Climatic Drivers and Heterogeneity of Diarrheal Disease, according to Pathogenic Class Open Access

Silver, Rachel (2017)

Permanent URL: https://etd.library.emory.edu/concern/etds/1c18dg67b?locale=en
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Abstract

Purpose: Global research that examines the relationship between climate and diarrheal diseases is lacking, especially regarding studies that demonstrate a link between climate and specific disease-causing agents. Among the existing data, positive associations between temperature and diarrheal disease incidence have been found, but uncertainty remains due to a lack of quantitative data on the effect that meteorological conditions have on risk by the specific individual pathogens responsible for causing diarrheal illness. In order to understand the effects that a changing climate is having on the incidence of infection, it is necessary to quantify and assess pathogen-specific seasonal patterns over a period of time, and the influence various weather factors have on this relationship.

Methods: This issue was examined through a systematic review of the literature focusing on three pathogens - E. coli, Cryptosporidium, and norovirus. Studies meeting specific inclusion and exclusion criteria that were conducted for a minimum of one full year were used for characterization of seasonal patterns. Available temperature and precipitation data for each study location was assessed through univariate, log-linear Poisson regression models and a pooled dataset for each pathogen was created for a meta-analysis using a generalized estimating equation modeling technique on each dataset.

Results: A positive correlation between mean monthly temperature and incidence of diarrheagenic E. coli and Cryptosporidium was found across all studies included in the pooled data analysis. Increases in the incidence of disease was found to be associated with a 1°C increase in mean monthly temperature for E. coli (8% increase, 95% CI: 5%-11%; P<0.0001) and mean 1-month lagged temperature Cryptosporidium (6% increase, 95% CI: 2%-10%; P=0.003) controlling for precipitation and country development stratum. Norovirus displayed a negative correlation between mean monthly temperature and incidence of disease, with (4% decrease, 95% CI: 0%-8%; P=0.05) per 1 degree Celsius increase, controlling for precipitation, country development, and new strain year.

Conclusions: These results demonstrate a heterogeneous relationship across pathogen class with ambient temperature, and suggests that an increase in mean monthly temperature corresponds to an increased incidence of diarrheagenic E. coli, and Cryptosporidium, and a decreased incidence of norovirus.

Table of Contents

INTRODUCTION

Background …………………………………………………………………………...1

E. coli ……………………………………………………………………………………4

Cryptosporidium …………………………………………………………………..4
Norovirus ………………………………………………………………………………5

METHODS

Hypothesis ……………………………………………………………………………….…………………7

Literature Search ………………………………………………………..………………………………7

Outcome Data: Extraction and Conversion …………………………………………………10

Independent Variables …………………………………………………………….…………………11

Seasonality Analysis ……………………………………………………………………………….…12

Statistical Modeling ……………………………………………………………………………….….13

RESULTS

Systematic Review ……………………………………………………………………………..….….16

Seasonality ……………………………………………………………………………………………….17

Association with Climate Variables …………………………………………………….……...18

DISCUSSION

Discussion …………………………………………………………………………………………..…….21

Future Directions …………………………………………………………………………..………….23

REFERENCES

References …………………………………………………………………………..……………..…….25

TABLES

Table 1a-c ………………………………………………………………………..……………..………..45

Table 2 …………………………………………………………………………..……………………..…..51

Table 3 …………………………………………………………………………..……………………..…..51

Table 4a-c ……………………………………………………………………..……………………..…..52

Table 5 ……………………………………………………………………..………………………….…..53

FIGURES AND FIGURE LEGENDS

Figure 1 …………………………………………………………………..………………………….…....54

Figure 2 …………………………………………………………………..……………………….….…..55

Figure 3 …………………………………………………………………..……………………….….…..56

Figure 4 …………………………………………………………………..…………….………….….…..57

Figure 5 …………………………………………………………………..……………………….….…..58

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