Association between disasters, damage and destruction of health facilities and public spending in BrazilAbstract
The study investigated the relationship between occurrence of damage and destruction of public health facilities and losses due to disasters, health care expenditure, the indicators of disaster risk management in Brazilian municipalities in the period from 2013 to 2020. The method was secondary data analyses using national datasets in the public domain. Only records of disasters recognised by the Federal Government were selected. In total, 19,026 records of disasters and their impacts were analysed, including 949 Brazilian municipalities. The results showed that as the number of dead, injured, and ill people grows, the number of damaged health facilities also tends to increase for all years. However, the correlation coefficient was weak, except for the years 2013, 2014 and 2015, which was moderate. The spatial distribution of municipalities with the highest disaster-related expenditures is concentrated in the states of Amazonas, Pará, Acre, Mato Grosso do Sul, and in some states in the Northeast. Hydrological (flooding and flash floods) have caused the most damage and destruction of health services. The indicators of disaster risk management do not explain the variation in the occurrence of damaged or destroyed facilities, because the coefficient values indicated a very weak correlation.
Key words
Natural Disasters; Health Facilities; Delivery of Health Care; Risk Management
INTRODUCTION
According to the United Nations Office for Disaster Risk Reduction (UNDRR 2025), a disaster is a severe disruption of the normal functioning of a community or society at any scale, caused by hazardous events that, through their interaction with conditions of exposure, vulnerability, and response capacity, result in one or more of the following consequences: human, material, economic, and environmental damage and losses. These impacts may test or exceed the response capacity of a community or society using its own resources, thereby requiring the mobilization of external assistance, which may involve neighboring jurisdictions or entities at the national or international level.
Disaster risk arises from the combination of hazard, exposure, and vulnerability. A hazard is a process, event, or human-induced activity that has the potential to result in fatalities, physical harm or other health effects, damage to assets, social and economic disturbance, or environmental deterioration. Exposure refers to the presence of people, services, or infrastructure in areas subject to these hazards. Vulnerability relates to the conditions that make these exposed elements more susceptible to harm, such as social, economic, or structural fragility. A disaster occurs when a hazard affects an exposed and vulnerable population, causing impacts that exceed its capacity to respond (UNDRR 2025).
In the first half of 2024, Brazil faced one of the worst natural disasters in history: the heavy rains in the Rio Grande do Sul (RS) state. The overall impact on the state was 88.9 billion reais and involved various sectors, such as infrastructure, production, and social services (Suarez et al. 2024).
The Civil Defense of the state of Rio Grande do Sul reported that 185 deaths and 23 missing persons have been recorded (Casa Militar Defesa Civil do Rio Grande do Sul 2025).
According to the Climate and Health Observatory of the Oswaldo Cruz Foundation (Fiocruz), the disaster may have impacted approximately 1,170 isolated doctor’s offices and 548 clinics and health centres. A survey by the RS Health Department showed that 663 health establishments which serve the Unified Health System in 179 municipalities suffered some damage. Of this total, 440 are part of primary health care, essential in responding to disease outbreaks and monitoring pre-existing chronic clinical conditions. In this scenario, the supply of care decreases, and the population does not have access to medicines and medical care, which increases the risk of death (Secretaria da Saúde do Estado do Rio Grande do Sul 2024, FIOCRUZ 2024a).
Actions to combat climate change still do not receive due attention from Brazilian authorities, mainly in relation to the prioritization of financing these actions, the impact of these changes on the health sector and strengthening the resilience capacity of health systems (Dall’Alba et al. 2024).
The impact of disasters goes far beyond the injured. In the post-disaster phase, chronic diseases and mental illness can worsen. These effects can be seen months or years after the disaster. A study by Fiocruz Minas has been following individuals since 2021 and evaluating the living and health conditions of the population of Brumadinho, after the disaster caused by the collapse of the Vale mining company’s dam in January 2019. One of the findings is exposure to metals, with a high percentage of detection, mainly arsenic. There is a notable increase in the diagnosis of diabetes among adults, from 8.7% in 2021 to 10.7% in 2023. Mental health also worsened. In 2021, 21.3% of adults reported a diagnosis of depression, and in 2023 it was 22.3%. These percentages are above the 10.2% reported by Brazilian adults evaluated in the National Health Survey, conducted by IBGE in 2019 (FIOCRUZ 2025).
