Exploration of the health status of experienced New Brunswick paramedics

Abstract

BACKGROUND:

Literature reports that paramedics represent an at-risk occupation for the development of health problems. At least half of the paramedic population presents at least one risk factor associated with a negative health condition. These reports may suffer a “mono-method bias” where most reported outcomes are based on a single screening tool approach (may attenuate or inflate the prevalence).

OBJECTIVE:

The current study characterizes the health status of a cohort of twenty-five experienced New Brunswick (Canadian province) paramedics.

METHODS:

To understand possible limitations of past research, health status was characterized using four different methods: two methods using only one health measure and two were combined methods, integrating outcomes from at least two health measures to determine the prevalence of a given health status.

RESULTS:

Mono-bias was observed when using the single health measure methods. The difference among the four methods highlighted that a third of the cohort seemed unaware of their health condition. This result shed additional light on paramedics’ health, where a high proportion of paramedics worked without knowledge of their health conditions. Based on a two health measures combined method, it was observed that only two-fifths of the current sample had no health conditions or could otherwise be considered as a “healthy”.

CONCLUSIONS:

Because the literature has focused on single screening methods, our results were difficult to compare. However, there was a consensus that paramedics represent an at-risk occupation comprised of health problems. This study was exploratory and should be the basis for further research.

Keywords

Mono-method bias musculoskeletal disorders (MSD)cardiovascular diseases (CVD)post-traumatic stress disorders (PTSD)paramedic health status

1 Introduction

Paramedics complete a variety of complex physical tasks and are often exposed to distressing events and major disasters. They are required to spend several hours sitting in their vehicle, and then suddenly deliver pre-hospital care and perform strenuous tasks in unknown and hazardous environments. These situations create conditions associated with the development of physical and psychosocial health problems [1, 2]. In addition to work related challenges, sedentary behavior and poor lifestyle habits may contribute to an increased risk of developing health problems that could impact quality of life as well as paramedics’ ability to deliver patient care [1 , 4]. Not surprisingly, we observe a high turnover rate within the Emergency Medical Service (EMS) [1 , 6].

Health problems among paramedics appear unevenly documented in the literature. Thirty years ago, the most important health problems causing absenteeism among UK paramedics were musculoskeletal disorders (MSD), cardiovascular diseases (CVD) and psychosocial health problems [7]. A recent report published in 2016, from the same UK EMS indicated that although the MSD impact remained the same, psychological factors have increased 11% (an increase from 5% in 1979 to 16% in 2016) and 6% of absences were due to “other known causes” [8]. Considering the fact that it is reported that CVDs are the second leading cause of paramedics’ fatalities [9, 10] and that paramedic work is considered at-risk with the development of CVDs [11, 12], it is odd that this UK governmental agency report contained no information about CVDs. In the USA, the Bureau of Labor statistics from the US Department of Labor revealed that half of the absences of paramedics were related to musculoskeletal injuries [13]. Musculoskeletal injuries refer to the combination of traumatic injuries and MSD. Traumatic injuries are due to an accident, that could include musculoskeletal lesion caused by vehicle collision, violence, slips, trips, falls; while MSD is a painful disorder developed through repetitive movements or cumulative trauma of bodily reaction and/or overexertion work-related task [14, 15]. The U.S. Department of Labor provides information on musculoskeletal injuries only (i.e. combining data from both, musculoskeletal injuries and MSD) also without reporting on CVD or psychosocial health problems. In Canada, there are no similar data available specifically for paramedics. Statistics Canada surveys do report causes of illness or disability (physical and psychological) by household based on the number of missing days from work for health technicians, which includes paramedics, but these do not provide data specific to the profession. It is therefore difficult to compare the paramedic data from the UK, USA and Canada, as the definitions and reporting methodologies differ. Nonetheless, based on the UK and USA public organization reports, paramedics do represent an at-risk occupation with developing health problems, which may cause a high rate of absenteeism.

The limitation of these public organization reports is the quantified absenteeism data rather than the specific health status of the paramedics on duty; the absenteeism data did not provide information about the individual working with health issues. To know how many paramedics are dealing with every day health conditions, this study refers to the paramedic’s health literature, where studies reveal that approximatively half of each respective paramedic sample reports MSD [16 –19]. Approximatively half of the reports have psychosocial issues and, more specifically, one quarter have severe symptoms of post-traumatic stress disorders (PTSD) [20]. Almost all of the paramedics (90%) were prone to develop CVDs [11]. Consequently, the literature supports the likelihood that a high number of paramedics likely work with conditions detrimental to their health daily.

Self-reported diagnostic and self-reported questionnaires have often been used as screening tools to describe and characterize paramedics’ health. This is understandable since these methods allow many of individuals to be evaluated easily and sometimes, there is no other option available (e.g. questionnaires in psychosocial health). However, these types of tools, may lead to a lack of precision. For example, weight and height used to assess obesity status could be self-reported or measured directly, but as mentioned by Stommel et al. [21], when individuals self-reported their measurement they have a tendency to over or under-estimate their values. Self-reported measures potentially distort reality as self-reported information can be influenced by a wide range of individual and social factors [22]. Despite this fact, paramedic health literature still relies on the use of self-reported measures. Often only one screening tool or one method is used to estimate a health condition, without validation against a second measure. As a result, the literature describing paramedics health may be limited by “mono-method bias” [23]. Using a single health measure tends to attenuate or inflate the results [23]. Application of multiple measures should increase and enhance the validity of health status estimates and provide a clearer report of paramedics’ health [22].

