The Epidemiological Profile and Morbidity-Mortality Patterns of Technological Disasters in the Americas from 2000 to 2021: a cross-sectional study

The Epidemiological Profile and Morbidity-Mortality Patterns of Technological Disasters in the Americas from 2000 to 2021: a cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Epidemiological Profile and Morbidity-Mortality Patterns of Technological Disasters in the Americas from 2000 to 2021: a cross-sectional study Andrea Fernández García, Rick Kye Gan, José Antonio Cernuda Martínez, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4169973/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Technological disasters in the Americas have significant public health and environmental implications, but there is limited epidemiological analysis of these events. This study aims to characterize the epidemiological profile of technological disasters in the Americas from 2000 to 2021, focusing on morbidity and mortality trends. Methods A retrospective cross-sectional study was conducted. The ANOVA test was applied in the mean rates calculated for each type of disaster. The Mann-Kendall test assessed the presence or absence of temporal trends, and the Dickey-Fuller augmented test was used to determine if the time series were stationary. Predictions were made up to the year 2030 to mean mortality rate per million inhabitants, mean rate of affected individuals per million inhabitants, and mean rate of injuries per million inhabitants. Results A total of 733 technological disasters were recorded in the Americas. Statistically significant differences were found between the mean rates of affected individuals and the mean mortality rates per million inhabitants for each type of technological disaster. No trends were identified. Conclusions The highest rates of fatalities and affected individuals occurred within industrial accidents. Technological Disasters Epidemiological Profile Public Health Crisis Morbidity Mortality Figures Figure 1 Figure 2 Figure 3 Figure 4 Background The United Nations Sendai Framework for Disaster Risk Reduction 2015–2030 defines a disaster as "a serious disruption of the functioning of a community or a society at any scale due to hazardous events interacting with conditions of exposure, vulnerability, and capacity, leading to one or more of the following: human, material, economic and environmental losses and impacts." 1 The Integrated Research on Disaster Risk (IRDR) Program in 2014 made a clear distinction between disasters that arise from natural hazards and those classified as technological hazards. 2 A technological disaster is a catastrophic event associated, entirely or partially, with human decisions, activities, or errors. These disasters are often related to technology control, malfunction, or management, as well as failures or breakdowns of systems, equipment, and engineering or industrial standards. Structural collapses (bridges, mines, and buildings), industrial accidents (chemical or nuclear explosions), and traffic accidents (land, air, and maritime) are examples of technological disasters. 2 Moreover, many of these technological disasters are accompanied by consequences such as environmental pollution. In fact, some technological disasters can be as severe as natural disasters. Sometimes, they are of an acute onset, while at other times, their effects may appear gradually and extend over years. 2 Between 2000 and 2021, a total of 5,390 technological disasters were reported, affecting 2,638,985 people, resulting in the death of 166,068 individuals, and causing economic losses totaling $ 63,178 million. 3 , 4 Some of the largest technological disasters in recent decades include the collapse of the Banqiao Dam (China, 1975, due to heavy rains combined with a design and engineering failure), the contamination incident in Bhopal (India, 1984, due to the toxic gas leak at the Union Carbide factory in Bhopal resulting from equipment failures and design defects, causing between 4,000 and 30,000 deaths), the Chernobyl nuclear accident (Ukraine, 1986, with over 200,000 deaths due to radiation exposure and significant environmental impact), and the Fukushima nuclear accident (Japan, 2011, which released 45,000 liters of highly radioactive water). Despite these events, technological disasters often receive less attention from the scientific community. 5 – 8 Technological disasters have a significant environmental and economic impact. Some of the costliest ones include the explosion in the port of Beirut (Lebanon, 2020), the oil spill from the sinking of the Prestige oil tanker (Spain, 2002), and the explosion and oil spill at the Deepwater Horizon platform (Gulf of Mexico, 2010). 4 In addition, technological disasters also have effects on the mental health of those affected, as evidenced by research conducted after the Gulf of Mexico oil spill, where elevated levels of depression and anxiety were increased, with a more significant impact on those who suffered economic losses due to the disaster. 9 , 10 This study aimed to examine the epidemiological profile of technological disasters that occurred in the Americas between 2000 and 2021 in terms of morbidity-mortality and to analyze their temporal trends. Methods A cross-sectional study of technological disasters in the American continent (North America, Central America, the Caribbean, and South America) was conducted, using the IRDR definition of technological disaster and data from EM-DAT database. 