112
		p = 0.608	p = 0.575	p = 0.005^*	p = 0.024^*

^*significant at 0.05.

Table 10 shows gender differences in the distribution of complaints about overall fatigue complaints (χ² = 7.866, p = 0.005) and headache and eyestrain (χ² = 5.112, p = 0.024). Women’s complaint rate was higher than that of men.

3.3.2 Subjective musculoskeletal pains

Table 11 shows the distribution of complainants for musculoskeletal pains during the past 12 months. The complaints’ rate was the highest at 61.1% in the upper extremities, followed by pain in the lower extremities (59.3%) and backache (42.3%).

Table 11
Ratios of subjective musculoskeletal pains

Gender Total Respondents (%) with musculoskeletal pains

(%) Backache Upper limb pain Lower limb pain

Female 985 477 666 673

100.0% 48.4% 67.6% 68.3%

Male 818 286 436 397

100.0% 35.0% 53.3% 48.5%

Total 1,803 763 1102 1070

100.0% 42.3% 61.1% 59.3%

χ² test χ² = 33.183 χ² = 38.528 χ² = 72.556

p < 0.001^* p < 0.001^* p < 0.001^*

Gender	Total	Respondents (%) with musculoskeletal pains
Female	985	477	666	673
	100.0%	48.4%	67.6%	68.3%
Male	818	286	436	397
	100.0%	35.0%	53.3%	48.5%
Total	1,803	763	1102	1070
	100.0%	42.3%	61.1%	59.3%
χ² test		χ² = 33.183	χ² = 38.528	χ² = 72.556
		p < 0.001^*	p < 0.001^*	p < 0.001^*

^*significant at 0.05.

There were gender differences in the distribution of complainants of backache (χ² = 33.183, p < 0.001), upper limb pain (χ² = 38.528, p < 0.001), and lower limb pain (χ² = 72.556, p < 0.001). Women’s complaint rate was higher than that of men in all areas. The complaint rates of lower extremity pain (68.3%) and upper extremity pain (67.6%) were very high in females, and the backache rate was 48.4%. On the other hand, men’s upper limb pain rate was the highest at 53.3%, followed by lower extremity pain rate (48.5%) and backache rate (35.0%).

3.3.3 Wellbeing and depression

Table 12 shows the average of wellbeing scores and depression distribution by men and women based on feeling over the past two weeks. In the mean test for the Wellbeing score, the male’s mean (12.147) was higher than the female’s mean (11.178) (t = –3.706, p < 0.001). The average wellbeing score in both men and women was less than 13, which is the criterion for depression.

Table 12
Results of subjective scores on wellbeing and depression symptoms

Gender Mean test of wellbeing scores Respondents (%) with depression symptoms^**

Yes No Total

Female Mean 11.178 538 447 985

SD 5.609 54.6% 45.4% 100.0%

Male Mean 12.147 384 434 818

SD 5.460 46.9% 53.1% 100.0%

Total Mean 11.617 922 881 1,803

SD 5.561 51.1% 48.9% 100.0%

Testing t = –3.706 χ² = 10.536

p < 0.001^* p < 0.001^*

Gender	Mean test of wellbeing scores	Respondents (%) with depression symptoms^**
Female	Mean	11.178	538	447	985
	SD	5.609	54.6%	45.4%	100.0%
Male	Mean	12.147	384	434	818
	SD	5.460	46.9%	53.1%	100.0%
Total	Mean	11.617	922	881	1,803
	SD	5.561	51.1%	48.9%	100.0%
Testing		t = –3.706	χ² = 10.536
		p < 0.001^*	p < 0.001^*

Note: SD = Standard deviation. ^*significant at 0.05, ^**Wellbeing score < 13.

In Table 12, the rate of depression complaints was 51.1%, and the female rate was 54.6%, which was higher than that of the male rate (46.9%) (χ² = 10.536, p = 0.001).

4 Discussion

This study compared the working environment, exposure to hazards, and health problems of the crop farmers aged ≥60 by gender. According to the results of this study, it was found that there are gender differences in the working environment, exposure to risk factors, and health problems.

This study showed that the proportion of single-person households among older female farmers accounted for 45.0%, higher than that of single-person households among older male farmers (13.4%). The ratio of under level of elementary school among female older farmers was 80.7%, higher than that of low education level among older male farmers (68.3%). Also, average incomes of older female farmers (1.565 million KRW) were lower than that of older males (1.791 million KRW). In other words, older female crop farmers showed economic vulnerability, low education, and living alone.

This phenomenon of aging farmers and living alone is a cause of increasing the amount of work and hours. In addition, it leads to the excessive physical burden, which causes fatigue to accumulate, causing chronic fatigue, pain in body parts, and health problems. Agricultural work has various work characteristics, from a low work height requiring a waist bent or squatting to a high work height requiring a waist stretch. Also, it has the features of repetitive work, awkward posture, handling of heavy objects, and mostly standing up during work [25 –28]. In this study, the subjective exposure of risk factors on a 7-point scale was highest in repetitive motion (4.777), followed by awkward posture (4.299), standing posture (3.793), high-temperature work (3.416), and heavy material handling (3.027). Among subjective scores on the exposure of risk factors, female’s repetitive motion (4.923) and awkward posture (4.415) scores were higher than that of repetitive motion (4.601) and awkward posture (4.159) scores.