The aim of this study was to describe the health status of New Brunswick (an Atlantic Canadian province) paramedics by using multiple screening methods. Because of the administration of several tools, it was expected that a mono-bias would be observed on the single health screening methods and would result in a wide variability of prevalence of health status among the participants. It was hypothesised that multiple screening approaches should prevent this bias.

2 Methodology

2.1 Design

A cross-sectional observational-based design was adopted to assess the health status among a group of Ambulance New Brunswick (ANB) paramedics. A series of self-reported questionnaires and physical measurements were administered to create a health profile of this cohort. This study received institutional research ethics board approval (CER#1213-059).

2.2 Participants

In collaboration with ANB, recruitment emails were sent out across the province of New Brunswick. Twenty-five (n = 25) paramedics responded positively to volunteering to participate in this project. No participants were excluded.

2.3 Procedures

All testing took place in the University laboratory. Upon arrival, each participant was briefed on the data collection procedures, read a letter of information and signed a consent form approved by the University’s research ethics board. Afterwards, the participant started sixty minutes of demographic and health assessment. Details of the assessment are provided in Table 1 with a brief description provided here. The chronological process of the assessment was as follows:

Participants’ weight and height were measured;

Participants performed a battery of musculoskeletal assessments;

Participants filled in demographic and health-related questionnaires;

Participants completed a physiological baseline period;

Participants performed step tests.

Table 1
Health profiling methodology

Health conditions Screening methods Health indicators Demographic characteristics

Self-diagnosis Screening tests (individually/combined) Diagnosis & screening tests combined BMI HRV Age Exp Educ

MSD X NQ X X X X X X

FMS

CVD X SDNN50 X X X X X X

YMCA

PTSD X PCL-C X X X X X X

RSES-22 (or) SOC-13

Health conditions	Screening methods	Health indicators	Demographic characteristics
MSD	X	NQ	X	X	X	X	X	X
FMS
CVD	X	SDNN50	X	X	X	X	X	X
YMCA
PTSD	X	PCL-C	X	X	X	X	X	X
RSES-22 (or) SOC-13

Note. (BMI) body mass index; (HRV) heart rate varaibility; (Exp) years of paramedic experience; (Educ) years of education; (MSD) elevated risk to develop musculoskeletal disorders; (CVD) elevated risk to develop cardiovascular diseases, (PTSD) elevated symptoms of post-traumatic stress disorders; (NQ) Nordic questionnaire; (FMS) functional movement screen; (YMCA) step test; (SDNN50) SDNN value below 50 ms; (PCL-C) civilian version of PTSD check list; (RSES-22) responses to stressful experiences scale; (SOC13) sense of coherence.

2.4 Measurements

2.4.1 Self-reported questionnaires

i) Demographic data

Self-reported questionnaire on gender, age, year of paramedical experience and total years of education.

ii) Diagnosed health problem

Self-reported survey on diagnosed MSD, CVD or PTSD.

iii) Nordic questionnaire (NQ)

NQ is a self-report validated tool measuring the history of MSD in the last 12 months and its occupational impact [24 –26]. The questionnaire is divided by anatomical regions: neck, shoulders, elbow, hands, wrists, upper back, lower back, hip, thighs, knees, ankles and feet. Three different outcomes are measured: 1) musculoskeletal complaints of at least one episode during the past 12 months; 2) activity limitations and occupational difficulties during the past 12 months; and 3) activity limitations and occupational difficulties during the last 7 days. There are two alternative responses: yes or no.

iv) Civilian version of PTSD check list (PCL-C)

PCL-C is a self-administered questionnaire for screening symptoms of PTSD in seventeen items that correspond to the symptoms of PTSD [27, 28]. The PCL-C has seventeen questions scored on a five-point scale (“1” = not at all, “2” = a little bit, “3” = moderately, “4” = quite a bit, “5” = extremely). As mentioned by the National center of PTSD of US Department of Veterans’ Affairs, “there is no absolute method of determining the correct cut-point”, however they suggested “36” as lowest normative cut-off point for specialized medical clinics like paramedics. The PCL-C questionnaire is a valid and a reliable tool (Cronbach α-value = 0.94; ICC r-value = 0.96) [27, 28].

v) 22-item Responses to Stressful Experiences Scale (RSES-22)

RSES-22 is a self-reported questionnaire measuring the level of resiliency. Low level of resiliency is associated negatively to the severity of PTSD symptoms [29]. RSES-22 provides a global score (0 to 88). This score is associated with three levels of coping: low level of resilience (0 to 49); moderate level of resilience (50 to 70); and high level of resilience (71 to 88). The RSES has been shown to be valid and reliable (Cronbach α-value = 0.91–0.93; ICC r-value = 0.87) [29].

