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INTRODUCTION: In most countries, COVID-19 mortality increases exponentially with age, but the growth rate varies considerably between countries. The different progression of mortality may reflect differences in population health, the quality of health care or coding practices. OBJECTIVE: In this study, we investigated differences in age-specific county characteristics of COVID-19 mortality in the second year of the pandemic. METHOD: Age-specific patterns of COVID-19 adult mortality were estimated according to county level and sex using a Gompertz function with multilevel models. RESULTS: The Gompertz function is suitable for describing age patterns of COVID-19 adult mortality at county level. We did not find significant differences in the age progression of mortality between counties, but there were significant spatial differences in the level of mortality. The mortality level showed a relationship with socioeconomic and health care indicators with the expected sign, but with different strengths. DISCUSSION AND CONCLUSION: The COVID-19 pandemic in 2021 resulted in a decline in life expectancy in Hungary not seen since World War II. The study highlights the importance of healthcare in addition to social vulnerability. It also points out that understanding age patterns will help to mitigate the consequences of the epidemic. Orv Hetil. 2023; 164(17): 643-650.
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COVID-19 , Pandemics , Adult , Humans , Life Expectancy , Age Factors , Hungary/epidemiology , MortalityABSTRACT
Small businesses have suffered disproportionately from the COVID-19 pandemic. We use near-real-time weekly data from the Small Business Pulse Survey (April 26, 2020 - June 17, 2021) to examine the constantly changing impact of COVID-19 on small businesses across the United States. A set of multilevel models for change are adopted to model the trajectories of the various kinds of impact as perceived by business owners (subjective) and those recorded for business operations (objective), providing insights into regional resilience from a small business perspective. The findings reveal spatially uneven and varied trajectories in both the subjectively and the objectively assessed impact of COVID-19 across the U.S., and the different responses to the pandemic shock can be explained by evolving health situations and public policies, as well as by the economic structure and degree of socioeconomic vulnerability in different areas. This study contributes to scholarship on small businesses and regional resilience, as well as identifying policies and practices that build economic resilience and regional development under conditions of global pandemic disruption. [ FROM AUTHOR] Copyright of International Regional Science Review is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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BACKGROUND: In Italy, inhaled corticosteroids (ICSs) are inappropriately prescribed to provide relief in URTI symptoms. Extreme variation in ICS prescribing has been described at regional and sub-regional level. During 2020, extraordinary containment measures were implemented in attempt to halt Coronavirus, such as social distancing, lockdown, and the use of mask. Our objectives were to evaluate the indirect impact of the SARS-CoV-2 pandemic on prescribing patterns of ICSs in preschool children and to estimate the prescribing variability among pediatricians before and during the pandemic. METHODS: In this real-world study, we enrolled all children residing in the Lazio region (Italy), aged 5 years or less during the period 2017-2020. The main outcome measures were the annual ICS prescription prevalence, and the variability in ICS prescribing, for each study year. Variability was expressed as Median Odds Ratios (MORs). If the MOR is 1.00, there is no variation between clusters (e.g., pediatricians). If there is considerable between-cluster variation, the MOR will be large. RESULTS: The study population consisted of 210,996 children, cared by 738 pediatricians located in the 46 local health districts (LHDs). Before the pandemic, the percentage of children exposed to ICS was almost stable, ranging from 27.3 to 29.1%. During the SARS-CoV-2 pandemic, the ICS prescription prevalence dropped to 17.0% (p < 0.001). In each study year, a relevant (p < 0.001) variability was detected among both LHDs and pediatricians working in the same LHD. However, the variability among individual pediatricians was always higher. In 2020, the MOR among pediatricians was 1.77 (95% CI: 1.71-1.83) whereas the MOR among LHDs was 1.29 (1.21-1.40). Furthermore, MORs remained stable over time, and no differences were detected in ICS prescription variability before and after pandemic outbreak. CONCLUSIONS: If on one hand the SARS-CoV-2 pandemic indirectly caused the reduction in ICS prescriptions, on the other the variability in ICS prescribing habits among both LHDs and pediatricians remained stable over the whole study time span (2017-2020), showing no differences between pre- pandemic and pandemic periods. The intra-regional drug prescribing variability underlines the lack of shared guidelines for appropriate ICS therapy in preschool children, and raises equity issues in access to optimal care.
