Factors Associated with Lost Time Injury among Paramedics in Victoria, Australia

Abstract

Background

The dynamic and uncontrolled nature of paramedic work frequently exposes these workers to physical and psychological injury. Often paramedic injury rates are estimated based on national injury surveillance data or compensation databases. These data sources tend to only capture cases of a more serious nature and overlook the broader factors that contribute to injury. This limits our understanding of the true burden of paramedic injury and the characteristics associated with increased injury severity.

Objectives

To describe the incidence and proportions of paramedic occupational injury in Victoria, Australia, and to determine the injury-related characteristics associated with lost time from work.

Methods

A retrospective analysis of paramedic injury report data from the single state-wide ambulance service in Victoria, Australia – Ambulance Victoria. Injuries reported between 1 January 2015 and 30 June 2020 were included. Chi-square tests of independence were used to explore shift and injury characteristic variables that may be associated with time lost from work.

Results

Over the study period, 7,591 paramedic injuries were reported that met the inclusion criteria, of which 2,124 (28%) resulted in lost time from work. The cumulative incidence of paramedic injury was 333.8 injuries per 1,000 FTE workers per year, and the rate of lost time injury was 93.0 per 1,000 FTE workers per year. Musculoskeletal injuries were the most frequently reported injury type irrespective of lost time status. Manual handling followed by psychological stressors were the two leading mechanisms of injury based on incidence. Psychological injury was associated with lost time from work (X²= 384.2, p < 0.001). Conversely, injury to the head and neck (X²= 7.5, p = 0.006), and upper limb injuries (X²= 104.5, p < 0.001), were more strongly associated with no lost time from work.

Conclusions

Paramedics working in Victoria have a higher rate of work-related injury than other Australian workers. Injury-related factors that are often overlooked, such as time, shift type, location, and injury characteristics, all contribute to an increased risk of lost time injury. An understanding of the factors that contribute to an increase in injury severity may facilitate the development and targeting of appropriate interventions.

Introduction

Paramedics and other emergency medical service (EMS) workers attend, assess, and manage medical, trauma, and psychosocial events outside of the hospital environment (1–5). Unique to this health care profession, the assessment and management of these events occurs in highly variable and uncontrolled work environments, such as peoples’ homes, poorly illuminated locations, unstable or uneven terrain, during adverse weather conditions, and on public roads during operational response driving. Furthermore, the nature of paramedic work also sees workers encounter other high-risk factors for physical and psychological injury, such as exposure to violent and aggressive patients or bystanders, and frequent exposure to trauma, social disadvantage, death, and dying (6). The regular exposure to these factors can increase the risk of paramedics becoming injured at work (6, 7).

While evidence indicates that paramedics are at increased risk of work-related injury, there are significant challenges that limit our understanding of the true size and burden of injury among these workers. In a recent systematic review (8), the incidence of paramedic occupational injury among the included studies varied considerably among studies using data obtained from national injury surveillance systems or compensation databases compared with EMS organization-level data, with injury rates ranging from 29.7 to 345.6 injuries per 1,000 workers per year. This variability is largely due to the differing data collection methods and case inclusion criteria used by various injury surveillance systems.

To date, much of the paramedic injury research exploring the magnitude of injury is based on data obtained from national injury surveillance systems and compensation databases (1–4, 7, 9–14). Although these sources typically provide high-quality data and capture most cases of severe injury, they often do not capture cases spanning the whole injury spectrum (15). This limits our understanding of the true burden of injury among this cohort.

There is further complexity when trying to directly compare injury rates from state-based or national injury surveillance systems and organizational-level injury reporting systems, given the inherent differences in the way the data are collected. This includes variability in case definitions and measurement of injury severity. Severity of injury is often quantified by metrics such as lost workdays, total compensation paid (16, 17), or lost time from work. Again, however, the definition of “lost time,” is highly variable. For example, the National Data Set for Compensation-Based Statistics compiled by Safe Work Australia measures lost time in working weeks and comprises the period for which compensation is paid (17). Conversely, the United States Bureau of Labor Statistics, classifies lost time as at least 1 day away from work with or without days of job transfer or restriction (18). Despite these variations in definitions, time lost from work can remain a valuable metric of injury severity.

However, by only including cases that meet the threshold for compensation or full shift absence, the number of time lost injuries is likely to be significantly underestimated. When trying to estimate the true magnitude of injury and understand the burden, broader definitions of lost time may be used. For example, classifying cases that result in any period of lost time from work, irrespective of duration, could be categorized as time lost cases. This will facilitate the capture and accurate classification of minor time lost injury cases that may have occurred in the late stages of a shift, or just prior to rostered days off.

While time lost from work is a useful metric for injury severity, it is only one aspect of understanding the true nature of paramedic work-related injury. Other factors such as the relationship between injury severity and various shift and injury-related characteristics are rarely explored, despite many injury surveillance systems routinely collecting the required information.

