A Profile of Farmers and Other Employed Canadians With Chronic Back Pain: A Population-Based Analysis of the 2009-2010 Canadian Community Health Surveys

Authors

  • Catherine Trask PhD,

    Corresponding author
    1. Centre for Health and Safety in Agriculture, College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
    • For further information, contact: Catherine Trask, PhD, Centre for Health and Safety in Agriculture, College of Medicine, University of Saskatchewan, 103 Hospital Drive, Saskatoon, Saskatchewan, Canada S7N 0W8; e-mail: catherine.trask@usask.ca.

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  • Brenna Bath PhD,

    1. School of Physical Therapy, College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
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  • Jesse McCrosky MMath,

    1. Department of Community Health and Epidemiology, College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
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  • Josh Lawson PhD

    1. Centre for Health and Safety in Agriculture, College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
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  • Funding: This research was funded by the University of Saskatchewan, College of Medicine, New Faculty Start-up fund (to Brenna Bath, PhD); a Canada Research Chair in Ergonomics and Musculoskeletal Health (Catherine Trask, PhD); and a Canadian Institutes of Health Research and Lung Association of Saskatchewan New Investigator Award (to Josh Lawson, PhD). The authors declare that they have no competing interests.

Abstract

Purpose

Chronic back disorders (CBDs) are a serious public health issue, both in the general population and among farmers. However, it is not clear whether all individuals with CBD should be treated the same, or if some subpopulations have special needs. This study's purpose was to determine the demographic, socioeconomic, co-morbidity, and other health characteristics of Canadian farmers and nonfarmers with self-reported CBD.

Methods

We performed a secondary analysis of the 2009-2010 Canadian Community Health Survey to develop a profile of adults with CBD comparing farmers (N = 350) to nonfarmer employed persons (N = 11,251). In addition to descriptive analysis, multiple logistic regression was used to control for possible confounding.

Findings

Our results indicate that farmers with CBD are significantly more likely to be older, less educated, and more often male and living rurally than nonfarmers with CBD. We found no difference between rates and type of co-morbidities between farmers and nonfarmers. However, the sociodemographic differences between farmers and nonfarmers with CBD may impact the design of effective interventions and have implications for health services planning and health care delivery. The information presented is anticipated to help address the identified need for musculoskeletal disorder prevention in agriculture.

Back disorders are a prevalent and expensive public health problem in the general population, with lifetime prevalence as high as 84%.[1-4] “Back disorder” represents a heterogeneous group of case definitions with no definitive imaging or clinical test for diagnosis[1, 5]; 95% of low back disorders are considered “nonspecific,”[6, 7] but they often demonstrate chronic, episodic, or recurrent manifestations.[8] Chronic back disorders (CBDs) are associated with tremendous loss of quality of life and disability,[9] and the economic consequences of CBD represent an enormous cost for society.[10-13] The estimated total cost of back disorders in the United States exceeds $100 billion per year; two-thirds of these costs are indirect, due to lost wages and reduced productivity.[12]

Back disorders are the most common regional musculoskeletal problem reported among farmers.[14-16] Across studies of many types of farming, the average lifetime prevalence of low back pain was 75%, and average 1-year prevalence was 48%.[14] Agriculture is a hazardous occupation,[17] with numerous prominent exposures, such as whole body vibration (WBV) and heavy lifting, that have been shown to increase risk of back disorders in other industries.[15, 18-21] The physical demands of farming combined with the very long work hours in planting and harvesting seasons[22] present a uniquely vulnerable scenario. Back problems can impact farmers’ productivity; a survey of Iowa farmers showed they had double the risk of low back pain as compared to the general working population, and they were 8 times more likely to make major changes in their work activities as a result of low back pain.[23] Similarly, results from a study of Irish farmers showed that farm income is lower when operators have musculoskeletal disorder-related disability.[24] Despite the importance of chronic back pain, there is limited information about chronic back pain in Canadian farmers.

