ORIGINAL RESEARCH
published: 15 October 2020
doi: 10.3389/fpubh.2020.578463
Framework for a Community Health
Observing System for the Gulf of
Mexico Region: Preparing for Future
Disasters
Edited by:
Nur A. Hasan,
University of Maryland, United States
Reviewed by:
Richard Kwok,
National Institute of Environmental
Health Sciences (NIEHS),
United States
Gulnihal Ozbay,
Delaware State University,
United States
*Correspondence:
Paul Sandifer
sandiferpa@cofc.edu
Specialty section:
This article was submitted to
Environmental Health,
a section of the journal
Frontiers in Public Health
Received: 02 July 2020
Accepted: 31 August 2020
Published: 15 October 2020
Citation:
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 AA, Rowles T,
Scott G, Seeman T, Solo-Gabriele H,
Svendsen E, Tincher T, Trtanj J,
Walker AH, Yehuda R, Yip F,
Yoskowitz D and Singer B (2020)
Framework for a Community Health
Observing System for the Gulf of
Mexico Region: Preparing for Future
Disasters.
Front. Public Health 8:578463.
doi: 10.3389/fpubh.2020.578463
Paul Sandifer 1*, Landon Knapp 1 , Maureen Lichtveld 2 , Ruth Manley 3 , David Abramson 4 ,
Rex Caffey 5 , David Cochran 6 , Tracy Collier 7 , Kristie Ebi 8 , Lawrence Engel 9 ,
John Farrington 10 , Melissa Finucane 11 , Christine Hale 12 , David Halpern 13 , Emily Harville 2 ,
Leslie Hart 14 , Yulin Hswen 15,16 , Barbara Kirkpatrick 17 , Bruce McEwen 18 , Glenn Morris 19 ,
Raymond Orbach 20 , Lawrence Palinkas 21 , Melissa Partyka 22 , Dwayne Porter 23 ,
Aric A. Prather 24 , Teresa Rowles 25 , Geoffrey Scott 23 , Teresa Seeman 26 ,
Helena Solo-Gabriele 27 , Erik Svendsen 28 , Terry Tincher 28 , Juli Trtanj 29 ,
Ann Hayward Walker 30 , Rachel Yehuda 31 , Fuyuen Yip 28 , David Yoskowitz 12 and
Burton Singer 19
1
Center for Coastal Environmental and Human Health, College of Charleston, Charleston, SC, United States, 2 School of
Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States, 3 Master’s Program in Environmental
and Sustainability Studies, College of Charleston, Charleston, SC, United States, 4 School of Global Public Health, New York
University, New York, NY, United States, 5 Department of Agricultural Economics and Agribusiness, Louisiana State University,
Baton Rouge, LA, United States, 6 School of Biological, Environmental, and Earth Sciences, University of Southern
Mississippi, Hattiesburg, MS, United States, 7 Huxley College of the Environment, Western Washington University,
Bellingham, WA, United States, 8 Department of Global Health, University of Washington, Seattle, WA, United States, 9 Gillings
School of Global Public Health, University of North Carolina, Chapel Hill, NC, United States, 10 Woods Hole Oceanographic
Institution, Woods Hole, MA, United States, 11 Rand Corporation, Pittsburg, PA, United States, 12 Harte Research Institute,
Texas A&M University-Corpus Christi, Corpus Christi, TX, United States, 13 Scripps Institution of Oceanography, La Jolla, CA,
United States, 14 Department of Health and Human Performance, College of Charleston, Charleston, SC, United States,
15
Computational Epidemiology Lab, Harvard Medical School, Boston, MA, United States, 16 Department of Epidemiology and
Biostatistics, Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA,
United States, 17 Gulf of Mexico Coastal Ocean Observing System, Texas A&M University, College Station TX, United States,
18
Laboratory of Neuroendocrinology, Rockefeller University, New York, NY, United States, 19 Emerging Pathogens Institute,
University of Florida, Gainesville, FL, United States, 20 Department of Mechanical Engineering, University of Texas, Austin, TX,
United States, 21 Suzanne Dworak-Peck School of Social Work, University of Southern California, Los Angeles, CA,
United States, 22 Mississippi-Alabama Sea Grant Consortium, Mobile, AL, United States, 23 Arnold School of Public Health,
University of South Carolina, Columbia, SC, United States, 24 Department of Psychiatry and Behavioral Sciences, University
of California, San Francisco, San Francisco, CA, United States, 25 National Marine Fisheries Service, National Oceanic and
Atmospheric Administration, Silver Spring, MD, United States, 26 David Geffen School of Medicine, University of California,
Los Angeles, Los Angeles, CA, United States, 27 Department of Civil, Architectural, and Environmental Engineering, University
of Miami, Coral Gables, FL, United States, 28 Division of Environmental Health Science and Practice, National Center for
Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, United States, 29 Office of Oceanic and
Atmospheric Research, National Oceanic and Atmospheric Administration, Silver Spring, MD, United States, 30 SEA
Consulting Group, Cape Charles, VA, United States, 31 Icahn School of Medicine at Mount Sinai, Bronx, NY, United States
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
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Community Health Observing System
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.
Keywords: health observing system, disasters, Gulf of Mexico, cohort studies, stress, COVID-19, allostatic load,
health surveillance
INTRODUCTION
mental and physical disorders (10, 11). Adverse physical health
effects of disasters beyond immediate and near-term injuries
are less well-studied than mental outcomes. However, a variety
of physical disorders have been linked to disaster experiences,
including cardiovascular disease (CVD); asthma and other
respiratory problems; digestive and intestinal complaints; eye,
skin, and throat irritation; elevated blood pressure and heart rate;
and some infectious and chronic diseases (12–15).
Repeated exposure to disaster events can amplify mental,
physical, and community health effects. Children and adolescents
may be especially vulnerable to impacts of multiple traumas (16–
20). Pregnant women and mothers with young children may also
be particularly vulnerable to disaster effects. Not surprisingly,
the U.S. Department of Health and Human Services emphasizes
that interventions should include elements specifically designed
for children and adolescents (21). Elderly people, especially
those with chronic conditions, are also of special concern
because of potential for loss or interruption of health care
and medications, inability to evacuate or move for treatment,
heightened vulnerability during transport and dislocation, and
loss of social contact and care mechanisms (15, 22–24). Now
added to the cumulative trauma effects of recent hurricanes
from Katrina through Michael as well as the DWH on the
The Gulf of Mexico (GoM) region has experienced frequent
disasters, including major and minor hurricanes and tropical
storms as well as the massive Deepwater Horizon oil spill (DWH)
(1). In addition to those disaster events emanating from Gulf
waters, there are 872 “highly hazardous chemical facilities” in
operation within 80 km of the GoM coast (2), and at least
three major chemical plant explosions occurred in 2019 alone.
The potential for chemical exposure is further exacerbated by
numerous oil spills and seeps, some of significant magnitude
and duration (3), and abandoned hazardous waste sites (4). Also,
the GoM has experienced frequent and sustained periods of
harmful algal blooms (HABs), with potential for human exposure
to HAB toxins via surface water, seafood, and air (5). The GoM
will likely experience continued frequent environmental and
technological disasters, especially in this era of climate change
and reduced environmental regulation (6–8). Most recently, the
GoM States have been challenged by the COVID-19 pandemic,
which differs from all of the others in its long duration and global
geographical coverage.
Mental health impacts are a dominant effect of disasters
(9), and disaster-related elevated stress may cause or exacerbate
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Community Health Observing System
GoM population is the COVID-19 pandemic, with no clear
end in sight. The recent social distancing and “stay at home”
interventions of COVID-19 have resulted in stressful intrahousehold dynamics, psychological issues, and major economic
concerns (25–28).
