SEARCH

SEARCH BY CITATION

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. References
  8. Supporting Information

Objective

Firefighting is a dangerous profession with high injury rates, particularly musculoskeletal (MS), but limited longitudinal data is available to examine predictors of MS injuries in this population.

Design and Methods

The relationship between personal individual, nonoccupational factors (e.g., demographic characteristics, body composition, fitness, and health behaviors) and incident injury and incident MS injury in a prospective cohort of 347 firefighters from the central United States was examined.

Results

Baseline weight status was a significant predictor of incident MS injury, with obese (BMI ≥ 30 kg m−2) firefighters 5.2 times more likely (95% CI = 1.1-23.4) to experience a MS injury than their normal weight (BMI = 18.5-24.9 kg m−2) colleagues over the course of the study. Similarly, firefighters who were obese based on WC (>102.0 cm) were almost three times as likely (OR = 2.8, 95% CI = 1.2-6.4) to have a MS injury at follow-up.

Conclusions

Findings highlight the importance of focusing on firefighters' body composition, nutrition and fitness as a means of decreasing risk for injury.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. References
  8. Supporting Information

Firefighters have high injury rates due to the jobs' significant demands and exposure to hazardous conditions [1]. The most commonly reported injuries are strains and sprains, often related to lifting [2], but limited prospective data is available that evaluates risk factors for these musculoskeletal (MS) injuries. Obesity has been associated with injury risk in comparable occupations. For example, overweight/obesity combined (body mass index; BMI ≥ 25) was a significant prospective predictor of low back pain and musculoskeletal injuries among infantry soldiers [3]. Heir and Eide [4] found that soldiers with higher BMIs were significantly more likely to experience a MS injury during basic training.

There are no currently available data about the longitudinal relationship between obesity and MS injuries among firefighters. Our study provides the first prospective evaluation of obesity as a risk factor for incident injury among firefighters in a population-based cohort. Given the high rates and associated costs of firefighter MS injuries [1, 2], we specifically examined the relationship between MS injury incidence and body composition, demographic factors, physical activity, and other health behaviors. Determining the role of obesity and other factors on injury risk prospectively provides insight into how to target injury prevention efforts for the fire service.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. References
  8. Supporting Information

Participants and procedures

Data for were collected as part of the firefighter injury and risk evaluation (FIRE) study (FEMA; EMW-2007-FP-0257), a longitudinal cohort designed to assess cardiovascular and injury risk factors in a population-based sample. All procedures were approved by the NDRI IRB. Firefighters were solicited from career (N = 11) and volunteer (N = 13) departments in the Missouri Valley region of the United States (US; Kansas, Missouri, Iowa, Nebraska, North Dakota, South Dakota, Colorado, Wyoming). Departments were randomly selected from the US Fire Department Census Database. Poston et al. [5] provide greater details of department selection. Of firefighters solicited (N = 736), 97% consented to participate.

Follow-up was completed at 9- (±1 month; career) or 6 months (±1 month; volunteer) after baseline assessment, based on fiscal considerations. Firefighters not available at the follow-up visit with the investigators were provided surveys with a stamped, addressed envelope and instructions to complete and send the survey to the research team. Three phone call attempts were made to contact firefighters who did not return the survey that was left for them. Only career firefighters were included in the current analyses because of their greater occupational exposures. Additionally, female firefighters were not included due to their low percentage in the fire service and our study sample (n = 21; 4.4% of the baseline career sample), which limited our ability to make gender-based inferences. Of those with complete injury data at baseline, 84.8% (n = 392) had follow-up injury data. However, 115 were injured at baseline and censored, so only male firefighters who were uninjured at baseline and who had follow up data (n = 301, 86.7% of those uninjured at baseline) were included in these analyses.

Primary measures

Injury

Injury questions were developed using information from the National Health Interview Survey [6], the National Institute of Standards and Technology [7] and a review of workers compensation data collection tools. Final items were tailored with assistance from fire service experts. Injury question were preceded with the explanation:

“The following questions are about injuries you have incurred since the baseline FIRE study assessment. An injury is anything for which you have completed an accident report for the department, reported to workers compensation, or received medical care (by a physician or other medical professional).”

Participants were asked to indicate the number of injuries sustained since the baseline assessment. Firefighters indicated injury type and location (i.e., where on their body), the duty performed, and the activity they were doing while engaged in that duty (e.g., lifting people, raising a ground ladder, overhaul). Injuries identified as “dislocations, sprains and strains” were classified as MS injuries.

