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Explaining Disaster and Pandemic Preparedness at the Nexus of Personal Resilience and Social Vulnerability: An Exploratory Study

Published online by Cambridge University Press:  19 June 2023

Regardt Ferreira ,
Clare EB Cannon Open the ORCID record for Clare EB Cannon [Opens in a new window] ,
Fred Buttell  and
Tim Davidson
Regardt Ferreira
Affiliation:
School of Social Work, Tulane University Department of Social Work, Stellenbosch University, South Africa
Clare EB Cannon*
Affiliation:
Department of Social Work, University of the Free State, Bloemfontein, South Africa Department of Human Ecology, University of California, Davis
Fred Buttell
Affiliation:
School of Social Work, Tulane University Department of Social Work, University of the Free State, Bloemfontein, South Africa
Tim Davidson
Affiliation:
School of Social Work, Tulane University
*
Corresponding author: Clare EB Cannon; Email: cebcannon@ucdavis.edu.
Rights & Permissions [Opens in a new window]

Abstract

Objective:

The purpose of this research was a pilot examination to identify and assess relationships among social vulnerability, personal resilience, and preparedness for a sample of US residents living in the Gulf South, who had experienced climate-related disaster (e.g., hurricanes) and the COVID-19 pandemic.

Methods:

Binary logistic regression was conducted using primary survey data collected in 2020 (n = 744) to identify statistically significant explanatory variables of sociodemographic characteristics and resilience, measured by the CD-RISC 10, of climate-related disaster, and pandemic preparedness.

Results:

Results indicate that respondents who identified as white, had more education, were in a relationship, and spoke English as a first language, as well as respondents who had exhibited greater resilience, were more likely to prepare for a climate-related disaster. Respondents who spoke English as a first language, had more education, and greater resilience were found to be statistically significant explanatory variables of pandemic preparedness. Respondents who prepared for disaster were also more likely to prepare for the pandemic.

Conclusions:

These findings provide insights into protective factors related to preparedness, including linkages between resilience and preparedness that can aid public health professionals in supporting resilience and preparedness efforts for impacted communities.

Keywords

preparednessdisasterresiliencesocial vulnerabilitypandemic

Information

Type
Original Research
Information
Disaster Medicine and Public Health Preparedness , Volume 17 , 2023 , e416
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of Society for Disaster Medicine and Public Health, Inc.

Introduction

Disasters are increasing in frequency and severity due to the effects of climate change, impacting people living in highly populated, urbanized, and low-lying coastal areas. Reference Pörtner, Roberts and Tignor1 Such a region is the Gulf Coast of the Southeastern US, which experiences an annual hurricane season from June through November. Reference Reidmiller, Avery and Easterling2 The rise in the number of people who experience disasters and the regularity and difficulty with which they experience them, makes disaster preparedness of vital importance. Reference Thomas, Leander-Griffith, Harp and Cioffi3

Preparedness has a range of definitions, and for the purposes of this article, is understood as collecting a set of supplies and/ or creating a plan that can be deployed when a disaster is either imminent or encountered. Reference Kohn, Eaton and Feroz4,Reference Verheul and Dückers5 Individuals continue to be unprepared for disaster, particularly in areas with increasing urbanization and population growth. Reference Gotham and Cannon6 For example, research suggests most US residents have not prepared an emergency kit, created an emergency meeting plan, nor developed a communication plan. 7,Reference Howe8 Before and concurrent to the pandemic, many have faced climate-related disasters, including those living on the US Gulf Coast. Little research has investigated preparedness related to infectious disease and climate change-induced disasters. Reference Phillips, Caldas and Cleetus9 Although the idea of pandemic preparedness is relatively new in the US, the novel Coronavirus 2019 (COVID-19) pandemic has spurred a growing body of research. Insights from such research can help inform mitigation and adaptation efforts through increasing preparedness to reduce harmful impacts from the current pandemic as well as future ones. Reference Naguib, Ellström, Järhult, Lundkvist and Olsen10

Furthermore, social vulnerability factors that negatively affect a community’s ability to respond to a disaster may also impact preparedness since it may limit an individual’s access to resources and assets. Reference Cannon, Twigg and Rowell11,Reference Flanagan, Gregory, Hallisey, Heitgerd and Lewis12 Research demonstrates that socially vulnerable populations are more likely to be impacted by disasters and have difficulty recovering from them. Reference Kendra, Clay and Gill13 For instance, homeownership, Reference Basolo, Steinberg and Burby14 and higher educational attainment lead to greater disaster preparedness, Reference Clay, Goetschius, Papas and Kendra15 while the roles of race and ethnicity in preparedness remain complex and difficult to decipher. Reference Donner and Lavariega-Montforti16,Reference Murphy, Cody, Frank, Glik and Ang17 Although research findings have been mixed regarding the relationship between age and preparedness, recent research has found that young and middle-aged adults (18 - 49), and seniors aged 75 and over, were more likely to prepare for disaster than those aged 65 - 74. Reference Cong, Chen and Liang18

With the increase in disasters and the number of people affected by them, an important but understudied question is how personal resilience may affect preparedness for disasters occurring sequentially or simultaneously, particularly for socially vulnerable populations. Personal resilience is understood as a person’s ability to cope with adversity. Reference Gulbrandsen19 Adapting the 3 models of resilience from stress-resistant theory (compensatory, challenge, and protective factors) to preparedness, Reference Zimmerman, Stoddard and Eisman20 this study completed 2 research objectives: (1) to explore patterns of social vulnerability factors on disaster and pandemic preparedness; and (2) to identify the effect of resilience on disaster and pandemic preparedness.