3 Distributions using absolute and relative frequencies were analyzed by year and over the entire study period. Central tendency (mean) and dispersion (standard deviation) parameters were used to calculate the mean rates of affected individuals, injuries, and fatalities per year and per million inhabitants. The chi-square test was applied to check for the presence or absence of an association between the number of each type of technological disaster and the year of occurrence. ANOVA test was used to determine whether there were statistically significant differences in the annual mean rates (for the period 2000–2021) per million inhabitants of deaths, injuries, and affected individuals by type of technological disaster. The Mann-Kendall test was applied to check for the presence or absence of temporal trends in the mean mortality rate per million inhabitants, the mean rate of affected individuals per million inhabitants, and the mean rate of injuries per million inhabitants for each type of technological disaster during the study period. Next, Augmented Dickey-Fuller (ADF) test was applied to determine whether the time series were stationary. The existence of stationarity implies that the mean, variance, and covariance of the analyzed variables are constant and will not change over time, making temporal predictions possible. Exponential smoothing was used to forecast the annual number of mean mortality rate per million inhabitants, mean rate of affected individuals per million inhabitants, and mean rate of injuries per million inhabitants up to the year 2030, along with their corresponding CI95%. An alpha value of 0.5 was used as the smoothing constant. Pearson correlation coefficient between the three variables studied (mean mortality rate per million inhabitants, mean rate of affected individuals per million inhabitants, and mean rate of injuries per million inhabitants) was calculated to check for any relationships between them. Lastly, the yearly frequency of deaths, injuries, and affected individuals per million population caused by technological accidents by using exponential smoothing was analyzed and made projections for each of these variables until 2030. In all calculations, the Stata v.15 statistical package was used. Results Between 2000 and 2021, a total of 733 technological disasters were recorded in the American continent. Of these, 562 (76.67%) were transportation accidents, 62 (8.46%) were industrial accidents, and 109 (14.87%) were miscellaneous accidents. Table 1 shows the absolute and relative frequencies of each type of technological disaster by year of occurrence. After applying the chi-square test, the difference was statistically non-significant. Table 1 also shows the annual mean rates of mortality, affected individuals, and injuries per million inhabitants, as well as the standard deviations for each of the mean rates. There were statistically significant differences between the mean rates of affected individuals per million inhabitants (F= 4.91; p= 0.008) and the mean mortality rates per million inhabitants (F= 2.97; p= 0.043) for each type of technological disaster. No statistically significant differences were found in the mean rates of injuries or casualties per million inhabitants. No trends were found in the mean mortality rate, mean rate of affected individuals per million inhabitants, or mean rate of injuries per million inhabitants for each type of technological disaster during the study period. On the other hand, it was found that the time series of the mean mortality rate (t= -5.20; p<0.000), the mean rate of injuries or casualties (t= -3.78; p= 0.001), and the mean rate of affected individuals (t= -2.34; p= 0.03) were stationary. The existence of stationarity implies that the mean, variance, and covariance of the variables analyzed are constant and will not change over time, allowing for temporal predictions. There was no significant correlation between the mean mortality rates, rates of injuries or casualties, and rates of affected individuals. Figures 1, 2, and 3, respectively, show the exponential smoothing of the series of mean mortality rates, injuries or casualties, and affected individuals, along with predictions up to the year 2030, and the upper and lower limits of the 95% confidence interval. Figure 4 illustrates the distribution of each type of technological disaster by country. Discussion Transportation accidents were the most frequent type of technological disaster during the study period on the American continent. Industrial accidents had the highest mean rates of deaths and affected individuals, while the highest mean rate of injuries occurred in miscellaneous or other types of accidents. A previous study 11 showed an increasing trend of technological disasters, which is expected to continue as industrialization and urbanization continue to grow. However, no trends were found for any of the variables studied in the present study. Freitas et al. 12 analyzed disasters in Brazil between 2013 and 2021 and found that while natural disasters were the most frequent, technological disasters also significantly impacted public health. Some of these disasters had important consequences for the health of the country's residents: prenatal exposure to contaminated water sources due to the Mariana dam disaster in the state of Minas Gerais in 2015 was associated with low birth weight. 13 Additionally, the fire at the Kiss nightclub in the Brazilian state of Rio Grande do Sul resulted in the death of 242 people and caused about 680 injuries. In addition to these consequences, mental health and psychiatric illnesses were observed in survivors and personnel involved in rescue efforts. 