Training the public health emergency response workforce: a mixed-methods approach to evaluating the virtual reality modality

Abstract

Objectives

To produce and evaluate a novel virtual reality (VR) training for public health emergency responders.

Design

Following a VR training designed to test key public health emergency responder competencies, a prospective cohort of participants completed surveys rating self-assessed skill levels and perceptions of training methods.

Setting

The VR training sessions were administered in a quiet room at the US Centers for Disease Control and Prevention (CDC), Atlanta, Georgia.

Participants

All participants volunteered from a list of CDC emergency international surge responders.

Outcome measures

Perceived impact of the training on responder skills was self-reported via a Likert 5-point scale questionnaire. Assessments were modelled according to the Expanded Technology Acceptance Model measuring participant perceived usefulness of and intention to use the new technology. Inductive coding of qualitative feedback resulted in the identification of central themes.

Results

From November 2019 to January 2020, 61 participants were enrolled. Most (98%) participants self-rated above neutral for all skills (mean 4.3; range 1.21–5.00). Regression modelling showed that the perceived ease of use of the VR and ability to produce demonstrable results as likely drivers of further use. Participants agreed that others would benefit from the training (97%), it was representative of actual response scenarios (72%) and they would use lessons learnt in the field (71%). Open-response feedback highlighted feeling being immersed in the training and its utility for public health responders.

Conclusions

At a time when a trained emergency public health workforce is a critical need, VR may be an option for addressing this gap. Participants’ impressions and feedback, in the setting of their high skill level and experience, highlighted the utility and benefit of using VR to deliver training. Further research is needed to determine skill acquisition through VR training among a pool of future responders with limited to no response experience.

Strengths and limitations of this study.

• This study is an investigation of virtual reality (VR) training replicating a public health emergency aimed at public health responders.

• This investigation applied validated mobile technology evaluation methodologies to assess public health emergency soft skill acquisition.

• The study enrolled a convenience sample limiting the generalisability of the results.

• Limited project funding affected the duration of the VR scenarios tested where a more robust training scenario could have a larger training impact.

Introduction

The risk of public health emergencies underscores the need to effectively train the public health workforce to rapidly respond to emergencies. Public health rapid response teams (RRTs) are often deployed immediately following a public health emergency; they are required to be timely and effective to mitigate the spread and impact of an emergency on vulnerable populations.¹ RRTs are often required to interact with diverse communities, cope with stigma, operate around mass death and morbidity and need to collaborate with multisectoral stakeholders in the field.^{2 3} Responders without proper training to manage these competing factors limit the effectiveness and impact of an RRT.

Simulation exercises have been the traditional gold standard for emergency training, creating an interactive environment that replicates the conditions that responders may face in the field.^4–6 However, simulations can be financially and logistically prohibitive and require the availability of trained experts, which is often limited in remote and low-income countries.^{4 7} Furthermore, simulations emphasise the one-time practice of a skill and often include limited validated assessment to document proficiency or improvement of skill.⁸

Virtual reality (VR) may be a tool to address these training challenges with its ability to create a 360 degree visual and audio immersive environment.^{6 9} VR requires minimal training to operate and can be run on a variety of equipment, such as inexpensive cardboard viewers with mobile phones, which can make training available to hard-to-reach populations in remote areas or socially distanced audiences.¹⁰ With application to industries such as aviation, medical procedures and emergency medical triage, studies comparing VR trainings to traditional simulations show similar levels of participant knowledge retention.^{4 11–13} Additionally, because VR training can take place several times without additional cost, performance improvement can be tracked and measured to demonstrate mastery of a desired task.^14–16

Despite successful use in other fields, the application of VR and its impact on public health emergency response training is yet to be fully explored.^{4 10 17} If RRT members can receive experiential training through VR, which hones their soft skills in an environment that recreates the stress and challenges of an emergency prior to responding to an emergency, they may be more effective in the field. To answer this question, we quantitatively and qualitatively assessed user experience and feedback of a VR training aimed at replicating the benefits of a simulation exercise in developing soft skills for emergency response.

Methods

Virtual reality production

In partnership with Foundry 45, a company that specialises in VR training development, the US Centers for Disease Control and Prevention (CDC) VR training team developed a custom VR public health emergency response scenario that is driven by participants’ choices. The VR headset immerses the user in the scenario while they turn their head to see the area around them and use a controller to select responses to prompts (figure 1).