Theoretical framework: adapting models of resiliency to preparedness

Resilience is an interdisciplinary term that refers to the ability to cope with adversity. Reference Cutter21,Reference Masten22 The nature and definition of resilience continues to be debated given the ambiguity of the term, unclear differences in general or specific forms of resilience, Reference Cavallo23 disagreements over its interpretations, Reference Moser, Meerow, Arnott and Jack-Scott24 and the perception of putting the responsibility of ‘bouncing back’ on an individual rather than focusing on systemic structures. Reference Adger, Hughes, Folke, Carpenter and Rockström25 Although its meaning has been debated, resilience continues to be an important concept in understanding how to manage and cope with adversity. Personal resilience, Reference Baker and Cormier26,Reference Cutter, Boruff and Shirley27 is an understudied area of disaster research which has tended to focus analysis at macro scales. Reference Weber, Pavlacic, Gawlik, Schulenberg and Buchanan28 Recent scholarship suggests that there is a significant link between increased personal resilience and increased disaster preparedness. Reference Fergus and Zimmerman29 However, the pathways of resilience and their relationships to preparedness are not yet well-understood.

There are 3 models of resilience within stress-resilient theory (i.e., compensatory, challenge, and protective) that describe the impacts of stress on an individual’s adaptation. Reference Zimmerman, Stoddard and Eisman20,Reference Fergus and Zimmerman29–Reference Osofsky and Osofsky32 A compensatory model describes a promotive factor, such as strong community ties, that counteracts a risk factor, such as unemployment. Reference Masten, Garmezy and Tellegen33 The challenge model describes when a risk factor, experienced at moderate levels, may enhance successful adaptation, Reference Luthar and Zelazo34 that is when individuals experience enough of a risk to overcome it, but not so much that it leads to a negative adaptation. The third model of resilience is a protective model in which access to resources or assets by an individual reduces the effects of a risk, thus, increasing adaptation. Reference Lightfoot, Lesen and Ferreira35

Although each of these models describe a different pathway between risk and protective factors, they have rarely been applied to disasters, particularly those associated with climate change-induced hazards such as hurricanes and flooding, or those from an infectious disease disaster such as a pandemic. Building on this research, in this study, we revise these conceptual models to include disaster preparedness and to add to the literature, in order to better understand the relationship between resilience and preparedness during disaster. We do this by specifying preparedness as an adaptation strategy to an emerging threat as illustrated in Figure 1. Reference Kohn, Eaton and Feroz4

Figure 1.

Conceptual model for stress-resistant theory of preparedness adapted from resiliency models of compensatory, challenge, and protective factors in Fergus & Zimmerman, 2005.

Given the theoretical models elucidated above, it could be the case that social vulnerability could increase preparedness through ‘challenges,’ Reference Luthar and Zelazo34 or post-traumatic growth. Reference Calhoun and Tedeschi36 Moreover, it is also possible that personal resilience, as a compensatory factor that has an independent and direct effect on preparedness, Reference Masten, Garmezy and Tellegen33 increases the likelihood of preparedness, as some research at the community level suggests. Reference Cuervo, Leopold and Baron37 Finally, the lack of social vulnerability, as individuals with access to resources, could point towards a protective factor model of preparedness. Reference Zimmerman, Stoddard and Eisman20 The current study seeks to test these potential relationships.

Pandemic preparedness

Although there are some similarities between preparedness for disasters associated with climate change and pandemic preparedness, there are some key differences. Reference Welby-Everard, Quantick and Green38 For instance, there are no clear temporal and geographic boundaries for the COVID-19 pandemic as opposed to the end of a hurricane season. Reference Palinkas, Springgate and Sugarman39 This nature of a pandemic may require individuals to prepare in unique ways necessitating this study.

There is a growing body of research into pandemic preparedness due to the COVID-19 pandemic. Reference Huang, Teoh, Wong and Wong40–Reference Babu, Khetrapal, John, Deepa and Narayan43 For instance, Huang et al. Reference Huang, Teoh, Wong and Wong40 found that countries who had experience with previous epidemics such as Severe Acute Respiratory Syndrome (SARS) or Middle Eastern Respiratory Syndrome (MERS) were more likely to have a lower COVID-19 incidence rate in 2020, suggesting an association between countries prior exposure to epidemics and their performance of containing the COVID-19 pandemic. Some prior disaster research suggests that positive adaptation was likely a result of skill development established during previous epidemic exposure. Reference Becker, Paton, Johnston, Ronan and McClure44 Yet, the relationship between pandemic and climate-related disaster preparedness remains unclear.

Hypotheses

Based on the literature reviewed above, we developed the following hypotheses:

  1. 1) Hypothesis 1a: Socially vulnerable residents (i.e., less educational attainment, English as a second language, and racial minority) are less likely to prepare for a disaster associated with climate change (e.g., hurricanes, flooding).

  2. 2) Hypothesis 1b: However even socially vulnerable individuals (i.e., less educational attainment, English as a second language, and racial minority) who exhibit greater resilience (as measured by the CD-RISC 10) are more likely to prepare for a disaster associated with climate change (e.g., hurricanes, flooding).

  3. 3) Hypothesis 2a: Socially vulnerable residents (i.e., less educational attainment, English as a second language, and racial minority) are less likely to prepare for a pandemic.

  4. 4) Hypothesis 2b: Individuals exhibiting greater resilience are more likely to prepare for a pandemic.

  5. 5) Hypothesis 3: Individuals who prepare for disasters are more likely to prepare for a pandemic.

To examine these research hypotheses, the current study employed binary logistic regression to analyze a non-probability sample of adults across the US Gulf Coast who have experienced at least 1 climate-related disaster (n = 744) in order to examine 3 theoretical models of resilience and to investigate the relationship among social vulnerability, resilience, and preparedness.

