625 Cluster analysis of weather and pollution features and its role in predicting acute cardiac or cerebrovascular events

Aims The impact of the interplay between weather and pollution features on the risk of acute cardiac and cerebrovascular events has not been entirely appraised. The aim of this study was to perform a comprehensive cluster analysis of weather and pollution features in a large metropolitan area, and their association with acute cardiac and cerebrovascular events. Methods and results Anonymized data on acute myocardial infarction (AMI) and acute cerebrovascular events were obtained from three tertiary care centre from a single large metropolitan area. Weather and pollution data were obtained averaging measurements from several city measurement stations managed by the competent regional agency for environmental protection, and from the Meteorologic Center of Italian Military Aviation. Unsupervised machine learning was performed with hierarchical clustering to identify specific days with distinct weather and pollution features. Clusters were then compared for rate of acute cardiac and cerebrovascular events with Poisson models. As expected, significant pairwise correlations were found between weather and pollution features. Building upon these correlations, hierarchical clustering, from a total of 1169 days, generated four separate clusters: Cluster 1, including 60 (5.1%) days, Cluster 2 with 419 (35.8%) days, Cluster 3 with 673 (57.6%) days, and Cluster 4 with 17 (1.5%) days, with significant between-cluster differences in weather and pollution features. Notably, Cluster 1 was characterized by low temperatures and high ozone concentrations (P < 0.001). Overall cluster-wise comparisons showed significant overall differences in adverse cardiac and cerebrovascular events (P < 0.001), as well as in cerebrovascular events (P < 0.001) and strokes (P = 0.001). Between-cluster comparisons showed that Cluster 1 was associated with an increased risk of any event, cerebrovascular events, and strokes in comparison to Cluster 2, Cluster 3, and Cluster 4 (all P < 0.05), as well as AMI in comparison to Cluster 3 (P = 0.047). In addition, Cluster 2 was associated with a higher risk of strokes in comparison to Cluster 4 (P = 0.030). Analysis adjusting for season confirmed the increased risk of any event, cerebrovascular events, and strokes for Cluster 1 and Cluster 2. Conclusions Unsupervised machine learning can be leveraged to identify specific days with a unique clustering of adverse weather and pollution features which are associated with an increases risk of acute cardiovascular events, especially cerebrovascular events.

A systematic analysis of the spatial migration patterns and the linkages between market components in Greater Manchester, United Kingdom

Purpose This paper aims to bring together research on housing market area, submarket and household migration into a systems approach that helps us gain a better understanding of the structure and dynamics of a housing market and identify housing problems for a large metropolitan area. Design/methodology/approach The paper uses a geographic information system (GIS)-based method with simple quantitative techniques, including spatial analysis, location analysis, house price clustering and cross-tabulation. The analysis is based on migration data from the 2011 Census, house price data from the Land Registry in 2011 for Greater Manchester at the ward level and the output areas level. Findings The results show that different submarkets and housing market areas had different patterns of spatial migration and connections with other areas. Through a systematic analysis of migration and house price in combination, it also found a close connection between destination submarkets and the ages of migrants and identified specific problematic patterns for a large metropolitan area. Research limitations/implications The interactions between the owner-occupied sector and the social and private rented sectors are arguably an important omission from the analysis. Also, it is acknowledged that clustering ward units based on price differentials is subject to distortions in terms of specification, size and shape. Moreover, the use of the large samples may result in very small p-values, leading to the problem of the rejection of the predefined hypothesis. Practical implications A systematic analysis of migration and house price in combination may be used to gain a better understanding of the housing market dynamics and identify housing problems systematically for a large metropolitan. It may help to identify low-demand areas, high-demand areas and assist planners with decisions in allocating suitable land for new housing constructions. Social implications The GIS-based method introduced in the paper could be considered as an effective approach to provide a better basis for determining policy interventions and public investment designed to allocate land resources effectively and improve transport systems to change existing problematic migration patterns. Originality/value This paper fills a gap in the international literature in relation to adopting a systems approach that analyses migration and house price data sets in combination to systematically explore migration patterns and linkages and identify housing problems for a large metropolitan area. This systems approach can be applied in any metropolitan area where migration and house price data are available.

Burnout prevention pilot intervention for healthcare workers during COVID-19

Background: Burnout is often prevalent among healthcare workers (HCWs) given the stressful nature of their work. COVID-19 has intensified HCW burnout, and little is known about burnout prevention interventions that may help alleviate HCW burnout during COVID-19.Methods: This study adopted a pre-experimental post-test only design. The sample (n = 53) was adult HCWs at a large metropolitan-area hospital. The intervention consisted of a memorial service that included music by a music therapist, chaplain support, and mindfulness-promoting provisions.Results: Results showed that 33.9 percent of participants reported currently feeling burned out and 98.1 percent of participants found the intervention helpful. Feedback from participants showed that they thoroughly appreciated the opportunity to pause and remember.Conclusion: Given the promising results of this pilot study, coupled with increased burden of the COVID-19 pandemic, burnout interventions for HCWs should be further explored.