Figure 1.

Two screenshots of the virtual reality interface for decision-making used during the emergency public health training scenarios show what the training participant sees. Background setting was captured separate from filming with an actor and was produced for the virtual reality format by coauthors (Foundry 45). *The person depicted is not a patient and the screenshot was taken with the actor’s knowledge and consent for publication.

Without existing public health emergency VR scenarios, we designed a VR training to test participants’ abilities to demonstrate compassion and empathy to those affected by the emergency; professionally address competing priorities from different response stakeholders in an unfamiliar setting; respond appropriately to unforeseen demands and respect and acknowledge the existing social hierarchy in a culture dissimilar to their own. To build out the VR content, we captured footage using a 360 degree camera during an in-person full-scale humanitarian emergency simulation. Separately, actors in front of green screen were given scripted narratives used to create the interactive scenarios portraying a responder (1) entering a refugee camp, (2) interacting with local militia, (3) collaborating with ministry of health and (4) assessing local health conditions. Foundry 45 conducted the final production and creation of the interface of the scenario into a VR programme.

The final VR training lasted 3 to 6 min with eight multiple-choice questions. Responses are scored best (three points) to worst (one point). At the end of the scenario, participants had the option to review ‘best’ answers and rewatch the scenario with ‘best’ answers shown compared with their own score.

Study sample

A convenience sample of 100 CDC employees who volunteer to deploy for the agency were recruited. The sample included employees of varying levels of public health emergency experience (such as first-time responders and seasoned multiemergency deployers) and varying types of expertise (including epidemiology, laboratory, communication, logistics, etc). Exclusion criteria included unavailability during study period, pregnancy, current inner ear infection, claustrophobia, cardiovascular disease, vision abnormalities or epileptic conditions as outlined in VR health and safety guidelines.^{18 19}

Investigation design

A pre-VR survey (online supplemental material 1) and two post-VR surveys, one quantitative and one qualitative, (online supplemental materials 2 and 3) were developed using Foundry 45 VR expertise and public health emergency response subject matter experts at CDC with a combined 60 years of experience. The assessment of participants’ perceptions of VR as the training modality was based on the Extended Technology Assessment Model (TAM2) a known and validated behavioural model used to evaluate user uptake of a new technology.^{20 21}

The pre-VR survey collected demographic data (sex, age, job category and previous response experience) as well as an initial self-assessment of key skills related to public health rapid response on a five-point Likert scale (one being low and five being the highest). The post-VR quantitative survey was administered immediately after they completed the VR simulation to assess the same skills in the pre-VR survey in addition to collecting their perceptions and experience of using the VR platform.

The post-VR qualitative survey included three questions on (1) VR as a training modality; (2) technical aspects and (3) training scenario; and an opportunity to provide additional comments. Participants had 1 week to complete the survey to allow time to reflect on the experience. All surveys were conducted via SurveyMonkey using a preassigned unique identifier for each participant to be able to link pre-VR and post-VR data.²²

Quantitative data analysis

Quantitative data analyses were conducted using the statistical software R (V.4.0.3).^{23 24} Descriptive analyses included χ² tests for frequencies of variables concerning participants’ demographic information, previous experience in public health emergency response, self-evaluation of response-associated soft skills and impressions of using the VR technology. Participants were stratified by years of public health experience into groupings: <1 year, 1–2 years, 2–4 years and >4 years. Continuous variables were analysed by one-way analysis of variance for means of overall scenario score and response time. All analyses were evaluated for significance at a p<0.05 level.

Logistic regression modelling was applied to participant impressions of using VR based on TAM2 elements, a validated behavioural model shown to be a useful measure of new technology uptake.²⁵ Questions assessing multiple elements were combined to create the elements of the TAM2 model and assessed based on Cronbach’s alpha where a value of 0.70 or greater was interpreted as a valid combination. Stepwise model selection was used to determine final models for all participants and for two groups disaggregated by age, Millennials (<40 years old) and non-Millennials (≥40 years old).

Qualitative data analysis

Qualitative survey data were thematically coded by two coders to ensure confirmability. Microsoft Excel (V.2018) was used to organise and document findings.²⁶ The codebook was developed with a lumping approach; all codes were defined as a cause of a discussion point (eg, how can this issue be addressed in the future, suggestions for improving future iterations). The final version contained 14 codes with four themes to indicate VR sentiment (‘success’, ‘challenge’, ‘suggestion’ and ‘neutral’) (figure 2). Each code was assigned to the statement based on the topic of the response’s content. General themes with no underlying codes were excluded due to lack of depth.

