Mental Health Impacts of the 2024 Ohio Tornadoes on People With Socioeconomic Disadvantages

Publication Date: 2025

Abstract

This report describes the mental health impacts of Ohio’s 2024 tornadoes, with a focus on how the disaster heightened anxiety, depression, and post-traumatic stress disorder (PTSD) among people with socioeconomic disadvantages in Franklin County, Ohio. The study also investigated how the tornadoes exacerbated mental health challenges in vulnerable communities and explored the coping mechanisms and resources that community members employed post-disaster. We used a mixed-methods approach, including surveys (N=521) and interviews (N=20) of adults from low-income households in Franklin County. Surveys provided quantitative measures of mental health conditions (i.e., anxiety, depression, and PTSD levels) and risk perceptions, while interviews offered qualitative perspectives on personal experiences, community challenges, and adaptive strategies. Our findings revealed significant disparities in mental health outcomes between participants who were highly impacted by severe weather in 2024 and those with low levels of impact. Highly impacted individuals reported elevated stress, which was compounded by financial strain and inadequate access to mental health resources. They also had significantly higher perceptions of risk severity and likelihood of future severe weather events. The qualitative analysis highlighted themes of community isolation, reliance on family and social ties, and mixed perceptions of local emergency communication. These results suggest that targeted mental health support, improved disaster preparedness programs, and culturally sensitive risk communication are essential to reducing vulnerability in these communities.

Introduction

Over the 4.5 months between January to mid-May 2024, Ohio experienced some of the most severe weather in its history, with central Ohio being the most impacted region (Murphy, 2025¹). The National Weather Service (n.d.²) confirmed that 66 tornadoes touched down and that another 123 damaging wind events (not including tornadoes), and 65 severe hail events also occurred in that time period. These severe weather events killed three people and injured at least 25 (Gelber, 2024³). In total, Franklin County, near Columbus, Ohio, experienced six tornadoes. An unusually lengthy snow season persisted in the county until the end of March, compounding the risks and difficulties that people experienced as they tried to navigate the post-tornado recovery. News reports documented how this rare breakout of severe weather caused a wave of panic among people in Franklin County (Gelber, 2024), especially among people in socioeconomically disadvantaged communities who already live in poor housing conditions. In our study, we focused on people in socioeconomically disadvantaged communities who experienced repeated severe and damaging wind events between the end of February and mid-May 2024. This report describes how the tornadoes intensified their mental health challenges and also reveals the coping strategies and resilience factors that they employed to mitigate these impacts.

Literature Review

The mental health consequences of disasters are not evenly distributed; research has shown that people in socioeconomically disadvantaged communities are more susceptible to adverse mental health outcomes post-disaster (Abukhalaf, 2024 ⁴; Lowe et al., 2020⁵; Neria et al., 2007⁶; Woodhall-Melnik & Grogan, 2019⁷). Studies have also shown that weather-related mental distress in socioeconomically disadvantaged communities is related to pre-existing factors such as poor housing conditions, fragile overall health, reduced access to health care, and economic limitations that hinder one’s ability to cope with disasters (First et al., 2021⁸; Laugharne et al., 2011). Weather-related disasters, exacerbated by climate change, are increasingly affecting mental health among socioeconomically disadvantaged communities and require further study (Goldmann & Galea, 2014⁹; Silveira et al., 2021¹⁰). Studies have shown that exposure to severe weather-related impacts—such as property damage, loss of loved ones, injuries, and fear for one's life—increases the risk of adverse mental health outcomes (First et al., 2021). Additionally, damage from natural hazard events can lead to residential instability, further negatively impacting mental health and well-being (Laugharne et al., 2011¹¹; Woodhall-Melnik & Grogan, 2019).

