Framework for a Community Health Observing System for the Gulf of Mexico Region: Preparing for Future Disasters

Linking coastal environmental and health observations for human wellbeing

Coastal areas have long been attractive places to live, work, and recreate and remain so even in the face of growing threats from global environmental change. At any moment, a significant portion of the human population is exposed to both positive and negative health effects associated with coastal locations. Some locations may be "hotspots" of concern for human health due to ongoing climatic and other changes, accentuating the need for better understanding of coastal environment-human health linkages. This paper describes how environmental and health data could be combined to create a coastal environmental and human health observing system. While largely based on information from the US and Europe, the concept should be relevant to almost any coastal area. If implemented, a coastal health observing system would connect a variety of human health data and environmental observations for individuals and communities, and where possible cohorts. Health data would be derived from questionnaires and other personal sources, clinical examinations, electronic health records, wearable devices, and syndromic surveillance, plus information on vulnerability and health-relevant community characteristics, and social media observations. Environmental data sources would include weather and climate, beach and coastal conditions, sentinel species, occurrences of harmful organisms and substances, seafood safety advisories, and distribution, proximity, and characteristics of health-promoting green and blue spaces. Where available, information on supporting resources could be added. Establishment of a linked network of coastal health observatories could provide powerful tools for understanding the positive and negative health effects of coastal living, lead to better health protections and enhanced wellbeing, and provide significant benefits to coastal residents, including the historically disadvantaged, as well as the military, hospitals and emergency departments, academic medical, public health, and environmental health programs, and others. Early networks could provide best practices and lessons learned to assist later entries.

Fast Methods for Drug Approval: Research Perspectives for Pandemic Preparedness

Public heath emergencies such as the outbreak of novel infectious diseases represent a major challenge for drug regulatory bodies, practitioners, and scientific communities. In such critical situations drug regulators and public health practitioners base their decisions on evidence generated and synthesised by scientists. The urgency and novelty of the situation create high levels of uncertainty concerning the safety and effectiveness of drugs. One key tool to mitigate such emergencies is pandemic preparedness. There seems to be, however, a lack of scholarly work on methodology for assessments of new or existing drugs during a pandemic. Issues related to risk attitudes, evidence production and evidence synthesis for drug approval require closer attention. This manuscript, therefore, engages in a conceptual analysis of relevant issues of drug assessment during a pandemic. To this end, we rely in our analysis on recent discussions in the philosophy of science and the philosophy of medicine. Important unanswered foundational questions are identified and possible ways to answer them are considered. Similar problems often have similar solutions, hence studying similar situations can provide important clues. We consider drug assessments of orphan drugs and drug assessments during endemics as similar to drug assessment during a pandemic. Furthermore, other scientific fields which cannot carry out controlled experiments may guide the methodology to draw defeasible causal inferences from imperfect data. Future contributions on methodologies for addressing the issues raised here will indeed have great potential to improve pandemic preparedness.

Allostatic load in the context of disasters

Environmental disasters, pandemics, and other major traumatic events such as the Covid-19 pandemic or war contribute to psychosocial stress which manifests in a wide range of mental and physical consequences. The increasing frequency and severity of such events suggest that the adverse effects of toxic stress are likely to become more widespread and pervasive in the future. The allostatic load (AL) model has important elements that lend themselves well for identifying adverse health effects of disasters. Here we examine several articulations of AL from the standpoint of using AL to gauge short- and long-term health effects of disasters and to provide predictive capacity that would enable mitigation or prevention of some disaster-related health consequences. We developed a transdisciplinary framework combining indices of psychosocial AL and physiological AL to produce a robust estimate of overall AL in people affected by disasters and other traumatic events. In conclusion, we urge researchers to consider the potential of using AL as a component in a proposed disaster-oriented human health observing system.

Risks and Opportunities to Ensure Equity in the Application of Big Data Research in Public Health

The big data revolution presents an exciting frontier to expand public health research, broadening the scope of research and increasing the precision of answers. Despite these advances, scientists must be vigilant against also advancing potential harms toward marginalized communities. In this review, we provide examples in which big data applications have (unintentionally) perpetuated discriminatory practices, while also highlighting opportunities for big data applications to advance equity in public health. Here, big data is framed in the context of the five Vs (volume, velocity, veracity, variety, and value), and we propose a sixth V, virtuosity, which incorporates equity and justice frameworks. Analytic approaches to improving equity are presented using social computational big data, fairness in machine learning algorithms, medical claims data, and data augmentation as illustrations. Throughout, we emphasize the biasing influence of data absenteeism and positionality and conclude with recommendations for incorporating an equity lens into big data research.

The CTSA University of Texas Health Science Center (UTHSC) Northeast-Tyler and Rio Grande Valley Success Story: How Rural, Underserved Academic Communities Rapidly Built a Robust Engine for Collaborative COVID-19 Clinical Research

In 2018, The University of Texas Health Science Center-Tyler and University of Texas Rio Grande Valley were invited to develop clinical research units for an existing Clinical and Translational Science Award (CTSA) consortium with the objective to equip medically underserved, economically disadvantaged communities and subsequently to deploy COVID-19 clinical trials in response to a public health emergency.