Figure 2.

Inductive and deductive codes with associated definitions and examples applied to participant feedback during qualitative analysis. TAM, Technology Assessment Model.

Patient and public involvement

No patients or public were involved in setting research priorities or defining research questions and outcome measures. No patients or public provided input into dissemination or results. Participant feedback/evaluation of technology was inherent to the study design. Local actors were used to create the VR scenario, but had no influence on scenario content or objectives.

Results

Participants’ characteristics

From November 2019 to January 2020, of the 100 participants recruited, 61 participants met enrolment criteria; of those, 52 completed both the pre-VR and post-VR quantitative surveys for inclusion in analysis and 33 (58.9%) completed the optional post-VR qualitative survey. Most participants were female (69.6%). Median age was 39 years (range: 32–65); 50.9% were classified as a Millennial. Median months of response experience was 19.5 months (range: 12–108 months). For 55.8% of participants, their response experience was primarily international-focused, 25.0% were primarily domestic-focused, and 19.2% had equal experiences in both.

Stratified by years of public health experience, there was no significant differences by sex, age group or job category across groups (p>0.05). Final scores for the scenario did not significantly differ between public health experience groups—median scores ranged between 21 and 22 (p>0.05).

Self-evaluation of soft skills

Overall, the pre-VR survey indicated a mean score of 4.31 (range: 1.21–5.00); 98% of participants ranked most of their abilities above 3.0 (‘neutral’). There were no significant differences of average soft skills scores across public health experience groups (p>0.05). The post-VR survey, administered immediately after completing the scenario, asked participants to evaluate whether the scenario influenced the same list of abilities assessed in the pre-VR survey. All groups averaged below 3.0 for 13 of 14 skills where only ‘working in conflict settings’ averaged above 3.0 (‘neutral’). While no significant differences resulted between groups, those with less than or equal to 1 year and more than 4 years’ experience consistently ranked the effect on their abilities higher than those with 1–2 years or 2–4 years’ experience (table 1).

Table 1.

Mean and SD of participants’ scoring of the effect of the virtual reality training on their abilities according to soft skills associated with emergency response grouped by years of response experience

Soft skills ‘As a public health responder, I am capable of…’	Effect of training on soft skills grouped by response experience
	Less than or equal to 1 year (n=6)	1–2 years (n=28)	2–4 years (n=9)	More than 4 years (n=13)	Total (n=56)	P value*
Addressing competing demands	3.00 (0.89)	2.21 (0.99)	2.78 (0.97)	2.50 (1.09)	2.45 (1.02)	0.15
Handling unforeseen demands	2.83 (0.98)	2.86 (1.11)	2.89 (1.05)	2.92 (1.16)	2.87 (1.07)	>0.50
Working in settings of mass death/illness	3.17 (1.17)	2.54 (1.26)	2.44 (1.24)	2.25 (0.97)	2.53 (1.18)	>0.50
Defining my agency’s role, capacity and limitations	2.17 (0.98)	2.79 (1.23)	2.67 (1.12)	2.67 (1.23)	2.67 (1.17)	>0.50
Showing compassion for host country colleagues	2.33 (0.82)	2.43 (1.03)	2.33 (1.00)	2.25 (0.97)	2.36 (0.97)	>0.50
Showing compassion for host country community	2.17 (0.75)	2.54 (1.07)	3.00 (1.32)	2.08 (1.08)	2.47 (1.10)	0.32
Negotiating cultural diplomacy	2.83 (0.98)	2.82 (1.16)	2.56 (0.88)	2.92 (1.31)	2.80 (1.11)	>0.50
Understanding an unfamiliar social hierarchy	2.50 (1.22)	2.46 (1.07)	2.56 (1.01)	2.58 (1.24)	2.51 (1.09)	>0.50
Understanding of a community’s interpretation of my outward appearance	2.50 (1.22)	2.32 (0.94)	2.78 (0.83)	2.58 (1.08)	2.47 (0.98)	>0.50
Delineating violations of policy and unfamiliar cultural norms	3.00 (0.63)	2.71 (1.01)	2.44 (0.88)	2.67 (1.30)	2.69 (1.02)	>0.50
Identifying and accounting for cultural norms when planning an intervention	2.83 (0.98)	2.46 (1.04)	2.56 (1.24)	2.83 (1.27)	2.60 (1.10)	>0.50
Handling compromises and stressors inherent to working in an austere environment	2.17 (0.98)	2.36 (1.10)	2.22 (1.09)	2.50 (1.24)	2.35 (1.09)	>0.50
Working in chaotic settings	2.50 (0.84)	2.43 (1.17)	2.44 (0.88)	2.50 (1.09)	2.45 (1.05)	>0.50
Working in conflict settings	3.00 (0.89)	3.14 (1.35)	3.22 (0.97)	2.67 (1.23)	3.04 (1.22)	>0.50

