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Meta-Analysis
. 2009 May;109(4):465-78.
doi: 10.1016/j.envres.2009.02.008. Epub 2009 Mar 27.

Seasonality of cryptosporidiosis: A meta-analysis approach

Jyotsna S Jagai  1 , Denise A Castronovo, Jim Monchak, Elena N Naumova
Affiliations
Meta-Analysis

Seasonality of cryptosporidiosis: A meta-analysis approach

Jyotsna S Jagai et al. Environ Res. 2009 May.

Abstract

Objectives: We developed methodology for and conducted a meta-analysis to examine how seasonal patterns of cryptosporidiosis, a primarily waterborne diarrheal illness, relate to precipitation and temperature fluctuations worldwide.

Methods: Monthly cryptosporidiosis data were abstracted from 61 published epidemiological studies that cover various climate regions based on the Köppen Climate Classification. Outcome data were supplemented with monthly aggregated ambient temperature and precipitation for each study location. We applied a linear mixed-effect model to relate the monthly normalized cryptosporidiosis incidence with normalized location-specific temperature and precipitation data. We also conducted a sub-analysis of associations between the Normalized Difference Vegetation Index (NDVI), a remote sensing measure for the combined effect of temperature and precipitation on vegetation, and cryptosporidiosis in Sub-Saharan Africa.

Results: Overall, and after adjusting for distance from the equator, increases in temperature and precipitation predict an increase in cryptosporidiosis; the strengths of relationship vary by climate subcategory. In moist tropical locations, precipitation is a strong seasonal driver for cryptosporidiosis whereas temperature is in mid-latitude and temperate climates. When assessing lagged relationships, temperature and precipitation remain strong predictors. In Sub-Saharan Africa, after adjusting for distance from the equator, low NDVI values are predictive of an increase in cryptosporidiosis in the following month.

Discussion: In this study we propose novel methodology to assess relationships between disease outcomes and meteorological data on a global scale. Our findings demonstrate that while climatic conditions typically define a pathogen habitat area, meteorological factors affect timing and intensity of seasonal outbreaks. Therefore, meteorological forecasts can be utilized to develop focused prevention programs for waterborne cryptosporidiosis.

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Figures

Figure 1
Figure 1
Flow chart of selection of studies utilized in the analysis including inclusion and exclusion criteria.
Figure 2
Figure 2
Map of Köppen Climate Classification with study locations marked. The size of the marker indicates the number of years of data utilized in this analysis from each location.
Figure 3
Figure 3
Meteorological characteristics of studies in Climate Category A (Tropical Climate). Monthly temperature z-score (Panel A), monthly precipitation z-score (Panel B), monthly cryptosporidiosis z-score (Panel C). For all Panel A–C the monthly mean is shown in blue and the annual mean is shown in red. Interpolation of cryptosporidiosis z-score in relation to precipitation and temperature z-score (Panel D).
Figure 4
Figure 4
Meteorological characteristics of studies in Climate Category B (Arid/Semi Arid Climate). Monthly temperature z-score (Panel A), monthly precipitation z-score (Panel B), monthly cryptosporidiosis z-score (Panel C). For all Panel A–C the monthly mean is shown in blue and the annual mean is shown in red. Interpolation of cryptosporidiosis z-score in relation to precipitation and temperature z-score (Panel D).
Figure 5
Figure 5
Meteorological characteristics of studies in Climate Category C (Humid Mid-Latitude Climate). Monthly temperature z-score (Panel A), monthly precipitation z-score (Panel B), monthly cryptosporidiosis z-score (Panel C). For all Panel A–C the monthly mean is shown in blue and the annual mean is shown in red. Interpolation of cryptosporidiosis z-score in relation to precipitation and temperature z-score (Panel D).
Figure 6
Figure 6
Meteorological characteristics of studies in Climate Category D (Cold Temperate Climate). Monthly temperature z-score (Panel A), monthly precipitation z-score (Panel B), monthly cryptosporidiosis z-score (Panel C). For all Panel A–C the monthly mean is shown in blue and the annual mean is shown in red. Interpolation of cryptosporidiosis z-score in relation to precipitation and temperature z-score (Panel D).
Figure 6
Figure 6
Meteorological characteristics of studies in Climate Category D (Cold Temperate Climate). Monthly temperature z-score (Panel A), monthly precipitation z-score (Panel B), monthly cryptosporidiosis z-score (Panel C). For all Panel A–C the monthly mean is shown in blue and the annual mean is shown in red. Interpolation of cryptosporidiosis z-score in relation to precipitation and temperature z-score (Panel D).
Figure 7
Figure 7
Interpolation of NDVI in relation to precipitation and temperature (Panel A) and precipitation z-score and temperature z-score (Panel B).
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