The use of ZIP and CART to model cryptosporidiosis in relation to climatic variables

Investigating the relationship between extreme weather and cryptosporidiosis and giardiasis in Colorado: A multi-decade study using distributed-lag nonlinear models

Environmentally-mediated protozoan diseases like cryptosporidiosis and giardiasis are likely to be highly impacted by extreme weather, as climate-related conditions like temperature and precipitation have been linked to their survival, distribution, and overall transmission success. Our aim was to investigate the relationship between extreme temperature and precipitation and cryptosporidiosis and giardiasis infection using monthly weather data and case reports from Colorado counties over a twenty-one year period. Data on reportable diseases and weather among Colorado counties were collected using the Colorado Electronic Disease Reporting System (CEDRS) and the Daily Surface Weather and Climatological Summaries (Daymet) Version 3 dataset, respectively. We used a conditional Poisson distributed-lag nonlinear modeling approach to estimate the lagged association (between 0 and 12-months) between relative temperature and precipitation extremes and the risk of cryptosporidiosis and giardiasis infection in Colorado counties between 1997 and 2017, relative to the risk found at average values of temperature and precipitation for a given county and month. We found distinctly different patterns in the associations between temperature extremes and cryptosporidiosis, versus temperature extremes and giardiasis. When maximum or minimum temperatures were high (90th percentile) or very high (95th percentile), we found a significant increase in cryptosporidiosis risk, but a significant decrease in giardiasis risk, relative to risk at the county and calendar-month mean. Conversely, we found very similar relationships between precipitation extremes and both cryptosporidiosis and giardiasis, which highlighted the prominent role of long-term (>8 months) lags. Our study presents novel insights on the influence that extreme temperature and precipitation can have on parasitic disease transmission in real-world settings. Additionally, we present preliminary evidence that the standard lag periods that are typically used in epidemiological studies to assess the impacts of extreme weather on cryptosporidiosis and giardiasis may not be capturing the entire relevant period.

Cryptosporidiosis threat under climate change in China: prediction and validation of habitat suitability and outbreak risk for human-derived Cryptosporidium based on ecological niche models

BACKGROUND

Cryptosporidiosis is a zoonotic intestinal infectious disease caused by Cryptosporidium spp., and its transmission is highly influenced by climate factors. In the present study, the potential spatial distribution of Cryptosporidium in China was predicted based on ecological niche models for cryptosporidiosis epidemic risk warning and prevention and control.

METHODS

The applicability of existing Cryptosporidium presence points in ENM analysis was investigated based on data from monitoring sites in 2011-2019. Cryptosporidium occurrence data for China and neighboring countries were extracted and used to construct the ENMs, namely Maxent, Bioclim, Domain, and Garp. Models were evaluated based on Receiver Operating Characteristic curve, Kappa, and True Skill Statistic coefficients. The best model was constructed using Cryptosporidium data and climate variables during 1986‒2010, and used to analyze the effects of climate factors on Cryptosporidium distribution. The climate variables for the period 2011‒2100 were projected to the simulation results to predict the ecological adaptability and potential distribution of Cryptosporidium in future in China.

RESULTS

The Maxent model (AUC = 0.95, maximum Kappa = 0.91, maximum TSS = 1.00) fit better than the other three models and was thus considered the best ENM for predicting Cryptosporidium habitat suitability. The major suitable habitats for human-derived Cryptosporidium in China were located in some high-population density areas, especially in the middle and lower reaches of the Yangtze River, the lower reaches of the Yellow River, and the Huai and the Pearl River Basins (cloglog value of habitat suitability > 0.9). Under future climate change, non-suitable habitats for Cryptosporidium will shrink, while highly suitable habitats will expand significantly (χ² = 76.641, P < 0.01; χ² = 86.836, P < 0.01), and the main changes will likely be concentrated in the northeastern, southwestern, and northwestern regions.

CONCLUSIONS

The Maxent model is applicable in prediction of Cryptosporidium habitat suitability and can achieve excellent simulation results. These results suggest a current high risk of transmission and significant pressure for cryptosporidiosis prevention and control in China. Against a future climate change background, Cryptosporidium may gain more suitable habitats within China. Constructing a national surveillance network could facilitate further elucidation of the epidemiological trends and transmission patterns of cryptosporidiosis, and mitigate the associated epidemic and outbreak risks.

