Related Experiment Video
Updated: Aug 7, 2025
07:31
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
7.2K
Total Excess Mortality Surveillance for Real-Time Decision-Making in Disasters and Crises
Carlos Santos-Burgoa1, Alejandra Garcia-Meza2, Maria Jose Talayero1
1The George Washington University.
Disaster Medicine and Public Health Preparedness
|March 13, 2023
Summary
Timely reporting of disaster deaths is crucial for effective interventions. Measuring total, all-cause, excess mortality (TEM) in real-time provides critical data for targeted disaster response and identifies vulnerable populations.
Area of Science:
- Public Health
- Disaster Management
- Epidemiology
Background:
- Crises like Hurricane Maria and COVID-19 highlight delays in death toll reporting.
- These delays hinder interventions, impact communication, and erode trust in response agencies.
- Challenges include defining disaster-attributable deaths and rapid vital statistics collection.
Purpose of the Study:
- To advocate for real-time Total, All-cause, Excess Mortality (TEM) measurement.
- To establish a continuous mortality surveillance system using readily available data.
- To provide timely information for effective disaster response.
Main Methods:
- Utilizing readily available death counts.
- Integrating geographic, demographic, and socioeconomic data.
- Establishing a baseline for a continuous mortality surveillance system.
Main Results:
- Real-time TEM can be a critical tool in emergency settings.
- TEM identifies mortality spikes, geographic disparities, and disproportionate impact on vulnerable groups.
- Provides authorities with timely information for targeted responses.
Conclusions:
- Measuring total, all-cause, excess mortality (TEM) should be the global standard for disaster impact assessment.
- Real-time TEM enables a more targeted and effective disaster response.
- This approach addresses critical gaps in current disaster mortality reporting.
Related Concept Videos
01:29Applications of GIS: Disaster Management and Emergency Response
123
Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
123
01:26Principles of Disease Surveillance
153
Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
153
01:18Steps in Outbreak Investigation
158
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
158
01:18Introduction To Survival Analysis
315
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
315
01:09Censoring Survival Data
168
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
168
01:25Statistical Methods for Analyzing Epidemiological Data
442
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
442