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Section 4: Study design
Chapter 4.4 Collection and management of good quality data
Research Methods for Health EDRM
Section navigation
- Section 4: Study design
- Chapter 4.4 Collection and management of good quality data
- Chapter 4.1 Basic principles in designing studies to assess the effects of interventions
- Chapter 4.2 Measuring the problem: Basic statistics
- Chapter 4.3 Cluster randomized trials
- Chapter 4.5 Advanced statistical techniques
- Chapter 4.6 Health-related risk modelling
- Chapter 4.7 Evaluating economic impacts in health emergency and disaster risk management
- Chapter 4.8 Geographic information systems
- Chapter 4.9 Real-time syndromic surveillance
- Chapter 4.10 Using logic models in research and evaluation of Health EDRM interventions
- Chapter 4.11 Researching communication and communicating research in Health EDRM
- Chapter 4.12 Qualitative research
- Chapter 4.13 Addressing complexity through mixed methods
- Chapter 4.14 Natural experiments in a hazard context
- Chapter 4.15 Monitoring and evaluation
Authors: Gouvea-Reis F, Dell’Aringa MF, Murray V.
Chapter 4.4 describes key aspects of data collection for research in health emergency and disaster risk management (Health EDRM), including:
- Different sources and methods for data collection, along with their advantages and limitations.
- Challenges involved in collecting data in disaster settings, and how these might be overcome.
- The importance of data quality, data storage and data sharing.
What is this chapter about?
The collection of reliable data for Health EDRM research and practice requires careful preparation and planning. Different methods can be used to gather data, and the local context, time and resources available should be considered in selecting the most suitable approach for a specific study.
This chapter discusses important aspects that should be considered before, during and after data are collected to ensure that it is of good quality when used in Health EDRM research. It explores planning and preparation processes, different methods for data collection, the challenges that a researcher may face when studying disasters and tools that might help them to address these challenges. Finally, the chapter discusses how to ensure good quality data are stored and made accessible to others, so that it can bring additional benefits.
Case studies presented in the chapter:
- Challenges in disaster data collection after the 2004 Indian Ocean Earthquake and Tsunami.
- An ecologic study to evaluate the impact of the 2011 Rio de Janeiro landslides in the utilization of public mental health services.
- The combination of cholera outbreak data and satellite environmental information to estimate cholera risk.
What are the key messages of this chapter?
- A specific research question and a data collection strategy that will provide adequate and sufficient information to answer this with the available resources are important for high quality research.
- It is fundamental to acknowledge that despite good preparation, challenges may occur. Anticipating how to deal with them can help researchers to overcome future barriers.
- A careful plan on how the collected data will be stored and shared in the long term will ensure that others benefit from the study.