Moreover, disasters can undermine the social and economic foundation on which vulnerable communities rely, leading to a rise in mental health disorders and a decline in quality of life (Bakić & Ajduković, 2021¹²). Socioeconomically disadvantaged communities are disproportionately affected by weather-related disasters due to factors such as limited access to healthcare, inadequate infrastructure, and reduced capacity to prepare for and recover from such events (Kang, 2023¹³). Lack of preparedness in health is due to the insufficient planning of health care services to meet the needs of vulnerable populations during disasters and the limited access that socioeconomically disadvantaged communities often have to essential health services during weather-related events.. The mental health risks associated with climate change and extreme weather include stress, anxiety, despair, depression, and suicidal ideation (Mason et al., 2020¹⁴). Extreme weather events can lead to psychological disorders such as post-traumatic stress disorder (PTSD), which are associated with loss, disruption, displacement, and cumulative impacts from repeated exposure to disasters (Fritze et al., 2008¹⁵; Kc et al., 2019¹⁶).

In summary, severe weather-related disasters exacerbate the mental health challenges faced by people with socioeconomic disadvantages. These individuals are at higher risk of adverse mental health outcomes due to fuel poverty, inadequate housing, low incomes, limited resources, reduced access to support services, and the compounding effects of chronic stress related to poverty. Addressing the mental health needs of socioeconomically disadvantaged populations in the aftermath of disasters is crucial for promoting resilience and recovery in vulnerable communities. While existing research has explored the impact of disasters on mental health, there is limited understanding of how specific events, like the recent tornadoes in Franklin County uniquely affect socioeconomically disadvantaged communities. This study seeks to fill this gap by examining the exacerbation of mental health issues and identifying effective coping mechanisms.

Research Questions

Our study aimed to promote resilience and capacity-building initiatives that empower individuals and communities to become more proactive and adaptive in managing disaster risks and enhancing their overall well-being. To do so, our data collection and analysis aimed to answer two main research questions:

• How did the recent tornadoes in Franklin County, Ohio, exacerbate the mental health issues among people living in socioeconomically disadvantaged communities?
• What coping mechanisms and resilience factors did people in socioeconomically disadvantaged communities in Franklin County employ to mitigate the adverse mental health effects of the recent tornadoes?

Research Design

Our study employed a mixed-methods approach, combining quantitative surveys with qualitative interviews to capture the complexity and heterogeneity of individuals' experiences. Quantitative data provided a basis for statistical analyses of mental health indicators and socioeconomic variables, while qualitative methods allowed people to describe their experiences in rich narratives and contextualize the social and psychological dimensions of their disaster recovery. By triangulating multiple sources of data, we enhanced the robustness and validity of our findings, offering a more comprehensive picture of the post-disaster mental health outcomes of people in socioeconomically disadvantaged communities in Franklin County.

Study Site

For our study site, we selected two neighboring zip codes (43223 and 43204) in Franklin County. Like many urban areas, socioeconomic hardships and environmental hazards are concentrated spatially in Franklin County, and we selected these two zip codes because of their higher levels of disadvantage. The median household income in the 43223 and 43204 zip codes was $31,143 (United States Zip Codes.org, n.d.-a¹⁷) and $40,815 (United States Zip Codes.org, n.d.-b¹⁸), respectively, numbers which are significantly lower than the median household income in all of Franklin County, which was $73,795 (Data USA, n.d.¹⁹), and the national median household income in the United States, which was $80,610 (U.S. Census Bureau, 2024²⁰).

Surveys

Survey Sampling Strategy and Recruitment

Participants were recruited via convenience sampling in busy areas, such as commercial plazas. We designed a flyer with information about the aim of the survey, types of survey questions, and a digital barcode that led to the online survey and distributed it at local businesses, houses of worship, and other public places with the targeted areas. In addition, we hired two community members who work at local nonprofit organizations to aid with recruitment. The two community partners set up a booth with iPads that had access to the survey in front of local businesses, such as grocery stores, in busy commercial plazas that are frequented by members of the entire community. This sampling approach helped us recruit a diverse sample of participants and minimize bias toward race, ethnicity, age, gender, education, and other variables.