Methods

This study used convenience sampling to recruit a sample of 744 adults during the COVD-19 pandemic and the 2020 Hurricane Season. Recent research suggests such approaches are important for exploratory studies into sub-populations, Reference Lehdonvirta, Oksanen, Räsänen and Blank45 while such a sampling strategy is often useful during a crisis given that it can be difficult to reach potential participants. Reference Norris46 Cross-sectional data were collected over a 12-month period from an online survey which was launched in the first week of April, 2020 and remained open until the last week of March, 2021. Following studies into other kinds of disasters, Reference Inoue and Yamaoka47 the survey was left open for this time period to account for an entire hurricane season in the Gulf South of the US during the COVID-19 pandemic. The study was approved by the Tulane University Social/Behavioral Institutional Review Board (approval number 2020-556). Distribution of the online survey was through personal social media accounts and advertisement on the Tulane University School of Social Work social media outlets and website for a period of 12 months. Consent was obtained through the online survey by agreeing to participate in the study and no identifying information was collected. Inclusion criteria required participants to be older than 18 years and living in the Southeastern US, residing within the Gulf Coast region. The survey included constructs focused on participants and their (a) disaster preparedness, (b) personal resilience, and (c) personal and household demographics. The online Qualtrics survey took an estimated 10 minutes to complete.

Measures

Dependent variable

The study was guided by 2 dichotomous dependent variables drawn from prior research. Reference Lindell and Perry48,Reference Liddell, Saltzman, Ferreira and Lesen49 The first variable focused on whether an individual has ever prepared for disasters to natural hazards such as hurricanes and floods (i.e., ‘Have you prepared for a natural disaster such as hurricanes, floods, etc.?’ coded as 1 = yes and 2 = no). The second question focused on whether an individual prepared for the COVID-19 pandemic (i.e., ‘Have you prepared for the COVID-19/ Coronavirus pandemic?’ coded as 1 = yes, 2 = no).

Independent variables

To assess the level of personal resilience, this study included the total score for the 10-item Connor Davidson Resilience Scale (CD-RISC 10), which evidences high internal consistency, construct validity, and test-retest reliability. Reference Gulbrandsen19,Reference Nrugham, Holen and Sund50 The CD-RISC 10 has been used with diverse samples across gender, age, and race/ ethnicity categories. Reference Patel, Saltzman, Ferreira and Lesen51 The scale utilizes an ordinal level, 5-point Likert scale ranging from 1 for ‘not at all’ to 5 for ‘nearly all the time,’ asking respondents to rate their own resilience by responding to 10 statements. Examples of some of the items are: (1) ‘I am able to adapt when changes occur;’ and (2) ‘I can deal with whatever comes my way.’ (All statements included in CD-RISC 10 can be found in Connor and Davidson. Reference Gulbrandsen19 ). The total score ranges from 0-40.

Social vulnerability was assessed using socio-demographic characteristics frequently used in disaster research. Reference Flanagan, Gregory, Hallisey, Heitgerd and Lewis12–Reference Cong, Chen and Liang18 These characteristics include age, gender (dichotomized as men and women), race (dichotomized as white and non-white), and ethnicity (dichotomized as not of Hispanic, Latino, or Spanish origin and of such origin), as well as relationship status (dichotomized as not in a relationship and in a relationship), employment (dichotomized as employed or not employed), English as a first language (dichotomized as yes or no)/ education (dichotomized as college or advanced degree and less than college education), and residential status (dichotomized as home ownership or not).

Data analysis

Binary logistic regression was conducted to examine factors associated with disaster and pandemic preparedness. In models 1 and 2 – social vulnerability variables including age, gender, race, and ethnicity, as well as English as a second language, relationship status, employment status, and education; in addition to residential status and resilience– were included to investigate predictors of group membership based on 2 outcomes: climate-related disaster preparedness and pandemic preparedness, respectively. Model 3 investigated the relationship between disaster preparedness and pandemic preparedness. Binary logistic regression is estimated using maximum likelihood estimation. Reference Osborne52 All analyses were performed using SPSS 28 (IBM Corp., Armonk, New York, USA).

Results

Descriptive statistics of the sample are presented in Table 1. The sample had an age range from 18 to 91 years of age with a mean of 47.68 years (SD = 15.35) and was mostly made up of women (80.9%, n = 602). The majority of the sample identified as white (83.1%, n = 602), 10.3% as Black (n = 77), 1.9% as mixed or bi-racial (n = 14), and 1.6% as other (n = 12), with 1.3% as Middle Eastern (n = 10), 1.1% as Asian (n = 8), and 0.7% as Native American or Alaska Native (n = 5). Regarding ethnicity, 94.5% of the sample reported they were not of Hispanic, Latino, or Spanish origin (n = 703). English as a second language was present among 17.7% of the sample (n = 132). In terms of relationship status, most participants indicated they were in a relationship (71.2%, n = 530). The majority of the sample was employed at the time of study participation (73.2%, n = 544). For education, only 1.7% had less than a high school diploma (n = 13), 4.4% of the sample indicated they had a high school diploma or GED equivalent (n = 33), 9.3% with some college (n = 69), 3.9% with an associate degree (n = 29), and 19.1% with a bachelor’s degree (n = 142) while 61.6% with a graduate degree (n = 458). More than 66.7% of the sample owned their home with 67.9% reporting homeownership (n = 505). Regarding preparedness, 75.4% of respondents reported that they had prepared for disaster (n = 561) and 80.6% reported that they were prepared for COVID-19 (n = 600). For resilience, the sample had a mean score of 30.75 (SD = 5.57), with a range from 9 to 40 on the CD-RISC 10.

Table 1.

