Robert J. Ursano

Bio: Robert J. Ursano is an academic researcher from Uniformed Services University of the Health Sciences. The author has contributed to research in topics: Poison control & Mental health. The author has an hindex of 69, co-authored 532 publications receiving 20891 citations. Previous affiliations of Robert J. Ursano include University of Oklahoma & University of North Carolina at Chapel Hill.

Public responses to the novel 2019 coronavirus (2019-nCoV) in Japan: Mental health consequences and target populations.

Abstract: In December 2019, cases of life-threatening pneumonia were reported in Wuhan, China. A novel coronavirus (2019-nCoV) was identified as the source of infection. The number of reported cases has rapidly increased in Wuhan as well as other Chinese cities. The virus has also been identified in other parts of the world. On 30 January 2020, the World Health Organization (WHO) declared this disease a ‘public health emergency of international concern.’ As of 3 February 2020, the Chinese government had reported 17 205 confirmed cases in Mainland China, and the WHO had reported 146 confirmed cases in 23 countries outside China. The virus has not been contained within Wuhan, and other major cities in China are likely to experience localized outbreaks. Foreign cities with close transport links to China could also become outbreak epicenters without careful public health interventions. In Japan, economic impacts and social disruptions have been reported. Several Japanese individuals who were on Japanese-government-chartered airplanes from Wuhan to Japan were reported as coronavirus-positive. Also, human-to-human transmission was confirmed in Nara Prefecture on 28 January 2020. Since then, the public has shown anxiety-related behaviors and there has been a significant shortage of masks and antiseptics in drug stores. The economic impact has been substantial. Stock prices have dropped in China and Japan, and other parts of the world are also showing some synchronous decline. As of 3 February 2020, no one had died directly from coronavirus infection in Japan. Tragically, however, a 37-year-old government worker who had been in charge of isolated returnees died from apparent suicide. This is not the first time that the Japanese people have experienced imperceptible-agent emergencies – often dubbed as ‘CBRNE’ (i.e., chemical, biological, radiological, nuclear, and high-yield explosives). Japan has endured two atomic bombings in 1945, the sarin gas attacks in 1995, the H1N1 influenza pandemic in 2009, and the Fukushima nuclear accident in 2011: all of which carried fear and risk associated with unseen agents. All of these events provoked social disruption. Overwhelming and sensational news headlines and images added anxiety and fear to these situations and fostered rumors and hyped information as individuals filled in the absence of information with rumors. The affected people were subject to societal rejection, discrimination, and stigmatization. Fukushima survivors tend to attribute physical changes to the event (regardless of actual exposure) and have decreased perceived health, which is associated with decreased life expectancy. Fear of the unknown raises anxiety levels in healthy individuals as well as those with preexisting mental health conditions. For example, studies of the 2001 anthrax letter attacks in the USA showed long-term mental health adversities as well as lowered health perception of the infected employees and responders. Public fear manifests as discrimination, stigmatization, and scapegoating of specific populations, authorities, and scientists. As we write this letter, the coronavirus emergency is rapidly evolving. Nonetheless, we can more or less predict expected mental/physical health consequences and the most vulnerable populations. First, peoples’ emotional responses will likely include extreme fear and uncertainty. Moreover, negative societal behaviors will be often driven by fear and distorted perceptions of risk. These experiences might evolve to include a broad range of public mental health concerns, including distress reactions (insomnia, anger, extreme fear of illness even in those not exposed), health risk behaviors (increased use of alcohol and tobacco, social isolation), mental health disorders (post-traumatic stress disorder, anxiety disorders, depression, somatization), and lowered perceived health. It is essential for mental health professionals to provide necessary support to those exposed and to those who deliver care. Second, particular effort must be directed to vulnerable populations, which include: (i) the infected and ill patients, their families, and colleagues; (ii) Chinese individuals and communities; (iii) individuals with pre-existing mental/physical conditions; and, last but not least, (iv) health-care and aid workers, especially nurses and physicians working directly with ill or quarantined persons. If nothing else, the death of the government quarantine worker must remind us to recognize the extent of psychological stress associated with imperceptible agent emergencies and to give paramount weight to the integrity and rights of vulnerable populations.

Five Essential Elements of Immediate and Mid–Term Mass Trauma Intervention: Empirical Evidence

Abstract: Given the devastation caused by disasters and mass violence, it is critical that intervention policy be based on the most updated research findings. However, to date, no evidence-based consensus has been reached supporting a clear set of recommendations for intervention during the immediate and the mid-term post mass trauma phases. Because it is unlikely that there will be evidence in the near or mid-term future from clinical trials that cover the diversity of disaster and mass violence circumstances, we assembled a worldwide panel of experts on the study and treatment of those exposed to disaster and mass violence to extrapolate from related fields of research, and to gain consensus on intervention principles. We identified five empirically supported intervention principles that should be used to guide and inform intervention and prevention efforts at the early to mid-term stages. These are promoting: 1) a sense of safety, 2) calming, 3) a sense of self- and community efficacy, 4) connectedness, and 5) hope.

