Publication Date:
2022-10-26
Description:
© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Sandifer, P., Knapp, L., Lichtveld, M., Manley, R., Abramson, D., Caffey, R., Cochran, D., Collier, T., Ebi, K., Engel, L., Farrington, J., Finucane, M., Hale, C., Halpern, D., Harville, E., Hart, L., Hswen, Y., Kirkpatrick, B., McEwen, B., Morris, G., Orbach, R., Palinkas, L., Partyka, M., Porter, D., Prather, A. A., Rowles, T., Scott, G., Seeman, T., Solo-Gabriele, H., Svendsen, E., Tincher, T., Trtanj, J., Walker, A. H., Yehuda, R., Yip, F., Yoskowitz, D., & Singer, B. Framework for a community health observing system for the Gulf of Mexico Region: preparing for future disasters. Frontiers in Public Health, 8, (2020): 578463, doi:10.3389/fpubh.2020.578463.
Description:
The Gulf of Mexico (GoM) region is prone to disasters, including recurrent oil spills, hurricanes, floods, industrial accidents, harmful algal blooms, and the current COVID-19 pandemic. The GoM and other regions of the U.S. lack sufficient baseline health information to identify, attribute, mitigate, and facilitate prevention of major health effects of disasters. Developing capacity to assess adverse human health consequences of future disasters requires establishment of a comprehensive, sustained community health observing system, similar to the extensive and well-established environmental observing systems. We propose a system that combines six levels of health data domains, beginning with three existing, national surveys and studies plus three new nested, longitudinal cohort studies. The latter are the unique and most important parts of the system and are focused on the coastal regions of the five GoM States. A statistically representative sample of participants is proposed for the new cohort studies, stratified to ensure proportional inclusion of urban and rural populations and with additional recruitment as necessary to enroll participants from particularly vulnerable or under-represented groups. Secondary data sources such as syndromic surveillance systems, electronic health records, national community surveys, environmental exposure databases, social media, and remote sensing will inform and augment the collection of primary data. Primary data sources will include participant-provided information via questionnaires, clinical measures of mental and physical health, acquisition of biological specimens, and wearable health monitoring devices. A suite of biomarkers may be derived from biological specimens for use in health assessments, including calculation of allostatic load, a measure of cumulative stress. The framework also addresses data management and sharing, participant retention, and system governance. The observing system is designed to continue indefinitely to ensure that essential pre-, during-, and post-disaster health data are collected and maintained. It could also provide a model/vehicle for effective health observation related to infectious disease pandemics such as COVID-19. To our knowledge, there is no comprehensive, disaster-focused health observing system such as the one proposed here currently in existence or planned elsewhere. Significant strengths of the GoM Community Health Observing System (CHOS) are its longitudinal cohorts and ability to adapt rapidly as needs arise and new technologies develop.
Description:
This project was supported in part by contract # C-231826 between the Gulf of Mexico Alliance, on behalf of the Gulf of Mexico Research Initiative, and the College of Charleston. The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of the Gulf of Mexico Alliance, the Gulf of Mexico Research Initiative, the College of Charleston, or the Centers for Disease Control and Prevention. Mention of private companies, trade names, or products does not imply endorsement of any kind.
Keywords:
Health observing system
;
Disasters
;
Gulf of Mexico
;
Cohort studies
;
Stress
;
COVID-19
;
Allostatic load
;
Health surveillance
Repository Name:
Woods Hole Open Access Server
Type:
Article