Demographic characteristics of the sample

Predicting preparedness to climate-change induced disaster

Table 2 presents results of the binary logistic regression model for disaster preparedness. These models sought to test hypotheses 1a and 1b – that social vulnerability characteristics and resilience predict disaster preparedness. For model 1, a test of the full model against a constant-only model was statistically significant (x 2 = 98.772, df = 10, P = 0.001). Prediction success for the cases used in the development of the model was moderate, with a moderate overall success rate of 75.4%.

Table 2.

Logistic regression analysis of sociodemographic characteristics and natural disaster preparedness

Note: n = 735.

a. df = 10, *P < 0.05, ** P < 0.01, *** P < 0.001.

5 of the predictor variables: race (Wald χ 2 = 6.312, df = 1, P = 0.012, CI .95 = 1.060, 1.6), English as a second language (Wald χ 2 = 46.826, df = 1, P = 0.01, CI .95 = 3.204, 8.159), relationship status (Wald χ 2 = 4.501, df = 1, P = 0.034, CI .95 = 1.01, 1.20), education (Wald χ 2 = 11.59, df = 1, P = 0.001, CI .95 = 1.11, 1.48) and resilience (Wald χ 2 = 10.11, df = 1, P = 0.001, CI .95 = 1.02, 1.09); were statistically significant predictors of group membership based on disaster preparedness. Based on the model, respondents who identified as white were 1.3 times more likely to report being prepared for a disaster than those who did not identify as white. Respondents who spoke English as a first language were 5.1 times more likely to prepare for disasters than those who spoke English as a second language. Respondents who were in a relationship were 1.11 times more likely to prepare for a disaster than those who were not in a relationship. Respondents with a college degree or advanced degree were 1.28 times more likely to prepare for a disaster than respondents with less education. Lastly, respondents who reported higher levels of resilience were 1.05 times more likely to prepare for a disaster than those who did not exhibit such levels of resilience.

Predicting preparedness to pandemic

To test hypotheses 2a and 2b (that social vulnerability characteristics and resilience predict pandemic preparedness), we performed a second binary logistic regression. Results from this model are presented in Table 3. In model 2, a test of the full model against a constant-only model was statistically significant (x 2 = 33.31, df = 10, P = 0.002). Prediction success for the cases used in the development of the model was modest, with an overall success rate of 81.1%. 3 of the independent variables including English as a second language (Wald χ 2 = 10.63, df = 1, P = 0.001, CI .95 = 1.375, 3.588), education (Wald χ 2 = 3.796, df = 1, P = 0.051, CI .95 = 0.999, 1.348), and resilience (Wald χ 2 = 6.612, df = 1, P = 0.010, CI .95 = 1.011, 1.084), were statistically significant predictors of group membership based on pandemic preparedness. According to this model, there is a 2.21 times greater likelihood of a respondent who speaks English as a first language to prepare for a pandemic compared to respondents who speak English as a second language. Respondents with more educational attainment were 1.16 times more likely to report pandemic preparedness than respondents with less educational attainment. Also, respondents who exhibited greater resilience were 1.05 times more likely to report pandemic preparedness when compared to respondents who exhibited less resilience.

Table 3.

Logistic regression analysis of sociodemographic characteristics and pandemic preparedness

Note: n = 735.

a. df = 10, P < 0.1†, P < 0.05, ** P < 0.01, *** P < 0.001.

Disaster preparedness predicts pandemic preparedness

To test hypothesis 3 (that individuals who prepare for disaster are more likely to prepare for a pandemic), we performed a third logistic binary regression. Results from this model are presented in Table 4. In model 3, a test of the full model against a constant-only model was statistically significant (x 2 = 39.402, df = 1, P < 0.001). Prediction success for the cases used in the development of the model was modest, with an overall success rate of 80.6%. The independent variable, disaster preparedness (Wald χ 2 = 40.541, df = 1, P < 0.001, CI .95 = 2.377, 5.134), was a statistically significant predictor of group membership based on pandemic preparedness. According to this model, respondents who report preparing for climate change-related disaster are 3.49 times more likely to prepare for a pandemic than those who did not prepare for a climate-related disaster.

Table 4.

Logistic regression analysis of disaster preparedness and pandemic preparedness

Note: n = 744.

a. df = 1, *** P < 0.001

Discussion

The findings here provide preliminary evidence for the theoretical relationships between preparedness as described above, although the support is not even across the models. The strongest empirical support was found for the hypothesis that social vulnerability predicts less disaster and pandemic preparedness. This study also provides preliminary empirical support for the role resilience plays in preparedness. These findings suggest that Gulf Coast residents who reported having higher levels of resilience, identifying as white, having more education, and being in a relationship, all while speaking English as a first language, were strong predictors of disaster preparedness. In many respects, such findings make sense as the study took place in the Gulf South in the United States; given the rate of structural racism across the US, Reference Bailey, Feldman and Bassett53 the Gulf South, as part of the Southeastern US, is rife with underserved communities amongst which social vulnerability is most prevalent. Reference Lightfoot, Lesen and Ferreira35

Importantly, resilience, in helping people adapt to a disaster by increasing their likelihood of preparing for disaster, may serve to counteract risk factors. Reference Zimmerman, Stoddard and Eisman20 Understanding this relationship between resilience and preparedness could be key to adaptation strategies for climate change-induced disasters. Reference Phillips, Caldas and Cleetus9,Reference Doppelt54,Reference Saxena, Qui and Robinson55 For instance, strategies and interventions that increase residents’ resilience may increase the chances that they will prepare for not just one disaster but for multiple disasters, a new reality that is expected to become more common for more and more people. Reference Pörtner, Roberts and Tignor1 Better preparation for disasters may be considered a compensatory factor, in part because preparation for one kind of disaster (e.g., hurricane) may spillover into preparation for another (e.g., pandemic), which probably has some ameliorating effect on impacts from vulnerabilities due to sociodemographic characteristics. Reference Cutter and Finch56 Moreover, the results of the logistic regression analysis suggest that increased personal resilience increases preparedness, which confirms research at the community-level, Reference Cuervo, Leopold and Baron37 while preparedness, may in turn, increase resilience. Reference Kapucu, Hawkins and Rivera57

