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- Research Article Work-related factors of knee osteoarthritis in Korean farmers: a cross-sectional study
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Han Soo Song1
, Dong Hwi Kim2, Gwang Chul Lee2, Kweon Young Kim3, So Yeon Ryu4, Chul Gab Lee1 -
Annals of Occupational and Environmental Medicine 2020;32:e37.
DOI: https://doi.org/10.35371/aoem.2020.32.e37
Published online: November 13, 2020
1Department of Occupational and Environmental Medicine, Chosun University Hospital, Gwangju, Korea.
2Department of Orthopedic Medicine, Chosun University Hospital, Gwangju, Korea.
3Department of Rehabilitation Medicine, Chosun University Hospital, Gwangju, Korea.
4Department of Preventive Medicine, Chosun University Hospital, Gwangju, Korea.
- Correspondence: Chul Gab Lee. Department of Occupational and Environmental Medicine, Chosun University Hospital, 365 Pilmun-daero, Dong-gu, Gwangju 61453, Korea. cglee@chosun.ac.kr
• Received: June 18, 2020 • Accepted: October 23, 2020
Copyright © 2020 Korean Society of Occupational & Environmental Medicine
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
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Background Several studies have reported a high prevalence of osteoarthritis (OA) of the knee among agricultural workers. We investigated work-related factors that increase the risk of knee OA among Korean farmers.
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Methods Data were extracted from the Jeonnam Center for Farmer's Safety and Health survey, conducted between 2013 and 2015. The sample included 489 farmers (man 240, woman 249). We defined knee OA as radiographic knee OA (≥ Kellgren-Lawrence grade 2) with symptoms (≥ Western Ontario and McMaster Universities Osteoarthritis, Korean version score 29.5). We considered covariates such as cumulative squatting working time (CSWT), cumulative heavy lifting working time (CLWT), body mass index (BMI), and history of knee injury. Odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) were calculated for knee OA and adjusted for relevant covariates.
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Results The results of multivariate logistic regression analysis indicated that knee OA was correlated by factors such as sex, age, BMI, history of knee injury, CSWT, and CLWT. Particularly, CSWT > 20,000 hours (OR: 2.83; 95% CI: 1.35–5.92; reference < 10,000 hours) and CLWT > 5,000 hours (OR: 2.62; 95% CI: 1.14–6.06; reference < 2,000 hours) were associated with an increased risk of knee OA after adjustment for covariates.
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Conclusions Squatting posture and heavy lifting associated with farm work might increase the risk of knee OA among Korean farmers.
BACKGROUND
Osteoarthritis (OA) is emerging as a public health challenge worldwide [1]. Population aging is increasing in the prevalence of OA. Particularly, knee OA, which accounts for the largest proportion of OA cases, contributes to the deterioration of quality of life and the increase of disability-adjusted life years [2,3].
In 2017, the World Health Organization-Global Burden of Disease study revealed that the percentage change in prevalence between 2007–2017 was 30.8% (vs. all causes 17.0%, neoplasms 40.6%, COPD 23.8%) [4]. In Korea, the prevalence of knee OA, defined by knee pain and radiologic findings, was estimated at 4.4% and 19.2% in men and women, respectively, according to the fifth Korean National Health and Nutrition Examination Survey [5]. As the prevalence of knee OA is rising, knee arthroplasty also markedly increased by 407% in 2010 compared to 2001. The 91% of those who received knee arthroplasty were women [6]. The presence of OA patients in Korean households can create catastrophic health expenditure [7].
Knee OA is a degenerative disease [8]. There are several risk factors for knee OA, such as lower limb malalignment, obesity, occupational activity, and knee injury [9]. Kneeling, squatting, lifting or carrying, climbing stairs, and heavy manual work have previously been reported as occupational risk factors [10,11,12]. Most studies have classified occupational risk factors for OA according to profession. Only few studies have attempted to identify the dose–response relationship between occupational risk factors and knee OA. In research on large-scale population, subjects' age distribution skewed toward higher age because knee OA occurs mainly in the elderly. Considering the known risk factor of knee OA, we could predict that the elderly who perform hard manual labor are vulnerable to knee OA. The average age of Korean farmers is approximately 60 years old. Despite the mechanization of agriculture, competitive price crops—field crops, fruit farming, and livestock—still require a higher amount of manual labor. A previous study has shown that the prevalence of knee OA among farmers is high [13]. This study therefore aimed to investigate the dose–response relationship between occupational risk factors and knee OA among Korean working farmers who perform heavy manual labor.
