Spatial Modelling Tools to Integrate Public Health and Environmental Science, Illustrated with Infectious Cryptosporidiosis

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.

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.

Clustering of cryptosporidiosis in Queensland, Australia, is not defined temporally or by spatial diversity

Cryptosporidiosis, caused by infection with Cryptosporidium spp., is a globally distributed disease that manifests as diarrhoea for which there is no effective treatment. The protozoan parasite Cryptosporidium is difficult to detect and control, and can lead to severe disease in young children and the immunocompromised. Individual outbreaks across Australia have predominately been reported in urban areas associated with recreational water, but investigation of spatiotemporal distribution of disease is limited. This study evaluated the spatial and temporal patterns of clusters of notified cases of cryptosporidiosis in the north-eastern Australian state of Queensland, which has the highest average notified cases nationally. A spatiotemporal analysis in SaTScan of 12,263 notified cases from mid 2001 to mid 2015 identified 79 statistically significant disease clusters (P < 0.05). Analyses of annual incidence and disease cluster formation across the state illustrated the substantial randomness of clustering with no clear geographic distribution. Outbreaks were observed temporally across all latitudes and in rural and urban settings, with the majority of clusters centred in major and regional cities. Whilst clusters appeared in areas of high incidence, high incidence itself was not a predictor of clusters. Clusters generally formed during the hottest months between January and April, and cases were primarily children aged 0 to <5 years. Spatiotemporal analysis at a statewide level is an important indicator of regional disease patterns and can act as a trigger for targeted epidemiological investigation.

Measuring and Visualizing Chlamydia and Gonorrhea Inequality: An Informatics Approach Using Geographical Information Systems

BACKGROUND

Health inequality measurements are vital in understanding disease patterns in identifying high-risk patients and implementing effective intervention programs to treat and manage sexually transmitted diseases.

OBJECTIVES

To measure and identify inequalities among chlamydia and gonorrhea rates using Gini coefficient measurements and spatial visualization mapping from geographical information systems. Additionally, we seek to examine trends of disease rate distribution longitudinally over a ten-year period for an urbanized county.

METHODS

Chlamydia and gonorrhea data from January 2005 to December 2014 were collected from the Indiana Network for Patient Care, a health information exchange system that gathers patient data from electronic health records. The Gini coefficient was used to calculate the magnitude of inequality in disease rates. Spatial visualization mapping and decile categorization of disease rates were conducted to identify locations where high and low rates of disease persisted and to visualize differences in inequality. A multiple comparisons ANOVA test was conducted to determine if Gini coefficient values were statistically different between townships and time periods during the study.

RESULTS

Our analyses show that chlamydia and gonorrhea rates are not evenly distributed. Inequalities in disease rates existed for different areas of the county with higher disease rates occurring near the center of the county. Inequality in gonorrhea rates were higher than chlamydia rates. Disease rates were statistically different when geographical locations or townships were compared to each other (p < 0.0001) but not for different years or time periods (p = 0.5152).

CONCLUSION

The ability to use Gini coefficients combined with spatial visualization techniques presented a valuable opportunity to analyze information from health information systems in investigating health inequalities. Knowledge from this study can benefit and improve health quality, delivery of services, and intervention programs while managing healthcare costs.

Spatial analysis of AIDS and the social determinants of health

INTRODUCTION

The social determinants of health (SDH) are factors that can influence the distribution of rates for acquired immunodeficiency syndrome (AIDS) in a given region. The objective of this study was to analyze SDHs related to AIDS.

METHOD

Ecological study, using spatial analyses techniques. 7,896 disease case reports were analyzed over a period of 11 years. Subjects were 13 years or older and residents of the state of Ceará, in the northeast of Brazil. The area of analysis was the municipality, calculating both the average rate of AIDS and the Freeman-Tukey transformed average rate for measuring softening. We used the Simple Linear Regression Model to make the spatial correlation between AIDS detection rates and SDH. A Geographic Information Systems (GIS) was used to manipulate georeferenced data.

RESULTS

High rates of AIDS could be found in cities with better living conditions. Additionally, there was a significant relationship between primary health care coverage and lower rates of the disease in Ceará.

CONCLUSION

Socioeconomic indicators with statistically significant correlation to the distribution of AIDS should be targeted by strategies policies in the fight against the disease.

Big Data's Role in Precision Public Health

Precision public health is an emerging practice to more granularly predict and understand public health risks and customize treatments for more specific and homogeneous subpopulations, often using new data, technologies, and methods. Big data is one element that has consistently helped to achieve these goals, through its ability to deliver to practitioners a volume and variety of structured or unstructured data not previously possible. Big data has enabled more widespread and specific research and trials of stratifying and segmenting populations at risk for a variety of health problems. Examples of success using big data are surveyed in surveillance and signal detection, predicting future risk, targeted interventions, and understanding disease. Using novel big data or big data approaches has risks that remain to be resolved. The continued growth in volume and variety of available data, decreased costs of data capture, and emerging computational methods mean big data success will likely be a required pillar of precision public health into the future. This review article aims to identify the precision public health use cases where big data has added value, identify classes of value that big data may bring, and outline the risks inherent in using big data in precision public health efforts.

The critical role of injecting drug users on the spatial distribution of hepatitis C virus; a study in the West of Iran

AIM

This study was conducted to provide a clear epidemiological picture of HCV spatial pattern.

BACKGROUND

Hepatitis C virus (HCV) is one of the major problems of public health, that its spatial and spatiotemporal pattern remain unclear in Hamadan province.

METHODS

We used the scan statistic to identify the spatial and spatiotemporal clusters of HCV in Hamadan province with an emphasis on considering the role of carrier's and injecting drug users (IDUs) cases. We repeated the same analysis to estimate the effect of some influencing factors on the formation of clusters. All HCV cases that had been recorded by deputy health of Hamadan University of Medical Sciences during 2008-2016 were included in this study.

RESULTS

The location of the purely spatial cluster for carriers, IDUs and total of cases were similar to each other, a cluster consisting of Toyserkan, Nahavand, Asadabad, Malayer and Bahar cities. However, after adjustment, the location of the identified cluster for both carries and IDUs cases changed to a cluster consisting of Asadabad, Bahar, Toyserkan and Nahavand cities. This cluster also observed for spatiotemporal clusters carriers, IDUs and total of cases even after adjustment.

CONCLUSION

Although further studies in individual level are needed, our results revealed that spatial distribution of HCV in Hamadan province (especially in clusters areas) can strongly dependent on the distribution of IDUs cases. Consequently, the effectiveness of HCV combating programs is subjected to properly controlling these case through various counseling, behavioral and therapeutic programs.