Although the region has a history of repeated, major
environmental disasters, the GoM lacks a significant, continuing
baseline of human health information that would enable the
identification, comparison, and mitigation of health outcomes
following disasters. This gap was highlighted by the National
Commission on the BP Deepwater Horizon Oil Spill and
Offshore Drilling (29), which specifically called out the need for
a “public health protocol requiring the collection of adequate
baseline data and long-term monitoring,” as well as by others
(30). Health data should be collected over a period long after
a given event has concluded in order to understand the full
magnitude of effects and better prepare to deal with impacts
of future disasters. Based on previous work (14, 31–33), studies
spanning multiple decades are warranted to gauge long-term and
transgenerational effects.
Major disasters like Hurricane Katrina, the DWH, and
COVID-19 underscore the necessity for establishing long-term
human health observations to improve disaster preparation.
A sustained health observing system is needed in the GoM,
analogous to observing systems that concentrate on highintensity, relatively low-frequency-of-occurrence extreme
weather events such as hurricanes (34–36). To be able to provide
evidence to inform prevention, preparedness, response, and
recovery actions, an effective disaster-focused health observing
system must have capacity to collect relevant health data from
cumulative impacts of sequential events and consequences of
slower-moving, potentially devastating occurrences such as
persistent environmental health threats, historical burdens of
health disparities, chronic chemical contamination, drought, and
climate change. Parker et al. (37) concluded that such planned
pre–post studies for disasters are “virtually non-existent” and
that “well-designed surveys with large probability-based samples
and longitudinal assessment across the life-cycle of a disaster and
across multiple disasters” are required. The current COVID-19
pandemic underscores the necessity to develop and implement
much more robust health surveillance systems in the US and
globally (38).
We propose the creation of a Gulf of Mexico Community
Health Observing System (GoM CHOS) focused on effects of
disasters on the health and well-being of people and their
communities, which would operate continuously, producing
pre-, during-, and post-disaster information. This will require the
integration of available human health information with new and
innovative approaches for measuring adverse health effects and
community vulnerability. The primary objectives of the proposed
GoM CHOS are to establish an ongoing system for the collection,
analysis, and interpretation of a broad range of mental, physical,
and community health data from a representative sample of GoM
residents. The proposed system will (1) provide a continuous
baseline of information against which to assess health impacts
of future environmental, technological, and other disasters,
individually and cumulatively; (2) implement an intensive data
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collection period in the immediate and near-term aftermath of
disasters; and (3) substantially enhance clinical databases, thereby
providing information for hypothesis generation and improving
clinical and public health research and practice. The framework
presented here includes observing system design, proposed
sampling area and population sampling approaches, participant
recruitment and retention, collection and assimilation of primary
and secondary data, data management, and system governance.
To the best of our knowledge, this is the first proposal for a
disaster-specific health observing system in any location, and the
system as outlined should be adaptable to many geographies and
kinds of disasters.
METHODS
Recognizing that no framework or platform existed for a
sustained community health observing system focused on
disaster impacts, the Research Board of the Gulf of Mexico
Research Initiative (GoMRI) commissioned the present work.
The project encompassed efforts to (1) identify a set of essential
data elements and (2) determine the potential for organizing
available data, ongoing health information collection efforts, new
health observing capacity, and technology into a comprehensive
community health observing system.
The project was led by two Principal Investigators (P. Sandifer
and B. Singer) and a Steering Committee of internationally
recognized experts. Two expert workshops were convened to
explore options for such an observing system, other subject
matter experts were consulted, and a large body of literature
and ongoing health surveys and studies was reviewed. Expert
workshop 1 focused on the overall concept of a health observing
system for the GoM region, while workshop 2 focused on
the potential to operationalize the allostatic load concept of
cumulative stress impacts on health for application in longterm health studies. Design of the proposed GoM CHOS was
also informed by the highly successful environmental observing
systems in place at regional, national, and global scales [e.g., GEO
www.earthobservations.org; https://www.earthobservations.org/
geoss.php NOAA’s National Weather Service (www.weather.
gov)], IOOS (ioos.noaa.gov) (39–42), and which provide
information on atmospheric, oceanic, climate, weather, and
biological conditions critical to life and livelihoods. Further
details are provided in (1), which serves as a repository for
information generated by the project.
RESULTS
GoM CHOS Framework
Based on essential requirements, guiding principles, and core
values identified during our first workshop (1), an observing
system framework was developed consisting of six levels of data
domains, illustrated as concentric circles (Figure 1). These data
domains encompass existing, large-scale surveys and studies as
well as three new GoM-specific cohort studies.
The outer blue ring is the observing system’s “backbone”
of national surveys and data domains, encompassing the
National Health and Nutrition Examination Survey (NHANES)
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reported enrollment of >348,000 participants, of which 271,000
had completed initial steps for participation (https://www.
researchallofus.org/data-snapshots/), although it is not clear
when the program will be fully operational. Despite its large
and diverse target sample, the All of Us program has significant
limitations relative to the GoM CHOS, including lack of a
statistically derived sampling plan, which restricts its utility in
epidemiological contexts (46). Strong points are its size and
national scope, emphases on enrolling underrepresented and
minority participants, and plans for sharing data, software, and
other program resources. Also, the All of Us questionnaires and
protocols are similar to those used successfully in the BRFSS,
NHANES, and NHIS surveys and have been validated in pilot
studies, making them attractive as potential templates for the new
cohort studies proposed here.
The three inner rings (Gold—Large Cohort; Yellow—Small
Cohort; and White—Disaster-Specific Cohorts) would be new
longitudinal cohort studies specifically designed for the GoM
CHOS. Workshop 1 participants identified new cohort studies as
essential elements of a GoM CHOS, and this finding is affirmed
by recommendations from the literature (19, 37, 47, 48). This
approach will enable a two-pronged sampling strategy: (1) a
prospective approach that ensures that data are collected across
the Gulf in anticipation of future disasters and (2) a responsive
component consisting of disaster-specific cohort(s) that will be
established immediately after a disaster. As envisioned, the new
cohort studies will be nested, with the Small Cohort being
a more intensively sampled subset of the Large Cohort, and
the Disaster-Specific Cohort(s) drawing participants from the
Large and Small Cohorts to the degree possible based on the
location and time and geographic scales of a specific disaster.
Participant recruitment and data collection methods will be
tailored to ensure comparability and interoperability of data
among all cohorts.
The Large GoM Cohort study (gold ring) is the largest
of the new longitudinal cohorts developed specifically for the
GoM. Its design was guided in part by other successful cohort
studies, such as the All of Us, CARDIA, Dunedin, Framingham,
MacArthur, MIDUS, and Wisconsin studies (1). As proposed,
it will contain proportional representation of participants from
coastal areas in all five states. The Large Cohort will include
sociodemographic and self-reported mental and physical health
information similar to that collected in the backbone studies.
In addition, clinically relevant data will be collected via clinical
visits, mobile monitoring, telemedicine methods, and remote
sensing. Some community health metrics also will be included
for all cohorts, as described below.
The yellow ring represents the Small GoM Cohort study,
conceived as a subset of the Large Cohort (but still Gulf-wide)
to provide more detailed health data. Small Cohort participants
could be chosen based solely on willingness to provide more
detailed health information, or to represent specific vulnerable
populations of the Gulf, or there could be multiple small cohorts,
each representing different geographic areas or serving initially as
demonstration projects. Targeted sampling in the Large or Small
Cohorts could facilitate the construction of other studies for more
intensive sampling for different purposes (49, 50).
