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MacFadden DR, Maxwell C, Bowdish D, Bronskill S, Brooks J, Brown K, Burrows LL, Clarke A, Langford B, Leung E, Leung V, Manuel D, McGeer A, Mishra S, Morris AM, Nott C, Raybardhan S, Sapin M, Schwartz KL, So M, Soucy JPR, Daneman N. Coronavirus Disease 2019 Vaccination Is Associated With Reduced Outpatient Antibiotic Prescribing in Older Adults With Confirmed Severe Acute Respiratory Syndrome Coronavirus 2: A Population-Wide Cohort Study. Clin Infect Dis 2023; 77:362-370. [PMID: 36999314 PMCID: PMC10425187 DOI: 10.1093/cid/ciad190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 03/23/2023] [Accepted: 03/29/2023] [Indexed: 04/01/2023] Open
Abstract
BACKGROUND Antibiotics are frequently prescribed unnecessarily in outpatients with coronavirus disease 2019 (COVID-19). We sought to evaluate factors associated with antibiotic prescribing in outpatients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. METHODS We performed a population-wide cohort study of outpatients aged ≥66 years with polymerase chain reaction-confirmed SARS-CoV-2 from 1 January 2020 to 31 December 2021 in Ontario, Canada. We determined rates of antibiotic prescribing within 1 week before (prediagnosis) and 1 week after (postdiagnosis) reporting of the positive SARS-CoV-2 result, compared to a self-controlled period (baseline). We evaluated predictors of prescribing, including a primary-series COVID-19 vaccination, in univariate and multivariable analyses. RESULTS We identified 13 529 eligible nursing home residents and 50 885 eligible community-dwelling adults with SARS-CoV-2 infection. Of the nursing home and community residents, 3020 (22%) and 6372 (13%), respectively, received at least 1 antibiotic prescription within 1 week of a SARS-CoV-2 positive result. Antibiotic prescribing in nursing home and community residents occurred, respectively, at 15.0 and 10.5 prescriptions per 1000 person-days prediagnosis and 20.9 and 9.8 per 1000 person-days postdiagnosis, higher than the baseline rates of 4.3 and 2.5 prescriptions per 1000 person-days. COVID-19 vaccination was associated with reduced prescribing in nursing home and community residents, with adjusted postdiagnosis incidence rate ratios (95% confidence interval) of 0.7 (0.4-1) and 0.3 (0.3-0.4), respectively. CONCLUSIONS Antibiotic prescribing was high and with little or no decline following SARS-CoV-2 diagnosis but was reduced in COVID-19-vaccinated individuals, highlighting the importance of vaccination and antibiotic stewardship in older adults with COVID-19.
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Affiliation(s)
- Derek R MacFadden
- Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Canada
- ICES, Toronto, Canada
- Division of Infectious Diseases, The Ottawa Hospital, Ottawa, Canada
| | - Colleen Maxwell
- ICES, Toronto, Canada
- Schools of Pharmacy and Public Health Sciences, University of Waterloo, Waterloo, Canada
| | - Dawn Bowdish
- Faculty of Health Sciences, McMaster University, Hamilton, Canada
| | | | - James Brooks
- Division of Infectious Diseases, The Ottawa Hospital, Ottawa, Canada
| | - Kevin Brown
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
- Public Health Ontario, Toronto, Canada
| | - Lori L Burrows
- Faculty of Health Sciences, McMaster University, Hamilton, Canada
| | | | - Bradley Langford
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
- Public Health Ontario, Toronto, Canada
| | - Elizabeth Leung
- Unity Health Toronto, Toronto, Canada
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Canada
| | - Valerie Leung
- Public Health Ontario, Toronto, Canada
- Michael Garron Hospital, Toronto East Health Network, Toronto, Canada
| | | | | | - Sharmistha Mishra
- ICES, Toronto, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
- Department of Medicine, University of Toronto, Toronto, Canada
- MAP Centre for Urban Health Solutions, St. Michael's Hospital, Unity Health Toronto, Toronto, Canada
- Institute of Medical Science, University of Toronto, Toronto, Canada
| | | | - Caroline Nott
- Division of Infectious Diseases, The Ottawa Hospital, Ottawa, Canada
| | - Sumit Raybardhan
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
- Pharmacy Department, North York General Hospital, Toronto, Canada
| | - Mia Sapin
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
| | - Kevin L Schwartz
- ICES, Toronto, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
- Public Health Ontario, Toronto, Canada
- Unity Health Toronto, Toronto, Canada
| | - Miranda So
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Canada
- Toronto General Hospital Research Institute, Toronto, Canada
| | - Jean-Paul R Soucy