We set a minimum survey sample size of 385 participants, which we determined by using the Z formula with a 95% confidence level and a 5% confidence interval. Survey participants were given $15 Amazon e-gift cards as incentives upon completing the survey. To ensure that each survey response came from a unique individual, we required participants to provide a valid and unique email address, which was cross-checked to prevent duplicate submissions and to manage the distribution of the $15 incentive. To confirm that respondents lived in the target area, we included a mandatory question asking for their ZIP code, and we only accepted responses from ZIP codes within the designated study region. Since the analysis was conducted at the household level, we limited participation to one respondent per household to avoid duplicate data and ensure consistency in household-level analysis. To receive the $15 incentive, each survey participant had to provide a unique email address and a unique physical address.

Survey Instrument

The survey contained questions related to demographics, housing conditions, risk perception, previous severe weather experiences, and mental health. We used measures accessed through the Disaster Research Response Resources Portal (DR2) hosted by the National Institute of Environmental Health Sciences. The survey instruments we accessed included the Duke Anxiety-Depression Scale (Duke University School of Medicine, n.d.²¹; Parkerson, 2023²²), the Short PTSD Rating Interview (SPRINT) (National Center for Post-Traumatic Stress Disorder, n.d.²³; Connor & Davidson, 2001²⁴), and the National Oceanic and Atmospheric Administration’s Tornado Post-Event Survey (Natural Hazards Center, n.d.²⁵). In addition, we used a survey tool previously designed and validated by the Florida Institute for Built Environment Resilience (FIBER) at the University of Florida that examines housing conditions, risk perception, and severe weather hazards (Abukhalaf et al., 2025²⁶). The survey was divided into three main sections: the first assessed risk perception and the impact of severe weather hazards in 2024; the second focused on anxiety, depression, and PTSD; and the third collected demographic information. On average, it took respondents 5-10 minutes to complete the survey. For the full survey instrument, see the Appendix A.

Survey Data Analysis

Survey data were analyzed using different statistical approaches, such as variance analysis and regression analysis, to identify patterns and relationships between the variables. Six key variables were measured: risk possibility, risk severity, Level of Impact by Severe Weather Events in 2024, anxiety level, depression level, and PTSD level. Each variable was constructed through a series of questions, including Likert scale items, and assigned a final score out of 100%.

• “Risk Severity” was measured using Questions 4–6 of the survey, each rated on a 1–5 scale (see Appendix A for more details). The average score across these questions was calculated and then converted into a percentage. This percentage served as the “Risk Severity” score for each survey response. Scores below 40% were categorized as Low, those between 40% and 60% as Medium, and scores above 60% as High.
• “Level of Impact by Severe Weather Events in 2024” was measured using Questions 7–13 of the survey, each rated on a 1–5 scale (see Appendix A for more details). The average score across these questions was calculated and then converted into a percentage. This percentage served as the “Level of Impact by Severe Weather Events in 2024” score for each survey response. Scores below 40% were categorized as Low, those between 40% and 60% as Medium, and scores above 60% as High.
• “Anxiety” was measured using Questions 20, 22, 23, 24, 25, and 27 of the survey, each rated on a 0–2 scale (see Appendix A for more details). The average score across these questions was calculated and then converted into a percentage. This percentage served as the “Anxiety” score for each survey response. Scores below 40% were categorized as Low, those between 40% and 60% as Medium, and scores above 60% as High.
• “Depression” was measured using Questions 21, 22, 24, 25, and 26 of the survey, each rated on a 0–2 scale (see Appendix A for more details). The average score across these questions was calculated and then converted into a percentage. This percentage served as the “Depression” score for each survey response. Scores below 40% were categorized as Low, those between 40% and 60% as Medium, and scores above 60% as High.
• “PTSD” was measured using Questions 28-33 of the survey, each rated on a 0–1 scale (see Appendix A for more details). The average score across these questions was calculated and then converted into a percentage. This percentage served as the “Anxiety” score for each survey response. Scores below 40% were categorized as Low, those between 40% and 60% as Medium, and scores above 60% as High.