FIGURE 1 | Diagram of a conceptual framework for a Gulf of Mexico
Community Health Observing System (GoM CHOS). The All of us study (green
ring) is under development by the National Institutes of Health and is expected
to provide useful comparison data, as well as other materials, if it becomes
fully operational as planned.
(https://www.cdc.gov/nchs/nhanes/index.htm), the Behavioral
Risk Factor Surveillance Survey (BRFSS) (https://www.cdc.gov/
brfss/index.html), and the National Health Interview Survey
(NHIS) (https://www.cdc.gov/nchs/nhis/index.htm). These are
ongoing cross-sectional studies conducted by public agencies,
and the data are available for research with restrictions to protect
privacy. In addition to other data, they collect information
on obesity, CVD, asthma, diabetes, and some other disorders
that may increase individuals’ vulnerability to disaster impacts
and may be useful for comparison with data derived from the
proposed cohort studies. BRFSS data are useful at state and
periodically at county levels, while NHANES and NHIS data
generally cannot be disaggregated for state-level comparisons.
Collectively, these surveys provide a wealth of demographic,
general health status, socioeconomic, and behavioral information
[see (43) and websites for each survey].
The purple ring is a proposed augmented GoM BRFSS, in
which additional questions pertinent to the GoM CHOS could
be developed and asked annually by State Health Departments
in the five GoM States, similar to the Gulf States Population
Survey (GSPS) (44) conducted following the DWH event. The
proposed new effort would enhance the richness of BRFSS
collections, both spatially and in the form of disaster-relevant
information. Implementation of an augmented BRFSS would
require agreement by and additional funding for each of the GoM
State Health Departments.
The orange ring is the NIH All of Us longitudinal cohort study,
which has a target enrollment of 1 million adult participants
that reflect the nation’s diversity, including groups historically
under-represented in biomedical research (45, 46). It began
enrolling participants in May 2018 and, as of 30 April 2020,
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The inner white circle, the Disaster-Specific Cohort, is
expected to be a considerably smaller cohort, established rapidly
following a specific disaster and would likely consist of more
exposed participants. However, if one considers a pandemic as
a disaster, then the Disaster-Specific Cohort(s) would likely have
to be larger and encompass a much greater geographic area
than contemplated in the current design. Leaving this example
aside for the moment, the white circle will be a further nested
collection where participants are recruited from the Small Cohort
as possible. Recruitment from the Large Cohort or of new
participants may be necessary depending on the disaster and its
characteristics. Creation of any disaster-specific cohort would be
coordinated with local and state public health and emergency
response officials, the CHOS community and scientific advisory
committees, and other organizations and officials as appropriate.
Design Options for Cohort Studies
Sampling Area
Based on the geographic location of numerous previous disasters
and the large size of the GoM region, we propose to limit the
area covered by the GoM CHOS to the counties immediately
along the Gulf coast. The National Oceanic and Atmospheric
Administration (NOAA) categorizes coastal counties as shoreline
or watershed (51, 52). Coastal shoreline counties have a coastline
bordering the ocean or Great Lakes or contain areas identified by
the Federal Emergency Management Agency (FEMA) as having
high risk for tidal and/or storm surge flooding (Figure 2A) and
are where the majority of economic production from coastal
and marine-related natural resources is concentrated. Coastal
watershed counties are those that lie immediately behind the
shoreline counties and whose residents affect the coast but are
generally less impacted by coastal disasters (Figure 2B). We
recommend the 68-shoreline counties across the five GoM states
be included as primary sampling targets for the GoM CHOS
data collection (Figure 2A). This limits the spatial scope of the
study area considerably, but still includes a substantial human
population (∼16,300,000 people) (53). In addition to being the
areas typically most affected by hurricanes and major oil spills,
people residing in the shoreline counties are more likely to be
exposed to airborne oil spill chemicals (55) and red-tide HAB
toxins (56). Many employees of coastal businesses may reside
further inland and therefore outside the initial study area. These
workers, their families and businesses, and social connections
that support them are vulnerable to disaster events and should
be considered for subsequent iterations of recruitment, with
the coastal watershed counties offering a potential option for
expanding the study area.
Although it will be impossible to state the actual sample size
to be targeted until funding and other operational matters are
decided, we did an example trial calculation [(1), p. 35]. This
resulted in estimated targeted sample sizes of between 96 and
1,031 participants for each of the 68 coastal counties included
in the framework, depending on the margin of error deemed
acceptable at time of implementation. The total estimated sample
for all counties was 6,528, 25,704, or 70,108 participants at 10, 5,
and 3% confidence levels, respectively.
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FIGURE 2 | Coastal shoreline (A) and shoreline plus watershed (B) counties in
the Gulf of Mexico region [adapted from (51), courtesy of G. Sataloff, NOAA].
Differences in color represent differences in relative population (53), with lighter
shades indicating lower population and darker high. (C) Coastal shoreline and
watershed counties showing relative levels of rural or urban
characteristics (54).
Population Sampling and Participant Recruitment
To the extent possible, the new cohorts should be created
as random and representative samples that proportionally and
adequately reflect health characteristics of the resident coastal
populations in the five GoM states. The volunteer participant
population should be composed of approximately equal numbers
of adult men and women (18 yr and older), with no upper
age limit, and children from age 3 to 18 with parental consent.
Gender-specific information, including pregnancy status, will
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be obtained via questionnaires and examinations. Because
health status and health-care access differ between rural and
non-rural areas (57), and socioeconomic factors may increase
vulnerability of rural residents (58), we propose a clustered,
stratified random sampling design with urban and rural shoreline
counties as the initial strata. Target counties and parishes would
be stratified by either the Rurality Level (54) (Figure 2C) or the
Center for Disease Control and Prevention (CDC)’s Urban-Rural
Classification Scheme for Counties (59). Additional stratification
at the U.S. Census Bureau tract or block group level or by ZIP
code could occur within each selected county or parish using
density of development derived from land cover data and/or
population density.
Subpopulations likely to be most at risk from future natural
and technological disasters can be defined by geographic location;
social determinants of health as defined by the six social capitals
(60); or other predisposing factors such as socioeconomic status
and preexisting chronic health problems. One potential tool for
identifying subpopulations is the Tapestry Segmentation dataset
curated by Esri, which uses sociodemographic characteristics to
classify U.S. neighborhoods into 67 distinct groupings from the
county to the block group level. While designed for the smallarea analysis of consumer markets, the groupings could either
be used as-is or the cluster analysis techniques could be adapted
using sociodemographic variables more specific to categorizing
vulnerability (61).
Members of minority, underserved, and disadvantaged
communities, including those with poorer health, typically yield
the lowest response rates and/or have been poorly represented
in epidemiological studies, potentially affecting the validity of
study results (33, 62, 63). Purposeful oversampling in urban–
suburban and rural areas may be necessary to ensure sufficient
vulnerable or at-risk individuals are included. Also, minority
communities are frequently located near industrial harbor
and port facilities, with resulting disproportionate exposure to
potentially health-damaging levels of petroleum hydrocarbons
(64). Adding participants beyond those selected randomly or
in the original sampling frame to meet project objectives of
inclusiveness is a fairly common practice (33, 65–67). A variety
of methods including adjusting for covariates of selection, inverse
probability weighting, and sensitivity analyses can be employed
to control for selection biases introduced by a targeted sampling
design (67). In addition, direct recruitment via federally qualified
health centers (FQHCs), involvement of trained community
health workers (CHWs) to identify volunteers, and other means
may be required.
Prior to recruitment of participants, the GoM CHOS should
initiate a broad community engagement effort employing a
community-based participatory research (CBPR) approach
(68–72) and specifically including environmental justice
communities. This should be a robust engagement and
awareness campaign to inform the public about the CHOS,
raise public awareness, provide information, and encourage
participation utilizing public news media, GoM Sea Grant
programs, healthcare providers, social media, community
organizations, pharmacies, churches, grocery and convenience
stores, and other willing outlets. The community engagement
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effort should begin at least 6 months before initiation of
recruitment efforts and continue at a reduced level over the life
of the CHOS.
