Lessons learned: Development of COVID-19 clinical staging models at a large urban research institution.

Background/Objective: The University of Illinois at Chicago (UIC), along with many academic institutions worldwide, made significant efforts to address the many challenges presented during the COVID-19 pandemic by developing clinical staging and predictive models. Data from patients with a clinical encounter at UIC from July 1, 2019 to March 30, 2022 were abstracted from the electronic health record and stored in the UIC Center for Clinical and Translational Science Clinical Research Data Warehouse, prior to data analysis. While we saw some success, there were many failures along the way. For this paper, we wanted to discuss some of these obstacles and many of the lessons learned from the journey. Methods: Principle investigators, research staff, and other project team members were invited to complete an anonymous Qualtrics survey to reflect on the project. The survey included open-ended questions centering on participants' opinions about the project, including whether project goals were met, project successes, project failures, and areas that could have been improved. We then identified themes among the results. Results: Nine project team members (out of 30 members contacted) completed the survey. The responders were anonymous. The survey responses were grouped into four key themes: Collaboration, Infrastructure, Data Acquisition/Validation, and Model Building. Conclusion: Through our COVID-19 research efforts, the team learned about our strengths and deficiencies. We continue to work to improve our research and data translation capabilities.

Does work from home reshape the urban rental structure? Early evidence from a rental gradient analysis in Auckland

Purpose: This study aims to identify the pandemic's impact on house rents by applying a rental gradient analysis to compare the pre-and post-COVID-19 periods in Auckland. The micro-level household census data from the Integrated Data Infrastructure of Statistics New Zealand is also applied to scrutinise this WFH trend as a robustness check. Design/methodology/approach: Since the outbreak of COVID-19, work-from-home (WFH) and e-commerce have become much more common in many cities. Many news reports have contended that households are leaving city centres and moving into bigger and better houses in the suburbs or rural areas. This emerging trend has been redefining the traditional theory of residential location choices. Proximity to central business district (CBD) is no longer the most critical consideration in choosing one's residence. WFH and e-commerce flatten the traditional bid rent curve from the city centre. Findings: The authors examined micro-level housing rental listings in 242 suburbs of the Auckland Region from January 2013 to December 2021 (108 months) and found that the hedonic price gradient models suggest that there has been a trend of rental gradient flattening and that its extent was almost doubled in 2021. Rents are also found to be increasing more in lower-density suburbs. Research limitations/implications: The results imply that the pandemic has accelerated the trend of WFH and e-commerce. The authors further discuss whether the trend will be a transient phenomenon or a long-term shift. Practical implications: Suppose an organisation is concerned about productivity and performance issues due to a companywide ability to WFH. In that case, some standard key performance indicators for management and employees could be implemented. Forward-thinking cities need to focus on attracting skilful workers by making WFH a possible solution, not by insisting on the primacy of antiquated nine-to-five office cultures. Social implications: WFH has traditionally encountered resistance, but more and more companies are adopting WFH policies in this post-COVID era. The early rental gradient and the micro-level household data analysis all confirm that the WFH trend is emerging and will likely be a long-term shift. Instead of resisting the change, organisations should improve their remote work policies and capabilities for this WFH trend. Originality/value: So far, empirical studies of post-COVID urban restructuring have been limited. This study aims to empirically test such an urban metamorphosis by identifying the spatial and temporal impacts of COVID on house rental gradients in the Auckland Region, New Zealand. The authors apply rental gradient analysis to test this urban restructuring hypothesis because the method considers the spatial-temporal differences, i.e. a difference-in-differences between pre-and post-pandemic period against the distance measured from the city centre. The method can control for the spatial difference and the endogeneity involved. © 2023, Emerald Publishing Limited.

Applied Regional Economic Research Can Improve Development Strategies and Drive Better Outcomes

The COVID-19 pandemic brought about an unimagined level of federal investment in regional economic development and much greater political attention to its priorities. Economic development researchers have an opportunity to contribute to an array of federally funded and pandemic-inspired regional experiments, many of which reflect shifting concerns about economic development and what constitutes success. Among these include the importance of addressing historical racial, ethnic, and gender inequalities;the value of research and development as a solution to major human problems;the severity of impending workforce shortages in key sectors;the fragility of many highly efficient global supply chains;and the inadequacy of our underinvested economic data infrastructure to help understand these issues. Researchers have a unique opportunity to examine the regional impacts of national issues by improving public investment logic models, advocating for an improved data infrastructure, and providing evidence to address the long-standing tension between growth and equity as competing economic development priorities.

Applied Regional Economic Research Can Improve Development Strategies and Drive Better Outcomes

The COVID-19 pandemic brought about an unimagined level of federal investment in regional economic development and much greater political attention to its priorities. Economic development researchers have an opportunity to contribute to an array of federally funded and pandemic-inspired regional experiments, many of which reflect shifting concerns about economic development and what constitutes success. Among these include the importance of addressing historical racial, ethnic, and gender inequalities;the value of research and development as a solution to major human problems;the severity of impending workforce shortages in key sectors;the fragility of many highly efficient global supply chains;and the inadequacy of our underinvested economic data infrastructure to help understand these issues. Researchers have a unique opportunity to examine the regional impacts of national issues by improving public investment logic models, advocating for an improved data infrastructure, and providing evidence to address the long-standing tension between growth and equity as competing economic development priorities.

Mortality after release from incarceration in New Zealand by gender: A national record linkage study.

