Hospital resource deficits in climate vulnerable United States counties: insights from a national survey

Introduction

Climate change presents one of the greatest challenges to humanity. It triggers an increase in severe natural disasters such as hurricanes, storms, and heatwaves, affecting not only the environment and infrastructure but also human health and the healthcare delivery system (1-6). The increased frequency and intensity of climate-driven natural disasters are placing new strains on healthcare facilities globally (7). Beyond extreme weather, climate change also influences longer-term determinants of health such as ambient temperature, land use, and biodiversity, as highlighted by recent systematic reviews (8,9). Multiple systematic reviews have shown that various climate-related events can affect at least one aspect of healthcare facilities (10,11). In addition, international guidance emphasizes the importance of building climate-resilient health systems, underscoring the global relevance of this issue (12). Extreme weather events, in particular, have been linked to a wide range of adverse health outcomes, including mental health issues, cardiovascular and pulmonary diseases, renal disease, trauma, reproductive health problems, and increased mortality (1-4,13-16). They also contribute to increased rates of infectious diseases, geriatric and pediatric conditions, higher mortality rates, and cancer cases (1,13,17). Furthermore, the effects of climate change are not uniformly distributed. Minority communities and socioeconomically vulnerable individuals are disproportionately affected by worse health care (6,18,19). Also, these groups are more likely to suffer from extreme weather events like storms, flooding, heatwaves, and infectious diseases and are less able to prepare and respond to disasters (19-21). In the United States, climate change has driven a surge in the frequency and destructiveness of large wildfires and a marked rise in the rapid intensification of hurricanes, heightening risks for affected populations (22,23).

Research has shown that healthcare facilities, which are critical for treating illnesses and providing disaster response, are vulnerable to these events (24,25).

The climate vulnerability score, introduced in January 2023 by the Environmental Defense Fund and Texas A&M University (26), represents the most comprehensive public database to assess climate vulnerabilities across the United States. It incorporates approximately 200 county level factors to calculate the Climate Vulnerability Index (CVI). The objective of this project is to bolster government initiatives, enabling communities and policymakers to prioritize resources and interventions in areas with heightened climate vulnerability (26).

Our research question was to analyze health care resources in the counties most vulnerable to natural disasters. To do this, we compared hospital characteristics in the most vulnerable counties to average hospital characteristics in the American Hospital Association (AHA) survey. To our knowledge, this is the first study to systematically examine the characteristics of hospitals located in the most climate-vulnerable counties in the United States.

We hypothesized that hospitals in climate-vulnerable regions would be smaller than those in non-vulnerable regions and would more often be rural referral hospitals, with less staffing and critical care resources.

Methods

We performed a retrospective observational study to examine the hospital and population characteristics in the highest climate vulnerable areas across the United States, using data collected between July and September 2024.

Data sources

CVI (27)

We used data from the U.S. CVI, accessed in 2024, which is compiled by the Environmental Defense Fund, Texas A&M University, and Darkhorse Analytics and draws on underlying indicators from 2017–2019 to avoid distortions related to the coronavirus disease 2019 (COVID-19) pandemic (26). The CVI identifies the intersection of vulnerability and climate change risks by integrating public health, social, economic, environmental, and climate data at the U.S. census tract level, using nearly 200 indicators. It combines baseline vulnerabilities that weaken resilience with the projected impacts of climate change on individuals and communities. This dataset is the most comprehensive effort to date to quantify and map cumulative vulnerabilities, risks, and disparities related to climate change across the United States. The data are publicly available and gives an overview to understand locally relevant determinants for climate vulnerability at a county level scale. Climate vulnerability data are presented in quintiles from lowest to highest climate vulnerability.

American Community Survey (ACS) (28)

The ACS is a yearly released database that provides detailed population and housing information to understand local communities and changes in communities. We used county-level data from 2021 to describe the demographic and socioeconomic characteristics of the populations served by the hospitals in our study.

