11 research outputs found

    Febrile illness diagnostics and the malaria-industrial complex: a socio-environmental perspective

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    Abstract Background Global prioritization of single-disease eradication programs over improvements to basic diagnostic capacity in the Global South have left the world unprepared for epidemics of chikungunya, Ebola, Zika, and whatever lies on the horizon. The medical establishment is slowly realizing that in many parts of sub-Saharan Africa (SSA), particularly urban areas, up to a third of patients suffering from acute fever do not receive a correct diagnosis of their infection. Main body Malaria is the most common diagnosis for febrile patients in low-resource health care settings, and malaria misdiagnosis has soared due to the institutionalization of malaria as the primary febrile illness of SSA by international development organizations and national malaria control programs. This has inadvertently created a “malaria-industrial complex” and historically obstructed our complete understanding of the continent’s complex communicable disease epidemiology, which is currently dominated by a mélange of undiagnosed febrile illnesses. We synthesize interdisciplinary literature from Ghana to highlight the complexity of communicable disease care in SSA from biomedical, social, and environmental perspectives, and suggest a way forward. Conclusion A socio-environmental approach to acute febrile illness etiology, diagnostics, and management would lead to substantial health gains in Africa, including more efficient malaria control. Such an approach would also improve global preparedness for future epidemics of emerging pathogens such as chikungunya, Ebola, and Zika, all of which originated in SSA with limited baseline understanding of their epidemiology despite clinical recognition of these viruses for many decades. Impending ACT resistance, new vaccine delays, and climate change all beckon our attention to proper diagnosis of fevers in order to maximize limited health care resources

    Erratum to: Estimation of the Gini coefficient for the lognormal distribution of income using the Lorenz curve

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    A data science based standardized Gini index as a Lorenz dominance preserving measure of the inequality of distributions

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    The Gini index is a measure of the inequality of a distribution that can be derived from Lorenz curves. While commonly used in, e.g., economic research, it suffers from ambiguity via lack of Lorenz dominance preservation. Here, investigation of large sets of empirical distributions of incomes of the World’s countries over several years indicated firstly, that the Gini indices are centered on a value of 33.33% corresponding to the Gini index of the uniform distribution and secondly, that the Lorenz curves of these distributions are consistent with Lorenz curves of log-normal distributions. This can be employed to provide a Lorenz dominance preserving equivalent of the Gini index. Therefore, a modified measure based on log-normal approximation and standardization of Lorenz curves is proposed. The so-called UGini index provides a meaningful and intuitive standardization on the uniform distribution as this characterizes societies that provide equal chances. The novel UGini index preserves Lorenz dominance. Analysis of the probability density distributions of the UGini index of the World’s counties income data indicated multimodality in two independent data sets. Applying Bayesian statistics provided a data-based classification of the World’s countries’ income distributions. The UGini index can be re-transferred into the classical index to preserve comparability with previous research
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