Environmental Factors Associated with Cryptosporidium and Giardia

Environmental factors significantly influence the transmission of intestinal protozoan diseases. Cryptosporidiosis and giardiasis are important zoonotic diseases characterized by diarrhea, and are mainly water or foodborne diseases caused by fecal-borne oocysts. The One Health approach effectively addresses environmentally influenced zoonotic diseases. However, the impact of environmental factors on the survival of Cryptosporidium/Giardia (oo)cysts or disease transmission is mostly uncharacterized. Associations between cryptosporidiosis and giardiasis incidence and environmental variables (e.g., climatic conditions, soil characteristics, and water characteristics) have been reported; however, the identified relationships are not consistently reported. Whether these are country-specific or global observations is unclear. Herein, we review the evidence for the influence of environmental factors on Cryptosporidium/Giardia and corresponding diseases from three perspectives: climatic, soil, and water characteristics. The (oo)cyst concentration or survival of Cryptosporidium/Giardia and the incidence of corresponding diseases are related to environmental variables. The associations identified varied among studies and have different levels of importance and lag times in different locations. This review summarizes the influence of relevant environmental factors on Cryptosporidium/Giardia from the One Health perspective and provides recommendations for future research, monitoring, and response.

Associations Between Eight Earth Observation-Derived Climate Variables and Enteropathogen Infection: An Independent Participant Data Meta-Analysis of Surveillance Studies With Broad Spectrum Nucleic Acid Diagnostics

Diarrheal disease, still a major cause of childhood illness, is caused by numerous, diverse infectious microorganisms, which are differentially sensitive to environmental conditions. Enteropathogen-specific impacts of climate remain underexplored. Results from 15 studies that diagnosed enteropathogens in 64,788 stool samples from 20,760 children in 19 countries were combined. Infection status for 10 common enteropathogens-adenovirus, astrovirus, norovirus, rotavirus, sapovirus, Campylobacter, ETEC, Shigella, Cryptosporidium and Giardia-was matched by date with hydrometeorological variables from a global Earth observation dataset-precipitation and runoff volume, humidity, soil moisture, solar radiation, air pressure, temperature, and wind speed. Models were fitted for each pathogen, accounting for lags, nonlinearity, confounders, and threshold effects. Different variables showed complex, non-linear associations with infection risk varying in magnitude and direction depending on pathogen species. Rotavirus infection decreased markedly following increasing 7-day average temperatures-a relative risk of 0.76 (95% confidence interval: 0.69-0.85) above 28°C-while ETEC risk increased by almost half, 1.43 (1.36-1.50), in the 20-35°C range. Risk for all pathogens was highest following soil moistures in the upper range. Humidity was associated with increases in bacterial infections and decreases in most viral infections. Several virus species' risk increased following lower-than-average rainfall, while rotavirus and ETEC increased with heavier runoff. Temperature, soil moisture, and humidity are particularly influential parameters across all enteropathogens, likely impacting pathogen survival outside the host. Precipitation and runoff have divergent associations with different enteric viruses. These effects may engender shifts in the relative burden of diarrhea-causing agents as the global climate changes.

Bayesian spatio-temporal modelling to assess the role of extreme weather, land use change and socio-economic trends on cryptosporidiosis in Australia, 2001-2018

BACKGROUND

Intensification of land use threatens to increase the emergence and prevalence of zoonotic diseases, with an adverse impact on human wellbeing. Understanding how the interaction between agriculture, natural systems, climate and socioeconomic drivers influence zoonotic disease distribution is crucial to inform policy planning and management to limit the emergence of new infections.

OBJECTIVES

Here we assess the relative contribution of environmental, climatic and socioeconomic factors influencing reported cryptosporidiosis across Australia from 2001 to 2018.

METHODS

We apply a Bayesian spatio-temporal analysis using Integrated Nested Laplace Approximation (INLA).

