Introduction

Environmental disasters in the United States are increasing in frequency and severity (Smith 2020). Disasters encompass geographic, structural, and social events contributing to or causing adverse human health effects, economic loss and environmental impacts (Chmutina and Von Meding 2019). Research is needed to address community concerns regarding potential impacts to human and environmental health, following the life-saving efforts often necessitated by a disaster (Miller et al. 2016). Characterizing chemical exposures is one such concern, as highlighted by the Deepwater Horizon oil spill (Gulf of Mexico), Hurricane Harvey (Texas), and recent wildfires in the Northwest (NW) United States. During Hurricane Harvey, there were 89 reported releases of air toxics, yet it was unclear which chemical contaminants were released, or in what quantities (Miller and Craft 2018). Additionally, there continue to be significant gaps in knowledge regarding the composition and subsequent health impacts of wildfire smoke in the Northwest (Aguilera et al. 2021). Disaster research aims to generate knowledge that will help populations support real-time response decisions during disasters, understand outcomes and recovery, and prepare for future disasters.

Disaster research has many challenges (Miller et al., 2016). Traditional research methodologies, including recruitment, retention, data collection, and data analysis, may be ill suited for disaster research (Bongers et al. 2008; Knack et al. 2006; Henderson et al. 2009). Disasters that involve potential chemical exposure require characterization of the type and quantity of chemicals in environmental media, yet a lack of baseline data complicates analysis (Knack et al. 2006; Lurie et al. 2013). Most exposure assessment tools rely on electricity, the internet, or trained data collection personnel. Biomonitoring technologies capable of detecting chemical analytes in urine or blood rely on physical infrastructure commonly impacted by disasters, such as controlled locations to collect and store samples. The digital collection of questionnaire or medical history data is similarly hampered (Henderson et al. 2009), especially when reliant on participant tracking and retention (Knack et al. 2006). There is a shared similarity amongst many of these challenges; they are dependent on pre-existing critical infrastructure. The challenges faced by disaster research are those faced on a much larger scale by communities dependent on these socio-technical systems (Choi et al. 2019). As such, disaster research must be carefully situated in this space to prevent additional demands on stressed or failing critical infrastructure (Choi et al. 2019).

Passive sampling is well-suited to disaster research in which characterization of chemical exposure is desired, as the sampler mimics biological membranes, thereby allowing analysis of bioavailable contaminants from air, water, soil, or sediment (Dixon et al. 2020; Anderson and Hillwalker 2008). The samplers do not require electricity, are low cost and low maintenance, are capable of assessing a breadth of chemicals (>1,530) (Bergmann et al. 2018), and can be configured as environmental or personal samplers (O’Connell et al. 2014). The samplers can be integrated with existing technological infrastructure, such as the NOAA Hazard Mapping System (Schroeder et al. 2008), questionnaire and biological data (Rohlman et al. 2019a), and US Census data for describing neighborhood socioeconomic status (Oluyomi et al. 2021) to provide a comprehensive sampling strategy following a disaster.

Beyond collecting chemical exposure data, obtaining consensus among researchers, communities, and regulatory agencies on which contaminants to evaluate is difficult (Reams et al. 2017). Identifying the research issues and subsequent prioritization requires coordination between multiple stakeholders, and should include community members (Miller et al. 2016). Following the Gulf of Mexico oil spill, the National Institute of Environmental Health Sciences (NIEHS) developed the Disaster Research Response (DR2) platform in 2012. The platform collates disaster-related materials and protocols, resources, and research tools; addresses issues of coordination between researchers, agencies, and public health; and collects timely health data following a disaster (Miller et al. 2016). This program also conducts trainings with first responders, regional and federal agencies, and researchers in environmental health (Miller et al. 2016). Efforts to increase coordination amongst researchers and their community partners have resulted in substantial changes to the ways in which disaster research is conducted, such as the implementation of a single IRB and standardized participant registries (Packenham et al. 2017; Collogan et al. 2004; Symanski et al. 2021).