METHODS
This cross-sectional study was conducted using data from Korean Farmer's Knee Cohort (KFKC). KFKC was initiated by the Jeonnam Center for Farmer's Safety and Health to investigate work-related factors and their impact on knee OA risk among Korean farmers. The cohort participants were recruited from 16 agricultural production units in the Jeonnam province in southwest Korea. Farmers were enrolled in the 2013–2015 years. In the present study, of the 550 registered cohort members, 489 farmers between the ages of 40 and 69 were selected.
We extracted data on the knee OA status and associated risk factors from the medical checkup and questionnaire results. The questionnaire-based survey was conducted face-to-face by trained researchers. Details on sex, age, previous knee injury, knee symptoms, and detailed information about agricultural physical activity were extracted from the questionnaire-based data. To measure how much time the farmers spent working in a specific position (squatting or lifting), we took the unit task exposure as the median value of each sub-periods exposure and then calculated cumulative squatting working time (CSWT) and cumulative heavy lifting working time (CLWT) by the following formula (Table 1):
Cumulative working time (hours) = Working years for lifetime × Working months per year × 4 × Working days per week × Working hours per day
Table 1
The matrix for calculation of cumulative squatting or heavy lifting working time
Researchers measured the weight and height of the cohort participants during a medical screening appointment. We calculated body mass index (BMI; kg/m2) based on height and weight data. Knee symptoms were investigated by the Korean version of Western Ontario and McMaster Universities Arthritis Index (K-WOMAC), which includes items such as pain, stiffness, and disability [14]. Knee OA in this study was confirmed with significant radiographic findings and symptoms reported in the structured questionnaire. The radiographic criteria were the knee Kellgren-Lawrence (KL) grade of 2 or higher [15]. The symptomatic criterion was the K-WOMAC score of 30 or higher [16].
The standardized weight-bearing anteroposterior and Rosenberg radiographs of the femorotibial joint were acquired. Interobserver reliability of KL grade was confirmed by 2 experienced orthopedic surgeons who used the Osteoarthritis Research Society International atlas [17]. The cut-off point for joint space narrowing was 4 mm based on the International Knee Documentation Committee (IKDC) radiographic scales [15,18]. Kappa for interobserver reliability of KL grade was 0.581 in the right knee and 0.598 in the left knee. Between the 2 grades, the more severe grade was used as the final KL grade.
We performed the χ2 test and logistic regression analysis to examine the association between knee OA and major risk factors. In the analysis, age was dichotomized as ≥ 60 and < 60, while BMI was dichotomized ≥ 25 or more and < 25. The presence of a previous knee injury was defined by answering ‘yes’ to a question: ‘Have you ever had a knee injury that required a cast?’ or ‘Have you ever experienced such pain in your knee that you were not able to perform farming duties for more than a day?”. CSWT was divided into < 10,000, 10,000–19,999, and ≥ 20,000 hours. CLWT was divided into 2,000, 2,000–4,999, and ≥ 5,000 hours. The CLWT 2,000 hours corresponds to the average annual working hours of Koreans. Multivariate logistic regression analysis was performed to obtain the odds ratios (ORs) and corresponding 95% confidence intervals (CIs) adjusted for major covariates. All statistical analyses were performed using IBM SPSS version 21.0 (IBM Co., Armonk, NY, USA). A p-value < 0.05 was considered statistically significant.
The present study protocol was reviewed and approved by the Institutional Review Board of Chosun University Hospital (approval No. 2013-12-006). All participants provided written informed consent for participation in the survey and use of their data for research purposes.
RESULTS
Table 2 shows the prevalence of significant knee symptoms by WOMAC, radiographic knee OA by KL grade, and radiographic knee OA with significant symptoms (radiographic OA with symptoms [SOA]) as the final outcome. The prevalence of radiographic knee OA was higher among older woman farmers. In total, 48 of 240 male farmers had radiographic knee OA; 14 cases of them were SOA. Moreover, 108 of 249 woman farmers had radiographic knee OA; 50 cases of them were SOA.