The experience of CBD may vary with the economic and scheduling pressures introduced by a farming occupation, or with the difference in service accessibility associated with rural living. Differences in productivity, pain, co-morbidities and function could signal CBD as more limiting or more needful of health services. Comparing the disability and co-morbidity profile of farmers versus nonfarmers with CBD will help identify subpopulations with greater need and can help plan delivery of appropriate services more effectively, with particular regard to health care access, availability, and need for co-morbidity care. The objectives of this study were to investigate the sociodemographics and profile of Canadians with CBD, with a particular focus on farmers. The primary research question is: What are the demographic, socioeconomic, health, and occupational characteristics of Canadian farmers and nonfarmers with self-reported CBDs? This will aid in determining if farmers have special needs or require special attention with regard to back pain and subsequent care.

Methods

Study Design and Data Source

We used data from Statistics Canada's 2009-2010 Canadian Community Health Survey (CCHS). The CCHS was designed to support health surveillance programs, provide data for research on small populations and rare characteristics, and provide a flexible survey instrument to address emerging health issues in Canada. It includes a rich range of content including sociodemographics, health status, health care utilization, and broad determinants of health. It was a cross-sectional study where respondents were selected using a complex survey design with a 2-phase stratified sampling plan designed to ensure adequate representation from each Canadian Health Region. Trained interviewers used a computer system of questions, skips, and prompts to conduct interviews either in person or by telephone. Proxy interviews were allowed where the selected respondent was incapable of completing the interview.

Study Population

The CCHS targets Canadians 12 years and older living in private dwellings in the 10 provinces and 3 territories. The survey did not include people living on reserves and other Aboriginal settlements, or residents of institutional and some noninstitutional collectives (eg, military bases, Canadian Armed Forces vessels, merchant and coast guard vessels, campgrounds, or parks). Approximately 130,000 Canadians took part in the 2009-2010 survey, which gave a participation rate of 72.3% of all those sampled for the survey.[25] Participation is defined as when a household agrees to participate in the survey and the person in that household selected for interview provided a response for at least some of the survey.[25] The survey was designed to be representative of approximately 98% of the Canadian population 12 years and older.[25]

The focus of our analysis was persons aged 18 years and older that reported suffering from a CBD.

Survey Data and Operational Definitions

We identified survey respondents with CBD using the question: “(Do you) have back problems, excluding fibromyalgia and arthritis?” This section of the survey is prefaced with the reminder: “Now I'd like to ask about certain chronic health conditions which (you) may have. We are interested in ‘long-term conditions’ which are expected to last or have already lasted 6 months or more and that have been diagnosed by a health professional.”

Occupation was determined through a series of open-ended questions that were coded using prompts from the computer-based interview software. Questions included “What kind of work are you doing?” and “What kind of business, industry or service is this?” “Farmer” occupations, including nonowner/operator agricultural workers, were identified using the North American Industry Classification System (NAICS) 2007 code provided in the survey data. A code beginning with 111xxx (crop) or 112xxx (animal) indicates farming occupation.[26] The nonfarmer group was identified as those that indicated that they had a job or business but did not have a NAICS 2007 code indicating they were a farmer. The code was derived in the interview process based on questions about the industry in which the respondent worked. During initial comparisons of farmers with the unrestricted nonfarming adult population, a healthy worker effect was observed, wherein more symptoms were found in the unemployed population than the employed population. To avoid this bias, we further restricted our sample to employed persons, leaving a total study population of 11,601 persons with CBD who were then divided into “farmer” and “nonfarmer” groups of 350 and 11,251 persons, respectively.

Rural/remote status was defined as those communities outside of either a census metropolitan area or a census agglomeration. Rural and small town communities were further subdivided by identifying the degree to which they are influenced, in terms of social and economic integration, by larger urban centers. Metropolitan influenced zone (MIZ) categories disaggregate the rural and small town population into 4 subgroups: strong, moderate, weak, and none. These urban/rural/remote categories are applied to those communities (cities, towns, and villages) that can be equated with the Statistics Canada designation census subdivision.[27] This approach is similar to that reported by Sibley et al.[28]

Sociodemographic and health status “variables of interest” were selected based on documented relationship to health[29-31] and included age group, gender, education, income quintile, MIZ as described above, ethnicity, marital status, smoking status, number of co-morbidities, depression probability, self-rated stress, self-rated mental health, self-rated work stress, self-rated overall health, Health Utility Index (CBD) pain and function level,[32] BMI, and reported loss of productivity due to CBD. In addition, the 5 most common co-morbidities among those with CBD (as determined using the full sample of adults with CBD) were investigated: arthritis, high blood pressure, migraines, asthma, and mood disorders. Further details regarding the variables of interest are presented in Table 1.