Given the inherent unpredictability of disasters, it is a challenge for researchers to collect baseline data, access knowledge regarding the extent of the disaster, and select study sites and participants (Bongers et al. 2008; Henderson et al. 2009; Knack et al. 2006). To address these compounded uncertainties, the inclusion of community voice enhances DR2 efforts (Miller et al. 2016; Errett et al. 2019). Research methodologies that engage communities for this purpose include citizen science (CS), community-engaged research (CEnR), and community-based participatory research (CBPR). CS has been defined as the participation of non-scientists in projects with public interest (engagement), yet where the main question or study design originated from academics (Woolley et al. 2016; O’Fallon and Finn 2015). CEnR is defined as research driven by academic interest and questions, yet engages the community in aspects of this endeavor, such as providing input on the study design or selection of the data collection tool (O’Fallon and Finn 2015). CBPR is situated within community knowledge and strengths, and uses a collaborative partnership model to co-develop research that addresses community concerns (Israel et al. 1998). Wooley et al. (2016) developed a framework for differentiating between these forms of engaged research (Figure 1) that parsed elements of engagement, involvement, and participation, with participation defined as that beyond a study subject. Whereas CBPR encompasses aspects of engagement, participation, and involvement (Israel et al. 1998), CS efforts rely on engagement around the research topic to increase participation in data collection. CEnR includes at least two elements of this framework (Figure 1). These research methodologies are designed to promote equitable research practices by prioritizing community priorities, needs, and strengths (Wallerstein et al. 2019). Indeed, CPBR focuses explicitly on the ways in which partnerships are formed and power is distributed (Wallerstein et al. 2019). For example, researchers and community members participate equally in the process, whereas CS and CEnR may be more heavily weighted toward the academic or community partner on aspects of research priorities, governance, data sharing, and communication (Cooper et al. 2021; Guerrini et al. 2021; Andress et al. 2020).

DR2 incorporates CS, CEnR, and CBPR to various extents. The benefits of CEnR are well catalogued (Israel et al. 1998). This framework has been applied to disaster planning (Wells et al. 2013) and disaster research (Miller et al. 2016). CBPR is an important, yet time-consuming undertaking (Israel et al. 1998), and thus difficult to implement following an unexpected disaster, wherein community partnerships may not yet exist. CS has been successfully used following disasters (Hicks et al. 2019). Studies that utilize forms of CS suggest that datasets developed by CS can inform disaster response and preparedness (Chari et al. 2019; Hicks et al. 2019). However, CEnR approaches appear to be well suited for conducting research during and after disasters, herein termed community-engaged disaster research (CEnDR). In this approach, community concerns drive research efforts (engagement), community context can account for existing or potential inequities and inform data methodology and collection tools (involvement), and community members may aid in the collection of data (participation). Relative to CEnR, CEnDR is subject to additional guidelines for working with impacted populations. These guidelines recognize the impact a disaster may have on infrastructure, to include reduced access to resources (medical facilities, clean water, food, transportation, school, and housing, among others), impacts on local researchers, and potential for research overload (Collogan et al. 2004; Ausbrooks et al. 2009).

Here, we discuss three case studies to describe the challenges of disaster research and lessons learned utilizing the CEnDR framework following these discrete disaster events.

Description of Case Studies

The following describes the subsequent research efforts to evaluate chemical exposures following the Deepwater Horizon oil spill (Gulf of Mexico), Hurricane Harvey (Texas), and wildfires (NW United States). Each study highlights various aspects of CEnDR, from informing study design to training community members to collect data (Table 1), along with the associated infrastructural challenges (Table 2). In all studies we prioritized the safety and well-being of participants and clarified that research participation was voluntary.

