Suchergebnisse

Background: Suicide is one of the leading causes of prisoners' death, and, as a group, inmates have higher suicide rates than their community counterparts. However, the prevalence of suicidal ideation among Chinese inmates and their risks of suicide remain unknown. Aims: This study investigates the prevalence of suicidal ideation among Chinese incarcerated populations and its risk and protective factors. Methods: We randomly selected three types of prisoners (N = 690) and a sample of college students (n = 248) from four correction facilities and a police academy in China. A self-report questionnaire with standardized scales translated from the West was administered to the samples. Results: The prevalence of suicidal ideation among Chinese inmates is similar to that found among inmates in the West. The correlates of suicidal ideation for the Chinese inmates are also in line with what has been found in general populations. Conclusions: It is suggested that Chinese inmates have the same risk factors and protective factors for suicidal ideation as the general population in China. Suicide prevention in Chinese prisons can learn from and inform suicide prevention work for the general population.

The fear among college students of different sensory status during the COVID-19 pandemic in Beijing was assessed with its correlates. The total sample of 686 cases was composed of students without sensory disabilities, students that are hard of hearing, and with a visual impairment. The measure of fear was adopted from an instrument assessing people's fear of flu published by WHO. The correlate variables beyond the demographics were all previously validated and documented tools. The students without sensory disabilities are more fearful than the students with sensory impairment. Females are more fearful than males. Social support reduces fear. Life satisfaction is negatively related to fear. While stress and anxiety increase fear, depression is negatively correlated with fear. Findings indicate that students with sensory disabilities are not the most fearful in a pandemic disaster like the current COVID-19, which may increase their risk of infection.

The identification of societal vulnerable counties and regions and the factors contributing to social vulnerability are crucial for effective disaster risk management. Significant advances have been made in the study of social vulnerability over the past two decades, but we still know little regarding China's societal vulnerability profiles, especially at the county level. This study investigates the county‐level spatial and temporal patterns in social vulnerability in China from 1980 to 2010. Based on China's four most recent population censuses of 2,361 counties and their corresponding socioeconomic data, a social vulnerability index for each county was created using factor analysis. Exploratory spatial data analysis, including global and local autocorrelations, was applied to reveal the spatial patterns of county‐level social vulnerability. The results demonstrate that the dynamic characteristics of China's county‐level social vulnerability are notably distinct, and the dominant contributors to societal vulnerability for all of the years studied were rural character, development (urbanization), and economic status. The spatial clustering patterns of social vulnerability to natural disasters in China exhibited a gathering–scattering–gathering pattern over time. Further investigations indicate that many counties in the eastern coastal area of China are experiencing a detectable increase in social vulnerability, whereas the societal vulnerability of many counties in the western and northern areas of China has significantly decreased over the past three decades. These findings will provide policymakers with a sound scientific basis for disaster prevention and mitigation decisions.

New features of natural disasters have been observed over the last several years. The factors that influence the disasters' formation mechanisms, regularity of occurrence and main characteristics have been revealed to be more complicated and diverse in nature than previously thought. As the uncertainty involved increases, the variables need to be examined further. This article discusses the importance and the shortage of multivariate analysis of natural disasters and presents a method to estimate the joint probability of the return periods and perform a risk analysis. Severe dust storms from 1990 to 2008 in Inner Mongolia were used as a case study to test this new methodology, as they are normal and recurring climatic phenomena on Earth. Based on the 79 investigated events and according to the dust storm definition with bivariate, the joint probability distribution of severe dust storms was established using the observed data of maximum wind speed and duration. The joint return periods of severe dust storms were calculated, and the relevant risk was analyzed according to the joint probability. The copula function is able to simulate severe dust storm disasters accurately. The joint return periods generated are closer to those observed in reality than the univariate return periods and thus have more value in severe dust storm disaster mitigation, strategy making, program design, and improvement of risk management. This research may prove useful in risk‐based decision making. The exploration of multivariate analysis methods can also lay the foundation for further applications in natural disaster risk analysis.