14 The American continent has one of the highest rates of transportation accidents. Colombia is the country with 70% of the deaths, with its residents having a three times higher probability of dying in such accidents compared to those in Spain and four times higher than the population in the United Kingdom. Furthermore, the risk of death due to transportation accidents is higher in people over 60 years old. 15 In Ecuador, in 2019, transportation accidents were the leading cause of death in adults aged 30 to 64. 16 Industrial activities increase the risk of explosions, fires, and maritime or railway disasters. 17 In addition to physical consequences (deaths or injuries), industrial disasters can also have a significant psychological impact. People involved in an industrial disaster can develop compound trauma due to job loss, social stigmatization, and anxiety associated with long-term physical health effects resulting from exposure to chemicals or toxins such as cancer, fertility problems, and teratogenic effects. 18 In the present study, industrial accidents had the highest mean rates of deaths and affected individuals among all types of technological disasters. There are various subtypes within industrial accidents, including chemical spills, collapses, explosions, gas leaks, radiation, and oil spills. 19 Individuals exposed to oil spills may experience respiratory symptoms such as coughing and difficulty breathing, but this type of disaster can also lead to hepatic, neurological, renal, endocrine, and hematological effects The oil spill caused by the sinking of the Deepwater Horizon oil rig in 2010 resulted in the largest oil spill ever recorded in the United States. 20 , 21 Nuclear disasters are a type of technological disaster with significant psychosocial effects. 22 Radiological accidents can trigger specific mental health disturbances that differ from those observed after natural disasters. 23 This may be because they are typically sudden-onset disasters, and the threat is invisible to the person, making fear of the potential consequences greater due to the uncertainty of the extent of exposure. 22 The Three Mile Island nuclear accident (Pennsylvania, 1979) was the most severe nuclear accident in the United States. 24 A study conducted on the population affected by this accident showed a slight increase in the risk of bronchus, trachea, lung cancer, and leukemia. 25 One of the most significant industrial accidents outside the American continent was the explosion at the ICMESA chemical plant near Seveso (Italy) in 1976. The release of high levels of 2,3,7,8-tetrachlorodibenzodioxin caused severe damage to both the population and the environment. A decrease in male and female fertility was observed, as well as a higher risk of cancer development. 6 This accident was the starting point for the publication of Directive 82/501/EEC on the Prevention and Control of Major Accidents, known as the SEVESO Directive. 26 This directive has been updated over the years, with the last adaptation in 2008 (SEVESO III). 27 The limitations of this study are related to the need for uniform criteria in different databases to define technological disasters and the varying quality with which countries report disasters and provide information. Conclusions Although natural disasters are the main type of disaster in terms of impact in the Americas, technological disasters also have a significant impact on public health, with high rates of deaths, injuries, and affected individuals, as well as on the environment, with very significant differences among countries in the Americas. Considering that the highest rates of deaths and affected individuals occur in the subgroup of industrial accidents, it would be advisable to review safety and response protocols for industrial accidents to reduce these rates as much as possible and ensure the health of workers and residents in affected areas. Therefore, countries must have an appropriate legal regulatory framework, especially in labor, industrial processes, and environmental areas. Abbreviations ADF Augmented Dickey-Fuller CI95% Confidence intervals at 95% IRDR Integrated Research on Disaster Risk Declarations Ethics approval and consent to participate: Not applicable Consent for publication: Not applicable Availability of data and material: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests: Not applicable Funding: Not applicable Authors' contributions: All authors contributed substantially to the design, data collection, data analysis, and risk assessment; drafted the article and reviewed the final manuscript; gave final approval of the version to be published and agreed to be accountable for all aspects of the work. Acknowledgements: Not applicable References United Nations Office for Disaster Risk Reduction. Report of the Open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction. United Nations. 2023. http://www.preventionweb.net/drr-framework/open-ended-working-group/ . Accessed 27 December 2023. Integrated Research on Disaster Risk. Peril Classification and Hazard Glossary. IRDR. 2014. http://www.irdrinternational.org/knowledge_pool/publications/173 . Accessed 28 December 2023. Centre for Research on the Epidemiology of Disasters. EM-DAT database. University of Lovaine. 2023. http://www.emdat.be . Accessed 28 December 2023. Centre for Research on the Epidemiology of Disasters. Technological Disasters: Trends & Transport accidents. University of Lovaine. 2023. http://www.cred.be/publications . Accessed 29 December 2023. US Depatment of the Interior. RCEM-Reclamation Consequence Estimating Methodology Interim. US Depatment of the Interior. 