Deepwater Horizon Oil Spill, Gulf of Mexico

On April 20, 2010, an industrial disaster on the Deepwater Horizon drilling rig in the Gulf of Mexico led to approximately 4.9 million barrels of oil spilled over nearly five months (Table 1). The oil spill impacted multiple communities and the economy, including recreational and commercial fishing, shrimping, and tourism (Lichtveld et al. 2016; Sumaila et al.; 2012). Following the disaster, we deployed to four states in the Gulf of Mexico (Louisiana, Alabama, Mississippi, and Florida). Here, community members were involved in study design, helped identify sites to deploy samplers, provided valuable logistical support, and remained engaged in monitoring and protecting the samplers over the course of multiple deployments. They also relayed important contextual information for interpreting environmental data, such as shoreline clean-up activities that occurred when researchers were not present. They remained engaged with the study via regular interactions with the researchers, study updates, and a workshop (Table 1, Figure 2).

Figure 2 

General timeline of community-engaged disaster research (CEnDR) case studies. The disaster duration refers to the initial event and does not encompass disaster recovery. Icons indicate areas of community engagement (forums, outreach via research dissemination), participation (focus groups, data collection), and involvement (community input on study design/methodology).

We set up environmental passive samplers (air, water) at shoreline sites within each state before oil from the spill reached the coastline, ensuring baseline data. Coordination with local and federal agencies was necessary to obtain permits for placing samplers in coastal waters. Sampling continued for a year, for a total of nine sampling events (Allan et al. 2012).

Here, we collected environmental baseline data, yet no personal samples were collected because of the lack of a pre-positioned institutional review board (IRB) protocol. However, by responding immediately using environmental samplers, the project documented pre-, during-, and post-spill oil chemical concentrations in nearshore environments in the Gulf of Mexico. These data have been widely utilized to help understand the impacts of the Deepwater Horizon oil spill.

The disaster response leveraged existing infrastructure such as organized community groups that categorized concerns, as well as a sampling technology that did not rely on disaster-impacted infrastructure. Based on lessons learned from this disaster research, the team initiated a pre-positioned disaster IRB for use in future disasters to allow collection of personal samples, and to improve opportunities for community involvement and participation (Rohlman et al. 2020).

Summary of disaster impacts on infrastructure and lessons learned

Disaster research is complicated by the unpredictable timing, the locations, and the severity inherent to disasters. As outlined in Table 2, there are additional challenges to disaster research. Here, we share lessons learned from conducting CEnDR following three disasters within the United States. As shown in Figure 2, community engagement, involvement, and participation were unique to the study design. While the three studies differed in disaster type, location, and approach (Table 1), they can inform a general sequence for CEnDR approaches (Figure 3). Researchers can prepare by developing training, IRB protocols, and tools that are well suited to disasters. While community partnerships may not exist prior to a disaster, principles of CEnDR may facilitate new partnerships and guide development of study designs, sampling approaches, and return of data. Finally, the return of data to individuals and communities can increase transparency and apply study results to community concerns.

Following identification of the challenges and opportunities posed by infrastructure before and during disasters, we developed a series of comprehensive lessons learned (Table 3). These lessons extend beyond the individual case studies, which focused on chemical disasters. Where possible, we have included specific examples, taken from researchers’ own accounts, to demonstrate the value of these recommendations for future CEnDR.

Conclusions

Increased collaboration and coordination, such as that facilitated by the DR2 program, has begun to address the need for shared resources, data collection tools, and protocols. However, identifying the research issue and prioritizing health concerns remain challenges unique to each disaster. Miller et al (2016) noted the importance of community involvement in this process yet acknowledged there is no organized infrastructure to support this. Additionally, the unpredictable nature of disasters often means that responding researchers may not have existing partnerships with the impacted community. Collaboration between researchers can leverage community partnerships; however, in a survey of Environmental Health Sciences Core Center directors, while collaboration was listed as necessary to increase community engagement, relevance, and equitable participation, it raised concerns of outside researchers potentially damaging trusted community partnerships (Errett et al. 2019).

