Aristotle's hierarchies of Governance depict forms of government relative to wealth, power and influence.

Aristotle's hierarchies of Governance depict forms of government relative to wealth, power and influence.

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In this three-part series we examine the extent to which disaster capitalism and the medical-industrial complex turned the pandemic into a 'golden' opportunity to enhance corporate profits which took place, in large part, at the taxpayer's expense through appropriation of public resources. In the first part we examine the rise of this predatory soc...

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... dominator hierarchies one level assumes pathologic agency at the expense of the entire system and thereby inhibits the creative potential and transformational capacity of the whole. Once dominator pathologies emerge within the social sphere, either in the form of autocracy or oligarchy, those in power manipulate dynamics in order to reap disproportionate benefit and, as a consequence, power and wealth concentrate in the hands of a few (Figure 2). Totalitarianism, the tyranny of a single absolutist dogma to the exclusion of all others, is the logical endpoint in cultures based on the dominator ethos. ...

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Background SARS-CoV-2 pandemic is primarily transmitted in households with massive healthcare systems burdens. The role of inactivated vaccines and ChAdOx1 nCoV-19 vaccination within-household transmission prevention remains unknown. Methods This observational, case-control study tracked 408 SARS-CoV-2 PCR-confirmed index cases from April to Septe...

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... Evidence suggests that future mass infectious outbreaks would be managed more efficiently and effectively on the ground at regional levels where consequences are most directly felt. 39,40 Limitations of this study are obvious: the conclusions made in this study are only as reliable as the validity of the data abstracted from the 9 sources that we used to assemble our database. [1][2][3][4][5][6][7][8][9][10] There is wide variation in case reporting from country to country and global outcomes could be potentially limited by inconsistencies. ...
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Objectives to assess COVID-19 mortality rates per country population. To determine what if any independent country-specific variables from 9 different databases were correlated. Design population based retrospective cohort study. Setting analysis of global COVID-19 treatment and containment strategies using data from 9 worldwide websites. Participants 108 countries worldwide. Interventions none. Main Outcome Measures were COVID-19 death rates per country population analyzed by univariate and multivariate analysis. The main outcome parameters were to determine if there are any correlations between the percentage of countrywide COVID-19 deaths/population by the countries’ percent vaccinated. Secondary outcome measures include the effect of other independent variables on COVID-19 death rates per country population including: health expenditures per capita, annual income per capita, COVID-19 tests per 1000 people, stringency index (a measure of each countries containment strategies), hydroxychloroquine score (a measure of each countries use), ivermectin score (a measure of each countries use), hypertension, obesity, diabetes, and specific countries and geographic locations. Results COVID-19 vaccination rates ranged from 0-99% in 108 countries. Univariate analysis demonstrates the following independent variables to correlate with COVID-19 deaths/population (correlation coefficient, p value): countrywide COVID-19 vaccination rates (+0.2936, p=0.002); healthcare costs per capita (+0.3212, p=0.0007), income per capita (+0.3051, p=0.0013), COVID-19 tests per 1000 population (+0.6981 p=0.0307); stringency index (+0.3098, p=0.0011); hydroxychloroquine index (-0.1337, p=0.0678); and ivermectin index (-0.1383, p=0.1535). Conclusions Increasing rates of COVID-19 vaccination are associated with increase COVID-19 death rates per country population (p=0.002). Other variables associated include healthcare costs per capita (+0.3212, p=0.0007), income per capita (+0.3051, p=0.0013), COVID-19 tests per 1000 population (+0.6981 p=0.0307); and stringency index (+0.3098, p=0.0011).