These limitations can potentially be offset by using organizational-level data directly from EMS workplace injury datasets. The broader case definitions and case inclusion criteria mean that these datasets have the potential to capture a greater number of injury cases, providing improved accuracy of injury rates. Furthermore, greater detail on the events associated with injury can be obtained by such datasets – including, for example, shift and call-out characteristics.

This study uses workplace injury data from a state-based EMS organization in Australia to describe the incidence and proportions of paramedic occupational injury in Victoria, Australia, and determine the injury-related characteristics associated with lost time from work.

Methods

Study Design

This study involved a retrospective analysis of paramedic injury and incident report data captured within the Ambulance Victoria (AV) centralized hazard, incident, and injury reporting database – the Health, Safety and Claims System (HSCS). All paramedic incident and injury reports that occurred between 1 January 2015 and 30 June 2020 that resulted in physical or psychological injuries were included in the study.

Setting

The Australian state of Victoria is the second most populous state with an estimated population of 6.68 million, of whom 77% live within the state capital of Melbourne (19). In the state of Victoria, all prehospital emergency care is provided by a single EMS agency. In the 2019–2020 financial year, AV responded to over 633,000 emergency cases across the state, using road, helicopter, and fixed-wing vehicles (20). As of the 2019–2020 financial year, AV’s operational workforce comprised 4,336.6 full-time equivalent (FTE) clinical staff, including intensive care paramedics, advanced life support paramedics, and other employed and volunteer on-road clinical staff (20).

Data Source

All AV hazard, incident, and injury reports are completed and managed online via the HSCS, an internally managed, centralized database established to capture all reported employee health and safety related incidents. The software is based on an adapted form of the RiskCloud.net health and safety architecture, developed by PAN Software. The system comprises more than 60 data elements and provides a systematic way of full lifecycle incident management control, from the initial reporting phase to investigation and control development.

Following an incident or injury, staff log onto the HSCS to complete an online report. Staff are required to report injuries within 48 hours of their occurrence, or as soon as they are available. The staffs completing the report are prompted to answer a series of questions relevant to the incident or injury. These questions focus on several elements, such as time, date, and location of the incident, demographic details, shift characteristics, and post-injury care. Injury specific characteristics, for example, the mechanism and agency of injury are also collected throughout the questionnaire and classified based on the Australian Type of Occurrence Classification System (21). Injuries of all severities are eligible to be reported into the HSCS, and the reporting of very minor injuries is strongly encouraged within the organization. Once submitted, the report is shared with relevant managers and organizational occupational health and safety staff for incident investigation, review, and control implementation. Responsible managers are required to conduct their investigations within 10 days of submission. Members of the health and safety team review submitted injury reports to ensure they are complete and accurate. There is the opportunity for user follow-up if clarification or further details are required. Injury reports relating to organizational priority areas (currently manual handling and violence-related cases) are reviewed monthly by specialist review groups to ensure completeness of the data.

Data Analysis

Prior to analysis, data were screened, and duplicate cases removed. Injuries affecting university student paramedics on clinical placement were excluded. Occupational injury was defined as any physical or psychological injury that occurred in the workplace, or as a result of work. Psychological injuries included any report of cognitive, emotional, or behavioral symptoms attributed to work, and supported with documentation from mental health care workers or other clinicians. Incident reports that were a result of exposure to potentially infectious agents were removed due to the focus of this article being on injury occurrence rather than hazard exposure. Further, due to incubation periods delaying symptom onset, and the potential for infectious disease exposures to occur outside of the workplace, it is difficult to definitively classify reports of illness as being of workplace origin. Therefore, in this article, cases of infectious disease exposure were only included if the incidents involved sharps, resulted in psychological injuries, or where exposure was attributed to occupational violence. In these circumstances, the cases were recoded to reflect the new classifications.

The primary outcome measure was lost time from work. Lost time was defined as any period lost from work where the individual was no longer able to undertake his or her rostered duties. This included any rostered time remaining in the individual’s current shift at time of injury, provided that the shift was not completed.

Paramedic injury rates were based on working estimates of 40 hours per week over 48 weeks per year. Hours worked per week and annual leave estimates were obtained from the standard AV paramedic full-time employee contract and the Ambulance Victoria Enterprise Agreement 2020. It must be noted that the estimated 40 work hours per week is conservative, as most full-time paramedics working in Victoria operate on a four-day-on, four-day-off cycle, and regularly undertake incidental overtime. Annual denominator data were obtained from publicly available AV annual reports. Injury rates were calculated by multiplying the total number of injuries each year by the total hours worked by 1,000 workers per year, divided by the total hours worked by the entire workforce. This provided an injury rate based on 1,000 FTE workers per year.

Time of injury (Time Occurred) was categorized into six, 4-hour epochs to differentiate between key shift time periods. The specific time periods were chosen to reflect the relationships between time of day and risk of shift worker injury observed in previous studies (22).