2023. http://damfailures.org/wp-content/uploads/2019/12/RCEM-CaseHistories20140304.pdf . Accessed 30 December 2023. Eskenazi B, Warner M, Brambilla P, Signorini S, Ames J, Mocarelli P. The Seveso accident: A look at 40 years of health research and beyond. Environ Int. 2018;121:71–84. De S, Banerjee N, Sabde Y. Respiratory morbidities and lung function abnormalities in survivors of Bhopal Gas Disaster: A cross-sectional study. Respir Investig. 2022;60(2):284–92. Centre for Research on the Epidemiology of Disasters. Technological disasters. University of Lovaine. 2023. http://www.cred.be/publications . Accessed 30 December 2023. Drescher CF, Schulenberg SE, Veronica Smith C. The deepwater horizon oil spill and the mississippi gulf coast: Mental health in the context of a technological disaster. Am J Orthopsychiatry. 2014;84(2):142–51. Osofsky HJ, Osofsky JD, Hansel TC. Deepwater horizon oil spill: mental health effects on residents in heavily affected areas. Disaster Med Public Health Prep. 2011;5(4):280–6. Jafari H, Jafari A, Nekoei-Moghadam M, Goharinezhad S. Morbidity and mortality from technological disasters in Iran: A narrative review. J Educ Health Promot. 2019;8(1):147. de Freitas AWQ, Witt RR, Veiga ABG. da. The health burden of natural and technological disasters in Brazil from 2013 to 2021. Cad Saude Publica. 2023;39(4). Defilipo EC, Chagas PSDC, Peraro-Nascimento A, Ribeiro LC. Factors associated with low birthweight: a case-control study in a city of Minas Gerais. Rev Saude Publica. 2020;54:71. Noal DdaS, Vicente LN, Weintraub ACA, de Fagundes M, Cabral SMS, SimoniACR KV, et al. Estratégia de Saúde Mental e Atenção Psicossocial para Afetados da Boate Kiss. Psicologia: Ciência e Profissão. 2016;36(4):932–45. Cardona AMS, Arango DC, Fernández DYB, Martínez AA. Mortality in traffic accidents with older adults in Colombia. Rev Saude Publica. 2017;51. Gómez García AR. Seguridad y salud en el trabajo en Ecuador. Arch Prev Riesgos Labor. 2021;24(3):232–9. Maltais D, Cherblanc J, Cadell S, Bergeron-Leclerc C, Pouliot E, Fortin G, et al. Factors Associated with Complicated Grief Following a Railway Tragedy. Illn Crises Loss. 2023;31(3):467–87. Tin D, Cheng L, Hata R, Hertelendy AJ, Hart A, Ciottone G. Descriptive Analysis of the Healthcare Aspects of Industrial Disasters Around the World. Disaster Med Public Health Prep. 2023;17:e400. Centre for Research on the Epidemiology of Disasters. Gloss Univ Lovaine. 2023. http// www.emdat.be/Glossary . Accessed 3 January 2024. Laffon B, Pásaro E, Valdiglesias V. Effects of exposure to oil spills on human health: Updated review. J Toxicol Environ Health B Crit Rev. 2016;19(3–4):105–28. Kwok RK, Engel LS, Miller AK, Blair A, Curry MD, Jackson WB, et al. The Gulf study: A Prospective Study of Persons Involved in the Deepwater Horizon Oil Spill Response and Clean-Up. Environ Health Perspect. 2017;125(4):570–8. Lagergren Lindberg M, Hedman C, Lindberg K, Valentin J, Stenke L. Mental health and psychosocial consequences linked to radiation emergencies-increasingly recognised concerns. J Radiol Prot. 2022 13;42(3). MCCormick LC, Tajeu GS, Klapow J. Mental health consequences of chemical and radiologic emergencies: a systematic review. Emerg Med Clin North Am. 2015;33(1):197–211. Lucchini RG, Hashim D, Acquilla S, Basanets A, Bertazzi PA, Bushmanov A, et al. A comparative assessment of major international disasters: The need for exposure assessment, systematic emergency preparedness, and lifetime health care. BMC Public Health. 2017;17(1):46. Han YY, Youk AO, Sasser H, Talbott EO. Cancer incidence among residents of the Three Mile Island accident area: 1982–1995. Environ Res. 2011;111(8):1230–5. European Union. Council Directive 82/501/EEC of 24 June 1982 on the major-accident hazards of certain industrial activities. European Union. http://eur-lex.europa.eu/eli/dir/1982/501/oj . Accessed 4 Janaury 2024. European Union, THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 16 December. (2008). REGULATION (EC) No 1272/2008 OF 2008 on classification, labelling and packaging of substances and mixtures, amending and repealing Directives 67/548/EEC and 1999/45/EC, and amending Regulation (EC) No 1907/2006. European Union. 2008. http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32008R1272 . Accessed 6 January 2024. Tables Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4169973","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":285543344,"identity":"0779d978-8897-4616-af89-cbf290d00545","order_by":0,"name":"Andrea Fernández García","email":"","orcid":"","institution":"Universidad de Oviedo","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"Fernández","lastName":"García","suffix":""},{"id":285543345,"identity":"7ba63b51-44da-4fd1-a160-7d83f81ba046","order_by":1,"name":"Rick Kye Gan","email":"","orcid":"","institution":"Universidad de 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Americas.\u003c/p\u003e","description":"","filename":"Onlinedrawingimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4169973/v1/8b1890c9c977ba99f5793973.png"},{"id":53958402,"identity":"07d9d28a-2207-4480-9fd1-50d15b5a2491","added_by":"auto","created_at":"2024-04-02 17:44:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":14098,"visible":true,"origin":"","legend":"\u003cp\u003eMean injury rate and its prediction up to the year 2030, along with the upper and lower 95% confidence interval limits, for technological disasters in the Americas between the years 2000 and 2021.