AHA (29)

The AHA collects data from approximately 6,000 hospitals across the United States. We obtained hospital characteristics from the 2016 Annual Survey Database. Only hospitals with a primary service code for general medical and surgical services were included, excluding specialty hospitals and long-term acute care providers. This restriction was chosen because these facilities are only partially captured in the AHA survey and often lack comparable structural features [e.g., surgical departments, intensive care unit (ICU) capacity, computed tomography (CT) scanners], which would have made direct comparisons inconsistent across hospital types.

Private equity (PE) ownership data (30)

The PE stakeholder project tracks data on the increasing number of hospitals that are PE-owned. We integrated their 2024 dataset to identify PE-owned hospitals.

Characteristics

Climate vulnerability definition and characteristics

We used the climate vulnerability county level quintiles to define the counties with the highest overall climate vulnerability. The overall climate vulnerability score consists of two main components: baseline community vulnerability and climate change risks. Baseline vulnerability reflects factors that may reduce resilience to climate change, while climate change risks reflect the direct and indirect impacts of climate change. In total it consists of 184 indicators to give a thorough overview of the most climate vulnerable communities. The baseline indicators include existing health, socioeconomic, infrastructure, and environmental vulnerabilities. In contrast, the climate change risk indicators incorporate data on extreme events, socioeconomic impacts post-event, and health impacts following a climate change event (26).

Hospital characteristics

Guided by prior frameworks on hospital surge capacity, system resilience, and climate-resilient health care facilities (31-33), we included a comprehensive set of structural and operational hospital characteristics: region; core-based statistical areas (as defined by the U.S. Office of Management and Budget); bed size; ownership; personnel data; community hospital status; critical access hospital status; rural referral center status; sole community provider status; Council of Teaching Hospitals and Health Systems status; hospital departments; number of Medicaid discharges; number of Medicare discharges; availability of imaging; and number of total admissions.

Population characteristics

We included racial composition of the population served, using the percentage of each race available in the ACS data. Further, we used the percentage of individuals living below the federal poverty threshold (as defined by the U.S. Census Bureau), the percentage with a bachelor’s degree, the percentage of disability, and the percentage of people without health insurance.

Data linkage

We merged hospital-level data from the AHA, ACS and CVI. The linkage was performed using county Federal Information Processing Standard (FIPS) codes as a common geographic identifier across datasets. Each hospital in the AHA dataset was assigned to a county based on the FIPS code provided in the AHA file. For hospitals located near county borders, we used the county of the hospital’s primary listed address. To identify hospitals under PE ownership, we merged the AHA data with a proprietary PE database using facility ID as the matching variable.

Statistical analysis

We assigned hospitals and population characteristics to county-level CVI quintiles, as defined by the CVI methodology. Hospital and population characteristics were summarized using medians with interquartile ranges for continuous variables and frequencies with percentages for categorical variables. To compare these characteristics across CVI quintiles, we employed statistical tests appropriate to each variable type. For continuous variables, we used the Kruskal-Wallis test due to non-normal distributions, confirmed by visual inspection. For categorical variables, we applied the Rao-Scott adjusted Chi-squared test to account for complex survey design. All analyses incorporated stratification by U.S. Census regions (Northeast, South, Midwest, and West) and clustering by hospital ID to account for geographic variation. Data manipulation and statistical analysis was performed using R version 4.3.0 (R Foundation, Vienna, Austria).

All analysis and presentation complied with the Strengthening the Reporting Observational Studies in Epidemiology Guidelines (STROBE). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was deemed exempt from institutional review by the Mass General Brigham Institutional Review Board (“Impact of climate change on benign and malignant conditions in the United States of America”, IRB: 2023P003498) because it only involved publicly available datasets and secondary analysis.

Results

We included data from 4,612 hospitals, located in 2,526 counties across the United States in the analysis. Of these, 682 (14.8%) were located in counties within the highest climate vulnerability quintile. The distribution of the climate-vulnerability for the hospitals is shown in Figure 1, with the vast majority, 634 (93%), located in the Southern region. Hospital and population characteristics across climate vulnerability quintiles are summarized in the online table (available at https://cdn.amegroups.cn/static/public/jhmhp-25-60-1.docx).

Figure 1 Distribution of county-level climate vulnerability across the United States.