Table 1. Description of (Independent) Variables Included in Analysis
VariableDescription (If Applicable) and Categories
  1. CA, census area; CMA, census metropolitan area.

Sociodemographic characteristics
OccupationFarming (NAICS 2007 code beginning with 111xxx (crop) or 112xxx (animal)); nonfarming (any other NAICS 2007 code)
Age18-34 y; 35-49 y; 50-64 y; ≥ 65 y. Categories based on quartiles and clinical relevance
GenderMale; female
EducationLess than secondary; secondary graduation; some postsecondary; postsecondary graduation
IncomeA StatsCan-derived variable addressing income adequacy. Quintile of adjusted ratio of total household income to the low income cut-off corresponding to household and community size
ResidenceUrban, strong metropolitan-influenced zones based on postal code. “Urban” residence includes communities with population ≥10,000 people (ie, CMA or CA). Smaller communities are divided into categories of relative rurality based on degree to which they are influenced by larger urban centers (ie, strong, moderate, or weak/no metropolitan-influenced zones[27])
EthnicityCaucasian; aboriginal; other
Marital statusSingle; married or common law; widowed or separated or divorced
Smoking statusNever smoked; former smoker; current smoker
Body mass index (BMI)Derived from self-reported height and weight.Underweight and normal (<25 kg/m2); overweight (25-29.9 kg/m2); obese (≥30 kg/m2)[33]
Health characteristics
Number of other co-morbidities/chronic conditionsIncludes “long-term conditions” which are expected to last or have already lasted 6 months or more and that have been diagnosed by a health professional. No other chronic conditions (other than CBD); 1or 2 chronic conditions (other than CBD); 3 or more chronic conditions (other than CBD). Category boundaries selected to maintain cell size.
Type of other co-morbiditiesPresence of top 5 co-morbidities associated with CBD: arthritis (excluding fibromyalgia); high blood pressure; migraine headaches; asthma; mood disorders (ie, depression, bipolar disorder, mania, or dysthymia)
Pain and physical functionHealth Utility Index (CBD) variable. Considers whether pain prevents person from performing activities of daily living. 5 categories: no pain or discomfort; pain prevents no activities; pain prevents a few activities; pain prevents some activities; pain prevents most activities
Depression probabilityA StatsCan-derived variable indicating the probability that the respondent would have been diagnosed as having experienced a major depressive episode in the past 12 months, if he/she had completed the long-form composite international diagnostic interview (CIDI).[34]
Self-rated stressAbility to handle day-to-day demands: not at all/not very; a bit; quite a bit/extremely. Collapsing of categories was performed to maintain equal-sized groups.
Self-rated mental healthIndicates the respondent's mental health status based on his/her own judgment: excellent; very good; good / fair / poor. Collapsing of categories was performed to maintain equal-sized groups.
Self-rated overall healthIndicates the respondent's health status based on his/her own judgment or his/her proxy: excellent/very good; good; fair/poor. Collapsing of categories was performed to maintain equal-sized groups.
Self-rated work stressIndicates level of stress encountered “most days at work”: not at all/not very; a bit; quite a bit/extremely. Collapsing of categories was performed to maintain equal-sized groups.
Loss of productivity due to back painIndicates the number of days lost: 0 d; 1-2 d; 3+ d. Collapsing of categories was performed to maintain equal-sized groups.

Statistical Analysis

All analyses were performed using Stata 12 software (StataCorp LP, College Station, Texas) with built-in survey data tools for probability weights and bootstrapping. Probability weights provided by Statistics Canada were used in all analyses. To account for the complex survey design, bootstrapping methods for robust variance estimation were employed using bootstrap weights provided by Statistics Canada to accurately estimate standard errors.

The descriptive analysis included calculation of proportions for categorical variables. A comparison between farmers and other employed persons was completed using a chi-square test suitable for complex survey data to determine statistical significance of the hypothesis that the given variable is distributed differently between farmers and nonfarmers.