*χ² statistical tests (p=0.05) were run to compare between groups based on years of emergency response experience (less than or equal to 12 months, 12–24 months, 24–48 months and greater than 48 months).

VR experience/perception

Participants described the training as ‘immersive’, ‘innovative’, ‘inventive’ and ‘engaging’ in the qualitative analysis. The mean score of VR perception/experience was 3.98 (range: 2.47–4.88); the highest rated was ‘VR is up and coming’ (mean=4.53) and lowest rated was ‘VR training was a more stressful environment…’ (mean=2.76). Overall, 97% of participants agreed that others would benefit from the training, 72% agreed that it was representative of actual response scenarios and 71% agreed that they would use lessons learnt during the training in the field. Stratified by public health experience, ‘I could use this VR training to practice my diplomacy skills’ was the only significantly different belief, with 2–4 years’ experience scoring the highest and 1–3 years’ experience scoring the lowest (p=0.031) (table 2).

Table 2.

Mean and SD of participants’ perceptions of differing elements of a virtual reality training scenario grouped by years of response experience

‘I believe that…’	Scoring participant perception of virtual reality grouped by response experience
	Less than or equal to 1 year (n=6)	1–2 years (n=28)	2–4 years (n=9)	More than 4 years (n=13)	Total (n=56)	P value*
Others in my agency would benefit from this training.	4.50 (0.55)	4.39 (0.74)	4.56 (0.53)	4.67 (0.49)	4.49 (0.63)	>0.50
Agency leadership would support further development of virtual reality trainings.	4.67 (0.52)	4.32 (0.67)	4.00 (0.50)	4.33 (0.89)	4.31 (0.69)	0.25
My supervisor would support using this modality of training for our work.	3.83 (0.75)	3.96 (0.69)	4.00 (0.87)	4.33 (0.65)	4.04 (0.72)	0.42
VR is an up-and-coming training modality.	4.33 (0.82)	4.50 (0.58)	4.56 (0.53)	4.67 (0.49)	4.53 (0.57)	>0.50
VR is a popular technology outside of the public health domain.	3.50 (1.05)	4.39 (0.74)	4.22 (0.97)	4.25 (0.87)	4.24 (0.86)	0.24
My coworkers and friends will be interested to hear I used VR.	4.33 (0.82)	4.25 (0.80)	4.33 (0.50)	4.25 (0.62)	4.27 (0.71)	>0.50
The VR training scenario was representative of an emergency response I have been to/heard about.	3.67 (1.21)	3.82 (0.90)	3.78 (1.20)	4.17 (0.58)	3.87 (0.92)	>0.50
VR training scenarios could be applied to my current, non-emergency, work.	3.67 (1.03)	3.36 (1.28)	3.78 (1.20)	4.17 (1.11)	3.64 (1.22)	0.18
It was difficult to make confident decisions in responding to questions in the scenario.	2.83 (1.17)	2.89 (1.07)	2.67 (1.22)	2.58 (1.00)	2.78 (1.07)	>0.50
This VR training improved my soft skills during an emergency response.	3.83 (0.75)	3.36 (0.78)	3.44 (0.73)	3.67 (1.07)	3.49 (0.84)	0.39
The VR training was a more stressful environment than other training modalities besides full-scale exercises.	2.67 (1.63)	2.93 (1.15)	2.44 (1.01)	2.67 (1.15)	2.76 (1.17)	>0.50
I could use this VR training to practice my diplomacy skills.	3.83 (0.98)	3.82 (0.77)	4.56 (0.53)	4.17 (1.19)	4.02 (0.89)	0.03
I could use this VR training to learn to better manage decision-making under stress.	4.00 (0.63)	4.00 (0.86)	4.11 (0.60)	4.08 (1.08)	4.04 (0.84)	>0.50
I will use the lessons learnt in this scenario during my next field experience.	4.17 (0.41)	3.68 (0.94)	4.11 (0.78)	4.00 (1.13)	3.87 (0.92)	0.34

*χ² statistical tests (p=0.05) were run to compare between groups based on years of emergency response experience (less than or equal to 12 months, 12–24 months, 24–48 months and greater than 48 months).

