Global Climate Indices - A Problem or a Solution to the Geospatial Analysis of Infectious Diseases | OMICS International
ISSN: 2157-7617
Journal of Earth Science & Climatic Change

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Global Climate Indices - A Problem or a Solution to the Geospatial Analysis of Infectious Diseases

Anil A. Panackal*
Department of Medicine, F. Edward Hebert School of Medicine, Uniformed Services University of the Health Sciences (USUHS), USA
Corresponding Author : Anil A. Panackal
Department of Medicine
F. Edward Hebert School of Medicine
Uniformed Services University of the Health Sciences (USUHS)
Tel: (202) 261-8084
E-mail: [email protected]
Received April 17, 2012; Accepted April 18, 2012; Published April 20, 2012
Citation: Panackal AA (2012) Global Climate Indices - A Problem or a Solution to the Geospatial Analysis of Infectious Diseases. J Earth Sci Climate Change 3:e102. doi:10.4172/2157-7617.1000e102
Copyright: © 2012 Panackal AA. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Many climatic and geological variables can modulate the association of infectious disease exposure and disease temporally. Temperature, precipitation, wind velocity and directionality, air humidity, barometric pressure, and seismic activity can be included to name a few. However, as we think about these covariates and how they interact to facilitate the development of geo-climatically influenced infectious diseases, several other parameters also come to mind such as diurnal cyclical fluctuations, deforestation, and global migratory patterns. Indeed, very quickly, our efforts to minimize residual confounding become laborious, with every newly discovered interaction opening up “a new can of worms.”
In 2003, Stenseth et al. [1] proposed advantages of a global climate index for determining the complex relationship between ecology and climate across time [1]. These investigators suggested that such a multivariate confounder score might enable the blending of climatic conditions by using a more holistic proxy [2]. Though beneficial for simplifying the analysis, the cost is loss of the ability to analyze components of the score with respect to exposure and outcome and problems with multiple inter-correlations. Once created, confounding can be controlled for using the usual methods of matching, stratification, and regression by the global climatic index. Several indices have been proposed by region of the world and are publically available via internet (e.g., available at: jhurrell/indices.html). For example, a Southern Oscillation Index (SOI) known as the multivariate ENSO Index (MEI) is calculated from sealevel pressure, zonal portions of the surface wind, sea and air surface temperatures, and total cloudiness proportion of the sky in the tropical Pacific (Available at: When this leads to a net rise or fall in sea surface temperature at the Central- East Pacific equatorial basin, phenomena commonly known as the El Niño and La Niña phases, respectively, develop. These have been associated with a variety of infectious diseases such as cholera, malaria, paracoccidoidomycosis, and perhaps influenza [3-6]. The resultant time series of such events by region can be overlaid by other environmental variables using spatial regression via geographical information systems to determine an independent association between the exposure of interest and infectious disease. Alternatively, a new composite index could be created incorporating climatologic variables with geologic, ecologic, and migratory co-variates that vary temporally with the climatologic index to avoid confounding within the climate change strata and over-fitting sparse data from multiple subdivisions. Host factors that are not time dependent on these environmental cues could be included separately (e.g. advanced AIDS and not malnutrition or ultraviolet irradiation). In addition, geo-climatic principal component analysis that creates a weighted summary variable of shared predictors, used when these outnumber the outcome observations, have been evaluated [7].
From a data collection view point, the same amount of data will need to be collected to minimize unmeasured confounding. However, the risk of model misspecification needs to be balanced against over analyzing, using variables that may not be highly influential. This is certainly a difficult problem to which many have tried to find the optimal solution. Ultimately, though, it will require a multidisciplinary approach coordinated by a group of climatologists, ecologists, geologists, epidemiologists, statisticians, information technologists, socio-behavioral scientists, and clinicians to determine a set of relevant global indices that can be useful for all fields and purposes.
Dr. Panackal is an Infectious Diseases Physician who works at the U.S. Department of State and holds an academic teaching appointment at USUHS, though the content of this editorial represents his own viewpoint and not of either entity.
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