RESULTS

We find that area-level risk of reported disease are associated with the proportions of the population under 5 and over 65 years of age, socioeconomic disadvantage, annual rainfall anomaly, and the proportion of natural habitat remaining. This combination of multiple factors influencing cryptosporidiosis highlights the benefits of a sophisticated spatio-temporal statistical approach. Two key findings from our model include: an estimated 4.6% increase in the risk of reported cryptosporidiosis associated with 22.8% higher percentage of postal area covered with original habitat; and an estimated 1.8% increase in disease risk associated with a 77.99 mm increase in annual rainfall anomaly at the postal area level.

DISCUSSION

These results provide novel insights regarding the predictive effects of extreme rainfall and the proportion of remaining natural habitat, which add unique explanatory power to the model alongside the variance associated with other predictive variables and spatiotemporal variation in reported disease. This demonstrates the importance of including perspectives from land and water management experts for policy making and public health responses to manage environmentally mediated diseases, including cryptosporidiosis.

Influence of weather and climate on cryptosporidiosis-A review

Studies have shown that climatic factors can significantly influence transmission of many waterborne diseases. However, knowledge of the impact of climate variability on cryptosporidiosis is much less certain. Associations between the incidence of cryptosporidiosis and climatic variables have been reported in several countries. Given that the identified relationships were not consistently reported across studies, it is not known whether these were country-specific observations or can be considered more globally. Variation in the disease risk in both low- and middle-income countries and high-income countries presents new challenges and opportunities to enact responsive changes in research and public health policies. Available epidemiological evidence of the influence of weather and climate on cryptosporidiosis is reviewed. Fourteen studies met the inclusion criteria, and most studies showed that the incidence of cryptosporidiosis is highly sensitive to climatic conditions, especially temperature, rainfall and relative humidity. The identified associations varied across studies, with different conditions of importance and lag times across different locations. Therefore, there is a need for countries at risk to assess Cryptosporidium transmission routes based on the spatiotemporal patterns of the disease and what role climate and other socio-ecological changes play in the transmission. Information gathering will then allow us to provide information for evidence-based control strategies and mitigation of transmission. This review offers new perspectives on the role of climate variability on Cryptosporidium transmission. It highlights different epidemiological approaches adopted and provides the potential for future research and surveillance to reduce the disease burden. By evaluating the epidemiological transmission of this organism in high-income countries, all mitigation strategies, for example filtration and water catchment management, can be used as exemplars of preventing infection in low- to middle-income countries.

Identifying the environmental drivers of Campylobacter infection risk in southern Ontario, Canada using a One Health approachs

BACKGROUND

Campylobacter bacteria infect both humans and animals. Sources of human exposure include contaminated food and water, contact with animals and/or their faeces, and contact with infected individuals. The objectives of this study were to: (a) identify environmental conditions associated with the occurrence of Campylobacter in humans in four regions of Ontario, and (b) identify pooled measures of effect across all four regions and potential sources of heterogeneity.

METHODS

To address objective 1, human Campylobacter cases from four health regions of Ontario, Canada were analysed using negative binomial regression and case cross-over analysis to identify relationships between environmental factors (temperature, precipitation and hydrology of the local watershed) and the risk of human infection. To address objective 2, meta-analytic models were used to explore pooled measures of effect and when appropriate, meta-regression models were used to explore potential sources of heterogeneity.

RESULTS

Human incidence exhibited strong seasonality with cases peaking in the late spring and summer. There was a decreasing yearly effect in three of the four health regions. A significant pooled effect was found for mean temperature after a 1-week lag (OR = 1.03, 95% CI 1.02, 1.04). No significant pooled effects were found for precipitation or water flow. However, increased precipitation was associated with lower odds of campylobacteriosis in Wellington and York regions at 2- and 3-week lags, respectively, from the case cross-over analysis.

CONCLUSION

These results demonstrate that a climatic factor (specifically, mean temperature in the week prior) was associated with human case occurrence after a biologically plausible time period, but hydrologic factors are not.