Our findings suggest that resilience also likely increases preparedness regardless of whether a disaster stems from climate change or a pandemic. This point is further supported by the finding that respondents who reported preparing for a disaster were more likely to prepare for the pandemic. Given that research into community resilience and preparedness suggests collaboration and connections of care are necessary to increase disaster preparedness, Reference Ma, Guo, Deng and Xu58 it is possible that similar connections are also needed on the individual or personal level in order to enhance preparedness. Though more research is needed to test this insight, these findings have practical implications. For instance, a personal resilience toolkit could be developed like the one to enhance community resilience for disaster preparedness developed for Los Angeles County, which focused on access to resources and self-sufficiency Reference Chandra, Williams and Plough59 and which may have impacts beyond just one kind of disaster. Additional research is needed to unpack personal resilience and identify its key mechanisms, which could aid in developing such a toolkit.

Our research also offers preliminary empirical support for the protective factor model of preparedness as it relates to resilience (see Figure 1 above). We found that being in a relationship also increased the likelihood of disaster preparedness. It could be the case that relationship status serves as a protective factor, as a couple may have more resources, such as more combined income or more social connections which they can draw from than those not in a relationship. Our study found that individuals with more education were more likely to prepare for both disasters associated with climate change (i.e., hurricanes, floods, etc.), similar to prior research, Reference Lightfoot, Lesen and Ferreira35,Reference Patel, Saltzman, Ferreira and Lesen51 and a pandemic. This study found that English as a first language was a protective factor that increased preparedness. This empirical finding makes intuitive sense given that English is the primary language used in the Southeastern US and many preparedness resources are provided almost exclusively in English as many places across the US unevenly address language access and inclusion. Reference Xiang, Gerber and Zhang60 It is important, given the link between language and disaster preparedness, that government and non-profit disaster preparedness organizations work to share information and resources in languages other than English. Reference Teo, Goonetilleke, Deilami, Ahankoob and Lawie61 Finally, we found that whiteness may work as a protective factor as it may increase access to resources because of racial hierarchies within the US, Reference Bailey, Feldman and Bassett53 and thus increase the likelihood of preparedness. Reference Lightfoot, Lesen and Ferreira35

Policies and interventions should aim to bolster residents from other racial categories as well by drawing on a range of mechanisms, such as increasing support and access to resources, and an increase in education with the aim to increase resilience. For instance, some research suggests increased education can be a protective factor against risk, Reference Righi, Lauriola, Ghinoi, Giovannetti and Soldati62,Reference Höfler63 meaning that an increase in public funding for community colleges, such as the proposed free community college plan proposed by the Biden Administration and the Cares Act of 2021 for Historically Black Colleges and Universities in the US, Reference Golden64 could help promote preparedness via increased access to higher education. Additionally, such initiatives support Sustainable Development Goal (SDG) #4, which is to ‘ensure inclusive and equitable quality education and promote life-long learning opportunities for all.’ 65

Limitations

There are several limitations to the current study. Although this research is important for gaining a better understanding of the relationship among social vulnerability, resilience, and preparedness; this study uses a cross-sectional design. Thus, we are unable to identify direct causality between explanatory and outcome variables. The sampling strategy, although important for gaining an understanding of sub-populations particularly for an exploratory study such as this, Reference Lehdonvirta, Oksanen, Räsänen and Blank45 and well suited to conducting research during an unfolding and evolving crisis, Reference Stratton66 led to a non-representative sample, with a selection bias for educated, employed, white women. Another limitation was that the survey was only distributed in English, thus limiting who could participate in the study. Given the data and sampling limitations, findings should be interpreted cautiously. Future research should consider collecting data from a more representative sample including specific questions on preparedness actions and behaviors, making surveys available in languages other than English, and using longitudinal analyses to better study the impacts of disaster on resilience and preparedness.

Conclusions

In sum, our study found some support for the compensatory and protective models of preparedness and did not find any support for the challenge model, because our analysis did not find that social vulnerability (i.e., Black and/ or English as Second language) promoted preparedness. Reference Lightfoot, Lesen and Ferreira35 There are limitations to this preliminary research, and it is possible that there are aspects of the theory that are not captured with this set of participants, research questions, and variables. Future research should build on this pilot exploration to consider both qualitative methods and refined quantitative approaches to better assess the role challenges play in both resilience and in promoting preparedness.

Author contribution

RJF contributed to the conceptualization, data curation, investigation, and formal analysis of this study, as well as the methodology and writing of the original draft; CEBC took part in the conceptualization, writing of the original draft, review, and editing of the finished work; FPB contributed to the conceptualization, methodology, investigation, and writing of the original draft, as well as the review and editing. TD took part in the writing of the original draft.