A review of GIS methodologies to analyze the dynamics of COVID-19 in the second half of 2020

COVID-19 has infected over 163 million people and has resulted in over 3.9 million deaths. Regarding the tools and strategies to research the ongoing pandemic, spatial analysis has been increasingly utilized to study the impacts of COVID-19. This article provides a review of 221 scientific articles that used spatial science to study the pandemic published from June 2020 to December 2020. The main objectives are: to identify the tools and techniques used by the authors; to review the subjects addressed and their disciplines; and to classify the studies based on their applications. This contribution will facilitate comparisons with the body of work published during the first half of 2020, revealing the evolution of the COVID-19 phenomenon through the lens of spatial analysis. Our results show that there was an increase in the use of both spatial statistical tools (e.g., geographically weighted regression, Bayesian models, spatial regression) applied to socioeconomic variables and analysis at finer spatial and temporal scales. We found an increase in remote sensing approaches, which are now widely applied in studies around the world. Lockdowns and associated changes in human mobility have been extensively examined using spatiotemporal techniques. Another dominant topic studied has been the relationship between pollution and COVID-19 dynamics, which enhance the impact of human activities on the pandemic's evolution. This represents a shift from the first half of 2020, when the research focused on climatic and weather factors. Overall, we have seen a vast increase in spatial tools and techniques to study COVID-19 transmission and the associated risk factors.

The U.S. Needs a National Human Health Observing System

The COVID-19 pandemic and increasing frequency and severity of environmental disasters reveal an urgent need for a robust health observing/surveillance system. With the possible exception of Brazil, we know of no such comprehensive health observing capacity. The US should create a national system of linked regionally-based health monitoring systems similar to those for weather, ocean conditions, and climate. Like those for weather, the health observing system should operate continuously, collecting mental, physical, and community health data before, during, and after events. The system should include existing cross-sectional health data surveys, along with significant new investment in regional longitudinal cohort studies. The recently described framework for a Gulf of Mexico Community Health Observing System is suggested as a potential model for development of a nation-wide system.

Towards integrated modeling of the long-term impacts of oil spills

Although great progress has been made to advance the scientific understanding of oil spills, tools for integrated assessment modeling of the long-term impacts on ecosystems, socioeconomics and human health are lacking. The objective of this study was to develop a conceptual framework that could be used to answer stakeholder questions about oil spill impacts and to identify knowledge gaps and future integration priorities. The framework was initially separated into four knowledge domains (ocean environment, biological ecosystems, socioeconomics, and human health) whose interactions were explored by gathering stakeholder questions through public engagement, assimilating expert input about existing models, and consolidating information through a system dynamics approach. This synthesis resulted in a causal loop diagram from which the interconnectivity of the system could be visualized. Results of this analysis indicate that the system naturally separates into two tiers, ocean environment and biological ecosystems versus socioeconomics and human health. As a result, ocean environment and ecosystem models could be used to provide input to explore human health and socioeconomic variables in hypothetical scenarios. At decadal-plus time scales, the analysis emphasized that human domains influence the natural domains through changes in oil-spill related laws and regulations. Although data gaps were identified in all four model domains, the socioeconomics and human health domains are the least established. Considerable future work is needed to address research gaps and to create fully coupled quantitative integrative assessment models that can be used in strategic decision-making that will optimize recoveries from future large oil spills.

Is Living in a U.S. Coastal City Good for One's Health?

Background: Evidence suggests that living close to “blue spaces” (water features), particularly coastlines, has salutary effects on human health. Methods: We analyzed five years of annual, self-reported general health and unhealthy days data from the Behavioral Risk Factor Surveillance System of the U.S. Centers for Disease Control and Prevention for 165 urban areas across the contiguous U.S. We compared health self-reports for people living in coastal vs. non-coastal urban areas and for residents of the disaster-prone Gulf of Mexico region vs. other locations. Coastal urban areas were defined as those having ≥50% of their population living within 20 km of a coast. Results: We found no overall health advantage of residing in a coastal urban location when all urban areas were considered. However, residents from non-Gulf of Mexico coastal urban areas reported modestly better health than residents from non-coastal areas. In contrast, self-reported health of Gulf coastal urban residents was significantly poorer than that of residents from other urban areas. Conclusions: The frequency of disasters and history of health and socioeconomic disparities in the Gulf region may be responsible, at least in part, for the apparent lack of health promoting effects of coastal location there.

Assessing the effects of disasters and their aftermath on pregnancy and infant outcomes: A conceptual model

Although many studies have examined broad patterns of effects on pregnancy and infant outcomes after disasters, the causes of adverse outcomes are not always clear. Disasters cause interrelated exposure to environmental pollutants, psychological stressors, and lack of health care, and interacts with other social determinants of health. This topical review examines the short- and long-term effects of disasters on pregnancy and how they are mediated by social, behavioral, and environmental effects. In the short term, disasters are associated with physical trauma, adverse environmental exposures, and unstable housing. In the longer term, disasters may lead to relocation, changes in family functioning, and negative economic effects. These aspects of disaster exposure, in turn, lead to lack of access to health care, increased stress and negative mental health outcomes, and negative behavioral changes, including smoking and substance use, poor nutrition, physical overexertion and limited activity, and reduction in breastfeeding. All of these factors interact with social determinants of health to worsen effects on the most vulnerable women, infants, and communities. Few interventions after disasters have been tested. With the increase in disasters due to climate change and the ongoing coronavirus pandemic, the models of effects of disasters and their human health consequences need increasing refinement, and, more importantly, should be applied to interventions that improve disaster prevention, mitigation, and response.