During disasters, the provision of services, including the health sector, may have their capacity exceeded due to the increased demand for injured patients and the direct damage and destruction to the physical structures of healthcare facilities. This, in turn, can reduce the number of hospital beds, health professionals and other essential resources for the full functioning of the health service (Silva et al. 2020a).
According to the Pan American Health Organisation (2018), in the Americas region, when a hospital does not work or continues to work only in a limited way, approximately 200,000 people are left without care, decreasing the chances of saving lives in disasters. In addition, about 67% of hospitals are located in disaster-prone areas.
In Brazil, there are still few national actions and the topic of safe hospitals is not treated as a priority (Cardoso & Oliveira 2020). Local-level analysis of vulnerabilities, including social and economic indicators and exposure to natural hazards, is vital for disaster risk assessment by local governments (Almeida et al. 2016).
The inherent risk of disasters can be associated with the population’s living conditions and socioeconomic inequalities, with the impact and consequences affecting the poorest (PAHO 2014). The consequences of natural disasters impact indicators such as the human development index and poverty at the municipal level. In Brazil, the change in precipitation tends to increase inequality (Fang et al. 2019).
Understanding disaster solely from a natural perspective is incomplete. The concept of natural disaster goes beyond human and material damage or environmental aspects. Unequal exposure to hazards, access to resources and opportunities implies different levels of vulnerability, which are influenced by social, economic, political and cultural aspects. Furthermore, it is questioned whether disasters are truly natural, or whether they result from activities, attitudes and behaviors (Blaikie et al. 2004, Kelman 2019).
Vulnerability also relates to people’s capabilities in the face of hazards, such as access to information, infrastructure and social relationships (UNDRR 2025).
The objectives of the present research were: i) to determine the relationship between the occurrence of damage and destruction of public health facilities and the indicators of disaster risk management of municipalities with public health facilities damaged or destroyed by disasters in the period from 2013 to 2020; and ii) analyse trends and associations between the variable “number of public health facilities damaged or destroyed” and other selected variables: spatial distribution of municipalities according to health care spending (in BRL), public health and emergency medical service and the number of public health facilities damaged or destroyed as a result of disasters; type of disaster; number of fatal casualties, injured and ill; health care expenditure, public health and emergency medical services in the period from 2013 to 2020.
MATERIALS AND METHODS
Selection of municipalities
Only records of disasters recognised by the Federal Government were selected (Figure 1). 949 municipalities with public health facilities damaged and destroyed in disasters were included.
With this recognition, it is possible to transfer federal resources to States and municipalities affected by emergency situation and state of public calamity (Brasil 2012). The request for federal recognition ensures that the disaster was absolute, depending on the presentation of reports about the damage caused by the disaster, risks, vulnerabilities, and reasons for the abnormality (Brasil 2022).
Data
Data were obtained from the following databases: i) Integrated Disaster Information System - S2iD, from the Ministry of Regional Development; ii) Cities@, from the Brazilian Institute of Geography and Statistics – IBGE, iii) Project Evaluation Health Systems Performance – PROADESS, from the Oswaldo Cruz Foundation – Fiocruz; and iv) National Registry of Healthcare Facilities – CNES (Brasil 2024a, b, IBGE 2024, FIOCRUZ 2024b).
The S2iD 2023 is a platform that integrates tools that provide information on disasters in Brazil that have been registered, recognised, or not by the federal government (Brasil 2020).
In S2iD, the tool “Management Report – Reported Damages” was used, and the following filters were selected: the period from 2013 to 2020, all Brazilian states and all types of disasters. This research turned into a report with municipalities affected by disasters and the number of public health facilities that suffered damage or were destroyed in these disasters, in addition to other indicators (material and human damage and economic losses).
Cidades@ is the system that makes available information from IBGE on the federative units of Brazil and has a tool to compare the indicators in several aspects, such as health, education, income level, and human development index. This database selected the following indicators: Municipalities’ disaster risk management. For the disaster risk management indicators, the selected year was 2020. This year was selected because it is the only year with available data on disaster risk management.