\u003c/p\u003e","description":"","filename":"Onlinedrawingimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4169973/v1/dc32cbbae14b36c91f4e577d.png"},{"id":53958361,"identity":"d54d0cdd-189f-46ae-b64d-2edee8c3f242","added_by":"auto","created_at":"2024-04-02 17:44:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":12205,"visible":true,"origin":"","legend":"\u003cp\u003eAverage rate of affected individuals and its prediction up to theyear 2030, along with the upper and lower limits of the 95% confidence interval, for technological disasters in the Americas between the years 2000 and 2021.\u003c/p\u003e","description":"","filename":"Onlinedrawingimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4169973/v1/1f8ff96165d41b935468cd8f.png"},{"id":53958341,"identity":"c576a185-cc36-4461-8449-375d898af417","added_by":"auto","created_at":"2024-04-02 17:44:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":12387,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency distribution of technological disasters in the Americas in the period 2000-2021 by type and country.\u003c/p\u003e","description":"","filename":"Onlinedrawingimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4169973/v1/4c0b643236fa1ccfabe9cc6b.png"},{"id":55265188,"identity":"ca52f3a5-d4d6-43da-b5c0-e353f7e84a0f","added_by":"auto","created_at":"2024-04-25 01:57:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":355700,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4169973/v1/c0168ea3-af47-48fb-a42b-552dc07b05c4.pdf"},{"id":53958405,"identity":"dc867f0c-de23-463d-9adf-3d9e52b99a9d","added_by":"auto","created_at":"2024-04-02 17:44:53","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":18795,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4169973/v1/d29f913147d49ec59a34c5b9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Epidemiological Profile and Morbidity-Mortality Patterns of Technological Disasters in the Americas from 2000 to 2021: a cross-sectional study","fulltext":[{"header":"Background","content":"\u003cp\u003eThe United Nations Sendai Framework for Disaster Risk Reduction 2015\u0026ndash;2030 defines a \u003cem\u003edisaster\u003c/em\u003e as \"a serious disruption of the functioning of a community or a society at any scale due to hazardous events interacting with conditions of exposure, vulnerability, and capacity, leading to one or more of the following: human, material, economic and environmental losses and impacts.\"\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e The Integrated Research on Disaster Risk (IRDR) Program in 2014 made a clear distinction between disasters that arise from natural hazards and those classified as technological hazards.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eA technological disaster is a catastrophic event associated, entirely or partially, with human decisions, activities, or errors. These disasters are often related to technology control, malfunction, or management, as well as failures or breakdowns of systems, equipment, and engineering or industrial standards. Structural collapses (bridges, mines, and buildings), industrial accidents (chemical or nuclear explosions), and traffic accidents (land, air, and maritime) are examples of technological disasters.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eMoreover, many of these technological disasters are accompanied by consequences such as environmental pollution. In fact, some technological disasters can be as severe as natural disasters. Sometimes, they are of an acute onset, while at other times, their effects may appear gradually and extend over years.\u003csup\u003e \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e \u003c/sup\u003e Between 2000 and 2021, a total of 5,390 technological disasters were reported, affecting 2,638,985 people, resulting in the death of 166,068 individuals, and causing economic losses totaling \u003cspan\u003e$\u003c/span\u003e63,178\u0026nbsp;million.\u003csup\u003e \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e \u003c/sup\u003e \u003c/p\u003e \u003cp\u003eSome of the largest technological disasters in recent decades include the collapse of the Banqiao Dam (China, 1975, due to heavy rains combined with a design and engineering failure), the contamination incident in Bhopal (India, 1984, due to the toxic gas leak at the Union Carbide factory in Bhopal resulting from equipment failures and design defects, causing between 4,000 and 30,000 deaths), the Chernobyl nuclear accident (Ukraine, 1986, with over 200,000 deaths due to radiation exposure and significant environmental impact), and the Fukushima nuclear accident (Japan, 2011, which released 45,000 liters of highly radioactive water). Despite these events, technological disasters often receive less attention from the scientific community.\u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTechnological disasters have a significant environmental and economic impact. Some of the costliest ones include the explosion in the port of Beirut (Lebanon, 2020), the oil spill from the sinking of the Prestige oil tanker (Spain, 2002), and the explosion and oil spill at the Deepwater Horizon platform (Gulf of Mexico, 2010).\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn addition, technological disasters also have effects on the mental health of those affected, as evidenced by research conducted after the Gulf of Mexico oil spill, where elevated levels of depression and anxiety were increased, with a more significant impact on those who suffered economic losses due to the disaster.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThis study aimed to examine the epidemiological profile of technological disasters that occurred in the Americas between 2000 and 2021 in terms of morbidity-mortality and to analyze their temporal trends.