Collective violence

2021 ◽  
pp. 393-400
Author(s):  
Barry S. Levy

War and other forms of armed conflict cause many adverse effects on health and the environment, including morbidity and mortality due to weapons; damage to the health-supporting infrastructure of society; contamination of air, water, and soil; forced displacement; violation of international agreements and human rights; diversion of resources; and promotion of additional violence. While conventional weapons account for the vast majority of fatal and non-fatal injuries during war, weapons of mass destruction (including nuclear and radiological weapons, chemical weapons, and biological agents) as well as antipersonnel landmines and unexploded ordnance pose additional threats. Public health workers and other health professionals can help to minimize the health and environmental consequences of war and other forms of armed conflict and to help end war itself. Categories of preventive measures include documentation; education and awareness-raising; advocacy and support for policies and programmes to minimize the consequences of, and help to reduce the risks of, war and other forms of armed conflict; and provision of preventive services. Public health frameworks of prevention can be useful in identifying opportunities for prevention and designing, implementing, and improving policies and programmes. These frameworks include levels of prevention (primary, secondary, and tertiary) and the host-agent-environment model.

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jinghua Li ◽  
Jingdong Xu ◽  
Huan Zhou ◽  
Hua You ◽  
Xiaohui Wang ◽  
...  

ABSTRACT Background Public health workers at the Chinese Centre for Disease Control and Prevention (China CDC) and primary health care institutes (PHIs) were among the main workers who implemented prevention, control, and containment measures. However, their efforts and health status have not been well documented. We aimed to investigate the working conditions and health status of front line public health workers in China during the COVID-19 epidemic. Methods Between 18 February and 1 March 2020, we conducted an online cross-sectional survey of 2,313 CDC workers and 4,004 PHI workers in five provinces across China experiencing different scales of COVID-19 epidemic. We surveyed all participants about their work conditions, roles, burdens, perceptions, mental health, and self-rated health using a self-constructed questionnaire and standardised measurements (i.e., Patient Health Questionnaire and General Anxiety Disorder scale). To examine the independent associations between working conditions and health outcomes, we used multivariate regression models controlling for potential confounders. Results The prevalence of depression, anxiety, and poor self-rated health was 21.3, 19.0, and 9.8%, respectively, among public health workers (27.1, 20.6, and 15.0% among CDC workers and 17.5, 17.9, and 6.8% among PHI workers). The majority (71.6%) made immense efforts in both field and non-field work. Nearly 20.0% have worked all night for more than 3 days, and 45.3% had worked throughout the Chinese New Year holiday. Three risk factors and two protective factors were found to be independently associated with all three health outcomes in our final multivariate models: working all night for >3 days (multivariate odds ratio [ORm]=1.67~1.75, p<0.001), concerns about infection at work (ORm=1.46~1.89, p<0.001), perceived troubles at work (ORm=1.10~1.28, p<0.001), initiating COVID-19 prevention work after January 23 (ORm=0.78~0.82, p=0.002~0.008), and ability to persist for > 1 month at the current work intensity (ORm=0.44~0.55, p<0.001). Conclusions Chinese public health workers made immense efforts and personal sacrifices to control the COVID-19 epidemic and faced the risk of mental health problems. Efforts are needed to improve the working conditions and health status of public health workers and thus maintain their morale and effectiveness during the fight against COVID-19.


2021 ◽  
Author(s):  
Duckhee Chae ◽  
Yunekyong Kim ◽  
Jeeheon Ryu ◽  
Keiko Asami ◽  
Jaseon Kim ◽  
...  

2021 ◽  
Author(s):  
Sarah E. Scales ◽  
Elizabeth Patrick ◽  
Kahler W. Stone ◽  
Kristina W. Kintziger ◽  
Meredith A. Jagger ◽  
...  

Author(s):  
Michael B. A. Oldstone

This chapter highlights the story of autism, the widespread acceptance of its incorrect cause, and the impact on use of vaccines, all stemming directly from deliberate, false reporting. The basic conflict is twofold. First, involvement of a scientific method that must be reproducible, be reliable, and possess substantial proof is in conflict with common/personal beliefs. Second, doctors, scientists, and public health workers, despite their mandate to listen to parents and patients concerning their opinions, must base medical conclusions on evidence that validates the outcome of each patient’s health issue. It is in this milieu that autism and the anti-vaccine groups still do battle. In 1998, Lancet, a usually respectable and reputable English journal, published Dr. Andrew Wakefield’s opinion that the measles, mumps, rubella (German measles) vaccine injected into the arms of children caused inflammation, leading to harmful chemicals entering the bloodstream through the gut (intestine). These factors, he said, traveled to the brain, where the harmful chemicals/toxins caused autism. In the face of this “fake news” about the source of autism and measles, the vaccination rate for measles dropped in the United Kingdom and Ireland.


2017 ◽  
Author(s):  
Chien-Chou Chen ◽  
Jen-Hsiang Chuang ◽  
Da-Wei Wang ◽  
Chien-Min Wang ◽  
Bo-Cheng Lin ◽  
...  

To balance the protection of geo-privacy and the accuracy of spatial patterns, we developed a geo-spatial tool (GeoMasker) intended to mask the residential locations of patients or cases in a geographic information system (GIS). To elucidate the effects of geo-masking parameters, we applied 2010 dengue epidemic data from Taiwan testing the tool’s performance in an empirical situation. The similarity of pre- and post-spatial patterns was measured by D statistics under a 95% confidence interval. In the empirical study, different magnitudes of anonymisation (estimated Kanonymity ≥10 and 100) were achieved and different degrees of agreement on the pre- and post-patterns were evaluated. The application is beneficial for public health workers and researchers when processing data with individuals’ spatial information.


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