vi) 13-item Sense of Coherence (SOC-13)

The SOC-13 is a self-reported questionnaire used to measure the sense of coherence. Low sense of coherence is associated negatively to the severity of PTSD symptoms [30 –34]. Each of the thirteen questions are scored on a seven-point Likert scale, resulting in a grand score between 13 and 91. Ibrahim et al. [34] indicated that the score could be categorized into three levels: low sense of coherence (0 to 55), medium sense of coherence (56 to 65) and high sense of coherence (66 to 91). The SOC-13 is a valid and reliable questionnaire (Cronbach α-value = 0.70–0.92; ICC r-value = 0.69–0.78) [30 –33].

2.4.2 Physical measurements

i) Body Mass Index (BMI)

Weight and height were measured with a 450KL Physician Beam scale (Health O Meter Professional, USA), which permitted the calculation of body mass index (BMI) (kg/m²) and used as a health indicator for this study.

ii) Functional Movement Screen (FMS^™)

Participants performed a Functional Movement Screen (FMS) consisting of a series of seven movements as developed by Cook et al. [35]: 1) deep squat; 2) hurdle step; 3) in-line lunge; 4) shoulder mobility; 5) active straight leg raise; 6) trunk stability push-up; and 7) rotary stability. Each movement was scored on a 4-point Likert scale, with 0 = pain, 1 = unable to perform movement, 2 = compensation required to perform movement, and 3 = able to perform movement, consistent with the instructions [35]. The summed FMS score can range from 0 to 21 [35 –37]. A systematic review and meta-analysis found that FMS has excellent inter-rater and intra-rater reliability, including a pass/fail cut-off of 14 points being valid in predicting a higher risk of injury [38]. For a detailed description of each movement, the reader is referred to Cook et al. (2006) [35].

iii) 5-minute physiological baseline

Before this measurement, a 3-lead (MLA2340, ADInstruments, USA) electrocardiogram (ECG) was placed on the participants to collect and monitor the heart activity. The electrode placement used LEAD II configuration according to Einthoven’s triangle [39]. The heart signals were recorded using the Bio Amp unit (FE132) and an eight channel PowerLab unit (PL3508) (AdInstruments, USA) and LabChart software (version 7, AdInstruments, USA). To measure the electrical activity of the heart, participants remained seated comfortably on a chair, in a quiet and dimly lit room for 5 minutes. Electric heart signal was computed to calculate heart rate variability values in order to evaluate the participant’s future risk of developing cardiovascular diseases (CVD) and to use as a health indicator with heart rate variability (HRV). An individual is considered at risk of developing future CVD when the value of the standard deviation time between inter-beat-intervals (SDNN) from the HRV calculation are lower than 50 milliseconds (ms) (SDNN50). SDNN50 is considerate as “low HRV” and represents an increasing risk of cardiovascular morbidity and mortality [40, 41]. Therefore, in this study the HRV is used as a screening tool and a health indicator.

iv) YMCA step test

After the physiological baseline measure, participants completed the YMCA 3-minute step test. The YMCA (Young Men’s Christian Association) step test is a submaximal cardiovascular fitness test designed to estimate fitness for individuals with low exercise capacity [42]. This step-test is a 3-min single-stage test, which is used to predict fitness levels using the number of heartbeats following the first minute of recovery. The YMCA step-test administration requires a 12-inch bench height, a chronometer and a metronome. The step frequency is 24 step cycles per minute (96 beats per minute). Heart rate was continuously monitored by ECG. After an adaptation period to understand the procedure (synchronized with a metronome), the YMCA step test was performed. Following exercise, participants were asked to sit quietly (without moving) for 1 minute. Beutner et al. [42] and Kasch et al. [43] have shown the YMCA step-test as a strong predictor of VO₂ max (r-value = 0.83). Participants were given a 10-minute break before moving on to other protocols. It was well-documented that a low level of cardiovascular fitness is associated with a high risk of mortality from CVD [44, 45].

2.5 Variables

All the data collected was divided into three health conditions (MSD, CVD, PTSD), two health indicators (HRV, BMI) and three demographic characteristics (age, year of experience, years of education) (Table 1). It should be noted that participants could have all health conditions.

i) Health conditions

Four methods were used to identify the presence of a health condition: i) diagnostic self-report by the participant (single measure method); ii) individual screening test (single measure method); iii) combination of two screening measures (questionnaire and/or physical measure) (multi-method approach); and iv) a combination of all methods (multi-method approach).

MSD: Elevated risk to develop future musculoskeletal disorders. Participants were considered as having elevated MSD risk if they either:

Method 1) self-reported being diagnosed with MSD;

Method 2)

had been screened positively with the NQ (work-related history of MSD in the last 12 month);

had been screened positively with the FMS (scored below the cut-off point) [38];

Method 3) had been screened positively for both tests (NQ and FMS);

Method 4) had been screened positively if they:

self-reported being diagnosed of MSD AND had been screened positively on at least one tool: NQ OR FMS;

OR considering the case that the participant did not report MSD diagnosis, the participant had been screened positively on both tools: NQ AND FMS.