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eA cross-sectional study of technological disasters in the American continent (North America, Central America, the Caribbean, and South America) was conducted, using the IRDR definition of technological disaster and data from EM-DAT database. \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDistributions using absolute and relative frequencies were analyzed by year and over the entire study period. Central tendency (mean) and dispersion (standard deviation) parameters were used to calculate the mean rates of affected individuals, injuries, and fatalities per year and per million inhabitants. The chi-square test was applied to check for the presence or absence of an association between the number of each type of technological disaster and the year of occurrence.\u003c/p\u003e \u003cp\u003eANOVA test was used to determine whether there were statistically significant differences in the annual mean rates (for the period 2000\u0026ndash;2021) per million inhabitants of deaths, injuries, and affected individuals by type of technological disaster. The Mann-Kendall test was applied to check for the presence or absence of temporal trends in the mean mortality rate per million inhabitants, the mean rate of affected individuals per million inhabitants, and the mean rate of injuries per million inhabitants for each type of technological disaster during the study period.\u003c/p\u003e \u003cp\u003eNext, Augmented Dickey-Fuller (ADF) test was applied to determine whether the time series were stationary. The existence of stationarity implies that the mean, variance, and covariance of the analyzed variables are constant and will not change over time, making temporal predictions possible. Exponential smoothing was used to forecast the annual number of mean mortality rate per million inhabitants, mean rate of affected individuals per million inhabitants, and mean rate of injuries per million inhabitants up to the year 2030, along with their corresponding CI95%. An alpha value of 0.5 was used as the smoothing constant.\u003c/p\u003e \u003cp\u003ePearson correlation coefficient between the three variables studied (mean mortality rate per million inhabitants, mean rate of affected individuals per million inhabitants, and mean rate of injuries per million inhabitants) was calculated to check for any relationships between them.\u003c/p\u003e \u003cp\u003eLastly, the yearly frequency of deaths, injuries, and affected individuals per million population caused by technological accidents by using exponential smoothing was analyzed and made projections for each of these variables until 2030. In all calculations, the Stata v.15 statistical package was used.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eBetween 2000 and 2021, a total of 733 technological disasters were recorded in the American continent. Of these, 562 (76.67%) were transportation accidents, 62 (8.46%) were industrial accidents, and 109 (14.87%) were miscellaneous accidents. Table 1 shows the absolute and relative frequencies of each type of technological disaster by year of occurrence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter applying the chi-square test, the difference was statistically non-significant. Table 1 also shows the annual mean rates of mortality, affected individuals, and injuries per million inhabitants, as well as the standard deviations for each of the mean rates. There were statistically significant differences between the mean rates of affected individuals per million inhabitants (F= 4.91; p= 0.008) and the mean mortality rates per million inhabitants (F= 2.97; p= 0.043) for each type of technological disaster.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo statistically significant differences were found in the mean rates of injuries or casualties per million inhabitants. No trends were found in the mean mortality rate, mean rate of affected individuals per million inhabitants, or mean rate of injuries per million inhabitants for each type of technological disaster during the study period.\u003c/p\u003e\n\u003cp\u003eOn the other hand, it was found that the time series of the mean mortality rate (t= -5.20; p\u0026lt;0.000), the mean rate of injuries or casualties (t= -3.78; p= 0.001), and the mean rate of affected individuals (t= -2.34; p= 0.03) were stationary. The existence of stationarity implies that the mean, variance, and covariance of the variables analyzed are constant and will not change over time, allowing for temporal predictions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere was no significant correlation between the mean mortality rates, rates of injuries or casualties, and rates of affected individuals. Figures 1, 2, and 3, respectively, show the exponential smoothing of the series of mean mortality rates, injuries or casualties, and affected individuals, along with predictions up to the year 2030, and the upper and lower limits of the 95% confidence interval. Figure 4 illustrates the distribution of each type of technological disaster by country.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTransportation accidents were the most frequent type of technological disaster during the study period on the American continent. Industrial accidents had the highest mean rates of deaths and affected individuals, while the highest mean rate of injuries occurred in miscellaneous or other types of accidents. A previous study\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e showed an increasing trend of technological disasters, which is expected to continue as industrialization and urbanization continue to grow. However, no trends were found for any of the variables studied in the present study.