The impact of safety behavior, perceived risk, and workplace resources on COVID outcomes for U.S. Aircraft Rescue and Firefighting personnel

Abstract

BACKGROUND:

Aircraft rescue and firefighting (ARFF) personnel are first responders located at airports in the United States who provide emergency response, mitigation, evacuation, and rescue of passengers and crew of aircraft at airports. The nature of their work puts ARFF personnel in close contact with travelers on a regular basis and at elevated risk for COVID-19 exposure.

OBJECTIVE:

In this study, we focused on safety behavior, perceived risk, and workplace resources to understand COVID-19 outcomes in the early pandemic among the overlooked worker population of ARFF personnel. The goal of this study was to examine how a self-reported positive COVID test were associated with safety behavior, perceived risk, and workplace resources.

METHODS:

Cross-sectional survey data were collected among ARFF personnel a year into the COVID-19 pandemic.

RESULTS:

Regression results showed that each additional unit increase in perceived susceptibility to COVID-19 was associated with a 133% increase in the odds of testing positive for COVID-19 (OR = 2.33, p <  0.05), and with each additional unit increase in perceived severity level, the odds of getting COVID-19 decreased by 47% (OR = 0.53, p <  0.05).

CONCLUSIONS:

Infection control among first responders may be improved by providing relevant information physical and emotional resources, and support that help shape perceptions of risk and adoption of prevention behaviors.

1. Introduction

Respiratory infections continue to be a deadly issue in the United States (U.S.) and globally. During the 2022–2023 and 2023–2024 respiratory virus seasons, there were three co-occurring outbreaks: COVID-19 (caused by the SARS-CoV-2 virus), the seasonal flu, and respiratory syncytial virus (RSV) infection [1]. In the 2022–2023 season, hospitalization rates per 100,000 residents for seasonal flu exceeded five of the 12 past seasons, RSV hospitalizations per 100,000 people exceeded the four previous seasons, and COVID-19 case surges continued [2]. Since the start of the COVID-19 pandemic, there have been over 700 million cases and almost seven million deaths globally, with the U.S. accounting for nearly 1.2 million deaths as of February 2024 [3]. Individuals who have had greater risk for respiratory virus exposures, hospitalizations, and death have been frontline workers, such as those who work in healthcare, public transit, and other industries with essential public-facing roles with close contact with others. Due to the nature of their work, even if wearing a form of respiratory protection, the volume and frequency of exposure to other individuals can make frontline workers highly susceptible to respiratory virus exposure [4–6]. One such worker group is first responders who answer to emergencies of all types, including medical. During a medical emergency where first responders are providing emergency and triage care, a definitive patient diagnosis is often unavailable. Thus, first responders provide care to patients with unknown exposures, symptoms, and variable transmission rates. The three aforementioned respiratory infections—COVID-19, seasonal flu, and RSV—all begin with non-descript influenza-like symptoms (e.g., fatigue, fever, chills) that first responders are potentially exposed to when providing emergency care; yet, they have no way discerning what they were potentially exposed to. Eliminating exposure altogether is not possible for first responders [7]. Thus, individual safety behavior and available workplace resources to prevent potential harmful respiratory exposures and manage the severity to which they will be exposed are of the utmost importance.

Aircraft rescue and firefighting (ARFF) personnel are a type of first responder located at all U.S. airports overseen by the Federal Aviation Administration (FAA) Federal Aviation Regulations (FAR) Part 139 [8]. ARFF are the only civilian fire protection services regulated by a government entity. ARFF duties involve emergency response mitigation, evacuation, and rescue of passengers and crew of aircraft at airports in the event of a fire. ARFF personnel are also responsible for medical response and first aid of anyone present at the airport –not only on the tarmac but also within airport terminals –24 hours a day, 365 days of the year. ARFF personnel are staffed at airports indexed in one of five categories A through E, with A being aircraft less than 90 feet to E being aircraft 200 feet and longer. In addition to airport index categorization, ARFF personnel are staffed based on number of daily departures from that airport. For example, Hartsfield-Jackson Atlanta International Airport, currently the world’s busiest airport, is Index E. With an average of 275,000 passengers per day and an average 2,000 of these passengers are transported to emergency rooms from the Atlanta airport per year for advance medical care that ARFF emergency medical services cannot provide –some of these cases involve passengers experiencing respiratory symptoms or events [9]. Thus, the greater the airport index, the greater the potential for ARFF personnel exposure due to the volume and frequency of close contact with travelers on a regular basis.

