Designing a Robust Methodology to Evaluate Emergency Health and Medical Responses to Disasters

Abstract

Introduction:Health and medical disciplines have traditionally preferred experimental and quasi-experimental methods to evaluate interventions. More recently, mathematical modeling was used to test intervention efficacy in the SARS-CoV-2 pandemic. The challenge for disaster researchers is neither approach suits examining phenomena about emergency health responses in disasters. This study applied an alternative methodology to examine questions of how and why emergency health and medical responses reduced mortality during six different natural hazard disaster events.Method:The case study methodology is orientated by the researcher’s perspective and ‘not assigned a fixed ontological, epistemological or methodological position’. This flexibility allows alignment of the researcher’s worldview with the methodology best fitting the research problem and its context, such as post-positivism. Qualitative case study design carefully links five key design elements and sequences, including research questions, propositions, a unit of analysis, data collection, and data analysis.Results:Six holistic single case studies described how and why the emergency health response reduced mortality risk of people affected by different disasters. An evidenced-based theoretical emergency health program logic model compared and contrasted inputs, activities, outputs, and outcomes between theoretical and actual responses. Rival explanations were tested before data collection for each single case and applied to challenge the logic model. Each case applied four strategies to increase the validity and reliability of the holistic single case study findings.Conclusion:Qualitative case study methodology provides a robust and flexible framework to examine complex questions about emergency health and medical responses, including questions about events, processes, activities, performance, and outcomes. The methodology is equally suited to real-time or retrospective studies. The strength of the approach is the high compatibility for examining phenomena within the context they occur, and linking program logic, data collection, and data analysis methods to the specific question being investigated.