Associations were assessed using logistic regression analyses. The strengths of associations were quantified by the odds ratio (OR) and 95% confidence interval (CI). Initially, a crude model was fitted including 1 independent variable and the dependent variable of farming status. To control for possible confounding, we then fitted a multiple logistic regression model. All variables with a significant unadjusted OR were included in the adjusted model and considered a “primary” variable; no stepwise procedure was used. We then assessed each other variable (all other variables of interest as well as province and region of residence) as a potential confounder. Each potential confounder was added to that adjusted model one at a time to assess whether or not it was a confounder. If the addition of the potential confounder in the model altered the OR of a “primary” variable by at least 10% and altered its coefficient by at least 20%, the variable was considered a confounder. In the event that a confounding effect was found, it was retained in the final model. No interaction effects were suspected or tested.

Respondents with missing data for variables included in the model were excluded case wise and assumed missing at random. Income adequacy was assessed from a smaller sample size, since not all respondents had information about household income. Due to missing data, the adjusted model was based on 4,613 respondents. An alternate model excluding income was created to assess if a superior model could be developed with a larger N, but there was no important advantage to the larger model.

Results

A sociodemographic profile of farmers and nonfarmers with chronic back pain is found in Table 2. A profile of health characteristics of farmers and nonfarmers with chronic back pain is found in Table 3. Adjusted and unadjusted ORs, with CIs, are presented in Table 4 to quantify any relationships between “farmer” occupational status and sociodemographic, health, and occupational characteristics among respondents with CBD.

Table 2. Sociodemographic Characteristics of Farmers and Nonfarmers With Chronic Back Disorder
Respondent CharacteristicFarmersNonfarmersP Valuea
  1. Values in bold indicate P < .05.

  2. a

    P value based on chi-square test.

  3. b

    Redacted due to small sample sizes.

N35011,251
Age  <.001
18-3414.0%22.2% 
35-4928.4%38.9% 
50-6446.3%35.5% 
65+11.3%3.5% 
Gender (male)70.6%52.9%<.001
Education  <.001
Less than secondary24.8%10.6% 
Secondary graduation26.6%16.5% 
Some postsecondary4.8%6.9% 
Postsecondary graduation43.7%66.0% 
Income quintile  .077
120.8%12.9% 
218.5%19.0% 
313.5%20.3% 
419.6%21.8% 
527.6%25.9% 
Metropolitan influenced zone  <.001
Census metropolitan areas or a census agglomeration21.5%73.1% 
Strongly influenced7.0%3.8% 
Moderately influenced25.1%6.6% 
Weak/uninfluenced + territories46.4%16.4% 
Ethnicity  .121
Caucasian92.1%83.7% 
Aboriginalb4.4% 
Otherb11.9% 
Marital status  <.001
Single10.2%18.4% 
Married + common – law82.8%70.4% 
Widowed + separated + divorced7.0%11.3% 
Smoking status  .534
Never smoked26.5%30.3% 
Former smoker45.1%41.8% 
Current smoker28.4%28.0% 
BMI  .965
Underweight / normal41.7%41.0% 
Overweight36.5%37.6% 
Obese21.9%21.4% 
Table 3. Health Characteristics of Farmers and Nonfarmers With Chronic Back Disorder
Respondent CharacteristicFarmersNonfarmersP valuea
  1. Values in bold indicate P < .05.

  2. a

    P value based on chi-square test.

  3. b

    Redacted due to small sample sizes.

N35011,251
No. of co-morbidities  .890
None38.2%37.9% 
1-248.9%48.1% 
3+12.9%14.1% 
Arthritis30.7%24.3%.041
High BP20.1%15.8%.135
Migraines12.3%19.4%.015
Asthma9.6%11.7%.383
Mood disorders7.3%10.1%.221
Depression scale predicted probability > = 0.95.5%8.8%.323
Self-rated stress  .958
Not at all/not very19.9%20.8% 
A bit45.4%44.4% 
Quite a bit/extremely34.7%34.8% 
Self-rated mental health  .231
Excellent25.2%30.6% 
Very good41.2%36.4% 
Good/fair/poor33.6%33.0% 
Self-rated work stress  .871
Not at all/not very20.9%22.0% 
A bit39.0%40.0% 
Quite a bit/extremely40.1%38.1% 
Self-rated overall health  .569
Excellent/Very Good53.0%51.4% 
Good32.2%36.0% 
Fair/Poor14.8%12.6% 
Health utility index pain/function  .862
No pain or discomfort60.8%60.0% 
Pain prevents no activities9.6%10.1% 
Pain prevents a few activities16.1%14.1% 
Pain prevents some activities10.7%11.1% 
Pain prevents most activities2.8%4.8% 
Loss of productivity (CCHS 2010 only)  .368
097.5%94.8% 
1-2 db1.9% 
3+ db3.3% 
Table 4. Probability of “Farmer” Occupation Respondents With CBD Exhibiting Sociodemographic, Health, and Occupational Characteristics
 UnadjustedAdjusted
Respondent CharacteristicOR95% CIOR95% CI
  1. Values in bold indicate P < .05.