For the question of whether there was a health problem in the past 12 months, the complaint rate of upper limb pain was the highest at 61.1%, followed by the lower extremity pain (59.3%) and backache (42.4%). That is, the rate of musculoskeletal pain was high. This study’s results are consistent with studies that farmers have a high rate of complaining of musculoskeletal symptoms such as low back pain and joint pain due to long-term intense labor and excessive physical burden [29]. A Eurofound [22] report found that women complained of more health problems than men, except for injuries and hearing problems. Among self-reported health problems, female’s complaint rates on upper limb pain (68.3%), lower extremity pain (67.6%), backache (48.4%), overall fatigue (44.8%), and headache and eyestrain (25.0%) were higher than that of male’s complaint rates on upper limb pain (48.5%), lower extremity pain (53.3%), backache (35.0%), overall fatigue (39.5%), and headache and eyestrain (19.4%). Although male’s exposure to risk factors was higher in all factors except for repetitive movements and uncomfortable postures, women’s pain complaint rate was higher than that of men. It is interpreted as a poorer physical condition than men, housework, or women’s sensitivity to physical pain [19 , 29]. Females’ occupational health problems do not occur biologically, but rather because the workplace is not aware of and accommodates diversity [30]. Similar exposure to the same risk factors may have a greater impact on females than males due to differences in physical or psychological factors [31]. The working environment should be easy and comfortable for all workers, including older female workers [32]. Kim and Jeong [33] insisted on a universal safety design concept that ensures a basic level of health and safety, and Baek and Jeong [34] proposed 46 guidelines based on six principles.

Musculoskeletal disorders are known as diseases that can be detected early and prevent progression with appropriate rest and treatment [9 , 28]. However, in this study, 64.0% of the crop farmers did not receive information on work-related safety and health. The outbreak of musculoskeletal disorders can reduce the health-related quality of life of farmers [20]. Older farmers may not be able to manage musculoskeletal disorders properly due to economic vulnerability, low education, and living alone [29]. Therefore, considering that health care facilities are insufficiently vulnerable, the development of a customized health promotion program is required [35]. Kee and Haslarm [13] reported that a participatory approach’s intervention showed advantages in terms of work efficiency, safety, and farmer satisfaction.

In this study, 51.1% showed depression symptoms by the WHO-5 index. In particular, the rate of depression symptoms in women (54.6%), with a high proportion of single-person households, was higher than that of men (46.9%). Therefore, it was found that active measures are needed for the elderly farmers. According to previous studies, in the case of older farmers living alone, support among families is already weak, and the majority are often experiencing economic difficulties [36]. For this reason, it is reported that access to medical institutions is low. Therefore, it is difficult for the elderly living alone to receive adequate treatment for chronic diseases, and the risk of depression is high [37].

5 Conclusion and Limitations of the Study

This study has some limitations. First, it is difficult to explain the relationship between the respondent’s working conditions and exposure to risk factors and health-related problems because it is a retrospective study. Second, since this study was analyzed based on the crop farmers, there may be differences from all farmers’ characteristics. Therefore, it is not easy to generalize the characteristics of farmers. Third, in this study, depression was screened using the WHO-5 index as a screening tool, while an accurate clinical diagnosis was not included. Thus, the generalization of the results requires attention. Also, further research is required to overcome these limitations.

Despite these limitations, this study systematically analyzed the working conditions, work-related hazard factors, and health-related problems of crop farmers and is considered to be meaningful as a basic data in determining the welfare policy of farmers. Also, it shows the severity of musculoskeletal pains and depression in elderly female farmers and elderly living alone, indicating that efforts and support are needed to improve working conditions for elderly farmworkers.

Funding

This research was financially supported by Hansung University.

Footnotes

Acknowledgments

The author is grateful to the Occupational Safety and Health Research Institute (OSHRI) and the Korea Occupational Safety and Health Agency (KOSHA) for providing the raw data from the KWCS.

Conflict of interest

The author declares no conflict of interest.

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Comparisons of working conditions and health-related problems between older male and female crop farmers

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1 Introduction

2 Method

2.1 Data collection and subjects

2.2 Research variables

3 Results

3.1 Characteristics of respondents

3.1.1 Distributions of respondents by gender and education level

3.2.1 Physical hazard exposures

Table 9 Providing safety and health information Gender Never Rarely Good Very good Female N = 985 262 388 255 80 % 26.6% 39.4% 25.9% 8.1% Male N = 818 199 304 254 61 % 24.3% 37.2% 31.1% 7.5% Total N = 1,803 461 692 509 141 % 25.6% 38.4% 28.2% 7.8% χ2 test χ2 = 5.951 p = 0.114

3.3.1 Self-reported physical health problems

5 Conclusion and Limitations of the Study

Funding

Footnotes

Acknowledgments

Conflict of interest

References

Table 9
Providing safety and health information

Gender Never Rarely Good Very good

Female N = 985 262 388 255 80

% 26.6% 39.4% 25.9% 8.1%

Male N = 818 199 304 254 61

% 24.3% 37.2% 31.1% 7.5%

Total N = 1,803 461 692 509 141

% 25.6% 38.4% 28.2% 7.8%

χ² test χ² = 5.951

p = 0.114