PROADESS presents data on the performance of the Brazilian health system from political, social, and economic perspectives, including aspects related to access, patient care capacity, and the guarantee of safety and patient rights. This database was selected for the study because it provides data on the number of health professionals (nurses and physicians) and hospital beds.
The CNES was used to obtain data on the number of health facilities in the municipalities. This database was chosen because it is the primary federal government database with complete information about the entire health services infrastructure and local installed capacity.
Selection of indicators
The indicators and their respective databases are presented in Figure 1. The basis for selecting research indicators is based on published studies that aimed to investigate the relationship between social, economic, geographic, health and disaster risk reduction indicators (Almeida et al. 2016, 2020).
These references were used because they present the Disaster Risk Indicators in Brazil (DRIB) Index 2020, which has been validated at the national level. Each indicator selected in the present study was based on one of the four components of the DRIB Index: (i) exposure, (ii) susceptibility, (iii) coping capacity, and (iv) adaptive capacity. The Supplementary Material - Table SI indicates the component associated with each indicator.
The DRIB was used in a study that aimed to develop local-scale disaster risk indicators and provide support to local decision-making for reducing the risk of disasters caused by flood events. For that, the chosen study area was the urban area of the municipality of Macau, on the northern coast of the state of Rio Grande do Norte, Northeastern Brazil (Almeida et al. 2023).
The selection of variables related to Civil Protection and Defense is due to the fact that Civil Defense Units in Brazil are fundamental to the implementation of the National Protection and Civil Defense Plan (PN-PDC 2025-2035), as these bodies coordinate actions to reduce disaster risk (Brasil 2025).
Sixty-three indicators were grouped into three categories: i) disaster damage and loss, ii) demography and health infrastructure, iii) disaster risk management. The complete list of indicators considered for analysis, grouped by category, is presented in the supplemental material.
The health care spending, public health and emergency medical service on the S2iD platform is in Brazilian Reais (R$). The conversion to US dollars used average commercial exchange rate for the period (June 2023), as provided by the Central Bank of Brazil.
For the study, the classification and definition of disasters were considered from the Brazilian Classification and Codification of Disasters (COBRADE 2023). According to COBRADE, natural and technological disasters, are divided into groups and subgroups. Natural disasters correspond to five groups: geological, hydrological, meteorological, climatological and biological. Technological disasters are divided into occurrences related to radioactive substances, hazardous products, urban fires, civil works and transportation of passengers and non-hazardous cargo.
Spatial analysis
The Geographic Information System (QGIS 2025), version 3.40.3 (Bratislava), was used to produce maps of Brazil showing municipalities with the highest numbers of public health facilities damaged or destroyed, as well as those with the highest expenditures from 2013 to 2020. The classes were defined solely for purposes of cartographic organization and visualization. Values were grouped into six classes using the quantile method, ensuring an even distribution of municipalities across ranges, without assigning qualitative categories or risk interpretations.
Statistical analysis
Data were harmonized at the municipal level using the IBGE code. Cross-referencing was performed between the data from the selected databases. Variables were classified as qualitative (nominal or ordinal) and quantitative (discrete or continuous). Variables and indicators were subjected to statistical analysis to enable comparison of values between municipalities. No imputation was performed for missing data. For variables relating to the type of disasters, number of dead, injured, and ill people in disasters, health care spending, public health and emergency medical services, and indicators of disaster risk management of municipalities, correlation coefficients were calculated to investigate the correlation with the primary variable of interest (number of public health facilities damaged and/or destroyed in disasters). Data normality was tested using the Kolmogorov-Smirnov test. Considering that normality was rejected, Spearman’s correlation coefficient was calculated.
The strength of a correlation (Fowler et al. 2009):
Value of coefficients:
0.00 to 0.19 A very weak correlation.
0.20 to 0.39 A weak correlation.
0.40 to 0.69 A moderate correlation.
0.70 to 0.89 A strong correlation.
0.90 to 1.00 A very strong correlation.
Also, the software R (version 4.0.2) was used to calculate the coefficient of determination R2 to investigate the association between the variables.
The integrated database data were tabulated in Microsoft Excel 2016 (Figure 1).
RESULTS
In total, 19,026 records of disasters and their impacts were analysed, including 949 Brazilian municipalities.
