Objectives To analyse the characteristics and predictors of death in hospitalized patients with coronavirus disease 2019 (COVID-19) in Spain. Methods A retrospective observational study was performed of the first consecutive patients hospitalized with COVID-19 confirmed by real-time PCR assay in 127 Spanish centres until 17 March 2020. The follow-up censoring date was 17 April 2020. We collected demographic, clinical, laboratory, treatment and complications data. The primary endpoint was all-cause mortality. Univariable and multivariable Cox regression analyses were performed to identify factors associated with death. Results Of the 4035 patients, male subjects accounted for 2433 (61.0%) of 3987, the median age was 70 years and 2539 (73.8%) of 3439 had one or more comorbidity. The most common symptoms were a history of fever, cough, malaise and dyspnoea. During hospitalization, 1255 (31.5%) of 3979 patients developed acute respiratory distress syndrome, 736 (18.5%) of 3988 were admitted to intensive care units and 619 (15.5%) of 3992 underwent mechanical ventilation. Virus- or host-targeted medications included lopinavir/ritonavir (2820/4005, 70.4%), hydroxychloroquine (2618/3995, 65.5%), interferon beta (1153/3950, 29.2%), corticosteroids (1109/3965, 28.0%) and tocilizumab (373/3951, 9.4%). Overall, 1131 (28%) of 4035 patients died. Mortality increased with age (85.6% occurring in older than 65 years). Seventeen factors were independently associated with an increased hazard of death, the strongest among them including advanced age, liver cirrhosis, low age-adjusted oxygen saturation, higher concentrations of C-reactive protein and lower estimated glomerular filtration rate. Conclusions Our findings provide comprehensive information about characteristics and complications of severe COVID-19, and may help clinicians identify patients at a higher risk of death.

New York State initiated a micro-cluster strategy ("hot spotting") that divides into three categories based on COVID-19 cases and hospital capacity, each with successively more restrictions: Yellow, Orange, and Red Zones. Our objectives were to evaluate the influence of hot spotting on mobility and subsequent mortality, and then to identify underlying social determinants of health associated with the neighborhoods most affected by hot spotting.Study Design: We combine several data sources in our analysis. Time-dependent data were obtained from SafeGraph for cellphone mobility at the Census Block Group, New York State Governor's Office for hot spotting, school and indoor dining, and NYC Department of Health and Mental Hygiene (DOHMH) for COVID-19 cases and mortality. Using the DOHMH's "Modified Zip Code Tabulation Areas" (MODZCTA), we matched these to community-level data obtained from 2018 American Community Survey 5-year estimates for population density. Our main outcomes are Average Median Percentage Time Home (AMPTH) and Device-Weighted Average Median Percentage Time Home (DWAMPTH) from SafeGraph Social Distancing Metrics summarized to MODZCTA boundaries. Home is defined as the common nighttime location of each mobile device over a 6-week period to a Geohash-7 granularity (w153m x w153m). We implemented the Wilcoxon rank-sum test with a <0.05 p-value threshold for each day since hot spotting policy to compare MODZCTA with any of the Zone's designation to those without designation. Our main outcomes are Average Median Percentage Time Home (AMPTH) and Device-Weighted Average Median Percentage Time Home (DWAMPTH) from SafeGraph Social Distancing Metrics summarized to MODZCTA boundaries.Population Studied: NYC residents from October 5, 2020, to December 31, 2020 (87 days total) using the 177 MODZCTA within NYC as geographic unit of analysis.Results: For the AMPTH measurement, MODZCTAs with hot spotting Zone's designation had 84 days (95% of the days) with statistically significantly lower mobility than non-intervention MODZCTAs, and for the DWAMPTH measurement, 83 days (97% of the days) had statistically significantly lower mobility. 58 of the days had p-value<0.001 for AMPTH and 49 had p-value<0.001 for DWAMPTH, and only a minority of days had p-value>0.1 (2 days for AMPTH and 3 for DWAMPTH). Looking at individual boroughs, Brooklyn had 42 statistically significant days for AMPTH and 49 for DWAMPTH, while Queens had 12 statistically significant days for AMPTH and 7 for DWAMPTH.Conclusions: New York State's micro-cluster focus Zones is associated with decreased mobility in high COVID-19 prevalence areas. Our study suggests that shutdowns targeted at small geographic areas may reduce mobility and thus can potentially help control COVID-19 spread.