Abstract
Monitoring health systems during and after natural disasters, epidemics, or outbreaks is critical for guiding policy decisions and interventions. In the case of natural disasters the effects on public health can be direct or indirect. Direct effects are defined as those resulting from the immediate destruction such as drowning and trauma from flying debris, while indirect effects are delayed, longer lasting, and, often harder to measure or detect. In the case of outbreaks and epidemics, lack of comprehensive testing or reporting can lead to challenges in measuring direct effects, while indirect effects can arise due to, for example, increased stress levels or reduced access to health services. When the effects are long lasting and difficult to detect in the short term, the accumulated effects can actually be devastating. Improved access to mortality data provides an opportunity to develop data-driven approaches that can help monitor health systems and quantify the effects of natural disasters, epidemics, or outbreaks. Here we describe a statistical methodology and software that facilitates data-driven approaches. Our work was motivated by events occurring in Puerto Rico after the passage of Hurricane María, but can be applied in other contexts such as estimating the effects of an epidemic in the presence of inaccurate reporting of cases. We demonstrate the utility of our tools by applying it to data related to six hurricanes and data related to the COVID-19 pandemic. We also searched for other unusual events during the last 35 years in Puerto Rico. We demonstrate that the effects of hurricanes in Puerto Rico are substantially worse than in other states, that the 2014 Chikungunya outbreak resulted in an unusually high mortality rate in Puerto Rico, that in the United States the excess mortality during the COVID-19 pandemic already exceeded 100,000 on May 9, 2020, and that the effects of this pandemic was worse for elderly black individuals compared to whites of the same age. We make our tools available through free and open source excessmort R package.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
No external funding was received by either of the authors.
Author Declarations
I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
Yes
The details of the IRB/oversight body that provided approval or exemption for the research described are given below:
Harvard Medical Campus Institutional Review Board
All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.
Yes
I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).
Yes
I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.
Yes
The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, University of Washington, and Vrije Universiteit Amsterdam.
Subject Area
Reviews and Context
0
Comment
0
TRIP Peer Reviews
0
Community Reviews
0
Automated Services
0
Blogs/Media
Author Videos