While respiratory hazards, such as COVID-19, are not completely avoidable the risk of infection can be mitigated by occupational safety and health (OSH) controls and workplace resources (e.g., via the provision of training, engineering controls, and personal protective equipment (PPE)) and safety behavior. Safety behavior comprises of safety compliance and safety participation. Safety compliance relates to an individual’s compliant behaviors that are consistent with organizational norms, the responsibilities of their position, and specific task requirements. Safety participation is proactive behaviors that contribute to an environment of safety in the organization or workplace [10, 11]. Literature has shown that safety behavior is affected by one’s perceived risk—a combination of perceived severity (i.e., a person’s feelings on the seriousness of contracting an illness or disease) and perceived susceptibility (i.e., a person’s subjective perception of the risk of acquiring an illness or disease); perceived risk and perceived severity are key constructs of the Health Belief Model (HBM) [11–13]. The HBM can be applied in the context of occupational health. According to the HBM, heightened perceived risk is associated with a greater inclination towards cautious behaviors which can reduce the likelihood of adverse health outcomes. COVID-related studies have found higher perceived risk may be related to lower infectivity [14, 15]. To the best of our knowledge, COVID outcomes and factors contributing to COVID infectivity has not been assessed among the essential but overlooked work population of ARFF personnel. Thus, in this study we focused on safety behavior, perceived risk, and workplace resources to understand COVID-19 outcomes during the early pandemic among ARFF personnel. For this study, we hypothesize the following which is also depicted in our conceptual framework (Fig. 1):

Fig. 1.

Hypothesized relationship between positive COVID diagnosis and safety behavior, perceived risk, and workplace resources.

H1: ARFF personnel with high safety behaviors were less likely to have reported COVID-19 infection in the early pandemic.

H2: ARFF personnel with higher perceived risk of COVID-19 were less likely to have reported COVID-19 infection in the early pandemic.

H3: ARFF personnel with access to greater workplace resources were less likely to have reported COVID-19 infection in the early pandemic.

2. Materials and Methods

2.1. Participants

Data were collected one year into the COVID-19 pandemic during April 2021. An online Qualtrics survey invitation (©2020, Provo, UT) was distributed to members of the ARFF Working Group email listserv. Utilizing convenience and chain referral sampling, data were collected from 241 ARFF personnel (236 consented, 26 submitted blank surveys, 55 left multiple items blank). Electronic informed consent was obtained prior to the start of the survey. No personally identifiable information was collected. This study was approved by the Indiana University Institutional Review Board (Protocol #10518).

2.2. Measures

This study adapted Zhang and colleague’s framework for assessing the relationship between COVID-relevant safety behavior outcomes and safety leadership among hospitality management during the early COVID-19 pandemic [11].

2.2.1. COVID Outcome

COVID outcome was measured by one binary item, “During the pandemic, have you tested positive for COVID-19?” The responses were coded as, 1 (yes) or 0 (no).

2.2.2. Safety Behaviors

Safety behaviors included (1) safety compliance, (2) safety participation, (3) mask use at work, and (4) mask use in public. Safety compliance was assessed utilizing the validated three-item safety compliance scale from Neal and Griffin (e.g., “I am using all the necessary safety equipment to do my job”) [11, 16]. Safety participation was also assessed with the validated three-item safety participation scale from Neal and Griffin (e.g., “I am promoting pandemic prevention and safety programs within the organization during the COVID-19 crisis”) [16]. Response options ranged from 1 (strongly disagree) to 5 (strongly agree). Responses were averaged to create an overall score for safety compliance and safety participation, with higher scores indicating a higher level of safety for each scale. The Cronbach’s alpha of safety compliance and safety participation with the current sample were 0.88 and 0.83, respectively. Mask use at work was measured by one item, “I wear an appropriate face covering or mask when I’m at work or performing my job and am unable to social distance from others” and mask use in public spaces was measured by one item “I wear an appropriate face covering or mask when I’m out in public spaces.” Participants answered using a 5-point scale (1 = strongly disagree to 5 = strongly agree), with a higher averaged score indicating greater mask use.