Table 2
The prevalence of SOA by sex and age among participants
Table 3 shows the relationships between the major covariates and the risk of SOA. Sex, age, previous knee injury, CLWT, and CSWT were significantly associated with SOA. In contrast, household income and agricultural work experience were not significantly associated with SOA.
Table 3
Association between SOA and major risk factors
Table 4 shows the results of multiple logistic regression analysis, examining the association between major risk factors and SOA. Sex, age, and BMI were significantly associated with SOA. History of knee injury was significantly associated with SOA. CLWT and CSWT were associated with SOA. After adjustment, the OR of CLWT over 5,000 hours was 2.62 (95% CI: 1.14–6.06) when the reference group was less than 2,000 hours. The OR of CSWT over 20,000 hours was 2.83 (95% CI: 1.35–5.92) when the reference group was less than 10,000 hours.
Table 4
The OR of SOA according to major risk factors
DISCUSSION
In the present study, long-term squatting and heavy lifting were associated with knee OA with symptoms among Korean working farmers. These results were adjusted for age, sex, obesity, and previous knee injury, known as confounding variables. Past studies have demonstrated moderate evidence for the relationship between kneeling/squatting or heavy lifting and knee OA. The combination of kneeling/squatting and heavy lifting showed a stronger relationship with knee OA [11,12]. Evidence in literature suggests that degenerative changes in the joint include the accumulation of biomechanical load. For the cultivation of low-floor crops, Korean farmers mainly use deep squat with feet flat on the ground, known as the Asian squat. As the knee flexion angle increases, the force on the knee joint also increases [19]. The thigh-calf contact during deep squat counteracts the forces of the knee joint [20]. Nevertheless, squatting by moving forward or sideways causes a higher rotational torque in the knee joint [21]. Additionally, long working hours cause fatigue in the thigh muscles, thereby increasing the varus moment [22].
Many studies showed that heavy lifting was related to knee OA. In previous studies, heavy lifting exposure was classified according to profession, frequency of heavy lifting, or maximum weight of heavy objects [12]. Therefore, only few studies attempted quantitative analysis for heavy lifting exposure. In the present study, quantification of exposure was performed by calculating the amount of working time related to heavy lifting. Heavy lifting of more than 5,000 hours in agriculture shows a significant relationship with knee OA. These results indicated that the burden of physical agricultural work might help explain the high prevalence of knee OA among farmers.
In our study, the adjusted OR for knee OA was 4.59 higher in women than in men. The previous knee OA studies have consistently shown that women are more vulnerable than men to knee OA, which also progresses more quickly in women than in men [23]. A previous study has found a higher prevalence of knee OA among Chinese women than white American women [24]. One study of knee OA in Asia, including participants from China, Japan, and Korea, showed that the prevalence of knee OA among Korean women was higher than among women from other countries [25]. Women's vulnerability to knee OA might be explained by menopausal changes to estrogen [26], femoral-tibia length [27], femoral bowing [28], socioeconomic differences [29], and squatting posture in daily life [30]. However, further research is needed to confirm the associations.
There was a correlation between obesity and knee OA in the present study, which is consistent with findings from previous studies. Previous studies on the impact of obesity on knee OA have shown that, compared to normal weight, BMI of 30 or more is a risk factor for OA. Moreover, large-scale studies have shown an increased risk of knee OA even among participants who were overweight (BMI 25–30) [31,32]. In many epidemiological studies, knee OA has been defined as a case of simultaneous radiologic knee OA and symptomatic knee OA [33]. Radiological knee OA is conventionally assessed with the KL grade [15]. However, there is a large variation in the reported reliability and validity of KL grade in each study [34]. These discrepancies might be due to the ambiguity in the description of each KL grade [35] and between-observer differences in judgments on joint space narrowing [36]. To remedy this problem, researchers use standard films that are read by experienced physicians [17]. We used the 4-mm narrowing as a threshold, suggested by the IKDC, as a significant joint space narrowing [15].
Imaging methods can also cause measurement errors. The sensitivity of radiographic knee OA depends on the weight load and knee flexion angle. For example, the Rosenberg view, taken at a 45-degree knee flexion state, is more sensitive than a standing view [37]. This might better reflect the state of the posterior of the knee joint. If the plateau of the tibia is not clear in the image, it can be checked in the lateral view. Since our study considered all 4 views, our knee OA assessment might have been more sensitive than the assessment in studies that only considered 1 or 2 views.