Hospital characteristics

Hospitals located in core-based statistical areas with the highest CVI were significantly different from those in lower quintiles in several key characteristics. Hospitals in the highest CVI areas were significantly more likely to be located in rural regions, with 34% classified as rural compared to 24% overall (P<0.001). They were less likely to be publicly owned, with 45% being public compared to 63% overall (P<0.001), and more likely to be for-profit institutions (24% vs. 16% overall; P<0.001). PE-owned hospitals were also more common in the highest CVI areas (6.2% vs. 3.8% overall; P<0.001). Additionally, hospitals in these areas were more likely to be classified as rural referral centers (7.2% vs. 4.0%, P<0.001) and sole community providers (11% vs. 6.9%, P<0.001).

Hospitals in the highest CVI areas were significantly smaller in terms of bed size, with a mean of 135 beds (IQR, 28, 165) compared to 167 beds (IQR, 28, 225) overall (P<0.001). They also had fewer ICU beds, with a mean of 10 (IQR, 0, 13) compared to 13 (IQR, 0, 17) overall (P<0.001).

Hospital services

Hospitals in the highest CVI quintile provided fewer critical services. For example, only 67% of hospitals in these areas offered intensive care services, compared to 73% overall (P<0.001). Additionally, hospitals in these areas were more likely to be classified as limited service hospitals, with 12% offering limited services compared to 13% overall (P=0.01).

Hospital personnel

Hospitals in the highest CVI quintile employed significantly fewer personnel overall. They had fewer full-time physicians, with a mean of 110 compared to 336 in less vulnerable areas (P<0.001). Similarly, they employed fewer registered nurses, with a mean of 187 compared to 267 overall (P<0.001). The total number of full-time staff was also lower, with a mean of 667 compared to 955 overall (P<0.001).

Imaging capabilities

Hospitals in the highest CVI areas had reduced imaging capabilities. For instance, 75% of hospitals had MRI machines compared to 79% overall (P=0.008), and 52% of hospitals in the highest CVI areas had advanced multi-slice CT scanners, compared to 64% overall (P<0.001).

Population characteristics

The populations served by hospitals in the most climate-vulnerable counties were significantly more likely to be non-Hispanic Black (24% vs. 10% nationally, P<0.001) and had higher rates of uninsured individuals (14% vs. 11%, P<0.001) and disabilities (18% vs. 14%, P<0.001). These communities also had lower education levels, with fewer residents holding bachelor’s degrees (18% vs. 28%, P<0.001) and higher poverty rates (20% vs. 14%, P<0.001).

Discussion

We integrated data from the CVI, the AHA, and the ACS to explore the characteristics of hospitals and populations in the most climate-vulnerable counties in the United States. Our findings reveal that hospitals in these high-risk areas are generally smaller, with fewer beds, physicians, and critical infrastructure such as CT scanners. These resource limitations may hinder their capacity to respond effectively to climate-related health emergencies, particularly during surges in demand (16). Despite serving smaller populations, these hospitals play an outsized role in their communities, as they are more likely to be rural referral centers or sole community providers.

While populations in these counties exhibit higher social vulnerability—such as greater proportions of Black Americans and indicators of socioeconomic disadvantage—the more immediate concern is the limited capacity of the hospitals serving them. Climate change is likely to exacerbate this mismatch. As natural disasters increase in frequency and severity, populations in high-vulnerability counties may face greater barriers to care, compounding existing disparities.

This relevance extends beyond healthcare, as strengthening hospital resilience and addressing inequities directly support the United Nations’ goals on health, reducing inequalities, and climate action (34). Recognizing these links highlights the global importance of addressing climate-related health disparities.

More frequent and severe natural disasters will impact hospitals and the populations they serve. Our findings highlight that the effects of climate change are not, and will not be, evenly distributed. In the United States, racial minorities and socioeconomically disadvantaged individuals are more likely to live in vulnerable areas, putting them at greater risk.