Assessing the impact of environmental exposures and Cryptosporidium infection in cattle on human incidence of cryptosporidiosis in Southwestern Ontario, Canada

Cryptosporidium is a waterborne parasite that causes diarrheal disease in humans and in cattle. Risk factors for human illness include contact with surface water such as lakes and rivers, exposure to contaminated municipal drinking water, as well as zoonotic transmission from livestock and agriculture. The objectives of this study are twofold: 1) to describe the temporal distribution of cryptosporidiosis in Southwestern Ontario; and 2) to determine the distribution of human cryptosporidiosis, in relation to exposures such as cryptosporidium positive cattle farms, weather events, and hydrological factors. Seasonal trends in 214 bovine and 87 human cases were assessed using regression models that predicted monthly case counts in relation to observed monthly case counts. A case-crossover approach was used to evaluate acute associations between daily environmental exposures, such as weather, hydrology, the presence of Cryptosporidium positive cattle farms within the region, and the risk of human Cryptosporidium infection. Annual seasonality was found for both human cases and bovine cases with human cases peaking in mid-summer and bovine cases peaking in late winter to early spring. Bovine cases that occurred 21 days prior to human cases were associated with a three-fold increase in the odds of human case occurrence. At both 9 and 14 days prior to human case onset, the odds of a human case increased twofold per 10-degree Celsius increase in air temperature. These results provide a preliminary hypothesis for the zoonotic transmission of cryptosporidiosis from cattle to humans via the environment and suggest that the timing of environmental conditions in relation to case occurrence is biologically plausible.

Towards a threshold climate for emergency lower respiratory hospital admissions

Identification of 'cut-points' or thresholds of climate factors would play a crucial role in alerting risks of climate change and providing guidance to policymakers. This study investigated a 'Climate Threshold' for emergency hospital admissions of chronic lower respiratory diseases by using a distributed lag non-linear model (DLNM). We analysed a unique longitudinal dataset (10 years, 2000-2009) on emergency hospital admissions, climate, and pollution factors for the Greater London. Our study extends existing work on this topic by considering non-linearity, lag effects between climate factors and disease exposure within the DLNM model considering B-spline as smoothing technique. The final model also considered natural cubic splines of time since exposure and 'day of the week' as confounding factors. The results of DLNM indicated a significant improvement in model fitting compared to a typical GLM model. The final model identified the thresholds of several climate factors including: high temperature (≥27°C), low relative humidity (≤ 40%), high Pm10 level (≥70-µg/m³), low wind speed (≤ 2 knots) and high rainfall (≥30mm). Beyond the threshold values, a significantly higher number of emergency admissions due to lower respiratory problems would be expected within the following 2-3 days after the climate shift in the Greater London. The approach will be useful to initiate 'region and disease specific' climate mitigation plans. It will help identify spatial hot spots and the most sensitive areas and population due to climate change, and will eventually lead towards a diversified health warning system tailored to specific climate zones and populations.

Advancing the framework for considering the effects of climate change on worker safety and health

In 2009, a preliminary framework for how climate change could affect worker safety and health was described. That framework was based on a literature search from 1988-2008 that supported seven categories of climate-related occupational hazards: (1) increased ambient temperature; (2) air pollution; (3) ultraviolet radiation exposure; (4) extreme weather; (5) vector-borne diseases and expanded habitats; (6) industrial transitions and emerging industries; and (7) changes in the built environment. This article reviews the published literature from 2008-2014 in each of the seven categories. Additionally, three new topics related to occupational safety and health are considered: mental health effects, economic burden, and potential worker safety and health impacts associated with the nascent field of climate intervention (geoengineering). Beyond updating the literature, this article also identifies key priorities for action to better characterize and understand how occupational safety and health may be associated with climate change events and ensure that worker health and safety issues are anticipated, recognized, evaluated, and mitigated. These key priorities include research, surveillance, risk assessment, risk management, and policy development. Strong evidence indicates that climate change will continue to present occupational safety and health hazards, and this framework may be a useful tool for preventing adverse effects to workers.