VR, virtual reality.

As a first-time user, I was pleasantly surprised with how easy it is to engage in VR. The scenarios shown were interesting and kept you on your toes, I think it is a good way to train you to think fast and react appropriately in the field.

Belief measures were combined to align with TAM2 elements evaluated by Cronbach’s alphas ranging from 0.42 to 0.87. Applying stepwise logistic regression and confirmed via backwards elimination, the subjective norm and results demonstrability elements contributed to 52% of the variance that determines a participant’s perceived usefulness. The elements perceived ease of use, perceived usefulness and subjective norm account for 38% of the variance in participant’s intention to use VR as a training modality. Results demonstrability was shown as a significant determinant of perceived usefulness for both Millennials and non-Millennials. Both groups indicated that perceived ease of use was a significant driver of their intention to use.

Most (>50%) participants citing VR as a preference to more traditional didactic styles such classroom lessons, online lectures or slide presentations. Qualitative data revealed overall positive feedback on utility:

I like it! It allows you to feel the awkwardness of being in an unfamiliar situation and still having to address emotional questions.

It’s not a panacea by any means, but I think the VR modality has real possibilities. It provides a sense of presence and emotional response that you can't get in lectures or third-person videos.

Qualitative data on ways to improve the VR for future iterations included longer duration, additional scenarios and an explanation of answers (ie, during a post-training debrief session between participants and facilitators). Participants also suggested that VR facilitators provide more background information (such as a pre-mission briefing) to contextualise decision-making and highlight necessary cultural norms and sensitivities.

Discussion

Training public health RRTs is critical to ensuring effective and efficient public health interventions during emergencies. Training modalities can be quite diverse and may occur in strained environments (remote participation, just-in-time training before deployment, limited internet connectivity, etc). Although limited by production scope and sample size, this study found a number of positive aspects and critiques that should guide further developments for utilising VR for training public health responders including the applicability to those with less public health experience and providing a realistic ‘picture’ of the stressors within an emergency.

There was no significant difference among correct scores during the scenario across public health experience or age groups. We expected that some correlation between familiarity with public health response work or exposure to technology at a younger age may affect performance in a VR training or willingness to use VR.²⁷ This may be a true result or could be due to limitations with the small sample size not powered appropriately to identify differences between relatively similarly experienced participants. Additionally, the training scenario was kept short as a pilot and may not have fully practised the intended soft skills to a measurable extent. Despite no significant differences between groups, several participants mentioned in the qualitative post-VR survey that the training would be ideal for responders with less experience. One participant with significant response experience noted that the training could ‘provide room for discussion and self-examination among first-timers or junior responders’. While judged to be lower experienced, interestingly, initial self-evaluation scores showed that, on average, those with less experience consistently rate themselves equally as or more confident in their abilities compared with their more experienced counterparts which may have affected analyses for significance between the public health experience groups.

The original TAM, based on behavioural models, proposed that perceived usefulness and perceived ease of use influence a participant’s intention to use a new technology which is predictive of their actual usage behaviour.²⁸ TAM2 builds on this model to suggest that several additional factors (subjective norm, image, job relevance, output quality and results demonstrability) influence perceived usefulness as well as subjective norm influencing intention to use directly and indirectly (figure 3).²⁰ The TAM2 model helped to identify the salient elements of VR that users found most impactful on whether they would be willing to use VR as a training modality. The most likely drivers for the willingness to use VR for training were the elements results demonstrability, perceived ease of use and subjective norm. These elements all contribute to one’s perception of VR as a tool and, furthermore, highlight the importance that participants put on leadership and peer endorsement, tangible impact on their skill development and the new technology being user-friendly. Variation in the level of the effects occurred, but models for all participants showed that these three elements contribute a significant portion of the likelihood that a participant will continue to use VR training.

Figure 3.