Descriptive statistics including frequencies and percentages were used to describe the characteristics of the sample. Chi-square tests of independence were used to explore shift and injury characteristic variables that may be associated with time lost from work. The strength of association was measured using Phi (Φ) for 2 × 2 variable contingency and Cramer’s V (Φc) for greater than 2 × 2 variable contingency. In the event of a statistically significant chi-square omnibus test finding, adjusted residuals were calculated to identify the specific variables contributing to the magnitude of the chi-square result within each category. Chi-square values and p-values were re-calculated. Given the number of variables included in the analyses, a Bonferroni correction was applied to reduce the risk of a Type 1 statistical error (23). The threshold for statistical significance was conservatively set to p < 0.01.

All statistical analyses were conducted using SPSS version 27 (IBM Corp., 2020).

Ethics

This study was approved by the Ambulance Victoria Research Governance Committee and the Monash University Human Research Ethics Committee.

Results

A total of 8,867 injury reports were submitted over the 5.5 year study period. Of these 7,591 paramedic injuries met the inclusion criteria for this study, of which 2,124 (28%) resulted in lost time from work.

Incidence of Injury

The cumulative incidence of paramedic injury over the entire study period was 333.8 injuries per 1,000 FTE workers per year (Table 1). The rate of lost time injury (LTI) over the study period was more than two-and-a-half times lower than the rate of injuries resulting in no lost time from work (93.0 vs. 240.8 injuries per 1,000 FTE workers per year).

Table 1. Incidence of injury.

Year	Workforce size (FTE)	Total injuries	Hours worked by 1,000 paramedics per year	Total hours worked by entire workforce	Injury rate per 1,000 FTE workers per year	LTI injury rate 1000 FTE workers per year	NLTI injury rate 1,000 FTE workers per year
2015	3654.8	1378	1,920,000	7,017,216	377.04	99.60	277.44
2016	3756.8	1392	1,920,000	7,213,056	370.53	103.28	267.25
2017	4155.7	1182	1,920,000	7,978,944	284.43	88.79	195.64
2018	4401.9	1433	1,920,000	8,451,648	325.54	89.05	236.49
2019	4518.7	1394	1,920,000	8,675,904	308.50	87.42	221.08
2020*	4822.8	812	1,920,000	4,629,888^#	336.73*	89.58*	247.16*
Total	–	7591	1,920,000	43,966,656	333.80	92.95	240.84

Abbreviations: FTE, full-time equivalent; LTI, lost time injury; NLTI, no lost time injury. Workforce size extracted from Ambulance Victoria annual reports. Workforce size = staffing numbers (FTE) minus other managerial, professional, and administrative staff. ^#Total hours worked by entire workforce over six months; *Injury rate is based on 6 months, not a full year.

The mean rates of injury based on injury type and mechanism are summarized in Table 2 and Table 3, respectively. Musculoskeletal injuries had the highest mean injury rate (212.0 per 1,000 FTE per year) of all injury types (Table 2). For mechanisms of injury, manual handling (198.4 per 1,000 FTE per year), followed by psychological stressors (43.6 per 1,000 FTE per year), were the two leading mechanisms based on rate of occurrence (Table 3).

Table 2. Mean rate of injury based on injury type.

Injury type	Total injuries over the study period	Mean injury rate per 1000 FTE workers per year
Musculoskeletal	4855	212.02
Psychological	553	24.15
Fractures	41	1.79
Other traumatic injuries	459	20.04
Neurological	221	9.65
Skin, subcutaneous, and soft tissue	607	26.51
Needlestick injuries	108	4.72
Other	731	31.92

Abbreviations: FTE, full-time equivalent.

Table 3. Mean rate of injury based on mechanism.

Mechanism of injury	Total injuries over the study period	Mean injury rate per 1000 FTE workers per year
Manual handling/ body stressing	4544	198.4
Psychological	999	43.6
Gravity	604	26.4
Vehicular	189	8.3
Object damage	311	13.6
Hazardous substance	178	7.8
Environment	175	7.6
Occupational violence and aggression	57	2.5
Other	313	13.7

Abbreviations: FTE, full-time equivalent.

When expanding this to examine trends in the rates of injury based on these injury characteristics, all injury types, with the exception of fractures, neurological, and musculoskeletal injuries, experienced overall increases between 2015 and 2020 (Figure 1). Similarly, hazardous substances, occupational violence, vehicle-related factors, and mechanisms classified as “other” also experienced overall increases in the rate of injury over the study period (Figure 2).

Figure 1. Injury rate trends based on injury type. Abbreviations: FTE, full-time equivalent.

Figure 2. Injury rate trends based on the mechanism of injury. Abbreviations: FTE, full-time equivalent.

Factors Associated with Lost Time Injury

The shift and injury-related characteristics associated with paramedic LTI are summarized in Table 4 and outlined below. Post hoc analyses with adjusted residuals for shift and injury-related characteristics associated with LTI are presented in Appendix A.