Untangling the Impacts of Climate Change on Waterborne Diseases: a Systematic Review of Relationships between Diarrheal Diseases and Temperature, Rainfall, Flooding, and Drought

Global climate change is expected to affect waterborne enteric diseases, yet to date there has been no comprehensive, systematic review of the epidemiological literature examining the relationship between meteorological conditions and diarrheal diseases. We searched PubMed, Embase, Web of Science, and the Cochrane Collection for studies describing the relationship between diarrheal diseases and four meteorological conditions that are expected to increase with climate change: ambient temperature, heavy rainfall, drought, and flooding. We synthesized key areas of agreement and evaluated the biological plausibility of these findings, drawing from a diverse, multidisciplinary evidence base. We identified 141 articles that met our inclusion criteria. Key areas of agreement include a positive association between ambient temperature and diarrheal diseases, with the exception of viral diarrhea and an increase in diarrheal disease following heavy rainfall and flooding events. Insufficient evidence was available to evaluate the effects of drought on diarrhea. There is evidence to support the biological plausibility of these associations, but publication bias is an ongoing concern. Future research evaluating whether interventions, such as improved water and sanitation access, modify risk would further our understanding of the potential impacts of climate change on diarrheal diseases and aid in the prioritization of adaptation measures.

Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella

Foodborne diseases have large economic and societal impacts worldwide. To evaluate how the risks of foodborne diseases might change in response to climate change, credible and usable climate information tailored to the specific application question is needed. Global Climate Model (GCM) data generally need to, both, be downscaled to the scales of the application to be usable, and represent, well, the key characteristics that inflict health impacts. This study presents an evaluation of temperature-based heat indices for the Washington D.C. area derived from statistically downscaled GCM simulations for 1971–2000—a necessary step in establishing the credibility of these data. The indices approximate high weekly mean temperatures linked previously to occurrences of Salmonella infections. Due to bias-correction, included in the Asynchronous Regional Regression Model (ARRM) and the Bias Correction Constructed Analogs (BCCA) downscaling methods, the observed 30-year means of the heat indices were reproduced reasonably well. In April and May, however, some of the statistically downscaled data misrepresent the increase in the number of hot days towards the summer months. This study demonstrates the dependence of the outcomes to the selection of downscaled climate data and the potential for misinterpretation of future estimates of Salmonella infections.

Global Climate Change and Children's Health

Rising global temperature is causing major physical, chemical, and ecological changes across the planet. There is wide consensus among scientific organizations and climatologists that these broad effects, known as climate change, are the result of contemporary human activity. Climate change poses threats to human health, safety, and security. Children are uniquely vulnerable to these threats. The effects of climate change on child health include physical and psychological sequelae of weather disasters, increased heat stress, decreased air quality, altered disease patterns of some climate-sensitive infections, and food, water, and nutrient insecurity in vulnerable regions. Prompt implementation of mitigation and adaptation strategies will protect children against worsening of the problem and its associated health effects. This technical report reviews the nature of climate change and its associated child health effects and supports the recommendations in the accompanying policy statement on climate change and children's health.

Cryptosporidiosis: A Disease of Tropical and Remote Areas in Australia

Cryptosporidiosis causes gastroenteritis and is transmitted to humans via contaminated water and food, and contact with infected animals and people. We analyse long-term cryptosporidiosis patterns across Australia (2001–2012) and review published Australian studies and jurisdictional health bulletins to identify high risk populations and potential risk factors for disease. Using national data on reported cryptosporidiosis, the average annual rate of reported illness was 12.8 cases per 100 000 population, with cycles of high and low reporting years. Reports of illness peak in summer, similar to other infectious gastrointestinal diseases. States with high livestock densities like New South Wales and Queensland also record a spring peak in illnesses. Children aged less than four years have the highest rates of disease, along with adult females. Rates of reported cryptosporidiosis are highest in the warmer, remote regions and in Aboriginal and Torres Strait Islander populations. Our review of 34 published studies and seven health department reports on cryptosporidiosis in Australia highlights a lack of long term, non-outbreak studies in these regions and populations, with an emphasis on outbreaks and risk factors in urban areas. The high disease rates in remote, tropical and subtropical areas and in Aboriginal and Torres Strait Islander populations underscore the need to develop interventions that target the sources of infection, seasonal exposures and risk factors for cryptosporidiosis in these settings. Spatial epidemiology can provide an evidence base to identify priorities for intervention to prevent and control cryptosporidiosis in high risk populations.

