Abstract

In a recent study, Wood et al. (2017 Phil. Trans. Roy. Soc. B 372, 20160122) utilized a novel set of spatial and temporal analyses to identify which factors were most strongly correlated with changes in human infectious disease burdens from 1990 to 2010 in 60 countries. Using the statistical analyses and findings of this article as a framework, I have identified three important insights and challenges that this research presents for disease biogeography moving forward. First, the main factor still limiting disease biogeography research progress is underreported or absent data – particularly in the case of neglected tropical diseases. Second, the use of disability-adjusted life years instead of indirect measures of disease burden should be a focal point of disease biogeography research since it allows for comparisons of lethal and non-lethal diseases. Finally, disease biogeography studies that utilize country-level statistical analyses may be better at identifying demographic and economic drivers than environmental or biological drivers.

Highlights

  • Understanding what factors drive global variation in human infectious disease burden has historically been challenging

  • This is true for regions/countries that do not have strong public health programs and often for Neglected Tropical Diseases (NTDs), which by their nature are understudied and poorly represented in health surveys (Hotez et al 2014, Stensgaard et al 2017)

  • The authors analyzed recently published information on disability-adjusted life years (DALYs, i.e., the total number of years lost to death/disability) from 1990 and 2010 for 24 infectious diseases tracked by the World Health Organization’s Global Burden of Disease Database in 60 countries of similar geographic size

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Summary

Introduction

Understanding what factors drive global variation in human infectious disease burden has historically been challenging. Studies that try to identify global drivers of infectious diseases must find ways to deal with data limitations and factors that are operating at different spatial and temporal scales.

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Conclusion
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