We categorized respondents as members of socioeconomically disadvantaged households if their incomes were below the threshold set by the U.S. Department of Housing and Urban Development’s ([HUD], 2023²⁷) 2023 Adjusted Home Income Limits Report for Ohio, which adjusts the amount of household income needed to meet its thresholds based on household size. We used the “Low Income” values from the Columbus Metro Area in the HUD report, as Franklin County falls within that metropolitan region. The low-income threshold for Columbus, Ohio, ranges from an annual income of $55,550 for a one-person household to $104,750 for an eight-person household. For the analysis, we adopted a binary coding system (0 and 1), where Low was coded as 0 and High as 1 for both variance and logistic regression analyses. The Medium values were excluded from the models as the binary system would only accept two values. Binary logistic regression is a statistical method used to understand how different factors affect the chance of something happening when there are only two possible outcomes—such as yes or no, high or low. It helps predict which outcome is more likely based on those factors. The results include something called an odds ratio, which shows how strongly each factor is linked to the outcome. If the odds ratio is above 1, it increases the chance of the outcome; below 1 means it lowers the chance; and if it’s 1, it has no effect.

Interviews

Interview Sampling Strategy and Recruitment

After completing the survey, we asked participants if they would like to participate in an interview at a later phase of the project. If they expressed interest, we asked them to provide their contact information so that the research team could follow up. The sample size for the interviews was 20 participants. Around a hundred of the survey participants expressed interest in the interview. Approximately 4 weeks later, we contacted them via phone and email, and 20 people agreed to be interviewed. The group included 12 women and eight men, ranging in age from 19 to 65 years old, and they were all below the HUD low-income threshold for Columbus, Ohio. The interviews were conducted in person at various locations, with interviewees selecting the venues that were most convenient for them. Some of these locations were private (such as the interviewee’s house) and some were public (such as a nearby park). Each interviewee was provided with $75 in compensation. The duration of the interviews ranged from 40 to 60 minutes. All interviews were conducted by the Principal Investigator, with community partners present throughout the process.

Interview Guide

Survey results were used to inform the design of the semi-structured interview guide. The interviews explored participants’ personal backgrounds and their connections to the community, followed by a deep dive into their experiences with severe weather events such as tornadoes or strong winds. Participants were asked how these events impacted their daily lives, mental health, and emotional well-being. The questions also focused on the broader mental health challenges within the community, how weather-related stress influences planning and relationships, and whether participants had received any mental health support. Additional topics included the effectiveness of local weather communication, financial impacts of severe weather, and how these stressors collectively shape individual and community mental health over time. (See Appendix B for more details on the interview questions.)

Interview Data Analysis

Interview data were thematically analyzed through continuous creation of and reflection on themes and sub-themes. We also used a combination of deductive and inductive coding, where codes were informed by interviewees, as well as pre-existing theories in mental health and disaster studies.

Ethics Statement

The Institutional Review Board (IRB) at University of Florida provided approval for this study (reference #ET00041349). All aspects of the study, including recruitment, data collection, analysis, and dissemination, were conducted in accordance with the principles outlined by the IRB. This included obtaining informed consent from all participants, protecting their confidentiality and privacy, and minimizing any potential risks or harm associated with their participation. We were fully aware that the study participants were from a particularly vulnerable population; therefore, everyone from our team—including community partners—involved in data collection and analysis was asked to finish and pass the following training modules from CONVERGE: (a) Disaster Mental Health (Adams et al., 2019²⁸), (b) Institutional Review Board (IRB) Procedures and Extreme Events Research (Wu et al., 2020²⁹), (c) Broader Ethical Considerations for Hazards and Disaster Researchers (Adams et al., 2021³⁰), (d) Social Vulnerability and Disasters (Adams et al., 2019³¹), and (e) Collecting and Sharing Perishable Data (Evans et al., 2021³²).

Results

Quantitative Findings

We received 909 completed survey responses. Our analysis identified 521 participants whose incomes were below the low-income threshold and which we categorized as socioeconomically disadvantaged. For the purposes of this report, we focused our analysis on this subgroup of 521 respondents. Table 1 provides an overview of their demographic characteristics. In terms of age, most were between the ages of 25 and 44, with the 35-39 group making up the largest proportion at 23.99. Gender distribution was relatively balanced, with males representing 52.40% and females 46.26%. A small percentage of respondents (1.34%) identified as non-binary or chose not to provide a gender response. Regarding family structure, a significant majority (83.49%) had dependents living with them. Most respondents lived in Ohio for more than two years, with 37.43% residing in the state for over 10 years.