Potential participants will be screened for enrollment in the
Large or Small Cohort based on response to an initial mail inquiry
about their willingness to participate and a limited amount
of information that would allow provisional classification of
vulnerability. Address-based sampling, which can be followed by
acquisition of relevant electronic contact information, may be a
better way to construct a participant sample frame than telephone
or internet-based sampling (1, 73). Participants who are willing
to provide more detailed information will be considered for
enrollment in the Small Cohort. The initial mailing may be
supplemented by personal contact to ensure recruitment of
sufficient numbers of vulnerable people and those willing to
provide additional information or samples as needed for the
Small Cohort. Initial screening criteria will include but may
not be limited to number of positive responses to vulnerability
screening questions, gender identification, age, socioeconomic
and educational status, race/ethnicity (e.g., African American,
Hispanic, Vietnamese-American, other), and preexisting health
conditions. Methods to bolster the number of participants in
subpopulations could include additional mailings targeted to
Census tracts known to be more heavily populated by people with
lower socioeconomic status (SES) characteristics, or contacts
with patrons of FQHCs (74). Another approach would be to train
and deploy CHWs (75, 76) who could serve as study ambassadors
in their communities, helping identify potential participants.
Although utilization of CHWs in recruitment may introduce
sampling bias, a wholly probabilistic sampling process may yield
more respondents who have significantly greater social and
health status than those of the overall community being studied
(63, 65, 67). The non-response error introduced by a solely
randomized sampling strategy is significant (77) and potentially
greater than the biases introduced by recruiting individuals via a
targeted approach (67).
Data Collection
Primary Data
New data will be gathered through a mixture of (a) survey
instruments; (b) clinical assessments of psychological and
physiological health; (c) collection, processing, analysis, and
banking of biospecimens and derived biomarkers; and (d)
wearable health devices. Telemedicine approaches, the provision
of medical services and information via electronic means, have
proven useful for remote treatment of a variety of mental and
physical ailments (78), collecting health data during a disaster
(79), and providing health care during the current COVID-19
pandemic (80). All data collection and management activities will
be conducted by appropriately trained and qualified personnel
either in a clinical setting or via use of telemedicine methods.
We recognize that there is growing concern about whether
or not the current practice of surveying by questionnaire is
sustainable (81). At this time, we do not see a viable alternative
to questionnaires for certain kinds of data, although such may
develop in the future. Establishment and maintenance of a robust
scientific advisory group that could assist the GoM CHOS to shift
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TABLE 1 | Types of data proposed for collection in Gulf of Mexico Community Health Observing System cohort studies.
PPI from questionnaires
PPI from questionnaires
Demographic information, including ethnicity, sex/gender identity,
marital/partner status, children, residential history
Socioeconomic information, including ability to deal with minor
financial emergencies
General health status
Personal health history, including chronic, and major diseases
Family health history, including chronic and major diseases, and
occupational history
Life history and behavioral factors, including alcohol, tobacco, and
illicit drug use, nutrition, exercise, sleep
Health care access and services utilization
Prescribed medications
Previous disaster/trauma experiences including in childhood
Residence and adequacy of housing
Known or suspected exposure to toxic or infectious
substances or organisms
Social, religious, tribal, community attachments, and
memberships
Marginalization and discrimination (political, racism, ethnic,
ageism, economic)
Feeling of security or insecurity in home and neighborhood
Level of trust in government/societal structures
Mental health measures
Physical health measures
Biospecimens
Anxiety: GAD-7
Depression: PHQ-8 or 9
PTSD/PTSS: PTSD Civilian
Resilience: CD-RISC-10
(Connor-Davidson Scale)
Alcohol abuse: AUDIT-C
Religiosity: RQ-12
General self-efficacy scale (GSES)
Social capital (adapted from
Loneliness scale (ULS-8)
Sense of control scale
Cognitive function (IQ or other)
Systolic & diastolic BP
Pulse (heart) rate
Height & weight
Waist–hip ratio
Body mass index
Lung function (FEV1/FEVC)
Cardiovascular fitness
Gum health
Balance
Ambulatory fitness (ability to rise,
stand, walk)
Blood
Plasma
Serum
Saliva
Urine
Hair
DNA, mtDNA, telomere length (buccal swab)
Nails (finger & toe)
Stool
Breath
Umbilical cord blood (when available)
All but PPI will be obtained in clinical settings. See Sandifer et al. (1) for reference citations.
should be sufficient for tracking health characteristics of the
GoM coastal populations. Questions for the Augmented BRFSS,
if implemented, should be prepared by Health Departments
in the five Gulf States, in collaboration with the CDC. PPI
will be elicited from questionnaires similar to and likely
derived from the ongoing data domains included here (All
of Us, BRFSS, NHANES, NHIS) and will include personal
and some community information. Similarity to established
survey instruments will ensure comparability of CHOS data to
national averages.
Collection of mental and physical health data (Table 1) will
be done by qualified personnel following established biomedical
protocols such as those detailed in the NIH GuLF study (87) and
the All of Us program (45). Any measurement values indicative of
a near-term health problem will be referred for evaluation, and,
if deemed prudent, the participant will be recommended for an
appropriate clinical examination. All data will be entered into a
computer database and an individual’s data will be made available
to that participant.
For mental health, screenings will target depression, anxiety,
PTSD, and others that are significantly associated with disasters
(Table 1). Additional psychological indicators that measure
social and interpersonal support, coping mechanisms, purpose
in life, satisfaction with life, quality of life, domestic conflict,
cognitive impairment, and experiences of specific disasters such
as oil spills, hurricanes, and floods may also be useful (1).
Proposed physical health measurements (Table 1) are based
on importance relative to disasters, use in other longitudinal
studies, and relative ease of collection. Biological specimens
will be collected from willing adult participants (18 yr and
onto new technological platforms as they develop over time will
be a crucial aspect of CHOS governance (see Discussion).
Institutional Review Board (IRB) protocols will be followed
for all data collection. Upon receipt of signed informed consent,
each participant will be provided detailed survey instruments
by mail or electronic means depending on preference. Surveys
may be filled out at home by the participant or with a trained
interviewer. Involvement of interviewers can be important
in situations where participants may have poor reading skills, lack
facility in English, and/or have poor health and environmental
literacy (72, 82, 83). Failure to provide for these circumstances
may lead to elevated levels of anxiety and non-participation or
non-compliance later in the study. However, in the presence
of an interviewer, people may tend to offer responses that are
considered socially desirable (84, 85). Unfortunately, no one
surveying mode is generally accepted for all circumstances (86).
A final decision as to how personally provided information
(PPI) will be collected likely will be made when the program
is implemented. For the present, we suggest that in most cases
the initial data questionnaires be filled out by the individual
participants, with the more expansive interviewer approach
reserved for the Disaster-Specific Cohort(s) or for special
language/literacy situations, possibly with assistance of CHWs.
Regardless of whether interviewers are involved or not, all
questionnaires, informed consent, and other significant program
documents should be provided in Spanish and Vietnamese, in
addition to English.
A suggested list of data and samples (Table 1) is presented
as a starting point for implementation decisions. This list was
developed based on expert input and literature review and
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older) and, if possible, for young children with parental consent.