Age
18-34    
35-491.160.68-1.971.060.57-1.96
50-642.061.25-3.411.690.90-3.18
65+5.042.80-9.094.011.92-8.35
Gender2.141.55-2.951.871.23-2.83
Education    
Less than secondary    
Secondary graduation0.690.42-1.111.180.67-2.09
Some postsecondary0.300.15-0.610.670.28-1.57
Postsecondary graduation0.280.19-0.430.490.28-0.85
Income quintile    
1    
20.600.31-1.160.520.25-1.09
30.420.23-0.760.290.14-0.57
40.560.30-1.030.440.21-0.89
50.660.39-1.140.440.23-0.84
Metropolitan influenced zone    
Census metropolitan areas or a census agglomeration    
Strongly influenced6.243.10-12.577.833.44-17.83
Moderately influenced12.897.93-20.9512.866.93-23.86
Weak/uninfluenced + territories9.636.23-14.908.895.04-15.68
Ethnicity    
Caucasian    
Aboriginal0.390.07-2.18  
Other0.460.20-1.07  
Marital status    
Single    
Married + common – law2.131.40-3.241.370.76-2.45
Widowed + separated + divorced1.130.59-2.150.580.24-1.39
Smoking status    
Never smoked    
Former smoker1.230.85-1.78  
Current smoker1.160.74-1.82  
No. of co-morbidities    
None    
1-21.010.71-1.43  
3+0.910.56-1.47  
Arthritis1.381.01-1.880.790.52-1.19
High BP1.350.91-1.99  
Migraines0.580.38-0.900.750.43-1.30
Asthma0.800.49-1.32  
Mood disorders0.710.40-1.23  
Depression scale predicted probability (> = 0.9)0.600.22-1.65  
Self-rated stress    
Not at all/not very    
A bit1.060.70-1.61  
Quite a bit/extremely1.040.67-1.62  
Self-rated mental health    
Excellent    
Very good1.370.95-1.981.420.89-2.27
Good/fair/poor1.240.83-1.841.200.69-2.09
Self-rated work stress    
Not at all/not very    
A bit1.030.68-1.551.300.79-2.13
Quite a bit/extremely1.110.74-1.671.851.08-3.16
Self-rated overall health    
Excellent/very good    
Good0.870.60-1.26  
Fair/poor1.140.72-1.80  
Health utility index pain/function    
No pain or discomfort    
Pain prevents no activities0.940.53-1.66  
Pain prevents a few activities1.130.69-1.84  
Pain prevents some activities0.950.59-1.54  
Pain prevents most activities0.590.19-1.80  
BMI    
Underweight/normal    
Overweight0.950.64-1.43  
Obese1.010.67-1.51  
Loss of productivity (CCHS 2010 only)    
0    
1-2 d0.690.12-3.99  
3+ d0.350.08-1.61  

Among Canadians with chronic back pain, farmers were significantly older, more often male, and far more likely to live in rural areas than nonfarmers (Table 2), even after adjusting for confounders (Table 4). In terms of health status proportions (Table 3), farmers with chronic back pain were more likely than nonfarmers to also have arthritis, but they were less likely to report migraine headaches.