Table 1. Select Demographic Characteristics of the Survey Sample

Table 2 shows the variance analysis that we conducted. Variance tests are statistical procedures used to determine whether there are significant differences in the distribution or spread of data across two or more groups. These tests help assess whether group differences are due to actual variation or random chance. As table 2 shows, survey participants with low severe weather impact in 2024 (less than 40%) had significantly lower perceptions of risk severity and risk possibility compared to those with high severe weather impact (greater than 60%). Specifically, participants with low severe weather impact had a mean risk possibility of 39%, while those with high impact had a mean of 63%. Similarly, perceptions of risk severity were significantly lower for participants with low impact (mean of 46%) compared to those with high impact (mean of 67%). Additionally, participants with high severe weather impact had significantly higher levels of anxiety, depression, and PTSD compared to those with low impact. For example, anxiety levels were higher among participants with high impact (mean of 55%) compared to those with low impact (mean of 43%). Depression levels were similarly higher for participants with high impact (mean of 59%) compared to those with low impact (mean of 44%). PTSD levels were also significantly higher in the high impact group (mean of 39%) compared to the low impact group (mean of 18%). When we refer to a value as "significantly higher" or "significantly lower" in this context, we mean that the difference is statistically significant—that is, it is unlikely to have occurred by chance based on the results of the statistical test.

Table 2. Variance Analysis Using Kruskal-Wallis Test

Table 3 shows a summary of the results from the logistic regression analysis that we conducted. This analysis further supports the findings from the variance analysis by demonstrating that survey participants who experienced low impact from severe weather events in 2024 were significantly less likely to develop high levels of anxiety, depression, and PTSD. In addition, they had lower perceptions of risk severity and risk possibility compared to those who experienced high impact. Participants who reported low impact from previous events were only 10% as likely to perceive a high possibility of a severe weather event compared to those who reported high impact (p < 0.001).The 95% confidence interval of 0.06 to 0.18 indicates a strong, statistically significant relationship between low impact and lower perceptions of risk possibility.

Table 3. Logistic Regression Analysis

Participants who reported low levels of impact from previous events were significantly less likely to perceive severe weather as highly threatening. Specifically, they were only 8% as likely to rate the severity of risk as high compared to those who reported high impact. This finding was strongly supported by the data. In terms of mental health, individuals with low impact were also less likely to report high levels of anxiety—they were about 25% as likely as those with high impact to experience strong anxiety symptoms. A similar pattern was seen with depression: low-impact participants were only 16% as likely to report high levels of depression. Finally, for symptoms of PTSD, those with low impact were 41% as likely to experience high levels of PTSD compared to those more heavily affected. In all models, the p-values are less than 0.05, which means the results are statistically significant, confirming that participants with low severe weather impact in 2024 had lower perceptions of risk and lower mental health issues compared to those with high impact.

Qualitative Findings

Our qualitative analysis of the 20 interviewees identified six main themes: (a) impact of severe weather events, (b) mental health and emotional responses, (c) financial and resource strain, (d) community and family relationships, (e) perception of local authority communication, and (f) coping mechanisms for mental health. See Table 4 for sub-themes, example codes, and representative quotes for each theme. Community and family relationships were essential coping factors, with 12 participants finding stability in family and long-standing connections. Additionally, there were mixed (some positive, some negative) perceptions of local authority communication, particularly around warning accuracy in 14 interviews and preparedness knowledge gaps in 11 interviews. Coping mechanisms for mental health varied, including healthy practices like breathing exercises (7 mentions), and unhealthy practices like substance use (6 mentions), showing diverse ways of managing stress.