Background: People who enter and leave places of incarceration experience considerable health inequities and are at increased risk of premature death compared to the general population. Causes of premature death in this population vary markedly between countries and so country-specific information is needed. Additionally, there is a lack of large population-based studies which can disaggregate mortality risk based on person and incarceration factors. This study is the first examination of mortality in the period following release from incarceration in New Zealand. Methods: We linked deidentified administrative data on incarceration and release between 1 January 1998 and 31 December 2016 with national mortality data for the same period to examine mortality after release in those who had been incarcerated for at least 1 day. Age standardised mortality rates and mortality ratios compared to the general New Zealand population were calculated separately for men and women, for releases from remand compared with prison, and by cause of death and time since release. Results: 90,195 individuals (13% women, 49% Maori) were followed up for 9.4 years after release from incarceration, with 4,764 deaths over the follow-up period. The overall standardised mortality ratio was 3.3 (95% CI 3.2, 3.4) compared to the general population, and higher for women (3.8) than men (2.7). The most common causes of death were cardiovascular disease, cancer and suicide. Rates of death were similar following release from remand versus prison, however suicide rates were highest following release from remand. Regardless of the type of incarceration, mortality was highest in the first month after release. Conclusion: Experience of incarceration in New Zealand is associated with high rates of mortality from both chronic conditions and external causes. There are urgent policy imperatives to recognise and actively address the increased health and mortality risks faced by people released from New Zealand prisons.

The role of the Dutch National Intensive Care Evaluation registry during the COVID-19 pandemic

In this paper we describe how the data infrastructure of an existing national quality registry was adapted to meet public health, research and capacity needs for information on COVID-19 hospitalisations in the Netherlands. Copyright © 2022, Netherlands Society of Intensive Care. All rights reserved.

Advancing genomic epidemiology by addressing the bioinformatics bottleneck: Challenges, design principles, and a Swiss example.

The SARS-CoV-2 pandemic led to a huge increase in global pathogen genome sequencing efforts, and the resulting data are becoming increasingly important to detect variants of concern, monitor outbreaks, and quantify transmission dynamics. However, this rapid up-scaling in data generation brought with it many IT infrastructure challenges. In this paper, we report about developing an improved system for genomic epidemiology. We (i) highlight key challenges that were exacerbated by the pandemic situation, (ii) provide data infrastructure design principles to address them, and (iii) give an implementation example developed by the Swiss SARS-CoV-2 Sequencing Consortium (S3C) in response to the COVID-19 pandemic. Finally, we discuss remaining challenges to data infrastructure for genomic epidemiology. Improving these infrastructures will help better detect, monitor, and respond to future public health threats.

Development and formative evaluation of the Canadian Armed Forces Surveillance and Outbreak Management System (CAF SOMS): applications for COVID-19 and beyond. / Élaboration et évaluation formative du Système de surveillance et de gestion des éclosions des Forces armées canadiennes (SSGE FAC) : applications durant la pandémie de COVID-19 et futures applications.

The COVID-19 pandemic highlighted limitations in the current public health data infrastructure, and the need for a comprehensive, real-time, centralized, user-friendly data management system suitable for both disease surveillance and outbreak management. To address these issues, the Canadian Forces Health Services Group developed the webbased Canadian Armed Forces Surveillance and Outbreak Management System (CAF SOMS). This paper details the development of the CAF SOMS, provides formative evaluation results and includes a discussion of the lessons learned and intent to use the CAF SOMS in future to enhance the CAF's disease surveillance and outbreak management capability beyond COVID-19.

The Canadian Armed Forces Surveillance and Outbreak Management System (CAF SOMS) was developed to address gaps in information management identified during the COVID-19 pandemic. Integrating a formative evaluation in the phased development and implementation helped to address issues with the system prior to its national roll-out. Lessons learned from its development, implementation and evaluation can inform further refinement and future applications of the CAF SOMS, and potentially of other public health information systems. Increased uptake of the system, integration with electronic records and alignment with the pan-Canadian Health Data Strategy may enhance responses to disease threats and improve CAF health outcomes.

Le Système de surveillance et de gestion des éclosions des Forces armées canadiennes (SSGE FAC) a été élaboré pour combler les lacunes en matière de gestion de l'information relevées pendant la pandémie de COVID-19. L'intégration d'une évaluation formative dans le processus d'élaboration et de mise en oeuvre par étapes a aidé à régler les problèmes liés au système avant son déploiement à l'échelle nationale. Les constats dégagés lors de l'élaboration, de la mise en oeuvre et de l'évaluation du système vont pouvoir contribuer à l'amélioration et aux applications futures du SSGE FAC et peut-être même d'autres systèmes d'information en santé publique. L'utilisation accrue du système, son intégration aux dossiers médicaux électroniques et son harmonisation avec la Stratégie pancanadienne de données sur la santé contribuerait vraisemblablement à lutter contre les menaces liées aux maladies et améliorer les résultats en matière de santé des FAC.

COVID-19 is spatial: Ensuring that mobile Big Data is used for social good.

The mobility restrictions related to COVID-19 pandemic have resulted in the biggest disruption to individual mobilities in modern times. The crisis is clearly spatial in nature, and examining the geographical aspect is important in understanding the broad implications of the pandemic. The avalanche of mobile Big Data makes it possible to study the spatial effects of the crisis with spatiotemporal detail at the national and global scales. However, the current crisis also highlights serious limitations in the readiness to take the advantage of mobile Big Data for social good, both within and beyond the interests of health sector. We propose two strategical pathways for the future use of mobile Big Data for societal impact assessment, addressing access to both raw mobile Big Data as well as aggregated data products. Both pathways require careful considerations of privacy issues, harmonized and transparent methodologies, and attention to the representativeness, reliability and continuity of data. The goal is to be better prepared to use mobile Big Data in future crises.