Chapter 2.4: Databases and registers as tools for disaster epidemiology
Authors: Schluter PJ, Kim HM.
Chapter 2.4 describes the three major types of databases and registers available to disaster epidemiology researchers, and their associated strengths and weaknesses by:
- Characterizing the salient differentiating features of these database and register types.
- Providing case studies and examples to illustrate these and their usage.
- Highlighting the strengths and weaknesses of each, and providing a global overview.
What is this chapter about?
Measuring the impacts on human health of disasters and other emergencies is a challenge. Databases and registers can help decision-makers to assess the effects of environmental exposures on human health in the disaster context.
This chapter describes and evaluates the strengths and weaknesses of ongoing, pre-existing, and post-disaster databases and registers for assessing health impacts of disasters. It describes opportunities to enhance such databases and registers through better linkages and expansion to include disaster planning and response research. The chapter includes three case studies illustrating the use of databases and registers in specific disasters
Case studies presented in the chapter
- Measuring the impact of integrated health system changes on emergency department attendances and acute admission, precipitated by the 2010-2011 Christchurch earthquakes.
- Understanding the role of peri-traumatic stress and disruption distress in predicting symptoms of major depression following exposure to the 2010-2011 Christchurch earthquakes.
- Using the World Trade Center Health Registry to determine longitudinal determinants of depression among World Trade Center Health Registry enrollees up to 15 years after the 9/11 attacks.
What are the key messages of this chapter?
- There are multiple and growing sources of data available for disaster epidemiology research. Knowledge of the exposome can be extended and developed by using and linking these data, and exploring how emergencies and disasters affect people’s likelihood of mortality, morbidity and life-course trajectories.
- The expediency of using routinely collected data is often offset by the coverage, depth and quality of the variables available to researchers. This often requires initiation of a post-disaster study, that is both specifically and contextually relevant to the disaster and the population affected.
- As more better quality and richer data are collected, Big Data, machine learning and data science are likely to play an increasingly important role in disaster epidemiology research. However, possible avenues to augment these quantitative data with qualitative information still need to be explored.
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