Table 4. Interview Themes, Codes, and Representative Quotes

The theme, impact of severe weather events, recurred in the interviews, highlighting both specific incidents and a general perception of increasing severity. Several participants shared personal experiences of weather-related damage, such as fallen trees and flooding, underscoring the tangible risks posed by these events. For instance, one participant mentioned, “We had a huge branch fall, and it destroyed part of our backyard.” Moreover, a significant number of interviewees (14) expressed concerns over the growing intensity and frequency of storms, with one participant noting, “The storms are getting stronger and more frequent. It's hard not to worry about what's next.” This sentiment reflects broader anxieties regarding the unpredictability of weather patterns, suggesting that people are not only reacting to past events but are increasingly concerned about future risks. Another key theme that emerged was mental health and emotional responses to these weather events, with many participants discussing heightened levels of anxiety and community stress. Participants revealed that personal anxiety related to storms was widespread, as evidenced by one comment: “Every time there's a storm, I get really anxious. I worry about the kids and what could happen.” In addition, 11 participants noted a collective sense of community-level stress, often exacerbated by financial strain, with one interviewee stating, “A lot of people seem more stressed and down. The costs and the weather are taking a toll on everyone.” The findings suggest that the mental health struggles of individuals are not isolated but shared across communities, reinforcing the idea that weather-related stress has a far-reaching impact on social and emotional well-being. The theme of financial and resource strain was also prevalent in the interviews, with many participants highlighting the economic challenges tied to severe weather. A common concern was the rising cost of utilities, particularly during extreme weather events, which 16 participants mentioned as a significant burden. One interviewee remarked, “The electric bill goes up every summer and winter. It’s almost doubled from a few years ago,” illustrating the financial pressure created by changing environmental conditions. Additionally, participants frequently mentioned reliance on community resources. Ten participants indicated that they turn to local support systems such as food pantries to help alleviate some financial stress. As one participant shared, “We go to the food pantry each week to save on groceries. It helps us pay other bills.” These insights suggest that the financial effects of extreme weather are not only individual challenges, but also shape the broader social fabric of the community.

Conclusions

Implications for Policy and Practice

This study had two main research questions. The first question was, “How did the recent tornadoes in Franklin County, Ohio exacerbate mental health issues among socioeconomically disadvantaged communities?” Our qualitative and quantitative analyses showed that the recent tornadoes in Franklin County have intensified feelings of vulnerability, stress, and anxiety. Limited resources make it difficult for residents to prepare adequately, exacerbating financial and emotional strain. The unpredictability of weather events and concerns about insufficient housing and infrastructure increase worry, while inconsistent warning systems lead to heightened anxiety or distrust in official communication. Many residents already struggle with financial instability, and the added risk of property damage or disruptions to daily life compounds mental health challenges like anxiety and depression. These findings were further supported by participants' own accounts of how weather-related unpredictability worsened existing stress, particularly among parents who expressed deep concern for their children's safety during storms. The anxiety surrounding extreme weather was also linked to a lack of preparedness, as many participants indicated that unclear or inconsistent warnings from local authorities left them feeling unprotected and vulnerable. Additionally, financial stressors, such as rising utility bills due to extreme weather, were mentioned as key aggravating factors, with several participants noting that the high costs of heating and cooling during severe weather made it difficult to manage everyday expenses. This compounded the emotional strain already felt by individuals facing low-income situations and limited access to mental health resources, further amplifying the challenges they faced in coping with these traumatic events. Social isolation and limited social support are significant issues for participants who lack transportation options or live in neighborhoods with minimal community engagement. Severe weather events can make travel difficult by creating hazardous conditions, limiting access to transportation, and preventing individuals from reaching support networks, particularly for those already facing transportation challenges in their communities. Isolation becomes particularly difficult to manage during extreme weather events, when travel to see family and friends is restricted. The feelings of loneliness and disconnection reported by interviewees indicate that socioeconomically disadvantaged individuals may struggle to maintain social ties, which are crucial for mental health resilience and emotional support (Abukhalaf et al., 2023a³³). Moreover, physical health issues create additional barriers for individuals already struggling with mental health and financial stress. Chronic pain or medical conditions make it more challenging to engage in work or daily activities. These health concerns often worsen during severe weather events due to pressure changes and increased humidity, which can lead to inflammation, joint stiffness, and difficulty managing symptoms, especially for vulnerable individuals. This can limit mobility or create hazards for those with health conditions (Cohen & Abukhalaf, 2022³⁴). Participants with physical health challenges may be more susceptible to severe weather’s impacts, adding to their sense of vulnerability and contributing to higher levels of perceived risk.

