The COVID-19 pandemic has brought into sharp relief the complexities of managing a coordinated strategy to minimize human health impacts whilst at the same time minimizing disruption to economic and other social systems. Most responses to date have been applied uniformly, without consideration of the variance in risk or in case

A review of pertinent literature documenting and exploring ethnic inequalities in health.• Evidence using existing data in a novel way to illustrate that: a. Ethnic inequalities in health between some groups are widening over time and that these differences are related to socioeconomic and broad spatial inequalities rather than inherent features of different ethnic groups.b. Ethnic inequalities in health are not fully explained by sociodemographic or geographic factors for which we had data at our disposal. Existing discussions of social and spatial inequalities in health are not therefore sufficient to capture the complex and multiplicative influences on ethnic differences in health.

BackgroundThe use of embedded smartphone sensors offers opportunities to measure physical activity (PA) and human movement. Big data—which includes billions of digital traces—offers scientists a new lens to examine PA in fine-grained detail and allows us to track people’s geocoded movement patterns to determine their interaction with the environment.ObjectiveThe objective of this study was to examine the validity of the Movn smartphone app (Moving Analytics) for collecting PA and human movement data.MethodsThe criterion and convergent validity of the Movn smartphone app for estimating energy expenditure (EE) were assessed in both laboratory and free-living settings, compared with indirect calorimetry (criterion reference) and a stand-alone accelerometer that is commonly used in PA research (GT1m, ActiGraph Corp, convergent reference). A supporting cross-validation study assessed the consistency of activity data when collected across different smartphone devices. Global positioning system (GPS) and accelerometer data were integrated with geographical information software to demonstrate the feasibility of geospatial analysis of human movement.ResultsA total of 21 participants contributed to linear regression analysis to estimate EE from Movn activity counts (standard error of estimation [SEE]=1.94 kcal/min). The equation was cross-validated in an independent sample (N=42, SEE=1.10 kcal/min). During laboratory-based treadmill exercise, EE from Movn was comparable to calorimetry (bias=0.36 [−0.07 to 0.78] kcal/min, t82=1.66, P=.10) but overestimated as compared with the ActiGraph accelerometer (bias=0.93 [0.58-1.29] kcal/min, t89=5.27, P<.001). The absolute magnitude of criterion biases increased as a function of locomotive speed (F1,4=7.54, P<.001) but was relatively consistent for the convergent comparison (F1,4=1.26, P<.29). Furthermore, 95% limits of agreement were consistent for criterion and convergent biases, and EE from Movn was strongly correlated with both reference measures (criterion r=.91, convergent r=.92, both P<.001). Movn overestimated EE during free-living activities (bias=1.00 [0.98-1.02] kcal/min, t6123=101.49, P<.001), and biases were larger during high-intensity activities (F3,6120=1550.51, P<.001). In addition, 95% limits of agreement for convergent biases were heterogeneous across free-living activity intensity levels, but Movn and ActiGraph measures were strongly correlated (r=.87, P<.001). Integration of GPS and accelerometer data within a geographic information system (GIS) enabled creation of individual temporospatial maps.ConclusionsThe Movn smartphone app can provide valid passive measurement of EE and can enrich these data with contextualizing temporospatial information. Although enhanced understanding of geographic and temporal variation in human movement patterns could inform intervention development, it also presents challenges for data processing and analytics.

Aims This study described the interplay between geographical and social inequalities in survival after incident acute myocardial infarction (AMI) and examined whether geographical variation in survival exists when accounting for sociodemographic characteristics of the patients and their neighbourhood. Methods Ringmap visualization and generalized linear models were performed to study post-AMI mortality. Three individual-level analyses were conducted: immediate case fatality, mortality between days 1 and 28 after admission and 365-day survival among patients who survived 28 days after admission. Results In total, 99,013 incident AMI cases were registered between 2005 and 2014 in Denmark. Survival after AMI tended to correlate with sociodemographic indicators at the municipality level. In individual-level models, geographical inequality in immediate case fatality was observed with high mortality in northern parts of Jutland after accounting for sociodemographic characteristics. In contrast, no geographical variation in survival was observed among patients who survived 28 days. In all three analyses, odds and rates of mortality were higher among patients with low educational level (odds ratio (OR) (95% credible intervals) of 1.20 (1.12–1.29), OR of 1.12 (1.01–1.24) and mortality rate ratio of 1.45 (1.30–1.61)) and low income (OR of 1.24 (1.15–1.33), OR of 1.33 (1.20–1.48) and mortality rate ratio of 1.25 (1.13–1.38)). Conclusion Marked geographical inequality was observed in immediate case fatality. However, no geographically unequal distribution of survival was found among patients who survived 28 days after AMI. Results additionally showed social inequality in survival following AMI.

ACTRN12612000641897.