This paper investigates three controversies involving potential causes and consequences of information bias in case and death definitions during the coronavirus disease (COVID-19) pandemic. First, evidence suggests China’s surveillance data were biased and misinterpreted by the World Health Organization (WHO), prompting the WHO to advise nations to copy China’s lockdowns. China appeared to use narrow diagnostic definitions that undercounted cases and deaths. Second, novel genomic data disseminated during the pandemic without adequate guidance from rigorous epidemiologic studies biased infection control policies in many countries. A novel genomic sequence of a virus is insufficient to declare new cases of a novel disease. Third, media reports of COVID-19 surveillance data in many nations appeared to be biased. Broadened surveillance definitions captured additional information, but unadjusted surveillance data disseminated to the public are not true cases and deaths. Recommendations include clarification of the proper use of diagnostic and surveillance case and death definitions to avoid information bias.

For 63 years scientists in the Atomic Bomb Casualty Commission and its successor, the Radiation Effects Research Foundation, have been assessing the long-term health effects in the survivors of the atomic bombings of Hiroshima and Nagasaki and in their children. The identification and follow-up of a large population (approximately a total of 200 000, of whom more than 40% are alive today) that includes a broad range of ages and radiation exposure doses, and healthy representatives of both sexes; establishment of well-defined cohorts whose members have been studied longitudinally, including some with biennial health examinations and a high survivor-participation rate; and careful reconstructions of individual radiation doses have resulted in reliable excess relative risk estimates for radiation-related health effects, including cancer and noncancer effects in humans, for the benefit of the survivors and for all humankind. This article reviews those risk estimates and summarizes what has been learned from this historic and unique study.

(Disaster Med Public Health Preparedness. 2011;5:S122-S133)

Understanding the relationship between infectious disease outbreaks and natural disasters is important in developing response and disaster risk reduction strategies. The aim of this study was to identify outbreaks associated with natural disasters during the past 20 y, and outline risk factors and mechanisms for postdisaster outbreaks. Review of the international disaster database (EM-DAT) and systematic review of the literature were conducted. The records of disaster events in EM-DAT during the past 20 y were screened. A literature search was carried out in the databases PubMed and Embase. Articles in English language published between 2000 and 2020 were searched. Data were extracted from articles and Narrative synthesis was used to summarize the findings. We found 108 events associated with epidemics, the majority being floods. We found 36 articles, most of them focused on outbreaks after floods. Risk factors and mechanisms that contributed to the outbreaks were mainly related to the consequences of disaster and its impact on the environment and living conditions of population. Infrastructure readiness and postdisaster measures play important roles in controlling the spread of epidemics after natural disasters. More evidence and research are required for better understanding of the association between natural disasters and infectious diseases outbreaks.

In testimony before US Congress on March 11, 2020, members of the House Oversight and Reform Committee were informed that estimated mortality for the novel coronavirus was 10-times higher than for seasonal influenza. Additional evidence, however, suggests the validity of this estimation could benefit from vetting for biases and miscalculations. The main objective of this article is to critically appraise the coronavirus mortality estimation presented to Congress. Informational texts from the World Health Organization and the Centers for Disease Control and Prevention are compared with coronavirus mortality calculations in Congressional testimony. Results of this critical appraisal reveal information bias and selection bias in coronavirus mortality overestimation, most likely caused by misclassifying an influenza infection fatality rate as a case fatality rate. Public health lessons learned for future infectious disease pandemics include: safeguarding against research biases that may underestimate or overestimate an associated risk of disease and mortality; reassessing the ethics of fear-based public health campaigns; and providing full public disclosure of adverse effects from severe mitigation measures to contain viral transmission.

Authors explore the influence of Russian aggression on Ukrainian health care and humanitarian crises. On February 24, 2022, Ukraine faced an unprovoked brutal Russian invasion resulting in multiple negative consequences. During the 3 mo of full-scale war, Russians damaged more than 600 hospitals and killed at least 12 medics. Access to health care is severely impacted due to security concerns, restricted mobility, broken supply chains, and mass population displacement. Health care continues to come under attack (including attacks against health facilities, transport, personnel, patients, supplies, and warehouses), with a total of 295 attacks on health care, resulting in 59 injuries and 76 deaths, reported between February 24 and June 15. Ministry of Health reported that 1658 medical workers had been forced to change their residence and became employed in other regions of the country. Russian aggression negatively influenced not only the medical system and economics, but it led to a humanitarian crisis in the whole of Ukraine.