This study has the following limitations. First, the participants were restricted to farmers living in the Jeonnam province in Korea. Therefore, the results of this study cannot be generalized to all farmers in Korea. However, the participants were members of the regional producer organizations or cultivation groups. Jeonnam province is the largest agricultural region in Korea. Second, the definition of radiological knee OA is generally applied by KL grade 2 or higher. However, there are no agreed criteria on the symptoms of knee OA. We used K-WOMAC to assess knee symptoms. However, other studies used a doctor's examination or a single questionnaire to assess knee symptoms. Differences in the methodology of symptom assessment should be considered when comparing the present and previous study findings. Third, CLWT and CSWT were insufficient to verify reliability and validity. The CLWT and CSWT had bimodal and right-skewed distribution. We categorized the cumulative working time into 3 levels. A small number of the intermediate level of CLWT and CSWT could make increase the OR's random error. Therefore, attention should be paid to interpreting the working time showing significant relevance to SOA in this study. Fourth, this study has limitations inherent to a cross-sectional study. Study participants were recruited to follow up the on development or progression of knee OA. Therefore, farmers with terminal stage knee or knee arthroplasty were excluded from the cohort registration. As a result, the impact of the burden of farm work on the risk of developing knee OA is likely to be underestimated. Fifth, considering sex differences in knee OA, it is reasonable to analyze men and women separately. However, this analysis was not attempted owing to the lack of cases. Despite these limitations, this study was the first study to identify work-related risk factors for knee OA among Korean farmers.
In conclusion, after adjusting confounding factors, cumulative squatting and heavy lifting working time had a dose-response relationship with knee OA among Korean farmers. The result suggests that agricultural work, such as long period squatting posture, heavy lifting is convincing evidence of the high prevalence of knee OA in Korean farmers.
Acknowledgments
The authors thank the Ministry of Agriculture, Food and Rural Affairs for financial and administrative assistance for this study.
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Funding: This research was supported by the Ministry of Agriculture, Food and Rural Affairs in Republic of Korea.
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Competing interests: The authors declare that they have no competing interests.
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Author Contributions:
NOTES
Abbreviations
BMI
body mass index
CI
confidence interval
CLWT
cumulative heavy lifting working time
CSWT
cumulative squatting working time
IKDC
International Knee Documentation Committee
KFKC
Korean Farmer's Knee Cohort
KL
Kellgren-Lawrence
K-WOMAC
Korean version of Western Ontario and McMaster Universities Arthritis Index
OA
osteoarthritis
OR
odds ratio
SOA
radiographic osteoarthritis with symptoms
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Work-related factors of knee osteoarthritis in Korean farmers: a cross-sectional study
Work-related factors of knee osteoarthritis in Korean farmers: a cross-sectional study
| Working years for lifttime (A) | Working months per 1 year (B) | Working days per 1 week (C) | Working hours per day (D) | ||||