The impact of a military air disaster on the health of assistance workers. A prospective study

Abstract: The worst peacetime disaster in United States Army history occurred on December 12, 1985 in Gander, Newfoundland. A charter airline carrying 248 soldiers home from peacekeeping duties in the Sinai Desert crashed after a refueling stop, killing all on board. After the crash, Army family assistance wo

Trends in mental illness and suicidality after Hurricane Katrina

Abstract: A representative sample of 815 pre-hurricane residents of the areas affected by Hurricane Katrina was interviewed 5-8 months after the hurricane and again 1 year later as the Hurricane Katrina Community Advisory Group (CAG). The follow-up survey was carried out to study patterns-correlates of recovery from hurricane-related post-traumatic stress disorder (PTSD), broader anxiety-mood disorders and suicidality. The Trauma Screening Questionnaire screening scale of PTSD and the K6 screening scale of anxiety-mood disorders were used to generate DSM-IV prevalence estimates. Contrary to results in other disaster studies, where post-disaster mental disorder typically decreases with time, prevalence increased significantly in the CAG for PTSD (20.9 vs 14.9% at baseline), serious mental illness (SMI; 14.0 vs 10.9%), suicidal ideation (6.4 vs 2.8%) and suicide plans (2.5 vs 1.0%). The increases in PTSD-SMI were confined to respondents not from the New Orleans Metropolitan Area, while the increases in suicidal ideation-plans occurred both in the New Orleans sub-sample and in the remainder of the sample. Unresolved hurricane-related stresses accounted for large proportions of the inter-temporal increases in SMI (89.2%), PTSD (31.9%) and suicidality (61.6%). Differential hurricane-related stress did not explain the significantly higher increases among respondents from areas other than New Orleans, though, as this stress was both higher initially and decreased less among respondents from the New Orleans Metropolitan Area than from other areas affected by the hurricane. Outcomes were only weakly related to socio-demographic variables, meaning that high prevalence of hurricane-related mental illness remains widely distributed in the population nearly 2 years after the hurricane.

Using Multivariate Statistics

Abstract: Thank you for downloading using multivariate statistics. As you may know, people have look hundreds times for their favorite novels like this using multivariate statistics, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful bugs inside their laptop. using multivariate statistics is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the using multivariate statistics is universally compatible with any devices to read.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Development of a new resilience scale: The Connor‐Davidson Resilience Scale (CD‐RISC)

Abstract: Resilience may be viewed as a measure of stress coping ability and, as such, could be an important target of treatment in anxiety, depression, and stress reactions. We describe a new rating scale to assess resilience. The Connor-Davidson Resilience scale (CD-RISC) comprises of 25 items, each rated on a 5-point scale (0–4), with higher scores reflecting greater resilience. The scale was administered to subjects in the following groups: community sample, primary care outpatients, general psychiatric outpatients, clinical trial of generalized anxiety disorder, and two clinical trials of PTSD. The reliability, validity, and factor analytic structure of the scale were evaluated, and reference scores for study samples were calculated. Sensitivity to treatment effects was examined in subjects from the PTSD clinical trials. The scale demonstrated good psychometric properties and factor analysis yielded five factors. A repeated measures ANOVA showed that an increase in CD-RISC score was associated with greater improvement during treatment. Improvement in CD-RISC score was noted in proportion to overall clinical global improvement, with greatest increase noted in subjects with the highest global improvement and deterioration in CD-RISC score in those with minimal or no global improvement. The CDRISC has sound psychometric properties and distinguishes between those with greater and lesser resilience. The scale demonstrates that resilience is modifiable and can improve with treatment, with greater improvement corresponding to higher levels of global improvement. Depression and Anxiety 18:76–82, 2003. & 2003 Wiley-Liss, Inc.

Meta-analysis of risk factors for posttraumatic stress disorder in trauma-exposed adults.

Abstract: Meta-analyses were conducted on 14 separate risk factors for posttraumatic stress disorder (PTSD), and the moderating effects of various sample and study characteristics, including civilian/military status, were examined. Three categories of risk factor emerged: Factors such as gender, age at trauma, and race that predicted PTSD in some populations but not in others; factors such as education, previous trauma, and general childhood adversity that predicted PTSD more consistently but to a varying extent according to the populations studied and the methods used; and factors such as psychiatric history, reported childhood abuse, and family psychiatric history that had more uniform predictive effects. Individually, the effect size of all the risk factors was modest, but factors operating during or after the trauma, such as trauma severity, lack of social support, and additional life stress, had somewhat stronger effects than pretrauma factors.