ABSTRACT
Understanding the impact of housing supply on housing price inflation is a particularly important issue from a policy-maker's perspective. Notwithstanding the impact of the great financial crisis (GFC) in 2007/08, the past 25 years has seen a significant increase in housing prices across a number of western economies. More recently, across countries, a common characteristic observed in housing markets appears to be the increase in price inflation in the aftermath of the Covid-19 pandemic. A key question which arises is whether housing price inflation can be assuaged somewhat by greater levels of housing supply? In this paper, we seek to quantify the impact of additional supply on price inflation in the Irish property market. While residential property markets in many countries experienced substantial swings in activity since the early 1990s, the Irish market has demonstrated particular volatility. Given such a high degree of volatility, it is plausible that the relationship between housing prices and its fundamental drivers could have changed over time. Crucially, therefore, we address this question using both a multiple breakpoint model and a Markov switching model to allow for the presence of structural changes in the Irish residential market over the period 1981–2019. Our results indicate a complex relationship between additional supply and house prices, with the impact varying over time. © 2022 Board of Trustees of the University of Illinois
ABSTRACT
Background: Most patients with cancer and COVID-19 will survive the acute illness. The longer-term impacts of COVID-19 on patients with cancer remain incompletely described. Methods: Using COVID-19 and Cancer Consortium registry data thru 12/31/2021, we examined outcomes of long-term COVID-19 survivors with post-acute sequelae of SARS-CoV-2 infection (PASC aka “long COVID”). PASC was defined as having recovered w/ complications or having died w/ ongoing infection 90+ days from original diagnosis;absence of PASC was defined as having fully recovered by 90 days, with 90+ days of follow-up. Patients with SARS-CoV-2 re-infection and records with low quality data were excluded. Results: 858 of 3710 of included patients (23%) met PASC criteria. Median follow-up (IQR) for PASC and recovered patients was 180 (98-217) and 180 (90-180) days, respectively. The PASC group had a higher rate of baseline comorbidities and poor performance status (Table). Cancer types, status, and recent anticancer treatment were similar between the groups. The PASC group experienced a higher illness burden, with more hospitalized (83% vs 48%);requiring ICU (29% vs 6%);requiring mechanical ventilation (17% vs 2%);and experiencing co-infections (19% vs 8%). There were more deaths in the PASC vs recovered group (8% vs 3%), with median (IQR) days to death of 158 (120-272) and 180 (130-228), respectively. Of these, 9% were attributed to COVID-19;15% to both COVID-19 and cancer;15% to cancer;and 23% to other causes. Conversely, no deaths in the recovered group were attributed to COVID-19;57% were attributed to cancer;and 24% to other causes (proximal cause of death unknown/missing in 38% and 19%, respectively). Cancer treatment modification was more common in the recovered group (23% vs 18%). Conclusions: Patients with underlying comorbidities, worse ECOG PS, and more severe acute SARS-CoV-2 infection had higher rates of PASC. These patients suffered more severe complications and incurred worse outcomes. There was an appreciable rate of death in both PASC and non-PASC, with cancer the dominant but not only cause in fully recovered patients. Further study is needed to understand what factors drive PASC, and whether longer-term cancer-specific outcomes will be affected.
ABSTRACT
The COVID-19 pandemic highlighted the need for timely information on the evolving economic impacts of such a crisis. During these periods, there is an increased need to understand the current state of the economy to guide the effective implementation of policy. This is made difficult by the fact that official estimates of economic indicators, such as those published by national statistical agencies, are released with a substantial lag. Using the case of Ireland, this article shows that the information contained in a panel of monthly economic indicators can be related to Quarterly National Accounts under the methodological framework of a dynamic factor model (DFM). The article also suggests that accounting for structural breaks improves the nowcasting performance of domestic demand. © 2022 Informa UK Limited, trading as Taylor & Francis Group.