The second research question was, “What are the possible coping mechanisms and resilience factors that can be employed by socioeconomically disadvantaged communities in Franklin County, Ohio to mitigate the adverse mental health effects of the recent tornadoes?” Participants reported various coping mechanisms. Many interviewees described using structured routines, mindfulness, and deep breathing exercises to manage their mental health. These methods, while effective for some, are often insufficient for individuals facing high-impact stressors without access to professional mental health support. For many participants, substance use serves as a primary coping strategy. Marijuana, smoking, and alcohol were frequently mentioned as ways to manage stress, anxiety, and depression. The reliance on these substances underscores a gap in accessible mental health resources, as highly impacted individuals among socioeconomically disadvantaged populations are more likely to lack affordable and effective treatment options. While these coping mechanisms provide short-term relief, their long-term impact on health and well-being could lead to additional challenges, emphasizing the need for healthier alternatives (Abukhalaf et al., 2023a; Cohen & Abukhalaf, 2022). Family and community support serve as crucial buffers for stress, particularly for those who are able to rely on family members or close friends. Participants with strong family connections reported feeling less isolated and better able to manage household responsibilities, which reduced their mental health burdens. Participants also reported avoidance and withdrawal as coping mechanisms. During severe weather events, individuals may feel heightened stress due to uncertainty, fear, or isolation. In response, some may resort to avoidance or withdrawal, minimizing social interactions and disengaging from community activities to cope with the increased pressure. However, this response can exacerbate feelings of isolation, hinder access to support networks, and further aggravate mental health struggles. Community resources, such as food pantries and government assistance, provide practical relief for financial stressors, though these are often overstretched during extreme weather seasons. Access to these resources remains essential, yet many participants feel they need more consistent and robust community support to manage ongoing stress. Our data suggests that effective support for socioeconomically disadvantaged communities affected by severe weather requires a multifaceted approach, involving individuals, the community -based organizations, and local government officials. Individuals can benefit from community-led mental health workshops focused on teaching coping skills such as mindfulness, grounding exercises, and resilience practices (Abukhalaf et al., 2023b³⁵; Cohen & Abukhalaf, 2022). Access to online mental health resources could further empower residents to manage stress without reliance on substances. At the same time, families and individuals should be encouraged to create personalized emergency preparedness plans that include gathering essential supplies, establishing safety protocols, and identifying local support networks (Abukhalaf et al., 2023a). Community organizations, such as the American Red Cross and United Way of Central Ohio, could provide guidelines and training on these measures. Community organizations should foster support networks that enable residents to connect, share resources, and offer emotional support. Initiatives like neighborhood support groups or emergency check-ins could be established to mitigate isolation, particularly during extreme weather events (Cohen et al., 2021a³⁶; Cohen & Abukhalaf, 2021b³⁷). Community centers and local nonprofits can offer workshops on stress management, physical fitness, and group counseling as alternatives to substance use. These initiatives could provide residents with healthier coping strategies and reduce reliance on substances (Abukhalaf et al., 2023b). In addition, community resources, including food pantries and utility assistance programs, should be expanded, especially during severe weather seasons. This could help alleviate financial pressures and ensure that residents have consistent access to essential services.

