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Soil Dust Aerosols and Wind as Predictors of Seasonal Meningitis Incidence in Niger

Background: Epidemics of meningococcal meningitis are concentrated in sub-Saharan Africa during the dry season, a period when the region is affected by the Harmattan, a dry and dusty northeasterly trade wind blowing from the Sahara into the Gulf of Guinea. Objectives: We examined the potential of cl...

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Autores principales: García-Pando, Carlos Pérez, Stanton, Michelle C., Diggle, Peter J., Trzaska, Sylwia, Miller, Ron L., Perlwitz, Jan P., Baldasano, José M., Cuevas, Emilio, Ceccato, Pietro, Yaka, Pascal, Thomson, Madeleine C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: NLM-Export 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4080544/
https://www.ncbi.nlm.nih.gov/pubmed/24633049
http://dx.doi.org/10.1289/ehp.1306640
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author García-Pando, Carlos Pérez
Stanton, Michelle C.
Diggle, Peter J.
Trzaska, Sylwia
Miller, Ron L.
Perlwitz, Jan P.
Baldasano, José M.
Cuevas, Emilio
Ceccato, Pietro
Yaka, Pascal
Thomson, Madeleine C.
author_facet García-Pando, Carlos Pérez
Stanton, Michelle C.
Diggle, Peter J.
Trzaska, Sylwia
Miller, Ron L.
Perlwitz, Jan P.
Baldasano, José M.
Cuevas, Emilio
Ceccato, Pietro
Yaka, Pascal
Thomson, Madeleine C.
author_sort García-Pando, Carlos Pérez
collection PubMed
description Background: Epidemics of meningococcal meningitis are concentrated in sub-Saharan Africa during the dry season, a period when the region is affected by the Harmattan, a dry and dusty northeasterly trade wind blowing from the Sahara into the Gulf of Guinea. Objectives: We examined the potential of climate-based statistical forecasting models to predict seasonal incidence of meningitis in Niger at both the national and district levels. Data and methods: We used time series of meningitis incidence from 1986 through 2006 for 38 districts in Niger. We tested models based on data that would be readily available in an operational framework, such as climate and dust, population, and the incidence of early cases before the onset of the meningitis season in January–May. Incidence was used as a proxy for immunological state, susceptibility, and carriage in the population. We compared a range of negative binomial generalized linear models fitted to the meningitis data. Results: At the national level, a model using early incidence in December and averaged November–December zonal wind provided the best fit (pseudo-R(2) = 0.57), with zonal wind having the greatest impact. A model with surface dust concentration as a predictive variable performed indistinguishably well. At the district level, the best spatiotemporal model included zonal wind, dust concentration, early incidence in December, and population density (pseudo-R(2) = 0.41). Conclusions: We showed that wind and dust information and incidence in the early dry season predict part of the year-to-year variability of the seasonal incidence of meningitis at both national and district levels in Niger. Models of this form could provide an early-season alert that wind, dust, and other conditions are potentially conducive to an epidemic. Citation: Pérez García-Pando C, Stanton MC, Diggle PJ, Trzaska S, Miller RL, Perlwitz JP, Baldasano JM, Cuevas E, Ceccato P, Yaka P, Thomson MC. 2014. Soil dust aerosols and wind as predictors of seasonal meningitis incidence in Niger. Environ Health Perspect 122:679–686; http://dx.doi.org/10.1289/ehp.1306640
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spelling pubmed-40805442014-07-11 Soil Dust Aerosols and Wind as Predictors of Seasonal Meningitis Incidence in Niger García-Pando, Carlos Pérez Stanton, Michelle C. Diggle, Peter J. Trzaska, Sylwia Miller, Ron L. Perlwitz, Jan P. Baldasano, José M. Cuevas, Emilio Ceccato, Pietro Yaka, Pascal Thomson, Madeleine C. Environ Health Perspect Research Background: Epidemics of meningococcal meningitis are concentrated in sub-Saharan Africa during the dry season, a period when the region is affected by the Harmattan, a dry and dusty northeasterly trade wind blowing from the Sahara into the Gulf of Guinea. Objectives: We examined the potential of climate-based statistical forecasting models to predict seasonal incidence of meningitis in Niger at both the national and district levels. Data and methods: We used time series of meningitis incidence from 1986 through 2006 for 38 districts in Niger. We tested models based on data that would be readily available in an operational framework, such as climate and dust, population, and the incidence of early cases before the onset of the meningitis season in January–May. Incidence was used as a proxy for immunological state, susceptibility, and carriage in the population. We compared a range of negative binomial generalized linear models fitted to the meningitis data. Results: At the national level, a model using early incidence in December and averaged November–December zonal wind provided the best fit (pseudo-R(2) = 0.57), with zonal wind having the greatest impact. A model with surface dust concentration as a predictive variable performed indistinguishably well. At the district level, the best spatiotemporal model included zonal wind, dust concentration, early incidence in December, and population density (pseudo-R(2) = 0.41). Conclusions: We showed that wind and dust information and incidence in the early dry season predict part of the year-to-year variability of the seasonal incidence of meningitis at both national and district levels in Niger. Models of this form could provide an early-season alert that wind, dust, and other conditions are potentially conducive to an epidemic. Citation: Pérez García-Pando C, Stanton MC, Diggle PJ, Trzaska S, Miller RL, Perlwitz JP, Baldasano JM, Cuevas E, Ceccato P, Yaka P, Thomson MC. 2014. Soil dust aerosols and wind as predictors of seasonal meningitis incidence in Niger. Environ Health Perspect 122:679–686; http://dx.doi.org/10.1289/ehp.1306640 NLM-Export 2014-03-17 2014-07 /pmc/articles/PMC4080544/ /pubmed/24633049 http://dx.doi.org/10.1289/ehp.1306640 Text en http://creativecommons.org/publicdomain/mark/1.0/ Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, “Reproduced with permission from Environmental Health Perspectives”); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
spellingShingle Research
García-Pando, Carlos Pérez
Stanton, Michelle C.
Diggle, Peter J.
Trzaska, Sylwia
Miller, Ron L.
Perlwitz, Jan P.
Baldasano, José M.
Cuevas, Emilio
Ceccato, Pietro
Yaka, Pascal
Thomson, Madeleine C.
Soil Dust Aerosols and Wind as Predictors of Seasonal Meningitis Incidence in Niger
title Soil Dust Aerosols and Wind as Predictors of Seasonal Meningitis Incidence in Niger
title_full Soil Dust Aerosols and Wind as Predictors of Seasonal Meningitis Incidence in Niger
title_fullStr Soil Dust Aerosols and Wind as Predictors of Seasonal Meningitis Incidence in Niger
title_full_unstemmed Soil Dust Aerosols and Wind as Predictors of Seasonal Meningitis Incidence in Niger
title_short Soil Dust Aerosols and Wind as Predictors of Seasonal Meningitis Incidence in Niger
title_sort soil dust aerosols and wind as predictors of seasonal meningitis incidence in niger
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4080544/
https://www.ncbi.nlm.nih.gov/pubmed/24633049
http://dx.doi.org/10.1289/ehp.1306640
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