Occurrence of public health facilities damaged and destroyed by frequency of dead, injured and ill
As the number of dead, injured, and ill people grows, the number of damaged public facilities also tends to increase for all years. However, according to Table I, the degree of these correlations is weak, with the coefficient being moderate only for 2013, 2014, and 2015. The interpretation of the result is that the closer it is to the absolute value of 1, the greater the correlation between the variables.
Spearman’s correlation coefficient of the number of dead, injured and ill variables by the number of damaged public health facilities from 2013 to 2020.
Figure 2 presents that several occurrences of deaths, injuries and ill where no public health facilities were destroyed. Additionally, there are also certain points to the left of the graph, indicating facilities were destroyed on occasions when there were few or no deaths, injuries or ill.
Number of dead, injured and ill variables by the number of destroyed public health facilities from 2013 to 2020.
By separately analysing the data by years, Spearman’s correlation coefficient of the number of dead, injured and ill variables by the number of destroyed public health facilities, showed that the with the exception of 2020, as the number of dead, injured, and ill people grows, the number of damaged public facilities tended to increase. In 2013, 2014 and 2015, the correlations were moderated.
Number of public health facilities damaged or destroyed by a type of disaster per year
From 2013 to 2015, floods were the leading cause of damage to health facilities, peaking in 2014 when over 60% of such facilities were affected by this type of disaster. In the following years, the number of health facilities damaged by floods declined, with no reported cases of flood-related damage in 2020. Flash floods accounted for 32.3% and 42.6% of the damage to health facilities in 2014 and 2017, respectively. Prior to 2018, heavy rainfall was responsible for less than 18% of the damage in any given year. However, in 2018, 2019, and 2020, heavy rainfall contributed to 38%, 56%, and 46.2% of the damage to health facilities, respectively (Figure 3). Over time, the impact of different disaster types on facility damage has varied. Nevertheless, it is evident that the contribution of flooding has decreased, while the impact of heavy rainfall has increased. In 2020, heavy rains had the greatest impact (approximately 45%), followed by windstorms (35%).
The R2 coefficient of determination was calculated to investigate the association between the number of public health facilities damaged or destroyed and the five most frequent types of disasters. The results were 0.11 and 0.06, respectively. This means there is a weak association, and these types of disasters are inadequate in explaining the variation in damaged or destroyed public health facilities.
Number of public health facilities damaged and/or destroyed by health care spending, public health and emergency medical service
In Figure 4, in 2013 and 2014 there were no expenses related to medical assistance, however, 2013 was the year with the highest number of public health facilities damaged. In 2015, some disasters were observed, still resulting in a large number of public health facilities damaged and higher Health care spending. After 2015, a reduction in the number of public health facilities damaged can be seen, with increased spending from 2017 to 2019. In 2020, there were few damaged facilities and low spending. Therefore, the variables behave differently each year. The Spearman’s correlation coefficient showed an association between healthcare spending (in US$), public health and emergency medical services by the number of public health facilities damaged and destroyed. However, this degree of association was weak.
Health care spending (in US$), public health and emergency medical services by number of public health facilities damaged (2013 to 2020).
Spatial distribution of municipalities according to health care spending (in BRL), public health and emergency medical service and the number of public health facilities damaged or destroyed as a result of disasters
When comparing the spatial distribution of the municipalities shown in Figures 5 and 6, it is observed a weak correspondence between structural damage and financial burden. The highest expenditures are concentrated in the states of Amazonas, Pará, Acre, Mato Grosso do Sul and some states in the Northeast. Regarding to municipalities with the highest number of damaged or destroyed public health facilities, they are located in the states of Amazonas and Pará. However, their distribution differs from that observed in other states.
Brazilian municipalities with public health facilities damaged and destroyed by disasters recognized by Federal Government (2013-2020).
Brazilian municipalities with health care spending (in BRL), public health and emergency medical services resulting from disasters recognized by the Federal Government (2013-2020).
Damage or destruction of public health facilities and indicators of disaster risk management
It was found that the indicators of disaster risk management were associated with the occurrence of damaged or destroyed public health facilities. However, according to the coefficient values, the indicators do not explain the variation in damaged or destroyed facilities.