CVD: Elevated risk to develop future cardiovascular diseases. Participants were identified as having high risk to develop CVD if they:

Method 1) self-reported being diagnosed with CVD;

Method 2)

scored “below average” on the YMCA step test [42];

had a HRV value lower than 50 milliseconds (ms) (SDNN50) [40];

Method 3) had been screened positively by both tests (YMCA and SDNN50)

Method 4) had been screened positively if they:

self-reported being diagnosed of CVD AND had been screened positively on at least one tool: YMCA OR SDNN50;

OR considering the case that the participant did not report CVD diagnosis, the participant had been screened positively on both tools: YMCA AND SDNN50.

PTSD: Elevated symptoms of post-traumatic stress disorders. Participants were considered to demonstrate elevated PTSD symptoms if:

Method 1) self-reported being diagnosed with PTSD;

Method 2)

had been screened positively for PCL-C (scored higher than 36) [27, 28];

had scored a low level of resilience (0 to 49) at the RSES-22 [29] or low level of sense of coherence (0 to 55) at the SOC-13 [34];

Method 3) had been screened positively by at least two tools (PCL-C and RSES-22 OR PCL-C and SOC-13);

Method 4) had been screened positively if they:

self-reported being diagnosed of PTSD AND had been screened positively on at least one tool: (PCL-C, RSES-22 or SOC-13).

OR considering the case that the participant did not report PTSD diagnosis, the participant had been screened positively on at least two tools (PCL-C and RSES-22 OR PCL-C and SOC-13).

ii) Health indicators

Body mass index (BMI).

Underweight, overweight and obesity are important risk factors associated with a variety of chronic diseases (e.g. MSD, CVD, cancer, type II diabetes, psychosocial issues). BMI provides an indicator of the general health state [46].

Heart rate variability (HRV).

In the literature, physical and psychosocial health state have been quantified by heart rate variability (HRV). HRV is the measurement of the variability in time intervals between successive heartbeats. HRV measured at rest can provide an indicator of general health state. More specifically, low rest HRV have been associated with a wide spectrum of physical and psychosocial disorders (e.g. obesity, poor cardiovascular health, anxiety disorders, PTSD) as well as being associated with unhealthy lifestyle (e.g. physical inactivity, sleep deprivation, alcohol and drug use) [40 , 47–56]. Essentially, HRV does not discriminate health condition, but it is an indicator of the effect of health condition on the autonomic nervous system (ANS). More specifically, it represents a reduction of vagal activity associated with individuals with poor health conditions. It is acknowledged that the ANS contributes to the regulation of cardiac activity. However, the mechanisms remain unclear in the literature on how poor health condition could affect cardiac activity, including HRV.

iii) Demographic characteristics

The scientific literature associates demographic characteristics with health issues. For example, age and years of experience are both associated positively with MSD [57], and the number of years of education is associated with a moderate decline in mortality risk [58].

2.6 Data processing

Some data from the physical tests and questionnaires were recorded on paper and subsequently transferred to electronic format. The ECG signal data collected during the baseline period and YMCA step test were prepared (i.e. 1 to 45 Hz bandpass filter and normalized) using LabChart software (i.e. LabChart software version 7, AdInstruments, USA) before the calculations.

i) 5-minute baseline period calculations

The ECG signal was collected in order to obtain heart rate variability (HRV) signal. In this study, HRV values were used to evaluate cardiovascular health and explore the physiological responses during the baseline period.

SDNN50: Standard deviation time between inter-beat-interval (IBI) below 50 ms. This value is calculated from across IBI. This represents an elevated risk of cardiovascular diseases [40, 41].

HRV as health indicator: Poincaré plot analysis is the measure of the dispersion of a geometric shape (i.e. ellipse or comet) formed by a pair of successive beats, thereby plotting the current inter-beat-interval (IBI) against the next IBI. Poincaré plot analysis is used to evaluate the dynamics of the heart rate variability signal and describe the activity of the sympathetic and parasympathetic modulation of heartbeat [59, 60]. SD1 (standard deviation of points perpendicular to the line-of-equality in milliseconds) measure the short-term HRV and is associated with the influence of parasympathetic activity; SD2 (standard deviation of points along to the line-of-equality in milliseconds) measures the long term HRV and is correlated with the sympathetic and parasympathetic activities; SD1/SD2 is the ratio (reported as a percent) between short and long interval variation and is used to measure the autonomic balance [59, 60].

ii) YMCA step test calculations

The number of heartbeats were summed during the first minute after the step test using LabChart software (i.e. LabChart software version 7, AdInstruments, USA). This calculation served to quantify participants’ level of cardiovascular fitness according to Kasch, et al.’s [43] seven levels (excellent, good, above average, average, below average, poor or very poor) categorized by age bracket and gender.

2.7 Descriptive statistics

Descriptive statistics were used to describe the prevalence, spread (mean, 95% confidence interval; M±95% CI), distribution of each health condition and health and demographic indicators among this paramedic sample. Descriptive statistics were computed with JASP version 0.8.0.1 (University of Amsterdam, The Netherlands).