\u003c/p\u003e \u003cp\u003eFreitas et al.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e analyzed disasters in Brazil between 2013 and 2021 and found that while natural disasters were the most frequent, technological disasters also significantly impacted public health. Some of these disasters had important consequences for the health of the country's residents: prenatal exposure to contaminated water sources due to the Mariana dam disaster in the state of Minas Gerais in 2015 was associated with low birth weight.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Additionally, the fire at the Kiss nightclub in the Brazilian state of Rio Grande do Sul resulted in the death of 242 people and caused about 680 injuries. In addition to these consequences, mental health and psychiatric illnesses were observed in survivors and personnel involved in rescue efforts.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe American continent has one of the highest rates of transportation accidents. Colombia is the country with 70% of the deaths, with its residents having a three times higher probability of dying in such accidents compared to those in Spain and four times higher than the population in the United Kingdom. Furthermore, the risk of death due to transportation accidents is higher in people over 60 years old.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e In Ecuador, in 2019, transportation accidents were the leading cause of death in adults aged 30 to 64.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIndustrial activities increase the risk of explosions, fires, and maritime or railway disasters.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e In addition to physical consequences (deaths or injuries), industrial disasters can also have a significant psychological impact. People involved in an industrial disaster can develop compound trauma due to job loss, social stigmatization, and anxiety associated with long-term physical health effects resulting from exposure to chemicals or toxins such as cancer, fertility problems, and teratogenic effects.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e In the present study, industrial accidents had the highest mean rates of deaths and affected individuals among all types of technological disasters. There are various subtypes within industrial accidents, including chemical spills, collapses, explosions, gas leaks, radiation, and oil spills.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Individuals exposed to oil spills may experience respiratory symptoms such as coughing and difficulty breathing, but this type of disaster can also lead to hepatic, neurological, renal, endocrine, and hematological effects The oil spill caused by the sinking of the Deepwater Horizon oil rig in 2010 resulted in the largest oil spill ever recorded in the United States.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eNuclear disasters are a type of technological disaster with significant psychosocial effects.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Radiological accidents can trigger specific mental health disturbances that differ from those observed after natural disasters.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e This may be because they are typically sudden-onset disasters, and the threat is invisible to the person, making fear of the potential consequences greater due to the uncertainty of the extent of exposure.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e The Three Mile Island nuclear accident (Pennsylvania, 1979) was the most severe nuclear accident in the United States.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e A study conducted on the population affected by this accident showed a slight increase in the risk of bronchus, trachea, lung cancer, and leukemia.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOne of the most significant industrial accidents outside the American continent was the explosion at the ICMESA chemical plant near Seveso (Italy) in 1976. The release of high levels of 2,3,7,8-tetrachlorodibenzodioxin caused severe damage to both the population and the environment. A decrease in male and female fertility was observed, as well as a higher risk of cancer development.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e This accident was the starting point for the publication of Directive 82/501/EEC on the Prevention and Control of Major Accidents, known as the SEVESO Directive.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e This directive has been updated over the years, with the last adaptation in 2008 (SEVESO III).\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe limitations of this study are related to the need for uniform criteria in different databases to define technological disasters and the varying quality with which countries report disasters and provide information.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eAlthough natural disasters are the main type of disaster in terms of impact in the Americas, technological disasters also have a significant impact on public health, with high rates of deaths, injuries, and affected individuals, as well as on the environment, with very significant differences among countries in the Americas.