Subject(s)
COVID-19 , SARS-CoV-2 , Humans , Child, Preschool , Pandemics , COVID-19/epidemiology , Communicable Disease Control , Adrenal Cortex Hormones/therapeutic use , Administration, InhalationABSTRACT
Existing studies show that the COVID-19 pandemic influences employee outcome in the work domain. However, the impact of business threat due to COVID-19 on employee daily insomnia remains unexplored. Addressing this research gap can help scholars understand the risks of COVID-19 in the non-work domain. Drawing on appraisal theories of emotion, we explore how and when business threat due to COVID-19 triggers employee insomnia. Using an experience sampling methodology where 89 employees are surveyed for 7 workdays, our multilevel analyses show that employee daily hope and workplace anxiety mediate the effects of business threat due to COVID-19 on employee insomnia. Furthermore, socially responsible human resource management (SRHRM) practices weaken the negative relationship between business threat due to COVID-19 and employees' hope and mitigate the positive relationship between business threat due to COVID-19 and their workplace anxiety. We also find that SRHRM practices influence the indirect effect of business threat due to COVID-19 on employee insomnia via workplace anxiety. Our study helps understand the underlying mechanisms in the relationship between business threat due to COVID-19 and employee insomnia and further sheds light on the role of SRHRM in mitigating the destructive effect of business threat due to COVID-19.
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Determining success factors for managing supply chains is a relevant aspect for companies. Then, modeling the relationship between inventory cost savings and supply chain success factors is a route for stating such a determination. This is particularly important in pharmacies and food nutrition services (FNS), where the advances made on this topic are still scarce. In this article, we propose and formulate a robust compromise (RoCo) multi-criteria model based on non-linear programming and time-dependent demand. The novelty of our proposal is in defining a score that allows us to measure the mentioned success factors in a simple way, in meeting together all three elements (RoCo multi-criteria, non-linear programming, and time-dependent demand) to state a new model, and in applying it to pharmacies and FNS. This model relates inventory cost savings for pharmacy/FNS and success factors across their supply chains. Savings of inventory costs are predicted by lot sizes to be purchased and computed by comparing optimal and true inventory costs. We utilize a system that records the movements and costs of products to collect the data. Factors, such as purchasing organization, economies of scale, and synchronized supply, are assumed using the purchase system, with these factors ranked on a Likert scale. We consider multilevel relationships between savings obtained for 79 pharmacy/FNS products, and success factor scores according to these products. To deal with the endogeneity bias of the relationships proposed, internal instrumental variables are employed by utilizing generalized statistical moments. Among our main conclusions, we state that the greatest cost savings obtained from inventory models are directly associated with low-success supply chain factors. In this association, the success factors operate as endogenous variables, with respect to inventory cost savings, given the simultaneity of their relationship with cost savings when inventory decision-making.
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Deaths from the COVID-19 pandemic have disproportionately affected older adults and residents in nursing homes. Although emerging research has identified place-based risk factors for the general population, little research has been conducted for nursing home populations. This GIS-based spatial modeling study aimed to determine the association between nursing home-level metrics and county-level, place-based variables with COVID-19 confirmed cases in nursing homes across the United States. A cross-sectional research design linked data from Centers for Medicare & Medicaid Services, American Community Survey, the 2010 Census, and COVID-19 cases among the general population and nursing homes. Spatial cluster analysis identified specific regions with statistically higher COVID-19 cases and deaths among residents. Multivariate analysis identified risk factors at the nursing home level including, total count of fines, total staffing levels, and LPN staffing levels. County-level or place-based factors like per-capita income, average household size, population density, and minority composition were significant predictors of COVID-19 cases in the nursing home. These results provide a framework for examining further COVID-19 cases in nursing homes and highlight the need to include other community-level variables when considering risk of COVID-19 transmission and outbreaks in nursing homes.