To address the potential underutilization of existing community resources, it is important to recognize the barriers that might prevent individuals from fully engaging with available services. These barriers could include lack of awareness, limited access to technology, transportation challenges, and social isolation. While programs like mental health workshops, emergency preparedness training, and support groups may already exist, their reach can be significantly limited if these obstacles are not addressed. Research is needed to understand how to overcome these barriers and ensure that services are accessible, relevant, and trusted by residents. For example, studies could explore how to improve the accessibility of online resources for those with limited digital literacy or connectivity. Additionally, community-led initiatives could be explored as a way to build trust and increase engagement. By involving residents in program planning and delivery, these initiatives could better reflect the needs and preferences of the community, ensuring higher participation. Furthermore, more focus could be placed on integrating services with other community touchpoints, such as schools, local businesses, and faith-based organizations, to enhance outreach and convenience. This comprehensive approach would help to ensure that vulnerable residents are aware of, and can access, the support they need during extreme weather events and other crises. Furthermore, local governments should work on creating clear, consistent communication strategies for emergency alerts, with specific guidance on actionable steps that recognize the resource limitations of community members (Abukhalaf & von Meding, 2020³⁸; Abukhalaf & von Meding, 2021³⁹). Regular public workshops on emergency preparedness can also help residents feel more equipped to handle severe weather events (Abukhalaf et al., 2022⁴⁰). Locally funded subsidies for heating and cooling costs, especially during extreme weather conditions, could help ease financial burdens for socioeconomically disadvantaged residents (Abukhalaf, 2024). These could be paired with programs focused on energy efficiency and conservation, ultimately reducing costs. Simultaneously, local governments can collaborate with local mental health organizations to provide free or low-cost counseling services, particularly for socioeconomically disadvantaged households (Cohen et al., 2021⁴¹; Cohen & Abukhalaf, 2021a). Expanding telehealth options and mobile mental health clinics can make these services more accessible to those with transportation limitations. Finally, local governments l could introduce resilience-building programs, including mental health literacy, financial literacy, and extreme weather preparedness (Cohen et al., 2021; Cohen & Abukhalaf, 2021b). Such programs could help residents build sustainable skills to manage crises, enhancing both individual and community resilience.

Limitations

Despite the comprehensive design and execution of this study, several limitations must be acknowledged. First, the cross-sectional nature of the research means that the data reflects only a snapshot in time, which limits the ability to capture the long-term effects of severe weather events on mental health. The mental health outcomes observed in the study may evolve over time, and additional longitudinal research would be needed to assess the durability of the impacts and the effectiveness of resilience-building strategies. Second, the reliance on self-reported data from surveys and interviews introduces the potential for response biases. Participants may have underreported or overreported their mental health symptoms or coping behaviors due to social desirability bias or lack of awareness of their mental health status. While efforts were made to ensure anonymity and reduce social pressures, these biases are an inherent challenge in mental health research, particularly in vulnerable populations. Another limitation is the potential for selection bias in the recruitment process. While surveys were distributed at various community locations, participation was voluntary, and the sample may not fully represent the diversity of experiences within the socioeconomically disadvantaged communities of Franklin County. The sample may be skewed towards individuals who are more engaged with the community or those who have easier access to the data collection sites. This could limit the generalizability of the findings to the broader population of socioeconomically disadvantaged residents. Additionally, although the study focused on socioeconomically disadvantaged communities in Franklin County, the findings may not be directly applicable to other regions with different socioeconomic, cultural, or environmental contexts. Further studies in other geographical areas are needed to confirm the broader applicability of the results.

Future Research Directions

Future research should further explore the mental health impacts of severe weather events on socioeconomically disadvantaged communities, especially in urban areas with limited disaster preparedness resources. Expanding sample sizes across diverse geographic locations could enhance the generalizability of findings, allowing researchers to compare different community responses and coping mechanisms based on unique environmental and socioeconomic factors. Additionally, integrating more behavioral constructs into the analysis—such as those from the Protective Behavior Model (Abukhalaf, 2023⁴²)—could offer deeper insights into the psychological processes driving risk perception, preparedness, and resilience. A longitudinal approach would also be valuable, tracking participants' mental health over time to assess the long-term effects of repeated severe weather exposure. Future studies might examine the specific roles that local institutions and government communication play in shaping public trust and preparedness, as mixed responses to warnings indicate a gap in effective risk communication. Finally, research should evaluate the effectiveness of community-centered resilience programs, such as peer support networks and localized mental health interventions, to determine the most impactful strategies for enhancing disaster resilience and reducing mental health burdens in these communities.

References