DISCUSSION
This study reveals as the number of dead, injured, and ill people grows, the number of damaged public facilities tended to increase. The probability of saving lives increases when the health service’s capacity for care is not compromised during a disaster. The physical structure of the institutions must be resistant to these events, and the equipment and critical infrastructure must be maintained, such as water supply, electricity, etc. Health teams must be prepared to face challenges such as the increased demand for hospitalisation of patients (Cardoso & Oliveira 2020).
A study that assessed the effect of flooding in Santa Catarina (SC) (2008) and Pernambuco (2010) showed that the municipality of Ilhota (SC) was left with practically no public health service because of the four local public health facilities two were damaged, and two others were destroyed. In Blumenau (SC), 60% of public facilities were damaged (thirty-eight) or killed (four). In Itajaí (SC), 52% were destroyed (twenty). In these municipalities, many people were affected by disasters, and there may have been an increase in the demand for health services (Londe et al. 2015).
A finding of the study was that hydrological disasters (flooding, flash floods and heavy rains) were the most involved in damage and destruction of health services. Corroborating this result, in Brazil, from November to December 2021, heavy rains affected two states: Bahia and Minas Gerais. Rainfall in these states was between 250 and 430% above that observed in the 1981-2010. A total of 574 municipalities were affected. The high level of rainfalls, which leads to floods and landslides, is related to a high number of deaths and also affects the functionality of hospitals (Marengo et al. 2023).
From 1991 to 2012, flash floods were the disaster most related to a higher number of deaths (58.15%). In the second position, mass movements appear (15.60%), followed by flooding (13.40%). Drought and dry spells accounted for 7.57% of all deaths (CEPED 2014).
In 19 years (1991 to 2010), of the total % of events (31,909) in Brazil, 57.8% were climatological events, mainly drought and dry spells. These events affected approximately 50 million people, which accounted for more than half of the total. Regarding mortality and morbidity, hydrological events were the most associated. Flooding and flash floods were related to 44.8% of mortality (Freitas et al. 2014).
These types of disasters may be among those that most compromise health facilities in Brazil, as they involve changes in precipitation and the location of hospitals in disaster-risk areas. The lack of urban planning and a governance capacity not focused on disaster risk reduction, which are factors present in Latin American countries’ small and medium-sized urban spaces, was associated with 80% of losses and damages due to disasters (Shekhar et al. 2022). Drought can compromise the functioning of health services, among other aspects, by interrupting the supply of water, electricity, inputs, etc.
The policy for safe hospitals in the face of disasters, led by PAHO, presents the hospital safety index, which is an instrument that classifies the risk of a service being compromised in a disaster and putting the lives of patients at risk. Three hospitals in Rio de Janeiro (Brazil), which have already been impacted by floods or are in risk areas, were assessed. All three hospitals received an intermediate rating, according to the index. This means that the functional and physical structure of these hospitals can put patients and the medical care team at risk and compromise the functioning of the health service, and short-term measures are required (Salles & Cavalini 2012).
The concept of “safe hospitals” is still little known and studied in Brazil. The country faces a worrying scenario of health services’ vulnerability to disasters, so the policy related to disaster risk management in health services has not yet been a priority for the Brazilian government. By March 2012, more than 1,400 hospitals in the Americas had been evaluated using the HSI, and 31 countries had reported using the HSI to define priorities and implement public policies (PAHO 2012).
Experience in Latin America and the Caribbean shows that the Hospital Safety Index (HSI) is an effective tool for guiding disaster risk reduction in the health sector. In the Dominican Republic, the systematic application of the index made it possible to identify structural, non-structural, and functional weaknesses in public hospitals, supporting the prioritization of interventions and investments aimed at strengthening the resilience of health services. The SMART Hospitals initiative emphasizes enhancing hospital resilience by incorporating environmentally friendly technologies, for example energy-related measures include the installation of solar panels. In Caribbean countries, SMART initiative integrated the HSI with energy efficiency and sustainability measures, demonstrating that relatively simple and low-cost interventions can significantly improve hospital safety while also reducing operational costs. These examples highlight the role of the HSI as a standardized diagnostic instrument that supports decision-making, guides action plans, and promotes tangible improvements in hospitals’ capacity to maintain essential functions during and after disasters (PAHO 2015, 2023).