ABSTRACT
Background: COVID-19 has been associated with immune modulation that may predispose infected patients to bacterial, viral, or fungal coinfections. Due to critical illness, > 70% of patients with severe COVID-19 receive empiric antibacterial or antifungal therapy, along with standard anti-COVID-19 treatments. However, the frequency of proven or probable secondary infections is < 10%. To our knowledge, there are no studies evaluating co-infections in patients with cancer and COVID-19, a vulnerable group with multiple risk factors for co-infections. We aim to describe the prevalence of bacterial, viral, and fungal co-infections, identify risk factors for coinfection, and investigate the potential impact of co-infections on mortality, in patients with a history of cancer and COVID-19. Methods: The CCC19 registry (NCT04354701) includes patients with active or prior hematologic or invasive solid malignancies reported across academic and community sites. We captured bacterial, fungal, or viral coinfections diagnosed within ±2 weeks from diagnosis of COVID-19, identified factors associated with an increased risk of having a coinfection, and evaluated the association of coinfections with 30-day all-cause mortality. Results: We examined 6732 patients with a history of cancer and a laboratory-confirmed diagnosis of SARS-CoV-2 reported to CCC19 by 82 sites between March 17, 2020 and February 3, 2021, with complete data on coinfection status. Median age was 65 (interquartile range: 55-75) years with 48% male, 52% non-Hispanic white, 19% non-Hispanic black, and 16% Hispanic. 5448 (81%) had solid tumors and 1466 (22%) had hematologic malignancies. Bacterial infections were reported in 823 patients (12%), including 296 Gram+ and 245 Gram- bacterial events. Documented viral (176 patients, 3%) and fungal (59 patients, 0.9%) co-infections were rare. The risk for co-infections increased with age, and they were more frequent among men, older patients, and those with diabetes, pulmonary or renal comorbid conditions, active progressive cancer, or hematologic malignancies (unadjusted P< 0.01). The frequency of reported co-infections decreased over the study period (divided into quartiles, Mantel-Haenszel P< 0.01). All-cause mortality rates were higher among those with bacterial (24% vs. 10%), viral (22% vs. 12%), and fungal (37% vs. 12%) coinfections compared to those without (unadjusted P< 0.01). Conclusions: The frequency of bacterial infections in patients with cancer and COVID-19 is relatively low. Viral and fungal co-infections are uncommon. Coinfections are associated with higher mortality rates. Several patient and tumor factors can be used for risk stratification and guide early empiric antimicrobial agent selection, which may improve clinical outcomes. These data could inform antimicrobial stewardship interventions in this tenuous patient population.
ABSTRACT
Background: In-hospital mortality among patients with cancer (pts) and COVID-19 infection is high. The frequency of, and factors associated with, donot- resuscitate (DNR) or do-not-intubate (DNI) orders at hospital admission (HA), and their correlation with care, has not been well studied. In November 2020, we began collecting this information for pts who were hospitalized at initial presentation in the CCC19 registry (NCT04354701). Methods: We investigated: 1. the frequency of, and factors associated with, DNR/DNI orders at HA;2. change in code status during HA;and 3. the correlation between DNR/DNI orders and palliative care consultation (PC), mortality or length of stay (LOS). We included hospitalized, adult pts with cancer and COVID-19 from 57 participating sites. Reported characteristics include age, ECOG performance status (PS), and cancer status. Comparative statistics include 2-sided Wilcoxon rank sum and Fisher's exact tests. Results: 744 pts had known baseline and/or changed code status (CS);most (79%) maintained their baseline CS (Table). Those with DNR±DNI orders at HA were older (median age 79 vs 69 yrs, p<0.001) and more likely to have: ECOG PS 2+ vs 0-1 (45% vs 22%, OR 3.95, p<0.001), metastatic disease (45% vs 35%, OR 1.72, p=0.005) and progressing cancer (32% vs 16%, OR 2.69, p<0.001), but equally likely to have received systemic anticancer therapy in the prior 3 months (38% vs 45%, p=0.15). N=192 pts with a change in CS from full to DNR±DNI were younger (median age 73), had better PS (37% ECOG PS 2+), and were less likely to have progressing cancer (23%) than those with DNR±DNI orders at baseline. However, their LOS was significantly longer, median 9 vs 6 days, p<0.001. Compared to those with DNR±DNI orders at HA, pts whose CS changed to DNR±DNI were more likely to die, OR 2.94, 95% CI 1.76-4.97, p<0.001. PC was obtained in 106 (14%) pts and associated with transition to DNR±DNI in 47 (44%), affirmation of admission CS in 58 (55%), and reversal in 1 (1%). Median LOS for pts receiving PC was 11 vs 6 days, p<0.001. Conclusions: In our sample, the majority of patients with cancer and COVID-19 were full code at hospital admission. DNR±DNI status, whether at baseline or assigned during the hospital course, was associated with worse prognosis. Longer length of stay for patients changing code status and/or receiving palliative care consultation was observed likely suggesting earlier palliative care consultation is an important, but likely underutilized component in the care of patients with cancer and COVID-19. (Table Presented).