Assessing environmental impacts on health in the Pacific Basin is challenged by significantly varying data types - quantities, qualities, and paucities - because of varying geographic sizes, environments, biodiversity, ecological assets, and human population densities, with highly varied and unequal socio-economic development and capacity to respond to environmental and health challenges. We discuss three case-based methodological examples from Pacific Basin environmental health impact assessments. These methods could be used to improve environmental health evidence at all country and regional levels across a spectrum of big data availability to no data. These methods are, 1) a risk assessment of airborne particulate matter in Korea based on the chemical composition of these particulates; 2) the use of system dynamics to appraise the influences of a range of environmental health determinants on child health outcomes in remote Solomon Islands; and 3) precision environmental public health methodologies based on comprehensive data collection, analyses, and modelling (including Bayesian belief networks and spatial epidemiology) increasing precision for good environmental health decision making to prevent and control a zoonotic disease in Fiji Islands. We show that while a common theme across the three examples is the value of high quality and quantity data to support stronger policy decisions and appropriate prioritizing of investment, it is also clear that for many countries in the Pacific Basin, sufficient data will remain a challenge to inform decision makers about environmental impact on health.
Keywords: Causal loop diagrams; Environmental Health Risk and Impact Assessment; Precision Environmental Public Health; System Dynamics.
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