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.

Informing healthcare operations with integrated pathology, clinical, and epidemiology data: Lessons from a single institution in Kenya during COVID-19 waves.

Pathology, clinical care teams, and public health experts often operate in silos. We hypothesized that large data sets from laboratories when integrated with other healthcare data can provide evidence that can be used to optimize planning for healthcare needs, often driven by health-seeking or delivery behavior. From the hospital information system, we extracted raw data from tests performed from 2019 to 2021, prescription drug usage, and admission patterns from pharmacy and nursing departments during the COVID-19 pandemic in Kenya (March 2020 to December 2021). Proportions and rates were calculated. Regression models were created, and a t-test for differences between means was applied for monthly or yearly clustered data compared to pre-COVID-19 data. Tests for malaria parasite, Mycobacterium tuberculosis, rifampicin resistance, blood group, blood count, and histology showed a statistically significant decrease in 2020, followed by a partial recovery in 2021. This pattern was attributed to restrictions implemented to control the spread of COVID-19. On the contrary, D-dimer, fibrinogen, CRP, and HbA1c showed a statistically significant increase (p-value <0.001). This pattern was attributed to increased utilization related to the clinical management of COVID-19. Prescription drug utilization revealed a non-linear relationship to the COVID-19 positivity rate. The results from this study reveal the expected scenario in the event of similar outbreaks. They also reveal the need for increased efforts at diabetes and cancer screening, follow-up of HIV, and tuberculosis patients. To realize a broader healthcare impact, pathology departments in Africa should invest in integrated data analytics, for non-communicable diseases as well.

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.

Issues on Japanese COVID-19 Exposure Notifications Application (COCOA)

This study investigates problems related to COCOA, which is a smartphone app officially provided by Japan's Ministry of Health, Labour and Welfare (MHLW) that is designed to notify users when they have been in close contact with coronavirus disease 2019 (COVID-19) positive persons, and thus help the government and healthcare organizations contain the spread of the virus. The information we have obtained thus far indicates that poor utilization rates of the app are due to significant program flaws, which caused the initial usage to be sluggish, as well as the failures of various health centers to adequately provide polymerase chain reaction (PCR) testing for COCOA notification recipients, which exacerbated sluggishness issues. Furthermore, a related survey revealed that although the government provides an integrated data system called the Health Center Real-time Information-sharing System on COVID-19 (Japanese abbreviation HER-SYS), information on fever outpatients (hospital names, locations, consultation times, presence or absence of PCR testing, etc.) corresponding to each local government is still not fully available. © 2022 IEEE.

COVID-19 Surveillance in the Biobank at the Colorado Center for Personalized Medicine: Observational Study.

BACKGROUND: Characterizing the experience and impact of the COVID-19 pandemic among various populations remains challenging due to the limitations inherent in common data sources, such as electronic health records (EHRs) or cross-sectional surveys. OBJECTIVE: This study aims to describe testing behaviors, symptoms, impact, vaccination status, and case ascertainment during the COVID-19 pandemic using integrated data sources. METHODS: In summer 2020 and 2021, we surveyed participants enrolled in the Biobank at the Colorado Center for Personalized Medicine (CCPM; N=180,599) about their experience with COVID-19. The prevalence of testing, symptoms, and impacts of COVID-19 on employment, family life, and physical and mental health were calculated overall and by demographic categories. Survey respondents who reported receiving a positive COVID-19 test result were considered a "confirmed case" of COVID-19. Using EHRs, we compared COVID-19 case ascertainment and characteristics in EHRs versus the survey. Positive cases were identified in EHRs using the International Statistical Classification of Diseases, 10th revision (ICD-10) diagnosis codes, health care encounter types, and encounter primary diagnoses. RESULTS: Of the 25,063 (13.9%) survey respondents, 10,661 (42.5%) had been tested for COVID-19, and of those, 1366 (12.8%) tested positive. Nearly half of those tested had symptoms or had been exposed to someone who was infected. Young adults (18-29 years) and Hispanics were more likely to have positive tests compared to older adults and persons of other racial/ethnic groups. Mental health (n=13,688, 54.6%) and family life (n=12,233, 48.8%) were most negatively affected by the pandemic and more so among younger groups and women; negative impacts on employment were more commonly reported among Black respondents. Of the 10,249 individuals who responded to vaccination questions from version 2 of the survey (summer 2021), 9770 (95.3%) had received the vaccine. After integration with EHR data up to the time of the survey completion, 1006 (4%) of the survey respondents had a discordant COVID-19 case status between EHRs and the survey. Using all longitudinal EHR and survey data, we identified 11,472 (6.4%) COVID-19-positive cases among Biobank participants. In comparison to COVID-19 cases identified through the survey, EHR-identified cases were younger and more likely to be Hispanic. CONCLUSIONS: We found that the COVID-19 pandemic has had far-reaching and varying effects among our Biobank participants. Integrated data assets, such as the Biobank at the CCPM, are key resources for population health monitoring in response to public health emergencies, such as the COVID-19 pandemic.

CoFIM: A Computational Framework for Proteomic and Metabolomic Integrated Data Analysis

Motivation: Coronavirus disease (COVID-19) struck the world in late 2019 and caused millions of deaths worldwide as an infectious disease caused by the SARS-CoV-2 virus. An effective and early diagnosis is truly pivotal, and thus, many studies were initiated for that. The existing studies have some limitations such as only focusing on one type of omics data. The study aims to develop a computational model which studies COVID-19 with the integration of metabolomics and proteomics data, therefore reaching the goal of detecting the virus early in the stage. Methods: The computational framework for integrating multi-omics data (CoFIM) consists of two parts. The first part is a statistical analysis of datasets. In this study, a series of statistical analyses including univariate and multivariate analyses were conducted to identify a number of potential biomarkers after pulling the data of severe patients and non-severe patients from a proteomic and metabolomics dataset of sera samples of COVID-19 patients. The second part is a machine learning model that was conducted to predict a patient's disease progression and provide more insightful information to understand the disease. Results: CoFIM integrates both proteomic and metabolomics data and provides a customizable and scalable framework to analyze the multi-omics data. CoFIM is demonstrated on the COVID-19 dataset and a number of biomarkers were detected. Several new protein biomarkers (IGKV1-12, PCOLCE, PGLYRP2, PCYOX1, LUM, IGHV1-46) were detected. We believe CoFIM will be widely used for multi-omics data analysis. © 2021 ACM.