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Nick Daneman
- ICES, Toronto, Canada
- Division of Infectious Diseases, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
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Hua L, Ran R, Li T. Analysis of COVID-19 outbreak in Hubei province based on Tencent's location big data. Front Public Health 2023; 11:1029385. [PMID: 37304123 PMCID: PMC10251770 DOI: 10.3389/fpubh.2023.1029385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 04/13/2023] [Indexed: 06/13/2023] Open
Abstract
Rapid urbanization has gradually strengthened the spatial links between cities, which greatly aggravates the possibility of the spread of an epidemic. Traditional methods lack the early and accurate detection of epidemics. This study took the Hubei province as the study area and used Tencent's location big data to study the spread of COVID-19. Using ArcGIS as a platform, the urban relation intensity, urban centrality, overlay analysis, and correlation analysis were used to measure and analyze the population mobility data of 17 cities in Hubei province. The results showed that there was high similarity in the spatial distribution of urban relation intensity, urban centrality, and the number of infected people, all indicating the spatial distribution characteristics of "one large and two small" distributions with Wuhan as the core and Huanggang and Xiaogan as the two wings. The urban centrality of Wuhan was four times higher than that of Huanggang and Xiaogan, and the urban relation intensity of Wuhan with Huanggang and Xiaogan was also the second highest in the Hubei province. Meanwhile, in the analysis of the number of infected persons, it was found that the number of infected persons in Wuhan was approximately two times that of these two cities. Through correlation analysis of the urban relation intensity, urban centrality, and the number of infected people, it was found that there was an extremely significant positive correlation among the urban relation intensity, urban centrality, and the number of infected people, with an R2 of 0.976 and 0.938, respectively. Based on Tencent's location big data, this study conducted the epidemic spread research for "epidemic spatial risk classification and prevention and control level selection" to make up for the shortcomings in epidemic risk analysis and judgment. This could provide a reference for city managers to effectively coordinate existing resources, formulate policy, and control the epidemic.
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Shen Q, Zhong T. Did Household Income Loss Have an Immediate Impact on Animal-Source Foods Consumption during the Early Stage of the COVID-19 Pandemic? Foods 2023; 12:1424. [PMID: 37048245 PMCID: PMC10093368 DOI: 10.3390/foods12071424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/15/2023] [Accepted: 03/22/2023] [Indexed: 03/30/2023] Open
Abstract
The outbreak of COVID-19 in 2020 caused extensive impact on household income and foods consumption. However, little attention has been paid to the immediate impact of income loss on animal-source foods consumption in the early stage of the COVID-19 pandemic. This paper aims to narrow this gap, and a total of 1301 valid samples of household food consumption surveys in Wuhan and Nanjing were obtained through specially designed online questionnaires. The surveys show that there were 69.6% (Wuhan) and 42.2% (Nanjing) of surveyed households whose animal-source foods consumption were affected, and there were 47.4% (Wuhan) and 18.9% (Nanjing) of surveyed households who suffered income loss. Furthermore, this paper makes an empirical study on the linkage between income loss and animal-source foods consumption. The results show that the pandemic affected household income, resulting in an immediate impact on animal-source foods consumption. This immediate impact may have been due to the combination of price increases, income loss and insufficient savings, which led to a "perfect storm" for animal-source foods consumption. Moreover, household income loss affected various animal-source foods consumption differently. For households suffering income losses, the odds of pork, beef and mutton, poultry, aquatic products, eggs and dairy products consumption being affected were increased by a factor of 1.894, 2.140, 2.773, 2.345, 1.802, 2.835, respectively, holding other variables constant. The results may be related to residents' consumption habits and food prices. During the COVID-19 pandemic, the reduction of animal-source foods consumption may have led to a state of tension concerning an increase in the development of nutrition intake and health, which may have led to increased food security risks.