2.2.3. Perceived Risk

Participants’ perceived risk of being infected with COVID-19 was measured using items from the Risk Behavior Diagnosis Scale [17], as adapted by Zhang and colleagues [11] to measure risk perception of COVID-19. Four items were used to measure perceived susceptibility (e.g., “I am at risk for contracting COVID-19 because of my job"), and five items were used to measure perceived severity (e.g., “COVID-19 is more deadly than most people realize"). The Cronbach’s alpha of perceived susceptibility and perceived severity were 0.84 and 0.90, respectively. The responses varied from 1 (strongly disagree) to 5 (strongly agree). Responses were averaged to create an overall score for perceived susceptibility and perceived severity, with higher scores indicating a higher level of perceived risk.

2.2.4. Workplace Resources

Participants’ safety resources to prevent COVID-19 were adopted from the Occupational Health Clinics for Ontario Workers survey. Six items were used to measure training effectiveness (e.g., “My training allows me to perform my job as safely as possible"), and nine items were used to measure resource adequacy (e.g., “My department provides me with the appropriate personal protective equipment needed to prevent COVID-19"). A score from 1 (strongly disagree) to 5 (strongly agree) was assigned to each of these responses. Responses were averaged to create an overall score for each scale, with higher scores indicating a higher level of resources for each scale. The Cronbach’s alpha of training effectiveness and resource adequacy with the current sample were 0.85 and 0.87, respectively.

2.2.5. Worker Health and Participant Characteristics

Participants’ general health before COVID-19 was measured by a single-item measure, “My overall health before the COVID-19 pandemic began in March 2020.” The range of responses varied from 1 (Excellent) to 5 (Terrible). Scores were reverse coded so that higher scores reflected better overall health before the onset of COVID-19 pandemic. Additional health measures were not added as single-item health measures have been found valid in other studies [18–20]. Participant characteristic data (e.g., demographic variables) such as gender, age, race, and educational level, were also collected.

2.3. Analysis

The goal of this study was to examine how the first self-reported positive COVID-19 test were associated with safety behavior, perceived risk, and workplace resources. To achieve this, hierarchical logistic regression modeling—which involved specifying a series of logistic regressions—was employed. This analysis allowed us to capture the effects of safety behavior, perceived risk, and workplace resources while adjusting for the effects of other confounding variables. First, a logistic model with positive COVID-19 test as dependent variables and the demographic variables as the independent variables was specified. Then the indicators of workplace resources, training effectiveness and resource adequacy, were added into the model. Third, the four additional independent variables, safety compliance, safety participation, mask use at work, and mask use in public spaces, were added. Last, perceived risk (perceived susceptibility and perceived severity) were added into the model. Raw data from Qualtrics were exported to CSV files. All data cleaning and analyses were conducted using SPSS statistics version 28.0.1.1 (Armonk, NY, IBM Corp).

3. Results

Table 1 summarizes the participants’ characteristics of the final study sample (N= 155) and descriptive statistics of the measured variables. Most participants had not tested positive for COVID-19 (n = 129, 83.2%) at the time of survey administration. There were more male participants (n = 143, 92.3%) than females. Most participants were White (n = 124, 80%), middle-aged (n = 64, 41.3% aged 50–59), and had a professional certification (n = 95, 61.3%). The participants’ overall health mean score before the COVID-19 pandemic was 4.36±0.63 along a 5-point scale, indicating good self-reported health. During the COVID-19 pandemic, the participants’ training effectiveness mean score was 4.73±0.03, their resource adequacy mean score was 4.57±0.04, their safety compliance mean score was 4.66±0.04, and their safety participation mean score was 4.23±0.06. While all these scores are on the higher end of a 5-point scale, training effectiveness was the highest. The participants had an average score of 4.20±0.08 for wearing a face mask in the workplace. When the participants were out in public spaces, the mean score for masking was the same as in the workplace, 4.20±0.10. The participants average perceived susceptibility score of COVID-19 was 3.98±0.07, and the perceived severity mean score of COVID-19 was 3.52±0.08. While their perceived susceptibility score was on the higher end of the scale, indicating their perceived risk of contracting the illness or disease, their perceived severity was closer to center of the scale, indicating a less severe perception of the seriousness of the illness or disease.