3 Results

Table 2 shows the wide variability of the prevalence of health conditions through screening methods. Self-reported diagnosis method (method one) identified the lower range of prevalence (4 to 16%) and the second method (individual screening tool) identified the higher range of prevalence (16 to 64%). The third and fourth method had a similar range of prevalence (12 to 32% and 12 to 36%), respectively.

Table 2
Prevalence of health conditions among a cohort of 25 paramedics

Health conditions Self-diagnosis Screening tests Diagnosis & screening tools combined

Individually Combined

MSD 12±12.7 NQ: 44±19.5 32±18.3 36±18.8

FMS: 44±19.5

CVD 16±14.4 YMCA: 64±18.8 28±17.6 32±18.3

SDNN50: 32±18.3

PTSD 4±7.7 PCL-C: 16±14.4 12±12.7 12±12.7

RSES-22, SOC-13: 28±17.6

Individuals w/ health conditions 32±18.3 88±12.7 52±19.6 60±19.2

Individuals w/o health condition (Healthy) 68±18.3 12±12.7 48±19.6 40±19.2

Health conditions	Self-diagnosis	Screening tests	Diagnosis & screening tools combined
MSD	12±12.7	NQ: 44±19.5	32±18.3	36±18.8
		FMS: 44±19.5
CVD	16±14.4	YMCA: 64±18.8	28±17.6	32±18.3
		SDNN50: 32±18.3
PTSD	4±7.7	PCL-C: 16±14.4	12±12.7	12±12.7
		RSES-22, SOC-13: 28±17.6
Individuals w/ health conditions	32±18.3	88±12.7	52±19.6	60±19.2
Individuals w/o health condition (Healthy)	68±18.3	12±12.7	48±19.6	40±19.2

Note. Results have been presented with the mean and 95% confidence interval (M±95% CI) of each health conditions; (MSD) elevated risk to develop musculoskeletal disorders; (CVD) elevated risk to develop cardiovascular diseases; (PTSD) elevated symptoms of post-traumatic stress disorders; (NQ) nordic questionnaire; (FMS) functional movement screen; (YMCA) step test; (SDNN50) SDNN value below 50 ms; (PCL-C) civilian version of PTSD check list; (RSES-22) responses to stressful experiences scale; (SOC13) sense of coherence.

Two participants (8%) who originally had self-reported health condition diagnosis (first method) have not been screened positively by the second and third method. More precisely, one participant was not identified with MSD, because the participant scored higher than the FMS threshold (i.e. 14), even if the participant had a self-reported work-related history of MSD in the last twelve months. The other participant was not identified with CVD, because the participant had a higher value than SDNN50 threshold (i.e. SDNN 50 ms), even if she/he had scored “below the average” on the YMCA step test.

3.1 Demographic descriptions of cohort characteristics

Twenty-five paramedics (8 women; 17 men) aged 38.5±3.5 (M±95% CI) years old, with an average of 15.3±3.3 years of paramedic experience as well as an average of 15.3±0.7 years of education, volunteered to take part in this study (Table 3).

Table 3
Distribution of demographic characteristics among a cohort of 25 paramedics

Categories Years intervals Prevalence (%) ±95% CI

Age (year) 20–29 12±12.7

30–39 44±19.5

40–49 32±18.3

50+ 12±12.7

Paramedic 5–9 24±16.7

experience 10–14 20±15.7

(years) 15–19 28±17.6

20+ 28±17.6

Education 13–14 40±19.2

(years) 15–16 40±19.2

17+ 20±15.7

Categories	Years intervals	Prevalence (%) ±95% CI
Age (year)	20–29	12±12.7
	30–39	44±19.5
	40–49	32±18.3
	50+	12±12.7
Paramedic	5–9	24±16.7
experience	10–14	20±15.7
(years)	15–19	28±17.6
	20+	28±17.6
Education	13–14	40±19.2
(years)	15–16	40±19.2
	17+	20±15.7

The individuals with an elevated risk to develop future CVD were older and more experienced than those with other health conditions (Table 4). The individuals who reported elevated symptoms of PTSD were younger and less experienced than those with other health conditions. The individuals without health conditions were younger (i.e. 8.5 years) than those identified with at least one health condition (Table 4).

Table 4

Demographic characteristics among a cohort of 25 paramedics with and without health condtions

Health conditions	Age (years)	Experience (years)	Education (years)
MSD	38.2±5.7	13.9±6.5	15.6±1.3
CVD	41.8±4.8	17.3±4.9	15.9±1.8
PTSD	33.3±2.4	11.0±5.7	15.0±3.0
All health conditions combined	40.3±4.3	15.8±4.5	15.6±1.0
Without health condition (healthy)	35.9±5.1	14.5±4.5	14.8±0.9
All paramedics cohort	38.5±3.3	15.3±3.2	15.3±0.7

Note. Results have been presented with the mean and 95% confidence interval (M±95% CI) of each demographic characteristic; (MSD) elevated risk to develop musculoskeletal disorders; (CVD) elevated risk to develop cardiovascular diseases, (PTSD) elevated symptoms of post-traumatic stress disorders.