The biospecimens identified, other than breath, are collected
routinely in longitudinal studies and provide indicators of
stress, frailty, health status, and exposures to potentially harmful
substances (1). Because of the expense involved in biospecimen
collection, storage, and analysis, it may be necessary to restrict
most biospecimen collections to the Small Cohort and any
Disaster Specific Cohort. Biospecimens will be aliquoted for
both near-term analysis and storage for later use. Biobanking
procedures will be based on established protocols [e.g., (45, 87)].
Choosing biomarkers is complicated by intended usages,
storage and analytical costs, and stability in storage.
Recommendations about biospecimen collection, and
biomarkers are based on current analytical capabilities and
are expected to change somewhat with future improvements
in collection, analysis, and use. Many of the biomarkers
recommended here (Table 2) have been widely used for a variety
of clinical and research purposes, including health and exposure
assessments and for calculating allostatic load (AL) (88). AL
conceptually refers to “the price the body pays for being forced
to adapt to adverse psychological or psychosocial or physical
situations” (89). Development of AL in an individual can be
thought of as the cumulative dysregulation of physiological
mediators of adaptation over time. While no formulation of
AL suitable for broad use is currently available, its potential for
dynamic operationalization is the subject of a paper currently
in preparation. Having a suite of biomarkers will support health
assessments and development of AL and other health evaluation
tools. Prior to implementation of the GoM CHOS, the list in
Table 2 and all other health parameters proposed should be
subjected to review by a panel of experts convened specifically
for that purpose. Also, a carefully designed and managed process
for reviewing requests for use of stored biological materials
should be an integral part of the system implementation plan
and should allow for input of supplementary resources from
research interests outside of the CHOS, thus expanding uses of
the observing system’s resources. This will likely require a data
sharing agreement.
Data collection intervals will be established at the time
of program implementation. Annual updates of data are
preferred, but program logistics may necessitate a different
interval. Ideally, this will not be longer than 2–4 years. Addon special studies or demonstration projects may require
different sampling schedules. Disaster-Specific Cohort sampling
will occur outside of the scheduled collection intervals as
needed following disaster events, but should not supersede
scheduled collections.
In addition to health data, monitoring for exposures to
contaminants, toxins, organisms, and conditions associated with
disasters should be an important component of the GoM CHOS
(Table 3). Exposures can involve inhalation, contact, ingestion,
and emotional pathways. Collection of exposure information
should include questionnaires, monitoring of ambient levels of
exposures of concern via existing databases and remote sensing,
and analyses of urine, blood, and other biospecimens as necessary
to assess body burdens of selected contaminants, toxins, or
microorganisms of concern.
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TABLE 2 | Biomarkers that have been used or suggested for use in assessing
allostatic load and health status in longitudinal or other studies or recommended
during an expert workshop.
Neuroendocrine
Cardiovascular & Respiratory
Cortisol (diurnal, salivary, or urinary)
Cortisone
Dehydroepiandrosterone (DHEA-S)
Insulin-like growth factor (ILGF)
Norepinephrine
Epinephrine
Dopamine
Aldosterone
Systolic blood pressure
Diastolic blood pressure
Mean arterial pressure
Heart rate (HR)
Peak respiratory flow (FEV1)
Cardiorespiratory fitness
Gum health
Anti-hypertensive medication
Glucose medication
Immune
Anthropomorphic
White blood cell count
Interleukin-6 (Il-6)
Tumor necrosis factor α
C-reactive protein
Fibrinogen
Leukocyte telomere length
Immunoglobulin E (IgE)
Cytomegalovirus (IgC)
Waist–hip ratio
Height
Weight
Waist–height ratio
Body mass index (BMI)
Facial age
Underweight (%)
Metabolic
Psychological/Cognitive
Total cholesterol (TC)
High-density lipoprotein (HDL)
Low density lipoprotein (LDL)
Lipoprotein
TC:HDL ratio
Triglycerides
Glucose
Insulin
Albumin
Glycosylated hemoglobin (HbA1c)
Creatinine (creatinine clearance)
Homocysteine
Urea nitrogen
Alkaline phosphatase
Apolipoprotein A, B100 ratio
Liver enzymes
mtDNA
Inflammation marker
IQ test
Sense of control
Sleep issues
Impairment of function
Feeling unsafe in neighborhood
Lack of neighborhood cohesion
Financial strain
Social isolation
Loneliness
Relationship conflict
Discrimination
Work stress
Caregiving stress
Items in bold type were most commonly used. See Sandifer et al. (1) for reference citations.
We anticipate that some cohort participants, particularly in
the Small and Disaster-Specific Cohorts, will have or agree to
be outfitted with wearable health devices (WHDs). Wearable
or portable monitoring devices for health-related parameters
including biomarkers, behaviors, and exposures are used widely
for persons dealing with chronic illnesses such as CVD and
diabetes, as well as by individuals to track personal health
indicators. WHDs come in an almost bewildering variety of
forms including wristbands; smartphones and apps; activity
trackers and smartwatches; specialized monitors; high-tech
patches; “smart” rings, clothing, glasses, contact lenses, ear
monitors, arm bands, and jewelry; and even temporary tattoos
that can be placed on or in the skin, as well as ingestible and
implantable devices. For large-scale, long-term studies, WHDs
should be non-invasive, inexpensive, simple, and relatively easy
for both participants and data recorders to use, light weight
and comfortable, relatively non-intrusive, robust and durable,
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parameters (1). Jiang et al. (97) demonstrated potential of a
specially modified patch to detect thousands of exposures to
microbes, insects, pets, wildlife, and chemicals. While many
technical and other challenges to the broad use of WHDs remain
(98), their potential to significantly enhance the specificity and
breadth of data collected make them worthy of incorporation
into longitudinal health studies.
TABLE 3 | Recommended list of environmental exposure information to be
collected from questionnaires, analyses of biospecimens, and/or analyses of
samples from homes, workplaces, or the environment of a particular disaster and
included in cohort studies.
Particulates (PM2.5 and nanoparticles)
Air temperature extremes (hot and cold)
Unclean/contaminated drinking or recreational water
Oil and its components and other chemicals*
Secondary Data
Contaminated or spoiled food
Secondary sources can play important roles in informing primary
data collection. Such sources include syndromic surveillance
systems (SyS), electronic health records (EHRs), remote sensing
(RS), social media, environmental data bases, volunteer health
workers, and community data sources to the extent feasible
and useful. EHRs and exposure data are expected to be most
important for the Small and Disaster-Specific Cohorts. To
guard against ecologic fallacy errors, supplemental data from
community surveys, remote sensing, and similar secondary
sources will not be assigned to individuals or used for
epidemiological purposes. Instead, this information is expected
to be used to help flesh out an individual’s surrounding
environmental context.
SyS is a public health early warning system that uses existing
electronic health information, primarily from chief complaint
forms, for early detection of disease outbreaks such as influenzalike illnesses (99). State SyS systems are coordinated through the
CDC and, in recent years, have expanded to encompass hazards
and disaster response (100–104). SyS could be more useful in
disaster health impact monitoring if they were strengthened by
increased sharing among the states, improving the base chief
complaint data formats, and incorporating more mental health,
hospital discharge diagnosis, Medicare and Medicaid, and death
data (1).
EHRs collected and maintained by clinical entities are
expected to be critical sources of health data, and GoM CHOS
participants will be asked to authorize access to their EHRs.
Willingness to do so will not be a requirement for participation
but likely will be a major factor in selecting candidates for the
Small and Disaster-Specific Cohorts. A well-established effort
making broad use of EHRs is the OneFlorida Clinical Research
Consortium [https://www.ctsi.ufl.edu/ctsa-consortium-projects/
oneflorida/, (105)]. OneFlorida resources include an IRB,
clinical informatics, community research facilitators, community
engagement programs, participant recruitment services,
information technology resources, research training and
education, and a statewide biorepository capacity. The NIH All
of Us program is also planning to make significant use of EHRs
(46). Both the OneFlorida and All of Us programs have or are
developing robust data management structures that could serve
as models or possibly collaboration opportunities for the GoM
CHOS program.
