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BackgroundIt is of interest to explore the variability in how the COVID-19 pandemic evolved geographically during the first twelve months. To this end, we apply inequality indices over regions to incidences, infection related mortality, and infection fatality rates. If avoiding of inequality in health is an important political goal, a metric must be implemented to track geographical inequality over time. MethodsThe relative and absolute Gini index as well as the Theil index are used to quantify inequality. Data are taken from international data bases. Absolute counts are transformed to rates adjusted for population size. ResultsComparing continents, the absolute Gini index shows an unfavorable development in four continents since February 2020. In contrast, the relative Gini as well as the Theil index support the interpretation of less inequality between European countries compared to other continents. Infection fatality rates within the EU as well as within the U.S. express comparable improvement towards more equality (as measured by both Gini indices). ConclusionsThe use of inequality indices to monitor changes in geographic in-equality over time for key health indicators is a valuable tool to inform public health policies. The absolute and relative Gini index behave complementary and should be reported simultaneously in order to gain a meta-perspective on very complex dynamics.
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COVID-19 is a major global crisis with unpredictable consequences. Many scientists have struggled to make forecasts about its impact. Especially, appropriate preparations for a second wave are needed not to move in a costly panic mode again. It is necessary to get ideas about worst case scenarios regarding incidences, hospitalization, or use of ICU resources. They can be described in terms of extreme quantiles (95%, 99%, 99.9%) of specific distributions that supposedly formalize the data mechanism behind future observations. Therefore, distributional issues do matter. Cirillo and Taleb argue that a natural and empirically correct framework for assessing and managing real risk in pandemics is provided by extreme value theory dealing with extrema and not averages. We explore this idea in more detail. In this paper we discuss the fat-tail patterns in the distribution of the global COVID-19 data by analyzing data from 66 countries worldwide. We also explore their relevance at a lower, regional scale perspective (national, federal state), which is in our opinion more relevant for planning measures against the epidemic spread. For this we analyze data from the German federal state of Bavaria. We conclude that fat-tail patterns are seen in global data, possibly reflecting the respective heterogeneity between different countries regarding incidences and fatalities during the ongoing epidemic. However, the disease activity at regional level seems to be better described by classical Poisson based models. To bridge the gap between regional and global phenomena we refer to mixtures of slim-tail distributions that may create fat-tail features. Especially in the beginning of a pandemic acting according to the "better safe than sorry" principle and taking extreme forecasts as the basis for the decisions might be justified. However, as the pandemic continues and control measures are partially lifted, there is a need for a careful discussion how to choose relevant distributions and their respective quantiles for future resource planning in order not to cause more harm as the pandemic itself.
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The use of methamphetamine is spreading globally and provokes the need for effective treatment options. Previous research showed increased psychiatric comorbidities in methamphetamine users, but its impact on treatment success is still unclear. This study investigates data from two German addiction rehabilitation centers including 108 methamphetamine using individuals. The participants were tested and interviewed at the beginning of the addiction treatment program and at the end of treatment after about six months. In total, 95% of the participants had at least another psychiatric diagnosis. At admission, substance related comorbid diagnoses (meaning abuse or addiction of other substances than methamphetamine) showed a significant effect on treatment dropout. Within the substance related diagnoses, the majority of participants (62%) suffered from cannabinoid dependency. Non-substance related comorbidities and the total number of comorbid diagnoses did not have an impact on treatment outcome. The most frequent non substance specific diagnosis at admission was a depressive disorder (15%). Diagnoses patients had at discharge did not show any effects on the treatment completion. Comparing diagnoses at admission and discharge revealed slight differences, which may rise from a better assessment at discharge due to the fact that clinicians got to know the patients better during the therapeutic process.