The effects likely will be even harder felt in climate-vulnerable counties because it has been shown that higher hospital quality provides better health care in the counties they serve (35) and that structural racism also exists in hospital care (18,19). Also, the population in these counties is disproportionally affected by cancer and show a higher cancer mortality than the rest of the population (36). Likely we will see even worse outcomes for disadvantaged groups if no action to better local health care, improve living conditions and fight against the CO₂ emissions.

The clinical impact of climate change is already evident. Emergency departments and intensive care units are facing increasing strain from climate-related events, such as heatwaves, hurricanes, and floods. These events drive up admissions for trauma, heat-related illnesses, and infectious diseases. Evidence suggests that these surges can overwhelm already under-resourced facilities (13,14,37-39).

Beyond clinical demands, hospitals also face operational risks: for example, Hurricane Katrina led to hospital closures, disrupted care, and long-term staffing losses. Private practices also suffered, leaving gaps in patient care that persisted long after the storm had passed (40-42).

Adapting to climate change will require investments in hospital infrastructure, particularly around temperature control and building resilience. Studies have shown that inadequate cooling during heatwaves increases mortality among vulnerable patients. Improvements such as upgrading Heating, Ventilation, and Air Conditioning systems and implementing heatwave response protocols will be critical for patient safety (8,43). Upgrading buildings, improving air conditioning systems, and developing heatwave response plans will be essential steps to protect patient health in the face of rising global temperatures.

However, infrastructure alone is not sufficient. Broader resilience also depends on strengthening workforce preparedness, service delivery, and governance structures, as emphasized in recent guidance on climate-resilient health systems. This includes coordination with other sectors to ensure continuity of critical functions such as transport access, power supply, communication, and information technology systems, as well as supporting staff who themselves face personal and household challenges during extreme events (12,44).

Our findings suggest that targeted policy support is needed to strengthen hospitals in climate-vulnerable regions. These institutions serve communities already at risk and must be equipped to manage future challenges. As noted by the Lancet Commission, climate adaptation efforts can also be opportunities to address longstanding health inequities (45). Directing resources to these hospitals may help mitigate both the immediate effects of climate change and historic disparities in healthcare access (45). At the same time, resilience to climate change extends beyond hospitals, with health promotion, primary care, and community networks playing a crucial role in reducing demand on hospital services and strengthening local capacity to cope with climate impacts (46).

An additional concern is the significantly higher prevalence of private for-profit and PE ownership among hospitals in the most vulnerable regions. Prior research has raised concerns that such ownership models may prioritize financial returns over long-term patient care, particularly through cost-cutting strategies that could compromise staffing and service quality (47). This dynamic warrants further investigation.

Ensuring continuity of care during disasters will also require regional coordination. Establishing hospital networks capable of redistributing patients and resources during acute events can build system resilience. Examples from past disasters underscore the importance of such coordination, yet more research is needed to develop and evaluate best practices. Future research should explore the accessibility and operability of hospitals during and after extreme weather events. Infrastructure damage, staff shortages, and supply chain disruptions are recurrent issues (19). Rather than suggesting a wholesale relocation of care infrastructure, like tech companies are doing with their server farms, future studies could examine how best to climate-proof existing facilities, particularly those serving high-risk populations. This includes evaluating the trade-offs between proximity to care and vulnerability to climate impacts, especially for patients with time-sensitive healthcare needs. At the same time, relocation of health services is inseparable from broader community displacement and requires multi-sectoral planning. As natural disasters increase in frequency, the growing unavailability of home insurance in disaster-prone areas may accelerate relocation, while mutual aid during compound events will become increasingly difficult.

Limitations

Although our study has several strengths, it also has some limitations. First, while analyzing population characteristics at the county level provides important context about the environment in which hospitals operate, it does not capture patient mobility across county lines. As a result, our estimates may not fully reflect the demographics of the actual patient populations each hospital serves. Nonetheless, prior research suggests that hospitals primarily serve the populations within their own counties, supporting the relevance of this geographic lens (9).

Second, the CVI incorporates population-level indicators—such as poverty, minority status, and education—in determining county-level vulnerability. Because of this overlap, some associations between hospital characteristics and population vulnerability were expected and should be interpreted accordingly. However, other variables we examined—such as disability status and health insurance coverage—are not included in the CVI. In addition, the CVI does not capture hospital characteristics or ownership structures such as PE, which represent important dimensions of vulnerability addressed in our analysis.