References

Pörtner, H-O, Roberts, DC, Tignor, M, et al. Climate change 2022: impacts, adaptation, and vulnerability. Working group II contribution to the IPCC Sixth Assessment Report. Cambridge University Press; 2022.Google Scholar
Reidmiller, DR, Avery, CW, Easterling, DR, et al. Impacts, risks, and adaptation in the United States: fourth national climate assessment. USGCRP. 2018:(2). doi: 10.7930/NCA4.2018 Google Scholar
Thomas, TN, Leander-Griffith, M, Harp, V, Cioffi, JP. Influences of preparedness knowledge and beliefs on household disaster preparedness. MMWR. 2015;64(35):965-971. doi: 10.15585/mmwr.mm6435a2 Google ScholarPubMed
Kohn, S, Eaton, JL, Feroz, S, et al. Personal disaster preparedness: an integrative review of the literature. Disaster Med Public Health Prep. 2012;6(3):217-231. doi: 10.1001/dmp.2012.47 CrossRefGoogle ScholarPubMed
Verheul, ML, Dückers, ML. Defining and operationalizing disaster preparedness in hospitals: a systematic literature review. Prehosp Disaster Med. 2020;35(1):61-68. doi: 10.1017/S1049023X19005181 CrossRefGoogle ScholarPubMed
Gotham, KF, Cannon, C. Circulating risks: coastal cities and the specter of climate change risk. The Routledge Handbook on Spaces of Urban Politics. 2018:393-403.CrossRefGoogle Scholar
United States Census Bureau. How ready are we? https://www.census.gov/library/visualizations/2018/comm/how-ready-are-wehtml Google Scholar
Howe, PD. Modeling geographic variation in household disaster preparedness across US states and metropolitan areas. Prof Georgr. 2018;70(3):491-503. doi: 10.1080/00330124.2017.1416301 CrossRefGoogle Scholar
Phillips, CA, Caldas, A, Cleetus, R, et al. Compound climate risks in the COVID-19 pandemic. Nat Clim Change. 2020;10(7):586-588.CrossRefGoogle Scholar
Naguib, MM, Ellström, P, Järhult, JD, Lundkvist, Å, Olsen, B. Towards pandemic preparedness beyond OVID-19. The Lancet Microbe. 2020;1(5):e185-e186. doi: 10.1016/S26665247(20)30088-4 CrossRefGoogle Scholar
Cannon, T, Twigg, J, Rowell, J. Social vulnerability, sustainable livelihoods, and disasters. Report to DFID conflict and humanitarian assistance department (CHAD); 2003.Google Scholar
Flanagan, BE, Gregory, EW, Hallisey, EJ, Heitgerd, JL, Lewis, B. A social vulnerability index for disaster management. J Homel Secur Emerg Manag. 2011;8(1):1-24. doi: 10.2202/1547-7355.1792 Google Scholar
Kendra, JM, Clay, LA, Gill, KB. Resilience and disasters. In Rodriguez, Quarantelli, Dynes (eds.) Handbook of Disaster Research. Springer, New York; 2018:87-107.CrossRefGoogle Scholar
Basolo, V, Steinberg, LJ, Burby, RJ, et al. The effects of confidence in government and information on perceived and actual preparedness for disasters. Environ Behav. 2009;41(3):338-364. doi: 10.1177/0013916508317222 CrossRefGoogle Scholar
Clay, LA, Goetschius, JB, Papas, MA, Kendra, J. Influence of mental health on disaster preparedness: findings from the behavioral risk factor surveillance system, 2007-2009. J Homel Secur Manag. 2014;11(3):375-392. https://doi.org/10.1515/jhsem-2014-0013 CrossRefGoogle Scholar
Donner, WR, Lavariega-Montforti, J. Ethnicity, income, and disaster preparedness in deep South Texas, United States. Disasters. 2018;42(4):719-733. doi: 10.1111/disa.12277 CrossRefGoogle ScholarPubMed
Murphy, ST, Cody, M, Frank, LB, Glik, D, Ang, A. Predictors of emergency preparedness and compliance. Disaster Med. Public Health Prep. 2009;3(2):1-10. doi: 10.1097/DMP.0b013e3181a9c6c5 Google Scholar
Cong, Z, Chen, Z, Liang, D. Barriers to preparing for disasters: age differences and caregiving responsibilities. Int J Disaster Risk Reduct. 2021; 61:102338. https://doi.org/10.1016/j.ijdrr.2021.102338 CrossRefGoogle Scholar
Gulbrandsen, C. Measuring older women’s resilience: evaluating the suitability of the Connor-Davidson Resilience Scale and the Resilience Scale. J Women Aging. 2016;28(3):225-237. doi: 10.1080/08952841.2014.951200 CrossRefGoogle ScholarPubMed
Zimmerman, MA, Stoddard, SA, Eisman, AB, et al. Adolescent resilience: promotive factors that inform prevention. Child Dev Perspect. 2013;7(4): doi: 10.1111/cdep.12042.CrossRefGoogle ScholarPubMed
Cutter, SL. Resilience to what? Resilience for whom? Georgr J. 2016;182(2):110-113. https://doi.org/10.1111/geoj.12174 CrossRefGoogle Scholar
Masten, AS. Global perspectives on resilience in children and youth. Child Dev. 2014;85(1):6-20. doi: 10.1111/cdev.12205 CrossRefGoogle ScholarPubMed
Cavallo, A. Integrating disaster preparedness and resilience: a complex approach using System of Systems. Aust J Emerg Manag. 2014;29(3):46-51.Google Scholar
Moser, S, Meerow, S, Arnott, J, Jack-Scott, E. The turbulent world of resilience: interpretations and themes for transdisciplinary dialogue. Climatic Change. 2019;153(1):21-40. doi: 10.1007/s10584-018-2358-0 CrossRefGoogle Scholar
Adger, WN, Hughes, TP, Folke, C, Carpenter, SR, Rockström, J. Social-ecological resilience to coastal disasters. Science. 2005;309(5737):1036-1039. doi: 10.1126/science.1112122 CrossRefGoogle ScholarPubMed