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Affiliation(s)
| | - Taiyang Zhong
- School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China;
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Ma S, Li S, Zhang J. Spatial and deep learning analyses of urban recovery from the impacts of COVID-19. Sci Rep 2023; 13:2447. [PMID: 36774395 PMCID: PMC9922321 DOI: 10.1038/s41598-023-29189-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 01/31/2023] [Indexed: 02/13/2023] Open
Abstract
This study investigates urban recovery from the COVID-19 pandemic by focusing on three main types of working, commercial, and night-life activities and associating them with land use and inherent socio-economic patterns as well as points of interests (POIs). Massive multi-source and multi-scale data include mobile phone signaling data (500 m × 500 m), aerial images (0.49 m × 0.49 m), night light satellite data (500 m × 500 m), land use data (street-block), and POIs data. Methods of convolutional neural network, guided gradient-weighted class activation mapping, bivariate local indicator of spatial association, Elbow and K-means are jointly applied. It is found that the recovery in central areas was slower than in suburbs, especially in terms of working and night-life activities, showing a donut-shaped spatial pattern. Residential areas with mixed land uses seem more resilient to the pandemic shock. More than 60% of open spaces are highly associated with recovery in areas with high-level pre-pandemic social-economic activities. POIs of sports and recreation are crucial to the recovery in all areas, while POIs of transportation and science/culture are also important to the recovery in many areas. Policy implications are discussed from perspectives of open spaces, public facilities, neighborhood units, spatial structures, and anchoring roles of POIs.
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Affiliation(s)
- Shuang Ma
- College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, 310058, China
| | - Shuangjin Li
- Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, 739-8529, Japan
| | - Junyi Zhang
- Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, 739-8529, Japan.
- School of Transportation, Southeast University, Nanjing, 211189, China.
- Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, 739-8529, Japan.
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Alexander P, Arneth A, Henry R, Maire J, Rabin S, Rounsevell MDA. High energy and fertilizer prices are more damaging than food export curtailment from Ukraine and Russia for food prices, health and the environment. NATURE FOOD 2023; 4:84-95. [PMID: 37118577 DOI: 10.1038/s43016-022-00659-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 11/04/2022] [Indexed: 04/30/2023]
Abstract
Higher food prices arising from restrictions on exports from Russia or Ukraine have been exacerbated by energy price rises, leading to higher costs for agricultural inputs such as fertilizer. Here, using a scenario modelling approach, we quantify the potential outcomes of increasing agricultural input costs and the curtailment of exports from Russia and Ukraine on human health and the environment. We show that, combined, agricultural inputs costs and food export restrictions could increase food costs by 60-100% in 2023 from 2021 levels, potentially leading to undernourishment of 61-107 million people in 2023 and annual additional deaths of 416,000 to 1.01 million people if the associated dietary patterns are maintained. Furthermore, reduced land use intensification arising from higher input costs would lead to agricultural land expansion and associated carbon and biodiversity loss. The impact of agricultural input costs on food prices is larger than that from curtailment of Russian and Ukrainian exports. Restoring food trade from Ukraine and Russia alone is therefore insufficient to avoid food insecurity problem from higher energy and fertilizer prices. We contend that the immediacy of the food export problems associated with the war diverted attention away from the principal causes of current global food insecurity.
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Affiliation(s)
- Peter Alexander
- School of Geosciences, University of Edinburgh, Edinburgh, UK.
- Global Academy of Agriculture and Food Security, The Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Midlothian, UK.
| | - Almut Arneth
- Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Garmisch-Partenkirchen, Germany
- Geography & Geo-ecology, Campus Süd, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Roslyn Henry
- Institute of Biological Sciences, University of Aberdeen, King's College, Aberdeen, UK
| | - Juliette Maire
- School of Geosciences, University of Edinburgh, Edinburgh, UK
| | - Sam Rabin
- Center for Environmental Prediction, School of Environmental & Biological Sciences, Rutgers University, New Brunswick, NJ, USA
| | - Mark D A Rounsevell
- School of Geosciences, University of Edinburgh, Edinburgh, UK
- Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Garmisch-Partenkirchen, Germany
- Geography & Geo-ecology, Campus Süd, Karlsruhe Institute of Technology, Karlsruhe, Germany
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