Thirdly, our study involved examining a broad set of hospital and population characteristics across climate vulnerability quintiles. Each characteristic was analyzed individually using appropriate statistical tests without formal adjustment for multiple comparisons. As a result, there is an increased risk of type I error, particularly for marginal associations. However, our aim was descriptive rather than hypothesis-driven, and the analyses reflect a systematic evaluation of all relevant hospital characteristics available in the AHA dataset rather than selective testing. Moreover, many of the observed differences were highly significant (P<0.001) and consistent across multiple domains (e.g., hospital size, ICU capacity, and staffing), which supports the robustness and substantive relevance of our findings despite the absence of multiplicity correction.

Fourth, our datasets are drawn from different years: the hospital characteristics from the AHA [2016], population characteristics from the ACS [2021], climate vulnerability indicators from the CVI [2017–2019], and PE ownership from 2024. We chose the most recent available sources for each domain to maximize accuracy, but acknowledge that this temporal misalignment could introduce bias if major changes occurred in the interim. However, prior literature suggests that hospital structural characteristics evolve slowly over time (48-50), and our sensitivity analysis with 2012 AHA data yielded consistent results, which supports the robustness of our findings despite the time gaps.

Finally, while our primary hospital data source—the AHA survey is from 2016 and may not fully reflect the current state of hospitals in 2024, we also conducted parallel analyses using the 2012 AHA dataset. The results from both years were highly consistent, which strengthens our confidence in the robustness and relevance of the findings despite the time lag. Changes in ownership, resource availability, or preparedness initiatives in recent years—particularly in climate-vulnerable areas are not captured in our dataset. In addition, our treatment of PE ownership as a binary variable does not capture the complexity of ownership structures, governance models, or investment strategies that may influence hospital preparedness and climate resilience. Lastly, although the highest climate-vulnerability counties in our study tended to have smaller and more rural populations, these communities remain highly relevant, as they face elevated climate risks in combination with limited hospital capacity, compounding their vulnerability.

Conclusions

Hospitals in the most climate-vulnerable counties in the United States are, on average, smaller and less well-resourced than those in counties with lower vulnerability. They have fewer beds, fewer intensive care units, and lower numbers of full-time clinical staff, despite playing a critical role in the health and stability of their communities. These facilities are disproportionately located in the Southern U.S. and are more often privately owned, for-profit institutions. Coupled with higher rates of uninsurance, disability, and poverty among the populations they serve, the limitations of these hospitals raise urgent concerns about healthcare system preparedness in the face of climate change.

Targeted investment is needed to strengthen infrastructure and build capacity within these hospitals. Enhancing their ability to meet rising demand—particularly during and after extreme weather events—is essential not only for protecting health but also for promoting equity in the era of climate change.

Acknowledgments

We utilized ChatGPT 4 for language revision to ensure grammatical accuracy of the manuscript. However, the content and analysis presented were independently generated and did not involve the use of any AI model.

Footnote

Peer Review File: Available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-25-60/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-25-60/coif). M.J. reports receiving research funding from the National Institute on Aging (USA), the National Institute on Minority Health and Health Disparities (USA), and the Department of Defense Congressionally Directed Medical Research Program (USA). Q.D.T. reports receiving honoraria for lectures from Bayer, Intuitive Surgical, Novartis, and Pfizer and receiving research funding from Pfizer. A.P.C. reports receiving research funding from the Bruce A Beal and Robert L Beal surgical fellowship of the BWH Department of Surgery, from the Prostate Cancer Foundation and American Cancer Society (#23YOUN25) and from a Physician Research Award from the Department of Defense Congressionally Directed Medical Research Program (#PC220342). The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was deemed exempt from institutional review by the Mass General Brigham Institutional Review Board (“Impact of climate change on benign and malignant conditions in the United States of America”, IRB: 2023P003498) because it only involved publicly available datasets and secondary analysis.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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