Data Imperfections in Environmental Epidemiology: A Case Study from Ecuadorian Amazon

The effective utilization of data in research is often hindered by inherent challenges, including inconsistency, imprecision, missing information, and redundancy. Data imperfections are a ubiquitous challenge in scientific research, and environmental epidemiology is no exception. Environmental epidemiology relies heavily on the presence of high-quality data to establish robust associations between environmental exposures and health outcomes. This work will explore common data imperfections encountered in environmental epidemiology research, focusing on their impact on research findings and presenting strategies for mitigation. Examples from an ongoing project in the Ecuadorian Amazon will be used to illustrate these challenges and solutions. This study aims at investigating links between environmental exposure to gold mining and adverse birth outcomes in communities living in Ecuadorian Amazon. The present study underscores the substantial ramifications of outcome data imperfections, encompassing imprecision, inconsistency over time, and the existence of missing values. It also addresses exposure data imperfection, which may arise from its unavailability and the challenges associated with its detection, particularly when it comes to illegal mining. Moreover, we will discuss the challenges of integrating these two types of data and the measures that can be taken to mitigate the adverse effects of these shortcomings. We will present our findings and explore potential strategies for addressing these limitations, such as the use of remote sensing and spatial analysis tools. This research emphasizes the critical need for robust data collection and analysis methods to accurately assess environmental health risks and inform effective public health interventions.