Table 4. Proportions and significance of shift and injury-related characteristics on lost time injury.

	All (n = 7591)	LTI (n = 2124)	NLTI (n = 5467)	X^2
Year, n (%)				X^2 (5, N = 7591) = 8.97, p = 0.110
2015	1378 (18)	364 (17)	1014 (19)
2016	1392 (18)	388 (18)	1004 (18)
2017	1182 (16)	369 (17)	813 (15)
2018	1433 (19)	392 (19)	1041 (19)
2019	1394 (18)	395 (19)	999 (18)
2020 (1 Jan-30 June)	812 (11)	216 (10)	596 (11)
Month, n (%)				X^2 (11, N = 7591) = 10.51, p = 0.486
January	679 (9)	177 (8)	502 (9)
February	626 (8)	181 (9)	445 (8)
March	742 (10)	193 (9)	549 (10)
April	701 (9)	191 (9)	510 (9)
May	764 (10)	214 (10)	550 (10)
June	770 (10)	218 (10)	552 (10)
July	583 (8)	153 (7)	430 (8)
August	596 (8)	166 (8)	430 (8)
September	519 (7)	165 (8)	354 (7)
October	527 (7)	156 (7)	371 (7)
November	549 (7)	147 (7)	402 (7)
December	535 (7)	163 (8)	372 (7)
Time occurred, n (%)				X^2 (5, N = 7291) = 39.25, p < 0.001. Φc = 0.073
00:00–04:00	712 (10)	168 (8)	544 (10)
04:01–08:00	814 (11)	259 (13)	555 (11)
08:01–12:00	1575 (22)	438 (21)	1137 (22)
12:01–16:00	1613 (22)	514 (25)	1099 (21)
16:01–20:00	1458 (20)	412 (20)	1046 (20)
20:01–23:59	1119 (15)	256 (13)	863 (17)
Unknown	300	77	223
Shift type, n (%)				X^2 (6, N = 7287) = 85.08, p < 0.001. Φc = 0.108
First day	1487 (20)	429 (21)	1058 (20)
Second day	1216 (17)	348 (17)	868 (17)
Afternoon	1158 (16)	366 (18)	792 (15)
First night	1496 (21)	379 (19)	1117 (21)
Second night	675 (9)	121 (6)	554 (11)
Overtime	367 (5)	79 (4)	288 (6)
Other	888 (12)	322 (16)	566 (11)
Unknown	304	80	224
Location type, n (%)				X^2 (8, N = 7586) = 49.32, p < 0.001. Φc = 0.081
On scene – Public area	535 (7)	160 (8)	375 (7)
On scene – Residential	1534 (20)	396 (19)	1138 (21)
On scene – Unspecified location	2060 (27)	547 (26)	1513 (28)
On scene – Hospital	823 (11)	195 (9)	628 (12)
On scene – Aged care facility	178 (2)	52 (3)	126 (2)
On scene – Other private location	479 (6)	117 (6)	362 (7)
Ambulance station	855 (11)	295 (14)	560 (10)
Enroute	442 (6)	124 (6)	318 (6)
Other	680 (9)	234 (11)	446 (8)
Unknown	5	4	1
Region, n (%)				X^2 (1, N = 7591) = 0.99, p = 0.320
Metropolitan	4838 (64)	1335 (63)	3503 (64)
Rural	2753 (36)	789 (37)	1964 (36)
Agency of injury, n (%)				X^2 (5, N = 7591) = 118.79, p < 0.001. Φc = 0.125
Equipment	2100 (28)	515 (24)	1585 (29)
Human	2851 (38)	760 (36)	2091 (38)
Physical agency	1105 (15)	292 (14)	813 (15)
Vehicle	588 (8)	185 (9)	403 (7)
Biological or chemical agency	388 (5)	107 (5)	273 (5)
Other	567 (8)	265 (13)	302 (6)
Bodily location of injury, n (%)				X^2 (6, N = 7586) = 445.28, p < 0.001. Φc = 0.242
Head and neck	565 (7)	130 (6)	435 (8)
Trunk	3040 (40)	863 (41)	2177 (40)
Upper limbs	1801 (24)	334 (16)	1467 (27)
Lower limbs	1031 (14)	288 (14)	743 (14)
Multiple locations	520 (7)	141 (7)	379 (7)
Psychological	533 (7)	345 (16)	188 (3)
Other	96 (1)	22 (1)	74 (1)
Unknown	5	1	4
Injury type, n (%)				X^2 (7, N = 7575) = 525.86, p < 0.001. Φc = 0.263
Musculoskeletal	4855 (64)	1316 (62)	3539 (65)
Psychological	553 (7)	355 (17)	198 (4)
Fractures	41 (1)	30 (1)	11 (0)
Other traumatic injuries	459 (6)	62 (3)	397 (7)
Neurological	221 (3)	81 (4)	140 (3)
Skin, subcutaneous, and soft tissue	607 (8)	105 (5)	502 (9)
Needlestick injuries	108 (1)	8 (0)	100 (2)
Other	731 (10)	166 (8)	565 (10)
Unknown	16	1	15
Mechanism of injury, n (%)				X^2 (8, N = 7370) = 189.52, p < 0.001. Φc = 0.160
Manual handling/ body stressing	4544 (62)	1209 (59)	3335 (63)
Psychological	999 (14)	421 (20)	578 (11)
Gravity	604 (8)	192 (9)	412 (8)
Vehicular	189 (3)	69 (3)	120 (2)
Object damage	311 (4)	64 (3)	247 (5)
Hazardous substance	178 (2)	36 (2)	142 (3)
Environment	175 (2)	21 (1)	154 (3)
Occupational violence and aggression	57 (1)	12 (1)	45 (1)
Other	313 (4)	39 (2)	274 (5)
Unknown	221	61	160
First aid treatment given, n (%)				X^2 (1, N = 7591) = 4.58, p = 0.032
Yes	2561 (34)	677 (32)	1884 (35)
No	5030 (66)	1447 (68)	3583 (66)
Medical treatment given, n (%)				X^2 (1, N = 7591) = 1114.31, p < 0.001. Φ = 0.383
Yes	2231 (29)	1219 (57)	1012 (19)
No	5360 (71)	905 (43)	4455 (82)
Percentage of shift complete, n (%)				X^2 (5, N = 7591) = 59.75, p < 0.001. Φc = 0.089
25% or less	1985 (26)	530 (25)	1455 (27)
26% to 50%	1584 (21)	389 (18)	1195 (22)
51% to 75%	1459 (19)	359 (17)	1100 (20)
76% to 100%	1688 (22)	531 (25)	1157 (21)
Overtime	275 (4)	89 (4)	186 (3)
Other	600 (8)	226 (11)	374 (7)