The local Health Surveillance bodies (state and municipal bodies) assess the architectural designs of healthcare facilities of interest to healthcare, specifically those involved in economic activity that potentially harms human health. A study showed that in the architectural projects evaluated by a specific health surveillance body, the second most identified irregularity was related to infrastructure aspects, such as nonconformities in floors, upper parts, walls, or other structures (Soldate & Oliveira 2022). A point to be considered is that this assessment of the Health Surveillance body does not link healthcare facility infrastructure and flow requirements to disaster risk management. Federal legislation does not provide for this type of assessment.
Another result of the study was that Spearman’s correlation coefficient showed an association between healthcare spending (in US$), public health and emergency medical services by the number of public health facilities damaged and destroyed. However, this degree of association was weak. In 2013 and 2014 there were no expenses related to medical assistance, however, 2013 was the year with the highest number of public health facilities damaged. In 2015, some disasters were observed, still resulting in a large number of public health facilities damaged and higher Health care spending. Therefore, the variables behave differently each year. The types of disasters and their different impacts may have influenced this variation from year to year.
In Brazil, from 2000 to 2015, the impact of disasters, in terms of damage and destruction of health facilities, generated expenses of US$ 830 million. Climatological disasters, which include drought and dry spells, were the most frequent in the country. However, they were unrelated to the highest costs resulting from damage and destruction of health facilities. Hydrological disasters were the ones that resulted in the most significant economic losses, being 3.2 and 3.6 times greater than those of meteorological (e.g. windstorms) and geological (e.g. landslides) disasters, respectively (Freitas et al. 2020).
The findings indicate a weak correspondence between the spatial distribution of municipal health care spending (in BRL), public health and emergency medical services, and the distribution of municipalities with damaged or destroyed public health facilities. Municipalities with high health care spending and low structural damage reinforce the evidence that the impacts of disasters on health systems are not limited to the physical destruction of facilities, as post-disaster costs are largely driven by functional and service-related factors, such as increased demand for care, exacerbation of chronic diseases, mental health needs, and disruptions in continuity of care. In contrast, municipalities that experience significant structural damage but report moderate spending may reflect limitations in local financial response capacity, associated with underfunding, delays in resource allocation, and dependence on state or federal funding mechanisms, a pattern widely described in the literature on disasters and health system resilience (Kruk et al. 2015, PAHO 2018, Freitas et al. 2014).
A study aiming to assess the economic impacts and costs of natural disasters on health facilities, based on S2ID data from 2000 to 2015, found that Pernambuco, Amazonas, and Santa Catarina stood out in terms of total costs in millions of Brazilian reais. Although the Northern Region recorded 719 events, which is less than one-tenth of the total disasters in the Northeast Region, the overall disaster costs in both regions were very similar, each exceeding one billion reais. The higher costs in the Northern Region, combined with its relatively low number of events, resulted in it having the highest cost per event (R$ 1,527,644.49), which was 7 to 9 times greater than the cost per event in the Northeast, Southeast, and South regions (Freitas et al 2020). There is consistency between these findings and the results of the present study, which showed that the highest expenditures are concentrated in the states of Amazonas, Pará, Acre, and Rondônia. However, the published study does not mention whether it considered only disasters officially recognized by the Federal Government as a criterion.
There are several hypotheses to explain why the Northern Region records fewer disasters than other regions while incurring higher expenditures. One of these is the occurrence of gradual, large-scale Amazonian floods in both Acre and in states such as Amazonas and Pará (Agência Fiocruz de Notícias 2021). This pattern may be related to the greater severity of these events.One of the most significant changes observed in recent decades is the marked rise in extremely severe flooding events. This increase in flooding has been associated with an intensification of the Walker circulation, driven by pronounced warming in the tropical Atlantic and concurrent cooling in the tropical Pacific (Barichivich et al. 2018).
Finally, the study indicated that indicators of disaster risk management do not explain the variation in the occurrence of damaged or destroyed facilities. Factors may have influenced this result. There are IBGE data, such as disaster risk management, which are obtained from surveys completed by municipal managers, who may have provided answers that are inconsistent with the reality of the localities, as they do not want to expose weaknesses and vulnerabilities. Furthermore, these surveys also function as instruments for evaluating management performance. Therefore, impartiality in participating in research is important.