Remote sensing (RS) is useful for examining the spatial
components of human–environment interactions (106), as
well as for describing the physical impacts and dynamics
of a disaster in near real-time. Air pollution, in particular
fine particulate matter (PM 2.5 ), is “the most consistent and
robust predictor of mortality from cardiovascular, respiratory,
Pesticides
Harmful bacteria and viruses
Harmful algal blooms/toxins
Mold
Overexposure to sunlight
Radioactivity
High levels of psychological and physiological stress
(The CDC Environmental Public Health Tracking Network includes a much more extensive
list of health effects and exposure indicators, including some community factors included
elsewhere in this paper; see https://ephtracking.cdc.gov/showIndicatorPages).
*For oil and its components, there are concerns with potential for polycyclic aromatic
hydrocarbons (PAH) to contaminate seafood. However, the list of PAHs typically measured
needs to be updated (90) as do lists of other chemicals of environmental concern related
to human health.
have small power demands and/or long battery duration, provide
accurate data, and include built-in security to protect data.
Smartphones are the most ubiquitous of passive sensors
(91). Already, 96% of Americans own a cellphone of some
type, 81% have a smartphone, and even 71% of those with
incomes <$30,000/year have a smartphone (92). Smartphones
should be a primary target for health monitoring due to their
near ubiquity, familiarity, amount of useful data collected, and
flexibility for addition of, or use in concert with, other monitoring
devices or apps. Smartphones and apps, smartwatches, Fitbit R
or similar types of activity and vital sign monitors have the
ability to collect relatively accurate readings for blood pressure
and heart rate in addition to providing location, relative
position, periods of activity, ambient light, and humidity. These
types of devices are being augmented rapidly with additional
sensors, such as for respiration, blood O2 levels, sweat, and
other parameters. Smartphones can also be used for “ecologic
momentary assessment,” such as getting repeated responses to a
few questions about a subject’s immediate experiences of anxiety,
pain, substance use, local environment, etc., over the course of a
day or other time frame instead of relying on a single response
that may be subject to recall bias (93).
Silicone wristbands appear to be the simplest, least expensive,
and easiest to deploy WHD for capturing potential contact and
inhalation exposure for a large number of potentially dangerous
chemicals, including polyaromatic hydrocarbons (PAHs),
polybrominated diphenyl ethers (PBDEs), polychlorinated
biphenyls (PCBs), flame retardants, pesticides, pharmaceuticals,
personal care products, dioxins, furans, some endocrinedisrupting chemicals, potential carcinogens, and other chemicals
(94–96). Rapid progress also is being made in development
of practical WHDs for cholesterol cortisol, glucose, and other
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Data Management
and other diseases in studies of long-term exposure to air
pollution” (107). Remotely sensed satellite data are being
paired with ground-based data and modeled PM 2.5 metrics
to create composite exposure data sets (108). Validation for
these technologies is ongoing, and the methods and uses are
expected to grow rapidly. RS is also vital to monitoring and
forecasting distribution of chemical contaminants such as oil
spills (109) and occurrence of harmful algal blooms (110, 111)
and harmful Vibrio bacteria (112). Additionally, a wealth of RS
tools exists to characterize landscapes and built environments,
including the Normalized Difference Vegetation Index (113–
115) and NOAA’s Coastal Change Analysis Program (116).
The GoM CHOS will rely on water and air quality databases
maintained by the US Environmental Protection Agency (EPA)
and NOAA, the Public Health Exposome database (117),
the National Centers for Environmental Information (NCEI)
(https://www.ncei.noaa.gov), the Human Health Exposure
Analysis Resource (HHEAR) supported by the National Institute
of Environmental Health Sciences (NIEHS) (https://www.niehs.
nih.gov/research/supported/exposure/hhear/index.cfm#), and
others for information related to exposures to toxic or infectious
agents. It should also track information from relevant animal
health assessments such as studies on marine mammals exposed
to oil spills and other contaminants (118, 119).
Additional supplemental data will be derived from the All of
Us (if implemented as planned), BRFSS, NHANES, and NHIS
surveys/studies, the American Community Survey (https://
www.census.gov/programs-surveys/acs), General Social Survey
(https://gss.norc.org/), Robert Wood Johnson Foundation
county health rankings (https://www.countyhealthrankings.
org/), the Southern Community Cohort Study (62, 74), CDC’s
National Environmental Public Health Tracking Network
(https://ephtracking.cdc.gov/), Southern Poverty Law Center’s
Hate Map (https://www.splcenter.org/hate-map), National
Flood Insurance Program’s Community Rating System (https://
www.fema.gov/national-flood-insurance-program-communityrating-system), and others as may be identified. Several
of these may provide information of value for identifying
community-level characteristics important to understanding
disaster impacts. Such characteristics include economic
vitality; industrialization; vulnerability of critical infrastructure;
community trauma recovery history; community decisionmaking; poverty; chronic health disparities; effectiveness
of government and other formal and informal institutions;
community organizations and support structures; green and
“blue” (water-associated) spaces; and parks and outdoor
gathering places.
A key goal of the health observing system proposed is to
allow segregation of data down to the individual level and
aggregation up to the level of a household, neighborhood,
or community. Coupling the original data collection of
the system’s cohort studies with ongoing, broad crosssectional health surveys, and connecting them with secondary
socioeconomic, community, and exposure information are
expected to build an archive of longitudinal health histories
that could be used to address cumulative effects of multiple
traumatic events.
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We envision data management and biospecimen storage
infrastructure for the GoM CHOS that would be managed by
a third-party entity and similar to, and to the extent possible
built upon, the All of Us and OneFlorida examples (Figure 3).
In the All of Us program, a Raw Data Repository receives and
stores all raw data in perpetuity in a secure system and facilitates
safe transfer to a Curated Data Repository (CDR) and other
systems. Only a small number of qualified personnel are allowed
access to the raw data. The CDR provides organized data for
access by users, but with robust protection of individual privacy.
A Participant Technology System Center (PTSC) facilitates
participant interaction with data and ensures access is recorded.
All data are encrypted in the system and for transfers. Similarly,
the OneFlorida Consortium has robust methods for handling,
integrating, and analyzing the large amounts of data derived
from EHRs. The All of Us and OneFlorida programs also
have established secure biobanking protocols for long-term
storage and access of biological specimens. For the GoM CHOS,
access procedures for data users will be established at program
implementation and will follow established security and use
procedures. The ability to perform analyses at the individual level
is vital to the calculation of numerous health metrics including
AL. Analyses utilizing raw data collections will also allow the
assigning of secondary data sets to individual participant files
based on location.
The third-party data center will curate the primary and
secondary data sets, accept new data sets, and ensure that
all data are routinely backed up. ISO-standard metadata will
be developed for each dataset generated. One option for
making the metadata more discoverable is to submit them to
NOAA’s National Marine Fisheries Service InPort system (120).
Additional options to improve data discovery should be explored
at program implementation.
