Disaster Data Doesn't Have to Be a Disaster Itself
2017 was a historic year for disasters in quantity, magnitude and destruction. They shattered prior cost records, left hundreds, possibly thousands, dead and pushed thousands more into already strained health systems with new and chronic injuries and illnesses. Early predictions are already calling for an above average hurricane season in 2018. Now is the time to take a new approach in examining what happened during these disasters so we can be better prepared to help survivors of future disasters.
I am a clinician-scientist who studies the health effects of disasters. For example, one of my recent studies showed that hospitalizations for older adults increase well beyond the initial impact of a disaster. I also put my money, and my advanced nursing training, where my mouth is, by deploying to disasters across the U.S. as a member of a federal disaster response team. I spent a month in Puerto Rico immediately after Hurricane Maria, where the personal stories of the clients I cared for matter most in informing my research.
Overwhelmingly, I took care of individuals with chronic disease needs that were not being met because of a health care system in shock. One of my first patients was a diabetic woman residing in a shelter. She had eaten the only food available that morning, a sugary pastry, and the last of her insulin was floating in a cooler of melted water. Her blood sugar was dangerously high. As a clinician, I needed data-informed strategies to understand how to treat her beyond just the episodic care she would receive in our temporary clinic.
We need large-scale, standardized data that can be analyzed to draw scientific and policy-relevant conclusions. Currently, evidence from past disasters relies heavily on after-action reports and case studies. However, there are multiple issues in emergency response that cannot be answered by ‘lessons learned’ compendiums alone.
This standardized system of data collection can improve response to future disasters, by better equipping disaster responders in reducing health consequences to people affected by disaster.
A systematic approach to collecting data after disasters that builds on causal evidence can more effectively influence policy change, and contribute to improving health. One possibility is using Common Data Elements (CDEs). The National Institutes of Health is using CDEs to collect data for a number of health conditions, including sleep disturbances, anxiety and neurologic disorders. By using CDEs, information is collected in the same way for each study population. It creates a standardized baseline which can be shared among research institutions across the world to study a diverse array of topics centered around a core concept—such as a disaster.
Encouraging—and funding—researchers to develop and use CDEs is one example of effective data use that will give us a wealth of information that can be used to compare and make generalizations across multiple populations and disasters. Being able to draw these comparisons can identify areas for improvement that can lead to being better prepared to save lives in the next disaster.
Disasters are unexpected. There’s always going to be an element of disorganization in a disaster which makes a standardized system for collecting to data a challenge. But I believe it is one that is surmountable with coordinated efforts on federal, state and local levels, funding, and with trained experts who are ready to deploy.
At this year’s March for Science, one popular sign said, “At the beginning of every disaster movie there’s a scientist being ignored.” Let’s not ignore the health effects of disasters. We need data to do science.
Sue Anne Bell is a nurse scientist and family nurse practitioner, with expertise in disaster response, community health and emergency care at the University of Michigan.
This blog post is part of a series focusing on different aspects of nursing research in recognition of National Nurses Week. Visit https://www.researchamerica.org/advocacy-action/national-nurses-week-2018 for more information.