\u003c/p\u003e \u003cp\u003eConsidering that the highest rates of deaths and affected individuals occur in the subgroup of industrial accidents, it would be advisable to review safety and response protocols for industrial accidents to reduce these rates as much as possible and ensure the health of workers and residents in affected areas. Therefore, countries must have an appropriate legal regulatory framework, especially in labor, industrial processes, and environmental areas.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eADF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAugmented Dickey-Fuller\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI95%\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence intervals at 95%\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIRDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntegrated Research on Disaster Risk\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate: Not applicable\u003c/p\u003e\n\u003cp\u003eConsent for publication: Not applicable\u003c/p\u003e\n\u003cp\u003eAvailability of data and material: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests: Not applicable\u003c/p\u003e\n\u003cp\u003eFunding: Not applicable\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions: All authors contributed substantially to the design, data collection, data analysis, and risk assessment; drafted the article and reviewed the final manuscript; gave final approval of the version to be published and agreed to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003eAcknowledgements: Not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUnited Nations Office for Disaster Risk Reduction. 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Emerg Med Clin North Am. 2015;33(1):197\u0026ndash;211.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLucchini RG, Hashim D, Acquilla S, Basanets A, Bertazzi PA, Bushmanov A, et al. A comparative assessment of major international disasters: The need for exposure assessment, systematic emergency preparedness, and lifetime health care. BMC Public Health. 2017;17(1):46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan YY, Youk AO, Sasser H, Talbott EO. Cancer incidence among residents of the Three Mile Island accident area: 1982\u0026ndash;1995. Environ Res. 2011;111(8):1230\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEuropean Union. Council Directive 82/501/EEC of 24 June 1982 on the major-accident hazards of certain industrial activities. European Union. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://eur-lex.europa.eu/eli/dir/1982/501/oj\u003c/span\u003e\u003cspan address=\"http://eur-lex.europa.eu/eli/dir/1982/501/oj\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 4 Janaury 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEuropean Union, THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 16 December. (2008). REGULATION (EC) No 1272/2008 OF 2008 on classification, labelling and packaging of substances and mixtures, amending and repealing Directives 67/548/EEC and 1999/45/EC, and amending Regulation (EC) No 1907/2006. European Union. 2008. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32008R1272\u003c/span\u003e\u003cspan address=\"http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32008R1272\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 6 January 2024.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Technological Disasters, Epidemiological Profile, Public Health Crisis, Morbidity, Mortality","lastPublishedDoi":"10.21203/rs.3.rs-4169973/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4169973/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTechnological disasters in the Americas have significant public health and environmental implications, but there is limited epidemiological analysis of these events. This study aims to characterize the epidemiological profile of technological disasters in the Americas from 2000 to 2021, focusing on morbidity and mortality trends.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective cross-sectional study was conducted. The ANOVA test was applied in the mean rates calculated for each type of disaster. The Mann-Kendall test assessed the presence or absence of temporal trends, and the Dickey-Fuller augmented test was used to determine if the time series were stationary. Predictions were made up to the year 2030 to mean mortality rate per million inhabitants, mean rate of affected individuals per million inhabitants, and mean rate of injuries per million inhabitants.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 733 technological disasters were recorded in the Americas. Statistically significant differences were found between the mean rates of affected individuals and the mean mortality rates per million inhabitants for each type of technological disaster. No trends were identified.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe highest rates of fatalities and affected individuals occurred within industrial accidents.\u003c/p\u003e","manuscriptTitle":"The Epidemiological Profile and Morbidity-Mortality Patterns of Technological Disasters in the Americas from 2000 to 2021: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-02 17:44:23","doi":"10.21203/rs.3.rs-4169973/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c4b4732a-3b2e-4c95-9ac2-c0637777e84b","owner":[],"postedDate":"April 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-24T19:01:33+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-02 17:44:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4169973","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4169973","identity":"rs-4169973","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}