Body composition

Body composition was determined by Body Mass Index (BMI), body fat percentage (BF%), and waist circumference (WC). Height was assessed with a portable stadiometer. Foot-to-foot bioelectrical impedance (Tanita 300; Tanita Corporation of America, Arlington Heights, IL) was used to assess weight and estimate BF%. The Tanita 300 demonstrated strong concurrent validity compared with dual energy X-ray absorptiometry (r = 0.94; P < 0.001) [8]. Nonstretchable tape measures were used to measure WC using standard procedures [9]. BMI was calculated as kg m−2. Standard cut-offs for obesity classifications were used for BMI, BF%, and WC [9].

Secondary measures

Physical activity

Physical activity was assessed with the self-report of physical activity (SRPA) questionnaire [10]. Torso strength and flexibility were assessed using methods recommended by the NFPA 1500 Standard on Fire Department Occupational Safety and Health Program [11].

Tobacco use

Tobacco use was determined using standard survey questions [12]. Participants were categorized as current smokers (i.e., smoking 100+ cigarettes in their lives, smoked in the past 30 days); former smokers (i.e., smoked 100+ cigarettes, no smoking in past 30 days); and never smokers (i.e., had not smoked 100 cigarettes in their lifetime). Current smokeless tobacco (SLT) users were firefighters who indicated using SLT in the previous 30 days.

Problematic alcohol use

Problematic alcohol use was measured with the CAGE questionnaire [13]. Each affirmative response to the four questions was summed to an overall score. Scores ≥ 2 are indicative of potential problematic alcohol use.

On-duty sleepiness

On-duty sleepiness was evaluated with the Epworth sleepiness scale (ESS). Responses were totaled and the standard cut-off for excessive daytime sleepiness (EDS; ESS > 11) was used [14].

Depression

Depression was assessed with the center for epidemiological studies short depression scale (CES-D 10). Those endorsing ≥10 items were considered to be in the range of concern for depression [15].

Analytic approach

Statistical analyses were performed with SPSS v19 (SPSS, Chicago, IL) and SAS 9.3 (SAS, Cary, NC). The first reported injury was used for any firefighter reporting multiple injuries. Logistic models were used to examine the relationships between body composition, demographics, and health behaviors at baseline and subsequent incidence of any injury and MS injury. Risk adjusted models examining the association between body composition and MS injury controlled for age, smoking status, and physical activity in a single model because these factors also have been predictive of injury. In addition, given the sampling strategy, the group-level factor “department” was entered into each model as a random effect.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. References
  8. Supporting Information

Participants' demographics are presented in Table 1. The most common injury types were dislocations, sprains and strains (57.1%), superficial injuries and open wounds (18.6%), and concussions/internal injuries (10.0%), fractures (4.3%), fire/chemical burns or scalds (4.3%) or other injuries not classified by the firefighter (11.8%). Firefighters were allowed to choose more than one type of injury resulting in the total not being 100%. Most injuries occurred during training (31.3%), on the fire ground (19.4%), returning from/responding to calls (19.4%), and on scene at nonfire calls (14.9%).

Table 1. Demographics of firefighters by injury status at follow-up
 Incident InjuryIncident MS Injury
Not injured; N = 255Injured; N = 46No MS injury; N = 295MS injury; N = 26
  1. Note: No statistically significant differences existed between groups of injured and noninjured firefighters.

Age, Years (SD)38.0 (10.0)38.5 (10.4)38.0 (10.0)38.6 (1.2)
Ethnicity (% White)91.289.190.796.2
% Married74.471.172.872.0
Education    
High School11.44.310.53.8
Some College64.263.062.673.1
College/Graduate degree24.432.626.923.1
Rank (%)    
Firefighter32.232.632.923.1
Firefighter/paramedic13.721.714.923.1
Driver operator22.715.221.423.1
Company officer (Lt, Capt)22.026.122.423.1
Chief (Asst, Deputy, Other)8.64.37.87.7
Other0.80.00.70.0
Years in fire service, years (SD)13.9 (9.0)13.9 (10.0)13.8 (8.9)14.7 (10.5)

Incidence of any injury during the 9 ± 1 month follow-up was 15.3% among firefighters who reported no injuries at baseline. No demographic, body composition, fitness, or health behavior measures were significant predictors of any injury longitudinally (see Table 2).