Discussion

Sociodemographic Characteristics

There were several significant differences in the socioeconomic characteristics of farmers and nonfarmers with CBD (Table 2). Farmers with back pain were significantly older than nonfarmers with back pain, a difference that persisted after adjusting for confounders (65+ age group OR 4.01, 95% CI = 1.92-8.35; Table 4). This may indicate that the inverted-U-shaped effect of age on back disorders[35] is shifted among farmers. Although increasing age has been linked with remission of back pain in the general population,[36] it may be that in farming occupations, older age is no longer associated with a protective effect. This may also be a reflection that the agricultural workforce is older and aging. In the United States, the average age of farmers increased from 54 to 57 years between 1997 and 2007.[37] Similar trends are seen in Canada, with the proportion of farmers over 55 increasing from 32% in 1991 to 48% in 2011.[38] Farmers also continue to work beyond typical retirement age, extending their exposure to occupational hazards.[14, 39] The trend of increasing farmer age has implications for health care planning for CBD in this group, as well as being informative for clinicians who may otherwise anticipate fewer work hours, less exposure to occupational risk factors, and a protective effect of age among older individuals.

A far larger proportion of the farmer CBD group is male than nonfarmers with CBD (OR 1.87, 95% CI = 1.23-2.83; Table 4). In general, back disorders are more common in women than in men.40,41 Farming is male-dominated; surveys show most of those doing full- and part-time farm work are male.[15, 42] Gender may have an impact on how health promotion materials are designed and disseminated, as well as health policies, service provision and health care utilization. Women are more likely than men to use health care for back pain, take more sick days from work, have a poor outcome after a single episode of low back pain, and develop persistent, chronic pain lasting more than 3 months.[43] Being married has been found to increase the odds of low back pain,[41] but after adjusting for confounders there were no significant differences in marital status between farmers and nonfarmers with CBD.

Nonfarmers with CBD were more likely to have some or completed postsecondary graduation than were farmers, although only the latter difference remained after adjusting for confounders (OR 0.49; 95% CI = 0.28-0.85; Table 4). As farming is a “blue-collar” occupation, one might expect a difference when compared with a mixed occupational group. Like many “blue-collar” occupations, farming is potentially hazardous and associated with several physical and psychosocial risk factors that can be associated with CBD. Among the many physical hazards are lifting and carrying heavy loads,[15, 18] working with the trunk in flexed or awkward positions,[15, 18, 20, 21] high work pace and workload,[44] exposure to WBV from farm vehicles,[18, 19] risk of trips and falls on slippery and uneven ground, accidents caused by the sudden unpredictable actions of livestock, or motor vehicle accidents.[16] Low educational attainment has been shown to be related to increased odds of low back pain, suggesting it may be acting as a surrogate measure of the physical exposures of these occupations.[41] Although the reasons for a disparity in education may be intuitive, more important are the implications this has for outreach and health promotion in this group. Adjustment for confounders aside, it is important to consider the communication needs and socio-cultural context of a less-educated target audience whenever prevention or rehabilitation information is disseminated.[45]

Although 28.5% of farmers in this study resided in cities or strongly influenced areas, farmers were far more likely to live in rural areas (ie, those areas with moderate or weak MIZ), even after adjusting for confounders (weak MIZ OR 8.89, 95% CI = 6.93-23.86; Table 4). It is not surprising that an occupation which in most cases requires access to arable or pastureland would be more often located in rural areas. Rural residence has been found to be associated with back pain,[41] and regardless of farming occupation, rural people may have decreased access to health care services to help them cope with back pain and return to productivity. It may be more difficult to access health services, there may be less preventive information available in a self-employment situation, and it may be harder to get preventive or early stage care, although a study using CCHS data found this was not the case for mental health services.[46] A study of dementia care reported that those in rural areas travel up to 500 km round-trip for diagnostic services, resulting in substantial costs once the time and expense of food, travel, and overnight accommodations for patients and accompanying family members are taken into account.[47] The issue of rurality has important implications for interpretation of CBD prevalence and appropriate strategies for prevention. Clearly, rural residence is also an important consideration for health services planning and should be the subject of future research on CBD.

Back disorders have been found to be more common among Caucasians than individuals of African descent,[40] and they are associated with smoking41,48 and obesity.[36, 41, 48] However, smoking, BMI, and ethnicity were not found to be significantly different between farmers and nonfarmers in this study (Table 4).

