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Disaster Risk Reduction and Health: The Potential of Health Registers for Public Health Monitoring

Introduction: The Sendai Framework seeks to substantially reduce disaster risk and losses in lives, livelihoods, health, and other assets including persons, communities, and countries. The framework focuses on reducing mortality while increasing population wellbeing, early warning, and promotion of health systems resilience. The use of scientific evidence to inform policy and formulate effective initiatives and interventions is crucial to disaster risk reduction within health. Different instruments and methodologies are available to guide policy and operations. The potential value of routinely collected patient data from health registers is that they can provide pre-event health and comparison group data without burdening affected populations. Aim: The current contribution aims to illustrate how health registers can help monitor the health impact of natural and human-made disasters. Methods: Patient data from health registers of general practitioners and other health professionals, sometimes combined with other registers and data sources, have been utilized to monitor the health impact of disasters and environmental hazards in the Netherlands, Norway, and Sweden since 2000. Results: Health registers allowed monitoring of mental health problems, medically unexplained symptoms, chronic health problems, and social problems. These were compared to groups not directly exposed. The health impact and care utilization was tracked after the fireworks explosion in Enschede affecting inhabitants of the neighborhood (2000; data range 1999-2005), children and parents after the Volendam café fire (2001; data range 2000-2006), Swedish survivors of the Tsunami in Southeast Asia (2004; data range 2004-2010), and parents of children affected by the terrorist attack on Utøya (2011; data range 2008-2014). Discussion: Health systems with registers have an important advantage when it comes to the potential for monitoring population health, and perhaps offer early warnings of pandemics. However, data generation should be closely connected to policy-making before and during the planning and evaluation of public health intervention.