Participant Retention
Participant retention is a problem in long-term studies due to
death, loss of interest, change of economic or health status, social
pressures, moving to another location, and other factors (121,
122). We propose to implement retention efforts similar to those
used in previous successful studies (33, 66, 87, 123). As noted by
Sandler et al. (87): “A key to high response rates and long-term
participation is not to simply contact participants when data are
needed but rather to maintain contact in small ways and provide
useful information including study results back to participants on
a regular basis.” Here, we propose that participants will receive
information updates quarterly and a participant newsletter at
least annually along with a request for information updates
and intent to remain in the system. The periodic newsletter of
the Wisconsin Longitudinal Study (https://www.wisls.info/) is
an excellent example of a participant communication tool. A
participant web portal also will be provided so that participants
can easily access their information, update addresses, or other
status information and ask questions, with support provided
by a GoM CHOS staff participant liaison. Participants without
regular electronic access will receive reports and other materials
by mail. On occasion, participants will be polled to solicit
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FIGURE 3 | Data and specimen pathways for the Gulf of Mexico Community Health Observing System (biobank refers to long-term frozen storage of biological
samples for later analysis).
funding needed to support dedicated staff and operations.
The consortium or other management entity also should be
responsible for regular reviews and assessments, financial and
technical audits, and establishment of scientific and community
advisory bodies to help guide the program. Although focused
primarily on clinical work, the OneFlorida Consortium is
an example of a highly successful program that might serve
as a model for organizing the GoM CHOS or even as a
potential partner. The OneFlorida governance structure includes
representatives from all partner organizations, an IRB, and a
scientific advisory board. Although the objectives of the GoM
CHOS differ from those of the OneFlorida Consortium, there
may be important opportunities for learning, partnering, sharing
of information and experience, and adaptation of organizational
approaches that could benefit the CHOS substantially. A GoM
CHOS consortium could be organized through a partnership
among entities already working in the GoM, such as the National
Academies of Science, Engineering, and Medicine (NASEM)
Gulf Research Program, the five Gulf State Health Departments,
the NIEHS, and other Federal agencies. Private-sector and
non-governmental organizations such as large health-oriented
philanthropic foundations and major industries that have
substantial work forces in the region (e.g., the petrochemical,
tourism, and fisheries industries in the Gulf, others in different
regions) could also be included. Some other governance options
are explored briefly by Sandifer et al. (1).
opinions about their perceived value of the system and any
criticisms or suggestions for improvements. Participants will be
contacted ∼6 months in advance of planned sampling intervals
to confirm appointment times and other matters, and again
at 3 and 1 month, and the week beforehand. Mobile phones,
email addresses, emergency or family contact information, social
media, and community organizations will be used as available
and appropriate to attempt to contact and follow-up with any
participants with whom contact has been lost or who move out of
the study region. CHWs can also facilitate tracking of individuals,
and subgroups with low-propensity for response can receive extra
attention to enhance participation and retention (121, 124). As
needed, new participants will be added to replace those lost
via attrition.
In addition to the participant portal, the GoM CHOS should
incorporate a public-facing web portal that will provide summary
information on the program, semi-annual or annual updates
on findings, and mechanisms for access to data by qualified
individuals and organizations, which will specifically include
State, County, and local Health Departments across the Gulf.
Governance
Implementation of the GoM CHOS will require a single sponsor
or more likely a consortium of several active institutional
partners to oversee and implement the system and provide
or raise the substantial amount of start-up and continuing
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DISCUSSION
health data to be collected, population sample sizes, and other
operational considerations.
As we were nearing completion of this study, the COVID19 pandemic caused by the SARS-CoV-2 virus engulfed the
world, infecting millions, killing hundreds of thousands, and
catastrophically impacting the U.S. and global economies.
More than any of the other kinds of disasters contemplated
here, this pandemic has drawn attention to the urgent need
for comprehensive, accurate, and rapidly responsive health
surveillance at local, regional, national, and global scales (38,
125–127). Like other disasters, long-term health effects of the
pandemic are expected to include serious mental health and
stress-associated impacts (25–28). These will be exacerbated by
the observed higher levels of COVID-19-related serious illness
and mortality among the elderly and others with underlying
health problems, people living or working in any type of close
proximity or communal arrangements such as nursing homes,
military installations, or prisons (126) or in areas with high levels
of air pollution (128); health-care workers and others employed
in occupations where social distancing is not possible; and
people of color where impacts are likely amplified by long-term
health disparities (129–131). In addition, men appear to suffer
more serious illness and possibly higher mortality rates, in part
perhaps due to sex differences in angiotensin-converting enzyme
2 (ACE2) receptors, relatively higher contribution of preexisting
diseases (i.e., CVD, hypertension, diabetes, and chronic lung
disease), higher risk behaviors (i.e., smoking and alcohol use),
and occupational exposure (130, 132–137). More recently, there
has been a rapid rise of cases of Multisystem Inflammatory
Syndrome in Children associated with COVID-19 (138). Another
complication may be the magnified stress and other health
issues likely to be associated with managing stay-at-home, social
distancing, and business closure directives related to this or
future pandemics while simultaneously responding to mandatory
evacuations necessitated by other, concurrent disaster events
such as hurricanes, floods, and wildfires, as has recently occurred
in the U.S. and elsewhere.
The need for broad-scale, regular sampling for COVID-19
and other emerging diseases in the U.S. and globally is real.
The urgency is even more apparent when one considers the
likelihood of continued problems with COVID-19, perhaps for
18–24 months (139) or longer, and the probability of more
emerging pandemic diseases in the near future, with the U.S.
as a likely major transmission node (140). Another reason for
a robust surveillance system that can detect excess morbidity
and mortality is the likelihood of corresponding spikes in the
numbers of indirect illnesses and deaths attributable to disrupted
health systems, delayed primary and preventive medical care,
and forestalled treatment of complex medical conditions such
as cancer or renal disease. These indirect consequences will
find expression in excess mortality rates, similar to the excess
deaths reported in events such as Hurricanes Katrina and Maria
(141–143). The duration and breadth of social and economic
disruptions associated with the global pandemic may extend
the period during which such excess mortality and morbidity
is observed. As the COVID-19 pandemic highlights, it is in
the U.S.’s self-interest to protect not only its own residents but
The GoM CHOS is designed to collect, curate, and disseminate
high-quality health-related data and biospecimens from
thousands of GoM residents, with special attention to the
most vulnerable and at risk. Data and information products
are intended for use to enhance understanding of health
effects of disasters, improve capacity to address immediate
and long-term disaster health impacts, aid in directing health
services to those most in need, and increase individual and
community resilience (1). Primary audiences are expected
to be public health personnel, emergency managers and
responders, clinical and academic researchers/practitioners,
and governmental agencies at all levels. Secondary users likely
will include community leaders, planners, and organizations;
natural resource managers; chambers of commerce, business
associations, and private businesses; charitable and other nongovernmental organizations; tribes and indigenous people; and
community members.
Any program of the scope proposed here has both risks
and benefits for participants. Based on experience elsewhere
(45, 87), overall, risks appear minimal, and limited primarily
to possible data breaches, uneasiness in providing certain kinds
of information, and fear, discomfort, or minor risks associated
with blood draws or other biospecimen collection. Programmatic
risks include lapses in program comprehensiveness, continuity,
or data security due to interruption in funding; management
failures; lack of clearly defined, standardized, and enforced
protocols; and poorly developed dispute resolution processes.
Benefits are expected to include a small financial or other
incentive, potential for identification of previously unrecognized
health problems, regular medical check-ups, personal genetic
information, being better prepared to deal with health impacts
of future disasters, detailed personal health information for
use in insurance or claims purposes, knowledge of personal
contribution to improve disaster public health responses in their
own region, and ultimately strengthened community resilience.
An additional benefit will be the development of preventive
public health strategies that alert the public in order to prevent
or minimize exposure to disaster-associated health hazards.