Table 1.

Sample Characteristics and Descriptive Statistics (N = 155)

Variables	Categories/Range	Study Sample M (SD) or N (%)
COVID Outcome	Yes	26 (16.77%)
	No	129 (83.22%)
Gender	Female	10 (6.45%)
	Male	143 (92.26%)
	Decline to answer	2 (1.29%)
Age	Age 20–29	6 (3.87%)
	Age 30–39	20 (12.9%)
	Age 40–49	54 (34.84%)
	Age 50–59	64 (41.29%)
	Age 60–64	10 (6.45%)
	Age 65 or above	1 (0.65%)
Race	White	124 (80%)
	Non-White	31 (20%)
Education	Non-university degree	95 (61.29%)
	Bachelor’s degree	37 (23.87%)
	Graduate degree	23 (14.84%)
Overall health before the COVID-19 pandemic	(1–5)	4.36 (0.63)
Workplace Resources
Training Effectiveness	(1–5)	4.73 (0.03)
Resource Adequacy	(1–5)	4.57 (0.04)
Safety Behavior
Safety Compliance	(1–5)	4.66 (0.04)
Safety Participation	(1–5)	4.23 (0.06)
Mask use at work	(1–5)	4.20 (0.08)
Mask use in public spaces	(1–5)	4.20 (0.10)
Perceived Risk
Perceived Susceptibility	(1–5)	3.98 (0.07)
Perceived Severity	(1–5)	3.52 (0.08)

Note: M = mean, SD = standard deviation, N = number of participants, %  = percentage.

A correlation matrix was calculated to assess the relationships between the outcome variable (COVID-19 infectivity) and all other measured variables (Table 2). Training effectiveness, resource adequacy, safety compliance, and safety participation were positively correlated with each other (all p’s <  0.01). Specifically, training effectiveness was positive correlated with resource adequacy (r = 0.55), safety compliance (r = 0.43), safety participation (r = 0.27), and mask use at work (r = 0.23), all p’s <  0.01. Resource adequacy was positive correlated with safety compliance (r = 0.46) as well as safety participation (r = 0.39), both p’s <  0.01. Additionally, mask wearing at workplaces, mask wearing in public spaces, safety compliance, and safety participation were positively correlated with each other (all p’s <  0.05). Specifically, the findings indicated a positive correlation between safety compliance and safety participation (r = 0.50), a moderate positive correlation between safety compliance and mask use at work (r = 0.37), and a relatively weaker positive correlation between safety compliance and mask use in public spaces (r = 0.19), all p’s <  0.05. Moreover, a moderate positive correlation between safety participation and mask use at work (r = 0.42), and a relatively weak positive correlation between safety participation and mask use in public spaces (r = 0.26) were found, both p’s <  0.01. Mask use at work also moderately correlated with mask use in public spaces (r = 0.39), p <  0.01.

Table 2.

Correlations Matrix of Variables

	1	2	3	4	5	6	7	8	9	10
1. COVID Outcome	1
2. Overall health before the COVID-19	–0.04	1
3. Training Effectiveness	0.09	–0.04	1
4. Resource Adequacy	0.06	0.12	0.55**	1
5. Safety Compliance	0.09	0.14	0.43**	0.46**	1
6. Safety Participation	–0.04	0.02	0.27**	0.39**	0.50**	1
7. Mask use at work	0.05	–0.09	0.23**	0.15	0.37**	0.42**	1
8. Mask use in public spaces	0.05	–0.08	0.09	–0.04	0.19*	0.26**	0.39**	1
9. Perceived Susceptibility	0.17*	–0.05	0.09	0.01	0.05	0.07	0.08	0.15	1
10. Perceived Severity	–0.11	–0.17*	–0.02	–0.07	0.07	0.20*	0.26**	0.49**	0.21*	1

Note: *Correlation is significant at the 0.05 level (2-tailed). **Correlation is significant at the 0.01 level (2-tailed).

