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OBJECTIVE: Severe weather events exacerbate existing health disparities due to poorly managed non-communicable diseases (NCDs). Our objective is to understand the experiences of staff, providers, and administrators (employees) of Federally Qualified Health Centers (FQHCs) in Puerto Rico and the US Virgin Islands (USVI) in providing care to patients living with NCDs in the setting of recent climate-related extreme events. METHODS: We used a convergent mixed-methods study design. A quantitative survey was distributed to employees at 2 FQHCs in Puerto Rico and the USVI, assessing experience with disasters, knowledge of disaster preparedness, the relevance of NCDs, and perceived gaps. Qualitative in-depth interviews explored their experience providing care for NCDs during recent disasters. Quantitative and qualitative data were merged using a narrative approach. RESULTS: Through the integration of quantitative and qualitative data, we recognize: (1) significant gaps in confidence and preparedness of employees with a need for more training; (2) challenges faced by persons with multiple NCDs, especially cardiovascular and mental health disorders; and (3) most clinicians do not discuss disaster preparedness with patients but recognize their important role in community resilience. CONCLUSION: With these results, we recommend strengthening the capacity of FQHCs to address the needs of their patients with NCDs in disasters.

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The U.S. Gulf of Mexico contains a complex network of existing, decommissioned, and abandoned oil and gas pipelines, which are susceptible to a number of stressors in the natural-engineered offshore system including corrosion, environmental hazards, and human error. The age of these structures, coupled with extreme weather events increasing in intensity and occurrence from climate change, have resulted in detrimental environmental and operational impacts such as hydrocarbon release events and pipeline damage. To support the evaluation of pipeline infrastructure integrity for reusability, remediation, and risk prevention, the U.S. Gulf of Mexico Pipeline and Reported Incident Datasets were developed and published. These datasets, in addition to supporting advanced analytics, were constructed to inform regulatory, industry, and research stakeholders. They encompass more than 490 attributes relating to structural information, incident reports, environmental loading statistics, seafloor factors, and potential geohazards, all of which have been spatially, and in some cases temporally matched to more than 89,000 oil and gas pipeline locations. Attributes were acquired or derived from publicly available, credible resources, and were processed using a combination of manual efforts and customized scripts, including big data processing using supercomputing resources. The resulting datasets comprise a spatial geodatabase, tabular files, and metadata. These datasets are publicly available through the Energy Data eXchange®, a curated online data and research library and laboratory developed by the U.S. Department of Energy's National Energy Technology Laboratory. This article describes the contents of the datasets, details the methods involved in processing and curation, and suggests application of the data to inform and mitigate risk associated with offshore pipeline infrastructure in the Gulf of Mexico.

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Driven by climate change, tropical cyclones (TCs) are predicted to change in intensity and frequency through time. Given these forecasted changes, developing an understanding of how TCs impact insular wildlife is of heightened importance. Previous work has shown that extreme weather events may shape species distributions more strongly than climatic averages; however, given the coarse spatial and temporal scales at which TC data are often reported, the influence of TCs on species distributions has yet to be explored. Using TC data from the National Hurricane Center, we developed spatially and temporally explicit species distribution models (SDMs) to examine the role of TCs in shaping present-day distributions of Puerto Rico's 10 Anolis lizard species. We created six predictor variables to represent the intensity and frequency of TCs. For each occurrence of a species, we calculated these variables for TCs that came within 500 km of the center of Puerto Rico and occurred within the 1-year window prior to when that occurrence was recorded. We also included predictor variables related to landcover, climate, topography, canopy cover and geology. We used random forests to assess model performance and variable importance in models with and without TC variables. We found that the inclusion of TC variables improved model performance for the majority of Puerto Rico's 10 anole species. The magnitude of the improvement varied by species, with generalist species that occur throughout the island experiencing the greatest improvements in model performance. Range-restricted species experienced small, almost negligible, improvements but also had more predictive models both with and without the inclusion of TC variables compared to generalist species. Our findings suggest that incorporating data on TCs into SDMs may be important for modeling insular species that are prone to experiencing these types of extreme weather events.

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The alarming increase in extreme weather events, such as severe storms with torrential rain and strong winds, is a direct result of climate change. These events have led to discernible shifts in forest structure and the carbon cycle, primarily driven by a surge in tree mortality. However, the impacts caused by these severe storms on the production and carbon increment from coarse woody debris (CWD) are still poorly understood, especially in the Brazilian Atlantic Forest. Thus, the goal proposed by the study was to quantify the CWD volume, necromass, and carbon stock before and after the occurrence of a severe storm and to determine the importance of spatial, structural, and qualitative variables of trees in the CWD carbon increment. The increase in carbon by the storm was 2.01 MgC ha-1, with a higher concentration in the CWD less decomposed and smaller diameter class. The forest fragment plots showed distinct increments (0.05-0.35 MgC), being influenced by spatial (elevation, declivity, and slope angle) structural (basal area) and qualitative factors (trunk quality and tree health), intrinsic to the forest. Thus, it is concluded that severe storms cause a large increase in carbon in CWD, making it essential to understand the susceptibility of forests to the action of intense rains and strong winds to model and monitor the future impacts of these extreme weather events on Atlantic Forest and other tropical forests in the world.

