Dr. Corinne Schuster-Wallace (University of Saskatchewan): creating an index to map vulnerability to

Dr. Corinne Schuster-Wallace is currently a water-health researcher within the Global Water Futures program and an Associate Professor in the Department of Geography and Planning at the University of Saskatchewan, Canada. Corinne has worked at the water-health nexus for over a decade and spent the last eight years working in an international, transdisciplinary context developing evidence for informed decision-making, creating tools to help local decision-makers collect the information required for understanding local water security and insecurity. She has broad experience with the environmental factors for, and environmental change impacts on, outbreaks of waterborne disease and the linkages with human health and well-being. In this Infectious Thoughts interview, we ask Dr. Corinne Schuster-Wallace about recent work integrating a range of social, biophysical and health data sets to develop the Water Associated Disease Index (WADI) and how this tool can help accelerate the prevention and control of dengue.

One of the challenges to accelerating collaboration between the WASH and health sectors is the absence in many spheres of a lingua franca, ie a common language when it comes to measurable targets and strategies. How has your research, which leverages global data-sets that are freely available, provided the metrics to improve the understanding and control of water-associated diseases?

An Ecohealth approach is a transdisciplinary, transectoral approach that examines the interaction between people and their environment i.e. the physical and social systems. This was the premise behind the development of the Water Associated Disease Index (WADI) – as far as available data sets would allow, we wanted to be able to understand how social as well as physical factors affect water-associated diseases. While environmental factors are critical to understanding water-related diseases, socio-economic conditions and some human behaviours alter exposure or response.

How can the WADI enhance and accelerate multisectoral collaboration?

WADI was developed to better understand the multifaceted elements of potential risk associated with a water-associated disease.

Through a comprehensive examination of the factors that play a role in transmission of the disease, people’s susceptibility to the disease, and factors that strengthen or undermine community resilience, WADI was designed in a way that crosses sectors. It was intended that WADI would aid resource allocation decisions through an understanding how different factors interact, where they interact to increase risk, and whether the drivers in a particular area were mainly in the physical or social systems, or across both. WADI is based on a geographical information system (GIS). Variable values can be altered to explore different scenarios and the tool driven, for example, by climate change scenarios.

The WADI was created to identify and visualize vulnerability to different water-associated diseases - what diseases have you focused on and what are the main risk factors that you try to encapsulate in this index?

WADI Dengue was the first application developed. We have also applied WADI to schistosomiasis and used downscaled climate data to examine potential changes over time. When designing WADI for specific diseases, we focus on factors that affect the viability of the vector, breeding habitats, the interaction between vector and people, and general population resilience.

Why was it important to integrate a range of social and biophysical determinants within the index? What are the benefits of offering results in map format?

It was important to integrate physical and social systems because socio-economic status and some human behaviours affect exposure, depending on the disease being studied. Visualisation through maps is a powerful tool for communicating findings. In Malaysia the maps were shared with public health officials as part of our assessment of WADI. The patterns on the maps not only matched their experiences, but also explained why the patterns existed.

What are some of the main findings from applying this to dengue vulnerability?

WADI dengue has been applied across geographic and time scales. Moving from sub-national to global application required a sensitivity assessment of the index in terms of the specific variables used and missing variables. Different countries use slightly different metrics and there are large data gaps in global databases, particularly for social variables.

Integration across data sets is an important requirement that seems to be gaining traction within the Sustainable Development Goals. Examining changes over time in NE Brazil demonstrated increases in vulnerability over time in remote regions resulting from land use intensification and increasing population densities while in more developed areas, increased access to piped water and solid waste collection services decreased susceptibility. At the global scale, regions in Europe and North America were identified as being conducive to dengue in the future given vector and virus introduction. This type of scenario development facilitates planning and resource allocation e.g. for mosquito surveillance programs.

What has the application of the WADI approach in a country such as Malaysia, with both extensive rural areas and highly developed urban centres, informed in terms of disease-control and policy recommendations?

WADI application to Malaysia identified two different types of increased vulnerability. The first was highly populated urban environments. The second was more rural, monsoon-affected eastern regions where communities were most affected during drier months.

Mountainous regions are not conducive to the vector, nor are forested regions with low population densities. As such, WADI supports resource allocation decisions in terms of where, when, and the types of interventions that are more likely to be effective.

At the local level, participatory mapping in an urban and rural community revealed differences in perspectives on breeding habitats. For example, the rural community identified lack of solid waste management as being a pr