Additionally, perceived severity was positively correlated with safety participation (r = 0.20), mask-wearing at workplaces (r = 0.26), mask-wearing in public spaces (r = 0.50), and susceptibility (r = 0.21), with all p’s <  0.05. Perceived severity also exhibited a negative correlation with overall health before COVID-19, r = –0.17, p <  0.05. Perceived susceptibility exhibited a positive correlation with COVID-19 outcome, with r = 0.17, p <  0.05.

Table 3 presents the results of hierarchical logistic regressions that assessed factors associated with testing positive for COVID-19 (N= 155). The R-squared increased from 0.03 (Step 1) to 0.12 (Step 4), indicating the proportion of variance explained by the predictors was increasing as more variables were added to the model. Findings from steps 1 to 3 showed that workplace resources (training effectiveness and resource adequacy) and safety behaviors (safety compliance, safety participation, mask use at work, mask use in public spaces) were not significantly associated with COVID-19 outcome (all p’s >  0.05). The last step of regression results showed that each additional unit increase in susceptibility to COVID-19 was associated with a 133% increase in the odds of testing positive for COVID-19 (OR = 2.33, p <  0.05), and with each additional unit increase in perceived severity level, the odds of getting COVID-19 decreased by 47% (OR = 0.53, p <  0.05). Follow-up variance inflation factor (VIF) tests were conducted, revealing no multicollinearity concerns. All VIF values were found to be below the established cutoff point of ten [21].

Table 3.

Results from hierarchical logistic regressions testing factors associated with testing positive for COVID-19 (N = 155)

	Step 1		Step 2		Step 3		Step 4
	O.R.	95% C.I.	OR	95% C.I.	OR	95% C.I.	OR	95% C.I.
Constant	0.69	–	0.01	–	0.002	–	0.001	–
Gender Female	2.30	(0.51, 10.34)	2.99	(0.62, 14.41)	4.14	(0.80, 21.40)	3.03	(0.56, 16.36)
Gender Decline	6.81	(0.37, 126.11)	12.16	(0.47, 316.84)	9.32	(0.38, 231.32)	9.02	(0.34, 236.54)
Age 30–39	1.44	(0.13, 15.89)	1.16	(0.10, 13.75)	0.77	(0.06, 9.75)	0.54	(0.041, 7.1)
Age 40–49	0.76	(0.08, 7.54)	0.57	(0.05, 6.14)	0.34	(0.03, 4.02)	0.29	(0.02, 3.71)
Age 50–59	0.92	(0.10, 8.79)	0.70	(0.07, 7.23)	0.53	(0.05, 5.67)	0.35	(0.03, 4.04)
Age 60–64	0.60	(0.03, 11.96)	0.47	(0.02, 10.08)	0.39	(0.01, 6.74)	0.31	(0.01, 7.48)
Age 65 or above	0.00	(0.00, 0.00)	0.00	(0.00, 0.00)	0.00	(0.00, 0.00)	0.00	(0.00, 0.00)
Non-white	1.21	(0.41, 3.53)	1.08	(0.36, 3.24)	1.11	(0.37, 3.36)	1.49	(0.47, 4.74)
Bachelor’s degree	1.08	(0.38, 3.12)	0.97	(0.33, 2.86)	0.98	(0.32, 3.03)	0.92	(0.28, 2.95)
Graduate degree	1.07	(0.30, 3.80)	1.15	(0.32, 4.07)	1.10	(0.30, 4.05)	0.89	(0.23, 3.50)
Overall health before the COVID-19	0.74	(0.37, 1.50)	0.76	(0.37, 1.55)	0.70	(0.33, 1.50)	0.64	(0.30, 1.40)
Workplace Resources
Training Effectiveness			2.93	(0.59, 14.47)	2.31	(0.44, 12.05)	2.05	(0.35, 12.14)
Resource Adequacy			1.00	(0.34, 2.90)	1.35	(0.41, 4.38)	1.23	(0.35, 4.25)
Safety Behavior
Safety Compliance					2.08	(0.58, 7.43)	1.93	(0.52, 7.16)
Safety Participation					0.52	(0.26, 1.04)	0.56	(0.27, 1.15)
Mask use at work					1.03	(060, 1.77)	1.06	(0.62, 1.82)
Mask use in public spaces					1.13	(0.72, 1.77)	1.33	(0.80, 2.2)
Perceived Risk
Perceived Susceptibility							2.33*	(1.05, 5.14)
Perceived Severity							0.53*	(0.30, 0.94)
Cox & Snell R-squared					0.03	0.05	0.07	0.12