Abbreviations: LTI, Lost Time Injury; NLTI, No Lost Time Injury; X², Pearson Chi Square; Φ, Phi; Φc, Cramer’s V. Percentages (%) were rounded up or down to the nearest integer. The percentages in each category may not add up to 100%.

Shift Characteristics

Time and Date

There was no significant difference between the LTI and no lost time injuries (NLTI) groups based on year of injury occurrence (X² 8.97, df = 5, p = 0.110) or calendar month (X² = 10.51, df = 11, p = 0.486). The greatest proportion of all injuries occurred between the hours of 08:01 and 20:00 (64%), when 66% of LTI and 63% of NLTI occurred. There was a statistically significant difference between the two groups for time of injury occurrence (X² = 39.25, df = 5, p < 0.001. Φc = 0.073). Pairwise comparisons with adjusted p-values showed that a significantly greater proportion of the LTI group had injuries that occurred between 12:01 and 16:00 compared to the NLTI group (X² = 14.75, p < 0.001).

Shift Completion

Percentage of shift completion was a measure of the proportion of the shift that the worker had completed at the time of his or her injury. There was a statistically significant difference between the percentage of the shift completed and the two groups (X² = 59.75, df = 5, p < 0.001. Φc = 0.089). The first quarter of the shift accounted for 26% of all injuries. Overall, 47% of all injuries occurred in the first half of the shift and 41% in the last half. Injuries occurring during overtime accounted for 4% of all injuries.

LTIs were more frequently reported in the last quarter of the shift (X² = 13.03, p < 0.001). Injuries resulting in lost time from work were more representative of cases where the proportion of shift completion was classified as “other” (X² = 30.36, p < 0.001). These cases may include injuries that did not occur on a specific shift.

Shift Type

There was a significant association between shift type and lost time (X² = 85.08, df = 6, p < 0.001. Φc = 0.108). Almost half (46%) of all injuries were reported to have occurred during afternoon shifts or nightshifts, whilst 37% occurred on either of the two dayshifts. The first dayshift and the first nightshift each accounted for the greatest singular proportion of LTI (21%) and NLTI (21%), respectively. Injuries occurring during afternoon shifts (X² = 8.64, p = 0.003) and shift types classified as “other” (X² = 33.76, p < 0.001) more commonly resulted in lost time from work.

Shift and Scene Location

More than one quarter of all injuries were categorized as occurring “on scene.” When expanding this classification to include public areas and the residential setting, this location accounted for 53% of all LTI and 56% of NLTI. Overall, there was a statistically significant variation between the two groups and location of injury (X² = 49.32, df = 8, p < 0.001. Φc = 0.081). A greater proportion of injuries classified as “other” (X² = 15,52, p < 0.001) or those that occurred at an ambulance branch (X² = 4.54, p < 0.001), resulted in LTI, while more hospital-related injuries resulted in NLTI (X² = 8.29, p = 0.004). Comprising of almost two-thirds of the workforce, the metropolitan region accounted for the largest proportion (64%) of all injuries. There was no statistically significant variation in proportion of injuries based on region (X² = 5.20, df = 5, p = 0.392. Φc = 0.026).