Table 2. Longitudinal predictors of any injury and MS injury
 Incident injuryIncident MS injury
  1. a

    Risk adjusted models examining the association between body composition and MS injury controlled for age, smoking status, and physical activity.

Body composition  
Obesity, BMI defined (%)  
Normal weighta
Overweight1.9 (0.7-5.2)1.8 (0.4-8.6)
Obese2.6 (0.9-7.4)5.2 (1.1-24.5)
Obesity, waist circumference (%)  
under 40 inchesa
over 40 inches1.9 (1.0-3.6)2.8 (1.2-6.4)
Obesity, body fat defined (%)  
Not obese <25%a
Obese1.3 (0.7-2.5)1.8 (0.8-4.0)
Demographics  
Age1.0 (1.0-1.0)1.0 (1.0-1.0)
Fitness  
SRPA  
Physical activity1.0 (0.8-1.1)1.0 (0.8-1.3)
Maximum torso strength  
Max/weight (SD)0.5 (0.2-1.4)0.8 (0.2-3.1)
Flexibility  
Average reach0.9 (0.8-1.0)1.0 (0.9-1.1)
Health behaviors  
Smoking  
Never/experimental
Former1.1 (0.5-2.5)0.8 (0.3-2.4)
Current0.9 (0.4-2.3)0.1(0.0-1.6)
Smokeless tobacco use  
Not current user
Current user1.9 (0.9-4.0)1.3 (0.5-3.3)
Problem drinking  
2 or less CAGE questionnaire
More than 2 on CAGE questions1.2 (0.5-3.1)1.4 (0.5-4.5)
Daytime sleepiness  
< 11 on Epworth Sleepiness Scale
≥11 on Epworth Sleepiness Scale1.9 (0.8-4.4)1.3 (0.4-4.0)
Depression  
<4 CESD-10
≥4 on CESD-101.2 (0.5-3.0)1.8 (0.6-5.2)

The incidence of MS injuries over the follow-up was 6.9% among those without MS injury at baseline. Only baseline body composition was a significant prospective predictor of MS injury, with obese (BMI ≥ 30 kg m−2) firefighters 5.2 times more likely (95% CI = 1.1-23.4) to experience a MS injury than their normal weight (BMI = 18.5-24.9 kg m−2) colleagues. Similarly, firefighters who were obese based on WC (>102.0 cm) were 2.8 times as likely (95% CI = 1.2-6.4) to have a MS injury at follow-up. Prospective analyses of MS injury including those with prevalent and incident injuries are available in Supporting Information. Both BMI and WC defined obesity remained significant predictors of incident MS injury after including department as a random factor in the models, resulting in no changes in the ORs and only small changes in the 95% CIs (95% CI = 1.1-24.5 for BMI-based obesity and 95% CI = 1.1-6.9 for WC-based obesity).

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. References
  8. Supporting Information

No demographic, body composition, fitness, or health behavior variables were significantly related to incident injury overall. However, obesity status, both based on BMI and WC, were significant prospective predictors of MS injury incidence. While the mechanism for how obesity increases MS injury risk is not clear, mechanisms have been suggested. Wearing et al. [16] proposed that obesity and excess body fat have harmful effects on soft tissue structures (e.g., tendons, cartilage, and fascia). The excess load on the locomotor system leads to altered mechanics in locomotor tasks which, in turn, raises stress on connective tissues leading to a higher risk of MS injury. Results from the study are consistent with prospective data demonstrating that increasing body mass is associated with greater disability risk and workers compensation claims [17]. This data suggests that an estimated incident rate of 90/1,000 firefighters are expected to incur a MS injury over a 12-month period. If all firefighters had a BMI of 25 or less, the rate of incident MS injuries would be expected to decline 60% (54/1,000).

Our findings underscore the significance of focusing on body composition (fitness and nutrition) as a means of decreasing risk of MS injuries. Obesity has been identified as a significant problem and as a detrimental risk factor for occupational health and safety among firefighters [5, 17, 18]. The current findings suggest injury prevention efforts should aimed at improving body composition in this population.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. References
  8. Supporting Information

Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. References
  8. Supporting Information

Additional Supporting Information may be found in the online version of this article.

FilenameFormatSizeDescription
oby20436-sup-0001-SuppInfo.docx19KSupporting Information

Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.