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Section 4: Study design
Chapter 4.6 Health-related risk modelling
Research Methods for Health EDRM
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- Section 4: Study design
- Chapter 4.6 Health-related risk modelling
- 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 4.4 Collection and management of good quality data
- Chapter 4.5 Advanced statistical techniques
- 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: Lam HCY, Huang Z, Chan EYY.
Chapter 4.6 describes some modelling methods that might be applied in research studies relevant to the following issues for health emergency and disaster risk management (Health EDRM):
- Short-term environmental health associations.
- Factors associated with the uptake of protection behaviours.
- Trends of influenza.
- Health-related vulnerability index.
What is this chapter about?
Although health-related risk modelling is well established, it is not always easily understood in the context of Health EDRM.
This chapter contributes to the understanding of health-related risk models that are applicable to Health EDRM. It discusses the use of statistical modelling to establish mathematical associations between variables related to health-related risks and shows how such models can be used to investigate short-term environmental health associations, factors associated with the uptake of protection behaviours, trends in influenza and the health-related vulnerability index.
Case studies presented in the chapter:
- Using a telephone survey to collect data on how people in Hong Kong acquired weather information during a cold spell.
- Forecast Model - Simulation Optimization (SIMOP) to forecast influenza epidemic infection curves in the USA.
- Principal components analysis (PCA) to develop a Heat Vulnerability Index in the UK.
- Factor analysis to develop a Health Vulnerability Index.
What are the key messages of this chapter?
- Time series analysis is widely used for establishing short-term associations between exposures and health outcomes.
- Factors associated with protective or preparedness behaviours can be identified by applying the multiple logistic regression method.
- Linking Antigenic Properties and Genetic Data, and Identification of Proposed Vaccine Strains are two ways of inference of phenotypic properties for influenza vaccine selection. They estimate the effectiveness of current vaccine strains for the emerging strains and identify new antigenic variants at an early stage of expansion
- In predicting influenza trends, epidemiological approaches, such as the susceptible-infections-removed models and agent-based models, consider human behaviours and address questions related to the impact of prevention measures.
- In constructing a health-related risk index, dimension reduction approaches such as principle component analysis (PCA) and factor analysis are widely used to simplify the display of multivariate data.