Injury Characteristics

Agency of Injury

Humans, either the injured individuals themselves due to fatigue or preexisting disability or illness (21), or other individuals, were identified as the single largest agency of injury (AOI) (38%), followed by injuries attributed to equipment (28%). There was a statistically significant difference in the proportions of AOI between the two groups (X² = 118.79, df = 5, p < 0.001. Φc = 0.125). AOI categorized as “other” accounted for a significantly larger proportion of LTI compared to NLTI.

Location of Injury

There was a significant association between bodily location of injury and injury classification (X² = 445.28, df = 6, p < 0.001. Φc = 0.242). Injuries to the trunk were the single most reported bodily location of injury for both injury classifications (40%). Psychological injuries were associated with LTI (X² = 384.16, p < 0.001), with the proportion of injury being more than five times greater than that of NLTI (16% versus 3%).

Injury Type

Musculoskeletal injures accounted for the greatest proportion (64%) of all injury types. Injuries classified as “other” (10%) and injuries to the skin, subcutaneous, and soft tissue (8%) were the next most prevalent. Psychological injuries were the resulting injury type in 7% of all injury cases. There was a significant difference in proportion of injury types between the two groups (X² = 525.86, df = 7, p < 0.001. Φc = 0.263). Significantly greater proportions of psychological injuries (X² = 386.91, p < 0.001), fractures (X² = 41.60, p < 0.001), and neurological injuries (X² = 8.41, p < 0.001) resulted in LTI in comparison to those resulting in NLTI.

Mechanism of Injury

Manual handling was identified as the primary mechanism of injury for all injuries (62%), followed by psychological impacts (14%). There was a statistically significant variation in proportions of mechanism of injury between the two groups (X² = 189.52, df = 8, p < 0.001. Φc = 0.160). A significantly greater proportion of psychological injuries (20% vs. 11%) (X² = 114.70, p < 0.001) and vehicle-related injuries (X² = 6.97, p = 0.008) were seen in the LTI group.

Post Injury Care

Two-thirds of all injuries did not receive first aid treatment. First aid was provided to 34% of all NLTI and 32% of all LTI. There was no statistically significant variation in the provision of first aid between the two groups (X² = 4.58, df = 1, p = 0.032. Φ = −0.025). Medical treatment was provided to 29% of all injuries. The majority (57%) of LTI received medical treatment compared to 19% of all NLTI (X² = 1114.31, df = 1, p < 0.001. Φ = 0.383).

Discussion

To our knowledge, this is the first study to use EMS agency level injury data to examine the incidence of paramedic injury in Victoria, Australia and determine the factors associated with lost time from work. Shift time, type, and location were associated with lost time from work following injury. Agencies classified as other; fractures, psychological, and neurological injuries; and psychological or vehicle related mechanisms were all associated with lost time from work.

Incidence of Injury

Over the 5.5 year study period, the mean rate of paramedic injury was 333.8 injuries per 1,000 FTE workers per year. This rate is consistent with mid to high injury rate estimates in other EMS injury studies (5, 24, 25), and considerably higher than estimates based on national injury surveillance system and compensation data (3, 4, 9, 11–13). However, when focusing on cases resulting in lost time, the mean LTI incidence of 93.0 injuries per 1,000 FTE workers per year in this study is consistent with paramedic injury rates based on hospital emergency department presentations (4, 13). When making comparisons to other emergency services, the mean LTI rate observed in this study is comparable to compensation rates seen in Australian police (91 per 1,000 FTE workers per year) and fire services (101 per 1,000 FTE workers per year) between 2004 and 2012 (9). Although these professions have their own unique challenges, they share many similar attributes with EMS, for example, lights and sirens driving, working in unfamiliar and uncontrolled environments, shift work, and repeat exposure to traumatic incidents.

In this study, musculoskeletal injuries had the highest rate of injury, with a mean of 212.0 injuries per 1,000 FTE workers per year over the study period. This rate is higher than those seen in previous EMS agency-level studies (5, 12, 24), but consistent with musculoskeletal injury rates of ambulance services after powerlift stretcher implementation (25). The incidence of musculoskeletal injuries in this study declined drastically in 2017, before remaining relatively stable for the remainder of the study. This reduction in 2017 coincides with AV’s gradual replacement of all manual lift stretchers to a powerlift model, which began in late 2016. As manual handling is the primary contributing factor to the occurrence of musculoskeletal injuries (3–5, 9, 11–13, 24), the significant reduction in the incidence of manual handling injuries from 2017 may be partially reflective of this engineering solution, but also other organization-wide programs and initiatives. For example, in 2016 all AV paramedics undertook mandatory in-person training focused on worker safety, such as how to conduct a dynamic risk assessment, the principles of risk management, and safety culture within the organization. The module focusing on organizational safety culture sought to provide clarity and direction of the safety-related behaviors, actions, and attitudes that are expected within the organization to drive behavioral change. Irrespective of the primary mitigating factor, the findings of this study may highlight the benefit of engineering solutions, safety awareness training, and changes in organizational safety culture on injury risk reduction in the workplace.

