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We analyzed the spread of the COVID-19 epidemic in 6 metropolitan regions with similar demographic characteristics, daytime commuting population and business activities: the New York metropolitan area, the Île-de-France region, the Greater London county, Bruxelles-Capital, the Community of Madrid and the Lombardy region. The highest mortality rates 30-days after the onset of the epidemic were recorded in New York (81.2 x 100,000) and Madrid (77.1 x 100,000). Lombardy mortality rate is below average (41.4 per 100,000), and it is the only situation in which the capital of the region (Milan) has not been heavily impacted by the epidemic wave. Our study analyzed the role played by containment measures and the positive contribution offered by the hospital care system. (www.actabiomedica.it).

Objective To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults. Design Population based cohort study. Setting and participants QResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020. Main outcome measures The primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period. Results 4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R 2 ); the D statistic was 3.37 (95% confidence interval 3.27 to 3.47), and Harrell’s C was 0.928 (0.919 to 0.938). Similar results were obtained for women, for both outcomes, and in both time periods. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths within 97 days was 75.7%. People in the top 20% of predicted risk of death accounted for 94% of all deaths from covid-19. Conclusion The QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves.

OBJECTIVE: To address the issue of limited national data on the prevalence and distribution of underlying conditions among COVID-19 deaths between sexes and across age groups.PATIENTS AND METHODS: All adult (≥18 years) deaths recorded in England and Wales (March 1, 2020, to May 12, 2020) were analyzed retrospectively. We compared the prevalence of underlying health conditions between COVID and non-COVID-related deaths during the COVID-19 pandemic and the age-standardized mortality rate (ASMR) of COVID-19 compared with other primary causes of death, stratified by sex and age group.RESULTS: Of 144,279 adult deaths recorded during the study period, 36,438 (25.3%) were confirmed COVID deaths. Women represented 43.2% (n=15,731) of COVID deaths compared with 51.9% (n=55,980) in non-COVID deaths. Overall, COVID deaths were younger than non-COVID deaths (82 vs 83 years). ASMR of COVID-19 was higher than all other common primary causes of death, across age groups and sexes, except for cancers in women between the ages of 30 and 79 years. A linear relationship was observed between ASMR and age among COVID-19 deaths, with persistently higher rates in men than women across all age groups. The most prevalent reported conditions were hypertension, dementia, chronic lung disease, and diabetes, and these were higher among COVID deaths. Pre-existing ischemic heart disease was similar in COVID (11.4%) and non-COVID (12%) deaths.CONCLUSION: In a nationwide analysis, COVID-19 infection was associated with higher age-standardized mortality than other primary causes of death, except cancer in women of select age groups. COVID-19 mortality was persistently higher in men and increased with advanced age.

The population-level case-fatality rate (CFR) associated with COVID-19 varies substantially, both across countries at any given time and within countries over time. We analyze the contribution of two key determinants of the variation in the observed CFR: the age-structure of diagnosed infection cases and age-specific case-fatality rates. We use data on diagnosed COVID-19 cases and death counts attributable to COVID-19 by age for China, Germany, Italy, South Korea, Spain, the United States, and New York City. We calculate the CFR for each population at the latest data point and also for Italy, Germany, Spain, and New York City over time. We use demographic decomposition to break the difference between CFRs into unique contributions arising from the age-structure of confirmed cases and the age-specific case-fatality. In late June 2020, CFRs varied from 2.2% in South Korea to 14.0% in Italy. The age-structure of detected cases often explains more than two-thirds of cross-country variation in the CFR. In Italy, the CFR increased from 4.2% to 14.0% between March 9 and June 30, 2020, and more than 90% of the change was due to increasing age-specific case-fatality rates. The importance of the age-structure of confirmed cases likely reflects several factors, including different testing regimes and differences in transmission trajectories; while increasing age-specific case-fatality rates in Italy could indicate other factors, such as the worsening health outcomes of those infected with COVID-19. Our findings lend support to recommendations for data to be disaggregated by age, and potentially other variables, to facilitate a better understanding of population-level differences in CFRs. They also show the need for well-designed seroprevalence studies to ascertain the extent to which differences in testing regimes drive differences in the age-structure of detected cases.

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Objective To determine predictors of in-hospital mortality related to COVID-19 in older patients. Design Retrospective cohort study. Setting and Participants Patients aged 65 years and older hospitalized for a diagnosis of COVID-19. Methods Data from hospital admission was collected from the electronic medical records. Logistic regression and Cox proportional-hazard models were used to predict mortality, our primary outcome. Variables at hospital admission were categorized according to the following domains: demographics, clinical history, comorbidities, previous treatment, clinical status, vital signs, clinical scales and scores, routine laboratory analysis and imaging results. Results Of a total of 235 Caucasian patients, 43% were male, with a mean age of 86 ± 6.5 years. Seventy-six patients (32%) died. Non-survivors had a shorter number of days from initial symptoms to hospitalization (p=0.007) and the length of stay in acute wards than survivors (p, Brief Summary: This study showed that male sex, functional decline, an increased fraction of inspired oxygen and the presence of crackles were the best predictors of mortality in the oldest old with COVID-19 and differences in age were not a significant prognostic factor within this very old group.