ABSTRACT
Caribbean countries face many challenges to effectively implement and benefit from the blue economy. This study synthesized current available information from the literature about the main blue economy activities in the Bahamas, Barbados, Belize, Guyana, Jamaica, Trinidad and Tobago, and Suriname, prior to the COVID-19 pandemic, to highlight their value in the context of blue economic recovery. This timestamp of data provides a point of comparison to understand the vulnerability of blue economy sectors to external shocks. The top performing sectors prior to the pandemic were shipping and tourism, both of which were significant contributors to the GDP. The other sectors (e.g. fisheries, aquaculture, pharmaceuticals, etc.) in some countries were well established and in others, at a minimum, displayed potential for continued development. To valorize the blue economy in a post pandemic recovery, there are three core areas of opportunity: sustainable resource extraction and production;cultivated economic development;and improved ecosystem economic valuations. Harnessing these opportunities will require a transition from a traditional ocean economy towards a coordinated blue economy, including the adoption of effective governance and sustainability principles, improved social, economic, and environmental valuations, and sustainable financing, as well as a more regional coordinated approach to the management of resources.
ABSTRACT
INTRODUCTION The clinical course of Coronavirus 19 disease (COVID-19) remains unpredictable. In the setting of pandemic disease, investigations such as arterial blood gases are difficult to obtain on admission. This retrospective study evaluated the ability of the systemic inflammatory response syndrome (SIRS), quick sequential organ failure assessment (qSOFA), and national early warning signs (NEWS) scores to predict in-hospital mortality. These scoring systems require limited data and may offer utility with regard to predicting outcomes on admission. METHODS Demographic, clinical, and outcome data were analyzed in 520 patients admitted for COVID-19 between March 20, 2020 and May 5, 2020. SIRS, qSOFA, and NEWS scoring was applied to each patient based on their admission characteristics. Receiver Operator Curve (ROC) analyses were completed to assess the predictive accuracy for in-hospital mortality. The need for ICU level of care was evaluated as a secondary composite outcome. Univariate regression analyses were completed to assess the predictive function of each scoring threshold. RESULTS Of 520 patients enrolled, 57.5% were male. Of the cohort, 53.8% were Hispanic, 33.3% were African American, 5.8% were Asian, and 7.3% were Caucasian. Mean age was 61.6 years of age. A total of 182 patients (35%) died in hospital. 179 patients (34.4%) required ICU level of care. NEWS demonstrated the greatest discrimination for in-hospital mortality (AUC 0.716 [95% CI, 0.671-0.762]) and ICU admission (AUC 0.641 [95% CI, 0.591-0.691]). The predictive ability of SIRS criteria was more modest for in-hospital mortality (AUC 0.611 [95% CI, 0.560-0.662]) and ICU admission (AUC 0.571 [95% CI, 0.520-0.622]). qSOFA demonstrated the least discrimination for in-hospital mortality (AUC 0.534 [95% CI, 0.479-0.588]) and ICU admission (AUC 0.545 [95% CI, 0.491-0.599]). A mortality rate of 59.76% was observed in patients with a NEWS score ≥ 5 (OR 3.354 [95% CI, 2.213-5.083]). Likewise, a mortality rate of 56.48% was observed in patients with a SIRS score ≥ 2 (OR 1.77 [95% CI, 1.21-2.59]), while a mortality rate of 30.33% was observed in patients with a qSOFA score ≥ 2 (OR 2.124 [95% CI, 1.442-3.129]). CONCLUSION Our findings suggest NEWS offers the best prognostic accuracy in predicting in-hospital mortality, as compared to qSOFA and SIRS scoring systems. (Table Presented).