3.2 Health indicators

Table 5 shows the BMI distribution among that cohort of NB paramedics. HRV values followed the same trend, where participants identifying with health conditions (elevated risk to develop MSD, elevated risk to develop CVD, elevated symptoms of PTSD) had lower values than individuals without physical health conditions (Table 6). More specifically, individuals with an elevated risk of CVD presented with lower HRV values for SD1 and SD2 and higher BMI than those with other health conditions. Conversely, individuals who reported elevated symptoms of PTSD also had lower values of SD1/SD2, but BMI values near the healthy BMI value.

Table 5
Distribution of BMI among a cohort of 25 paramedics

Classification BMI (kg/m²) Prevalence (%) ±95% CI

Normal weight 18.5 –24.9 24±16.7

Overweight 25.0 –29.9 44±19.5

Obese class I 30.0 –34.9 20±15.7

Obese class II 35.0 –39.9 12±12.7

Classification	BMI (kg/m²)	Prevalence (%) ±95% CI
Normal weight	18.5 –24.9	24±16.7
Overweight	25.0 –29.9	44±19.5
Obese class I	30.0 –34.9	20±15.7
Obese class II	35.0 –39.9	12±12.7

Note. Classification of Body Mass Index (BMI) is based on: Health Canada. Canadian Guidelines for Body Weight Classification in Adults: Quick Reference Tool for Professionals. 2003.

Table 6

Health indicators among a cohort of 25 paramedics with and without health condtions

Health conditions	HRV			BMI (kg/m²)
	SD1 (ms)	SD2 (ms)	SD1/SD2 (%)
MSD	17.0±10.6	76.2±33.0	20.4±6.7	29.0±2.9
CVD	11.1±4.8	50.5±14.5	20.7±4.3	31.6±2.6
PTSD	15.2±9.6	89.9±50.8	16.2±4.3	25.4±4.7
All health conditions combined	15.4±6.3	66.6±20.7	21.7±4.1	29.2±2.2
Without health condition (healthy)	33.2±13.1	100.7±25.6	31.7±6.5	26.6±2.6
All paramedics cohort	23.4±7.7	81.9±17.5	26.2±4.2	28.2±1.7

Note. Results have been presented with the mean and 95% confidence interval (M±95% CI) of each health conditions; (SD1) standard deviation of points perpendicular to the line-of-equality in milliseconds (ms); (SD2) standard deviation of points along to the line-of-equality in milliseconds (ms); (BMI) body mass index; (MSD) elevated risk to develop musculoskeletal disorders; (CVD) elevated risk to develop cardiovascular diseases, (PTSD) elevated symptoms of post-traumatic stress disorders.

4 Discussion

The current study assessed the health status of a cohort of experienced New Brunswick paramedics. The approach used in this research was original, since the health status of the participants was estimated by four methods: i) self-reported diagnosis; ii) one validated screening tool; iii) two validated screening tools combined; and iv) self-reported diagnosis and screening tools combined. Also, participant health status, HRV and BMI values were used as health indicators.

As expected, the prevalence of health conditions as calculated by different methods varied widely. Three tendencies were observed: i) self-reported diagnosis defined the lowest prevalence; ii) individual tests found the highest prevalence; and iii) both combined methods (two screening tools combined, self-reported diagnosis and screening tools combined) had moderate, yet similar prevalence and also showed a “moderate” prevalence than other methods.

Considerable differences emerged when identifying the prevalence of health status using self-report diagnosis relative to the use of a combined method. This difference could be explained by the fact that a third of this cohort of experienced paramedics did not seem aware of their own health status. Paradoxically, paramedics possess the knowledge and competency to identify the majority of health problems, and some paramedics work on health prevention and intervention programs with populations at risk of diabetes and CVD [61]. It appears that paramedics may have underreported their health status when using self-report methods. This might suggest that paramedics are so busy taking care of others they forget or run out of time to take care of themselves, or perhaps are not aware of their health status.

Single measure methods (i.e. self-report and single screeners), produced the most extreme (lowest and highest) prevalence estimates across the four methods. That result confirms the presence of “mono-bias” among those methods, where prevalence attenuation may be present when estimating prevalence from self-report (the first method) and prevalence inflation when estimating prevalence using single screens (the second method). The combined method approaches (i.e. the third and fourth methods) prevented this bias by requiring at-least two measurements to identify the presence of a health condition. Therefore, this paper shares the opinion of Lenderink et al. [22], that multiple measures enhance the validity of the health status.

In that context, the fourth method is considered the best to evaluate the prevalence of health conditions among a cohort of experienced paramedics. Because this method used at least two health measures, it confirmed the self-reported diagnosis and identified individuals who have health conditions without knowing. Therefore, the observations show a high prevalence of poor health conditions among this cohort of paramedics. Furthermore, all presenting signs or symptoms of chronic diseases among paramedics in this project were related to a decreasing variability of the HRV indicating a negative influence of their health condition on their ANS.