Baker, LR, Cormier, L. Disasters and Vulnerable Populations: Evidence Based Practice For The Helping Professions. Springer Publishing Company; 2014.CrossRefGoogle Scholar
Cutter, SL, Boruff, BJ, Shirley, WL. Social vulnerability to environmental hazards. Soc Sci Q. 2012;84(2):242-261. doi: 10.1111/1540-6237.8402002 CrossRefGoogle Scholar
Weber, MC, Pavlacic, JM, Gawlik, EA, Schulenberg, SE, Buchanan, EM. Modeling resilience, meaning in life, posttraumatic growth, and disaster preparedness with two samples of tornado survivors. Traumatology. 2020;26(3):266. https://doi.org/10.1037/trm0000210 CrossRefGoogle Scholar
Fergus, S, Zimmerman, MA. Adolescent resilience: a framework for understanding healthy development in the face of risk. Annu Rev Public Health. 2005;26: 399-419. doi: 10.1146/annurev.publhealth.26.021304.144357 CrossRefGoogle Scholar
Masten, AS, Obradovic, J. Disaster preparation and recovery: lessons from research on resilience in human development. Ecol Soc. 2008;13(1). doi: 10.5751/ES-02282-130109 CrossRefGoogle Scholar
Kronenberg, ME, Hansel, TC, Brennan, AM, et al. Children of Katrina: lessons learned about post-disaster symptoms and recovery patterns. Child Dev. 2010;81(4): 1241-1259. https://doi.org/10.1111/j.1467-8624.2010.01465.x CrossRefGoogle Scholar
Osofsky, JD, Osofsky, HJ. Hurricane Katrina and the Gulf Oil Spill: lessons learned about short-term and long-term effects. Int J Psychol. 2021; 56(1):56-63. doi: 10.1002/ijop.12729 CrossRefGoogle ScholarPubMed
Masten, AS, Garmezy, N, Tellegen, A, et al. Competence and stress in school children: the moderating effects of individual and family qualities. J Child Psychol Psychiatry. 1988;29(6):745-764. doi: 10.1111/j.1469-7610.1988.tb00751.x CrossRefGoogle ScholarPubMed
Luthar, SS, Zelazo, LB. Research on resilience: an integrative review. Resilience and vulnerability: adaptation in the context of childhood adversities. 2003;2:510-549. https://doi.org/10.1017/CBO9780511615788.023 CrossRefGoogle Scholar
Lightfoot, ES, Lesen, AE, Ferreira, RJ. Gender and resilience in Gulf Coast communities: risk and protective factors following a technological disaster. Int J Disaster Risk Reduct. 2020;50:101716. https://doi.org/10.1016/j.ijdrr.2020.101716 CrossRefGoogle Scholar
Calhoun, LG, Tedeschi, RG. The foundations of posttraumatic growth: an expanded framework. Handbook of Post-traumatic growth. 2014;3-23.Google Scholar
Cuervo, I, Leopold, L, Baron, S. Promoting community preparedness and resilience: a Latino immigrant community–driven project following Hurricane Sandy. Am J Public Health. 2017;107(S2): S161-S164.CrossRefGoogle ScholarPubMed
Welby-Everard, P, Quantick, O, Green, A. Emergency preparedness, resilience and response to a biological outbreak. BMJ Mil Health. 2020;166(1):37-41. doi: 10.1136/jramc-2019-001323 Google ScholarPubMed
Palinkas, LA, Springgate, BF, Sugarman, OK, et al. A rapid assessment of disaster preparedness needs and resources during the COVID-19 Pandemic. Int J Environ Res Public Health. 2021;18(2):425. https://doi.org/10.3390/ijerph18020425 CrossRefGoogle ScholarPubMed
Huang, J, Teoh, JY-C, Wong, SH, Wong, MCS. The potential impact of previous exposure to SARS or MERS on control of the COVID-19 pandemic. Eur J Epidemiol. 2020; 35(11):1099-1103. https://doi.org/10.1007/s10654-020-00674-9 CrossRefGoogle ScholarPubMed
Whytlaw, JL, Hutton, N, Yusuf, JEW, et al. Changing vulnerability for hurricane evacuation during a pandemic: issues and anticipated responses in the early days of the COVID-19 pandemic. Int J Disaster Risk Reduct. 2021;61:102386. doi: 10.1016/j.ijdrr.2021.102386 CrossRefGoogle ScholarPubMed
Sirleaf, EJ, Clark, H. Report of the independent panel for pandemic preparedness and response: making COVID-19 the last pandemic. Lancet. 2021;398(10295):101-103. doi: 10.1016/S0140-6736(21)01095-3 CrossRefGoogle ScholarPubMed
Babu, GR, Khetrapal, S, John, DA, Deepa, R, Narayan, KV. Pandemic preparedness and response to COVID-19 in South Asian countries. Int J Infect Dis. 2021;104:169-174. doi: 10.1016/j.ijid.2020.12.048 CrossRefGoogle ScholarPubMed
Becker, JS, Paton, D, Johnston, DM, Ronan, KR, McClure, J. The role of prior experience in informing and motivating earthquake preparedness. Int J Disaster Risk Reduct. 2017;22:179-193. https://doi.org/10.1016/j.ijdrr.2017.03.006 CrossRefGoogle Scholar
Lehdonvirta, V, Oksanen, A, Räsänen, P, Blank, G. Social media, web, and panel surveys: using non-probability samples in social and policy research. Policy Internet. 2021;13(1):134-155. https://doi.org/10.1002/poi3.238 CrossRefGoogle Scholar
Norris, FH. Disaster research methodology: past progress and future directions. J Trauma Stress. 2006;19(2):173-84. doi: 10.1002/jts.20109. http://www.redmh.org/research/general/REDMH_methods.pdf CrossRefGoogle ScholarPubMed