Health Characteristics

The presence of other chronic health conditions is common among people with CBD.[49] These other co-morbidities are important to consider within the CBD population as it may signal additional health care needs of individuals with CBD and may represent clusters of symptoms that could or should be addressed in tandem, particularly for folks with time or geographical barriers to health care access. However, there were few significant differences between farmers and nonfarmers with respect to health status (Table 3). Farmers were more likely than nonfarmers to also have arthritis, and they were less likely than nonfarmers to report also having migraine headaches, although neither of these relationships persisted after adjusting for potential confounders such as age, sex, area of residence, and education. There were also no significant differences between farmers and nonfarmers with respect to the number of co-morbidities, high blood pressure, asthma, mood disorders, depression, self-rated stress, self-rated mental health, and self-rated work stress (Table 4). Mental health status has been shown to be closely linked to back pain; the presence of anxiety, and depression predict both disability and number of health care contacts.[50] Symptom remission is less likely among those with depressive symptoms or low self-rated health.[36] Anxiety, depression, somatisation symptoms, stressful responsibility, and mental stress at work are related to higher risk of back disorders,[40] with mixed results for high work pace, high job demands, and low job control.[51] Farmers in particular have been found previously to have increased depression and job stress as a risk factor for low back disorders.[48] Although mental and emotional sequelae have been shown to be important in treating low back disorders and returning patients to function, the results of this study do not suggest a disproportionate coincidence of mental health issues in famers with CBD.

Self-rated pain and function are related to the ability to participate in activities of daily living and fulfill one's goals. The degree of “disability” depends on the demands one is asked to fulfill.52,53 Productivity loss is similar in that the degree of disability is related to the demands of the job; for example, clerical work may be possible to perform with back pain, whereas heavy lifting may not. A differential in work ability could have a severe impact on a farmer's economic status; farm income is lower when operators have musculoskeletal-related disability.[24] A survey of Iowa farmers showed they had double the risk of low back pain as compared to the general working population, and they were 8 times more likely to make major changes in their work activities as a result of low back pain.[23] Despite an anticipated differential, there were no statistically significant differences between farmers and nonfarmers with CBD with respect to self-rated overall health, pain and function index, and loss of productivity (Table 4).

Strengths and Limitations

The CCHS dataset is a nationally representative sample of Canadians, allowing us to complete the first study known to the authors that investigates CBD and includes a Canada-wide population of farmers. Careful survey design, large sample size, and postweighting based on Census and other Statistics Canada data ensure the survey is representative of the target population. The CCHS is representative of approximately 98% of the Canadian population 12 years or older; exclusions include persons living on reserves and other aboriginal settlements, or in institutions.[25]

This study also includes a broad range of sociodemographic and other health factors, which are not available in most studies of back disorders. The CCHS also experienced a relatively high participation rate, which has become rare in population-based studies in recent years. This reduces the potential bias and improves the generalizability of results.

However, there are some limitations. As a broad health survey, information on all possible confounders, occupational exposures, and the nature of back pain itself was not available in the CCHS. Farming status is determined through open-ended questions with follow-ups by the surveyor to determine the specific occupational code. However, no specific definitions are given in the CCHS question guidebook, so respondents are left to interpret the term “farmer” as a representation of a heterogeneous occupational group spanning many different commodities, cultivation methods, work tasks, activities, and occupational exposures. Similarly, the survey section on chronic disorders directs respondents thus: “We are interested in ‘long-term conditions’ which are expected to last or have already lasted 6 months or more and that have been diagnosed be a health professional.” Although back pain is often associated with episodic and recurrent manifestations,[8] a consistent definition is problematic; it is not clear what the case definition should be54 as there are no definitive diagnostic tests and most patients have few objective physical findings.[1, 5]

Conclusions

Back pain is a serious public health issue, both in the general population[55] and among farmers.[14-16] The central issue addressed by this study is this: Should all individuals with CBD be treated the same, or do some subpopulations have special needs? Our results indicate that farmers with CBD are older, less educated, more often male, and living rurally, than are nonfarmers (Table 4). We found no difference between rates and type of co-morbidities between farmers and nonfarmers (Table 4). However, the sociodemographic differences between farmers and nonfarmers with CBD may have an impact on the design of effective interventions, and they may have implications for health services planning and health care delivery. For example, older, less-educated, rural farmers may be less likely to have access to the internet[56] and, therefore, may be less likely to be reached by educational campaigns or patient information resources than their nonfarming counterparts. In this case, it would be more appropriate for agencies and clinicians to employ alternate means of health promotion and patient education. The information presented is anticipated to help address the identified need for prevention of musculoskeletal disorder disability in the agriculture industry.[57]

Ancillary