|---|---|---|---|---|---|---|---|
| Criterion (years) | Rating | Criterion (months) | Rating | Criterion (days) | Rating | Criterion (hours) | Rating |
| < 5 | 2.5 | < 1 | 0.5 | < 1 | 0.5 | < 1 | 0.5 |
| 5–9 | 7.0 | 1–2 | 2.0 | 1–2 | 1.5 | 1–2 | 1.5 |
| 10–19 | 14.5 | 3–6 | 4.5 | 3–4 | 3.5 | 3–4 | 3.5 |
| 20–29 | 24.5 | 7–9 | 8.0 | 5–6 | 5.5 | 5–6 | 5.5 |
| 30–39 | 34.5 | 9–12 | 10.5 | 7 | 7.0 | 7–8 | 7.5 |
| ≥ 40 | 40.0 | > 8 | 8.0 | ||||
| Variables | Men (n = 240) | Women (n = 249) | ||||
|---|---|---|---|---|---|---|
| 40–49 years (n = 45) | 50–59 years (n = 109) | 60–69 years (n = 86) | 40–49 years (n = 49) | 50–59 years (n = 119) | 60–69 years (n = 81) | |
| (A) WOMAC ≥ 30 | 6 (13.3) | 17 (15.6) | 19 (22.1) | 5 (10.2) | 39 (32.8) | 42 (51.9) |
| (B) KL grade ≥ 2 | 3 (6.7) | 21 (19.3) | 23 (26.7) | 9 (18.4) | 41 (34.5) | 58 (71.6) |
| (A and B) SOA | 1 (2.2) | 5 (4.6) | 8 (9.3) | 2 (4.1) | 18 (15.1) | 30 (37.0) |
| Variables | Total | Non-SOA | SOA | pa | |
|---|---|---|---|---|---|
| Sex | < 0.001 | ||||
| Men | 240 | 226 (94.2) | 14 (5.8) | ||
| Women | 249 | 199 (79.9) | 49 (20.1) | ||
| Age (years) | < 0.001 | ||||
| < 60 | 322 | 296 (92.9) | 26 (8.1) | ||
| ≥ 60 | 167 | 129 (77.2) | 38 (22.8) | ||
| BMI (kg/m2) | 0.032 | ||||
| < 25 | 267 | 240 (89.4) | 27 (10.6) | ||
| ≥ 25 | 222 | 185 (83.7) | 37 (16.3) | ||
| Previous knee injury | 0.039 | ||||
| No | 344 | 306 (89.0) | 38 (11.0) | ||
| Yes | 145 | 119 (82.1) | 26 (17.9) | ||
| CLWT (hours) | 0.001 | ||||
| < 2,000 | 156 | 147 (94.2) | 9 (5.8) | ||
| 2,000–4,999 | 59 | 54 (91.5) | 5 (8.5) | ||
| ≥ 5,000 | 274 | 224 (81.8) | 50 (18.2) | ||
| CSWT (hours) | < 0.001 | ||||
| < 10,000 | 239 | 226 (94.6) | 13 (5.4) | ||
| 10,000–19,999 | 64 | 56 (87.5) | 8 (12.5) | ||
| ≥ 20,000 | 186 | 143 (76.9) | 43 (23.1) | ||
| Variables | Unadjusted | Adjusteda | |||
|---|---|---|---|---|---|
| OR | 95% CI | OR | 95% CI | ||
| Sex | |||||
| Men | 1.00 | 1.00 | |||
| Women | 4.06 | 2.18–7.56 | 4.59 | 2.33–9.06 | |
| Age (years) | |||||
| < 60 | 1.00 | 1.00 | |||
| ≥ 60 | 3.35 | 1.95–5.76 | 4.06 | 2.21–7.43 | |
| BMI (kg/m2) | |||||
| < 25 | 1.00 | 1.00 | |||
| ≥ 25 | 1.78 | 1.04–3.06 | 2.25 | 1.23–4.13 | |
| Previous knee injury | |||||
| No | 1.00 | 1.00 | |||
| Yes | 1.78 | 1.04–3.03 | 2.13 | 1.15–3.97 | |
| CLWT (hours) | |||||
| < 2,000 | 1.00 | 1.00 | |||
| 2,000–4,999 | 1.51 | 0.49–4.71 | 1.32 | 0.38–4.55 | |
| ≥ 5,000 | 3.65 | 1.74–7.64 | 2.62 | 1.14–6.06 | |
| CSWT (hours) | |||||
| < 10,000 | 1.00 | 1.00 | |||
| 10,000–19,999 | 2.53 | 1.00–6.40 | 1.65 | 0.59–4.61 | |
| ≥ 20,000 | 5.19 | 2.70–9.99 | 2.83 | 1.35–5.92 | |
Table 1 The matrix for calculation of cumulative squatting or heavy lifting working time
Cumulative working time = A × B × 4 × C × D.
Table 2 The prevalence of SOA by sex and age among participants
Values are presented as number (%).
SOA: radiographic osteoarthritis with symptoms; WOMAC: Western Ontario and McMaster Universities Arthritis Index; KL: Kellgren-Lawrence.
Table 3 Association between SOA and major risk factors
Values are presented as number (%).
SOA: radiographic osteoarthritis with symptoms; OR: odds ratio; CI: confidence interval; BMI: body mass index; CLWT: cumulative heavy lifting working time; CSWT: cumulative squatting working time.
aThe
Table 4 The OR of SOA according to major risk factors
SOA: radiographic osteoarthritis with symptoms; OR: odds ratio; CI: confidence interval; BMI: body mass index; CLWT: cumulative heavy lifting working time; CSWT: cumulative squatting working time.
aAdjusted by all variables.
