Objectives: Acute stroke has a high morbidity and mortality in elderly population. Baseline confounding illnesses, initial clinical examination, and basic laboratory tests may impact prognostics. In this study, we aimed to establish a model for predicting in-hospital mortality based on clinical data available within 12 hours of hospital admission in elderly (≥ 65 age) patients who experienced stroke. Design: Retrospective observational cohort study. Setting: Academic comprehensive stroke center. Patients: Elderly acute stroke patients—2005–2009 (n = 462), 2010–2012 (n = 122), and 2016–2017 (n = 123). Interventions: None. Measurements and Main Results: After institutional review board approval, we retrospectively queried elderly stroke patients’ data from 2005 to 2009 (training dataset) to build a model to predict mortality. We designed a multivariable logistic regression model as a function of baseline severity of illness and laboratory tests, developed a nomogram, and applied it to patients from 2010 to 2012. Due to updated guidelines in 2013, we revalidated our model (2016–2017). The final model included stroke type (intracerebral hemorrhage vs ischemic stroke: odds ratio [95% CI] of 0.92 [0.50–1.68] and subarachnoid hemorrhage vs ischemic stroke: 1.0 [0.40–2.49]), year (1.01 [0.66–1.53]), age (1.78 [1.20–2.65] per 10 yr), smoking (8.0 [2.4–26.7]), mean arterial pressure less than 60 mm Hg (3.08 [1.67–5.67]), Glasgow Coma Scale (0.73 [0.66–0.80] per 1 point increment), WBC less than 11 K (0.31 [0.16–0.60]), creatinine (1.76 [1.17–2.64] for 2 vs 1), congestive heart failure (2.49 [1.06–5.82]), and warfarin (2.29 [1.17–4.47]). In summary, age, smoking, congestive heart failure, warfarin use, Glasgow Coma Scale, mean arterial pressure less than 60 mm Hg, admission WBC, and creatinine levels were independently associated with mortality in our training cohort. The model had internal area under the curve of 0.83 (0.79–0.89) after adjustment for over-fitting, indicating excellent discrimination. When applied to the test data from 2010 to 2012, the nomogram accurately predicted mortality with area under the curve of 0.79 (0.71–0.87) and scaled Brier’s score of 0.17. Revalidation of the same model in the recent dataset from 2016 to 2017 confirmed accurate prediction with area under the curve of 0.83 (0.75–0.91) and scaled Brier’s score of 0.27. Conclusions: Baseline medical problems, clinical severity, and basic laboratory tests available within the first 12 hours of admission provided strong independent predictors of in-hospital mortality in elderly acute stroke patients. Our nomogram may guide interventions to improve acute care of stroke.

Urban scaling analysis, the study of how aggregated urban features vary with the population of an urban area, provides a promising framework for discovering commonalities across cities and uncovering dynamics shared by cities across time and space. Here, we use the urban scaling framework to study an important, but under-explored feature in this community—income inequality. We propose a new method to study the scaling of income distributions by analysing total income scaling in population percentiles. We show that income in the least wealthy decile (10%) scales close to linearly with city population, while income in the most wealthy decile scale with a significantly superlinear exponent. In contrast to the superlinear scaling of total income with city population, this decile scaling illustrates that the benefits of larger cities are increasingly unequally distributed. For the poorest income deciles, cities have no positive effect over the null expectation of a linear increase. We repeat our analysis after adjusting income by housing cost, and find similar results. We then further analyse the shapes of income distributions. First, we find that mean, variance, skewness and kurtosis of income distributions all increase with city size. Second, the Kullback–Leibler divergence between a city’s income distribution and that of the largest city decreases with city population, suggesting the overall shape of income distribution shifts with city population. As most urban scaling theories consider densifying interactions within cities as the fundamental process leading to the superlinear increase of many features, our results suggest this effect is only seen in the upper deciles of the cities. Our finding encourages future work to consider heterogeneous models of interactions to form a more coherent understanding of urban scaling.

During the COVID-19 pandemic, physical distancing, mobility restrictions and self-isolation measures were implemented around the world as the primary intervention to prevent the virus from spreading. Urban life has undergone sweeping changes, with people using spaces in new ways. Stockholm is a particularly relevant case of this phenomenon since most facilities, such as day care centres and schools, have remained open, in contrast to cities with a broader lockdown. In this study, we use Twitter data and an online map survey to study how COVID-19 restrictions have impacted the use of different locations, services and amenities in Stockholm. First, we compare the spatial distribution of 87,000 geolocated tweets pre-COVID-19 and during the COVID-19 pandemic. Second, we analyse 895 survey responses asking people to identify places they ‘still visit’, ‘use more’, ‘avoid’ and self-report reasons for using locations. The survey provides a nuanced understanding of whether and how restrictions have affected people. Service and seclusion were found to be important; therefore, the accessibility of such amenities was analysed, demonstrating how changes in urban habits are related to conditions of the local environment. We find how different parts of the city show different capacities to accommodate new habits and mitigate the effects of restrictions on people’s use of urban spaces. In addition to the immediate relevance to COVID-19, this paper thus contributes to understanding how restrictions on movement and gathering, in any situation, expose more profound urban challenges related to segregation and social inequality.

We present a novel metric for measuring relative connection between parts of a city using geotagged Twitter data as a proxy for co-occurrence of city residents. We find that socioeconomic similarity is a significant predictor of this connectivity metric, which we call “linkage strength”: neighborhoods that are similar to one another in terms of residents’ median income, education level, and (to a lesser extent) immigration history are more strongly connected in terms of the of people who spend time there, indicating some level of homophily in the way that individuals choose to move throughout a city’s districts.