Objective: Hurricane Katrina struck the US Gulf Coast on August 29, 2005, causing unprecedented damage to numerous communities in Louisiana and Mississippi. Our objectives were to verify, document, and characterize Katrina-related mortality in Louisiana and help identify strategies to reduce mortality in future disasters.

Methods: We assessed Hurricane Katrina mortality data sources received in 2007, including Louisiana and out-of-state death certificates for deaths occurring from August 27 to October 31, 2005, and the Disaster Mortuary Operational Response Team's confirmed victims' database. We calculated age-, race-, and sex-specific mortality rates for Orleans, St Bernard, and Jefferson Parishes, where 95% of Katrina victims resided and conducted stratified analyses by parish of residence to compare differences between observed proportions of victim demographic characteristics and expected values based on 2000 US Census data, using Pearson chi square and Fisher exact tests.

Results: We identified 971 Katrina-related deaths in Louisiana and 15 deaths among Katrina evacuees in other states. Drowning (40%), injury and trauma (25%), and heart conditions (11%) were the major causes of death among Louisiana victims. Forty-nine percent of victims were people 75 years old and older. Fifty-three percent of victims were men; 51% were black; and 42% were white. In Orleans Parish, the mortality rate among blacks was 1.7 to 4 times higher than that among whites for all people 18 years old and older. People 75 years old and older were significantly more likely to be storm victims (P < .0001).

Conclusions: Hurricane Katrina was the deadliest hurricane to strike the US Gulf Coast since 1928. Drowning was the major cause of death and people 75 years old and older were the most affected population cohort. Future disaster preparedness efforts must focus on evacuating and caring for vulnerable populations, including those in hospitals, long-term care facilities, and personal residences. Improving mortality reporting timeliness will enable response teams to provide appropriate interventions to these populations and to prepare and implement preventive measures before the next disaster. (Disaster Med Public Health Preparedness. 2008;2:215–223)

All levels of government are authorized to apply coronavirus disease 2019 (COVID-19) protection measures; however, they must consider how and when to ease lockdown restrictions to limit long-term societal harm and societal instability. Leaders that use a well-considered framework with an incremental approach will be able to gradually restart society while simultaneously maintaining the public health benefits achieved through lockdown measures. Economically vulnerable populations cannot endure long-term lockdown, and most countries lack the ability to maintain a full nationwide relief operation. Decision-makers need to understand this risk and how the Maslow hierarchy of needs and the social determinants of health can guide whole of society policies. Aligning decisions with societal needs will help ensure all segments of society are catered to and met while managing the crisis. This must inform the process of incremental easing of lockdowns to facilitate the resumption of community foundations, such as commerce, education, and employment in a manner that protects those most vulnerable to COVID-19. This study proposes a framework for identifying a path forward. It reflects on baseline requirements, regulations and recommendations, triggers, and implementation. Those desiring a successful recovery from the COVID-19 pandemic need to adopt an evidence-based framework now to ensure community stabilization and sustainability.

Many countries have enacted a quick response to the unexpected coronavirus disease 2019 (COVID-19) pandemic by using existing technologies. For example, robotics, artificial intelligence, and digital technology have been deployed in hospitals and public areas for maintaining social distancing, reducing person-to-person contact, enabling rapid diagnosis, tracking virus spread, and providing sanitation. In this study, 163 news articles and scientific reports on COVID-19-related technology adoption were screened, shortlisted, categorized by application scenario, and reviewed for functionality. Technologies related to robots, artificial intelligence, and digital technology were selected from the pool of candidates, yielding a total of 50 applications for review. Each case was analyzed for its engineering characteristics and potential impact on the COVID-19 pandemic. Finally, challenges and future directions regarding the response to this pandemic and future pandemics were summarized and discussed.