Inoue, M, Yamaoka, K. Social factors associated with psychological distress and health problems among elderly members of a disaster-affected population: subgroup analysis of a 1-year post-disaster survey in Ishinomaki area, Japan. Disaster Med Public Health Prep. 2017;11(1):64-71. doi: 10.1017/dmp.2016.147 CrossRefGoogle ScholarPubMed
Lindell, MK, Perry, RW. The protective action decision model: theoretical modifications and additional evidence. Risk Anal. 2012;32(4):616-632. doi: 10.1111/j.1539-6924.2011.01647.x CrossRefGoogle ScholarPubMed
Liddell, JL, Saltzman, LY, Ferreira, RJ, Lesen, AE. Cumulative disaster exposure, gender and the protective action decision model. Progress Disaster Sci. 2020;5:100042. https://doi.org/10.1016/j.pdisas.2019.100042 Google Scholar
Nrugham, L, Holen, A, Sund, AM. Associations between attempted suicide, violent life events, depressive symptoms, and resilience in adolescents and young adults. J Nerv Ment Dis. 2010;198(2):131-136. doi: 10.1097/NMD.0b013e3181cc43a2 CrossRefGoogle ScholarPubMed
Patel, MM, Saltzman, LY, Ferreira, RJ, Lesen, AE. Resilience: examining the impacts of the Deepwater Horizon oil spill on the Gulf Coast Vietnamese American community. Soc Sci. 2018;7(10):203. https://doi.org/10.3390/socsci7100203 CrossRefGoogle Scholar
Osborne, JW. Regression & linear modeling: best practices and modern methods. Sage Publications; 2019.Google Scholar
Bailey, ZD, Feldman, JM, Bassett, MT. How structural racism works—racist policies as a root cause of US racial health inequities. N Engl J Med. 2021;384(8):768-773.CrossRefGoogle ScholarPubMed
Doppelt, B. Transformational resilience: how building human resilience to climate disruption can safeguard society and increase wellbeing. Routledge; 2017.CrossRefGoogle Scholar
Saxena, A, Qui, K, Robinson, SA. Knowledge, attitudes, and practices of climate adaptation actors towards resilience and transformation in a 1.5oC world. Environ Sci Policy. 2018;80:152-159. doi: 10.1016/j.envsci.2017.11.001 CrossRefGoogle Scholar
Cutter, SL, Finch, C. Temporal and spatial changes in social vulnerability to natural hazards. Proc Natl Acad Sci USA. 2008;105:(7):2301-2306. doi: 10.1073/pnas.0710375105 CrossRefGoogle Scholar
Kapucu, N, Hawkins, CV, Rivera, FI. Disaster preparedness and resilience for rural communities. RHCPP. 2013;4(4):215-233. https://doi.org/10.1002/rhc3.12043 Google Scholar
Ma, Z, Guo, S, Deng, X, Xu, D. Community resilience and resident’s disaster preparedness: evidence from China’s earthquake-stricken areas. Nat Hazards. 2021;108(1):567-591. doi: 10.1007/s11069-021-04695-9 CrossRefGoogle Scholar
Chandra, A, Williams, M, Plough, A, et al. Getting actionable about community resilience: the Los Angeles county community disaster resilience project. Am J Public Health. 2013; 103(7):1181-1189. doi: 10.2105/AJPH.2013.301270 CrossRefGoogle ScholarPubMed
Xiang, T, Gerber, BJ, Zhang, F. Language access in emergency and disaster preparedness: an assessment of local government ‘whole community’ efforts in the United States. Int J Disaster Risk Reduct. 2021;55:102072. https://doi.org/10.1016/j.ijdrr.2021.102072 CrossRefGoogle Scholar
Teo, M, Goonetilleke, A, Deilami, K, Ahankoob, A, Lawie, M. Engaging residents from different ethnic and language backgrounds in disaster preparedness. Int J Disiaster Risk Reduct. 2019;39:101245. https://doi.org/10.1016/j.ijdrr.2019.101245 CrossRefGoogle Scholar
Righi, E, Lauriola, P, Ghinoi, A, Giovannetti, E, Soldati, M. Disaster risk reduction and interdisciplinary education and training. Progress Disaster Sci. 2021;10:100165. https://doi.org/10.1016/j.pdisas.2021.100165 CrossRefGoogle Scholar
Höfler, M. Psychological resilience building in disaster risk reduction: contributions from adult education. Int J Disaster Risk Sci. 2014;5(1):33-40. https://doi.org/10.1007/s13753-014-0009-2 CrossRefGoogle Scholar
Golden, H. ‘I have a future:’ How Biden’s free community college plan could transform education. The Guardian. Published July 19, 2021. https://www.theguardian.com/education/2021/jul/19/biden-plan-free-community-college-transform-higher-education Google Scholar
UN Department of Economic and Social Affairs. Sustainable development goals. https://sdgs.un.org/goals Google Scholar
Stratton, SJ. Population research: convenience sampling strategies. Prehosp Disaster Med. 2021;36(4):373-374. doi: 10.1017/S1049023X21000649 CrossRefGoogle ScholarPubMed
Figure 0

Figure 1. Conceptual model for stress-resistant theory of preparedness adapted from resiliency models of compensatory, challenge, and protective factors in Fergus & Zimmerman, 2005.

Figure 1

Table 1. Demographic characteristics of the sample

Figure 2

Table 2. Logistic regression analysis of sociodemographic characteristics and natural disaster preparedness

Figure 3

Table 3. Logistic regression analysis of sociodemographic characteristics and pandemic preparedness

Figure 4

Table 4. Logistic regression analysis of disaster preparedness and pandemic preparedness