4.1 Health status

i) MSD

Garrison et al.’s parallels the work of this study. They measured the risk of developing MSD by combining the self-reported diagnosis of MSD, FMS cut-off score and/or the history of MSD among college athletes. Considering the discrepancies of the age and occupational activity among the respective populations of this paper (i.e. college athlete, experienced paramedics) it remains difficult to compare the studies together. Only a few studies have indirectly measured musculoskeletal health with the musculoskeletal questionnaires. At this time there are no known recent studies combining methods (e.g. questionnaire with functional assessment of paramedics).

The data available are divided into two types: 1) international studies based on the prevalence of paramedics working with MSD; and 2) North American and UK data based on the stated cause of sickness absence. For a better comparison, results for this paper were limited to the first type of study. A Swedish study (n = 1500) observed that half of the paramedics sampled had neck, shoulder and low back pain [63]. A Danish study (n = 1691), observed that two-fifths of their paramedics sample suffered neck and shoulder problems and two-fifths had low-back problems [16]. A Japanese study (n = 1550) observed that one-third had neck-shoulder problems and two-thirds had low back problems [17]. These international studies present higher lower back pain prevalence with Japanese paramedics reporting higher rates than Danish paramedics (66% vs. 40%). These previous studies present a higher prevalence than the current study (32%). This discrepancy might be explained by the mono-method bias, because these studies used a single method of screening (questionnaires).

In addition, the authors reported that less years’ experience was associated with having poor musculoskeletal health. However, the results did not confirm this observation, because the participants with an elevated risk of developing MSD had similar years of experience compared to individuals without health conditions.

ii) CVD

One-third of the experienced NB paramedics had an elevated risk of developing CVD. At this time, there is no known paramedic study that has evaluated risk of developing CVD (based on self-report diagnosis, HRV at rest and/or heart rate during physical effort (i.e., climbing stairs task, with YMCA step test)). Recent papers are limited and focused (with questionnaires) on the prevalence of risk factors of CVD (e.g. BMI, tobacco consumption, lipidemia, blood pressure, life-style, diet) [11 , 64–66]. Although there are only a few dated scientific articles concerning paramedics and CVDs, CVDs were identified as an important health problem. For the period 1970-1972 in the UK, more than half of all paramedic mortalities were CVD-related [9]. In 1998, Rodgers reported that the second highest cause of early retirement between 1988 to 1992 for paramedics in the UK was CVD [67]. Maguire et al. [10] reported CVD as the second leading cause of fatalities among paramedics in the USA. Considering a lack of previous research, it is difficult to compare the findings of the current study. However, it is well documented that paramedics are an occupational population at risk of developing CVD. Furthermore, the current results show that paramedics who had elevated risk of developing CVD were older and more experienced than other participants (i.e. other health conditions or healthy paramedics). That could be explained in two ways: first, the normal deconditioning process associated with aging and its relationship to increases in sedentary behaviour, physical inactivity, poor diet, and secondly the nature of paramedics’ work, work-shift, physical and psychosocial demands that may progressively increase the risk of CVD [1 , 64–66].

iii) PTSD

The literature presents a discrepancy among the prevalence of PTSD, which makes it difficult to compare with this study. Berger et al. [68] revealed that the worldwide prevalence of PTSD is 15% for paramedics (without significant geographic difference between North-America, Europe and Australia.), while Carleton et al. [20] reveal higher rates in Canada, where 25% of paramedics were identified with PTSD. This difference across these two important studies, could be explained by the methodological differences. They both used a single approach (questionnaire), where they might be affected by mono-method bias. Resulting in an amplification for Carleton et al. [20] study and an attenuation for Berger et al. [68] paper, without using another form of cross-validation that could explain the differences between the studies. Additionally, both papers used various instrumentations (e.g. PCL-C, PCL-5, PDS) that could accentuate the differences between the studies.

In addition, Carleton et al. [20] reported that age and years of experience were associated positively with having elevated symptoms of PTSD. However, the results of the current study show that individuals with elevated symptoms of PTSD were younger and less experienced compared to other participants. That could be explained by the “healthy workers effect”, where injured or traumatized workers often transition away, leaving only the healthy workers in the cohort. In other words, it could mean that the older workers in our cohort may have had better practices or may have simply been more resistant to the demands of the workplace [69, 70].

4.2 Health indicators

i) BMI

The number of paramedics classified as obese in this study is similar to the literature which has reported that 50% of paramedics are overweight and more than half of these individuals are considered obese [1 , 66]. The result of the current study shows that paramedics with an elevated risk of CVD have higher BMI among all participants, confirming what the literature has reported: that paramedics with obesity have positive relationships with CVD [1 , 64–66].

ii) HRV

The results for HRV follow the same trend, where the paramedics who presented signs or symptoms of health conditions had lower HRV values than paramedics who did not have a health condition. The results in this study are consistent with the literature [40 , 71]. More specifically, the lowest HRV values were observed among paramedics with an elevated risk of CVD, confirming Hillebrand et al. [40, 41], where low HRV values represent an increased risk of cardiovascular morbidity and mortality.