The weak correlation coefficients and modest R² values observed in this study indicate that the relationships between disaster-related damage to public health facilities and disaster risk management are limited in their explanatory and predictive capacity. Similar findings have been reported in recent disaster research. For example, a study found a weak correlation between building damage and loss of life from landslides, highlighting that physical damage metrics alone are insufficient to explain complex human and systemic outcomes (Van Wyk de Vries et al. 2025)
These results reinforce the notion that disaster impacts on health systems are inherently multifactorial. Health expenditure patterns are shaped not only by the extent of physical damage but also by pre-existing health system capacity, governance structures, emergency funding mechanisms, political priorities, and the timing of budget execution relative to disaster occurrence. As demonstrated in the landslide study, outcomes of interest may depend more strongly on contextual and social factors than on direct measures of damage intensity.
In addition, the use of secondary administrative data introduces important limitations, including heterogeneous reporting standards across municipalities, potential underreporting of damage, and uncertainty in expenditure attribution. Such data constraints may attenuate statistical associations and contribute to low coefficients of determination. Furthermore, linear models may be inadequate to capture non-linear dynamics, threshold effects, or delayed responses that characterize post-disaster recovery processes in health systems.
Another study, which examined the relationship between disaster impacts and social indicators, demonstrated that health outcomes following disasters are only weakly associated with single exposure or damage-related variables when analyzed at aggregated levels, emphasizing the dominant role of broader social, economic, and structural determinants. Similarly, the low correlation coefficients and modest R² values observed in our analysis suggest that damage to public health facilities or disaster risk management capture only a limited portion of the variability in post-disaster health system responses. As highlighted in the European study, social vulnerability, inequality, baseline health system capacity, and governance context may exert a stronger influence on health impacts than direct measures of disaster damage alone (Özden & Erbaydar 2024).
Taken together, these findings suggest that weak correlations should not be interpreted as an absence of meaningful relationships, but rather as evidence of the complexity of disaster–health system interactions. As emphasized by recent disaster research, including the study on landslides, caution is warranted when interpreting simple statistical associations, and future studies may benefit from longitudinal designs, non-linear modeling approaches, and the inclusion of contextual variables to better capture these dynamics.
The federal government budget to be allocated to disaster mitigation has been significantly reduced, which reduces the execution of studies of risk areas (USP 2023, Kuhn et al. 2022). In the African continent’s experience in dealing with various disasters, including those caused by infectious biological agents, in the proposed design of the structure of the health system applied to disaster risk management, the allocation of funds for disaster preparedness and also for public health emergencies is one of the central elements of this proposal (Olu 2017).
The National Protection and Civil Defense Plan presents strategies for disaster risk management. For the implementation of this Plan, civil defense units need to be structured to develop actions to reduce the risk of disasters. However, in Brazil, 72 % of municipal civil defense units there is no budget for implementing their activities (Marchezini et al. 2025).
Municipal Master Plans need to incorporate disaster risk reduction. Disaster risk management necessarily involves investments in population engagement strategies and educational actions to develop preparedness and response capacity to achieve practical actions in disasters (Vieira & Alves 2020, Silva et al. 2020b).
Some measures that can help prevent, prepare for and reduce the risk of disasters, such as floods, are the development of contingency plans, development of efficient early warning systems for natural disasters, risk communication and urban planning with investments in appropriate infrastructure. Specifically, in relation to health services, training teams in disaster management, mental health support, securing backup resources (e.g. beds, staff, medicines, etc.) is recommended (Marengo et al. 2024, Wu et al. 2024).
A study assessed the performance of a hydrodynamic model in analysing the extension of prone-to-flooding areas based on the simulation of a case study, which corresponded to an extreme flooding event that occurred in São Caetano do Sul, São Paulo, Brazil. This tool proved robust in identifying areas prone to flooding in urban regions. Local managers should consider the possibility and feasibility of using this technology to prevent and respond to these events (Escobar-Silva et al. 2023).
Flooding occurs frequently in Japan. In 1977, an urban flood management regulation was implemented to reduce the risk of these events. This regulation addresses the study of the watershed and its risks and is not restricted to the urban area level. That is, an infrastructure capable of resisting the impacts of flooding was developed in an entire river basin, along with a risk analysis based on climate change (IADB 2017).