Development of a practical, large-scale health observing
system requires reliance on proven technologies to ensure
reliability and cost-effectiveness of data gathered. At the same
time, long-term success of a structured surveillance platform
necessitates incorporation of new methods and technologies
as their capabilities are verified. Such improvements are likely
to occur in the rapidly evolving fields of WHDs, diagnostic
tests, improved assessments of AL, genetic “fingeprinting” and
integrated personal omics profiling (http://snyderlab.stanford.
edu/iPOP.html) and digital health. Since health histories are
of primary interest in longitudinal cohort studies, more
personal, and less technology-based approaches such as nuanced,
individual visual health histories (e.g., the Pictal Health system,
https://www.pictalhealth.com/) also could be considered for
inclusion. We recommend that, once necessary commitments to
establish a GoM CHOS are made, an expert team be convened to
develop final study design and implementation plans, including
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impacts, and acute, sustained, and cumulative stress. Some
ongoing longitudinal studies are already being adapted for such
purposes (155). Data from longitudinal cohort studies could
be integrated with information from one or more national
cross-sectional studies that enrolled a new, randomly selected
participant wave each year. Combined with rapid testing,
tracking and modeling supplied via public, academic, and private
sector entities, such an approach could deliver short- and longterm data to public health officials, clinicians, researchers, and
the public to support robust interventions to future pandemics
and other environmental and economic catastrophes.
also the global population by taking all appropriate measures to
prevent and mitigate future pandemics.
While many responsibilities appropriately belong in the public
sector, much could be gained by developing and supporting
robust public–academic–private partnerships that harness the
powers, public health, and funding capacities of government; the
cutting-edge research and analytical capabilities of academia; and
the technological strengths and nimbleness of private businesses
to address future health threats including pandemics. Examples
abound of recent actions along these lines, such as Canada’s
and California’s use of a private company, Blue Dot (https://
bluedot.global/), to help track outbreaks, and enlistment of
the private sector for rapid production of personal protective
equipment and other biomedical necessities in the U.S. Israel
even applied its military and intelligence-gathering infrastructure
to enhance COVID-19 tracking (https://www.bbc.com/news/
world-middle-east-52579475), although that approach raised
concerns about personal privacy and potential for government
overreach. Overall, however, these types of responses have
appeared primarily as ad hoc, off-the-cuff reactions to the current
COVID-19 threat, without consideration of long-term plans,
needs, and consequences. This kind of approach is wholly
insufficient for the future. One immediate need is for the Federal
government to pre-certify a wide range of public (e.g., State
Health Departments), academic, and private sector biomedical
and clinical laboratories and require data sharing and interlaboratory validation to ensure that adequate, accurate, rapid, and
dependable sample analysis capacity is online prior to any future
pandemics. It is equally important for all involved to speak with
one voice, based on the available data.
Implementing regional and nationwide systems modeled on
the GoM CHOS could be a momentous step toward meeting this
need, especially if coupled to major academic (e.g., OneFlorida),
business (e.g., Blue Dot), and local surveillance capacities (144).
In particular, longitudinal cohort studies of the types described
here that encompass large, well-characterized, and representative
population samples could be expanded as needed to incorporate
testing for COVID-19 and other infectious organisms at regular
intervals. Such testing might focus on up-to-date technology
based on minimally invasive, often self-administered, sampling
[e.g., tests of saliva (145, 146), nasopharyngeal swabs (144), dried
blood spots (147–149), urine (150, 151), and perhaps smartphone
apps or other mobile monitoring health devices] to complement
existing disease surveillance efforts. Broad scale surveillance
for SARS-CoV-2 in wastewater is also possible, as already
demonstrated in multiple localities (152–154). The observing
system could adapt rapidly as new technologies for identifying
SARS-CoV-2 and other emerging infectious agents are validated
and become available and provide significant and timely
information for infection tracing efforts. Equally important,
longitudinal cohorts would allow tracking of pandemic health
effects over a relatively long time period, including incidence and
prevalence of asymptomatic disease; pediatric disease (https://
www.nih.gov/news-events/news-releases/study-determineincidence-novel-coronavirus-infection-us-children-begins);
possible recurrence or reinfection; and interaction with other
health factors such as underlying chronic disease, psychosocial
Frontiers in Public Health | www.frontiersin.org
DATA AVAILABILITY STATEMENT
This is a derivative work. As such no new data were generated
and data were not analyzed. Many previous studies were reviewed
and a new health observing (surveillance) system was designed
focused on effects disasters have on human health, with a
geographic focus on the Gulf of Mexico region. Literature review
and expert workshops informed the design of the new system.
Detailed results of the review and design are summarized in a
supporting Technical Report (1).
AUTHOR CONTRIBUTIONS
PS and BS were responsible for concept formulation, funding,
workshops, and overall content. PS had primary writing,
literature review, and project management responsibilities, with
yeoman support from LK and RM. LK contributed significantly
to writing, review, and workshop planning. ML, TC, BM, TS, and
RY were members of the project Steering Committee. DA, TC,
LE, YH, BM, GM, LP, AP, TS, ES, and DY were invited workshop
speakers. PS, BS, LK, ML, RM, DA, TC, KE, JF, MF, CH, EH,
LH, BM, GM, RO, LP, MP, DP, GS, HS-G, FY, and DY reviewed
and/or edited manuscript drafts. All authors except MP and FY
participated in the expert workshop and all authors contributed
ideas, read, and approved the submitted version.
FUNDING
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.
ACKNOWLEDGMENTS
The authors dedicate this paper to the memory of BM (January
17, 1938–January 2, 2020): Colleague, mentor, friend, role
model, and source of inspiration across the biological and
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Community Health Observing System
social sciences. We are indebted to the many experts who
contributed to this work through sharing of ideas, review of
draft materials, and other ways. Dr. Charles Wilson, Chief
Scientific Officer for the Gulf of Mexico Initiative (GoMRI),
and Dr. Rita Colwell, Chair of the GoMRI Research Board,
provided continuous support and encouragement. Mr. Michael
Feldman, Ms. Callan Yanoff, and others at the Consortium
for Ocean Leadership managed all logistical arrangements for
our expert workshops and assisted in other ways, both large
and small, over the life of the project. Drs. ML and EH of
Tulane University volunteered two of their MPH students, Ms.
Kaitlin Gibson and Ms. Tingting Li, to prepare an annotated
bibliography of publications on human health effects of the
DWH oil spill. We are especially grateful to Mr. Gabe Sataloff of
the Office of Coastal Management, Charleston, SC for preparing
the sampling area mapping data and to Ms. Catherine Polk for
the professional rendition of the figures. Ms. Kayli Paterson
assisted with reference citations. Dr. Anita Chandra, Rand
Corporation, provided input for community health metrics, and
Drs. Christopher Rea (NASEM Gulf Research Program) and
Jennifer Rusiecki (Uniformed Services University) participated
in the early stage of the project. Drs. Stephen Sempier and MP
of the Mississippi-Alabama Sea Grant Consortium arranged for
publication of a final project report and supporting information
as a Technical Report of the Mississippi-Alabama Sea
Grant Consortium.
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Conflict of Interest: AW was employed by the SEA Consulting Group and MF
was employed by the Rand Corporation, a non-profit research organization. All
authors declare that the research was conducted in the absence of any commercial
or financial relationships that could be construed as a potential conflict of interest.
Copyright © 2020 Sandifer, Knapp, Lichtveld, Manley, Abramson, Caffey, Cochran,
Collier, Ebi, Engel, Farrington, Finucane, Hale, Halpern, Harville, Hart, Hswen,
Kirkpatrick, McEwen, Morris, Orbach, Palinkas, Partyka, Porter, Prather, Rowles,
Scott, Seeman, Solo-Gabriele, Svendsen, Tincher, Trtanj, Walker, Yehuda, Yip,
Yoskowitz and Singer. This is an open-access article distributed under the terms
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is cited, in accordance with accepted academic practice. No use, distribution or
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