Despite the fact there are only a few studies that measured HRV amongst individuals with MSD, our results corroborate the findings of Hallman et al. [72], where they observed lower HRV values from individuals who suffered chronic neck-shoulder musculoskeletal pain. They suggest that the individuals suffering from chronic musculoskeletal pain reduced their physical activity and/or leisure time, leading them to diminished parasympathetic activity caused by increasing time of inactivity or rest period.

The link between elevated symptoms of PTSD and HRV has already been identified in the literature [51, 52]. Our results support this. Paramedics often work in highly stressful conditions, where they are required to manage difficult situations such as violence, patient interaction, life and death responsibility, time pressure, frequent traumatic event exposure as well as risks of death, injury and to contracting infectious diseases. A high level of occupational stress among paramedics has been shown to increase the risk of developing poor psychosocial health conditions (such as PTSD) [6 , 74].

4.3 Practical implications

The results of the current study demonstrate that the single screening methods of quantifying paramedic health status might be problematic, causing either an underestimation of prevalence when using self-reported diagnosis or an overestimation when using individual screening tests. In order to avoid a mono-bias, it is recommended to administer at least one additional screening tool, to confirm a self-reported diagnosis. Furthermore, this study highlights the prospect of underreporting of health status within this paramedic cohort. In other words, it appears that a high proportion of paramedics lack awareness of their own health, or perhaps are focussed so intently on the health of their patients, they have not adequately considered their own health. Because of this population is highly health literate, it is unlikely that a health promotional campaign would yield value. Instead the routine administration of a battery of screening tests similar to those used in this study would provide paramedics with important data for support in the awareness of their health status. If prompted through such a medical surveillance-based approach, it is possible that some paramedics would become more aware of their health and could decide to change and improve their health status if warranted.

In addition, the present study has shown that HRV could be used as a health indicator. Several studies have shown the positive effects of lifestyle changing and training by increasing HRV value. Portable HRV measurement systems are available for a low cost (apps on a smartphone). These types of tools should be further explored to identify whether they can be of assistance to paramedics as a monitoring tool.

4.4 Study limitations

The recruitment for this project was challenging. ANB is an Emergency Medical Service (EMS) including over 1000 paramedics, dispatchers, and flight nurses across the province of New Brunswick (NB). Recruitment emails were sent out to all NB paramedics. Of those emails, only twenty-five (25) paramedics responded positively to volunteer in this project. This limited the sample size of this project. Upon reflection, one reason that could explain the lack of participation is that the project was identified as “health assessment”. During the recruitment period, interviews with the paramedics revealed that, part of their concerns with participating in the study was related to a fear of being branded as “not fit for duty”.

5 Conclusion

The current study characterized the occupational health of a cohort of NB paramedics by using four different methods: two methods that used only one health measure and two others that combined at least two health measures. Mono-bias was observed when using single health measure methods. The difference between the four methods suggested that a third of the cohort may be unaware of their health condition or choose to under report. Based on the two health measures method, it was observed that less than forty percent would be considered as “healthy”. Despite the fact that the study was limited by a low level of participation, paramedic health literature appears to be suffering of mono-method bias for the prevalence of MSD and PTSD; and because the literature was dated and focused on CVDs’ risk factors, it was difficult to compare the results with the known literature. Nevertheless, New Brunswick or worldwide paramedics represent an at-risk occupation associated with an increased prevalence of health problems. The most important aspect of this study is that the method proposed should improve health researchers in the conception of study design. Additionally, based on the current results and limitations, we suggest schools, professional associations and EMS, to promote the participation to future health study among paramedic workers and individuals pursuing a paramedic career. These organizations should perform health screening tools to heighten health awareness, as well as educate the risks related to health conditions and the health conditions associated with paramedics’ duty. Future research is needed to explore how paramedic job performance is affected by health conditions.

Conflict of interest

None to report.

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Exploration of the health status of experienced New Brunswick paramedics

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1 Introduction

2 Methodology

2.1 Design

2.2 Participants

2.3 Procedures

2.4.1 Self-reported questionnaires

2.4.2 Physical measurements

2.5 Variables

2.6 Data processing

2.7 Descriptive statistics

3 Results

Table 5 Distribution of BMI among a cohort of 25 paramedics Classification BMI (kg/m2) Prevalence (%) ±95% CI Normal weight 18.5 –24.9 24±16.7 Overweight 25.0 –29.9 44±19.5 Obese class I 30.0 –34.9 20±15.7 Obese class II 35.0 –39.9 12±12.7

4.1 Health status

4.2 Health indicators

4.3 Practical implications

4.4 Study limitations

5 Conclusion

Conflict of interest

References

Table 5
Distribution of BMI among a cohort of 25 paramedics

Classification BMI (kg/m²) Prevalence (%) ±95% CI

Normal weight 18.5 –24.9 24±16.7

Overweight 25.0 –29.9 44±19.5

Obese class I 30.0 –34.9 20±15.7

Obese class II 35.0 –39.9 12±12.7