Wallerstein et al. (2019) identified multiple strategies underpinning successful, robust academic-community partnerships. These strategies included recognizing and including community strengths and knowledge, facilitating communication, and representing and addressing community-defined outcomes for the research (Wallerstein et al. 2019). These are strategies that mesh well with the CEnDR framework of prioritizing engagement, involvement, and participation, and can be adaptive to disaster scenarios. For example, CEnDR must be responsive to impacts from a disaster on critical infrastructure (Choi et al. 2019), as well as competing community priorities. As discussed above, the nature of a disaster may preclude the deliberate, careful development of a community partnership. Given the impacts of a disaster, communities may view the need for active engagement, involvement, or participation as a barrier. However, in our experience with CEnDR, having independent academic research groups engaging with local communities provided clear benefits. Smaller organizations were more nimble and able to deploy rapidly and scale projects appropriately to be responsive to community needs. The use of data-sharing agreements between researchers and communities, as well as the development of community advisory boards, may help mitigate concerns related to jeopardizing relationships with partners.

There is the related concern that without comprehensive collaboration between researchers, communities may receive multiple research requests. Increased collaboration under a CEnDR framework can reduce multiple requests and lower burdens to participation by aligning research goals and study designs. In our case studies, we identified a need to coordinate with other researchers that sought to characterize exposures following a disaster. Following the Gulf of Mexico oil spill, there was a call for increased collaboration to reduce such fatigue (Finucane et al. 2020). To enhance collaboration and reduce multiple, related community requests, platforms like the DR2 program may help connect disaster-impacted communities with researchers. While it is critical to have local participation in disaster research, it is not feasible to have a multitude of hyper-local groups that maintain capacity for research on a wide range of potential impacts from unpredictable disaster events. Rather, a national framework that connects communities and researchers within and outside of their region, and concurrently allows research groups to coordinate their efforts, would be mutually beneficial.

A CEnDR approach provides for the return of individual- and community-level data. The return of data has multiple benefits, including increased transparency and giving participants knowledge and agency to make informed decisions regarding their environment and their health (National Academies of Sciences and Medicine 2018). In the aforementioned case studies, all communities received final data, with Hurricane Harvey and NW wildfire participants receiving individual-level data. The return of data is embedded in the pre-positioned IRB and communicated as a data-sharing policy with all study participants. The ability to host focus groups, as was done with Hurricane Harvey, allowed us to better understand new and existing problems that may have been exacerbated by the hurricane.

A final challenge in disaster research includes the collection of baseline data (Knack et al. 2006). While CEnDR may be useful in identifying areas in which to collect such information, prioritized based on community knowledge of an area, data collection is reliant upon funding. The lack of funding was identified as the predominant barrier to conducting disaster research among EHS CC directors (Errett et al. 2019). Expanding existing rapid response funding mechanisms to accommodate and prioritize CEnDR can increase the relevance of research and increase coordination between researchers.

In sum, these case studies highlighted important issues in terms of the existing physical, technological, and social infrastructure for disaster research. Simultaneously, the studies also identified opportunities for CEnDR to leverage underlying strengths and to build an infrastructure for improving disaster response research. The infrastructure proposed here relates to existing technology, to include passive samplers, questionnaires, and the DR2 program that enhances collaboration. The case studies additionally recognize that while CEnDR positively engages with infrastructure, the nature of doing research during disasters also identifies the limitations of such an approach. CEnDR is further subject to limitations of the tools used (requirements for underlying technological and physical infrastructure), and the methods for engaging communities, recognizing differing levels of community involvement, engagement, and participation. CEnDR may not be appropriate for all disaster-response research given the caveats discussed above. The decision to move forward with a CEnDR study should be done in consultation with community groups and liaisons, with discussions around time commitments, compensation for community time and knowledge, and clear expectations regarding the use and dissemination of study data.

These case studies help inform a structure for long-term CEnDR that recognizes the disaster research continuum, from preparing for disasters through disaster response and recovery. The use of CEnDR, which includes community voice and knowledge, and transparent data-sharing policies can mitigate many challenges associated with disaster research and ensure that research is responsive to and informed by community needs.