Graphic representation of the Extended Technology Acceptance Model (TAM2) used to predict user beliefs contributing to future use of a technology (adapted from model presented in Venkatesh and Davis²⁰). VR, virtual reality.

Comparing VR to other traditional training styles, participants found this novel experience has potential over more traditional didactic styles such as classroom lessons, online lectures or slide presentations. A VR environment may more greatly impact demonstrable skills compared with the soft skills assessed in this study. Participants suggested that VR could serve as a complementary component used in conjunction with traditional training approaches. In particular, it was suggested as a viable potential component of a pre-deployment briefing.

Feedback also featured a variety of suggestions for future scenarios, ways to improve the current scenario or other ways they believe that VR would enhance training outside of RRTs. Participants reported that they would have benefited from ‘additional sensory’ inputs, a ‘pre-mission briefing’ or clearer ‘visual cues’ within the training. Any designers and/or facilitators of VR training for similar purposes should include the design and evaluation of the entire training experience (including introduction of the technology, training objectives, use of VR training and a post-training debrief with users) rather than limiting training to the time spent in the VR environment.

There were several limitations in this study. In production, limited project funding affected the duration of the scenario and lead to removal of certain learning elements. The less robust training scenario could have limited the impact of the training as well as continuity of the narrative. As a pilot project to assess whether there was utility in investigating this modality further, future efforts could more easily expand the scope and depth of the training scenarios. VR trainings could be burdened with expensive initiation costs, but have a longer lifespan than in-person simulations.

The convenience sampling method along with the low sample size may have affected our ability to look at differences between the stratified groups as well as to make more generalised conclusions. Additionally, those who responded to the optional post-VR qualitative survey may be more likely to express a more polar sentiment. Issues with the technology (such as headsets malfunctioning and internet connectivity issues at the training sites) and design within the VR scenario (such as lack of visual cues and vantage point of the user within the simulation) may have impacted participants’ perceptions of the pilot training. Finally, without scores available for the individual questions during the scenario, any analysis for internal validity was not possible which made it difficult to assess the questions for use in future iterations.

Nevertheless, building on the results of this investigation, future VR trainings may benefit from longer scenarios that focus on one to two major learning points allowing enough time to present the issues more organically and approach them from multiple perspectives, which would, ideally, deepen the training and impact on those skills. Participants readily identified additional scenarios that they would find helpful to their development. For instance, trainings that highlight harsher realities could expose responders to difficult scenarios allowing them to begin developing strategies to handle sensitive topics such as mass destruction following a hurricane/earthquake, or mass death during an outbreak in an overly packed health centre. Furthermore, following a series of trainings on addressing soft skills, technical trainings focused on role specific skills could be developed to provide more targeted trainings for all roles on an RRT. The repetition would allow for refining technical skills such as survey administration or infection prevention and control assessments.

Conclusion

This is the first systematic investigation of using VR as a potential training modality for public health RRTs. In this study, VR was found to be an acceptable training tool with data indicating high usability and excitement. Most participants rated their existing rapid response soft skills very high prior to undergoing the VR scenario therefore resulting skill acquisition was difficult to measure. Regardless, this highly experienced group of responders consistently echoed VR’s applicability for those with minimal experience and thus VR training of soft skills may be more suited for less experienced responders or included in a briefing programme alongside traditional didactic methods prior to deploying.

The results described here will serve to inform those producing a VR-based training on which aspects resonated with participants most and what would drive them to reuse VR for their training. Focus should be put on developing trainings that are concrete examples of the work a responder would expect in the field while maintaining a smooth interface. Additional scenarios were highly requested by participants in the qualitative postsurvey. Future trainings of this type could highlight harsh realities of emergencies so that participants learn the appropriate technical skills as well as identify how they may respond emotionally and mitigate any unintended reactions.

The SARS-CoV-2 response underscored the need for an experienced emergency response workforce in all areas of the world. VR offers a remote option for training the future public health workforce and may revolutionise how training is applied one day.

Data availability statement

Data are available upon reasonable request. The data that support the findings of this study are available from the corresponding author, DBugli, upon reasonable request.

Ethics statements

Patient consent for publication

Not required.

Ethics approval

This study involves human participants. This investigation was reviewed and approved in accordance with US Centers for Disease Control and Prevention Institutional Review Board human subjects expedited review process outlined in 45 CFR 46.110(b)(1), categories 4 and 7. Participants gave informed consent to participate in the study before taking part.