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OBJECTIVES: To explore the health impacts of Hurricane Maria (HM) on HIV care outcomes among people living with HIV who use drugs. METHODS: Using data from an ongoing cohort study in San Juan, Puerto Rico (Proyecto PACTo), we measured differences in HIV care outcomes (viral load, viral suppression, and CD4 counts) before and after HM using assessments conducted at 6-month intervals. Generalized estimating equations were used to assess factors associated with HIV care outcomes. RESULTS: All HIV care outcomes showed a deterioration from pre-HM values to post-HM values (mean viral load increased, CD4 counts decreased, and rate of viral suppression decreased) after controlling for pre-HM sociodemographic and health characteristics. In addition to HM, age (aIRR = 1·01), being homeless (aIRR = 0·78) and having health insurance (aIRR = 1·6) were independently associated with viral suppression. PARTICIPANTS: 219 participants completed follow-up visits between April 2017 and January 2018, before and after HM. CONCLUSIONS: People living with HIV who use drugs in Puerto Rico experienced poorer HIV outcomes following HM. Socio-environmental factors contributing to these outcomes is discussed in the context of disaster response, recovery, and program planning.

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Mangrove forests have been reported as sinks for metals because of the immobilization of these elements in their soils. However, climate change may alter the functioning of these ecosystems. We aimed to assess the geochemical dynamics of Mn, Cu, and Zn in the soils of a mangrove forest dead by an extreme weather event in southeastern Brazil. Soil samples were collected from dead and live mangroves adjacent to each other. The physicochemical parameters (total organic carbon, redox potential, and pH), total metal content, particle size, and metal partitioning were determined. Distinct changes in the soil geochemical environment (establishment of suboxic conditions) and a considerable loss of fine particles was caused by the death of the mangroves. Our results also showed a loss of up to 93 % of metals from soil. This study highlights the paradoxical role of mangroves as potential metal sources in the face of climate change.

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BACKGROUND: Hurricanes are the immediate ways that people experience climate impacts in the Caribbean. These events affect socio-ecological systems and lead to major disruptions in the healthcare system, having effects on health outcomes. In September 2017, Puerto Rico (PR) and the United States Virgin Islands (USVI) experienced one of the most catastrophic hurricane seasons in recent history (Hurricane Irma was a Category 5 and Hurricane María was a Category 4 when they hit PR). OBJECTIVE: This study examines environmental stressors experienced by women with gynecologic (GYN) cancers from PR and USVI who received oncologic cancer care in PR, in the aftermath of the hurricanes. METHODS: A descriptive qualitative study design was used to obtain rich information for understanding the context, barriers, knowledge, perspectives, risks, vulnerabilities, and attitudes associated to these hurricanes. We performed focus groups among GYN cancer patients (n = 24) and key-informant interviews (n = 21) among health-care providers and administrators. Interviews were conducted from December 2018-April 2019. RESULTS: Environmental health stressors such as lack of water, heat and uncomfortable temperatures, air pollution (air quality), noise pollution, mosquitos, and rats ranked in the top concerns among cancer patients and key-informants. CONCLUSIONS: These findings are relevant to cancer patients, decision-makers, and health providers facing extreme events and disasters in the Caribbean. Identifying environmental secondary stressors and the most relevant cascading effects is useful for decision-makers so that they may address and mitigate the effects of hurricanes on public health and cancer care.

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BACKGROUND: There remains a dearth of cross-city comparisons on the impact of climate change through extreme temperature and precipitation events on road safety. We examined trends in traffic fatalities, injuries and property damage associated with high temperatures and heavy rains in Boston (USA) and Santo Domingo (Dominican Republic). METHODS: Official publicly available data on daily traffic outcomes and weather conditions during the warm season (May to September) were used for Boston (2002-2015) and Santo Domingo (2013-2017). Daily maximum temperatures and mean precipitations for each city were considered for classifying hot days, warm days, and warm nights, and wet, very wet, and extremely wet days. Time-series analyses were used to assess the relationship between temperature and precipitation and daily traffic outcomes, using a quasi-Poisson regression. RESULTS: In Santo Domingo, the presence of a warm night increased traffic fatalities with a rate ratio (RR) of 1.31 (95% CI [confidence interval]: 1.00,1.71). In Boston, precipitation factors (particularly, extremely wet days) were associated with increments in traffic injuries (RR 1.25, 95% CI: 1.18, 1.32) and property damages (RR 1.42, 95% CI: 1.33, 1.51). CONCLUSION: During the warm season, mixed associations between weather conditions and traffic outcomes were found across Santo Domingo and Boston. In Boston, increases in heavy precipitation events were associated with higher traffic injuries and property damage. As climate change-related heavy precipitation events are projected to increase in the USA, the associations found in this study should be of interest for road safety planning in a rapidly changing environment.