The rates of psychological injury, and injury attributed to psychological stressor exposure in this study, were between one-and-a-half to three times greater than the rate of mental health claims among Australian paramedics and ambulance officers between 2006 and 2015 (9, 14). Despite the paramedic rate of psychological injury seen in this study being greater than existing research, it is likely that these figures are still an underrepresentation of the true effects of psychological injury among these workers. In a large Australian survey focused on the mental health of emergency services personnel (26), stigma was identified as a significant barrier to psychological injury reporting. While Australian paramedics tend not to hold stigmatizing mental health attitudes to their colleagues, self-stigma is common, with 58% of surveyed paramedics stating that they would avoid telling others about their mental health issues, primarily due to embarrassment and thoughts of being able to self-manage (26). Consequently, stigma and self-stigma may be factors driving the underreporting of psychological injuries, diminishing the psychological injury rates presented in this article.

Another explanation for the higher rates of all types of injury seen in this study compared to other EMS injury studies, and a strength of agency-level data, is greater case capture due to lower thresholds for injury reporting and case inclusion (8). However, often agency-level data, including those used in this study, are limited by the measure used for injury severity. As with this study, lost time is often used in industry as a metric for severity, as it is reflective of lost productivity and easily measured (16). Though, one of the challenges with this approach is the inability of a binary classification system to be able to differentiate between the extent or complexity of an injury, as injuries that meet the threshold for lost time classification may vary considerably in terms of severity (16). Despite the injury rate of LTI being consistent with previous studies based on hospital emergency department presentations, measures such as lost workdays or compensation payments may be more representative of the severity of injury as they provide a clear indication of the burden and consequences of injury on the individual, and the organization (16). Although there are significant limitations to using lost time as a metric for injury severity, it does still have some merit. While lost time as a binary measure is unable to differentiate between other LTI cases based on severity, when comparing to injury cases that resulted in no lost time, it can still serve as a high-level measure of effect on agencies, albeit without a clearly defined consequence, where no other more appropriate metrics are available.

Factors Associated with Lost Time Injury

In this study, there was a relationship between various case characteristics and LTI. Time of injury, location, shift type, and proportion of shift completion, were all associated with lost time from work.

A significantly greater proportion of LTIs occurred between the hours of peak operational workload for the organization (12:01–16:00).Previous studies have highlighted a strong link between high call volume, infrequent or absent rest breaks, and long shift duration with paramedic physical and psychological injury (5, 27–33). One of the key risk factors associated with paramedic injury during high demand periods, and with shift work in general, is fatigue. High rates of fatigue are commonly reported among EMS workers. In one study, 56% of respondents reported experiencing fatigue while at work (34). Fatigue is a strong risk factor for paramedics sustaining physical and psychological injuries (28, 35–39) as it impedes cognition leading to reduced alertness, increased risk taking behaviors, and poor decision-making and judgment (37).

Agencies of injury that were classified as “other” were associated with lost time from work. These cases may be representative of an agency that is not clearly identifiable or definable, for example musculoskeletal injury due to repetitive stress on tissues over an extended period, or psychological injury due to cumulative exposure to traumatic events. Of all injury types and mechanisms examined in this study, psychological injuries and injuries attributed to psychological stressors had the strongest association with lost time from work. Psychological distress and injury are pervasive in the EMS sector (40, 41), and have profound effects on the individuals, their families, professional and social networks, and the organizations in which they are employed (42, 43). While there is some understanding of the factors and circumstances that contribute to paramedic work-related psychological injury, for example sleep disturbances (34, 36, 44) and exposure to traumatic incidents (45, 46), there is little understanding of the broader, more indirect factors, such as case and dispatch characteristics, that may increase the risk of sustaining a psychological injury at work. Further exploration of these broader factors and how they influence the occurrence of psychological injuries among paramedics is required to understand how to target risk reduction measures and interventions, appropriately, and effectively, in a timely and considered manner.

Limitations

This study has several limitations. Due to data only being captured for half of the year of 2020, the incidence of injury and proportions presented in this study for that year may not be representative of the actual rates that occurred. Furthermore, despite the broad definition of lost time for work used in this study being advantageous in capturing more injury cases then other national injury surveillance definitions, the broad definition means it cannot be reliably used a metric of injury severity. Consequently, lost time may not be the most appropriate metric to be used in this study – this was a limitation of the dataset used. Another limitation of the dataset used in this study was its restriction to include data used for administrative purposes only. This meant that the provided dataset did not include demographic data and was limited in its scope to capture shift and injury-related characteristics. As this study sought to describe and define the factors associated with paramedic injury, by only focusing on basic shift and injury-related characteristics, this study is unable to provide a robust and holistic understanding of the influence of broader factors related to paramedic injury.