By analysing data records from a hospital and the Rio de Janeiro State Secretariat (Brazil), where a landslide disaster occurred in 2011, twelve lessons learned were identified. Among them: rescue teams should identify local people who could volunteer to help, as they know the place and can provide important information; communication between those involved (operation command, teams, hospitals, others) is critical; teams trained in disaster risk management and the existence of contingency plans for the health services and authorities involved (Pereira et al. 2013).
A study carried out in rural areas of Iran aimed to identify the factors influencing the implementation of protective measures prior to the occurrence of flooding. The findings indicated that the perception of not believing in climate change, along with the opinion that the severity of flood impacts is greatly exaggerated, had a significant negative influence on protective responses to floods (Askari et al. 2025).
A proposal for a Drought-Related Disaster Risk Warning System in Brazil addresses the following aspects: risk knowledge (mapping of threats and vulnerabilities), monitoring (understanding of the dynamic process of disaster occurrence based on environmental, social, and economic indicators), and communication of the threat to the target audience (risk communication training) (Cunha et al. 2019).
This study has limitations. Other confounding factors may have influenced the association between variables and indicators estimate. Control of potential confounding factors was not applied. The consequences of disasters vary according to the vulnerability profile and the social, economic, health and environmental dimensions (Silveira & Oliveira 2023, Silva et al. 2024). Further investigation should be use the Structural Equation Modeling that is a robust statistical method can be used in disaster risk reduction to examine the complex relationships among multiple observable and latent factors—social, economic, cultural, and psychological—that influence community vulnerability, resilience, and preparedness (Askari et al. 2025).
Missing data may have influenced the results. When the S2iD system’s time series of disasters in Brazil from 2013 to 2020 was considered for the analysis, and data from indicators of disaster risk management and response capacity of the municipalities from IBGE were cross-referenced only for the year 2020, the temporal variation of disasters in the period from 2013 to 2020 may have influenced the results.
In conclusion, hydrological (flooding and flash floods) and meteorological disasters (heavy rains) caused more damage and destruction of public health facilities during the study period. It should be noted that the sample studied consisted of disasters recognized by the Federal Government. Therefore, the results cannot be generalized to all disasters that occurred or to the entire universe of Brazilian municipalities. In the last three years of the period assessed (2018-2020), heavy rainfall had the greatest impact on damage to public health facilities. The material and human damage caused by the disasters was significant and can be characterised as a public health problem. Investing in prevention makes it possible to reduce the risk of deaths and economic losses.
As the number of dead, injured, and ill people grows, the number of damaged public facilities tended to increase for all years. The correlation was moderate for the three years studied. Based on the results, it was possible to understand that, although disasters exert an influence that may have some effect on the number of damaged and destroyed public health facilities, the values of the association coefficients suggest that the variation in this number cannot be explained by the indicators evaluated in the study. Other factors may also play a significant role in determining infrastructure impact. This indicates a multifactorial dynamic not fully captured by the indicators used.
It is urgent and critical for countries to work to anticipate, plan for, and reduce disaster risk in order to effectively protect people, communities, and countries, as well as their livelihoods, health, culture, heritage, socioeconomic assets, and ecosystems, thereby strengthening resilience. Reducing vulnerability and exposure to risk is essential, through actions focused on addressing the underlying drivers of disaster risk, such as the consequences of poverty and inequality, climate change, unplanned and rapid changes resulting from urbanization, and the lack of regulation and financial incentives for disaster risk reduction (United Nations 2025).
Further research on the subject is needed, addressing disaster risk management and the capacity for preparedness, response and recovery of health services in the face of disasters. Furthermore, there is a need to map health facilities located in disaster-risk areas and study the influence of the urbanisation process and climate change on the occurrence of disasters. The policy of safe hospitals in the face of disasters and public health emergencies must receive due attention and be incorporated into the country’s strategic agenda.
Acknowledgements
The authors acknowledge the University College London (UCL), the Brazilian Health Regulatory Agency—Anvisa, and the University of Brasília—UnB.
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Edited by
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Handling editor
Helton Santiago
The datasets used in this study are publicly available from open-access databases. The derived dataset generated through data processing and integration is available from the corresponding author upon reasonable request.
