Background The clinical presentation of COVID-19 in patients admitted to hospital is heterogeneous. We aimed to determine whether clinical phenotypes of patients with COVID-19 can be derived from clinical data, to assess the reproducibility of these phenotypes and correlation with prognosis, and to derive and validate a simplified probabilistic model for phenotype assignment. Phenotype identification was not primarily intended as a predictive tool for mortality. MethodsIn this study, we used data from two cohorts: the COVID-19@Spain cohort, a retrospective cohort including 4035 consecutive adult patients admitted to 127 hospitals in Spain with COVID-19 between Feb 2 and March 17, 2020, and the COVID-19@HULP cohort, including 2226 consecutive adult patients admitted to a teaching hospital in Madrid between Feb 25 and April 19, 2020. The COVID-19@Spain cohort was divided into a derivation cohort, comprising 2667 randomly selected patients, and an internal validation cohort, comprising the remaining 1368 patients. The COVID-19@HULP cohort was used as an external validation cohort. A probabilistic model for phenotype assignment was derived in the derivation cohort using multinomial logistic regression and validated in the internal validation cohort. The model was also applied to the external validation cohort. 30-day mortality and other prognostic variables were assessed in the derived phenotypes and in the phenotypes assigned by the probabilistic model. Findings Three distinct phenotypes were derived in the derivation cohort (n=2667)-phenotype A (516 [19%] patients), phenotype B (1955 [73%]) and phenotype C (196 [7%])-and reproduced in the internal validation cohort (n=1368)phenotype A (233 [17%] patients), phenotype B (1019 [74%]), and phenotype C (116 [8%]). Patients with phenotype A were younger, were less frequently male, had mild viral symptoms, and had normal inflammatory parameters. Patients with phenotype B included more patients with obesity, lymphocytopenia, and moderately elevated inflammatory parameters. Patients with phenotype C included older patients with more comorbidities and even higher inflammatory parameters than phenotype B. We developed a simplified probabilistic model (validated in the internal validation cohort) for phenotype assignment, including 16 variables. In the derivation cohort, 30-day mortality rates were 2•5% (95% CI 1•4-4•3) for patients with phenotype A, 30•5% (28•5-32•6) for patients with phenotype B, and 60•7% (53•7-67•2) for patients with phenotype C (log-rank test p<0•0001). The predicted phenotypes in the internal validation cohort and external validation cohort showed similar mortality rates to the assigned phenotypes (internal validation cohort: 5•3% [95% CI 3•4-8•1] for phenotype A, 31•3% [28•5-34•2] for phenotype B, and 59•5% [48•8-69•3] for phenotype C; external validation cohort: 3•7% [2•0-6•4] for phenotype A, 23•7% [21•8-25•7] for phenotype B, and 51•4% [41•9-60•7] for phenotype C).Interpretation Patients admitted to hospital with COVID-19 can be classified into three...

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Economic contributions of the green industry in the United States were estimated for 2013 using information on industry output, value added, employment and domestic/international exports, retail sector lawn and garden product sales, and economic multipliers from Impact Analysis for Planning (IMPLAN) regional economic models for each state. Direct industry output for all sectors was estimated at $136.44 billion (B), and total output contributions, including indirect and induced regional economic multiplier effects of export sales, were $196.07 B. The total value-added contribution to gross domestic product (GDP) was $120.71 B, including labor income contributions of $82.47 B, other property income contributions of $28.91 B and business taxes paid to local, state, and federal governments of $9.30 B. The industry had direct employment of 1,599,662 full-time and part-time jobs, and total employment contributions of 2,035,636 jobs in the broader economy. The largest individual industry sectors in terms of employment and GDP contributions were landscaping and horticultural services (1,105,526 jobs, $54.70 B); greenhouse, nursery, and floriculture production (240,809 jobs, $20.36 B); and lawn and garden equipment and supplies stores (217,798 jobs, $12.87 B). The top 10 states in terms of employment contributions were California (245,267 jobs), Florida (197,073), Texas (149,364), Ohio (77,664), Pennsylvania (77,569), Illinois (76,254), New York (73,676), North Carolina (72,014), Georgia (64,066), and Michigan (63,189). Green industry contributions represented 0.72% of U.S. GDP and 1.11% of total workforce employment, and it contributed over 1.0% of GDP in five states, and over 1.25% of employment in 10 states. Employment contributions averaged 0.6 jobs/mile2 of land area and 6.4 jobs per 1000 persons in the U.S. population, while GDP contributions averaged $34,176/mile2 and $382 per capita. Since 2007–08, green industry contributions in 2013 increased by 4.4% for employment and 2.7% for GDP in inflation-adjusted terms. Growth in the industry was highest for wholesale and retail trade, whereas production and manufacturing declined. Although the green industry has grown slowly in recent years, it remains an important contributor to national, state, and local economies.

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