Note: O.R. = Odds Ratio; C.I. = Confidence Interval; *p <  0.05.

4. Discussion

Aircraft rescue and firefighting (ARFF) personnel have elevated exposures to respiratory hazards due to the nature of their work. In this study—adapting the HBM, as well as Zhang and colleague’s framework on COVID-19 relevant outcomes—safety behavior, perceived risk, and workplace resources were assessed to understand what relationship, if any, it had with COVID-19 outcomes among this specific worker population during the early pandemic [11]. The participants were predominantly white, middle-aged, male ARFF personnel who had not yet tested positive for COVID at the time of survey administration. They reported high levels of training effectiveness, resource adequacy, safety participation, and mask use. We had three main hypotheses for this study: 1) Those with high safety behaviors were less likely to report COVID infection; 2) Those with higher perceived risk of COVID were less likely to have reported COVID infection; and 3) Those with greater workplace resources were less likely to have reported COVID infection. Based on the results from the hierarchical regression, H2 was partially supported given that perceived severity decreased the odds of a positive COVID test. The results of this study have implications beyond the early COVID-19 pandemic that can be applied to OSH considerations for first responder personnel –especially for future large-scale respiratory outbreaks.

The significant finding that emerged from this study pertained to testing positive for COVID-19 and perceived risk, specifically perceived susceptibility and perceived severity. First, perceived susceptibility had a significant positive weight and increased odds ratio with the COVID outcome; in other words, those who perceived themselves to be more susceptible to COVID-19 reported COVID-19 infection. Participants may have had pre-existing conditions or comorbidities—items that were not captured in the scope of this study—that make their immune systems vulnerable to infection, which increased perceived susceptibility [22, 23]. It is estimated that 60% of Americans have at least one comorbidity so it is highly likely ARFF personnel, may also have comorbidities [24]. In a systematic review and meta-analysis of 11 pre-existing comorbidities with COVID-19 mortality, it found that those with cardiovascular disease, hypertensions, diabetes, congestive heart failure, chronic kidney disease, and cancer had greater risk of mortality compared to patients that did not [23]. There could have also been other factors, such as the elevated media coverage of COVID leading to stress [25, 26] resulting in greater allostatic load which can weaken the immune system [27, 28]. Thus, with future inevitable public health outbreaks it may be beneficial for employers to measure (e.g., survey, informal interviews, focus groups) the perceived susceptibility of their employees to the specific “infection/crisis”, to provide workplace accommodation policies [29] and/or heightened training and job-specific prevention measures (detailed below) to decrease overall risk perception.

Second, perceived severity had a significant negative weight and decreased odds ratio with the COVID-19 outcome. In other words, those with greater perceived severity of COVID-19 may have been more likely to take precautions to prevent contracting the virus and thusly did not report COVID-19 infection at the time of survey administration. Several studies on perceived severity and COVID-19 found those with greater perceived severity were more likely to engage in precautionary behaviors and practice greater self-control when it came to public health measures (e.g., isolation, limiting social activities) [30–32]. This is consistent with studies among general populations where elevated perceived harm of COVD-19 outcomes were predictive of safety behaviors [33, 34].

