The Seasonality and Climatic Drivers of Cryptosporidiosis Open Access

Tyndall, Leigh (2014)

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Purpose: There is much uncertainty about the relationship between climate and diarrheal disease in the scientific literature, due to a lack of studies that target this question, a lack of studies of the relationships between climate and individual pathogens, and also true heterogeneity of effect. This study attempts to address these factors through an analysis of cryptosporidiosis specifically, and its relationship with temperature (°C) and rainfall (mm), testing for heterogeneity both within and between datasets.

Methods: All US cryptosporidiosis cases reported monthly between 1997-2011 were obtained from the National Notifiable Disease Surveillance System (NNDSS). These data were analyzed with monthly temperature and precipitation data, using generalized linear model and generalized estimating equation regression analyses to calculate incidence rate ratios for each state, nine climate regions, and for the US as a whole. Heterogeneity of results was assessed using the I2 statistic. A systematic review of the literature was also performed, searching for studies worldwide that presented at least one full year of monthly data on cryptosporidiosis incidence. These data were extracted, matched with climate data for the same periods, and analyzed separately. The results were compared to the NNDSS analysis.

Results: There is an overall positive relationship between temperature and cryptosporidiosis in the US--for every 1 °C increase in temperature, cryptosporidiosis case incidence increases by 2.51%. This is supported by the global literature review which reports a 2.96% increase in cryptosporidiosis for every 1 °C increase in temperature worldwide. There is much variability in the relationship between precipitation and cryptosporidiosis in the US, which may be due to local geographic and temporal factors. There was no significant heterogeneity in results between states, but considerable heterogeneity between climate regions.

Conclusion: In general, there is a positive relationship between cryptosporidiosis and temperature, shown both in the US and worldwide. The relationship between cryptosporidiosis and precipitation is not as clear and is likely due to factors not considered in this study. The relationship between these climatic variables and cryptosporidiosis cases was remarkably consistent across states and between the US and global analyses. This suggests the temperature-disease relationship is robust to varying conditions.

Table of Contents

Background 1
Purpose and Motivation for Study 3
Literature Review 4
Cryptosporidiosis 4
Cryptosporidium Parasite 4
Cryptosporidiosis Epidemiology 4
Clinical Symptoms of Cryptosporidiosis 5
Cryptosporidiosis Treatment and Prevention 5
Risk factors for Cryptosporidiosis 6
Seasonality 7
In Temperate Regions 7
In Tropical Regions 9
Climatic Drivers 10
Temperature and Rainfall as Climatic Drivers 10
Infectious Diarrheal Disease 11
Cryptosporidiosis and Climatic Drivers 14
National Surveillance Data 17
National Notifiable Disease Surveillance System 18
Hypothesis 20
Outcome Variables 20
Systematic Review 20
National Notifiable Diseases Surveillance System 22
Predictor Variables 24
Climate-related Variables 24
Demographic Variables 25
Methods of Analysis 26
Summarizing Data and Seasonality Analysis 26
Statistical Analysis 27
Systematic Review 29
National Notifiable Disease Surveillance System Data 30
Season Strength 30
Seasonality across US Climate Regions 31
Generalized Linear Modeling 32
United States and US Climate Regions 32
Heterogeneity between States and Climate Regions 34
Generalized Estimating Equations Modeling 35
Season Strength and Seasonality 37
Temperature 39
Precipitation 42
Well Use 44
Heterogeneity within NNDSS Data 45
Comparison between NNDSS and Meta-analysis 45
Pooled Analysis GEE Model 45
Temperature 47
Precipitation 48
Limitations 50
Recommendations 53
References 59
Table 1: Characteristics of States and Regions included in study 76
Table 2: Regression results for overall Generalized Estimating Equations analysis of NNDSS data 78
Table 3: Regression results for overall Generalized Estimating Equations analysis of global literature review data, developed countries in the northern hemisphere and all studies 79
Figure 1. 80
Figure 2. 81
Figure 3. 82
Figure 4a. 83
Figure 4b. 84
Figure 5 a and b. 85
Figure 6 a and b. 86
Figure 7 a and b. 87
Appendix A. Detailed search protocol 88
Appendix B. PRISMA flow diagram 91
Appendix C. Registration Information and Data Use Restrictions Agreement 92
Appendix D. Map of NCDC climate regions 94
Appendix E. IRB Declaration Letter 95
Appendix F. Meta-data table for studies 96
Appendix G. Plot of Cryptosporidiosis Season Strength against Temperature and Precipitation Season Strengths 97
Appendix H. Correlations between demographic variables and plots 98
Appendix I. Supplementary Maps of Temperature and Precipitation Season Strength 100

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