Conclusion

Using data obtained for a single state-wide ambulance service in Victoria, Australia, the findings of this study further reinforce the increased risk of injury paramedics encounter at work, with an incidence of injury considerably higher than that of the Australian general population. Key findings to inform risk prevention strategies included the role that shift time and duration have on the severity of injury, musculoskeletal injuries accounting for the greatest proportion of LTI, and psychological injuries be strongly associated with absence from work. Despite this study describing several factors associated with paramedic injury, further research is required to explore additional broader factors that may contribute to the occurrence of injury at work, so that risk reduction strategies and interventions can be targeted appropriately. This will entail linkage with additional organizational data sources and fields such as patient, case, dispatch, practitioner, and organizational factors, and link these with compensation data to better understand the broader factors contributing to paramedic injury and the overall burden.

Disclosure Statement

The authors report there are no competing interests to declare.

Appendix A.

Post-hoc analysis with adjusted residuals and Bonferroni correction

Table

	Adjusted residual
	LTI	NLTI	X^2	Adjusted p-value
Time occurred
00:00–04:00	−2.80	2.80	7.84	0.005
04:01–08:00	2.52	−2.52	6.35	0.012
08:01–12:00	−0.27	0.27	0.07	0.787
12:01–16:00	3.84	−3.84	14.75	<0.001
16:01–20:00	0.17	−0.17	0.03	0.865
20:01–23:59	−4.21	4.21	17.72	<0.001
Shift type
First day	0.77	−0.77	0.59	0.441
Second day	0.48	−0.48	0.23	0.631
Afternoon	2.94	−2.94	8.64	0.003
First night	−2.62	2.62	6.86	0.009
Second night	−6.15	6.15	37.82	<0.001
Overtime	−2.85	2.85	8.12	0.004
Other	5.81	−5.81	33.76	<0.001
Location type
On scene – Public area	1.05	−1.05	1.10	0.294
On scene – Residential	−2.08	2.08	4.33	0.038
On scene – Unspecified location	−1.65	1.65	2.72	0.099
On scene – Aged care facility	0.38	−0.38	0.14	0.704
On scene – Hospital	−2.88	2.88	8.29	0.004
On scene – Other private location	−1.77	1.77	3.13	0.077
Ambulance station	4.54	−4.54	20.61	<0.001
Enroute	0.05	−0.05	0	0.960
Other	3.94	−3.94	15.52	<0.001
Agency of injury
Equipment	−4.15	4.15	17.22	<0.001
Human	−1.99	1.99	3.96	0.047
Physical agency	−1.25	1.25	1.56	0.211
Vehicle	1.96	−1.96	3.84	0.050
Biological or chemical agency	0.08	−0.08	0.01	0.936
Other	10.34	−10.34	106.92	<0.001
Bodily location of injury
Head and neck	−2.74	2.74	7.51	0.006
Trunk	0.64	−0.64	0.41	0.522
Upper limbs	−10.22	10.22	104.45	<0.001
Lower limbs	−0.04	0.04	0	0.968
Multiple locations	−0.46	0.46	0.21	0.646
Psychological	19.60	−19.60	384.16	<0.001
Other	−1.11	1.11	1.23	0.267
Injury type
Musculoskeletal	−2.38	2.38	5.66	0.017
Psychological	19.67	−19.67	386.91	<0.001
Fractures	6.45	−6.45	41.60	<0.001
Other traumatic injuries	−7.15	7.15	51.12	<0.001
Neurological	2.90	−2.90	8.41	0.004
Skin, subcutaneous, and soft tissue	−6.14	6.14	37.70	<0.001
Needlestick injuries	−4.81	4.81	23.14	<0.001
Other	−3.37	3.37	11.36	0.001
Mechanism of injury
Manual handling/ body stressing	−3.36	3.36	11.29	0.001
Psychological	10.71	−10.71	114.70	<0.001
Gravity	2.17	−2.17	4.71	0.030
Vehicular	2.64	−2.64	6.97	0.008
Object damage	−2.98	2.98	8.88	0.003
Hazardous substance	−2.34	2.34	5.48	0.019
Environment	−4.77	4.77	22.75	<0.001
Occupational violence and aggression	−1.17	1.17	1.37	0.242
Other	−6.25	6.25	39.06	<0.001
Medical treatment given
Yes	33.38	−33.88	1114.22	<0.001
No	−33.88	33.88	1114.22	<0.001
Percentage of shift complete
25% or less	−1.48	1.48	2.19	0.139
26% to 50%	−3.41	3.41	11.63	0.001
51% to 75%	−3.19	3.19	10.18	0.001
76% to 100%	3.61	−3.61	13.03	<0.001
Overtime	1.65	−1.65	2.72	0.099
Other	5.51	−5.51	30.36	<0.001

Abbreviations: LTI, lost time injury; NLTI, no lost time injury; X², Pearson chi-square.