This finding further supports and provides additional evidence regarding the importance of risk messaging during a pandemic or when significant health exposures are present. This not only includes public messaging from public service organizations, but targeted intra-organizational risk communication. Risk messaging should be coherent and targeted [35] so that potential risk severity is understood as perceptions of health risks inform decisions about protective behaviors [36]. Given known challenges with communication across emergency service and fire service organizations [37], risk communication strategies need to be formulated and presented to addresses the unique nature of operations and structure within emergency and fire service organizations. Pollack and colleagues [38] suggest applying behavior change models and using theory to inform the development of messages that can sustain behavior change in these organizations. More specific guidance, including five practical guidelines for effective health and risk communication strategies related to the pandemic and COVID, is presented by Porat and colleagues [39]. These strategies might be translated to intra-organizational interventions or strategies to bolster appraisals of risk in the context of work.

Implications of the findings of the present research potentially extend beyond respiratory illness, such as COVID-19. The importance of risk communication in promoting and fostering protective behaviors is one that needs to be further explored in emergency medical response and fire service organizations. In an occupation that requires frequent exposures to a variety of hazards, it seems that when hazard severity is appropriately appraised, actions to reduce exposures are completed and health consequences are averted. As such, a focus on risk severity within interventions and communication strategies within emergency and fire service organizations may be beneficial in protecting emergency responders and firefighters from injuries.

4.1. Practical Implications for Management and OSH Practitioners

Implications for ARFF leadership and OSH practitioners who oversee this and similar worker population should focus on infection control outcomes. Infection control among first responders may be improved by providing relevant information physical (e.g., training, administrative controls, personal protective equipment) and mental (e.g., employee assistance programs, supporting emotional wellbeing) resources and support that may influence risk perception and preventative health behaviors. First, identifying barriers to first responders adopting preventative measures and finding ways to minimize those barriers can encourage the uptake of workplace safety behaviors; in the HBM the perceived risk of a health outcome and the perceived benefit vs. cost of taking action are important predictors of individuals taking preventative action [40]. Additionally, providing up-do-date, evidence-based relevant information on the severity of outcomes for a future public health emergency; providing just-in-time and/or regular training on how to safely respond to emergencies by taking an all-hazards approach; and fostering positive workplace safety climate through clear leadership support for OSH can affect risk perception [41, 42]. Information presented in this study can inform future strategies management and OSH practitioners can take to safeguard the health of first responders, such as ARFF personnel, during the next public health emergency or respiratory hazard outbreak.

4.2. Limitations

This study is not without its limitations. First, this was a cross-sectional study so causation cannot be inferred. Second, the National Fire Protection Association nor the ARFF Working Group keeps track of the number of total ARFF personnel nationwide, so we were not able to provide the response rate for our survey. Third, we did not ask for a detailed health history so there may be pre-existing health conditions or other factors affecting the participants’ perceived risk of COVID that was not accounted for. Lastly, the generalizability of these findings to other first responders, such as police, may be limited due to differences in job duties, training, and risk exposure.

5. Conclusion

In summary, this study has demonstrated significant associations between the perceived risk factors of COVID-19, which include perceived severity and perceived susceptibility, and the COVID-19 outcome among personnel of ARFF services during the early pandemic. Perceived susceptibility was associated with increased likelihood of COVID-19, but perceived severity was associated with decreased likelihood of reported COVID-19. To formulate effective interventions aimed at safeguarding the wellbeing of first responders engaged in emergency response activities, subsequent investigations should explore the underlying connections between perceived risk factors and COVID-19 outcomes using longitudinal data.

Ethical approval

This study was deemed exempt by the Indiana University Institutional Review Board (Protocol #10518) on March 23, 2021.

Informed consent

Written informed consent was obtained from all study participants prior to the start of the study. No personally identifiable information was presented in this article.

Conflict of interest

The authors report there are not competing interests to declare.

Funding

This study was not funded by any external source.