The parasite Cryptosporidium is a common cause of gastroenteritis worldwide. Ineffectively focused interventions are partly why the disease remains a challenge to control. In this study, we describe the geographical, seasonal and demographic characteristics of reported cryptosporidiosis in Australia from 2001–2012. We combine this analysis of illnesses with evidence published in peer review articles and state health bulletins to identify high disease risk areas and populations. We find that rates of reported cryptosporidiosis are highest in warm, remote areas and in Aboriginal and Torres Strait Islander populations’ dominated regions. Our review of the published literature and health reports highlights a focus on short term outbreaks in metropolitan areas. This negligible overlap between areas with high disease rates and areas of public health focus is of concern. Public health interventions and promotion programs to prevent and control diarrhoea need to focus on remote and Indigenous dominated Australia to reduce the currently high rates in these regions and populations.

Climate variability, weather and enteric disease incidence in New Zealand: time series analysis

BACKGROUND

Evaluating the influence of climate variability on enteric disease incidence may improve our ability to predict how climate change may affect these diseases.

OBJECTIVES

To examine the associations between regional climate variability and enteric disease incidence in New Zealand.

METHODS

Associations between monthly climate and enteric diseases (campylobacteriosis, salmonellosis, cryptosporidiosis, giardiasis) were investigated using Seasonal Auto Regressive Integrated Moving Average (SARIMA) models.

RESULTS

No climatic factors were significantly associated with campylobacteriosis and giardiasis, with similar predictive power for univariate and multivariate models. Cryptosporidiosis was positively associated with average temperature of the previous month (β =  0.130, SE =  0.060, p <0.01) and inversely related to the Southern Oscillation Index (SOI) two months previously (β =  -0.008, SE =  0.004, p <0.05). By contrast, salmonellosis was positively associated with temperature (β  = 0.110, SE = 0.020, p<0.001) of the current month and SOI of the current (β  = 0.005, SE = 0.002, p<0.050) and previous month (β  = 0.005, SE = 0.002, p<0.05). Forecasting accuracy of the multivariate models for cryptosporidiosis and salmonellosis were significantly higher.

CONCLUSIONS

Although spatial heterogeneity in the observed patterns could not be assessed, these results suggest that temporally lagged relationships between climate variables and national communicable disease incidence data can contribute to disease prediction models and early warning systems.

Risk factor analysis and spatiotemporal CART model of cryptosporidiosis in Queensland, Australia

BACKGROUND

It remains unclear whether it is possible to develop a spatiotemporal epidemic prediction model for cryptosporidiosis disease. This paper examined the impact of social economic and weather factors on cryptosporidiosis and explored the possibility of developing such a model using social economic and weather data in Queensland, Australia.

METHODS

Data on weather variables, notified cryptosporidiosis cases and social economic factors in Queensland were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics, respectively. Three-stage spatiotemporal classification and regression tree (CART) models were developed to examine the association between social economic and weather factors and monthly incidence of cryptosporidiosis in Queensland, Australia. The spatiotemporal CART model was used for predicting the outbreak of cryptosporidiosis in Queensland, Australia.

RESULTS

The results of the classification tree model (with incidence rates defined as binary presence/absence) showed that there was an 87% chance of an occurrence of cryptosporidiosis in a local government area (LGA) if the socio-economic index for the area (SEIFA) exceeded 1021, while the results of regression tree model (based on non-zero incidence rates) show when SEIFA was between 892 and 945, and temperature exceeded 32°C, the relative risk (RR) of cryptosporidiosis was 3.9 (mean morbidity: 390.6/100,000, standard deviation (SD): 310.5), compared to monthly average incidence of cryptosporidiosis. When SEIFA was less than 892 the RR of cryptosporidiosis was 4.3 (mean morbidity: 426.8/100,000, SD: 319.2). A prediction map for the cryptosporidiosis outbreak was made according to the outputs of spatiotemporal CART models.

CONCLUSIONS

The results of this study suggest that spatiotemporal CART models based on social economic and weather variables can be used for predicting the outbreak of cryptosporidiosis in Queensland, Australia.