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The relationship between rainfall, especially extreme rainfall, and increases in waterborne infectious diseases is widely reported in the literature. Most of this research, however, has not formally considered the impact of exposure measurement error contributed by the limited spatiotemporal fidelity of precipitation data. Here, we evaluate bias in effect estimates associated with exposure misclassification due to precipitation data fidelity, using extreme rainfall as an example. We accomplished this via a simulation study, followed by analysis of extreme rainfall and incident diarrheal disease in an epidemiologic study in Ecuador. We found that the limited fidelity typical of spatiotemporal rainfall data sets biases effect estimates towards the null. Use of spatial interpolations of rain-gauge data or satellite data biased estimated health effects due to extreme rainfall (occurrence) and wet conditions (accumulated totals) downwards by 35%-45%. Similar biases were evident in the Ecuadorian case study analysis, where spatial incompatibility between exposed populations and rain gauges resulted in the association between extreme rainfall and diarrheal disease incidence being approximately halved. These findings suggest that investigators should pay greater attention to limitations in using spatially heterogeneous environmental data sets to assign exposures in epidemiologic research.

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Extreme heat episodes are becoming more common worldwide, including in tropical areas of Australia, India, and Puerto Rico. Higher frequency, duration, and intensity of extreme heat episodes are triggering public health issues in most mid-latitude and continental cities. With urbanization, land use and land cover have affected local climate directly and indirectly encouraging the Urban Heat Island effect with potential impacts on heat-related morbidity and mortality among urban populations. However, this association is not completely understood in tropical islands such as Puerto Rico. The present study examines the effects of heat in two municipalities (San Juan and Bayamón) within the San Juan metropolitan area on overall and cause-specific mortality among the population between 2009 and 2013. The number of daily deaths attributed to selected causes (cardiovascular disease, hypertension, diabetes, stroke, chronic lower respiratory disease, pneumonia, and kidney disease) coded and classified according to the Tenth Revision of the International Classification of Diseases was analyzed. The relations between elevated air surface temperatures on cause-specific mortality were modeled. Separate Poisson regression models were fitted to explain the total number of deaths as a function of daily maximum and minimum temperatures, while adjusting for seasonal patterns. Results show a significant increase in the effect of high temperatures on mortality, during the summers of 2012 and 2013. Stroke (relative risk = 16.80, 95% CI 6.81-41.4) and cardiovascular diseases (relative risk = 16.63, 95% CI 10.47-26.42) were the primary causes of death most associated with elevated summer temperatures. Better understanding of how these heat events affect the health of the population will provide a useful tool for decision makers to address and mitigate the effects of the increasing temperatures on public health. The enhanced temperature forecast may be a crucial component in decision making during the National Weather Service Heat Watches, Advisories, and Warning process.

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We evaluated the influence of storm waves on the intertidal community structure of urbanized and non-urbanized areas of a sandy beach on the northern coast of Rio de Janeiro, Brazil. The macrofauna was sampled before (PREV) and after two storm wave events (POEV I; POEV II) in 2013 and 2014. Significant differences in community structure between PREV and POEV I in the urbanized sector demonstrate higher macrofauna vulnerability, and the community recovery within 41days on this scenario of less frequent events in 2013. On the other hand, significant differences in the macrofauna only in the urbanized sector between PREV and POEV II also highlight macrofauna vulnerability and community recovery failure within 42days on this scenario of more frequent storm in 2014. Urbanization and wave height were the variables that most influenced species, indicating that high storm wave events and increasing urbanization synergism are a threat to the macrofauna.

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El presente trabajo parte de una breve fundamentación teórica que soporta una estructura metodológica tendiente al cálculo de cambio climático, el estudio de la variabilidad climática en el territorio de las cuencas de los ríos Zulia y Pamplonita en Norte de Santander y la identificación inicial de medidas potenciales de adaptación al cambio climático y la variabilidad climática. En su desarrollo se identificaron y espacializaron las tendencias de cambio climático y las alteraciones más probables de los fenómenos de variabilidad climática asociados al ciclo ENSO6 para el período 1981-2010 en las cuencas objeto de estudio. Los resultados obtenidos muestran que la temperatura media ha cambiado entre 0,1 y 0,4ºC por década en las cuencas, la precipitación ha aumentado de 0 a 250 mm/década y los ciclos ENSO generan alteraciones en la variabilidad climática de las cuencas de manera diferenciada a nivel estacional e interanual, especialmente en los trimestres Dic-Ene-Feb, Jun-Jul-Ago y Sep-Oct-Nov.

This work is based on a brief theoretical foundation that supports a methodological structure aimed at the calculations of climate change, the study of climate variability in the territory of the basins of the Zulia and Pamplonita rivers in Norte de Santander and the initial identification of potential measures of adaptation to climate change and climate variability..In its development, the climate change trends and the most likely alterations of the climate variability phenomena associated with the ENSO cycle for the period 1981-2010 in the basins of the object of study were identified and spatialized. The results obtained show that the average temperature has changed between 0.1 and 0.4°C per decade in the basins, precipitation has increased from 0 to 250 mm/decade and the ENSO cycles generate alterations in the climate variability of the basins on a differentiated manner on a seasonal and year-on-year level, especially in the Dec-Jan-Feb, Jun-Jul-Aug and Sep-Oct-Nov quarters.

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